Research ArticleOpen accessIn the abstractAn Enhanced Algorithm for Small Object Detection based on Thermal Imaging Using YOLOv8-EPBRavina Gupta, Sarika Jain, Manoj KumarObject detection is one of the most important and challenging problems in the computer vision domain. Using the power of deep models, researchers have carefully explored and made significant contributions to increasing the effectiveness of object detection and related tasks, such as object identification, localization, and segmentation. This progress is due to the rapid progress of deep learning in the … …Journal of Computer Science, Vol. 21, Issue 6 · 13 Jun 2025 · 4,218 accessesView PDFCite
Research ArticleOpen accessIn the abstractSmart Talent Sourcing Through Advanced Skill Profiling TechniqueAmey Krishnanath Shet Tilve, Gaurang Sitaram Patkar, Vadiraj Gururaj Inamdar et al.The hiring process often struggles with aligning job seekers' skills to employers' requirements, leading to inefficiencies and mismatches. To address this challenge, a dual-functionality system is proposed that leverages Natural Language Processing (NLP) techniques, including BERT for embedding textual information and cosine similarity to rank resumes according to their alignment with job descriptions and to recommend suitable candidates to employers … …Journal of Computer Science, Vol. 21, Issue 2 · 18 Jan 2025 · 4,976 accessesView PDFCite
Research ArticleOpen accessIn the abstractRandom Forest-Based Genome Analysis for Disease Association and SNP Marker IdentificationXiangdong LiuAt present, genomic data analysis has the problems of insufficient model interpretation and limited computational efficiency in disease association research. As a powerful machine learning algorithm, Random Forest has demonstrated excellent ability in processing complex and multi-dimensional data sets and has gradually become a popular tool in the field of bioinformatics. The purpose of this paper is to explore how … …American Journal of Biochemistry and Biotechnology, Vol. 21, Issue 2 · 25 Jul 2025 · 5,536 accessesView PDFCite
Research ArticleOpen accessIn the abstractStroke Risk Assessment with Classical ML MethodsMohammad Aref Alshraideh, Najwan Alshraideh, Abedalrahman Alshraideh et al.Stroke, often caused by a disruption in the supply of essential oxygen, blood, and nutrients to the brain, represents a significant global health challenge. Due to limited resources, developing countries like Ethiopia face unique obstacles in identifying and treating strokes. This study explores the potential of Machine Learning (ML) techniques to predict stroke risk and facilitate early detection and intervention. … …Journal of Computer Science, Vol. 21, Issue 1 · 18 Dec 2024 · 4,657 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Novel Optimized Machine Learning Approach for Early Prediction of Heart Disease Using Bio-Inspired AlgorithmsIndira Priyadarsini, I.S. Siva Rao, P. Swetha et al.This study presents a novel approach for early prediction of heart disease using bio-inspired optimization algorithms to select and optimize features. The proposed approach utilizes a Genetic Algorithm (GA), Bat Algorithm (BA), Bee Algorithm (BA), and Ant Colony Optimization (ACO) to optimize a set of four features for improved prediction accuracy. The optimization process aims to identify the most relevant … …Journal of Computer Science, Vol. 21, Issue 1 · 4 Dec 2024 · 5,235 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Method for Fast and Robust Intensity Based 2D/3D Registration in Differentiable FrameworkAgung Alfiansyah, Izzati Muhimmah, Arif Sasongko et al.This study introduces an innovative intensity-based registration method designed to align 3D image data volumes, such as Computed Tomography (CT), with 2D data, such as X-ray images, a crucial component of intra-operative navigation in image-guided surgery. Leveraging advanced autodifferentiable reconstruction techniques based on the GPU-accelerated Siddon algorithm, the proposed approach facilitates the generation of differentiable Digitally Reconstructed Radiographs (DRRs), enabling … …Journal of Computer Science, Vol. 20, Issue 12 · 24 Oct 2024 · 4,825 accessesView PDFCite
Research ArticleOpen accessIn the abstractStock Closing Price Forecasting Using LSTM, Sentiment Analysis, Kalman FilterAssel Abdildayeva, Galymzhan Nurtugan, Guldana Taganova, Ardak AkhmetovaThe article explores the problem of predicting the closing prices of stocks using an innovative approach that combines methods of machine learning, sentiment analysis and noise filtering. The focus is on the development and testing of a comprehensive model that integrates recurrent neural networks (LSTM) for time series analysis, the GPT-3 language model for processing and analyzing textual data from … …Journal of Computer Science, Vol. 20, Issue 11 · 4 Sep 2024 · 6,266 accessesView PDFCite
Research ArticleOpen accessIn the abstractSales Forecasting Models: Comparison between ARIMA, LSTM and ProphetDmitry BrykinSales forecasting is crucial for business planning and resource allocation. Data-driven approaches have become popular in this field. This study compares the performance of three forecasting models: Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) and Prophet within the context of specific sales categories derived from acquiring data provided by a bank. This study uses a time series dataset … …Journal of Computer Science, Vol. 20, Issue 10 · 1 Aug 2024 · 8,006 accessesView PDFCite
Research ArticleOpen accessIn the abstractMulti-Temporal Sentinel-2 Images for Classification AccuracyYuhendra, Eva YuliantiThe term ‘multi-temporal’ refers to data recorded by sensors scanning the same scene at different dates and times. In this research, Sentinel-2A images were selected for the analyses. Sentinel-2 is a very new program of the European Space Agency (ESA) designed for fine spatial resolution global monitoring. Land Cover–Land Use (LCLU) classification tasks can take advantage of the fusion of … …Journal of Computer Science, Vol. 15, Issue 2 · 22 Feb 2019 · 7,959 accessesView PDFCite
Research ArticleOpen accessIn the abstractDeep Learning Approach to Mitigate DDoS Attacks in SDNHema Surendrakumar Dhadhal, Paresh Kotak, Parvez Faruki, Atul GonsaiThe rise of Distributed Denial of Service (DDoS) attacks remains a significant obstacle to network security and availability. This abstract presents a new hybrid model for DDoS mitigation in Software-Defined Networking (SDN) environments, combining a Semi-supervised Deep Extreme Learning Machine (Semi-Deep ELM) with a hybrid architecture. SDN's centralized control and programmability create an ideal platform for implementing advanced mitigation strategies. … …Journal of Computer Science, Vol. 20, Issue 9 · 5 Jul 2024 · 5,345 accessesView PDFCite