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175 results for “Reliable Data”
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Research ArticleOpen accessIn the abstractDistributed Vehicle Tracking System Using Image Processing TechniquesIehab Abdul Jabbar Kamil, Mohanad Abdulsalam Al-AskariIn this part of the research, an implementation of a distributed vehicle tracking system that uses image processing techniques for real-time automobile surveillance is done. The system uses OpenCV, Pytesseract and Optical Character Recognition (OCR) for the extraction of the license plate information from vehicle images to identify and track vehicles. By doing so, distributed computing enables the system to … …Journal of Computer Science, Vol. 21, Issue 8 · 9 Oct 2025 · 2,263 accessesView PDFCite
Research ArticleOpen accessIn the abstractAnalyzing Temperature-Dependent Thermal Properties of Biomaterials Using Machine Learning MethodsArmaghan Shalbaftabar, Kristen Rhinehardt, Dhananjay KumarThis research utilizes machine learning models ANN, Random Forest, and Decision Tree to predict material properties of Ti-based biomaterials, including Young’s modulus, density, thermal conductivity, and specific heat at various temperatures. Data was sourced using web scraping and Plot Digitizer, validated against literature, and analyzed in Excel. The ANN model achieved strong performance, with R² = 0.980874 for TiAl and … …American Journal of Engineering and Applied Sciences, Vol. 18, Issue 1 · 12 Sep 2025 · 4,634 accessesView PDFCite
Review ArticleOpen accessIn the abstractA Review on Ultra Reliable and Low Latency Communications (PHY and MAC Layer Perspectives)Prinu Chacko Philip, Mohammed Abdel-HafezFifth Generation (5G) wireless network plays major attention in the communication scenario due to the advanced features in data transmission. 5G is designed to overcome the issues, such as data rate, reliability, throughput, latency and capacity in the wireless network domains. The 5G wire-less services aim to increase the data rate through massive communication services. In this survey, well known … …Journal of Computer Science, Vol. 16, Issue 8 · 1 Sep 2020 · 6,991 accessesView PDFCite
Review ArticleOpen accessIn the abstractArtificial Intelligence Techniques and External Factors used in Crime Forecasting in Violence and Property: A ReviewRebaz Mala Nabi, Soran Ab. M. Saeed, Habibollah HaronCrime forecasting is beneficial in providing useful information to authorities in planning effective crime prevention measures. The two types of analysis used in crime forecasting are univariate and multivariate. Comparatively, multivariate analysis provides better forecasting accuracy because of its ability to discover crime patterns not previously seen. Crime is strongly influenced by several external factors, including economic, social and demographic. … …Journal of Computer Science, Vol. 16, Issue 2 · 19 Feb 2020 · 7,918 accessesView PDFCite
Research ArticleOpen accessIn the abstractAnalytical Fractured Reservoir Characterization by using Geological and Petrophysical LogsZohreh Movahed, Farzaneh AghajariDue to structural complexities in some wells, higher than the expected thickness of the Asmari formation is found. Fracture intensity and deep-rooted fractures broadly increase the risk of unpredictable water production. The well-test analysis is not sufficient in describing fracture properties. Unfortunately, the core condition is poor in the fractured zones and cannot be used to provide reliable information. So, … …Current Research in Geoscience, Vol. 12, Issue 1 · 3 Dec 2022 · 9,927 accessesView PDFCite
Research ArticleOpen accessIn the abstractPhishing Website Detection Using Improved Multilayered Convolutional Neural NetworksHadia Bibi, Syed Rehan Shah, Mirza Murad Baig et al.The internet has become an essential part of many fields: Communication, entertainment, commerce, industrial production, agriculture, etc. Unfortunately, online users are vulnerable to various attacks; this could lead to financial damages and loss of personal information. Phishing is seen as an internet threat and a cybercrime where anyone can capture personal information and data by posing as a reliable source. … …Journal of Computer Science, Vol. 20, Issue 9 · 6 Jul 2024 · 6,578 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
Research ArticleOpen accessIn the abstractTAGN-IDC: Implicit Discrete Constraints for Tsunami Aware Graph Neural Networks in Spatial Evacuation RoutingSularno, Wendi Boy, Putri Anggraini et al.Tsunami evacuation route requires rapid identification of safe evacuation paths under dynamically changing hazard conditions. A major challenge is incorporating hard binary road-blockage constraints caused by tsunami inundation while maintaining end-to-end differentiability for deep learning models. Existing approaches commonly rely on continuous relaxations or stochastic sampling techniques, which either compromise physical feasibility or increase computational complexity. This study proposes the … …Journal of Computer Science, Vol. 22, Issue 9 · 24 Sep 2026 · 704 accessesView PDFCite
Research ArticleOpen accessIn the abstractForecasting Corporate Financial Distress Using Explainable Machine Learning: Implications for Risk Management TechnologyKanjana Hinthaw, Warawut NarkbunnumFinancial distress prediction is a critical task in financial risk management, yet it remains challenging due to class imbalance, complex financial structures, and limited model interpretability. This study develops an integrated machine learning framework that combines feature selection, class imbalance handling, threshold optimization, and Explainable Artificial Intelligence (XAI) to improve both predictive performance and decision-making reliability. Using a dataset of … …Journal of Computer Science, Vol. 22, Issue 7 · 22 Jul 2026 · 1,130 accessesView PDFCite
Research ArticleOpen accessIn the abstractAnimal Health Prediction Using Hybrid CNN Based BiLSTM Classification Model: A Deep Learning ApproachJ. Rathi, A. SumathiAccurate prediction of animal diseases is vital in veterinary medicine, as it can substantially enhance animal health outcomes and mitigate economic losses, making it a critical task that warrants attention and innovative solutions. This paper proposes a novel approach to animal condition classification, leveraging an Auto-encoder-based feature selection process and an Improved Hybrid Convolutional Neural Networks (CNN) with Bidirectional (Bi-LSTM) … …Journal of Computer Science, Vol. 22, Issue 5 · 20 May 2026 · 1,481 accessesView PDFCite