- Software Engineering, Information Technology, Machine Learning, Algorithms, Automatic Control, Bioinformatics, and 61 moreNatural Language Processing, CAD, Testing, Computer Vision, Speech Recognition, Data Mining, Fuzzy Logic, AI Planning, Computational Complexity Theory, Intelligent Systems, Information Retrieval, Knowledge Representation, Mechatronics, Multimedia, Cognitive Informatics, Neural Networks, Parallel Processing, Pattern Recognition, Pervasive Computing, Ambient Intelligence, Programming Languages, Practical Reasoning, Evolution, Semantic Web, Web Technologies, Soft Computing, Software Architecture, Hardware Architectures and Implementations, Web Intelligence, Evaluation, Web Engineering, Genetic Algorithms, Artificial Neural Networks, Philosophy of Artificial Intelligence, Intelligence, Image segmentation, Face Recognition, Object Oriented Design and Programming, Research, Research and Publications, Databases, Artificial Intelligence, Fuzzy Control, Fuzzy set theory, Support Vector Machines, Deep Learning, Computer Science, Cognitive Science, System Modeling and Simulation, Integration Technology of Automation Systems, Philosophy of Mind, Optimization Technology, Dynamic Systems and Control, Simulation, Text Mining, Classification (Machine Learning), Smart spaces, Complex Event Processing, Information Systems, Software Evolution, and Roboticsedit
- The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed... moreThe International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.edit
5th International Conference on NLP Trends & Technologies (NLPTT 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing technologies and... more
5th International Conference on NLP Trends & Technologies (NLPTT 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing technologies and its applications.
Research Interests: Ontology, Applied Ontology, Phonology, Ontology (Computer Science), Text Mining, and 12 moreMorphology, Ontology of Music, Phonetics-Phonology Interface, Morphology and Syntax, Semantic Business Processing, Chunking Syntax, Semantic Processing, Text Mining, Search, Machine Learning, Chunking, Text-mining, Data mining and Text mining, and Semantic Text Processing
2 nd International Conference on NLP and Machine Learning Trends (NLMLT 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of NLP and Machine Learning. Authors are... more
2
nd International Conference on NLP and Machine Learning Trends (NLMLT 2024) will
provide an excellent international forum for sharing knowledge and results in theory, methodology
and applications of NLP and Machine Learning. Authors are solicited to contribute to the Conference
by submitting articles that illustrate research results, projects, surveying works and industrial
experiences that describe significant advances in the areas of NLP and Machine Learning Trends.
nd International Conference on NLP and Machine Learning Trends (NLMLT 2024) will
provide an excellent international forum for sharing knowledge and results in theory, methodology
and applications of NLP and Machine Learning. Authors are solicited to contribute to the Conference
by submitting articles that illustrate research results, projects, surveying works and industrial
experiences that describe significant advances in the areas of NLP and Machine Learning Trends.
Research Interests: Machine Learning, Classification (Machine Learning), Statistical machine learning, Automatic Classification (Machine Learning), Unsupervised Machine Learning, and 8 moreMachine Learning and Pattern Recognition, MAchine Learning Algorithms, Machine Learning Model, Machine Learning Big Data, Applied Machine Learning, Extreme Learning Machines, Machine Learning/Data Mining in Healthcare Big Data, and Machine Learning in Python
3 rd International Conference on Machine Learning, NLP and Data Mining (MLDA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Machine Learning, Natural... more
3
rd International Conference on Machine Learning, NLP and Data Mining (MLDA 2024) will
provide an excellent international forum for sharing knowledge and results in theory, methodology and
applications of Machine Learning, Natural Language Computing and Data Mining
rd International Conference on Machine Learning, NLP and Data Mining (MLDA 2024) will
provide an excellent international forum for sharing knowledge and results in theory, methodology and
applications of Machine Learning, Natural Language Computing and Data Mining
Research Interests: Machine Learning, Applications of Machine Learning, Unsupervised Machine Learning, Machine Learning and Pattern Recognition, Artificial Intelligence and Machine Learning, and 7 moreMAchine Learning Algorithms, Machine Learning Model, Machine Learning Big Data, Applied Machine Learning, Extreme Learning Machines, Machine Learning & Data Mining In Pattern Recognition, and Machine Learning in Python
3 rd International Conference on Artificial Intelligence Advances (AIAD 2024) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the area advanced Artificial... more
3
rd International Conference on Artificial Intelligence Advances (AIAD 2024) will act as a major
forum for the presentation of innovative ideas, approaches, developments, and research projects in the
area advanced Artificial Intelligence. It will also serve to facilitate the exchange of information between
researchers and industry professionals to discuss the latest issues and advancement in the research area.
Core areas of AI and advanced multi-disciplinary and its applications will be covered during the
conferences.
rd International Conference on Artificial Intelligence Advances (AIAD 2024) will act as a major
forum for the presentation of innovative ideas, approaches, developments, and research projects in the
area advanced Artificial Intelligence. It will also serve to facilitate the exchange of information between
researchers and industry professionals to discuss the latest issues and advancement in the research area.
Core areas of AI and advanced multi-disciplinary and its applications will be covered during the
conferences.
Research Interests: Artificial Intelligence, AI Planning (Artificial Intelligence), History of Artificial Intelligence, Artificial General Intelligence, Artificial Intelligence and Law, and 7 moreArtificial Intelligence (AI), Ethics of Artificial Intelligence, Philosophy of Artificial Intelligence, Artificial Intelligence in Medicine, Artificial Intelligence And Techniques, Computer Science and Artificial Intelligence, and Implementing Artificial Intelligence for Simulating and Optimizing
5th International Conference on Advances in Artificial Intelligence Techniques (ArIT 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence... more
5th International Conference on Advances in Artificial Intelligence Techniques (ArIT 2024) will
provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Artificial Intelligence and its advances. The Conference looks
for significant contributions to all major fields of the Artificial Intelligence in theoretical and
practical aspects. The aim of the Conference is to provide a platform to the researchers and
practitioners from both academia as well as industry to meet and share cutting-edge development
in the field.
provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Artificial Intelligence and its advances. The Conference looks
for significant contributions to all major fields of the Artificial Intelligence in theoretical and
practical aspects. The aim of the Conference is to provide a platform to the researchers and
practitioners from both academia as well as industry to meet and share cutting-edge development
in the field.
Research Interests: Adaptive Learning Systems, Organizations as complex adaptive systems, Complex Adaptive Systems, Information Fusion, Sensor Information Fusion, and 11 moreEngineering of Self-Adaptive Software Systems, Adaptive Neuro-Fuzzy Inference Systems, Human Centered Information Fusion, Data and Information Fusion, Soft Information Fusion, Hearing Aid Algorithms, AI Search Algorithms, AI algorithms, Algorithms Aided Design, Information Fusion Methods, and Automated Machine Learning
4 th International conference on AI, Machine Learning in Communications and Networks (AIMLNET 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial... more
4
th International conference on AI, Machine Learning in Communications and Networks
(AIMLNET 2024) will provide an excellent international forum for sharing knowledge and
results in theory, methodology and applications of Artificial Intelligence, Machine Learning
impacts on Communications and Networks. The Conference looks for significant contributions
to all major fields of the Artificial Intelligence, Machine Learning and networks in theoretical
and practical aspects. The aim of the Conference is to provide a platform to the researchers and
practitioners from both academia as well as industry to meet and share cutting-edge
development in the field.
th International conference on AI, Machine Learning in Communications and Networks
(AIMLNET 2024) will provide an excellent international forum for sharing knowledge and
results in theory, methodology and applications of Artificial Intelligence, Machine Learning
impacts on Communications and Networks. The Conference looks for significant contributions
to all major fields of the Artificial Intelligence, Machine Learning and networks in theoretical
and practical aspects. The aim of the Conference is to provide a platform to the researchers and
practitioners from both academia as well as industry to meet and share cutting-edge
development in the field.
Research Interests: Machine Learning, Classification (Machine Learning), Applications of Machine Learning, Statistical machine learning, Unsupervised Machine Learning, and 9 moreMachine Learning and Pattern Recognition, MAchine Learning Algorithms, Machine Learning Model, Machine Learning Big Data, Applied Machine Learning, Extreme Learning Machines, Data Science, Machine Learning, Quantum Machine Learning, and Machine Learning in Python
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Research Interests: Machine Learning, Classification (Machine Learning), Applications of Machine Learning, Statistical machine learning, Unsupervised Machine Learning, and 9 moreArtificial Intelligence and Machine Learning, MAchine Learning Algorithms, Machine Learning Model, Machine Learning Big Data, Applied Machine Learning, Extreme Learning Machines, Data Science, Machine Learning, Quantum Machine Learning, and Machine Learning in Python
5 th International Conference on Big Data, Machine Learning and IoT (BMLI 2024) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Big Data, Machine Learning... more
5
th International Conference on Big Data, Machine Learning and IoT (BMLI 2024) will act as a
major forum for the presentation of innovative ideas, approaches, developments, and research projects
in the areas of Big Data, Machine Learning and IoT.
th International Conference on Big Data, Machine Learning and IoT (BMLI 2024) will act as a
major forum for the presentation of innovative ideas, approaches, developments, and research projects
in the areas of Big Data, Machine Learning and IoT.
Research Interests: Machine Learning, Applications of Machine Learning, Statistical machine learning, Unsupervised Machine Learning, MAchine Learning Algorithms, and 7 moreMachine Learning Model, Machine Learning Big Data, Applied Machine Learning, Extreme Learning Machines, Data Science, Machine Learning, Quantum Machine Learning, and Machine Learning in Python
4 th International Conference on Big Data, IOT & NLP (BINLP 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Big Data, IoT and NLP. Authors are solicited to... more
4
th International Conference on Big Data, IOT & NLP (BINLP 2024) will provide an excellent
international forum for sharing knowledge and results in theory, methodology and applications of Big
Data, IoT and NLP. Authors are solicited to contribute to the conference by submitting articles that
illustrate research results, projects, surveying works and industrial experiences that describe
significant advances in the areas of Big Data, Internet of Things and NLP.
th International Conference on Big Data, IOT & NLP (BINLP 2024) will provide an excellent
international forum for sharing knowledge and results in theory, methodology and applications of Big
Data, IoT and NLP. Authors are solicited to contribute to the conference by submitting articles that
illustrate research results, projects, surveying works and industrial experiences that describe
significant advances in the areas of Big Data, Internet of Things and NLP.
Research Interests:
10th International Conference on Artificial Intelligence and Applications (AI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its... more
10th International Conference on Artificial Intelligence and Applications (AI 2024) will provide an
excellent international forum for sharing knowledge and results in theory, methodology and applications of
Artificial Intelligence and its applications. The Conference looks for significant contributions to all major
fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the
Conference is to provide a platform to the researchers and practitioners from both academia as well as
industry to meet and share cutting-edge development in the field.
excellent international forum for sharing knowledge and results in theory, methodology and applications of
Artificial Intelligence and its applications. The Conference looks for significant contributions to all major
fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the
Conference is to provide a platform to the researchers and practitioners from both academia as well as
industry to meet and share cutting-edge development in the field.
Research Interests: Artificial Intelligence, AI Planning (Artificial Intelligence), History of Artificial Intelligence, Future of artificial intelligence, Artificial General Intelligence, and 5 moreArtificial Intelligence and Law, Artificial Intelligence (AI), Ethics of Artificial Intelligence, Philosophy of Artificial Intelligence, and Artificial Intelligence in Medicine
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications... more
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Research Interests: Artificial Intelligence, AI Planning (Artificial Intelligence), History of Artificial Intelligence, Future of artificial intelligence, Artificial General Intelligence, and 7 moreArtificial Intelligence and Law, Artificial Intelligence (AI), Ethics of Artificial Intelligence, Artificial Intelligence in Education, Philosophy of Artificial Intelligence, Artificial Intelligence in Medicine, and Computer Science and Artificial Intelligence
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications... more
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Research Interests: Artificial Intelligence, AI Planning (Artificial Intelligence), History of Artificial Intelligence, Future of artificial intelligence, Artificial General Intelligence, and 5 moreArtificial Intelligence and Law, Artificial Intelligence (AI), Ethics of Artificial Intelligence, Artificial Intelligence in Education, and Artificial Intelligence And Techniques
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications... more
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Research Interests: Artificial Intelligence, AI Planning (Artificial Intelligence), History of Artificial Intelligence, Artificial General Intelligence, Artificial Intelligence and Law, and 5 moreEthics of Artificial Intelligence, Philosophy of Artificial Intelligence, Artificial Intelligence in Medicine, Artificial Intelligence And Techniques, and Philosophy of Mind and Artificial Intelligence & AI
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications... more
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Artificial Intelligence & applications. Topics of interest include, but are not limited to, the following:
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Artificial Intelligence & applications. Topics of interest include, but are not limited to, the following:
Research Interests: Natural Language Processing, Mobile Robotics, Autonomous Robotics, Automatic Control, Artficial Neural Networks, and 7 moreOpenCv or Computer Vision, Arabic Natural Language Processing, Artifical Neural Networks, Automatic Gate Control Using Plc, Computer Aided Design (CAD), Recent Trends in Fdi in India, and Publication to International Journal on Recent Trends In Engineering and Technology
Complicated policy texts require a lot of effort to read, so there is a need for intelligent interpretation of Chinese policies. To better solve the Chinese Text Summarization task, this paper utilized the mT5 model as the core framework... more
Complicated policy texts require a lot of effort to read, so there is a need for intelligent interpretation of
Chinese policies. To better solve the Chinese Text Summarization task, this paper utilized the mT5 model
as the core framework and initial weights. Additionally, In addition, this paper reduced the model size
through parameter clipping, used the Gap Sentence Generation (GSG) method as unsupervised method,
and improved the Chinese tokenizer. After training on a meticulously processed 30GB Chinese training
corpus, the paper developed the enhanced mT5-GSG model. Then, when fine-tuning the Chinese Policy
text, this paper chose the idea of “Dropout Twice”, and innovatively combined the probability distribution
of the two Dropouts through the Wasserstein distance. Experimental results indicate that the proposed
model achieved Rouge-1, Rouge-2, and Rouge-L scores of 56.13%, 45.76%, and 56.41% respectively on
the Chinese policy text summarization dataset.
Chinese policies. To better solve the Chinese Text Summarization task, this paper utilized the mT5 model
as the core framework and initial weights. Additionally, In addition, this paper reduced the model size
through parameter clipping, used the Gap Sentence Generation (GSG) method as unsupervised method,
and improved the Chinese tokenizer. After training on a meticulously processed 30GB Chinese training
corpus, the paper developed the enhanced mT5-GSG model. Then, when fine-tuning the Chinese Policy
text, this paper chose the idea of “Dropout Twice”, and innovatively combined the probability distribution
of the two Dropouts through the Wasserstein distance. Experimental results indicate that the proposed
model achieved Rouge-1, Rouge-2, and Rouge-L scores of 56.13%, 45.76%, and 56.41% respectively on
the Chinese policy text summarization dataset.
Research Interests:
The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at any given time.... more
The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified
our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at
any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative
to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms.
Motivated by this necessity, we present this paper to contribute to developing an automated system for
detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to
previous experiments on the same dataset. We employed the stacking ensemble machine learning method,
utilizing four various feature extraction techniques to optimize performance within the stacking ensemble
learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear
Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we
achieved superior results compared to traditional machine learning classifier models. The stacking classifier
achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing
the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an
accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.
our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at
any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative
to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms.
Motivated by this necessity, we present this paper to contribute to developing an automated system for
detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to
previous experiments on the same dataset. We employed the stacking ensemble machine learning method,
utilizing four various feature extraction techniques to optimize performance within the stacking ensemble
learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear
Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we
achieved superior results compared to traditional machine learning classifier models. The stacking classifier
achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing
the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an
accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.
Research Interests:
Classifying the ECG dataset is the main technique for diagnosing heart disease. However, the focus of this field is increasingly on prediction, with a growing dependence on machine learning techniques. This study aimed to enhance the... more
Classifying the ECG dataset is the main technique for diagnosing heart disease. However, the focus of this
field is increasingly on prediction, with a growing dependence on machine learning techniques. This study
aimed to enhance the accuracy of cardiovascular disease classification using data from the PhysioNet
database by employing machine learning (ML). The study proposed several multi-class classification
models that accurately identify patterns within three classes: heart failure rhythm (HFR), normal heart
rhythm (NHR), and arrhythmia (ARR). This was accomplished by utilizing a database containing 162 ECG
signals. The study employed a variety of techniques, including frequency-time domain analysis, spectral
features, and wavelet scattering, to extract features and capture unique characteristics from the ECG
dataset. The SVM model produced a training accuracy of 97.1% and a testing accuracy of 92%. This work
provides a reliable, effective, and human error-free diagnostic tool for identifying heart disease.
Furthermore, it could prove to be a valuable resource for future medical research projects aimed at
improving the diagnosis and treatment of cardiovascular diseases.
field is increasingly on prediction, with a growing dependence on machine learning techniques. This study
aimed to enhance the accuracy of cardiovascular disease classification using data from the PhysioNet
database by employing machine learning (ML). The study proposed several multi-class classification
models that accurately identify patterns within three classes: heart failure rhythm (HFR), normal heart
rhythm (NHR), and arrhythmia (ARR). This was accomplished by utilizing a database containing 162 ECG
signals. The study employed a variety of techniques, including frequency-time domain analysis, spectral
features, and wavelet scattering, to extract features and capture unique characteristics from the ECG
dataset. The SVM model produced a training accuracy of 97.1% and a testing accuracy of 92%. This work
provides a reliable, effective, and human error-free diagnostic tool for identifying heart disease.
Furthermore, it could prove to be a valuable resource for future medical research projects aimed at
improving the diagnosis and treatment of cardiovascular diseases.
Research Interests:
This paper focuses on an experimental study that used passive sonar sensors as the primary information source for the submerged target in order to identify, classify, and recognize naval targets. Surface vessels and submarine generate a... more
This paper focuses on an experimental study that used passive sonar sensors as the primary information
source for the submerged target in order to identify, classify, and recognize naval targets. Surface vessels
and submarine generate a specific sound either by propulsion systems, auxiliary equipment or blades of
their propellers, producing information known as the "acoustic signature" that is unique to each type of
target. Consequently, the analysis and classification of targets depend on the processing of the frequencies
produced by these vibrations (sound). utilizing the TPWS (Two-Pass-Split Windows) filter, this work aims
to develop a novel technique for target identification and classification utilizing passive sonars. This
technique involves processing the target's signal in the time-frequency domain. subsequently, in order to
improve the frequency lines of the target noise and decrease the background noise, a TPSW algorithm is
implemented in the frequency domain. By integrating narrowband and broadband analysis as inputs of an
artificial intelligence model that can classify a target into one of the categories given in the training phase,
the target has finally been classified. Our findings demonstrated that the suggested approach is dependent
upon the size of the target noise data collection and the noise-to-effective-signal ratio.
source for the submerged target in order to identify, classify, and recognize naval targets. Surface vessels
and submarine generate a specific sound either by propulsion systems, auxiliary equipment or blades of
their propellers, producing information known as the "acoustic signature" that is unique to each type of
target. Consequently, the analysis and classification of targets depend on the processing of the frequencies
produced by these vibrations (sound). utilizing the TPWS (Two-Pass-Split Windows) filter, this work aims
to develop a novel technique for target identification and classification utilizing passive sonars. This
technique involves processing the target's signal in the time-frequency domain. subsequently, in order to
improve the frequency lines of the target noise and decrease the background noise, a TPSW algorithm is
implemented in the frequency domain. By integrating narrowband and broadband analysis as inputs of an
artificial intelligence model that can classify a target into one of the categories given in the training phase,
the target has finally been classified. Our findings demonstrated that the suggested approach is dependent
upon the size of the target noise data collection and the noise-to-effective-signal ratio.
Research Interests:
Artificial Narrow Intelligence is in the phase of moving towards the AGN, which will attempt to decide as a human being. We are getting closer to it by each day, but AI actually is indefinite to many, although it is no different than any... more
Artificial Narrow Intelligence is in the phase of moving towards the AGN, which will attempt
to decide as a human being. We are getting closer to it by each day, but AI actually is indefinite to
many, although it is no different than any other set of mathematically defined computer operations in its
core. Generating new data from a pre-trained model introduces new challenges to science & technology. In
this work, the design of such an architecture from scratch, solving problems, and introducing alternative
approaches are what has been conducted. Using a deep thinker, Tolstoy, as an object of study is a source
of motivation for the entire research.
to decide as a human being. We are getting closer to it by each day, but AI actually is indefinite to
many, although it is no different than any other set of mathematically defined computer operations in its
core. Generating new data from a pre-trained model introduces new challenges to science & technology. In
this work, the design of such an architecture from scratch, solving problems, and introducing alternative
approaches are what has been conducted. Using a deep thinker, Tolstoy, as an object of study is a source
of motivation for the entire research.
Research Interests:
This study reviews studies on Artificial Intelligence (AI) maturity models (MM) in automotive manufacturing. To stay competitive, SMEs in the automotive industry need to embrace digitalization. SMEs employ a large segment of the USA's... more
This study reviews studies on Artificial Intelligence (AI) maturity models (MM) in automotive
manufacturing. To stay competitive, SMEs in the automotive industry need to embrace digitalization. SMEs
employ a large segment of the USA's workforce. The benefits of operational efficiency, quality
improvement, cost reduction, and innovative culture have made SMEs more aggressive about
digitalization. Digitalizing operations with Artificial Intelligence are on the rise. In this paper, AI
applications in SMEs are examined through the lens of an AI maturity model.
manufacturing. To stay competitive, SMEs in the automotive industry need to embrace digitalization. SMEs
employ a large segment of the USA's workforce. The benefits of operational efficiency, quality
improvement, cost reduction, and innovative culture have made SMEs more aggressive about
digitalization. Digitalizing operations with Artificial Intelligence are on the rise. In this paper, AI
applications in SMEs are examined through the lens of an AI maturity model.
Research Interests:
This work uses artificial intelligence to create an automatic solution for the modulation's classification of various radio signals. This project is a component of a lengthy communications intelligence process that aims to find an... more
This work uses artificial intelligence to create an automatic solution for the modulation's classification of
various radio signals. This project is a component of a lengthy communications intelligence process that
aims to find an automated method for demodulating, decoding, and deciphering communication signals. As
a result, the work we did involved selecting the database required for supervised deep learning, assessing
the performance of current methods on unprocessed communication signals, and suggesting a deep
learning network-based method that would enable the classification of modulation types with the best
possible ratio between computation time and accuracy. In order to use the current automatic classification
models as a guide, we first conducted study on them. As a result, we suggested an ensemble learning
strategy based on Transformer Neural Network and adjusted Res Net that takes into account the difficulty
of forecasting in low Signal Noise Ratio (SNR) scenarios while also being effective at extracting multiscale characteristics from the raw I/Q sequence data. Ultimately, we produced an architecture for
communication signals that is simple to work with and implement. With an accuracy of up to 95%, this
solution's optimum and sturdy architecture decides the type of modulation on its own.
various radio signals. This project is a component of a lengthy communications intelligence process that
aims to find an automated method for demodulating, decoding, and deciphering communication signals. As
a result, the work we did involved selecting the database required for supervised deep learning, assessing
the performance of current methods on unprocessed communication signals, and suggesting a deep
learning network-based method that would enable the classification of modulation types with the best
possible ratio between computation time and accuracy. In order to use the current automatic classification
models as a guide, we first conducted study on them. As a result, we suggested an ensemble learning
strategy based on Transformer Neural Network and adjusted Res Net that takes into account the difficulty
of forecasting in low Signal Noise Ratio (SNR) scenarios while also being effective at extracting multiscale characteristics from the raw I/Q sequence data. Ultimately, we produced an architecture for
communication signals that is simple to work with and implement. With an accuracy of up to 95%, this
solution's optimum and sturdy architecture decides the type of modulation on its own.
Research Interests:
Facial Recognition is integral to numerous modern applications, such as security systems, social media platforms, and augmented reality apps. The success of these systems heavily depends on the performance of the Face Recognition models... more
Facial Recognition is integral to numerous modern applications, such as security systems, social media
platforms, and augmented reality apps. The success of these systems heavily depends on the performance
of the Face Recognition models they use, specifically Convolutional Neural Networks (CNNs). However,
many real-world classification tasks encounter imbalanced datasets, with some classes significantly
underrepresented. Face Recognition models that do not address this class imbalance tend to exhibit poor
performance, especially in tasks involving a wide range of faces to identify (multi-class problems). This
research examines how class imbalance in datasets impacts the creation of neural network classifiers for
Facial Recognition. Initially, we crafted a Convolutional Neural Network model for facial recognition,
integrating hybrid resampling methods (oversampling and under-sampling) to address dataset imbalances.
In addition, augmentation techniques were implemented to enhance generalization capabilities and overall
performance. Through comprehensive experimentation, we assess the influence of imbalanced datasets on
the performance of the CNN-based classifier. Using Pins face data, we conducted an empirical study,
evaluating conclusions based on accuracy, precision, recall, and F1-score measurements. A comparative
analysis demonstrates that the performance of the proposed Convolutional Neural Network classifier
diminishes in the presence of dataset class imbalances. Conversely, the proposed system, utilizing data
resampling techniques, notably enhances classification performance for imbalanced datasets. This study
underscores the efficacy of data resampling approaches in augmenting the performance of Face
Recognition models, presenting prospects for more dependable and efficient future systems
platforms, and augmented reality apps. The success of these systems heavily depends on the performance
of the Face Recognition models they use, specifically Convolutional Neural Networks (CNNs). However,
many real-world classification tasks encounter imbalanced datasets, with some classes significantly
underrepresented. Face Recognition models that do not address this class imbalance tend to exhibit poor
performance, especially in tasks involving a wide range of faces to identify (multi-class problems). This
research examines how class imbalance in datasets impacts the creation of neural network classifiers for
Facial Recognition. Initially, we crafted a Convolutional Neural Network model for facial recognition,
integrating hybrid resampling methods (oversampling and under-sampling) to address dataset imbalances.
In addition, augmentation techniques were implemented to enhance generalization capabilities and overall
performance. Through comprehensive experimentation, we assess the influence of imbalanced datasets on
the performance of the CNN-based classifier. Using Pins face data, we conducted an empirical study,
evaluating conclusions based on accuracy, precision, recall, and F1-score measurements. A comparative
analysis demonstrates that the performance of the proposed Convolutional Neural Network classifier
diminishes in the presence of dataset class imbalances. Conversely, the proposed system, utilizing data
resampling techniques, notably enhances classification performance for imbalanced datasets. This study
underscores the efficacy of data resampling approaches in augmenting the performance of Face
Recognition models, presenting prospects for more dependable and efficient future systems
Research Interests:
The global upswing in cardiovascular disease (CVD) cases presents a critical challenge. While the ultimate goal remains elusive, improving CVD prediction accuracy is vital. Machine learning and deep learning are crucial for decoding... more
The global upswing in cardiovascular disease (CVD) cases presents a critical challenge. While the
ultimate goal remains elusive, improving CVD prediction accuracy is vital. Machine learning and deep
learning are crucial for decoding complex health data, enhancing cardiac imaging, and predicting disease
outcomes in clinical practice. This systematic literature review meticulously analyses CVD using machine
learning techniques, with a particular emphasis on algorithms for classification and prediction. The metaanalysis covers 343 references from 2020 to November 2023, preceding a thorough examination of 65
selected references. Acknowledging current hurdles in CVD classification methods that impede practical
use, this systematic literature review (SLR) is conducted.
The study provides valuable insights for researchers and healthcare professionals, facilitating the
integration of clinical applications in machine learning settings related to CVD. It also aids in promptly
identifying potential threats and implementing precautionary measures. The study also recognizes
prevalent classical machine learning methods, emphasizing their clinically relevant diagnostic outcomes.
Deliberating on current trends, algorithms, and potential areas for future research offers a comprehensive
insight into the present state of affairs.
ultimate goal remains elusive, improving CVD prediction accuracy is vital. Machine learning and deep
learning are crucial for decoding complex health data, enhancing cardiac imaging, and predicting disease
outcomes in clinical practice. This systematic literature review meticulously analyses CVD using machine
learning techniques, with a particular emphasis on algorithms for classification and prediction. The metaanalysis covers 343 references from 2020 to November 2023, preceding a thorough examination of 65
selected references. Acknowledging current hurdles in CVD classification methods that impede practical
use, this systematic literature review (SLR) is conducted.
The study provides valuable insights for researchers and healthcare professionals, facilitating the
integration of clinical applications in machine learning settings related to CVD. It also aids in promptly
identifying potential threats and implementing precautionary measures. The study also recognizes
prevalent classical machine learning methods, emphasizing their clinically relevant diagnostic outcomes.
Deliberating on current trends, algorithms, and potential areas for future research offers a comprehensive
insight into the present state of affairs.
Research Interests:
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications... more
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Artificial Intelligence & applications. Topics of interest include, but are not limited to, the following:
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Artificial Intelligence & applications. Topics of interest include, but are not limited to, the following:
Research Interests:
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications... more
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Artificial Intelligence & applications. Topics of interest include, but are not limited to, the following:
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Artificial Intelligence & applications. Topics of interest include, but are not limited to, the following:
Research Interests: Artificial Intelligence, Applications of Artificial Intelligence in Manufacturing, History of Artificial Intelligence, Future of artificial intelligence, Artificial General Intelligence, and 6 moreArtificial Intelligence and Law, Ethics of Artificial Intelligence, Philosophy of Artificial Intelligence, Artificial Intelligence And Techniques, Artificial Intelligence and Nural Networks, and AI algorithms
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications... more
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open
access peer-reviewed journal that publishes articles which contribute new results in all areas of
the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for
professionals and researchers in all fields of AI for researchers, programmers, and software and
hardware manufacturers. The journal also aims to publish new attempts in the form of special
issues on emerging areas in Artificial Intelligence and applications.
Authors are solicited to contribute to the journal by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances
in the areas of Artificial Intelligence & applications.
access peer-reviewed journal that publishes articles which contribute new results in all areas of
the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for
professionals and researchers in all fields of AI for researchers, programmers, and software and
hardware manufacturers. The journal also aims to publish new attempts in the form of special
issues on emerging areas in Artificial Intelligence and applications.
Authors are solicited to contribute to the journal by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances
in the areas of Artificial Intelligence & applications.
Research Interests: Artificial Intelligence, Fuzzy Logic, History of Artificial Intelligence, Future of artificial intelligence, Artificial General Intelligence, and 10 moreArtificial Intelligence and Law, Artificial Intelligence in Education, Fuzzy Logic Programming, Philosophy of Artificial Intelligence, Artificial Intelligence And Techniques, Lógica Fuzzy, Artificial Intelligence and Nural Networks, AI Search Algorithms, AI algorithms, and Algorithms Aided Design
Designing metroidvania games often poses a unique challenge to video game developers, namely the difficulty of consistently preventing soft-locking, which hinders or blocks the player’s ability to traverse through levels effectively [1].... more
Designing metroidvania games often poses a unique challenge to video game developers, namely the
difficulty of consistently preventing soft-locking, which hinders or blocks the player’s ability to traverse
through levels effectively [1]. As a result, many turn to hand-making all levels to ensure the level’s
traversability, but in the process often forsaking the ability to rely on procedural generation to lessen the
time and burden on human game developers [2]. On the other hand, when developers rely on popular ways
of procedural generation such as using perlin noise, they find themselves unable to control those
procedural algorithms to guarantee certain characteristics of the outputs such as traversability [3]. Our
paper aims to present a procedural solution that can also effectively guarantee the traversability of the
generated level. Our method uses Answer Set Programming (ASP) to verify generation based on
restrictions we place, guaranteeing the outcome to be what we want [4]. The generation of a level is
divided into rooms, which are first mapped out in a graph to ensure traversability from a starting room to
an ending boss area. The rooms’ geometry is then generated accordingly to create the full level. Using
perlin noise, we were also able to create a demonstration of how traversability works in another form of
procedural generation, and compare it with our methodology to identify strengths and weaknesses. To
demonstrate our method, we applied our solution as well as the perlin noise algorithm to a 2D
metroidvania game made in the Unity game engine and conducted quantitative tests on the ASP method to
assess how well our method works as a level generator [5].
difficulty of consistently preventing soft-locking, which hinders or blocks the player’s ability to traverse
through levels effectively [1]. As a result, many turn to hand-making all levels to ensure the level’s
traversability, but in the process often forsaking the ability to rely on procedural generation to lessen the
time and burden on human game developers [2]. On the other hand, when developers rely on popular ways
of procedural generation such as using perlin noise, they find themselves unable to control those
procedural algorithms to guarantee certain characteristics of the outputs such as traversability [3]. Our
paper aims to present a procedural solution that can also effectively guarantee the traversability of the
generated level. Our method uses Answer Set Programming (ASP) to verify generation based on
restrictions we place, guaranteeing the outcome to be what we want [4]. The generation of a level is
divided into rooms, which are first mapped out in a graph to ensure traversability from a starting room to
an ending boss area. The rooms’ geometry is then generated accordingly to create the full level. Using
perlin noise, we were also able to create a demonstration of how traversability works in another form of
procedural generation, and compare it with our methodology to identify strengths and weaknesses. To
demonstrate our method, we applied our solution as well as the perlin noise algorithm to a 2D
metroidvania game made in the Unity game engine and conducted quantitative tests on the ASP method to
assess how well our method works as a level generator [5].
Research Interests:
This study investigates whether transformer models like ChatGPT (GPT4, MAR2023) can generalize beyond their training data by examining their performance on the novel Cipher Dataset, which scrambles token order. The dataset consists of 654... more
This study investigates whether transformer models like ChatGPT (GPT4, MAR2023) can generalize
beyond their training data by examining their performance on the novel Cipher Dataset, which scrambles
token order. The dataset consists of 654 test cases, and the analysis focuses on 51 text examples and 13
algorithmic choices. Results show that the models perform well on low-difficulty ciphers like Caesar and
can unscramble tokens in 77% of the cipher examples. Despite their reliance on training data, the model's
ability to generalize outside of token order is surprising, especially when leveraging large-scale models
with hundreds of billions of weights and a comprehensive text corpus with few examples. The original
contributions of the work focus on presenting a cipher challenge dataset and then scoring historically
significant ciphers for large language models to descramble. The real challenge for these generational
models lies in executing the complex algorithmic steps on new cipher inputs, potentially as a novel
reasoning challenge that relies less on knowledge acquisition and more on trial-and-error or out-ofbounds responses.
beyond their training data by examining their performance on the novel Cipher Dataset, which scrambles
token order. The dataset consists of 654 test cases, and the analysis focuses on 51 text examples and 13
algorithmic choices. Results show that the models perform well on low-difficulty ciphers like Caesar and
can unscramble tokens in 77% of the cipher examples. Despite their reliance on training data, the model's
ability to generalize outside of token order is surprising, especially when leveraging large-scale models
with hundreds of billions of weights and a comprehensive text corpus with few examples. The original
contributions of the work focus on presenting a cipher challenge dataset and then scoring historically
significant ciphers for large language models to descramble. The real challenge for these generational
models lies in executing the complex algorithmic steps on new cipher inputs, potentially as a novel
reasoning challenge that relies less on knowledge acquisition and more on trial-and-error or out-ofbounds responses.
Research Interests:
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications... more
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Artificial Intelligence & applications. Topics of interest include, but are not limited to, the following:
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Artificial Intelligence & applications. Topics of interest include, but are not limited to, the following:
Research Interests: Neural Networks, Automatic Control, Parallel Processing, Artificial Neural Networks, Hybrid Intelligent Systems, and 12 moreParallel Computing, Storage, Image Processing, Spiking Neural Networks, Automatic Gate Control Using Plc, Parallel and Distributed Processing, Hybrid Intelligent Decision Support Systems, Automatic Street Light Control System, Hearing Aid Algorithms, AI Search Algorithms, Parallel Distributed Processing, AI algorithms, Hybrid intelligent systems, Adaptation and Learning Systems, and Algorithms Aided Design
The Witch is a typical stereotype-busting character because its description has changed many times in a long history. This paper is an attempt to understand the visual interpretations and character positioning of the Watch by many... more
The Witch is a typical stereotype-busting character because its description has changed many times in a
long history. This paper is an attempt to understand the visual interpretations and character positioning of
the Watch by many creators in different eras, AI is being used to help summarize current stereotypes in
witch design, and to propose a way to subvert the Witch stereotype in current popular culture. This study
aims to understand the visual interpretations of witches and character positioning by many creators in
different eras, and to subvert the stereotype of witches in current popular culture. This study provides
material for future research on character design stereotypes, and an attempt is proposed to use artificial
intelligence to break the stereotypes in design and is being documented as an experiment in how to subvert
current stereotypes from various periods in history. The method begins by using AI to compile stereotypical
images of contemporary witches. Then, the two major components of the stereotype, "accessories" and
"appearance," are analyzed from historical and social perspectives and attributed to the reasons for the
formation and transformation of the Witch image. These past stereotypes are designed using the design
approach of "extraction" "retention" and "conversion.", and finally the advantages and disadvantages of
this approach are summarized from a practical perspective. Research has shown that it is feasible to use AI
to summarize the design elements and use them as clues to trace history. This is especially true for
characters such as the Witch, who have undergone many historical transitions. The more changes there
are, the more elements can be gathered, and the advantage of this method increases. Stereotypes change
over time, and even when the current stereotype has become history, this method is still effective for newly
created stereotypes.
long history. This paper is an attempt to understand the visual interpretations and character positioning of
the Watch by many creators in different eras, AI is being used to help summarize current stereotypes in
witch design, and to propose a way to subvert the Witch stereotype in current popular culture. This study
aims to understand the visual interpretations of witches and character positioning by many creators in
different eras, and to subvert the stereotype of witches in current popular culture. This study provides
material for future research on character design stereotypes, and an attempt is proposed to use artificial
intelligence to break the stereotypes in design and is being documented as an experiment in how to subvert
current stereotypes from various periods in history. The method begins by using AI to compile stereotypical
images of contemporary witches. Then, the two major components of the stereotype, "accessories" and
"appearance," are analyzed from historical and social perspectives and attributed to the reasons for the
formation and transformation of the Witch image. These past stereotypes are designed using the design
approach of "extraction" "retention" and "conversion.", and finally the advantages and disadvantages of
this approach are summarized from a practical perspective. Research has shown that it is feasible to use AI
to summarize the design elements and use them as clues to trace history. This is especially true for
characters such as the Witch, who have undergone many historical transitions. The more changes there
are, the more elements can be gathered, and the advantage of this method increases. Stereotypes change
over time, and even when the current stereotype has become history, this method is still effective for newly
created stereotypes.
Research Interests:
The Witch is a typical stereotype-busting character because its description has changed many times in a long history. This paper is an attempt to understand the visual interpretations and character positioning of the Watch by many... more
The Witch is a typical stereotype-busting character because its description has changed many times in a
long history. This paper is an attempt to understand the visual interpretations and character positioning of
the Watch by many creators in different eras, AI is being used to help summarize current stereotypes in
witch design, and to propose a way to subvert the Witch stereotype in current popular culture. This study
aims to understand the visual interpretations of witches and character positioning by many creators in
different eras, and to subvert the stereotype of witches in current popular culture. This study provides
material for future research on character design stereotypes, and an attempt is proposed to use artificial
intelligence to break the stereotypes in design and is being documented as an experiment in how to subvert
current stereotypes from various periods in history. The method begins by using AI to compile stereotypical
images of contemporary witches. Then, the two major components of the stereotype, "accessories" and
"appearance," are analyzed from historical and social perspectives and attributed to the reasons for the
formation and transformation of the Witch image. These past stereotypes are designed using the design
approach of "extraction" "retention" and "conversion.", and finally the advantages and disadvantages of
this approach are summarized from a practical perspective. Research has shown that it is feasible to use AI
to summarize the design elements and use them as clues to trace history. This is especially true for
characters such as the Witch, who have undergone many historical transitions. The more changes there
are, the more elements can be gathered, and the advantage of this method increases. Stereotypes change
over time, and even when the current stereotype has become history, this method is still effective for newly
created stereotypes.
long history. This paper is an attempt to understand the visual interpretations and character positioning of
the Watch by many creators in different eras, AI is being used to help summarize current stereotypes in
witch design, and to propose a way to subvert the Witch stereotype in current popular culture. This study
aims to understand the visual interpretations of witches and character positioning by many creators in
different eras, and to subvert the stereotype of witches in current popular culture. This study provides
material for future research on character design stereotypes, and an attempt is proposed to use artificial
intelligence to break the stereotypes in design and is being documented as an experiment in how to subvert
current stereotypes from various periods in history. The method begins by using AI to compile stereotypical
images of contemporary witches. Then, the two major components of the stereotype, "accessories" and
"appearance," are analyzed from historical and social perspectives and attributed to the reasons for the
formation and transformation of the Witch image. These past stereotypes are designed using the design
approach of "extraction" "retention" and "conversion.", and finally the advantages and disadvantages of
this approach are summarized from a practical perspective. Research has shown that it is feasible to use AI
to summarize the design elements and use them as clues to trace history. This is especially true for
characters such as the Witch, who have undergone many historical transitions. The more changes there
are, the more elements can be gathered, and the advantage of this method increases. Stereotypes change
over time, and even when the current stereotype has become history, this method is still effective for newly
created stereotypes.
Research Interests:
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications... more
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Research Interests:
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications... more
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Research Interests: Robotics, Bioinformatics, Programming Languages, Natural Language Processing, Fuzzy Logic, and 10 moreAutomatic Control, Parallel Processing, Mechatronics Engineering, Hybrid Intelligent Systems, AI algorithms, Artificial Intelligence tools, Data Mining and Machine Learning Tools, Reasoning and Evolution, Recent Trends and Developments of AI, Big DatA, and • Computer Vision and Speech Understanding
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications... more
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications
Research Interests: Bioinformatics, Information Retrieval, Computer Vision, Natural Language Processing, Mechatronics, and 12 moreFuzzy Logic, Automatic Control, Parallel Processing, Hybrid Intelligent Systems, Computer Vision and Pattern Recognition, Computer Aided Design (CAD), Machine Learning and Machine Tools, Speech Understanding, Model, heuristic and AI planning strategies, AI algorithms, Artificial Intelligence tools, and Intelligent System Architectures
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications... more
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Research Interests: Robotics, Programming Languages, Artificial Intelligence, Information Technology, Natural Language Processing, and 7 moreMachine Learning, Fuzzy Logic, Structural Bioinformatics, Web Intelligence, Deep Learning, AI algorithms, and Analysis of IT systems, security and monitoring of the hardware and software infrastructures of an IT node;
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications... more
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Research Interests: Robotics, Bioinformatics, Programming Languages, Information Retrieval, Natural Language Processing, and 13 moreFuzzy Logic, Pattern Recognition, Neural Networks, Automatic Control, Knowledge Representation, Hybrid Intelligent Systems, AI algorithms, Intelligent System Architectures, Web Intelligence Applications & Search, Software and Hardware Architectures, Data Mining and Machine Learning Tools, • Computational Theories of Learning, and • Computer Vision and Speech Understanding
More infectious virus variants can arise from rapid mutations in their proteins, creating new infection waves. These variants can evade one’s immune system and infect vaccinated individuals, lowering vaccine efficacy. Hence, to improve... more
More infectious virus variants can arise from rapid mutations in their proteins, creating new infection waves. These variants can evade one’s immune system and infect vaccinated individuals, lowering vaccine efficacy. Hence, to improve vaccine design, this project proposes Optimus PPIme – a deep learning approach to predict future, more infectious variants from an existing virus (exemplified by SARS-CoV-2). The approach comprises an algorithm which acts as a “virus” attacking a host cell. To increase infectivity, the “virus” mutates to bind better to the host’s receptor. 2 algorithms were attempted – greedy search and beam search. The strength of this variant-host binding was then assessed by a transformer network we developed, with a high accuracy of 90%. With both components, beam search eventually proposed more infectious variants. Therefore, this approach can potentially enable researchers to develop vaccines that provide protection against future infectious variants before they emerge, pre-empting outbreaks and saving lives.
Research Interests: RNA viruses, Virus-Host Interactions, Computer Virus, Deep Learning, Virus, and 15 moreComputer Virus and Antivirus, Power transformers, Transformers, Condition monitoring of power transformers, Current Transformers, Perancangan Software Anti Virus, Virus Vector Relationships, Electric Instrument Transformers, Transformerstation, Deeper Learning, Deep Learning Algorithm in Object Recognition, Deep learning algorithms, Deep Reinforcement Learning, Deep Learning, Neural Languages and NLP, and Neural Networks / Deep Learning
A major source of information can be taken from financial news articles, which have some correlations about the fluctuation of stock trends. In this paper, we investigate the influences of financial news on the stock trends, from a... more
A major source of information can be taken from financial news articles, which have some correlations about the fluctuation of stock trends. In this paper, we investigate the influences of financial news on the stock trends, from a multi-instance view. The intuition behind this is based on the news uncertainty in random news occurrences and the lack of annotation for every single financial news. Under the scenario of Multiple Instance Learning (MIL) where training instances are arranged in bags, and a label is assigned for the entire bag instead of instances, we develop a flexible and adaptive multi-instance learning model and evaluate its ability in directional movement forecast of Standard & Poor's 500 index on financial news dataset. Specifically, we treat each trading day as one bag, with certain amounts of news happening on each trading day as instances in each bag. Experiment results demonstrate that our proposed multiinstance-based framework gains outstanding results in terms of the accuracy of trend prediction, compared with other state-of-art approaches and baselines.
Research Interests: Natural Language Processing, Multiple Instance Learning, Text Classification, Natural Languages Processing/Machine Translation, Machine Learning, Natural Language Processing, and 15 moreTexture Classification, Arabic Natural Language Processing, Natural Language Processing, Machine Translation, Machine Learning,natural Language Processing, English Text Classification, Stylometric Text Classification, Natural Language Processing(NLP), Natural Language Processing (NLP)(AI), Arabic Text Classification, Natural Language Processing Text Mining, Financial News, Short Text Classification, Automatic Text Classification, Text Genre Classification, and Stock Trend Forecasting
Additive manufacturing is an emerging and crucial technology that can overcome the limitations of traditional manufacturing techniques to accurately manufacture highly complex parts. X-ray Computed Tomography (XCT) is a widely used method... more
Additive manufacturing is an emerging and crucial technology that can overcome the limitations of traditional manufacturing techniques to accurately manufacture highly complex parts. X-ray Computed Tomography (XCT) is a widely used method for non-destructive testing of AM parts. However, detection and segmentation of defects in XCT images of AM have many challenges due to contrast, size, and appearance of defects. This study developed deep learning techniques for detecting and segmenting defects in XCT images of AM. Due to a large number of required defect annotations, this paper applied image processing techniques to automate the defect labeling process. A single-stage object detection algorithm (YOLOv5) was applied to the problem of defect detection in image data. Three different variants of YOLOv5 were implemented and their performances were compared. U-Net was applied for defect segmentation in XCT slices. Finally, this research demonstrates that deep learning techniques can improve the automatic defect detection and segmentation in XCT data of AM.
Research Interests: Additive Manufacturing, Additive Manufacturing and 3D printing, X ray Computed Tomography, X-ray Computed Micro Tomography, Laser Additive Manufacturing, and 15 moreAdditive Layer Manufacturing, Convolutional Neural Networks, X-ray Computed Tomography, Convolutional Neural Network [CNN], Additive Manufacturing Process, Additive manufacturing technologies, Design for Additive Manufacturing, Convolutional neural networks superpixel, Directed Acyclic Graph Deep Convolutional Neural Network, Convolutional adversarial neural network, deep convolutional neural networks, Region-based Convolutional Neural Network (RCNN), Multi-Channel Convolutional Neural Network (MC-CNN), U-Net, and YOLOV5
The authors present a new Framework of Artificial Intelligence which analyzes the key elements of transformational AI in industry. The State of the Art of Artificial Intelligence is gleaned from an examination of what has been done in the... more
The authors present a new Framework of Artificial Intelligence which analyzes the key elements of transformational AI in industry. The State of the Art of Artificial Intelligence is gleaned from an examination of what has been done in the past, presently in the last decade and what is predicted for future decades. The paper will highlight the biggest changes in AI, important influencers to adoption/diffusion and give examples of how these technologies have and will be applied in three key industrial sectors, including agriculture, education and healthcare. Next the research examines seven driving triggers of cost, speed, accuracy, diversity/inclusion, interdisciplinary research/collaboration and ethics/trustworthiness that are accelerating AI development and concludes with a discussion of what are the critical success factors for industry to be transformational in AI.
Research Interests: Artificial Intelligence, AI Planning (Artificial Intelligence), Diffusion of Innovations, Diffusion Tensor Imaging, Future of artificial intelligence, and 15 moreArtificial General Intelligence, Technology development, Artificial Intelligence (AI), Diffusion MRI, Philosophy of Artificial Intelligence, Diffusion, Technology Adoption, Artificial Intelligence and Machine Learning, Artificial Intelligence And Techniques, Bass diffusion model, Artificial Intelligence applications, Technology Trends, Artificial Intelligence and Robotics, Artificial Intelligence and Nural Networks, and Driving Triggers
Deep Learning (DL) algorithms have shown impressive performance in diverse domains. Among them, audio has attracted many researchers over the last couple of decades due to some interesting patterns–particularly in classification of audio... more
Deep Learning (DL) algorithms have shown impressive performance in diverse domains. Among them, audio has attracted many researchers over the last couple of decades due to some interesting patterns–particularly in classification of audio data. For better performance of audio classification, feature selection and combination play a key role as they have the potential to make or break the performance of any DL model. To investigate this role, we conduct an extensive evaluation of the performance of several cutting-edge DL models (i.e., Convolutional Neural Network, Efficient Net, Mobile Net, Supper Vector Machine and Multi-Perceptron) with various state-of-the-art audio features (i.e., Mel Spectrogram, Mel Frequency Cepstral Coefficients, and Zero Crossing Rate) either independently or as a combination (i.e., through ensembling) on three different datasets (i.e., Free Spoken Digits Dataset, Audio Urdu Digits Dataset, and Audio Gujarati Digits Dataset). Overall, results suggest feature selection depends on both the dataset and the model. However, feature combinations should be restricted to the only features that already achieve good performances when used individually (i.e., mostly Mel Spectrogram, Mel Frequency Cepstral Coefficients). Such feature combination/ensembling enabled us to outperform the previous state-of-the-art results irrespective of our choice of DL model.
Research Interests: Feature Selection, Deep Learning, Automatic Feature Selection, EEG SVM feature selection, Audio Features, and 15 moreAudio Classification, Audio Scene Classification, Deep Learning Algorithm in Object Recognition, Ensemble Feature Selection, Feature Subset Selection, Deep learning algorithms, Sequential Floating Feature Selection (SFFS), Deep Reinforcement Learning, Machine Learning, Deep Learning, Machine Learning & Deep Learning Diagnostics, Multimodal Deep Learning, Deep Learning, Neural Languages and NLP, Neural Networks / Deep Learning, Ensembling, and Deep Learning Applications.
This paper presents a deep learning model that can be used for data standardization tasks. With applications such as insurance processing, accounting, and government forms processingbeing able to standardise data that is presented in a... more
This paper presents a deep learning model that can be used for data standardization tasks. With applications such as insurance processing, accounting, and government forms processingbeing able to standardise data that is presented in a nonstandard format would be impactful across many industries. Restaurant receipt images from CORD dataset were used to build the model. These images were previously processed with OCR then pre-processed to create JSON files that contain the OCR'ed data and metadata of the information on those images. The main challenge in building the model is that the size of the data set is very small (1,000 images). In order to overcome this challenge, an augmentation stage was employed to generate more training samples out of the existing ones. While this standardization problem can be modelled as a classification task, it has been decided to attempt using a regression model that can predict the total on a receipt.
Research Interests: Machine Learning, Applications of Machine Learning, Statistical machine learning, Deep Learning, Machine Learning and Pattern Recognition, and 15 moreArtificial Intelligence and Machine Learning, MAchine Learning Algorithms, Machine Learning Model, Regression Model, Machine Learning Big Data, Applied Machine Learning, Healthcare Insurance, Deep learning algorithms, Data augmentation, Deep Reinforcement Learning, Data Science, Machine Learning, Machine Learning, Deep Learning, Deep Learning, Neural Languages and NLP, Neural Networks / Deep Learning, and Data Standardisation
Rapid technological growth has made Artificial Intelligence (AI) and application of robots common among human lives. The advancements undertaken to make designs with human similarities or adaptations to the society are elaborated in... more
Rapid technological growth has made Artificial Intelligence (AI) and application of robots common among human lives. The advancements undertaken to make designs with human similarities or adaptations to the society are elaborated in detail. The increasing manufacturing and use of robots for industrial purposes have been related to their operating mechanisms. The experiments and laboratory testing of these devices is analysed in form tables to show the statistical side of the technology. This report explains the technological aspects and laboratory experiments that have been advanced to increase knowledge on these digital technologies. This study aims to present an overview of two developing technologies: artificial intelligence (AI) and robots and their potential applications. The product variety is a primary characteristic of each of these specialties. In addition, they may be described as disruptive, facilitating, and transdisciplinary.
Research Interests: Artificial Intelligence, Future of artificial intelligence, Artificial General Intelligence, Parallel Robots, Artificial Intelligence (AI), and 15 moreGame, Autonomous Robots, Mobile Robots, Philosophy of Artificial Intelligence, Robots, Autonomous Mobile Robots, Artificial Intelligence and Machine Learning, Industrial Robots, Artificial Intelligence And Techniques, Visual SLAM for mobile robots, Trust Game, Artificial Intelligence applications, Drones and Robots, KUKA Robots, and Artificial Intelligence and Nural Networks
The viral outbreak of COVID-19 that started in the year 2019, radically changed our everyday life, with a detrimental impact on the simple, daily habits of citizens. In many countries around the world, the usage of mask is necessary as a... more
The viral outbreak of COVID-19 that started in the year 2019, radically changed our everyday life, with a detrimental impact on the simple, daily habits of citizens. In many countries around the world, the usage of mask is necessary as a protection measure against covid-19. Every service, organization, various stores, schools, universities, hospitals, companies and many other places, which are attended by hundreds of people every day, make the use of a mask necessary to enter them. This fact requires the control of the persons when they enter the respective spaces to determine if they are wearing a mask when entering the area. In this research we compared performance on YOLOv4 and the Tiny-YOLOv4 algorithm on images, recorded video, and real time video. In the next step we will implement the YOLOv4 TFlite and Tiny YOLOv4 TFlite model for mobile applications using the Android Studio platform. On the proposed dataset YOLOv4 achieved 92.91% mAP and training took around 2 hours for 1000 iterations. On the other hand, YOLOv4-tiny achieved 74.75% mAP and training took less than half an hour for 1000 iterations. For further improvement we convert YOLOv4 and YOLOv4-tiny to YOLOv4 TFlite and YOLOv4-tiny TFlite respectively. After this step we compare YOLOv4 TFlite and YOLOv4-tiny TFlite model performance on mobile device. YOLOv4 TFlite achieved 96.92% accuracy on real time video at 5017ms and YOLOv4-tiny 74.72% accuracy on real time video at 491ms.
Research Interests: Object Class Detection, Deep Learning, Object Detection and Tracking, Spherical Object Detection, Object Detection, and 15 moreReal Time Object Detection and Tracking, Object Detection Using Laser Scanner, Deep learning algorithms, Deep Reinforcement Learning, Applied Deep Learning, Machine Learning & Deep Learning Diagnostics, Multimodal Deep Learning, YOLOv4, Deep Learning, Neural Languages and NLP, Neural Networks / Deep Learning, face mask detection, Mask detection, Deep Learning Applications., Tiny-YOLOv4, and Real-time videos
Social networking services – such as Facebook.com and Twitter.com – are fast-growing enterprise platform that has become a prevalent and essential component of daily life. Due to its popularity, Twitter draws many spammers or other fake... more
Social networking services – such as Facebook.com and Twitter.com – are fast-growing enterprise platform that has become a prevalent and essential component of daily life. Due to its popularity, Twitter draws many spammers or other fake accounts to post malicious links and infiltrate legitimate users' accounts with many spam messages. Therefore, it is crucial to recognize and screen spam tweets and spam accounts. As a result, spam detection is highly needed but still a difficult challenge. This article applied several Bio-inspired optimization algorithms to reduce the features' dimensions in the first stage. Then we used several classification schemes in the second stage to enhance the spam detection rate in three real Twitter data collections. The performance of the chosen classifiers also revealed that Random Forest and C4.5 classifiers achieved the highest Accuracy, Precision, Recall, and F1-score even on class imbalance.
Research Interests: Machine Learning, Classification (Machine Learning), Social Media, Applications of Machine Learning, SPAM, and 14 moreTwitter, Statistical machine learning, Batteries, Pso, Bat, Classification, Cuckoo Search, Machine Learning and Pattern Recognition, Spam Filtering, MAchine Learning Algorithms, Cuckoo, Machine Learning Big Data, Data Mining Classification, and Twitter Data Analysis
Conventional methods for causal structure learning from data face significant challenges due to combinatorial search space. Recently, the problem has been formulated into a continuous optimization framework with an acyclicity constraint... more
Conventional methods for causal structure learning from data face significant challenges due to combinatorial search space. Recently, the problem has been formulated into a continuous optimization framework with an acyclicity constraint to learn Directed Acyclic Graphs (DAGs). Such a framework allows the utilization of deep generative models for causal structure learning to better capture the relations between data sample distributions and DAGs. However, so far no study has experimented with the use of Wasserstein distance in the context of causal structure learning. Our model named DAG-WGAN combines the Wasserstein-based adversarial loss with an acyclicity constraint in an auto-encoder architecture. It simultaneously learns causal structures while improving its data generation capability. We compare the performance of DAG-WGAN with other models that do not involve the Wasserstein metric in order to identify its contribution to causal structure learning. Our model performs better with high cardinality data according to our experiments.
8th International Conference on Artificial Intelligence and Soft Computing (AISO 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence,... more
8th International Conference on Artificial Intelligence and Soft Computing (AISO 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
Research Interests: Computer Science, Artificial Intelligence, Human Computer Interaction, Computer Vision, Machine Learning, and 9 moreApplications of Machine Learning, Statistical machine learning, Artificial Neural Networks for modeling purposes, Deep Learning, Inteligencia artificial, MAchine Learning Algorithms, Machine Learning Model, Machine Learning Big Data, and Artificial Neural Network
International Conference on NLP and Machine Learning Trends (NLMLT 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of NLP and Machine Learning. Authors are... more
International Conference on NLP and Machine Learning Trends (NLMLT 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of NLP and Machine Learning. Authors are solicited to contribute to the Conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of NLP and Machine Learning Trends.
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10th International Conference of Artificial Intelligence and Fuzzy Logic (AI & FL 2022) provides a forum for researchers who address this issue and to present their work in a peer-reviewed forum. Authors are solicited to contribute to the... more
10th International Conference of Artificial Intelligence and Fuzzy Logic (AI & FL 2022) provides a forum for researchers who address this issue and to present their work in a peer-reviewed forum. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe
significant advances in the following areas, but are not limited to these topics only.
significant advances in the following areas, but are not limited to these topics only.
Research Interests: Robotics, Bioinformatics, Computer Science, Artificial Intelligence, Information Technology, and 15 moreNatural Language Processing, Machine Learning, Data Mining, Mechatronics, Fuzzy Logic, Computational Biology, Soft Computing, Fuzzy Sets, Numerical Analysis, Automatic Control, Fuzzy Systems, Fuzzy Clustering, Fuzzy and Neuro-Fuzzy Systems, Adaptive Neuro-Fuzzy Inference Systems, and Fuzzy Image Modeling
Artificial intelligence techniques are still revealing their pros; however, several fields have benefited from these techniques. In this study we applied the Decision Tree (DT-CART) method derived from artificial intelligence techniques... more
Artificial intelligence techniques are still revealing their pros; however, several fields have benefited from these techniques. In this study we applied the Decision Tree (DT-CART) method derived from artificial intelligence techniques to the prediction of the creditworthy of bank customers, for this we used historical data of bank customers. However we have adopted the flowing process, for this purpose we started with a data preprocessing in which we clean the data and we deleted all rows with outliers or missing values, then we fixed the variable to be explained (dependent or Target) and we also thought to eliminate all explanatory (independent) variables that are not significant using univariate analysis as well as the correlation matrix, then we applied our CART decision tree method using the SPSS tool. After completing our process of building our model (DT-CART), we started the process of evaluating and testing the performance of our model, by which we found that the accuracy and precision of our model is 71%, so we calculated the error ratios, and we found that the error rate equal to 29%, this allowed us to conclude that our model at a fairly good level in terms of precision, predictability and very precisely in predicting the solvency of our banking customers.
Research Interests: Artificial Intelligence, Data Mining, Project Risk Management, Banking, Risk Management, and 15 moreRisk Assessment & Risk Management, Data mining (Data Analysis), Distributed Data Mining, Decision Trees, Data Warehousing and Data Mining, Philosophy of Artificial Intelligence, Credit Risk, Privacy Preserving Data Mining, Data Mining – Concepts and Techniques, Decision Tree Classification, Decision Tree, Credit Risk in Banking, Artificial Intelligence And Techniques, Credit Risk Management, and ID3 Decision Tree Algorithm
Intelligent Object Framework (IOF) is a new communication standard over a wireless network supporting existing multiple sets of architectural solutions. The Framework consists of a framework design that enables devices of different... more
Intelligent Object Framework (IOF) is a new communication standard over a wireless network supporting existing multiple sets of architectural solutions. The Framework consists of a framework design that
enables devices of different platforms to communicate by a common data exchange model via a device management controller. This paper provides a descriptive analysis of functional requirements for the IOF.
The purpose of the proposed system is to provide a platform independent device (Intelligent Object) management by utilization of set components. The functional requirements focus on deriving primary
functionality of server and client applications by description of required inputs, behaviours and outputs
enables devices of different platforms to communicate by a common data exchange model via a device management controller. This paper provides a descriptive analysis of functional requirements for the IOF.
The purpose of the proposed system is to provide a platform independent device (Intelligent Object) management by utilization of set components. The functional requirements focus on deriving primary
functionality of server and client applications by description of required inputs, behaviours and outputs
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High accuracy in cancer prediction is important to improve the quality of the treatment and to improve the rate of survivability of patients. As the data volume is increasing rapidly in the healthcare research, the analytical challenge... more
High accuracy in cancer prediction is important to improve the quality of the treatment and to improve the rate of survivability of patients. As the data volume is increasing rapidly in the healthcare research, the analytical challenge exists in double. The use of effective sampling technique in classification algorithms always yields good prediction accuracy. The SEER public use cancer database provides various prominent class labels for prognosis prediction. The main objective of this paper is to find the effect of sampling techniques in classifying the prognosis variable and propose an ideal sampling method based on the outcome of the experimentation. In the first phase of this work the traditional random sampling and stratified sampling techniques have been used. At the next level the balanced stratified sampling with variations as per the choice of the prognosis class labels have been tested. Much of the initial time has been focused on performing the pre-processing of the SEER data set. The classification model for experimentation has been built using the breast cancer, respiratory cancer and mixed cancer data sets with three traditional classifiers namely Decision Tree, Naïve Bayes and K-Nearest Neighbour. The three prognosis factors survival, stage and metastasis have been used as class labels for experimental comparisons. The results shows a steady increase in the prediction accuracy of balanced stratified model as the sample size increases, but the traditional approach fluctuates before the optimum results.
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According to this fact that educational system is the base of constant development in every country and this system educates human-forces and this forces,are accelerators and a factor, of achieving the goals of development,the educational... more
According to this fact that educational system is the base of constant development in every country and this system educates human-forces and this forces,are accelerators and a factor, of achieving the goals of development,the educational system can play, Major role in the context economic behavior, in this context some concepts are regarded as behavioral targets and performance.In educational system, handling artificial intelligence, in teaching and learning process, had a surprising evolution through educational advantages, making job, respecting customers rights and customer relationship management, to assist priority and citizenship, correct investment through formal markets. Science-Based economy, resistible economy and a positive view to job and Iran capital,including concepts which can be institutionalize in to the educational system. In this paper it is decided to pose a new method, creating a proper cultural and scientific bed, this helps.That the educational system behavioral goals, better and stable being achieved. The method presented in this paper is general and based on handling artificial intelligence, information technology and electronic content management that means in an intelligent educational system.The educational goals can be better achieved and managed by new technology of education.
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Wireless sensor network (WSN) are powered by batteries to perform various sensing tasks in a given environment. The measurements made by the sensors are sometimes unreliable and erroneous due to noise in the sensor or hardware failure.... more
Wireless sensor network (WSN) are powered by batteries to perform various sensing tasks in a given environment. The measurements made by the sensors are sometimes unreliable and erroneous due to noise in the sensor or hardware failure. For a large scale WSN to be economically feasible, it is important to ensure that the faulty node does not affect the overall behaviour of the system. In this paper a binary fault-tolerant event detection technique has been proposed for the non-symmetric errors and its performance has been analysed. Theoretical analysis and simulation show that almost 97 percent of faults can be corrected even when 10 percent sensor nodes are faulty.
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In a Power plant with a Distributed Control System (DCS), process parameters are continuously stored in databases at discrete intervals. The data contained in these databases may not appear to contain valuable relational information but... more
In a Power plant with a Distributed Control System (DCS), process parameters are continuously stored in databases at discrete intervals. The data contained in these databases may not appear to contain valuable relational information but practically such a relation exists. The large number of process parameter values are changing with time in a Power Plant. These parameters are part of rules framed by domain experts for the expert system. With the changes in parameters there is a quite high possibility to form new rules using the dynamics of the process itself. We present an efficient algorithm that generates all significant rules based on the real data. The association based algorithms were compared and the best suited algorithm for this process application was selected. The application for the Learning system is studied in a Power Plant domain. The SCADA interface was developed to acquire online plant data.
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Great knowledge and experience on microbiology are required for accurate bacteria identification. Automation of bacteria identification is required because there might be a shortage of skilled microbiologists and clinicians at a time of... more
Great knowledge and experience on microbiology are required for accurate bacteria identification. Automation of bacteria identification is required because there might be a shortage of skilled microbiologists and clinicians at a time of great need. There have been several attempts to perform automatic background identification. This paper reviews state-of-the-art automatic bacteria identification techniques. This paper also provides discussion on limitations of state-of-the-art automatic bacteria identification systems and recommends future direction of automatic bacteria identification.
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Nowadays, asynchronous motors have wide range use in many industrial applications. Field oriented control (FOC) and direct torque control (DTC) are commonly used methods in high performance vector control for asynchronous motors.... more
Nowadays, asynchronous motors have wide range use in many industrial applications. Field oriented control (FOC) and direct torque control (DTC) are commonly used methods in high performance vector control for asynchronous motors. Therefore, it is very important to identify clearly advantages and disadvantages of both systems in the selection of appropriate control methods for many industrial applications. This paper aims to present a new and different perspective regarding the comparison of the switching behaviours on the FOC and the DTC drivers. For this purpose, the experimental studies have been carried out to compare the inverter switching frequencies and torque responses of the asynchronous motor in the FOC and the DTC systems under different working conditions. The dSPACE 1103 controller board was programmed with Matlab/Simulink software. As expected, the experimental studies showed that the FOC controlled motors has a lessened torque ripple. On the other hand, the FOC controlled motor switching frequency has about 65-75% more than the DTC controlled under both loaded and unloaded working conditions.
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ABSRACT Many of the robotic grasping researches have been focusing on stationary objects. And for dynamic moving objects, researchers have been using real time captured images to locate objects dynamically. However, this approach of... more
ABSRACT Many of the robotic grasping researches have been focusing on stationary objects. And for dynamic moving objects, researchers have been using real time captured images to locate objects dynamically. However, this approach of controlling the grasping process is quite costly, implying a lot of resources and image processing.Therefore, it is indispensable to seek other method of simpler handling… In this paper, we are going to detail the requirements to manipulate a humanoid robot arm with 7 degree-of-freedom to grasp and handle any moving objects in the 3-D environment in presence or not of obstacles and without using the cameras. We use the OpenRAVE simulation environment, as well as, a robot arm instrumented with the Barrett hand. We also describe a randomized planning algorithm capable of planning. This algorithm is an extent of RRT-JT that combines exploration, using a Rapidly-exploring Random Tree, with exploitation, using Jacobian-based gradient descent, to instruct a 7-DoF WAM robotic arm, in order to grasp a moving target, while avoiding possible encountered obstacles. We present a simulation of a scenario that starts with tracking a moving mug then grasping it and finally placing the mug in a determined position, assuring a maximum rate of success in a reasonable time.
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In economical societies of today, using cash is an inseparable aspect of human's life. People use cash for marketing, services, entertainments, bank operations and so on. This huge amount of contact with cash and the necessity of knowing... more
In economical societies of today, using cash is an inseparable aspect of human's life. People use cash for marketing, services, entertainments, bank operations and so on. This huge amount of contact with cash and the necessity of knowing the monetary value of it caused one of the most challenging problems for visually impaired people. In this paper we propose a mobile phone based approach to identify monetary value of a picture taken from a banknote using some image processing and machine vision techniques. While the developed approach is very fast, it can recognize the value of the banknote by an average accuracy rate of about 97% and can overcome different challenges like rotation, scaling, collision, illumination changes, perspective, and some others.
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The feature selection or extraction is the most important task in Opinion mining and Sentimental Analysis (OSMA) for calculating the polarity score. These scores are used to determine the positive, negative, and neutral polarity about the... more
The feature selection or extraction is the most important task in Opinion mining and Sentimental Analysis (OSMA) for calculating the polarity score. These scores are used to determine the positive, negative, and neutral polarity about the product, user reviews, user comments, and etc., in social media for the purpose of decision making and Business Intelligence to individuals or organizations. In this paper, we have performed an experimental study for different feature extraction or selection techniques available for opinion mining task. This experimental study is carried out in four stages. First, the data collection process has been done from readily available sources. Second, the pre-processing techniques are applied automatically using the tools to extract the terms, POS (Parts-of-Speech). Third, different feature selection or extraction techniques are applied over the content. Finally, the empirical study is carried out for analyzing the sentiment polarity with different features.
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Recently the use of the Monte-Carlo Tree Search algorithm, and in particular its most famous implementation, the Upper Confidence Tree can be seen has a key moment for artificial intelligence in games. This family of algorithms provides... more
Recently the use of the Monte-Carlo Tree Search algorithm, and in particular its most famous implementation, the Upper Confidence Tree can be seen has a key moment for artificial intelligence in games. This family of algorithms provides huge improvements in numerous games, such as Go, Havannah, Hex or Amazon. In this paper we study the use of this algorithm on the game of Mr Jack and in particular how to deal with a specific decision-making process.Mr Jack is a 2-player game, from the family of board games. We will present the difficulties of designing an artificial intelligence for this kind of games, and we show that Monte-Carlo Tree Search is robust enough to be competitive in this game with a smart approach.
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The improvement of health and nutritional status of the society has been one of the thrust areas for social developments programmes of the country. The present states of healthcare facilities in India are inadequate when compared to... more
The improvement of health and nutritional status of the society has been one of the thrust areas for social developments programmes of the country. The present states of healthcare facilities in India are inadequate when compared to international standards. The average Indian spending on healthcare is much below the global average spending. Indian healthcare Industry is growing at the rapid pace of more than 18%, the fastest in the world. The prospects for Indian healthcare are to the tune of USD 40 billion, while global market is USD 1660 trillion. India has all the prospects to become medical tourism destination of the world, because it has a large pool of low-cost scientifically trained technical personal and is one of the favoured counties for cost effective healthcare. As per the reports of Global Burden of Neurological Disorders Estimations and Projections survey there is big shortage of neurologist in India and around the world. So Authors would like to develop an innovative IT based solution to help doctors in rural areas to gain expertise in Neuro Science and treat patients like expert neurologist. This paper aims to survey the Soft Computing techniques in treating neural patient's problems used throughout the world
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Temporally extended actions have been proved to enhance the performance of reinforcement learning agents. The broader framework of 'Options' gives us a flexible way of representing such extended course of action in Markov decision... more
Temporally extended actions have been proved to enhance the performance of reinforcement learning agents. The broader framework of 'Options' gives us a flexible way of representing such extended course of action in Markov decision processes. In this work we try to adapt options framework to model an operating system scheduler, which is expected not to allow processor stay idle if there is any process ready or waiting for its execution. A process is allowed to utilize CPU resources for a fixed quantum of time (timeslice) and subsequent context switch leads to considerable overhead. In this work we try to utilize the historical performances of a scheduler and try to reduce the number of redundant context switches. We propose a machine-learning module, based on temporally extended reinforcement-learning agent, to predict a better performing timeslice. We measure the importance of states, in option framework, by evaluating the impact of their absence and propose an algorithm to identify such checkpoint states. We present empirical evaluation of our approach in a maze-world navigation and their implications on "adaptive timeslice parameter" show efficient throughput time.
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Object detection and recognition are important problems in computer vision and pattern recognition domain. Human beings are able to detect and classify objects effortlessly but replication of this ability on computer based systems has... more
Object detection and recognition are important problems in computer vision and pattern recognition domain. Human beings are able to detect and classify objects effortlessly but replication of this ability on computer based systems has proved to be a non-trivial task. In particular, despite significant research efforts focused on meta-heuristic object detection and recognition, robust and reliable object recognition systems in real time remain elusive. Here we present a survey of one particular approach that has proved very promising for invariant feature recognition and which is a key initial stage of multi-stage network architecture methods for the high level task of object recognition.
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Ontologies are being used to organize information in many domains like artificial intelligence, information science, semantic web, library science. Ontologies of an entity having different information can be merged to create more... more
Ontologies are being used to organize information in many domains like artificial intelligence, information science, semantic web, library science. Ontologies of an entity having different information can be merged to create more knowledge of that particular entity. Ontologies today are powering more accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0, also termed as the semantic web, ontologies will play a more important role. Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can yield basic information about an entity. This paper proposes an automated method for ontology creation, using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning. Concepts drawn from these domains help in designing more accurate ontologies represented using the XML format. This paper uses document classification using classification algorithms for assigning labels to documents, document similarity to cluster similar documents to the input document, together, and summarization to shorten the text and keep important terms essential in making the ontology. The module is constructed using the Python programming language and NLTK (Natural Language Toolkit). The ontologies created in XML will convey to a lay person the definition of the important term's and their lexical relationships.
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All types of machine automated systems are generating large amount of data in different forms like statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we are discussing issues,... more
All types of machine automated systems are generating large amount of data in different forms like statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we are discussing issues, challenges, and application of these types of Big Data with the consideration of big data dimensions. Here we are discussing social media data analytics, content based analytics, text data analytics, audio, and video data analytics their issues and expected application areas. It will motivate researchers to address these issues of storage, management, and retrieval of data known as Big Data. As well as the usages of Big Data analytics in India is also highlighted.
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Big Data is used to store huge volume of both structured and unstructured data which is so large and is hard to process using current / traditional database tools and software technologies. The goal of Big Data Storage Management is to... more
Big Data is used to store huge volume of both structured and unstructured data which is so large and is hard to process using current / traditional database tools and software technologies. The goal of Big Data Storage Management is to ensure a high level of data quality and availability for business intellect and big data analytics applications. Graph database which is not most popular NoSQL database compare to relational database yet but it is a most powerful NoSQL database which can handle large volume of data in very efficient way. It is very difficult to manage large volume of data using traditional technology. Data retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are available. This paper describe what is big data storage management, dimensions of big data, types of data, what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic structure of graph database, advantages, disadvantages and application area and comparison of various graph database.
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This paper presents a method for calculating the light generated current, the series resistance, shun resistance and the two components of the reverse saturation current usually encountered in the double diode representation of the solar... more
This paper presents a method for calculating the light generated current, the series resistance, shun resistance and the two components of the reverse saturation current usually encountered in the double diode representation of the solar cell from the experimental values of the current-voltage characteristics of the cell using genetic algorithm. The theory is able to regenerate the above mentioned parameters to very good accuracy when applied to cell data that was generated from pre-defined parameters. The method is applied to various types of space quality solar cells and sub cells. All parameters except the light generated current are seen to be nearly the same in the case of a cell whose characteristics under illumination and in dark were analyzed. The light generated current is nearly equal to the short-circuit current in all cases. The parameters obtained by this method and another method are nearly equal wherever applicable. The parameters are also shown to represent the current-voltage characteristics well.
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In the magnitude of internet one need to devote extra time to investigate anticipated resource, especially when one need to search information from documents. For the higher range internet there is serious need to demand the essentiality... more
In the magnitude of internet one need to devote extra time to investigate anticipated resource, especially when one need to search information from documents. For the higher range internet there is serious need to demand the essentiality to discover the reserved resources. One of the solutions for information retrieval from document repository is to attach tags to documents. Numerous online social bookmarking services permit users to attach tags with resources which are eventually meta-data, frequently stated as folksonomy. In current paper, authors implemented this model for information retrieval by utilizing these tags, after retrieving by using delicious API and synthesize tag cloud in an Indian University to search and retrieve information from document repository.
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Internet is the boon in modern era as every organization uses it for dissemination of information and e-commerce related applications. Sometimes people of organization feel delay while accessing internet in spite of proper bandwidth.... more
Internet is the boon in modern era as every organization uses it for dissemination of information and e-commerce related applications. Sometimes people of organization feel delay while accessing internet in spite of proper bandwidth. Prediction model of web caching and prefetching is an ideal solution of this delay problem. Prediction model analysing history of internet user from server raw log files and determine future sequence of web objects and placed all web objects to nearer to the user so access latency could be reduced to some extent and problem of delay is to be solved. To determine sequence of future web objects, it is necessary to determine proximity of one web object with other by identifying proper distance metric technique related to web caching and prefetching. This paper studies different distance metric techniques and concludes that bio informatics based distance metric techniques are ideal in context to Web Caching and Web Prefetching.
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Electric power is a basic need in today's life. Due to the extensive usage of power, there is a need to look for an alternate clean energy source. Recently many researchers have focused on the solar energy as a reliable alternative power... more
Electric power is a basic need in today's life. Due to the extensive usage of power, there is a need to look for an alternate clean energy source. Recently many researchers have focused on the solar energy as a reliable alternative power source. Photovoltaic panels are used to collect sun radiation and convert it into electrical energy. Most of the photovoltaic panels are deployed in a fixed position, they are inefficient as they are fixed only at a specific angle. The efficiency of photovoltaic systems can be considerably increased with an ability to change the panels angel according to the sun position. The main goal of such systems is to make the sun radiation perpendicular to the photovoltaic panels as much as possible all the day times. This paper presents a dual axis design for a fuzzy inference approach-based solar tracking system. The system is modeled using Mamdani fuzzy logic model and the different combinations of ANFIS modeling. Models are compared in terms of the correlation between the actual testing data output and their corresponding forecasted output. The Mean Absolute Percent Error and Mean Percentage Error are used to measure the models error size. In order to measure the effectiveness of the proposed models, we compare the output power produced by a fixed photovoltaic panels with the output which would be produced if the dual-axis panels are used. Results show that dual-axis solar tracker system will produce 22% more power than a fixed panels system.
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Advancement in information and technology has made a major impact on medical science where the researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer is one such disease killing large... more
Advancement in information and technology has made a major impact on medical science where the researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer is one such disease killing large number of people around the world. Diagnosing the disease at its earliest instance makes a huge impact on its treatment. The authors propose a Binary Bat Algorithm (BBA) based Feedforward Neural Network (FNN) hybrid model, where the advantages of BBA and efficiency of FNN is exploited for the classification of three benchmark breast cancer datasets into malignant and benign cases. Here BBA is used to generate a V-shaped hyperbolic tangent function for training the network and a fitness function is used for error minimization. FNNBBA based classification produces 92.61% accuracy for training data and 89.95% for testing data.
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Generating images from a text description is as challenging as it is interesting. The Adversarial network performs in a competitive fashion where the networks are the rivalry of each other. With the introduction of Generative Adversarial... more
Generating images from a text description is as challenging as it is interesting. The Adversarial network performs in a competitive fashion where the networks are the rivalry of each other. With the introduction of Generative Adversarial Network, lots of development is happening in the field of Computer Vision. With generative adversarial networks as the baseline model, studied Stack GAN consisting of two-stage GANS step-by-step in this paper that could be easily understood. This paper presents visual comparative study of other models attempting to generate image conditioned on the text description. One sentence can be related to many images. And to achieve this multi-modal characteristic, conditioning augmentation is also performed. The performance of Stack-GAN is better in generating images from captions due to its unique architecture. As it consists of two GANS instead of one, it first draws a rough sketch and then corrects the defects yielding a high-resolution image.
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5th International Conference on NLP & Big Data (NLPD 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing and Big data.
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The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications .The journal... more
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications .The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
Research Interests:
2nd International Conference on Machine Learning, NLP and Data Mining (MLDA 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Machine Learning, Natural... more
2nd International Conference on Machine Learning, NLP and Data Mining (MLDA 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Machine Learning, Natural Language Computing and Data Mining. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only.
Research Interests:
11th International Conference of Artificial Intelligence and Fuzzy Logic (AI & FL 2023) provides a forum for researchers who address this issue and to present their work in a peer-reviewed forum. Authors are solicited to contribute to the... more
11th International Conference of Artificial Intelligence and Fuzzy Logic (AI & FL 2023) provides a forum for researchers who address this issue and to present their work in a peer-reviewed forum. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only.
Research Interests:
International Conference on Machine Learning and IoT (MLIoT 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Machine Learning and Internet of Things (IoT).... more
International Conference on Machine Learning and IoT (MLIoT 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Machine Learning and Internet of Things (IoT).
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only.
Research Interests:
International Conference on Machine Learning and IoT (MLIoT 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Machine Learning and Internet of Things (IoT).... more
International Conference on Machine Learning and IoT (MLIoT 2023) will provide an excellent
international forum for sharing knowledge and results in theory, methodology and applications of
Machine Learning and Internet of Things (IoT).
Authors are solicited to contribute to the conference by submitting articles that illustrate research results,
projects, surveying works and industrial experiences that describe significant advances in the following
areas, but are not limited to these topics only.
international forum for sharing knowledge and results in theory, methodology and applications of
Machine Learning and Internet of Things (IoT).
Authors are solicited to contribute to the conference by submitting articles that illustrate research results,
projects, surveying works and industrial experiences that describe significant advances in the following
areas, but are not limited to these topics only.
Research Interests:
10th International Conference on Signal Processing (CSIP 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Signal and Image Processing. The Conference looks... more
10th International Conference on Signal Processing (CSIP 2023) will provide an excellent
international forum for sharing knowledge and results in theory, methodology and applications
of Signal and Image Processing. The Conference looks for significant contributions to all major
fields of the Signal and Image Processing in theoretical and practical aspects. The aim of the
conference is to provide a platform to the researchers and practitioners from both academia as
well as industry to meet and share cutting-edge development in the field.
international forum for sharing knowledge and results in theory, methodology and applications
of Signal and Image Processing. The Conference looks for significant contributions to all major
fields of the Signal and Image Processing in theoretical and practical aspects. The aim of the
conference is to provide a platform to the researchers and practitioners from both academia as
well as industry to meet and share cutting-edge development in the field.
Research Interests:
4th International Conference on Data Mining & Machine Learning (DMML 2023) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Data Mining and Machine... more
4th International Conference on Data Mining & Machine Learning (DMML 2023) will act
as a major forum for the presentation of innovative ideas, approaches, developments, and
research projects in the areas of Data Mining and Machine Learning. It will also serve to facilitate
the exchange of information between researchers and industry professionals to discuss the latest
issues and advancement in the area of Big Data and Machine Learning.
Authors are solicited to contribute to the conference by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances in
Data Mining and Machine Learning.
as a major forum for the presentation of innovative ideas, approaches, developments, and
research projects in the areas of Data Mining and Machine Learning. It will also serve to facilitate
the exchange of information between researchers and industry professionals to discuss the latest
issues and advancement in the area of Big Data and Machine Learning.
Authors are solicited to contribute to the conference by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances in
Data Mining and Machine Learning.
Research Interests: Machine Learning, Data Mining, Classification (Machine Learning), Statistical machine learning, Educational Data Mining, and 9 moreData mining (Data Analysis), Distributed Data Mining, Machine Learning and Pattern Recognition, Machine Learning Model, Big Data / Analytics / Data Mining, Machine Learning Big Data, Applied Machine Learning, Extreme Learning Machines, and Quantum Machine Learning
9 th International Conference on Artificial Intelligence and Applications (AI 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its... more
9
th International Conference on Artificial Intelligence and Applications (AI 2023) will
provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Artificial Intelligence and its applications. The Conference
looks for significant contributions to all major fields of the Artificial Intelligence, Soft
Computing in theoretical and practical aspects. The aim of the Conference is to provide a
platform to the researchers and practitioners from both academia as well as industry to meet and
share cutting-edge development in the field.
th International Conference on Artificial Intelligence and Applications (AI 2023) will
provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Artificial Intelligence and its applications. The Conference
looks for significant contributions to all major fields of the Artificial Intelligence, Soft
Computing in theoretical and practical aspects. The aim of the Conference is to provide a
platform to the researchers and practitioners from both academia as well as industry to meet and
share cutting-edge development in the field.
Research Interests:
4th International Conference on Machine Learning Techniques and NLP (MLNLP 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Machine Learning Techniques and... more
4th International Conference on Machine Learning Techniques and NLP (MLNLP 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Machine Learning Techniques and NLP.
Research Interests:
11th International Conference of Artificial Intelligence and Fuzzy Logic (AI & FL 2023) provides a forum for researchers who address this issue and to present their work in a peer-reviewed forum. Authors are solicited to contribute to the... more
11th International Conference of Artificial Intelligence and Fuzzy Logic (AI & FL
2023) provides a forum for researchers who address this issue and to present their work in a
peer-reviewed forum. Authors are solicited to contribute to the conference by submitting articles
that illustrate research results, projects, surveying works and industrial experiences that describe
significant advances in the following areas, but are not limited to these topics only.
2023) provides a forum for researchers who address this issue and to present their work in a
peer-reviewed forum. Authors are solicited to contribute to the conference by submitting articles
that illustrate research results, projects, surveying works and industrial experiences that describe
significant advances in the following areas, but are not limited to these topics only.
Research Interests:
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the... more
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.
Research Interests: Image Processing, Machine Learning, Optical Fiber Communications, Wireless Communications, Digital Signal Processing, and 15 moreApplications of Machine Learning, Satellite Communications, Video Processing, Image segmentation, Emerging Technologies, Video and Data Transmission over Mobile Networks, Mobile Communications, Image and Video Processing, Wireless Power Transmission, Transmission and Distribution, Electronics and Communications Engineering, Digital Signal Processing and Its Applications, Communications, Cyber forensics and information security, and Automatic Storage and Retrieval System
org/bmli/index Scope 4 th International Conference on Big Data, Machine Learning and IoT (BMLI 2023) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Big... more
org/bmli/index Scope 4 th International Conference on Big Data, Machine Learning and IoT (BMLI 2023) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Big Data, Machine Learning and IoT. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in Big Data, Machine Learning and IoT.
Research Interests: Machine Learning, Classification (Machine Learning), Applications of Machine Learning, Statistical machine learning, Big Data, and 14 moreMachine Learning and Pattern Recognition, MAchine Learning Algorithms, Internet of Things (IoT), Big Data Analytics, Big Data / Analytics / Data Mining, Big Data Visualisation, Machine Learning Big Data, Applied Machine Learning, Extreme Learning Machines, Big Data Technologies, Big Data Security, Big Data Applications, Application of Big Data, and Internet of Things (IoTs)
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications... more
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Artificial Intelligence & applications. Topics of interest include, but are not limited to, the following:
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Artificial Intelligence & applications. Topics of interest include, but are not limited to, the following:
Research Interests: Artificial Intelligence, History of Artificial Intelligence, Automatic Control, Future of artificial intelligence, Automatic Flight Control Systems, and 6 moreArtificial Intelligence and Law, Artificial Intelligence in Education, Philosophy of Artificial Intelligence, AI Search Algorithms, AI algorithms, and Algorithms Aided Design
Facial recognition (FR) is a pattern recognition problem, in which images can be considered as a matrix of pixels.There are manychallenges that affect the performance of face recognitionincluding illumination variation, occlusion, and... more
Facial recognition (FR) is a pattern recognition problem, in which images can be considered as a matrix of
pixels.There are manychallenges that affect the performance of face recognitionincluding illumination
variation, occlusion, and blurring. In this paper,a few preprocessing techniques are suggested to handle the
illumination variationsproblem. Also, other phases of face recognition problems like feature extraction and
classification are discussed. Preprocessing techniques like Histogram Equalization (HE), Gamma Intensity
Correction (GIC), and Regional Histogram Equalization (RHE) are tested inthe AT&T database. For
feature extraction, methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis
(LDA), Independent Component Analysis (ICA), and Local Binary Pattern (LBP) are applied. Support
Vector Machine (SVM) is used as the classifier. Both holistic and block-based methods are tested using the
AT&T database. For twelve different combinations of preprocessing, feature extraction, and classification
methods, experiments involving various block sizes are conducted to assess the computation performance
and recognition accuracy for the AT&T dataset.Using the block-based method, 100% accuracy is achieved
with the combination of GIC preprocessing, LDA feature extraction,and SVM classification using 2x2
block-sizingwhile the holistic method yields the maximum accuracy of 93.5%. The block-sized algorithm
performs better than the holistic approach under poor lighting conditions.SVM Radial Basis Function
performs extremely well on theAT&Tdataset for both holistic and block-based approaches
pixels.There are manychallenges that affect the performance of face recognitionincluding illumination
variation, occlusion, and blurring. In this paper,a few preprocessing techniques are suggested to handle the
illumination variationsproblem. Also, other phases of face recognition problems like feature extraction and
classification are discussed. Preprocessing techniques like Histogram Equalization (HE), Gamma Intensity
Correction (GIC), and Regional Histogram Equalization (RHE) are tested inthe AT&T database. For
feature extraction, methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis
(LDA), Independent Component Analysis (ICA), and Local Binary Pattern (LBP) are applied. Support
Vector Machine (SVM) is used as the classifier. Both holistic and block-based methods are tested using the
AT&T database. For twelve different combinations of preprocessing, feature extraction, and classification
methods, experiments involving various block sizes are conducted to assess the computation performance
and recognition accuracy for the AT&T dataset.Using the block-based method, 100% accuracy is achieved
with the combination of GIC preprocessing, LDA feature extraction,and SVM classification using 2x2
block-sizingwhile the holistic method yields the maximum accuracy of 93.5%. The block-sized algorithm
performs better than the holistic approach under poor lighting conditions.SVM Radial Basis Function
performs extremely well on theAT&Tdataset for both holistic and block-based approaches
Research Interests:
Firearm Shootings and stabbings attacks are intense and result in severe trauma and threat to public safety. Technology is needed to prevent lone-wolf attacks without human supervision. Hence designing an automatic weapon detection using... more
Firearm Shootings and stabbings attacks are intense and result in severe trauma and threat to public
safety. Technology is needed to prevent lone-wolf attacks without human supervision. Hence designing
an automatic weapon detection using deep learning, is an optimized solution to localize and detect the
presence of weapon objects using Neural Networks. This research focuses on both unified and II-stage
object detectors whose resultant model not only detects the presence of weapons but also classifies with
respective to its weapon classes, including handgun, knife, revolver, and rifle, along with person
detection. This research focuses on YOLOv5 (You Look Only Once) family and Faster RCNN family for
model validation and training. Pruning and Ensembling techniques were applied to YOLOv5 to
enhance their speed and performance. YOLOv5 models achieve the highest score of 78% with an
inference speed of 8.1ms. However, Faster R-CNN models achieve the highest AP 89%.
safety. Technology is needed to prevent lone-wolf attacks without human supervision. Hence designing
an automatic weapon detection using deep learning, is an optimized solution to localize and detect the
presence of weapon objects using Neural Networks. This research focuses on both unified and II-stage
object detectors whose resultant model not only detects the presence of weapons but also classifies with
respective to its weapon classes, including handgun, knife, revolver, and rifle, along with person
detection. This research focuses on YOLOv5 (You Look Only Once) family and Faster RCNN family for
model validation and training. Pruning and Ensembling techniques were applied to YOLOv5 to
enhance their speed and performance. YOLOv5 models achieve the highest score of 78% with an
inference speed of 8.1ms. However, Faster R-CNN models achieve the highest AP 89%.