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Research ArticleOpen accessIn the abstractEffectual Deep Learning Based Digital Image Forgery Detection Model Using Ameliorate CNN, VGG 16 and VGG 19 for Copy Move and Image Splicing Classification With Enhanced PerformancePreeti Dhiman, Usha ChauhanIn this research, a pioneer passive approach for detecting and localizing tampering in images is proposed. In the past, image forgeries have created a huge impact in the world. Developments of various image processing technologies made it easier to tamper images with the objective to cover up or hide a significant feature in an image. In some situation, such as … …Journal of Computer Science, Vol. 22, Issue 10 · 3 Oct 2026 · 568 accessesView PDFCite
Research ArticleOpen accessIn the abstractComparative Study of BERT Based Architectures for Multi Task News Classification and Threat DetectionA. Mustain Billah, Sani M IsaWe present a comparative analysis of single-task and multi-task BERT-based models for Indonesian news classification across two objectives: category prediction (five classes) and threat detection (binary). Using 8,951 annotated news titles, single-task baselines achieved weighted F1 scores of 0.84±0.01 (category) and 0.87±0.00 (threat). Multi-task hybrids integrating CNN, LSTM, and Bi-LSTM layers performed comparably overall and improved minority class threat detection, … …Journal of Computer Science, Vol. 22, Issue 3 · 25 Mar 2026 · 1,124 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Dynamic Lifelong Learning Network for Real-Time Stock Prediction With Reinforcement TuningTalent Mawere, Selvaraj Rajalakshmi, Venu Madhav Kuthadi, Otlhapile DinakanyaneCompanies and investors require accurate market forecasting to make more informed decisions. Traditional methods for predicting stock market performance have become less useful considering the dynamic and volatile nature that characterizes stock markets today. In this paper, we propose an innovative Dynamic Lifelong Learning Network for stock market prediction. The model incorporates a hybrid convolutional long short-term memory with an … …Journal of Computer Science, Vol. 22, Issue 2 · 20 Feb 2026 · 1,822 accessesView PDFCite
Research ArticleOpen accessIn the abstractIntelligent Traffic Light System Using Convolutional Neural Network to Optimize Vehicle Flow in IndonesiaBhustomy Hakim, Fergie Joanda Kaunang, Yemima Monica Geasela, Regina HillaryTraffic congestion remains a persistent challenge in major Indonesian cities due to rapidly increasing vehicle density and the continued reliance on fixed-time traffic signal scheduling. This study aims to develop an intelligent traffic light system that dynamically adjusts sequencing based on real-time road conditions. A dataset of 902 CCTV images was collected from intersections in six large cities across Indonesia, … …Journal of Computer Science, Vol. 22, Issue 2 · 17 Feb 2026 · 2,155 accessesView PDFCite
Research ArticleOpen accessIn the abstractRemote Sensing Image Classification Using Convolutional Neural Network (CNN) and Transfer Learning TechniquesMustafa Majeed Abd Zaid, Ahmed Abed Mohammed, Putra SumariThis study investigates the classification of aerial images depicting transmission towers, forests, farmland, and mountains. To complete the classification job, features are extracted from input photos using a Convolutional Neural Network (CNN) architecture. Then, the images are classified using Softmax. To test the model, we ran it for ten epochs using a batch size of 90, the Adam optimizer, and … …Journal of Computer Science, Vol. 21, Issue 3 · 11 Feb 2025 · 10,117 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 abstractClassification of Geographical Land Structure Using Convolution Neural Network and Transfer LearningMustafa Majeed Abd Zaid, Ahmed Abed Mohammed, Putra SumariSatellite imagery has dramatically revolutionized the field of geography by giving academics, scientists, and policymakers unprecedented global access to spatial data. Manual methods typically require significant time and effort to detect the generic land structure in satellite images. This study can produce a set of applications such as urban planning and development, environmental monitoring, disaster management, etc. Therefore, the research … …Journal of Computer Science, Vol. 20, Issue 12 · 24 Oct 2024 · 5,079 accessesView PDFCite
Research ArticleOpen accessIn the abstractIntegration of Cyber-Physical Systems, Digital Twins and 3D Printing in Advanced Manufacturing: A Synergistic ApproachAntreas Kantaros, Theodore GanetsosThе dynamic fiеld of advancеd manufacturing has sееn a significant transformation with thе convеrgеncе of Cybеr-Physical Systеms (CPS), Digital Twins (DT) and 3D Printing tеchnologiеs. A comprеhеnsivе analysis of thе intеgration of thеsе cutting-еdgе tеchnologiеs is presented, highlighting thеir synеrgistic potеntial and thе impact on thе еcosystеm of industry 4.0. Thе intricatе intеrplay bеtwееn CPS, which amalgamatеs computing еlеmеnts with … …American Journal of Engineering and Applied Sciences, Vol. 17, Issue 1 · 19 Jan 2024 · 5,940 accessesView PDFCite
Research ArticleOpen accessIn the abstractAdaptive Synthetic Oversampling Algorithm for Handling Class Imbalance in Multi-Class Data Stream ClassificationPriya S., Annie UthraConcept drift and class imbalanced data are major challenging processes involved in modern streaming data classification. Particularly, when integrated with difficult factors like the existence of noise, overlapping class distribution, concept drift, and data imbalance can considerably affect the classifier results. In addition, various challenges affect the performance of the existing oversampling schemes such as SMOTE and its derivatives. Regardless … …Journal of Computer Science, Vol. 18, Issue 7 · 26 Jul 2022 · 5,809 accessesView PDFCite
Research ArticleOpen accessIn the abstractImproved Classification Model using CNN for Detection of Alzheimer’s DiseaseRagavamsi Davuluri, Ragupathy RengaswamyAlzheimer’s Disease (AD) is commonly called a neurodegenerative disorder and it is a common form of dementia. There is no permanent cure for this brain disease hence the early diagnosis of such disease using medical imaging system is highly significant. Machine learning models play a vital role in the detection of AD. Since most of the conventional machine learning models … …Journal of Computer Science, Vol. 18, Issue 5 · 30 May 2022 · 6,252 accessesView PDFCite