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332 articles for “data driven model”
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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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Implement Artificial Intelligence and Machine Learning for Engineering Design, Predictive Modeling, and Optimizing Polymer Nanocomposites
Abstract: Polymer nanocomposites are high performance engineered materials obtained by inclusion of nano-sized fillers into the polymer matrix to enhance mechanical, thermal, electrical, barrier and functional properties. However, the complex and non-linear interactions among polymer chemistry, nanofiller characteristics, filler concentration, dispersion, interfacial bonding and processing conditions make it challenging to anticipate and maximize their properties. Artificial intelligence (AI) and machine learning (ML) offer powerful data-driven solutions to these difficulties by establishing …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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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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The Intersection of Bioinformatics and Cellular Function in Disease Modeling
Abstract: The integration of bioinformatics and cellular biology has revolutionized our understanding of disease mechanisms, offering unprecedented opportunities to model complex biological systems. Bioinformatics is an interdisciplinary field that merges biology, computer science, and statistics, offering advanced tools to analyze vast biological datasets. Cellular functions, including gene expression, protein interactions, and metabolic pathways, form the foundation of physiological and pathological states. Disruptions in these processes can result in diseases like cancer, …
Published in International Journal of Cell Biology and Cellular Functions · Vol. 2, Issue 2, 2024 · pp. 1–7 Read article
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Role of Quantum Chemistry in Catalysis: A Comprehensive Review
Abstract: Catalysis plays a crucial role in modern chemical manufacturing, energy conversion, and environmental protection by enabling chemical reactions to occur more rapidly, selectively, and with reduced energy consumption. A fundamental understanding of catalytic processes at the atomic and electronic levels is essential for the rational design and optimization of catalysts. Quantum chemistry has emerged as a powerful theoretical and computational framework that enables detailed investigation of electronic structure, reaction energetics, …
Published in Journal of Catalyst & Catalysis · Vol. 13, Issue 1, 2026 · pp. 01–16 Read article
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Strategic Solutions: How Mathematics Reshapes Industrial Landscapes
Abstract: One of the earliest and most fundamental fields of the physical sciences is mathematics. It has a significant impact on industrial enterprises' bottom lines and enhances their performance in the current data-driven market. One subfield of applied mathematics is industrial mathematics. It concentrates on issues that arise in the industry and seeks answers that are pertinent to the sector. The use of mathematical models and techniques to diverse industry difficulties …
Published in International Journal of Industrial and Product Design Engineering · Vol. 2, Issue 1, 2024 · pp. 16–23 Read article
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A Mathematical Perspective on Recent Cloud-Computing Trends for Scalable and Secure Social-Media Platforms
Abstract: Cloud computing underpins modern social-media platforms by providing elastic compute, storage, and data-processing pipelines capable of absorbing highly bursty workloads. This paper surveys recent cloud-native trends—serverless and event-driven design, container orchestration, edge/CDN offload, streaming analytics, and privacy-enhancing security controls—and formalizes their impact through a compact mathematical model. We express workload volatility using arrival-rate functions, use queueing-based capacity sizing to derive auto-scaling rules, and formulate an optimization objective that balances cost …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 35–40 Read article
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The Significance and Applications of Parallel Computing in the Modern Era
Abstract: This article explores the advancements in parallel computing, focusing on its applications in various domains such as scientific simulations, big data analytics, artificial intelligence, and real-time processing. We discuss the architectural shifts from traditional single-core processors to multi-core and many-core systems, along with the role of graphics processing unit (GPU)-based computing and specialized hardware like tensor processing units (TPUs) and field programmable gate arrays (FPGAs). Furthermore, the article examines contemporary …
Published in Recent Trends in Parallel Computing · Vol. 12, Issue 1, 2025 · pp. 24–38 Read article
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Adapting Traditional Land Use Planning with Smart City Solutions - Case of New Delhi
Abstract: This research paper explores how traditional land use planning can be effectively blended with Smart City solutions to shape the future of New Delhi. As the city grapples with rapid urbanization, rising traffic congestion, and severe environmental pressures, the need for adaptive and sustainable urban development has become more urgent than ever. Although New Delhi’s planning framework—rooted in Master Plans and zoning regulations—has evolved over the decades, it still struggles …
Published in Recent Trends in Civil Engineering & Technology · Vol. 15, Issue 3, 2025 · pp. 24–47 Read article
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The Green Cost of Generative Ai: Environmental Sustainability Implications of Large-Scale Ai Systems
Abstract: Generative Artificial Intelligence (GenAI) has advanced rapidly in scale and complexity, enabling powerful capabilities in automated content creation, multimodal reasoning and real-time decision support across sectors. While these systems offer significant technological and economic benefits, their environmental implications are not fully examined. Large-scale GenAI models rely on high-performance computing infrastructure that consumes substantial energy and resources throughout their lifecycle, raising critical sustainability concerns. This paper offers a sustainability-oriented assessment of …
Published in International Journal of Sustainability · Vol. 3, Issue 1, 2026 · pp. 12–20 Read article
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Deep Learning Enhanced Compressive Sensing for Wireless IoT Data Optimization and Weather Monitoring.
Abstract: This research explores the application of deep learning and compressive sensing in order to optimize data traffic in non-orthogonal multiple access (NOMA)-based wireless internet of things (IoT) networks and weather monitoring. Such a framework would be very effective and overcome pilot attacks and reconstruction losses for secure data transmission. In this regard, a strong communication model has been adopted based on power-domain NOMA for simultaneous wireless transmission by multiple IoT …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 2, 2024 · pp. 20–36 Read article
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A Hybrid Multi-Physics Ensemble Deep Learning Framework for Simultaneous Prediction of Thermal and Electrical Conductivity in Functional Polymer-Based Nanocomposites
Abstract: Polymer-based nanocomposites have become promising materials for applications in energy storage, flexible electronics, biomedical devices, aerospace components, and advanced engineering because of their tunable thermal and electrical properties. Reliable prediction of these properties is essential for accelerating material design; however, existing analytical models and conventional machine learning techniques often fail to represent the complex interactions among filler characteristics, polymer matrices, processing conditions, and interfacial transport phenomena. This work presents HMEP-Net, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1062–1082 Read article
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Optimizing Urban Mobility with AI-Based Traffic Management
Abstract: Urban mobility is a pressing concern in modern cities, plagued by issues like traffic congestion and pollution. This research involves, "Optimising Urban Mobility with AI-based Traffic Management", delves into the potential of Artificial Intelligence (AI) to revolutionize traffic management. Focusing on AI algorithms, data analytics, and sensor technologies, the research aims to enhance traffic flow, reduce congestion, and improve overall efficiency. Through statistical analysis and simulations, the research evaluates the …
Published in Trends in Transport Engineering and Applications · Vol. 12, Issue 3, 2025 · pp. 34–43 Read article
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Image-Based Quantitative Mapping of Structure Property Relationships in Polymer Composite Materials
Abstract: The performance of polymer composite materials is intrinsically governed by their microstructural architecture, which is shaped by manufacturing conditions and constituent interactions. Despite extensive experimental characterization efforts, establishing transparent and quantitative structure–property relationships from microstructural images remains a challenge. In this study, an explainable image-driven framework is developed to systematically correlate microstructural features with composite property indicators. Microstructure images are processed to identify voids, fibers, and filler phases, from which …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 188–196 Read article
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Advancements in AI-Driven Sound Spectrogram Analysis: From Deep Learning to Quantum and Neuromorphic Processing
Abstract: The rapid advancement of artificial intelligence (AI) has significantly reshaped the field of audio signal processing, with sound spectrogram analysis emerging as a central research focus. Spectrograms provide a rich time–frequency representation of audio signals, making them particularly suitable for data-driven learning approaches. This paper presents an in-depth and original review of modern AI-based techniques applied to spectrogram analysis, highlighting their growing impact across critical application areas such as healthcare …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 13, Issue 1, 2026 · pp. 01–06 Read article
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Ethical Considerations in AI-Driven Rehabilitation Robotics: Balancing Innovation and Responsibility
Abstract: Artificial intelligence (AI) is transforming robotics rehabilitation by introducing advanced capabilities such as adaptive therapy, real-time feedback, and personalized assistance, significantly improving outcomes for individuals with neurological and physical impairments. These AI-powered systems offer high levels of precision and consistency in therapy delivery, making them especially beneficial in pediatric and adult rehabilitation settings where engagement and tailored interventions are crucial. However, the integration of AI in healthcare also presents critical …
Published in Journal of Mechatronics and Automation · Vol. 12, Issue 3, 2025 · pp. 24–29 Read article
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Enhancing Robot Autonomy: Integrating AI for Advanced Decision-making in Autonomous Robotic Systems
Abstract: The capabilities of autonomous robotic systems have been drastically changed by the rapid progress in artificial intelligence (AI) technologies. In this work, we investigate the integration of AI approaches to improve robot autonomy by presenting even more advanced mechanisms for decision-making. Almost all traditional robotic systems involve predefined algorithms, making them unable to cope with dynamic environments. They can also help with learning based on machine learning and deep learning …
Published in Journal of Advancements in Robotics · Vol. 11, Issue 3, 2024 · pp. 28–37 Read article
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Artificial Intelligence in Drug Repurposing: A Short Impact Assessment
Abstract: Artificial intelligence (AI) in pharmaceutical repurposing has become a game-changing tool that opens new avenues for the application of new drugs that have already been approved. Traditional drug discovery is a lengthy and expensive process, whereas AI can rapidly analyze vast datasets of biological, chemical, and clinical information to predict drug-disease interactions. AI-driven techniques, such as machine learning, natural language processing, and deep learning, enable the identification of potential repurposing …
Published in Trends in Drug Delivery · Vol. 11, Issue 3, 2024 · pp. 42–45 Read article
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Quantitative Image-Based Assessment of Degradation Patterns in Polymer-Based Medical Implants
Abstract: Polymer-based medical devices are widely used in clinical practice, where long-term material degradation can compromise performance and patient safety. Traditional polymer degradation studies predominantly rely on laboratory-based experiments, which often fail to capture real-world operational and usage conditions. In this study, a multimodal, data-driven framework is proposed for the quantitative assessment of degradation patterns in polymer-based medical devices using publicly available clinical failure data. Structured operational parameters, including cumulative usage …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1510–1518 Read article
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A Framework for Privacy-preserving AI Models in Cloud Computing: Challenges and Solutions
Abstract: The growing adoption of cloud computing for deploying artificial intelligence (AI) models has led to significant advancements in sectors such as healthcare, finance, and e-commerce. However, the integration of AI with cloud computing raises critical privacy concerns, particularly when handling sensitive data. This paper presents a comprehensive framework for implementing privacy-preserving AI models in cloud environments, addressing the unique challenges, and proposing effective solutions. The suggested framework employs advanced privacy-preserving …
Published in Journal of Operating Systems Development & Trends · Vol. 11, Issue 3, 2024 · pp. 1–12 Read article