The education system worldwide has been affected by the Corona Virus Diseases 2019 (COVID-19) pandemic, resulting in the interruption of all educational institutions. Moreover, as a precautionary measure, the lockdown has been imposed that has severely affected the learning processes, especially assessment activities, including exams and viva. In such challenging situations, E-learning platforms could play a vital role in conducting seamless academic activities. In spite of all the advantages of remote learning systems, many hurdles and obstacles, like a selection of suitable learning resources/material encounter by individual users based on their interests or requirements. Especially those who are not well familiar with the internet technology in developing countries and are in need of a platform that could help them in resolving the issues related to the online virtual environment. Therefore, in this work, we have proposed a mechanism that intelligently and correctly predicts the appropriate preferences for the selection of resources relevant to a specific user by considering the capabilities of diverse perspectives users to provide quality online education and to make work from home policy more effective and progressive during the pandemic. The proposed system helps teachers in providing quality online education, familiarizing them with advanced technology in the online environment. It also semantically predicts the preferences for virtual assistance of those users who are in need of learning the new tools and technologies in short time as per their institutional requirements in order to meet the quality standards of online education. The experimental and statistical results have demonstrated that the proposed virtual personalized preferences system has improved overall academic activities as compared to the current method. The proposed system enhanced user's learning abilities and facilitated them in selecting short courses while using different online education tools adopted/suggested by the institutions to conduct online classes/seminars/webinars etc., as compared to the conventional classes/activities.

/pdf/enabling-remote-learning-system-for-virtual-personalized-22yquu73n9.pdf

Enabling remote learning system for virtual personalized preferences during COVID-19 pandemic.

Software needs modifications and requires revisions regularly. Owing to these revisions, retesting software becomes essential to ensure that the enhancements made, have not affected its bug-free functioning. The time and cost incurred in this process, need to be reduced by the method of test case selection and prioritization. It is observed that many nature-inspired techniques are applied in this area. African Buffalo Optimization is one such approach, applied to regression test selection and prioritization. In this paper, the proposed work explains and proves the applicability of the African Buffalo Optimization approach to test case selection and prioritization. The proposed algorithm converges in polynomial time (O(n2)). In this paper, the empirical evaluation of applying African Buffalo Optimization for test case prioritization is done on sample data set with multiple iterations. An astounding 62.5% drop in size and a 48.57% drop in the runtime of the original test suite were recorded. The obtained results are compared with Ant Colony Optimization. The comparative analysis indicates that African Buffalo Optimization and Ant Colony Optimization exhibit similar fault detection capabilities (80%), and a reduction in the overall execution time and size of the resultant test suite. The results and analysis, hence, advocate and encourages the use of African Buffalo Optimization in the area of test case selection and prioritization. 

/pdf/fault-coverage-based-test-case-prioritization-and-selection-203l1jre.pdf

Fault Coverage-Based Test Case Prioritization and Selection Using African Buffalo Optimization

ABSTRACT In the present paper, we propose a new approach for monthly streamflow prediction based on the extreme learning machine (ELM) and the metaheuristic bat algorithm (Bat-ELM). The performance of the Bat-ELM was compared to that of ELM, support vector regression (SVR), Gaussian process regression (GPR), multilayer perceptron neural network (MLPNN), and generalized regression neural network (GRNN). The proposed models were applied using data from three hydrometric stations located in the Cheliff Basin, Algeria. The results showed that the Bat-ELM was more satisfactory than the standalone models. The Bat-ELM achieved the highest numerical performance with correlation coefficient and Nash-Sutcliffe efficiency ranging from 0.927 to 0.973 and from 0.846 to 0.944, respectively, much higher than the respective values obtained using the MLPNN, GRNN, SVR, GPR and ELM approaches. The obtained results demonstrate that the Bat-ELM is an interesting alternative algorithm for predicting high and extreme streamflow.

Monthly streamflow prediction using hybrid extreme learning machine optimized by bat algorithm: a case study of Cheliff watershed, Algeria

In software development industry, component based software development is an emerging research area which helps to characterize the quality of software, especially software component reusability. Finding good quality software components are internally strong cohesive which reduces the maintenance effort and fasten the development of a software. In this research paper, a new three phase model is proposed for an effective software component reusability prediction. In the first phase, the real time input data are collected from the python programs with 70 number of instances and 16 number of attributes. In the second phase, extra tree classifier is applied to select the best attributes from the collected data on the basis of Gini index. In the final phase, selected attributes are fed to enhanced Harris hawks optimization algorithm for selecting the best reusable software components from the python programs, where the selected best reusable components are adapter, template, singleton, proxy, factory, facade and state. In the experimental phase, proposed model performance is analyzed by means of mean square error, f-test, P-value and sum of squares. Simulation outcome revealed that the proposed model achieved better performance in software component reusability prediction compared to conventional Harris hawks optimization algorithm. The proposed model almost improved 2% similarity value related to Harris hawks optimization algorithm and other comparative models in terms of f-test, mean square error, and P-value.

Software component reusability prediction using extra tree classifier and enhanced Harris hawks optimization algorithm

Software quality measurement considers a very crucial process in developing software. Achieving high-quality software leads to produce software with high efficiency, and reliability and to reduce the software cost. This study intends to explain the most important factors that have direct effects on the software quality measurement process. In addition, this study summarized the software quality models; where each model could be classified into various scopes such as reliability, functionality, usability, portability, and maintainability. Moreover, the most recent techniques, which are used in software quality measurement, are concluded. Furthermore, the vital metrics that play an important role in the measurement process have been determined. Last but not least, the most commonly used datasets in software quality measurement in many aspects and scopes have been presented. As a result, this paper drew the road map for future work in the field of software engineering to accomplish more research work in developing novel software quality measurement approaches based on machine learning techniques such as deep learning methods and ensemble techniques that may give promising results.

Software Quality Measurement Analysis based on Techniques, Criteria, Metrics, Models, and Datasets

Component-based software engineering is currently a development strategy used to improve complex embedded systems. The engineers have to deal with a large number of quality requirements (e.g. safety, security, availability, reliability, maintainability, portability, performance, and temporal correctness requirements), hence the development of complex embedded systems is becoming a challenging task. Enhancement of the quality prediction in component-based software engineering systems using soft computing techniques is the foremost intention of the research. Therefore, this paper proposes an extreme learning machine (ELM) classifier with the ant colony optimization algorithm and Nelder-Mead (ACO-NM) soft computing approach for component quality prediction. To promote efficient software systems and the ability of the software to work under several computer configurations maintainability, independence, and portability are taken as three core software components metrics for measuring the quality prediction. The ELM uses AC-NM for updating its weight to transform the quality constraints into objective functions for providing a global optimum quality prediction. The experimental results have shown that the proposed work gives an improved performance in terms of Sensitivity, Precision, Specificity, Accuracy, Mathews correlation coefficient, false positive rate, negative predictive value, false discovery rate, and rate of convergence.

A novel quality prediction model for component based software system using ACO–NM optimized extreme learning machine

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A novel quality prediction model for component based software system using ACO–NM optimized extreme learning machine