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Machine Learning Methods for Trust-based Selection of Web Services
( Muhammad Hasnain ),( Imran Ghani ),( Muhammad F. Pasha ),( Seung R. Jeong ) 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.1
Web services instances can be classified into two categories, namely trusted and untrusted from users. A web service with high throughput (TP) and low response time (RT) instance values is a trusted web service. Web services are not trustworthy due to the mismatch in the guaranteed instance values and the actual values achieved by users. To perform web services selection from users' attained TP and RT values, we need to verify the correct prediction of trusted and untrusted instances from invoked web services. This accurate prediction of web services instances is used to perform the selection of web services. We propose to construct fuzzy rules to label web services instances correctly. This paper presents web services selection using a well-known machine learning algorithm, namely REPTree, for the correct prediction of trusted and untrusted instances. Performance comparison of REPTree with five machine learning models is conducted on web services datasets. We have performed experiments on web services datasets using a ten k-fold cross-validation method. To evaluate the performance of the REPTree classifier, we used accuracy metrics (Sensitivity and Specificity). Experimental results showed that web service (WS1) gained top selection score with the (47.0588%) trusted instances, and web service (WS2) was selected the least with (25.00%) trusted instances. Evaluation results of the proposed web services selection approach were found as (asymptotic sig. = 0.019), demonstrating the relationship between final selection and recommended trust score of web services.
A Review on Floating Photovoltaic Technology (FPVT)
Hasnain Yousuf,Muhammad Quddamah Khokhar,Muhammad Aleem Zahid,Jaeun Kim,김영국,조은철,조용현,이준신 한국태양광발전학회 2020 Current Photovoltaic Research Vol.8 No.3
A novel energy production system which has fascinated a wide consideration because of its several benefits that are called floating photovoltaic technology (FPVT). The FPVT system that helps to minimize the evaporation of water as well as an increase in energy production. For the research purposes, both electrical and mechanical structure requires studying of these systems for the development of FPVT power plants. From different points of views, numerous researches have been directed on FPVT systems that have evaluated these systems. The present research article give a logical investigation and up to date review that shows the different features and components of FPVT systems as an energy production system is offered. This articles reviewing the FPVT that gets the attention of the scientists who have the investigational stage and involuntary inspection of FPVT systems in addition to influence of implementing these systems on the water surface. Also, a comprehensive comparison has been constructed that shows the cons and pros of various types of solar systems that could be installed in various locations. In this review, it has been found that solar energy on the roof of a dwelling house generally has a power of 5 to 20 kW, while the inhabitants of commercial buildings generally have a power of 100 kW or more. The average power capacity of a floating solar panel is 11% more of the average capacity of a solar panel installed on the ground. Studies show that 40% of the water in open reservoirs is lost through evaporation. By covering only 30% of the water surface, evaporation can be reduced by 49%. The global solar panel market exceeds 100 GW and the capacity of 104 GW will bring the annual growth rate to 6%. In 2018, the world's total photovoltaic capacity reached 512 GW, an increase of 27% compared to the total capacity and about 55% of the renewable resources newly created that come from photovoltaic systems. It has been also predicted by this review that in 2025 the Solar technology including the FPVT system will increase by 7.38% that is 485.4 GW more of today installed power worldwide.
Muhammad Hasnain,Imran Ghani,Muhammad Fermi Pasha,Ishrat Hayat Malik,Shahzad Malik 한국인터넷방송통신학회 2019 International Journal of Internet, Broadcasting an Vol.11 No.2
Research classification of software modules was done to validate the approaches proposed for addressing limitations in existing classification approaches. The objective of this study was to replicate the experiments of a recently published research study and re-evaluate its results. The reason to repeat the experiment(s) and re-evaluate the results was to verify the approach to identify the faulty and non-faulty modules applied in the original study for the prioritization of test cases. As a methodology, weconducted this study to re-evaluate the results of the study. The results showed that binary logistic regression analysis remains helpful for researchers for predictions, as it provides an overall prediction of accuracy in percentage. Our study shows a prediction accuracy of 92.9% for the PureMVC Java open source program, while the original study showed an 82% prediction accuracy for the same Java program classes. It is believed by the authors that future research can refine the criteria used to classify classes of web systemswritten in various programming languages based on the results of this study.
A Comprehensive Review on Regression Test Case Prioritization Techniques for Web Services
( Muhammad Hasnain ),( Imran Ghani ),( Muhammad Fermi Pasha ),( Chern Hong Lim ),( Seung Ryul Jeong ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.5
Test Case Prioritization (TCP) involves the rearrangement of test cases on a prioritized basis for various services. This research work focuses on TCP in web services, as it has been a growing challenge for researchers. Web services continuously evolve and hence require reforming and re-execution of test cases to ensure the accurate working of web services. This study aims to investigate gaps, issues, and existing solutions related to test case prioritization. This study examines research publications within popular selected databases. We perform a meticulous screening of research publications and selected 65 papers through which to answer the proposed research questions. The results show that criteria-based test case prioritization techniques are reported mainly in 41 primary studies. Test case prioritization models, frameworks, and related algorithms are also reported in primary studies. In addition, there are eight issues related to TCP techniques. Among these eight issues, optimization and high effectiveness are most discussed within primary studies. This systematic review has identified that a significant proportion of primary studies are not involved in the use of statistical methods in measuring or comparing the effectiveness of TCP techniques. However, a large number of primary studies use ‘Average Percentage of Faults Detected’ (APFD) or extended APFD metrics to compute the performance of techniques for web services.
A Review on Degradation of Silicon Photovoltaic Modules
Hasnain Yousuf,Muhammad Quddamah Khokhar,Muhammad Aleem Zahid,Jaeun Kim,Youngkuk Kim,Sung Bae Cho,Young Hyun Cho,Eun-Chel Cho,Junsin Yi 한국신재생에너지학회 2021 신재생에너지 Vol.17 No.1
Photovoltaic (PV) panels are generally treated as the most dependable components of PV systems; therefore, investigations are necessary to understand and emphasize the degradation of PV cells. In almost all specific deprivation models, humidity and temperature are the two major factors that are responsible for PV module degradation. However, even if the degradation mode of a PV module is determined, it is challenging to research them in practice. Long-term response experiments should thus be conducted to investigate the influences of the incidence, rates of change, and different degradation methods of PV modules on energy production; such models can help avoid lengthy experiments to investigate the degradation of PV panels under actual working conditions. From the review, it was found that the degradation rate of PV modules in climates where the annual average ambient temperature remained low was -1.05% to -1.16% per year, and the degree of deterioration of PV modules in climates with high average annual ambient temperatures was -1.35% to -1.46% per year; however, PV manufacturers currently claim degradation rates of up to -0.5% per year.
A machine learning framework for performance anomaly detection
Muhammad Hasnain,Muhammad Fermi Pasha,Imran Ghani,정승렬,Aitizaz Ali 한국인터넷정보학회 2022 인터넷정보학회논문지 Vol.23 No.2
Web services show a rapid evolution and integration to meet the increased users' requirements. Thus, web services undergo updates and may have performance degradation due to undetected faults in the updated versions. Due to these faults, many performances and regression anomalies in web services may occur in real-world scenarios. This paper proposed applying the deep learning model and innovative explainable framework to detect performance and regression anomalies in web services. This study indicated that upper bound and lower bound values in performance metrics provide us with the simple means to detect the performance and regression anomalies in updated versions of web services. The explainable deep learning method enabled us to decide the precise use of deep learning to detect performance and anomalies in web services. The evaluation results of the proposed approach showed us the detection of unusual behavior of web service. The proposed approach is efficient and straightforward in detecting regression anomalies in web services compared with the existing approaches.