Soft Computing

7th International Conference on Artificial Intelligence and Applications (AI 2021) 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. 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.

9th International Conference on Soft Computing, Artificial Intelligence and Applications (SAI 2020) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. 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.  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

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.

Fault diagnosis of industrial machineries become very much important for improving the quality of the manufacturing as well as for reducing the cost for product testing. In modern manufacturing scenario, a fast and reliable diagnosis system has turned into a challenging issue in the complex industrial environment. In this work, the diagnosis of gearbox is considered as a mean of health monitoring system by used lubricant. The proposed methodology has been performed on the basis of wear particle analysis in gearbox at offline stage. Possible wear characterization has been done by image vision system to interpret into soft computing techniques like fuzzy inference and neural network mechanisms. Basically, the maintenance policy has been taken with the help of fuzzy expert system, which has been described in the present work.

The application of soft computing in decision support system in disease prediction is one of the emerging interdisciplinary research areas in the field of computer science. Machine learning algorithms plays an important role in risk prediction of diseases. Attribute selection among the dataset is the key factor that influences prediction accuracy. Mathew's correlation coefficient performance metric is also taken into account. Particle swarm optimization algorithm is altered and applied for performing attribute selection. Improved fuzzy artificial neural network classifier performs the prediction task.

'El Diario de Juárez' is a local newspaper in a city of 1.5 million Spanish-speaking inhabitants that publishes texts of which citizens read them on both a website and an RSS (Really Simple Syndication) service. This research applies natural-language-processing and machine-learning algorithms to the news provided by the RSS service in order to classify them based on whether they are about a traffic incident or not, with the final intention of notifying citizens where such accidents occur. The classification process explores the bag-of-words technique with five learners (Classification and Regression Tree (CART), Naïve Bayes, kNN, Random Forest, and Support Vector Machine (SVM)) on a class-imbalanced benchmark; this challenging issue is dealt with via five sampling algorithms: synthetic minority oversampling technique (SMOTE), borderline SMOTE, adaptive synthetic sampling, random oversampling, and random undersampling. Consequently, our final classifier reaches a sensitivity of 0.86 and an area under the precision-recall curve of 0.86, which is an acceptable performance when considering the complexity of analyzing unstructured texts in Spanish.

The demand for credit is increasing constantly. Banks are looking for various methods of credit evaluation that provide the most accurate results in a shorter period in order to minimize their rising risks. This study focuses on various methods that enable the banks to increase their asset quality without market loss regarding the credit allocation process. These methods enable the automatic evaluation of loan applications in line with the sector practices, and enable determination of credit policies/strategies based on actual needs. Within the scope of this study, the relationship between the predetermined attributes and the credit limit outputs are analyzed by using a sample data set of consumer loans. Random forest (RF), sequential minimal optimization (SMO), PART, decision table (DT), J48, multilayer perceptron (MP), JRip, naïve Bayes (NB), one rule (OneR) and zero rule (ZeroR) algorithms were used in this process. As a result of this analysis, SMO, PART and random forest algorithms are the top three approaches for determining customer credit limits.

Nowadays, systems are under serious security threats caused by malicious software, commonly known as malware. Such malwares are sophisticatedly created with advanced techniques that make them hard to analyse and detect, thus causing a lot of damages. Polymorphism is one of the advanced techniques by which malware change their identity on each time they attack. This paper presents a detailed systematic and critical review that explores the available literature, and outlines the research efforts that have been made in relation to polymorphic malware analysis and their detection.

The present paper deals with an inventory management system with ramp type and quadratic demand rates. A constant deterioration rate is considered into the model. In the two types models, the optimum time and total cost are derived when demand is ramp type and quadratic. A structural comparative study is demonstrated here by illustrating the model with sensitivity analysis.

Proximal Support Vector machine based on Least Mean Square Algorithm classi-fiers (LMS-SVM) are tools for classification of binary data. Proximal Support Vector based on Least Mean Square Algorithm classifiers is completely based on the theory of Proximal Support Vector Machine classifiers (PSVM). PSVM classifies binary pat- terns by assigning them to the closest of two parallel planes that are pushed apart as far as possible. The training time for the classifier is found to be faster compared to their previous versions of Support Vector Machines. But due to the presence of slack variable or error vector the classification accuracy of the Proximal Support Vector Machine is less. So we have come with an idea to update the adjustable weight vectors at the training phase such that all the data points fall out-side the region of separation and falls on the correct side of the hyperplane and to enlarge the width of the separable region.To implement this idea, Least Mean Square (LMS) algo...

Experiments were conducted to control the exhaust emissions from two-stroke, single cylinder, spark ignition (SI) engine, with alcohol blended gasoline (80% gasoline, 10% methanol, 10% ethanol by volume) having copper coated combustion chamber [CCCC, copper-(thickness, 300 μ) coated on piston crown, inner side of cylinder head] provided with catalytic converter with sponge iron as catalyst and compared with conventional SI engine (CE) with pure gasoline operation. Aldehydes were measured by wet chemical method. Exhaust emissions of CO and UBHC were evaluated at different values of brake effective pressure, while aldehydes were measured at full load operation of the engine. A microprocessor-based analyzer was used for the measurement of CO/UBHC in the exhaust of the engine. Copper coated combustion chamber with alcohol blended gasoline considerably reduced pollutants in comparison with CE with pure gasoline operation. Catalytic converter with air injection significantly reduced pollu...

This paper proposes a new grammar-guided genetic programming (GGGP) system by introducing two original genetic operators: crossover and mutation, which most influence the evolution process. The first, the so-called grammar-based crossover operator, strikes a good balance between search space exploration and exploitation capabilities and, therefore, enhances GGGP system performance. And the second is a grammar-based mutation operator, based on the crossover, which has been designed to generate individuals that match the syntactical constraints of the context-free grammar that defines the programs to be handled. The use of these operators together in the same GGGP system assures a higher convergence speed and less likelihood of getting trapped in local optima than other related approaches. These features are shown throughout the comparison of the results achieved by the proposed system with other important crossover and mutation methods in two experiments: a laboratory problem and the real-world task of breast cancer prognosis.

Computers have a direct impact on our lives nowadays. Human's interaction with the computer has modified with the passage of time as improvement in technology occurred the better the human computer interaction became. Today we are facilitated by the operating system that has reduced all the complexity of hardware and we undergo our computation in a very convenient way irrespective of the process occurring at the hardware level. Though the human computer interaction has improved but it's not done yet. If we come to the future the computer's role in our lives would be a lot more rather our life would be of the artificial intelligence. In our future the biggest resource would be component of time and wasting time for a key board entry or a mouse input would be unbearable so the need would be of the computer interaction environment that along with the complexity reduction also minimizes the time wastage in the human computer interaction. Accordingly in our future the computation would also be increased it would not be a simple operating system limited to a computer it would be computers managing our entire life activities hence fall out of domain of present computers electronic based architecture .In this research paper we propose a model that shall be meeting the future human computer interaction needs possessing linguistic human computer interference environment based on surface technology, automation and photonic computing, which would be reliable, efficient and quicker satisfying all the future artificial intelligence pre requisites.

This paper presents the study of Support Vector Regression (SVR) to forecast the future streamflow discharge
using past streamflow and rainfall data, which is closely related to regularization network and Gaussian
processes. A Gaussian Radial Basis Function (RBF) kernel framework was built on the dataset to optimize the
tuning parameters and to obtain the moderated output. It has been observed from various studies that the
prediction ability of RBF kernel function is better for regression problems. The training process of Support
Vector Machine (SVM) involves the selection of both kernel parameters and regularization constants. The
constants such as γ, ε, C and σ are the parameters of SVR which were optimized to obtain the desired outputs.
Where parameter C determines trade-off between model complexity and degree to which deviations are larger
than ε are tolerated in optimization formulation, and the parameter ε controls the width of ε –sensitive zone. The
study area include the upstream part of Jayakwadi dam. The prediction is based on flow at two Gauge and
Discharge Sites (GDS) i.e. Kopargaon and Nagamthan and rainfall data at three Standard Rain gauge Stations
(SRG) i.e. Rahata, Khirdisathe, and Wadala Mahadev in Maharashtra, India. The daily data values of these
stations for 18 years were collected from Hydrological Data User Group (HDUG), Nashik. About 70% of data
were used for training purpose and remaining 30% of data were used for testing purpose. The experimental
results showed that the SVR algorithm is reliable and efficient method for streamflow prediction, which has an
important impact on the water resources management of region.
Keywords: Support Vector Machine (SVM), Support Vector Regression (SVR), Radial Basis Function (RBF)
Kernel, ε-sensitive zone, regularization parameters.

Face recognition is one of the important biometric authentication research areas for security purposes in many fields such as pattern recognition and image processing. However, the human face recognitions have the major problem in machine learning and deep learning techniques, since input images vary with poses of people, different lighting conditions, various expressions, ages as well as illumination conditions and it makes the face recognition process poor in accuracy. In the present research, the resolution of the image patches is reduced by the max pooling layer in convolutional neural network (CNN) and also used to make the model robust than other traditional feature extraction technique called local multiple pattern (LMP). The extracted features are fed into the linear collaborative discriminant regression classification (LCDRC) for final face recognition. Due to optimization using CNN in LCDRC, the distance ratio between the classes has maximized and the distance of the featu...

Information Technology (IT) infrastructure and related research communities can help tackle environmental challenges in developing countries through environmentally sustainable models of economic development. The paper sought to examine the status of current and emerging environmentally friendly technologies, equipment and applications in supporting programs that play a role in addressing environment degradation in Kenya. It also sought to underscore the role of IT in environmentally sustainable consumption. The paper examines what constitutes environment degradation and explores the negative effects of IT infrastructure on the environment. The consequences of E-waste on environment are discussed followed by green IT as part of the solution to environment degradation as a result of adoption of IT. The papers also discuss the available IT infrastructure that can be used to combat the challenges of environment degradation. The paper ends with possible IT infrastructure measures that c...

There is a lot of hype about social networks and related software within the concept Web 2.0 in the business and technical worlds. Given the fact that information is being widely spread in an exponential way with rooted dependencies among networks of social networks, we could find our digital identity exposed in ways that we could consider as inappropriate. Further than censorship, there is an arising need of knowing what is happening with the information we are delivering somewhere in the net, managing the distribution and the quality of detailed information. This paper discusses on which ways social identity management could be approached and further analyzed.

Reliability is a critical program evaluation criterion so much so that program writing (expressing one’s
computational ideas) will be of no use if the end result is not consistent. Programmers cognizant pertaining
to a particular programming language selected for solving a particular computational problem will yield
the desired result. This can only be achieved if the program written in a particular programming language
either structured (C, C++) or Objected-Oriented (Java, C#, Ruby) is gauge with the criteria’s for
evaluating program reliability. Adopting a soft-computing based approach will help model solutions to
program reliability utilizing linguistic variables. Simulating a soft-computing system comprising of genetic
algorithm and fuzzy logic for determining program language reliability was the rationale behind this
research paper. The reliability of programming language could be determined in terms of “Reliable”,
“Moderate Reliable” and “Not Reliable” based on the possessed reliability criteria of the examined
programming language.

Depression disorder is common in primary care, but its diagnosis is complex and controversial due to the
conflicting, overlapping and confusing nature of the multitude of symptoms, hence the need to retain cases in a
case base and reuse effective previous solutions for current cases. This paper proposes a neuro-fuzzy-Case Base
Reasoning (CBR) driven decision support system that utilizes solutions to previous cases in assisting physicians
in the diagnosis of depression disorder. The system represents depression disorder with 25 symptoms grouped
into five categories. Fuzzy logic provided a means for handling imprecise symptoms. Local similarity between
the input cases and retrieved cases was achieved using the absolute deviation as the distance metric, while
adaptive neuro-fuzzy inference system handled fuzzy rules whose antecedents are the mapped local similarities
of each category of symptoms for global similarity measurement, upon which the retrieved cases are ranked. The
5 best matched cases are subjected to the emotional filter of the system for diagnostic decision making. This
approach derives strengths from the hybridization since the tools are complementary to one another.

International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.

Abstract: Applications of face recognition systems have been fairly well-established. However, since Facial Image Recognition System basically depends on the source images as object recognition and methodology, good analysis results from abnormal images containing a blur or noise have still problems. It is therefore, important to develop efficient method to recognize these abnormal images. To analyze the abnormal image, we used introduction of Backpropagation Neural Networks called BNN. Prior to using BNN approach, segmentation as pre-processing and image extraction using zoning method through filtering, grayscaling, thresholding and zoning calles FGTZ were conducted. To confirm the effectiveness of our study, blur images were varied from level 1-5 with 10 variations including variations of poses and lighting. The results showed that pre-processing techniques and extraction methods can generate representative facial features whereas the overall of system containing various blur leve...

Machine learning methods used for prediction and decision
support are of great concern nowadays. Methods for learning
implicit, non-symbolic knowledge provide better predictive
accuracy. But Methods for learning explicit, symbolic
knowledge produce more comprehensible models. Hybrid
machine learning models combine strengths of both
knowledge representation of model types. In this research we
compare predictive accuracy and comprehensibility of
explicit, implicit, and hybrid machine learning models. This
research based on predicting terrorist groups responsible of
attacks in Middle East and North Africa from year 2009 up to
2013 by comparing various standard, ensemble, hybrid, and
hybrid ensemble machine learning methods namely; Naïve
Bayes, K-nearest neighbours, Decision Tree, Support Vector
Machine; Hybrid Hoeffding Tree, Functional Tree, Hybrid
Naïve Bayes with Decision Table, Classification via
Clustering; Random Forests; and Stacking classifiers.
Afterwards compare the results obtained from conducting the
experiments according to four different performance
measures. Experiments were carried out using real world
data represented by Global terrorism Database (GTD) from
National Consortium for the study of terrorism and
Responses of Terrorism (START).

several neural networks controllers for robotics manipulators have been developed during the last decades due to their capability to learn the dynamics properties and the improvements in the global stability of the system. In this paper, two control and identification schemes for a two links robotic manipulator implementing neural networks are presented. A multilayer feedforward neural network and a model reference adaptive controller are used to estimate the inverse dynamic of this mechanism.
An online training algorithm based on the error dynamics is used on the adaptive neural network controller but the neural networks are trained offline with a backpropagation algorithm. The design and architecture of the neural networks are explained along with the identification procedure of the robotic system. Simulations and comparisons with a PD controller are done to test the performance of the neural network controller.

The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.