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1089 articles for “data modelling”
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Modeling Galaxy Formation in a Hierarchical Universe: A Fiducial Approach and Comparison with Observational Data
Abstract: We have developed a detailed model to understand how galaxies form in the framework of hierarchical theories of structure formation. Our model accounts for key processes like the formation and merging of dark matter halos, the heating and cooling of gas inside these halos, the regulation of star formation driven by energy from evolving stars and supernovae, galaxy mergers, and the changes in star populations over time. This approach is …
Published in International Journal of Universe · Vol. 1, Issue 1, 2025 · pp. 30–36 Read article
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Study on Cloud Computing’s Deployment Models
Abstract: Cloud computing represents a significant technological advancement in the information technology industry. It is one of the fastest-growing technologies, where computing resources are managed and allocated across the globe via the internet. Today, cloud computing is a key topic in many computer science curricula due to its extensive impact on various computing domains, particularly big data, which would be impossible without cloud computing. Cloud computing is an internet-based technology that …
Published in Current Trends in Information Technology · Vol. 14, Issue 3, 2024 · pp. 26–33 Read article
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CNN-Based Wound Segmentation: A Review of Models and Performance Evaluation
Abstract: Deep learning, particularly convolutional neural networks (CNNs), has altered medical image processing by automating and precisely segmenting complex medical pictures. Wound segmentation, a critical application in automated wound assessment, is essential for wound size estimation, classification, and healing progress monitoring. This study presents a comprehensive review of CNN-based wound segmentation models, focusing on their architectures, methodologies, and performance on diverse datasets. Four deep learning models, including two U-Net variants (5-layer …
Published in Current Trends in Signal Processing · Vol. 15, Issue 1, 2025 · pp. 33–46 Read article
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Animal Species Prediction Using Deep Learning
Abstract: In the face of escalating biodiversity loss, effective monitoring of animal species is critical for conservation efforts. This study presents a deep learning approach for species detection and a multimodal feature identification technique for animals vulnerable to poaching. The suggested prediction system recognizes objects automatically by the application of deep learning techniques to detect objects and then recognize them by using computer vision techniques, and it is triggered when an …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 1, 2026 · pp. 14–22 Read article
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A Comprehensive Analysis of Machine Learning Models for Credit Card Fraud Detection
Abstract: This paper presents an indepth comparison of various machine learning models—Logistic Regression, Support Vector Classification (SVC), and Neural Networks (NN)—in the context of credit card fraud detection. The analysis spans multiple performance metrics, including accuracy, F1 score, precision, recall, and computational efficiency. Logistic Regression demonstrates competitive performance in terms of accuracy, but its poor precision renders it unsuitable for fraud detection tasks. Conversely, the Neural Network exhibits balanced precision and …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 Read article
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Quantitative Structure-activity Relationship in Computer-aided Drug Design: A Review
Abstract: Quantitative Structure-Activity Relationship stands at the forefront of Computer-Aided Drug Design, providing a systematic framework for understanding the relationship between the chemical structure of molecules and their biological activity. The present review delves into the multifaceted realm of quantitative structure-activity relationship methodologies within the landscape of drug discovery. Through an exploration of diverse quantitative structure-activity relationship models, molecular descriptors, validation techniques, and recent advancements, the present article aims to elucidate …
Published in Research and Reviews: A Journal of Pharmaceutical Science · Vol. 15, Issue 2, 2024 · pp. 55–63 Read article
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The Poisson–Uma Distribution with Properties and Applications to Model Thunderstorm Events
Abstract: The discrete data available in any field of knowledge is influenced by several known and unknown factors and the factors which affect the discrete data are stochastic. The stochastic nature of discrete data is a challenge for statisticians to model and analyze with the existing discrete distributions. In the present paper, Poisson-Uma distribution, the Poisson compound of Uma distribution, has been proposed to model over-dispersed data of thunderstorm events. The …
Published in Research & Reviews : Journal of Statistics · Vol. 13, Issue 1, 2024 · pp. 20–30 Read article
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Designing an AI-Based Platform for Stock Market Prediction
Abstract: The AI-Based Platform for Stock Market Prediction is an advanced tool designed to forecast stock prices and market trends using artificial intelligence. This platform combines machine learning algorithms, real-time financial data, and sentiment analysis to provide investors with actionable insights. The platform uses advanced predictive techniques like Long Short-Term Memory (LSTM) networks and Gradient Boosting Machines to generate precise and reliable forecasts. Additionally, it incorporates interactive visualizations and portfolio optimization …
Published in E-Commerce for Future & Trends · Vol. 12, Issue 3, 2025 · pp. 14–19 Read article
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Developing an AI-Based Novel Forecasting Framework for Surface Irregularity in Metal Matrix Materials
Abstract: Surface irregularity in metal matrix materials (MMM) signifies the deviations from smoothness, influencing structural integrity and performance frequently arising from the manufacturing process along with intrinsic material characteristics that influence effectiveness. Limitations in data, model interpretability and complexity are the difficulties that impede artificial intelligence (AI) based surface irregularity in MMM. In this study, we suggested a novel framework of Gaussian regression fused multi-strategy adaptive boosting classifier (GR-MABC) for the …
Published in Journal of Polymer & Composites · Vol. 12, Issue 5, 2024 · pp. 48–56 Read article
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An Empirical Analysis of Cost, Time, and Quality Relationships Using Correlation and Regression Techniques
Abstract: Cost, time, and quality have been recognized as the three key dimensions that determine construction project performance and are commonly represented through the "Project Management Triangle." However, despite their theoretical linkage, there is limited empirical evidence to quantify the nature of their relationships, particularly within developing construction markets. This paper seeks to contribute to addressing this shortfall by investigating the specific influence of cost and schedule variations upon quality outcomes …
Published in Journal of Construction Engineering, Technology & Management · Vol. 16, Issue 1, 2026 · pp. 52–60 Read article
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In-silico Approach of Few Selected Phytoconstituents on Newer Cancer Targets
Abstract: Background: Cancer’s high death rates are mainly due to, drug resistance and unmet medical demands. It necessitates novel anticancer medications. AI tools aid in efficient and faster drug discovery by analyzing data, modeling processes and optimizing pipeline stages. Aim: The aim of this present study is to evaluate phytoconstituents against novel and newer cancer targets. Methodology: The ligands Daidzein, Resveratrol and Genistein were targeted against the Glutamate dehydrogenase (PDB ID …
Published in Research and Reviews: A Journal of Pharmaceutical Science · Vol. 15, Issue 3, 2024 · pp. 12–17 Read article
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Artificial Intelligence in Cybersecurity: Emerging Trends, Technological Advancements, and Future Directions for Cyber Defense
Abstract: Artificial Intelligence (AI) is revolutionizing the field of cybersecurity by automating complex security tasks, improving threat detection capabilities, and enhancing the precision of threat response mechanisms. With the rapid evolution of cyber threats such as malware, ransomware, phishing, and data breaches, conventional security systems are often insufficient to provide timely and accurate protection. AI, powered by machine learning algorithms and neural networks, enables the analysis of vast datasets to detect …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 103–112 Read article
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Survey of Predictive Models for Safe Route Predicting Using Machine Learning Techniques
Abstract: Safe route prediction is essential for the well-being and security of individuals in urban and rural environments. Machine learning techniques leverage historical data, real-time information, and algorithms to estimate the safety levels of different routes. The objective of safe route planning is to minimize risks, including crime-prone areas and accidents, reducing potential harm, property damage, and emotional distress. However, challenges arise from the complex and dynamic nature of urban environments, …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 11, Issue 1, 2024 · pp. 13–22 Read article
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A Review on Predicting Wear and Friction of PTFE Composites - Fillers to Machine Learning Models
Abstract: Polytetrafluoroethylene (PTFE) composites, a self-lubricating material with low friction, became an indispensable material in engineering applications where load carrying capacity and wear are crucial. The pure PTFE has poor mechanical strength and wear resistance which can be enhanced by the addition of fillers in appropriate volume fraction. The wear performance is dependent on various factors such as fillers, operating parameters, environmental conditions as well as manufacturing attributes. This makes the …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 114–128 Read article
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QSAR Modeling Techniques: A Comprehensive Review of Tools and Best Practices
Abstract: Quantitative Structure–Activity Relationship (QSAR) modeling has become an essential tool in drug discovery, toxicity assessment, and environmental chemistry. By correlating chemical structure with biological activity or toxicity, QSAR enables the prediction of compound behavior without extensive experimental testing. This approach not only saves time and resources but also supports ethical practices by reducing reliance on animal studies. The evolution of QSAR from basic linear models to advanced machine learning and …
Published in International Journal of Cheminformatics · Vol. 3, Issue 1, 2025 · pp. 56–63 Read article
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Greener 3D Printing: The Role of Artificial Intelligence in Sustainable Polymer and Composite Manufacturing
Abstract: The integration of sustainable materials with additive manufacturing (AM) technologies marks a significant step towards environmentally responsible production. Biodegradable polymers, recycled thermoplastics, and bio-based composites, when used in 3D printing, offer the potential to reduce the ecological footprint of manufacturing. However optimizing the interplay between material properties process parameters, and product performance remains a complex challenge. This review examines how artificial intelligence (AI) is being applied to address these challenges …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 288–300 Read article
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Differential Privacy-Aware Data Sanitization for Multi-Level Security
Abstract: Multi-level security (MLS) models are fundamental for enforcing mandatory access control in high-security environments such as government, military, healthcare, and finance. However, traditional MLS frameworks, including the Bell-LaPadula and Biba models, often create rigid data silos, preventing efficient data utilization. Differential privacy (DP) presents a novel solution by enabling controlled information leakage while preserving confidentiality. By injecting statistical noise into query results, DP allows lower-clearance users to access sanitized versions …
Published in International Journal of Computer Science Languages · Vol. 3, Issue 1, 2025 · pp. 42–52 Read article
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Enzyme Stability Prediction using BERT and CNN-A Deep Learning Approach for Enhanced Biocatalysis
Abstract: An important factor in determining the efficacy of industrial enzymes used in various biotechnological applications is their stability. The goal of this study is to develop a predictive model for industrial enzyme stability, which is essential to the efficiency of these enzymes in biotechnological applications. The research takes a comprehensive strategy to comprehend the parameters affecting enzyme stability by combining statistical analysis, deep learning algorithms (BERT and CNN), and molecular …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 14, Issue 2, 2024 · pp. 19–35 Read article
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AI-Driven Optimization of Biopolymer Composite Formulations Using IoT Data Streams
Abstract: Biodegradable polymer composites have emerged as a sustainable alternative to petroleum-based materials in packaging, biomedical, and structural applications. However, traditional formulation techniques for reinforced polymer composites often lack precision and fail to adapt to real-time variations during processing, resulting in suboptimal material performance. This research proposes a real-time AI-IoT-enabled framework to optimize biopolymer composite formulations. The goal is to intelligently tune composite properties such as mechanical strength, moisture resistance, and …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 85–100 Read article
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Statistical Models for Predicting Genetic Variability and Disease Susceptibility
Abstract: Differences in genetics are key to understanding why some individuals are more prone to certain diseases than others. Recent advancements in genomic research, combined with statistical modeling techniques, have made significant strides in predicting disease risk based on genetic factors. This review explores the application of statistical models for predicting genetic variability and their role in disease susceptibility. We discuss traditional methods like linear regression and genome-wide association studies (GWAS), …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 1, 2025 · pp. 30–34 Read article