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1126 articles for “predict”
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Machine Learning in Nuclear Medical Applications: A Review of Research Frontiers
Abstract: Nuclear medicine, encompassing PET, SPECT, and targeted radionuclide therapy, generates high-dimensional, quantitative data uniquely suited for machine learning (ML) analysis. This review synthesizes current research applications of ML across six key domains. Positron emission tomography (PET), single-photon emission computed tomography (SPECT), and targeted radionuclide therapy are examples of nuclear medicine modalities that generate high- dimensional, quantitative datasets that are particularly well-suited for machine learning (ML)-driven analysis. These imaging methods provide …
Published in Journal of Nuclear Engineering & Technology · Vol. 16, Issue 1, 2026 · pp. 19–24 Read article
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Machine Learning Assisted Design and Analysis of Polymer Composite Materials for Sustainable Renewable Energy Systems
Abstract: Accurate prediction and optimization of polymer composite properties is of paramount importance in the design of these lightweight, durable, and sustainable materials within renewable energy technologies. This work will provide a holistic machine learning-assisted framework that unites materials informatics with domain-specific features and state-of-the-art ML methodologies in the prediction of the mechanical properties of polymer composites, such as tensile strength. This includes embedding several ensemble models, including Random Forest and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 391–402 Read article
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Structure Property Correlation of Polymer Dielectrics Using Electrical Response Data
Abstract: Polymer dielectrics are foundational to insulation, capacitors, embedded passives, and flexible electronics, where performance is governed by the frequency-dependent electrical response rather than a single dielectric constant. This study presents a spectroscopy-aware structure–property correlation framework that transforms dielectric response data into physically interpretable spectral fingerprints and learns mappings from polymer descriptors to these fingerprints for prediction and interpretation. Broadband spectra are standardized on a log-frequency grid and parameterized using relaxation-informed …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 315–324 Read article
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Computational Modeling of Polymer Semiconductors for Electronic Applications
Abstract: Polymer semiconductors have become important materials in modern electronic applications because they combine semiconducting behavior with mechanical flexibility, low-cost processing, and tunable molecular structure. Their growing use in organic field-effect transistors, organic photovoltaics, organic light-emitting diodes, and flexible sensing devices has increased the need for accurate computational approaches that can predict material properties and device performance before experimental fabrication. This paper reviews the major computational modeling techniques used for polymer …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 132–146 Read article
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Fracture Analysis of Functionally Graded Material (FGM) Plates Using Extended Finite Element Method: A Review.
Abstract: Functionally Graded Materials (FGMs), have drawn a lot of interest in various engineering applications due to their superior mechanical properties and ability to withstand extreme conditions. Fracture analysis in FGMs focuses on understanding how cracks initiate and propagate within these complex materials. The stress distribution becomes irregular due to spatial property variations which produces different crack paths than what occurs in homogeneous materials. The examination of FGM plates under fracture …
Published in International Journal of Fracture Mechanics and Damage Science · Vol. 4, Issue 1, 2026 · pp. 9–15 Read article
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Explainable Artificial Intelligence in Personalized Medicine: Emerging Clinical Perspectives
Abstract: The convergence of artificial intelligence (AI) and precision medicine has transformed contemporary healthcare by enabling data-driven clinical decision-making, individualized therapeutic interventions, and predictive diagnostics. However, despite remarkable advances in machine learning (ML) and deep learning (DL), the widespread adoption of AI in healthcare remains constrained by the “black-box” nature of many computational systems. Clinicians, regulatory agencies, and patients increasingly demand transparency, interpretability, and trustworthiness in AI-guided medical recommendations. Explainable Artificial …
Published in Emerging Trends in Personalized Medicines · Vol. 3, Issue 2, 2026 · pp. 13–29 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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Multi-Scale Analysis of Polymer Based Energy Storage Systems for High Performance Battery Applications
Abstract: The energy storage systems based on polymers are becoming promising materials for the next generation of high performance batteries because of their excellent mechanical flexibility, improved safety, and favorable electrochemical properties. Even with computational tools in Python, polymer-based energy storage systems remain plagued by poor ionic conductivity, complicated electrochemical reactions and potential thermal runaway. Therefore, a multi-scale model is proposed to improve battery performance, thermal stability, reliability, and large-scale deployment …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1035–1048 Read article
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Enhance Thermal and Conductive Properties through Graph Neural Network-Based Machine Learning-Driven Advanced Polymer Material Design
Abstract: Advanced polymer materials are widely used in modern engineering and manufacturing because of their lightweight nature, flexibility, durability, and adaptability to different applications. However, designing polymer materials with enhanced thermal and electrical properties remains a challenging task. The performance of polymers is influenced by a complex combination of molecular structures, filler materials, processing parameters, and nanoscale interactions. Conventional optimization methods often require extensive experimental trials and computational resources, making it …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 Read article
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Generative Design of Bioactive Orthopedic Composites for Fracture Repair Using an Integrated Conditional GAN–Transformer Framework: A Multi-Objective Approach
Abstract: Orthopedic composite implants for fracture repair must simultaneously satisfy conflicting mechanical and biological demands: high fracture toughness, sufficient compressive stiffness, and bioactive surface chemistry enabling osteoblast adhesion and mineralization. Existing design approaches rely on trial-and-error experimentation, yielding sub-optimal trade-offs between these objectives. This paper presents an integrated conditional Generative Adversarial Network–Transformer (cGAN-T) framework for fully computational, multi-objective generative design of hydroxyapatite (HA)-reinforced polymer composite microstructures targeting Orthopedic fracture repair. A …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 21–35 Read article
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Machine-Learning-Assisted Development of Polymer-Biochar Composite Adsorbents for the Removal of Heavy Metals from Gomti River Water
Abstract: Rapid urbanization, industrial discharge, and agricultural runoff pose a significant threat to freshwater sustainability and public health. Within these ecosystems, polymer pollutants—such as microplastics, nanoplastics, synthetic fibres, and additive residues—have emerged as persistent vectors capable of adsorbing and transporting toxic heavy metals. Because these polymeric contaminants dynamically interact with conventional aquatic parameters to alter pollutant mobility and ecological risk profiles, there is an urgent need to transition from passive environmental …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 72–95 Read article
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Thermal Performance Analysis and Optimization of Pin-Fin Heat Sink Using CFD, Taguchi Method, and Machine Learning
Abstract: Efficient thermal management is essential for improving the performance and reliability of modern engineering systems and electronic devices. This study presents the design, simulation, and optimization of a pin-fin heat sink using SolidWorks for three-dimensional modeling and ANSYS for thermal and computational fluid dynamics (CFD) analysis. Four different pin-fin geometries, namely square, pentagon, octagon, and circular fins, are considered to evaluate their thermal performance under varying operating conditions. Aluminum is …
Published in Trends in Mechanical Engineering & Technology · Vol. 16, Issue 2, 2026 Read article
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Google Play Store Analysis
Abstract: Google play store consists of millions of applications and several thousand apps are added on the play store every day. The competition is so fierce that it is really difficult for the developers to find out whether the app that is the product of his hard work is going to be successful or not. The main goal of the study is to create a tool that helps developers and organizations …
Published in Journal of Open Source Developments Read article
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Advanced Quantitative Analysis of Dynamic Recovery during Warm Working of FCC Alloys
Abstract: Metal working processes of warm working require an advanced quantitative analysis of dynamic and static recovery of dislocations to control microstructures and crucial shape forming properties. Feasibility of dynamic recovery by thermal glide and static recovery by climb of constricted jogs, applicable to ausforming, nano bulk forming by SPD and normalizing has been addressed to design new steels, used for hulls, and tribologically superior bearings. Predictive dislocation recovery analysis shows …
Published in Journal of Materials & Metallurgical Engineering Read article
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Heat Sink-PCM Device for PV Efficiency Improvement
Abstract: This paper is focused on the development of a second stage of a previous work where the PV panel efficiency is improved by using a hybrid system made up of a PCM attached to the rear side of the panel. This evolved stage incorporates a heat sink unit to improve the distribution of the heat removed from the panel; this improvement reduces the elapsed time to reach the steady state …
Published in Journal of Thermal Engineering and Applications Read article
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Use of the Computer Statistics in Oncology
Abstract: Currently, the use of computer statistics and computer statistical modeling in oncology for obtaining an accurate diagnosis, determination of the choice of treatment method and its correction in the process of ongoing treatment, prediction of the outcome of the disease, and evaluation of the effectiveness of the chosen treatment tactics is a decisive factor. The use of computer statistics, based on an adapted scientific and statistical package of the SSP …
Published in Research & Reviews : Journal of Statistics Read article
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Time Series Forecasting Based on PyAF and fbProphet
Abstract: Time series forecasting is the technique of predicting future events using previous data. Time series data includes information that is collected and recorded at regular intervals, such as daily stock prices, monthly sales figures, or hourly temperature readings. The purpose of time series forecasting is to use previous data to create accurate forecasts about the future values of a given variable. This can be beneficial for a range of applications, …
Published in International Journal of Information Security Engineering · Vol. 1, Issue 1, 2023 · pp. 32–36 Read article
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Engine Performance Analysis of Cottonseed Based Biodiesel Using Design Expert Statistical Based Tool
Abstract: Biodiesel is a biofuel acquired by substance forms from vegetable oils or creature fats and liquor that can be utilized in diesel engines alone or mixed with diesel oil. It is characterized as the mono-alkyl esters of unsaturated fats got from vegetable oils or creature fats. In basic words biodiesel is the item that gotten when vegetable oil or creature fat is artificially responded with a liquor to deliver unsaturated …
Published in International Journal of Industrial and Product Design Engineering · Vol. 1, Issue 1, 2023 · pp. 1–5 Read article
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In Silico Analysis and Docking Study of the Active Phyto Compounds of Ginkgo Biloba Against Alzheimer's Amyloid-Beta Protein
Abstract: Objective: Alzheimer's disease, an age-related progressive neurological condition, arises due to the accumulation of amyloid-beta protein within the brain. In this study, an attempt was made to explore the potential of natural compounds derived from Ginkgo, known for their diverse medicinal properties, in the prevention of the disorder by employing molecular docking techniques, conducting drug-likeness prediction assessments, and performing ADME analysis. Methods: Amyloid beta protein was retrieved from the PDB …
Published in International Journal of Cell Biology and Cellular Functions · Vol. 1, Issue 2, 2023 Read article
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A Molecular Docking Study: Targetting HIV-1 Integrase Protein Against Selected Phytocompounds from Calophyllum Lanigerum
Abstract: Objectives: The most common form of HIV that causes AIDS is HIV-1. The World Health Organization has estimated roughly 75 million plus HIV-1 infections till date and roughly 40 million deaths (as of 2021). Thus, this study was done to identify natural compounds from Calophyllum lanigerum, a medicinal plant largely endemic to South-East Asia, to inhibit the spread of this disease. It did so by using molecular docking methods, Lipinski …
Published in International Journal of Molecular Biotechnological Research · Vol. 1, Issue 2, 2023 · pp. 43–55 Read article