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Research ArticleOpen accessIn the abstractEmpirical Study on Modified Pre-Trained CNN Architectures for Fitzpatrick17k Skin Diseases Prediction ModellingSteven Matthew, Nur Afny Catur Andryani, Rojali et al.Preventing missed diagnosis of skin diseases is critical for enhancing patient outcomes. Technology support empowered by AI based algorithms have been recently developed to minimize the burden. Convolutional Neural Networks (CNNs) are increasingly utilized for image classification tasks including skin's diseases prediction modelling. Its advancement architectures such as pre-trained models, VGG16 andResNet50 provide a strong foundation to up-skill the prediction … …Journal of Computer Science, Vol. 21, Issue 7 · 8 Jul 2025 · 3,934 accessesView PDFCite
Research ArticleOpen accessIn the abstractMultivalent Optimizer-Based Hybrid Genetic Algorithm for Task Scheduling in Cloud ApplicationsMeena Malik, Bhavna Gupta, Chander Prabha et al.Cloud computing platforms provide on-demand online services without the need for direct user management. Generally, big clouds distribute functions over multiple data centers at distant locations. The major facilities offered by clouds are based upon virtual machines which provide benefits in terms of low scheduling cost, improved accessibility and availability of cloud services. While transferring the tasks for effective scheduling, … …Journal of Computer Science, Vol. 21, Issue 2 · 31 Dec 2024 · 4,900 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Air-Written Real-Time Multilingual Numeral String Recognition System Using Deep Convolutional Neural NetworksMeenal Jabde, Chandrashekhar Himmatrao Patil, Amol D. Vibhute, Shankar MaliAir-writing is a modern practice for free-space writing of characters or words with hand or finger movements. In the new era, hand gestures are commonly used for Human-Computer Interaction (HCI) or controlling machines in several applications. Writing with a finger or holding any object in hand and 3-D movement in the air is helpful for several applications. However, it is … …Journal of Computer Science, Vol. 20, Issue 12 · 8 Nov 2024 · 5,248 accessesView PDFCite
Research ArticleOpen accessIn the abstractLeveraging Machine Learning Techniques to Analyze Consumer Mindset Metrics Embedded in Arabic Dialect Texts Across Social Media PlatformsSafa Khaled Al Sarairah, Mohd Heikal Husin, Noor Farizah IbrahimAs social media grows in popularity around the world, analyzing Arabic texts on these platforms can provide important insights into consumer attitudes and behavior. The complexity and diversity of Arabic and its dialects, however, make it a challenging task. This research raises these challenges by using and comparing the performance of Machine Learning (ML) models for classifying social media comments … …Journal of Computer Science, Vol. 22, Issue 5 · 5 Jun 2026 · 1,301 accessesView PDFCite
Research ArticleOpen accessIn the abstractPredictive Mathematical Modeling and Classification of Retail Sales Orders Using AI Machine Learning TechniquesMohammad Subhi Al-Batah, Mowafaq Salem Alzboon, Hamzeh ZureigatThis study presents a systematic mathematical model known as a low-code approach to classifying retail sales orders by size using AI machine learning techniques within the Orange Data Mining platform. Leveraging a real-world sales dataset sourced from Kaggle, we implemented and evaluated ten classification models, including ensemble learners (AdaBoost, Gradient Boosting), probabilistic classifiers (Naïve Bayes), distance-based methods (kNN), and interpretable … …Journal of Computer Science, Vol. 22, Issue 3 · 10 Mar 2026 · 1,567 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Integrated Disease Progression Model to Analyze Electronic Health Records With Multimodal Datasets Using Deep Learning TechniquesThulaganyo Dimakatso, Venumadhav Kuthadi, Rajalakshmi Selvaraj, Othapile DinakenyaneIn contemporary clinical environments, precisely simulating the emergence of disease is an intricate task owing to heterogeneously derived data and variability in the record of diagnoses. The research presents a stable classification model that integrates image data with formalized patient data to track patterns in disease through various stages. The suggested approach combines a multi-stage analytical methodology that includes systematic … …Journal of Computer Science, Vol. 22, Issue 2 · 19 Feb 2026 · 1,678 accessesView PDFCite
Research ArticleOpen accessIn the abstractBoosting Arabic Named Entity Recognition with K-Fold Cross Validation on LSTM and Bi-LSTM ModelsHamid Sadeq Mahdi Alsultani, Ahmed H. AliwyNamed-Entity-Recognition(NER) is one of the most important Information-Extraction (IE) use cases, whichis used to improve the performance of Natural Languages Processing (NLP) tasks,such as Relation-Extraction (RE), Question-Answering (QA). Recently, Arabic NER is tackled in differentways by researchers. In this study, we assess the performance of two widelyused models, namely, LSTM and Bi-LSTM on the NER task in the Arabic languageand … …Journal of Computer Science, Vol. 18, Issue 9 · 6 Sep 2022 · 6,317 accessesView PDFCite
Research ArticleOpen accessIn the abstractAnimal Health Prediction Using Hybrid KNN Based Vector Neighbor Classification Model: A Machine Learning ApproachJ. Rathi, A. SumathiAnimal condition disease prediction is a crucial task in veterinary medicine, and accurate prediction can significantly improve animal health and reduce economic losses. This paper proposed a novel hybrid classification model that combines an Improved Fuzzy-based feature selection process with a Hybrid KNN-based Vector Neighbor Classification model to enhance animal condition disease prediction. The proposed model aims to improve the … …Journal of Computer Science, Vol. 21, Issue 9 · 21 Oct 2025 · 2,900 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
Research ArticleOpen accessIn the abstractA GUI-Enabled, Automated LSTM-Based Inverse Kinematics Pipeline for 6-DOF Robotic Arms Using MATLAB and CoppeliaSimWaleed Abdulrahman Saleh Al-Akwa, Mohammed Abdulwahab Ahmed DabaExisting deep learning-based inverse kinematics (IK) solutions often target specific robotic arms and require significant modifications when applied to different configurations. To support early-phase design and testing of 6-degree-of-freedom (6-DOF) robotic arms, this study presents a fast and adaptable IK solution through a user-friendly interface. Unlike traditional numerical methods that are computationally intensive, sensitive to initial conditions, and may not … …Journal of Mechatronics and Robotics, Vol. 9, Issue 1 · 26 May 2025 · 8,252 accessesView PDFCite