Advanced persistent threat (APT) is a serious concern in cyber-security that has matured and grow... more Advanced persistent threat (APT) is a serious concern in cyber-security that has matured and grown over the years with the advent of technology. The main aim of this study is to establish an effective identification model for APT attacks to prevent and reduce their influence. Machine learning has the potential as well as substantial background to detect and predict cyber-security threats including APT. This study utilized several boosting-based machine learning methods to predict various types of APTs that are consistent in cyber-security domain. Furthermore, Explainable Artificial Intelligence (XAI) was coupled with the predictions to provide actionable insights to the domain stakeholders as well as practitioners in this domain. The results, particularly XGBoost with weighted F1 score of 0.97 and SHapley Additive exPlanations (SHAP)-based explanation, prove that boosting methods as well as machine learning models paired with XAI are indeed promising in handling cyber-security-relat...
Artificial intelligence has significantly enhanced the research paradigm and spectrum with a subs... more Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in the real world domain. Artificial intelligence, the driving force of the current technological revolution, has been used in many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, and most importantly the healthcare sector. With the rise of the COVID-19 pandemic, several prediction and detection methods using artificial intelligence have been employed to understand, forecast, handle, and curtail the ensuing threats. In this study, the most recent related publications, methodologies and medical reports were investigated with the purpose of studying artificial intelligence’s role in the pandemic. This study presents a comprehensive review of artificial intelligence with specific attention to machine learning, deep learning, image processing, object detection, image segmentation, and few-sh...
Yoga has become an integral part of human life to maintain a healthy body and mind in recent time... more Yoga has become an integral part of human life to maintain a healthy body and mind in recent times. With the growing, fast-paced life and work from home, it has become difficult for people to invest time in the gymnasium for exercises. Instead, they like to do assisted exercises at home where pose recognition techniques play the most vital role. Recognition of different poses is challenging due to proper dataset and classification architecture. In this work, we have proposed a deep learning-based model to identify five different yoga poses from comparatively fewer amounts of data. We have compared our model’s performance with some state-of-the-art image classification models-ResNet, InceptionNet, InceptionResNet, Xception and found our architecture superior. Our proposed architecture extracts spatial, and depth features from the image individually and considers them for further calculation in classification. The experimental results show that it achieved 94.91% accuracy with 95.61% ...
Proceedings of the International Conference on Computing Advancements
Most of the modern processors achieve instruction level parallelism by executing instructions wit... more Most of the modern processors achieve instruction level parallelism by executing instructions without the original program order. Although the instructions can execute without preserving the original program sequence, they must commit in-order once execution ends.A Reorder Buffer (ROB) is typically used in traditional out-of-order processors to ensure the in-order commitment of the instructions, which has the ability to impede the processor entirely if the head of ROB is obstructed with an instruction. In this paper, we present an alternate approach mechanism for reorder buffer to do out-of-order commit of instructions. The proposed mechanism extends the ROB by adding a Stalled Instruction Zone (SI-Zone) which accommodates batches of dependent instructions and allows out-of-order commit for the ROB. The particular instruction that makes initial stall and other dependent instructions are moved to SI-zone to make way for the normal instructions to commit at the ROB. The SI-zone commits the instructions in batch mode once the stall is cleared. The outcome is an efficient processor that is ROB-based and increases both the effective size of ROB and number of physical registers.
The devastating impact of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) pandem... more The devastating impact of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) pandemic almost halted the global economy and is responsible for 6 million deaths with infection rates of over 524 million. With significant reservations, initially, the SARS-CoV-2 virus was suspected to be infected by and closely related to Bats. However, over the periods of learning and critical development of experimental evidence, it is found to have some similarities with several gene clusters and virus proteins identified in animal-human transmission. Despite this substantial evidence and learnings, there is limited exploration regarding the SARS-CoV-2 genome to putative microRNAs (miRNAs) in the virus life cycle. In this context, this paper presents a detection method of SARS-CoV-2 precursor-miRNAs (pre-miRNAs) that helps to identify a quick detection of specific ribonucleic acid (RNAs). The approach employs an artificial neural network and proposes a model that estimated accuracy of 98...
2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD), 2021
Parasites and viruses are responsible for attacking the immune system of human body and destroyin... more Parasites and viruses are responsible for attacking the immune system of human body and destroying it in the process which makes detection of parasites and virus a penultimate task in modern medical analysis. The detection process is often characterized with symptoms, microscopic tests. However, little research work has been carried out to exploit and garner the capability of deep learning to classify and detect the parasites and virus. In our research work, we propose a new deep neural network based image processing approach to classify the parasites, germs and virus for the case of Entamoeba parasite which leads to Amebiasis disease and John Cunningham virus which is responsible for Progressive multifocal leukoencephalopathy (PML). We have applied our own deep convolutional neural network to classify the JC virus and entamoeba using immunohistochemistry(IHC) images and microscope images from a dataset that We have collected from various neuropathology laboratories, and researchers. Our model achieved overall classification accuracy of 77% and F1-score of 76% with a strong narrative of rationale for utilization of batch normalization and dropout layer in deep learning model.
2021 24th International Conference on Computer and Information Technology (ICCIT), 2021
The technological explosion has paved the way for agriculture to flourish exponentially thus cont... more The technological explosion has paved the way for agriculture to flourish exponentially thus contributing to better yield of crops through the aid of machine learning, the Internet of things, mechanical systems in agriculture. In our research work, we have investigated various types of dry beans followed by a deep neural network based approach to classify the beans automatically. The results shows that our approach had an accuracy of 93.44%, and an F-1 score of 94.57%, with the dataset that consisted of 7 varieties of dry beans. Our results, which performed substantially better in comparison to traditional machine learning approaches aided us to devise further research scopes in the field of agricultural machine learning.
2021 24th International Conference on Computer and Information Technology (ICCIT), 2021
k-means is the most widely used clustering algorithm which is an unsupervised technique that need... more k-means is the most widely used clustering algorithm which is an unsupervised technique that needs assumptions of centroids to begin the process. Hence, the problem is NP-hard and needs careful consideration and optimization to get a better quality of clusters of data. In this work, a meta-heuristic based genetic algorithm is proposed to optimize the centroid initialization process. The proposed method includes tournament selection, probability-based mutation, and elitism that leads to finding the optimal centroids for the clusters of a given dataset. Nine different and diversified datasets were used to test the performance of the proposed method in terms of the davies-bouldin index and it performed better in all the datasets than the standard k-means and minibatch k-means algorithm.
2017 20th International Conference of Computer and Information Technology (ICCIT), 2017
The importance of online news article has evolved notably with the advancement of information and... more The importance of online news article has evolved notably with the advancement of information and technology. However, some of the news are violent as well as obnoxious. So, identifying and categorizing online news article automatically is important as well as remains challenging. Using opinion mining and sentiment analysis, we propose an intuitive approach of detecting positive or negative news from an online news article. Our approach consists of a sentence identification phase, followed by a dynamic library of predefined negative and positive strings and at last marking whether the paragraph is positive, negative or neutral. Our approach detects the polarity of online news articles with around 91% accuracy rate. Sentence type identification before using dynamic dictionary of positive and negative words is the key factor which resolves the issue of finding out the part of the sentence which holds the polarity of the sentence.
Coronavirus (COVID) has claimed numerous lives since its outbreak in late 2019. It is estimated t... more Coronavirus (COVID) has claimed numerous lives since its outbreak in late 2019. It is estimated that around 72 million people are affected by the virus and a toll on human life has reached 1.6 millions as of December 2020 making it one of the worst pandemic in recorded history.
Modern scientific and technical advancement is characterized by the further penetration of electr... more Modern scientific and technical advancement is characterized by the further penetration of electronic devices and systems practically in all areas of a science and engineering. In the industry, tangible growth of diagnosis of computer facilities and the radio-electronic equipment are observed with the massive increase of electronics products. Detection and correction of errors in electronic blocks emerged as a growing research area with the advancement of hardware technology. In this research, we have reviewed various methods and addressed a novel approach that is aided by fragmentation to detect the error in electronic block and solves the error. Aus. J. Bus. Sco S. & IT. Vol 5(1), April 2019, P 1-6
Advanced persistent threat (APT) is a serious concern in cyber-security that has matured and grow... more Advanced persistent threat (APT) is a serious concern in cyber-security that has matured and grown over the years with the advent of technology. The main aim of this study is to establish an effective identification model for APT attacks to prevent and reduce their influence. Machine learning has the potential as well as substantial background to detect and predict cyber-security threats including APT. This study utilized several boosting-based machine learning methods to predict various types of APTs that are consistent in cyber-security domain. Furthermore, Explainable Artificial Intelligence (XAI) was coupled with the predictions to provide actionable insights to the domain stakeholders as well as practitioners in this domain. The results, particularly XGBoost with weighted F1 score of 0.97 and SHapley Additive exPlanations (SHAP)-based explanation, prove that boosting methods as well as machine learning models paired with XAI are indeed promising in handling cyber-security-relat...
Artificial intelligence has significantly enhanced the research paradigm and spectrum with a subs... more Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in the real world domain. Artificial intelligence, the driving force of the current technological revolution, has been used in many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, and most importantly the healthcare sector. With the rise of the COVID-19 pandemic, several prediction and detection methods using artificial intelligence have been employed to understand, forecast, handle, and curtail the ensuing threats. In this study, the most recent related publications, methodologies and medical reports were investigated with the purpose of studying artificial intelligence’s role in the pandemic. This study presents a comprehensive review of artificial intelligence with specific attention to machine learning, deep learning, image processing, object detection, image segmentation, and few-sh...
Yoga has become an integral part of human life to maintain a healthy body and mind in recent time... more Yoga has become an integral part of human life to maintain a healthy body and mind in recent times. With the growing, fast-paced life and work from home, it has become difficult for people to invest time in the gymnasium for exercises. Instead, they like to do assisted exercises at home where pose recognition techniques play the most vital role. Recognition of different poses is challenging due to proper dataset and classification architecture. In this work, we have proposed a deep learning-based model to identify five different yoga poses from comparatively fewer amounts of data. We have compared our model’s performance with some state-of-the-art image classification models-ResNet, InceptionNet, InceptionResNet, Xception and found our architecture superior. Our proposed architecture extracts spatial, and depth features from the image individually and considers them for further calculation in classification. The experimental results show that it achieved 94.91% accuracy with 95.61% ...
Proceedings of the International Conference on Computing Advancements
Most of the modern processors achieve instruction level parallelism by executing instructions wit... more Most of the modern processors achieve instruction level parallelism by executing instructions without the original program order. Although the instructions can execute without preserving the original program sequence, they must commit in-order once execution ends.A Reorder Buffer (ROB) is typically used in traditional out-of-order processors to ensure the in-order commitment of the instructions, which has the ability to impede the processor entirely if the head of ROB is obstructed with an instruction. In this paper, we present an alternate approach mechanism for reorder buffer to do out-of-order commit of instructions. The proposed mechanism extends the ROB by adding a Stalled Instruction Zone (SI-Zone) which accommodates batches of dependent instructions and allows out-of-order commit for the ROB. The particular instruction that makes initial stall and other dependent instructions are moved to SI-zone to make way for the normal instructions to commit at the ROB. The SI-zone commits the instructions in batch mode once the stall is cleared. The outcome is an efficient processor that is ROB-based and increases both the effective size of ROB and number of physical registers.
The devastating impact of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) pandem... more The devastating impact of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) pandemic almost halted the global economy and is responsible for 6 million deaths with infection rates of over 524 million. With significant reservations, initially, the SARS-CoV-2 virus was suspected to be infected by and closely related to Bats. However, over the periods of learning and critical development of experimental evidence, it is found to have some similarities with several gene clusters and virus proteins identified in animal-human transmission. Despite this substantial evidence and learnings, there is limited exploration regarding the SARS-CoV-2 genome to putative microRNAs (miRNAs) in the virus life cycle. In this context, this paper presents a detection method of SARS-CoV-2 precursor-miRNAs (pre-miRNAs) that helps to identify a quick detection of specific ribonucleic acid (RNAs). The approach employs an artificial neural network and proposes a model that estimated accuracy of 98...
2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD), 2021
Parasites and viruses are responsible for attacking the immune system of human body and destroyin... more Parasites and viruses are responsible for attacking the immune system of human body and destroying it in the process which makes detection of parasites and virus a penultimate task in modern medical analysis. The detection process is often characterized with symptoms, microscopic tests. However, little research work has been carried out to exploit and garner the capability of deep learning to classify and detect the parasites and virus. In our research work, we propose a new deep neural network based image processing approach to classify the parasites, germs and virus for the case of Entamoeba parasite which leads to Amebiasis disease and John Cunningham virus which is responsible for Progressive multifocal leukoencephalopathy (PML). We have applied our own deep convolutional neural network to classify the JC virus and entamoeba using immunohistochemistry(IHC) images and microscope images from a dataset that We have collected from various neuropathology laboratories, and researchers. Our model achieved overall classification accuracy of 77% and F1-score of 76% with a strong narrative of rationale for utilization of batch normalization and dropout layer in deep learning model.
2021 24th International Conference on Computer and Information Technology (ICCIT), 2021
The technological explosion has paved the way for agriculture to flourish exponentially thus cont... more The technological explosion has paved the way for agriculture to flourish exponentially thus contributing to better yield of crops through the aid of machine learning, the Internet of things, mechanical systems in agriculture. In our research work, we have investigated various types of dry beans followed by a deep neural network based approach to classify the beans automatically. The results shows that our approach had an accuracy of 93.44%, and an F-1 score of 94.57%, with the dataset that consisted of 7 varieties of dry beans. Our results, which performed substantially better in comparison to traditional machine learning approaches aided us to devise further research scopes in the field of agricultural machine learning.
2021 24th International Conference on Computer and Information Technology (ICCIT), 2021
k-means is the most widely used clustering algorithm which is an unsupervised technique that need... more k-means is the most widely used clustering algorithm which is an unsupervised technique that needs assumptions of centroids to begin the process. Hence, the problem is NP-hard and needs careful consideration and optimization to get a better quality of clusters of data. In this work, a meta-heuristic based genetic algorithm is proposed to optimize the centroid initialization process. The proposed method includes tournament selection, probability-based mutation, and elitism that leads to finding the optimal centroids for the clusters of a given dataset. Nine different and diversified datasets were used to test the performance of the proposed method in terms of the davies-bouldin index and it performed better in all the datasets than the standard k-means and minibatch k-means algorithm.
2017 20th International Conference of Computer and Information Technology (ICCIT), 2017
The importance of online news article has evolved notably with the advancement of information and... more The importance of online news article has evolved notably with the advancement of information and technology. However, some of the news are violent as well as obnoxious. So, identifying and categorizing online news article automatically is important as well as remains challenging. Using opinion mining and sentiment analysis, we propose an intuitive approach of detecting positive or negative news from an online news article. Our approach consists of a sentence identification phase, followed by a dynamic library of predefined negative and positive strings and at last marking whether the paragraph is positive, negative or neutral. Our approach detects the polarity of online news articles with around 91% accuracy rate. Sentence type identification before using dynamic dictionary of positive and negative words is the key factor which resolves the issue of finding out the part of the sentence which holds the polarity of the sentence.
Coronavirus (COVID) has claimed numerous lives since its outbreak in late 2019. It is estimated t... more Coronavirus (COVID) has claimed numerous lives since its outbreak in late 2019. It is estimated that around 72 million people are affected by the virus and a toll on human life has reached 1.6 millions as of December 2020 making it one of the worst pandemic in recorded history.
Modern scientific and technical advancement is characterized by the further penetration of electr... more Modern scientific and technical advancement is characterized by the further penetration of electronic devices and systems practically in all areas of a science and engineering. In the industry, tangible growth of diagnosis of computer facilities and the radio-electronic equipment are observed with the massive increase of electronics products. Detection and correction of errors in electronic blocks emerged as a growing research area with the advancement of hardware technology. In this research, we have reviewed various methods and addressed a novel approach that is aided by fragmentation to detect the error in electronic block and solves the error. Aus. J. Bus. Sco S. & IT. Vol 5(1), April 2019, P 1-6
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