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704 articles for “Predictive Models”
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An Innovative AI-Integrated Approach for Identifying the Tensile Robustness of Polymeric Materials
Abstract: Polymeric materials have so many applications and character similar to flexibility, robustness and lightweight nature they are essential to a large variety of industries. Though, it is difficult to establish their tensile robustness appropriately, particularly in a variety of environmental situation. Provide a recommended Artificial Intelligence (AI)-integrated method to decide the issues of rapidly ascertaining the tensile robustness of the polymeric material. Using machine learning (ML), this study, predicted and …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 90–97 Read article
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Damage Evolution and Delamination Resistance in Polymer Matrix Functionally Graded Laminates
Abstract: Functionally graded laminates (FGLs) in polymer-matrix systems represent a promising pathway to enhance damage tolerance and delay delamination in advanced structural composites. In this study, we explore the mechanisms of damage initiation, propagation, and delamination resistance in polymer matrix functionally graded laminates (PM-FGLs) through a combined experimental–computational approach. Laminates with linear, exponential, and bio-inspired gradation profiles were fabricated using vacuum-assisted resin transfer molding (VARTM) and additive manufacturing techniques. Comprehensive mechanical …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 321–337 Read article
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AI-Based Preventive Healthcare Using Quantum Computing
Abstract: With its improved performance and capabilities, quantum machine learning (QML) is becoming a promising field, especially in the healthcare industry for tasks like early heart disease prediction. In this work, a Quantum Support Vector Classifier (QSVC) is proposed as the basic classifier for a bagging ensemble learning model. Shapley Additive explanations (SHAP) are used to evaluate the significance of each attribute in the predictions in order to improve explainability. Using …
Published in Journal of Nanoscience, NanoEngineering & Applications · Vol. 15, Issue 2, 2025 Read article
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Artificial Intelligence and Machine Learning Applications in Optimizing Air Conditioning Systems
Abstract: The growing demand for air conditioning systems, especially in the wake of climate change and increasing global temperatures, has led to a significant increase in energy consumption. This, in turn, contributes to the growing concerns of environmental sustainability and operational costs. As a result, there is a pressing need for innovative solutions to optimize the performance and energy efficiency of air conditioning (AC) systems. Artificial Intelligence (AI) and Machine Learning …
Published in Journal of Refrigeration, Air conditioning, Heating and ventilation · Vol. 12, Issue 1, 2025 · pp. 38–43 Read article
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Integrating Biotechnology, Physiology, and Agroecological Practices for Sustainable Crop Production and Protection
Abstract: Global agriculture is currently confronting a wide range of complex challenges, including a rapidly growing population, climate change, increasing pest and disease pressures, soil degradation, water scarcity, and the urgent need for sustainable intensification of crop production. Addressing these issues requires integrated strategies that combine crop improvement (through modern breeding and biotechnology), precision agronomic practices related to soil, irrigation, and nutrition, as well as advancements in plant physiology, molecular biology, …
Published in International Journal of Trends in Horticulture · Vol. 2, Issue 2, 2025 · pp. 1–6 Read article
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The Fundamental Premises of Efficient Linear Regression and Applying Regression
Abstract: Linear regression is a statistical technique for estimating the value of a dependent variable from an independent variable. Linear regression is a way to assess how two variables are related. A dependent variable is predicted using this modelling technique based on one or more independent factors. Many analyses are based on linear regression. Sometimes the data must be changed to satisfy the needs of the analysis, or extra room must …
Published in Trends in Machine design Read article
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House Price Estimation Using Linear Regression: A Machine Learning Perspective
Abstract: House price prediction plays a crucial role in the real estate industry, helping buyers, sellers, and investors make well-informed decisions. Accurate estimation of property values enables stakeholders to assess market trends, plan investments, and minimize financial risks. This study focuses on the application of linear regression, a fundamental and widely used machine learning algorithm, to predict house prices based on multiple influencing factors. These factors include location, property size, number …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 1, 2026 Read article
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Strategy for Improving Software Maintenance Using Machine Learning for Security Requirements: A Review
Abstract: Within the area of software technical education, the significance of software defect discovery has increased as a research focus to enhance program reliability. By maximizing testing resources and assisting developers in identifying potential problems using program defect predictions, program dependability is increased. Applying software engineering (SE) techniques to critical and intricate systems, like networking and security systems, is imperative. Traditional methods of predicting software maintainability have limitations, particularly in balancing …
Published in International Journal of Information Security Engineering · Vol. 2, Issue 2, 2024 · pp. 36–48 Read article
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Structure–Property Modeling of Cement-Based Multi-Component Composites Using Ensemble Machine Learning and Explainable Feature Attribution
Abstract: Accurate prediction of compressive strength is central to structure–property optimization, quality control, and sustainability-driven design in cement-based composite materials. Cementitious systems represent heterogeneous multi-phase composites composed of reactive binder matrices and dispersed aggregate phases, whose macroscopic mechanical performance emerges from complex nonlinear interactions among constituents and curing-dependent microstructural evolution. This study develops a data-driven structure–property modeling framework to quantify the nonlinear dependence of compressive strength on multi-component composite composition and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 112–131 Read article
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Investigate an Implementation Study of TI-6AL-4V Lattice-based Scaffold Design Using Finite Element Analysis
Abstract: Implementation studies are advised to determine the effectiveness and performance of bone scaffolding in morphology, which has been demonstrated scientifically. The rehabilitation of bone defects, which is still a complex problem in orthopedic surgery, was the subject of an implementation study we provided. Biomaterial scaffolding porosity and pore size are essential in both in vivo and in vitro bone development. However, it has been linked to various drawbacks, including poor …
Published in International Journal of Biochemistry and Biomolecule Research · Vol. 1, Issue 1, 2023 · pp. 28–35 Read article
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Retinal Disease Detection Using Deep CNN
Abstract: Age-related macular degeneration, glaucoma, and diabetic retinopathy are the three main causes of blindness in the globe. To avoid visual loss, early identification and treatment of these disorders are essential. The goal of this research is to create an automated method for detecting retinal diseases by analyzing retinal fundus pictures with machine learning techniques. Python and the Tkinter package for the graphical user interface are used in the construction of …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 13, Issue 2, 2024 · pp. 46–50 Read article
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A Review on Advances in Polymer Composites and Nanocomposites: Design, Applications, and Life Cycle Assessment
Abstract: Materials based on polymer composites and nanocomposites have won the main status in several regions of industry by mare possessing excellent properties and broad range of application. This review focuses on the advancements in the design of fiber-reinforced polymers, structural composites, multifunctional composites, and biomimetic and eco-friendly composites. Emerging developments in biomedical composites, polymer foams, and smart composites are explored, highlighting their applications in medical, aerospace, automotive, and structural engineering. …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 244–251 Read article
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A New Computational Method for Dust and Gas Dynamics in Protoplanetary Discs
Abstract: The simultaneous evolution of dust and gas in protoplanetary discs regulates essential events in planet formation, such as dust accumulation, migration, and the initiation of gravitational instabilities. Nevertheless, precisely modelling this interaction continues to provide a significant computing problem owing to the extensive variety of spatial and temporal scales involved. In this study, we introduce an innovative computational framework for simulating dust-gas dynamics in protoplanetary discs, integrating a two-fluid hydrodynamical …
Published in International Journal of Universe · Vol. 1, Issue 2, 2025 · pp. 35–46 Read article
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The Impact of Bacteria and Biostimulants on Crude Oil Breakdown in Loamy Soil
Abstract: This study investigates the influence of bacteria and biostimulants—specifically Bryophyllum pinnatum leaves soaked in both water and ethanol—on the degradation of crude oil in loamy soil. A laboratory-scale bioremediation setup was employed using a batch reactor model and first-order degradation kinetics to assess total petroleum hydrocarbon (TPH) breakdown under controlled conditions. Various analytical tools and procedures, including gas chromatography (Agilent 6890) and microbial media such as nutrient agar and mineral …
Published in International Journal of Pollution: Prevention & Control · Vol. 3, Issue 2, 2025 · pp. 15–22 Read article
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Symmetry Breaking in Mathematical Models: Bifurcation, Chaos, and Pattern Formation
Abstract: Symmetry breaking serves as a central organizing principle in the understanding of nonlinear systems across physics, biology, chemistry, and engineering. When a system transitions from a symmetric state to an asymmetric configuration, it often signals the onset of new structures, dynamic behaviors, or even chaotic regimes. This review explores symmetry breaking from the theoretical and mathematical perspectives of bifurcation theory, chaos theory, and pattern formation. We discuss how small parameter …
Published in Emerging Trends in Symmetry · Vol. 1, Issue 2, 2025 · pp. 25–30 Read article
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Tribological Performance and Wear Coefficient Prediction of AA2024–TiC Composites via Python-Based Machine Learning
Abstract: Determining wear coefficient accurately serves as a critical factor to maximize engineering materials' tribological characteristics. The experiment examines the wear characteristics of TiC-reinforced AA2024 aluminum alloy subjected to different tribological operating conditions. A pin-on-disc tribometer performed wear tests under different conditions of load and TiC weight fraction and sliding speed and duration. ANOVA statistical results show that load intensity and TiC reinforcement density stand out as principal variables that affect …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 1099–1112 Read article
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Mechanical Performance Assessment of Hybrid FRP Laminates with Carbon Fiber Core Using Experimental and Numerical Approaches
Abstract: The high strength-to-weight ratio, corrosion resistance and design flexibility of Fiber-reinforced polymer (FRP) composites have attracted considerable attention in aerospace, automotive and structural applications. This work presents an experimental and finite element study on the tensile and flexural behavior of epoxy-based hybrid FRP laminates. Five laminate configurations were manufactured, including a unidirectional carbon fiber laminate and four hybrid laminates, Kevlar–Carbon–Kevlar (K/C/K), Glass–Carbon–Glass (G/C/G), Kevlar–Carbon–Glass (K/C/G), and Glass–Carbon–Kevlar (G/C/K). For all …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Sentiment Analysis of E-Commerce Reviews using Machine Learning
Abstract: In e-commerce, sentiment pertains to the emotional responses, opinions, or perceptions that customers have about their online shopping experiences, including factors like product quality, service, and various processes such as ordering, shipping, and customer support. Sentiment analysis, which involves machine learning techniques, plays a crucial role in deciphering these sentiments. By using sentiment analysis, companies can obtain valuable insights from customer feedback from diverse online sources, including social media, surveys, …
Published in Journal of Operating Systems Development & Trends · Vol. 11, Issue 3, 2024 · pp. 25–37 Read article
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Real-Time Air Quality Prediction Using IoT-Integrated Polymer Sensors and Recurrent Neural Networks
Abstract: Real-time air quality monitoring remains a critical challenge in urban environments, where traditional sensor infrastructures often suffer from limited responsiveness, poor scalability, and high deployment costs. The increasing prevalence of NO₂ pollution, a key contributor to respiratory and cardiovascular ailments, demands advanced sensing platforms capable of both accurate detection and predictive inference. Existing methods either rely on rigid electronic sensors lacking adaptability or on statistical forecasting models that fail to …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 332–347 Read article
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Artificial Neural Network Based Prediction of Impact Loads and Thickness in CFRP and GFRP Composite Laminates
Abstract: Recent technological advancements, particularly the integration of neural networks, have facilitated a predictive approach to complex engineering problems, especially those involving composite materials with directional properties. The scarcity of literature on predicting impact damage using experimental and ultrasonic flaw detection data motivated this study. Experimental assessment of impact damage on carbon fiber/epoxy (CFRP) and glass fiber/epoxy (GFRP) composites was conducted using low-velocity drop weight impact testing. Damage assessment employed an …
Published in International Journal of Fracture Mechanics and Damage Science · Vol. 2, Issue 1, 2024 · pp. 34–45 Read article