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1966 articles for “Models” (ranking capped at the first 2,000 matches — narrow the search to see the rest)
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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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Generative Artificial Intelligence with Emphasis on Large Language Models: Review and Current Trends
Abstract: Generative Artificial Intelligence deals with AI systems that generate new content, such as text, and images. It accomplishes this by using data patterns of texts and images that already exist. Generative AI began an era of major advancement in AI, producing more refined and human-like results. Large Language Models, LLMs, is a part of Generative AI with applications in Natural Language Processing such as text generation, translation, summarization, sentiment detection …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 1, 2025 · pp. 40–46 Read article
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Exploring Technologies for Extractive Text Summarization: A Review of Transformer and Reinforcement Learning Models
Abstract: In recent years, the size of information on the Internet has increased exponentially. Therefore, a solution is needed to transform large amounts of raw data into useful information the human brain can understand. Automatic Text Summarization (ATS) is a part of Natural Language Processing (NLP) that aims to take long texts and shorten them, keeping the most important information in a clear and easy-to-understand way. This research report explores methods …
Published in Current Trends in Signal Processing · Vol. 15, Issue 1, 2025 · pp. 1–6 Read article
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Impact of Adaptive Learning, LMS, and Personalized Skill Development on Industry Recruitment Alignment
Abstract: This paper discusses the impact of adaptive learning on the development of skill and self-learning, and it portrays a methodology framework to design an adaptive learning system that works with industry recruitment objectives. The paper distinguishes different approaches to learning, namely teacher-centric and student-centric, rigid and adaptive, competitive and collaborative, and explains that each learner has their own learning style, preference, and level of knowledge and skill acquisition. The paper …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 12, Issue 1, 2025 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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Continuous Learning in Language Models: A Survey of Streaming Data Processing Techniques
Abstract: The integration of continual learning with Large Language Models (LLMs) and Natural Language Processing (NLP) represents a transformative step toward creating adaptive, intelligent systems capable of functioning effectively in ever-changing environments. Traditional LLMs are typically trained on large, pre-collected datasets, which limits their ability to evolve as new information emerges. Continual learning, in contrast, enables models to acquire new knowledge incrementally without the need for complete retraining, thereby supporting long-term …
Published in Recent Trends in Programming languages · Vol. 12, Issue 3, 2025 · pp. 23–34 Read article
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Global Supply Chain Agility Through ERP Systems: A Decision Support Model for Emerging Economies
Abstract: In today’s highly dynamic and interconnected global markets, supply chain agility—the capacity to sense environmental changes and respond rapidly and effectively—has emerged as a critical success factor. This is particularly significant for firms operating in emerging economies, where market volatility, infrastructural limitations, policy uncertainties, and resource constraints pose persistent challenges. Enterprise Resource Planning (ERP) systems, with their ability to integrate cross-functional processes, centralize data, and provide real-time decision-making support, offer …
Published in Journal of Production Research & Management · Vol. 15, Issue 3, 2025 · pp. 31–37 Read article
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Adversarial Attacks on Machine Learning Models in Cybersecurity: A Systematic Literature Review
Abstract: Adversarial machine learning (AML) is a field that is growing swiftly, especially as machine learning models are employed more and more in places where security is critical. This review goes into great depth over 746 publications from the Scopus database, with an emphasis on the connection between AML and network security. Using Biblioshiny and Scopus tools, we looked at trends in publications, study fields, productive authors, collaboration networks, and theme …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 1, 2025 · pp. 23–38 Read article
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Experimental Analysis and Predictive Modeling of Mechanical Behavior in Epoxy Composites Reinforced with Waste Tyre Rubber Particles
Abstract: The disposal of end-of-life tyres poses a significant environmental and resource challenge owing to their large volumes and non-biodegradable nature. In this work, we explore the incorporation of waste tyre rubber particles (WTRP) into an epoxy resin matrix to develop sustainable polymer composites and examine their mechanical behavior both experimentally and through predictive modelling. Composites with differing epoxy: WTRP ratios (80:20, 75:25, 70:30 wt.%) and varying rubber particle mesh sizes …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1754–1765 Read article
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A Review of Solar-Powered Electric Vehicle Models Handling Unpredictable Changes in Modern Power Grids
Abstract: The transition to Electric Vehicles (EVs) is a critical strategy for mitigating global warming and reducing dependence on diminishing fossil fuel reserves. However, the environmental benefits of EVs are significantly diminished if the charging power is sourced from carbon-intensive electrical grids. To achieve true sustainability, it is vital to integrate Renewable Energy Sources (RES), particularly solar energy, into the charging infrastructure. Beyond transportation, EVs offer a unique opportunity to act …
Published in Trends in Electrical Engineering · Vol. 16, Issue 1, 2025 Read article
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Assessing Urban Surface Runoff Management Using the SWAT Model, GIS, and the Rational Method: A Case Study of Karapakkam
Abstract: Urban surface runoff management encompasses strategies aimed at regulating rainwater flow, reducing urban flooding, enhancing groundwater recharge, and contributing to disaster risk mitigation for sustainable urban development. This study seeks to optimize surface runoff management in urban settings by using the soil and water assessment tool (SWAT) model integrated with geographic information systems (GIS) to minimize flood risks. The research focuses on the peak runoff in Karapakkam, a locality within …
Published in International Journal of Environmental Planning and Development Architecture · Vol. 4, Issue 1, 2026 · pp. 37–48 Read article
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Lightweight Models for Per-PC Energy Consumption Forecasting: Comparative Study with ML and DL Approaches
Abstract: We have collected primary data from automated logging of parameters like CPU utilization, estimated power, active or idle state, user logging activity, and the type of day. Additionally, survey data showed user awareness, energy-saving behaviour, and PC usage patterns. The data is pre-processed and merged by applying processes such as data cleaning, normalization, and feature extraction, i.e., determining the peak active timings and downtime. Developed lightweight prediction models based on …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 17, Issue 1, 2026 Read article
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Open Educational Resources and Open Access Publishing in Higher Education: Policies, Challenges, and Collaborative Models
Abstract: Open Educational Resources (OER) and Open Access (OA) publishing are transforming higher education by promoting equitable access to knowledge, reducing costs, and fostering collaborative innovation. While OER focus on openly licensed teaching and learning materials, OA ensures unrestricted online access to scholarly research. Together, they form an integrated open knowledge ecosystem aligned with global policy frameworks such as the UNESCO OER Recommendation (2019) and the Budapest Open Access Initiative. This …
Published in International Journal of Trends in Humanities · Vol. 3, Issue 1, 2026 · pp. 7–12 Read article
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A Reviewed Study On Cpu-Optimized Parameter-Efficient Fine- Tuning For Large Language Models To Increase Accuracy Using Lora
Abstract: The fast proliferation of Large Language Models (LLMs) has increased the need to optimize the process of fine-tuning but the existing workflows that require a GPU are still expensive, intensive, and unavailable to most researchers. This paper is driven by the desire to have a more cost-efficient and democratized version by examining a CPU-efficient implementation of Parameter-Efficient Fine-Tuning (PEFT) based on Low-Rank Adaptation (LoRA). The major purpose of the study …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 Read article
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Polymer Composite-Integrated Food Waste Management Across the Supply Chain: Quantification, Process Modelling, and Techno-Economic Valorization
Abstract: Food waste produced throughout the global food supply chain constitutes one of the most impactful forms of inefficiency within the current food production system, producing roughly 931 million tons per year and resulting in economic losses above $1 trillion worldwide each year. One aspect that has not been sufficiently studied systematically is how polymer composite materials, such as membrane separation systems, polymer-coated extraction equipment, fiber-reinforced polymer (FRP) biorefinery infrastructure, and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 819–836 Read article
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Molecular Docking, QSAR Modeling, and ADMET Evaluation of Novel Pyrazolo-Pyrimidine Derivatives as Potential CDK-2 Inhibitors for Cancer Therapy
Abstract: Cyclin-dependent kinase-2 (CDK-2) is an essential regulator in cell cycle progression and is an important therapeutic target in cancer drug development. In the present study, an integrated computational approach involving molecular docking studies, QSAR modeling, ADMET prediction, and artificial intelligence-based analysis was used to identify pyrazolo-pyrimidine derivatives as potential CDK-2 inhibitors. Based on the molecular docking results, it was found that selected compounds exhibited high binding affinity towards the ATP …
Published in Research and Reviews: A Journal of Pharmacology · Vol. 16, Issue 2, 2026 Read article
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Study of an Improved Quantum Particle Swarm Optimization-Based Framework for Neural Network Optimization in Modelling of Polymer Data
Abstract: The accurate forecasting of polymer viscosity at various physicochemical conditions has been quite critical due to the nonlinear interactions and interrelations between the variables. This paper suggests a better hybrid modelling framework, which involves the use of Artificial Neural Networks (ANN) and more advanced versions of Quantum Particle Swarm Optimization (QPSO) to better predict polymer viscosity. The input parameters taken are, namely, log (shear rate), polymer concentration, NaCl concentration, Ca …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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A Hybrid Mathematical Model for Epidemic Outbreak Forecasting Using Machine Learning and Cloud Computing
Abstract: The increasing frequency of infectious disease outbreaks has emphasized the necessity for intelligent epidemic surveillance systems capable of predicting disease spread at an early stage. Conventional outbreak detection approaches rely heavily on delayed statistical reporting and manual monitoring techniques, resulting in reduced responsiveness during critical periods. This paper presents a mathematical predictive framework for epidemic outbreak detection using machine learning and cloud computing technologies. The proposed framework integrates the Susceptible–Infected–Recovered …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 13, Issue 2, 2026 · pp. 01–06 Read article
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Automated Machine Learning System for Model Selection and Hyperparameter Optimization
Abstract: The proliferation of machine learning applications in various scientific and industrial domains has given rise to an urgent need for developing principled, automated techniques for optimal architecture selection and hyperparameter tuning for machine learning models without human expert intervention. In this paper, we introduce the Automated Machine Learning System for Model selection and hyperparameter Optimization (AMLSMO)—a state-of-the-art, all-encompassing AutoML system that combines the power of meta-learning-based warm-starting, Bayesian Optimization with …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 15–23 Read article
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Modelling of Wind-Wave Misalignment for Floating Offshore Wind Turbines
Abstract: The influence of the combined effects of wind and waves onto the performance of a floating offshore wind turbine (FOWT) is analyzed. A study of the wind conditions relative to the position of the aerodynamic rotor has been made, analyzing how the angle of incidence of the wind (angle of attack) varies with the wind direction and the inclination of the wind turbine mast as a consequence of the undulatory …
Published in Journal of Offshore Structure and Technology Read article