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Research ArticleOpen accessIn the abstractThe Stanford Dependency Relations for Commonsense Knowledge Representation of Winograd Schema Challenge (WSC)Nesreen Alsharman, Raja Masadeh, Ibrahim Ali Jawarneh, Ahmad Al-Rababa’aAn alternative to the Turing Test that could offer a more accurate measurement of artificial intelligence is the Winograd Schema Challenge (WSC). It presents a number of coreference resolution issues that cannot be resolved without the use of human behavior reasoning. A certain type of Commonsense Knowledge (CSK) is necessary for Winograd schema. In order to handle the representation of … …Journal of Computer Science, Vol. 20, Issue 9 · 10 Jul 2024 · 4,743 accessesView PDFCite
Research ArticleOpen accessIn the abstractPrognosis of Dementia Using Early Fusion Approach with Digital Clock Drawing and Trail-Making TestsShridevi Karande, Vrushali KulkarniDementia poses a substantial global public health challenge. Emerging evidence suggests that COVID-19's neurological impact may aggravate dementia incidences. Timely recognition and management can significantly decelerate dementia progression and enhance affected individuals' quality of life. In the realm of cognitive assessment, the Clock Drawing Test (CDT) and Trail-Making Test (TMT) stand as prominent tools. Existing research predominantly focuses on the … …Journal of Computer Science, Vol. 20, Issue 8 · 10 Jun 2024 · 4,720 accessesView PDFCite
Research ArticleOpen accessIn the abstractResearch on Cotton Top Bud Target Detection Algorithm Based on Improved RetinaNetJikui Zhu, Shijie Lin, Fengkui Zhang et al.To solve the problems of low accuracy and high miss rate in the recognition of cotton apical buds during mechanical topping, an enhanced method based on the RetinaNet network is proposed for the accurate identification of cotton apical buds under natural light. The traditional RetinaNet algorithm is validated to improve the recall rate and average accuracy of cotton apical bud … …American Journal of Biochemistry and Biotechnology, Vol. 20, Issue 1 · 17 Apr 2024 · 5,128 accessesView PDFCite
Research ArticleOpen accessIn the abstractA 2-Tier Stacking Ensemble Classifier for Disease ClassificationK. Sukanya Varshini, R. Annie UthraDiarrhea, dysentery, and dehydration are top of the list of high mortality-causing diseases in children under the age group of five. Despite the tremendous growth of machine learning in the field of medical research, still, some areas remain untouched, especially, the pediatric department. Since medical data is used in the proposed work, even a slight increase in the accuracy of … …Journal of Computer Science, Vol. 19, Issue 7 · 7 Jun 2023 · 5,785 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Non-Intrusive Experiment to Examine the Visual Attention Data on Exposure to a Brand Using Eye TrackingSurya Bahadur Thapa, Prasun Gahlot, Komal Suryavanshi et al.The objective of this study is to understand the influence of articulation on sports sponsorship posters of a sporting event. An eye tracking experiment was conducted to examine the visual attention data on exposure to the sponsor’s brand while viewing an advertisement poster. The respondents were asked to fill out a questionnaire based on what they could recall while viewing … …Journal of Computer Science, Vol. 19, Issue 3 · 28 Feb 2023 · 5,908 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Ensemble of Gaussian Mixture Model and Support Vector Machines for Network Intrusion DetectionOlujimi Daniel Alao, Sheriff Alimi, Shade Oluwakemi Kuyoro et al.NetworkIntrusion Detection Systems (NIDS) can protect computer networks and computersystems by detecting abnormal network packets and taking agreed action plans,such as notifying an administrator or rejecting the network packets. In this study,the aim is the implementation of NIDS with improved performance using anensemble of Support Vector Machines (SVMs) and the Gaussian Mixture Model(GMM). Four SVMs with Radial Basis Function (RBF), … …Journal of Computer Science, Vol. 18, Issue 9 · 21 Sep 2022 · 6,114 accessesView PDFCite
Research NotesOpen accessIn the abstractMetadata Stewardship and Quality in Telecom SectorBhola Gan Chaudhuri, Shalli RaniThe spread of the telecom network infrastructure, constellations of satellites and the use of the Internet of Things (IoT) have brought about unprecedented volumes of heterogeneous information which facilitate the contemporary monitoring systems of the environment and the earth. Metadata control and information quality have also become significant in the telecom industry in controlling data reliably, enhancing data services, and … …Journal of Computer Science, Vol. 22, Issue 10 · 22 Oct 2026 · 335 accessesView PDFCite
Research ArticleOpen accessIn the abstractProbabilistic Learning Models for Secure E-Commerce Transaction AnalysisAnanth Maria Pushpa, S. SubramaniaanThe tremendous expansion of e-commerce requires well-developed security mechanisms of transactions coupled with cryptography systems. This paper explores how probabilistic learning algorithms can be used to promote fraud and transaction validation in e-commerce systems that are cryptographically secured. A study compared different machine learning models to analyze retail data, including Naïve Bayes, Bayes Network, and Logistic learning. The results showed … …Journal of Computer Science, Vol. 22, Issue 11 · 20 Oct 2026 · 506 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Hybrid Machine Learning Approach to Predictive Analytics in Precision Agriculture for Sustainable Crop OptimizationJignesh Kariya, Pankaj MudholkarDriven by data-enabled technologies, precision agriculture is evolving into an innovative way to increase agricultural productivity and sustainability. Conventional agricultural practices face several challenges, such as poor soil quality, inappropriate crop selection, improper resource utilization, and unpredictable climate conditions. To address these limitations, this paper proposes a hybrid machine learning framework for sustainable crop optimization and predictive analysis in precision … …Journal of Computer Science, Vol. 22, Issue 10 · 10 Oct 2026 · 865 accessesView PDFCite
Research ArticleOpen accessIn the abstractLeveraging Modified Cascaded Fully Convolutional Neural Networks and Optimized Swin U-Net for Liver Lesion PredictionSugandha Chakraverti, Tejaswi Khanna, Vijay ShuklaLiver lesions are abnormal growths that can develop for various reasons, some of which are noncancerous (benign) while others are cancerous. Machine learning and, deep learning techniques are employed for liver health assessment and lesion detection, but the quality and, diversity of training data can hinder the performance, leading to overfitting or underfitting. These models often need extensive pre-processing and … …Journal of Computer Science, Vol. 22, Issue 10 · 8 Oct 2026 · 372 accessesView PDFCite