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Research ArticleOpen accessIn the abstractLatent Feature-Based Trust-Aware Model for Service Delegation in Social Internet of Things (SIoT)Rahul, Venkatesh, Satish B BasapurThe integration of social networking concepts into the Internet of Things (IoT) paradigm has given rise to Social IoT (SIoT) ecosystems, aiming to address challenges related to network navigation, service discovery, and service composition. A fundamental issue in SIoT is the careful selection of trustworthy devices that provide services. A service provider can offer multiple and diverse services, and different … …Journal of Computer Science, Vol. 21, Issue 12 · 9 Jan 2026 · 1,802 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Fuzzy Clustering Based Method for the Spatio-Temporal Data AnalysisAnahita Zolghadr, Afsaneh JalalianSpatiotemporal data is a type of data that is collected by the sensors. This type of data has two spatial and temporal dimensions. There are many challenges in analyzing spatiotemporal big data. Common evaluation metrics of clustering methods are not appropriate for spatiotemporal data. Previous clustering methods and the conventional evaluation metrics are efficient for data like time series with … …Journal of Computer Science, Vol. 18, Issue 6 · 13 Jun 2022 · 5,638 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Experimental Study on the Embedded Software Development Approach Using TI-RTOSAloysio Augusto Rabello de Carvalho, Luiz Eduardo Galvão MartinsEmbedded systems have experienced significant growth in recent years. These systems, which power a wide range of devices from smart appliances to industrial machinery, have become integral to our daily lives and industries. However, this rapid expansion introduces new challenges for developers. A critical decision involves whether to employ a Real-Time Operating System (RTOS) or to forgo it, depending on … …Journal of Computer Science, Vol. 21, Issue 6 · 17 Jul 2025 · 3,068 accessesView PDFCite
Research ArticleOpen accessIn the abstractGreedy Algorithm Implementation for Test Case Prioritization in the Regression Testing PhaseAkira Wahyu Putra, Nilo LegowoThis study examines the implementation of Test Case Prioritization (TCP) using the Greedy Algorithm (GA) to enhance regression testing efficiency within a financial technology company's software development cycle. With testing durations increasing significantly, this study aims to address inefficiencies by applying the Greedy Algorithm to optimize test suite size and fault detection. The research methodology involves applying the Greedy Algorithm … …Journal of Computer Science, Vol. 21, Issue 2 · 9 Jan 2025 · 7,558 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Systematic Literature Review for Implementing Data Ops in the Data Warehouse Lifecycle during the ETL PhaseAhmed Bahaa, Sherif Ragab Eldemerdash, Hanan FahmyNowadays, no one can deny the importance of Data Ware House (DWH) in all organizations. The most important components in Data Ware House (DWH) are the Extraction, Transformation, Loading (ETL) phase. Data cleaning is a basic piece of the transformation stage in Data Warehousing. This may affect critical activities such as data collection and decision-making in various organizations Data Ops … …Journal of Computer Science, Vol. 17, Issue 11 · 10 Nov 2021 · 7,547 accessesView PDFCite
Review ArticleOpen accessIn the abstractA Systematic Literature Review of Software Defect Prediction Using Deep LearningAhmed Bahaa, Enas Mohamed Fathy, Ahmed Sharaf Eldin et al.The approaches associated with software defect prediction are used to reduce the time and cost of discovering software defects in source code and to improve the software quality in the organizations. There are two approaches to reveal the software defects in the source code. The first approach is concentrated on the traditional features such as lines of code, code complexity, … …Journal of Computer Science, Vol. 17, Issue 5 · 22 May 2021 · 10,967 accessesView PDFCite
Research ArticleOpen accessIn the abstractSystem Error Estimate using Combination of Classification and Optimization TechniqueKhalid AlattasThe representation of software must produce flawless significances without any inadequacies. Software imperfection evaluations scheme determines defective mechanisms in software. The eventual creation would have minor or negligible shortcomings to harvest great eminence software. Software quality metrics are a division of software metrics that spotlight the quality aspects of the product. The software flaw prediction system helps in the early … …Journal of Computer Science, Vol. 17, Issue 3 · 1 Apr 2021 · 6,588 accessesView PDFCite
Research ArticleOpen accessIn the abstractMeasuring Test Data Uniformity in Acceptance Tests for the FitNesse and Gherkin NotationsDouglas Hiura Longo, Patrícia Vilain, Lucas Pereira da SilvaThis paper presents two metrics designed to measure the data uniformity of acceptance tests in FitNesse and Gherkin notations. The objective is to measure the data uniformity of acceptance tests in order to identify projects with lots of random and meaningless data. Random data in acceptance tests hinder communication between stakeholders and increase the volume of glue code. The main … …Journal of Computer Science, Vol. 17, Issue 2 · 25 Feb 2021 · 7,572 accessesView PDFCite
Research ArticleOpen accessIn the abstractCapsule Defect Detection and Classification Using Enhanced MobileNet ModelWenqing Bian, Yongzhi Huang, Baolian Shan, Yanting ChangThe quality of capsules has a direct impact on human health. However, defects inevitably arise during the capsule manufacturing process, and manual sorting is required. Most studies on capsule defects focus on detecting surface imperfections and those on internal capsule defects remain relatively limited. This study classifies four types of capsules using the deep learning model MobileNet, aiming to accurately … …American Journal of Biochemistry and Biotechnology, Vol. 20, Issue 4 · 5 Nov 2024 · 5,203 accessesView PDFCite
Research ArticleOpen accessIn the abstractPM2.5 Prediction Using Homogenous and Heterogenous Ensemble Learning: A Comprehensive EvaluationShrabani Medhi, Minakshi GogoiAir pollution is a global issue. PM2.5 is considered to be the most dangerous pollutant. Prediction of PM2.5 concentration is important so that effective measures can be taken beforehand. A multitude of machine learning methodologies have been employed in forecasting PM2.5 levels, utilizing diverse combinations of ensemble classifiers and regressors. However, there are three important issues that need to be … …Journal of Computer Science, Vol. 20, Issue 9 · 20 Jun 2024 · 5,176 accessesView PDFCite