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133 results for “Architectures”
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Research ArticleOpen accessEvery word in the titleHybrid Deep Learning Models for Text Classification: Performance Evaluation of TriDistilBERT and BiGRU ArchitecturesAmira Samy TalaatText can be a valuable source of information, but its unstructured nature makes analysis challenging and time-consuming. Machine Learning (ML) algorithms can efficiently analyze and structure text, enabling organizations to automate processes and uncover insights that support better decision-making. This study focuses on applying ML to a classification problem using two datasets. Four deep learning models are introduced, combining Bi … …Journal of Computer Science, Vol. 21, Issue 9 · 10 Oct 2025 · 2,792 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleScalable and Advanced Framework for Hate Speech Detection on Social Media Using BERT and GPT-2 through Encoder and Decoder ArchitecturesUsman, Nabeela Hasan, Syed Mohammad Khurshid QuadriHate speech is a major problem on social media platforms. Every day, numerous instances of hateful behavior based on race, ethnicity, religion, or gender are witnessed on social media. Most of the leading social media platforms like Instagram, Facebook, Twitter Reddit, etc., have strong community guidelines that condemn and restrict the exchange of hateful language/content in any form. Despite the … …Journal of Computer Science, Vol. 21, Issue 3 · 7 Feb 2025 · 7,325 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA Hybrid Method of Long Short-Term Memory and Auto-Encoder Architectures for Sarcasm DetectionMohammed M. AL-Ani, Nazlia Omar, Ahmed Adil NafeaSarcasm detection is considered one of the most challenging tasks in sentiment analysis and opinion mining applications in the social media. Sarcasm identification is therefore essential for a good public opinion decision. There are some studies on sarcasm detection that apply standard word2vec model and have shown great performance with word-level analysis. However, once a sequence of terms is being … …Journal of Computer Science, Vol. 17, Issue 11 · 1 Dec 2021 · 7,187 accessesView PDFCite
Literature ReviewOpen accessIn the keywordsObject Tracking in Wireless Sensor Networks: Challenges and SolutionsShereen Ismail, Eman Alkhader, Said ElnaffarWireless Sensor Networks (WSNs) are small, inexpensive and battery-operated sensor nodes that are deployed over a geographical area. WSNs are used in many applications such border patrolling, military intrusion detection, wildlife animal monitoring, surveillance of natural disasters and healthcare systems. Mobile object tracking is a vital task in all these applications. The goal of this work is to highlight the … …Journal of Computer Science, Vol. 12, Issue 4 · 1 May 2016 · 7,777 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA Simulation-Based Comparison of Multimedia Traffic Prioritization Schemes for High-Performance Input-Queued Packet SwitchesSalman A. AlQahtaniThis study investigates the problem of enabling Quality of Service (QoS) of multimedia traffic at the input port of high-performance input-queued packet switches using a simulation-based evaluation. We focus on the possibility of assuring QoS of multimedia traffic in such switches by implementing traffic prioritization at the input port where each input-queue has been modified to provide a separate buffer … …Journal of Computer Science, Vol. 2, Issue 4 · 30 Apr 2006 · 7,074 accessesView PDFCite
Review ArticleOpen accessIn the abstractHuman Activity Recognition at the Edge: A Systematic Review of Machine Learning Models, Deployment Constraints, and Latency Energy Accuracy Trade offsJose Antonio Rojas Guillén, Wini Ebelin Quispe Bautista, Arturo Gamarra MorenoHuman Activity Recognition (HAR) has become a key component of intelligent healthcare monitoring, assisted living, sports analytics, smart environments, and wearable computing. However, the migration of HAR models from cloud-centered architectures to edge, embedded, mobile, and wearable platforms introduces constraints related to computational capacity, memory footprint, battery consumption, inference time, and real-time responsiveness. This study presents a systematic literature review … …Journal of Computer Science, Vol. 22, Issue 8 · 7 Sep 2026 · 593 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Comparative SCAPS-1D Study of Lead-Free CsSnCl₃-Based Perovskite SCs Employing Different ETLsRony Tota, Shak Mahmudul Hasan, Md. Morsalin et al.Cesium tin chloride (CsSnCl3) has drawn considerable interest as a non-toxic absorber candidate for next-generation photovoltaic devices, offering an attractive combination of affordability, thermal robustness, and competitive conversion performance suitable for clean-energy deployment. In the present work, the photovoltaic behaviour of CsSnCl₃-based SCs was numerically examined using the SCAPS-1D software. Three distinct device stacks were constructed by varying the ETL, … …American Journal of Engineering and Applied Sciences, Vol. 19, Issue 1 · 22 May 2026 · 1,737 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Novel Deep Learning Approach for Tomato Leaf Disease Detection Using Optimized CNN ArchitectureSunil K. Vithalani, Vipul K. DabhiEarly disease detection in plants is essential for sustaining agricultural yield and guaranteeing food security. A comparative analysis of transformer-based and convolutional based deep learning models for classifying tomato leaf diseases is presented. Specifically, it examines the performance of a Vision Transformer (ViT), tested both in a form of scratch training setup and through transfer learning, against well-known CNN architectures … …Journal of Computer Science, Vol. 22, Issue 1 · 7 Feb 2026 · 3,917 accessesView PDFCite
Research ArticleOpen accessIn the abstractSelf-Supervised Contrastive Learning for Steering Angle Prediction in Autonomous Driving SimulationsMaxson Phang, Suryadiputra LiawatimenaPredicting steering angles is a crucial task in autonomous driving, with end-to-end deep learning being a widely used approach. However, such models often suffer from overfitting to learned trajectories, limiting their generalization to new data. To address this, self-supervised contrastive learning is explored as an alternative, allowing models to learn meaningful representations without labeled data. The primary objective is to … …Journal of Computer Science, Vol. 21, Issue 9 · 20 Oct 2025 · 2,581 accessesView PDFCite