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567 articles for “model evaluation”
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Modelling -Based Evaluation of Hybrid Natural Synthetic Fiber Polymer Composites for Sustainable Energy Applications
Abstract: The growing need of lightweight, high-performance, and green energy system materials has increased the research on hybrid polymer composites. This paper gives a modelling-based evaluation of polymer matrix composites which are reinforced using natural fibers like jute, sisal, bamboo in a combination with synthetic glass fibers to be used in sustainable energy sources. An analytical model has been used to assess the effect of hybrid fiber composition on mechanical, thermal, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 682–688 Read article
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Detection and Classification of Diabetic Retinopathy Using Deep Learning Techniques
Abstract: This project delves into the evaluation of three prominent deep learning architectures Basic CNN, ResNet, and DenseNet for their efficacy in detecting diabetic retinopathy from retinal images. Utilizing a diverse dataset, the study employs standard deep learning frameworks to train and validate each model. The focus extends to exploring the potential benefits of transfer learning on a limited dataset. Evaluation metrics like specificity, sensitivity, and accuracy are employed for a …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 13, Issue 2, 2024 · pp. 64–69 Read article
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Smart Water Harvester
Abstract: Smart water harvester is a wiser use of Data Science in the optimization of rainwater harvesting, taking into account the forecast of precipitation and ideal catchment areas, and basically image processing using machine learning. In that respect, the system, via predictive algorithms like Random Forests, predicts the amount of rainfall by taking into consideration historical and real-time data, while Digital Elevation Models (DEM) and visualization methodologies of images make geographical …
Published in Journal of Water Resource Engineering and Management · Vol. 11, Issue 3, 2024 · pp. 1–7 Read article
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A Comprehensive Analysis of Classification Methods for Churn Prediction in Financial Services
Abstract: Persistent issues that affect long-term revenue in the banking sector include excessive client attrition. Customary churn models depend on measures related to customer satisfaction, which often result in low predictive accuracy due to their subjective nature. This study proposes an effective early warning model to address customer churn in financial services. Data is preprocessed through cleaning, one-hot encoding, Z-score normalization, and Min-max scaling. To handle class imbalance, the SMOTE algorithm …
Published in Journal of Advanced Database Management & Systems · Vol. 12, Issue 2, 2025 · pp. 47–61 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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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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Modelling and Performance Evaluation of a Multiport Converter for Active Balancing of Lithium- ion Battery Cells
Abstract: In this paper, a flexible multiport DC-DC converter-based active cell balancing technique for Li-ion battery packs is presented. Cell balancing plays an important role in terms of safety, capacity utilization, and battery lifespan. For Li-ion cells in series configuration, the difference in voltages and states of charge (SOCs) leads to imbalance issues which might negatively affect their performance and shorten their lifespan. To solve these problems, the proposed technique utilizes …
Published in International Journal of Electrical Power and Machine Systems · Vol. 4, Issue 2, 2026 Read article
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A Comparison of Different Generative AI Models
Abstract: Generative models have significantly advanced the field of artificial intelligence by allowing machines to produce complex and realistic outputs such as images, text, and other forms of data. Among the leading frameworks in this domain are generative adversarial networks (GANs), variational autoencoders (VAEs), and architectures based on Transformers. Each model offers specific benefits and drawbacks concerning design structure, training demands, and range of applications. This paper provides a detailed comparison …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 1, 2025 · pp. 16–22 Read article
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Modeling Dispersed Count Data: Evaluating the Conway–Maxwell–Poisson Regression with COVID-19 Mortality Data
Abstract: Count data are prevalent in diverse fields such as biology, healthcare, psychology, and marketing, characterized by non-negativity and inherent heteroskedasticity, often exhibiting overdispersion or underdispersion. Traditional Poisson regression, which assumes equal mean and variance, is inadequate for such dispersed data. To address this, various generalized linear models (GLMs) and their extensions, including negative binomial (NB) and Conway–Maxwell–Poisson (CMP) regressions, are utilized. This study evaluates the performance of CMP regression compared …
Published in Research & Reviews : Journal of Statistics · Vol. 13, Issue 3, 2024 · pp. 18–26 Read article
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Enhancement in Biomedical Polymer Nanocomposites: Biocompatibility and Mechanical Property Predictions using Machine Learning
Abstract: A machine learning (ML)-based framework is developed and validated through experimental analysis and comparative modeling to enhance system dependability and improve prediction performance. The proposed framework includes key stages such as data preprocessing, feature evaluation, model training, and performance benchmarking to determine the most effective prediction technique. Several machine learning models were evaluated, including Ensemble models, Artificial Neural Networks (ANN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 275–296 Read article
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Methodology for Evaluating Code Synthesis in Large Language Models: ChatGPT and Copilot: A Review
Abstract: The authors introduce a comprehensive framework to assess the code-generation capabilities of large language models, focusing on ChatGPT and Copilot through a benchmark suite of 25 program synthesis tasks. Their main goal was to show why making proper comparisons is important, they did not focus on choosing the newest models, since they keep changing frequently. The critique examines how the methodology addresses both functional and non-functional aspects of code. In …
Published in Recent Trends in Programming languages · Vol. 12, Issue 3, 2025 · pp. 01–07 Read article
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Comparison of Models of Machine Learning and Hyperparameter optimization methods on various datasets
Abstract: The most likely phase in achieving powerful and robust machine learning models is probably the hyperparameters tuning step. The traditional exhaustive methods of search (Grid Search and others) ensure that the search space is covered, but are computationally very inexpensive; random search is less expensive and can still miss good regions; and lastly, the modern model-based and population-based methods (Bayesian Optimization, Tree-structured Parzen Estimator (TPE), Genetic Algorithms) are thought to …
Published in Recent Trends in Programming languages · Vol. 13, Issue 1, 2026 Read article
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Impact of Total Quality Maintenance Parts on The Operation of Electric Motor in Thermal Desorption Unit
Abstract: Thermal desorption Unit been a vital equipment with complex applications in the sections of waste and minerals treatments, suffers intensively in the managerial section as a whole. This paves a way towards the aim of this research work which covers the type of the impact of Total Quality maintenance electric motor parts on the Operation of the Thermal Desorption Unit [TDUs] using that of the Halden Nigeria Limited, located within …
Published in International Journal of Advance in Molecular Engineering · Vol. 2, Issue 1, 2024 · pp. 14–25 Read article
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Evaluating AI-Driven Adaptive Learning Models in Mathematics: A Contemporary Perspective
Abstract: Artificial Intelligence (AI) continues to transform mathematics education through data-driven personalization and adaptive learning technologies. This study investigates how AI-enabled adaptive platforms influence student performance and engagement in mathematics classrooms. Using a quantitative approach across two institutions, pre- and post-assessment results were compared between students using AI-assisted adaptive learning tools and those receiving conventional instruction. The findings reveal that AI-driven learners demonstrated significantly higher gains in conceptual understanding and engagement …
Published in Recent Trends in Mathematics · Vol. 3, Issue 1, 2026 · pp. 8–12 Read article
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Modeling of ZnO Based Nano Sensor Device for Evaluating Electronic Interaction with NO2 Pollutant: Combining Multiphysics Simulation and DFT Study
Abstract: This study focuses on the designing and modeling of a sensor device employing zinc oxide (ZnO) nanowires (NWs), and the evaluation of the chemical response of the same in the presence of nitrogen dioxide (NO2), which is an acute harmful pollutant for human health and environment. Experimentally synthesized ZnO nanostructures were used as the basis for modeling of the ZnO NWs based sensor device along with a single ZnO NW, …
Published in Journal of Polymer & Composites · Vol. 13, Issue 2, 2025 · pp. 211–219 Read article
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Collaborative Robotics and Smart Automation: Enhancing Human–Robot Synergy in Industry 5.0
Abstract: Industry 5.0 marks a paradigm shift from efficiency-centric automation to a human-centred, sustainable, and collaborative production environment . In this context, collaborative robots, commonly referred to as cobots, play a central role by enabling direct and safe interaction between humans and machines within shared workspaces. These systems are designed to support human operators by undertaking repetitive, precision-intensive, and physically demanding tasks, thereby allowing humans to focus on supervisory control, problem-solving, …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 4, Issue 1, 2026 · pp. 22–29 Read article
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Atmospheric Modeling: A Comprehensive Review of Numerical Approaches and Applications
Abstract: Atmospheric modeling plays a crucial role in understanding and predicting atmospheric processes, weather patterns, and climate variability. This review synthesizes current methodologies and applications across several types of atmospheric models, including numerical weather prediction (NWP), climate models, air quality models, and chemical transport models. We explore the intricacies of data assimilation, model evaluation, parameterization, and the importance of high-performance computing in advancing model accuracy and efficiency. Special emphasis is placed …
Published in International Journal of Atmosphere · Vol. 1, Issue 2, 2024 · pp. 16–21 Read article
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A Study on the Influence of Cashew and Pawpaw Leaf on Soil Density Treatment of Contaminated Swampy and Clay Soils
Abstract: Soil density is a key parameter in assessing soil health and structure, often employed as a potential index in bioremediation studies. This study investigates the impact of bioremediation treatments using Cashew Leaf (Anacardium occidentale) and Pawpaw Leaf (Carica papaya) on the density of swampy and clay soils over time. The results show that both treatments, in varying quantities, influence the soil's density by reducing the post-contamination density to levels closer …
Published in International Journal of Advance in Molecular Engineering · Vol. 3, Issue 2, 2025 · pp. 1–9 Read article
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To Evaluate the Performance of the Selected Hybrid Systems and Validation of Mathematical Model with the Experimental Data
Abstract: The main aim of this paper to evaluate the performance of the PV-Wind hybrid systems and validation of mathematical model with the experimental model. In the research paper, the experimental model of PV-Wind Hybrid system has been installed at a height of 22 meters in the School of Energy and Environmental studies, DAVV, Indore, and M.P., India. The theoretical calculation of the wind generator output has also been compared with …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 16, Issue 2, 2025 · pp. 20–28 Read article
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Network Intrusion Detection System Using Decision Tree
Abstract: This paper presents a novel approach to network intrusion detection systems (NIDS) using advanced decision tree algorithms to address critical limitations in existing IDS solutions. Traditional IDSs often struggle with high false positive and negative rates, lack of scalability, and poor interpretability. Our proposed IDS leverages decision trees to enhance detection accuracy, interpretability, and scalability, thereby improving network security. Decision trees are chosen for their adaptive learning capabilities, transparent decision-making …
Published in Journal Of Network security · Vol. 12, Issue 2, 2024 · pp. 22–33 Read article