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360 articles for “prediction tool”
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Jaccard Index Versus Preferential Attachment: A Comparative Study of Similarity Based Link Prediction Techniques in Complex Networks
Abstract: Link prediction is a critical task in network analysis that aims to forecast potential connections between nodes. Numerous methods have been developed to address this challenge, with similarity-based techniques gaining substantial attention due to their simplicity and effectiveness. This research work presents a comprehensive review of two prominent similarity-based link prediction techniques, namely the Jaccard Index and Preferential Attachment. The Jaccard Index measures the similarity between two nodes based on …
Published in Journal of Computer Technology & Applications · Vol. 14, Issue 2, 2023 · pp. 7–11 Read article
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A Conceptual Framework for AI-Integrated Metabolomics in Predictive Health Systems for Resource-Constrained Environments
Abstract: The increasing prevalence of non-communicable diseases (NCDs) continues to place a significant strain on healthcare systems, particularly in low- and middle-income regions where access to early diagnostic infrastructure is limited. Conventional healthcare approaches remain largely reactive, often detecting diseases at advanced stages when treatment effectiveness is reduced. This challenge underscores the need for predictive, cost-effective, and data-driven healthcare solutions. This study presents a conceptual framework that integrates metabolomics with artificial …
Published in Emerging Trends in Metabolites · Vol. 3, Issue 2, 2026 Read article
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Simulation and Analysis of Battery Pack Using the Multi Scale Multi-Domain Battery Model
Abstract: The creation of sophisticated simulation models has been made necessary by the need for reliable and effective battery packs in energy storage systems and electric vehicles. This study focuses on the simulation and analysis of battery packs using a multi-scale multi-domain battery model. The model enables a thorough knowledge of battery pack behavior across a range of operating situations by integrating the electricity, thermal, and mechanical domains. Multi-scale modeling bridges …
Published in International Journal of Electro-Mechanics and Material Behaviour · Vol. 2, Issue 2, 2024 · pp. 31–46 Read article
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A Combined ECG and PPG Signal Powered Artificial Intelligence-Based Prediction Model for Stroke
Abstract: Stroke is one of the most common causes of morbidity and mortality around the world, and emphasis on prevention and early detection strategies cannot be overstated. This review aims to integrate techniques of artificial intelligence with electrocardiogram and photoplethysmogram signals to enhance stroke prediction and monitoring of cardiovascular health. All in all, the application of artificial intelligence that incorporates machine learning, deep learning, or hybrid models gives robust tools toward …
Published in Journal of Control & Instrumentation · Vol. 16, Issue 2, 2025 · pp. 18–26 Read article
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Machine Learning Based House Price Forecasting
Abstract: This research endeavours to craft a predictive model leveraging machine learning to estimate the market value of houses in Delhi. By integrating Python and its powerful libraries, pandas for data processing, Plot for interactive visualizations, scikit-learn for implementing machine learning algorithms, XGBoost for boosting the model's prediction accuracy, and to evaluate the model's performance cross-validation techniques are used. An interactive user interface is created using a Flask web application to …
Published in Current Trends in Information Technology · Vol. 14, Issue 1, 2024 · pp. 5–11 Read article
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Advanced Airline Operations Control System Using NodeJS
Abstract: This research paper presents the development of an Advanced Airline Operations Control System (AAOCS) using NodeJS, a lightweight and scalable JavaScript runtime environment. Leveraging NodeJS's event-driven architecture and non-blocking I/O model, the system facilitates efficient management of flight operations, crew scheduling, resource allocation, and real-time decision-making in the aviation industry. Key features include real-time decision support tools, a microservices-based architecture for scalability, integration with external data sources and APIs, and …
Published in Journal of Control & Instrumentation · Vol. 15, Issue 1, 2024 · pp. 45–51 Read article
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Pathophysiology Reimagined: Integrating Systems Biology and AI for Disease Understanding
Abstract: Pathophysiology, the study of disease mechanisms at molecular, cellular, and systemic levels, has traditionally relied on reductionist approaches that often fail to capture the complex, dynamic, and interconnected nature of biological systems. Diseases such as cancer, neurodegenerative disorders, and infectious diseases arise from intricate interactions among genetic, epigenetic, metabolic, and environmental factors, necessitating integrative, data-driven methodologies for a deeper understanding. Systems biology has emerged as a powerful approach by leveraging …
Published in Research and Reviews: A Journal of Pharmaceutical Science · Vol. 16, Issue 2, 2025 · pp. 63–71 Read article
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Hybrid Additive-Subtractive Manufacturing of Multi-Material Functionally Graded Components: Integration of Laser Powder Bed Fusion with High-Speed CNC Finishing for Aerospace Applications
Abstract: The synergy involved in the merging of additive and subtractive manufacturing technologies is the game changer to generate multi-material functionally graded components to be used in the aerospace industries. The paper is an in-depth review of a proposed hybrid additive-subtractive manufacturing, which synergistically merges laser powder bed fusion (LPBF) fashioning with rapid computer numerical control finishing production processes. The multi-material deposition, thermal issues, and optimization of post-processing are the challenges …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 398–418 Read article
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In Silico Analysis and Docking Study of the Active Phytocompounds of Bacopa monnieri Against Alzheimer Disease
Abstract: Objective: Alzheimer’s is a neurodegenerative disease and is the cause of 60–70% of cases of dementia. It infected a million people worldwide. An effort was undertaken to explore the potential of natural compounds found in Bacopa monnieri, a plant renowned for its extensive medicinal properties in Indian Ayurveda, to combat the disease. This was achieved through molecular docking studies, evaluation of drug-likeness, and comprehensive ADME (absorption, distribution, metabolism, and excretion) …
Published in Research and Reviews : Journal of Computational Biology · Vol. 13, Issue 2, 2024 · pp. 1–13 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 · pp. 21–29 Read article
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A Hybrid Machine Learning Approach for Enhanced Patient Diagnosis and Health Outcome Prediction
Abstract: Rapid and accurate diagnosis is essential to present day practitioners of medicine, yet can be complicated by the enormous volume and complexity inherent in clinical data. To this end, we here propose a hybrid machine learning model in combination with Recursive Feature Elimination (RFE) and ensemble voting to enhance the diagnostic accuracy by integrating multiple models. Trained on real-world electronic health data, including lab results, demographics and medical history, the …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 13, Issue 2, 2026 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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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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AI-Driven Topology Optimization of Woven Fiber-Reinforced Composite Chassis Structures for Electric Vehicles Under Crash Loading
Abstract: The structural design of an electric vehicle (EV) chassis represents a unique engineering challenge to achieve minimal weight while meeting occupants' safety requirements during high-energy crash conditions without compromise to the battery housing's integrity or the geometrical constraints of the electric powertrain package. In this paper, a single framework is proposed to integrate physics-based artificial intelligence (AI) surrogate models using PINNs, CNN-accelerated topology optimization, and FEA to design woven fiber-reinforced …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 72–89 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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Integrate AI and IoT to Develop Sustainable Polymer Structural Materials Processing Optimization: Enabled Monitoring Strategies for Performance and Lifecycle Assessment
Abstract: The need for long-lasting structural polymer materials that are both environmentally friendly and highly mechanically effective is driving demand for these materials as the industrial sector continues to grow. Optimizing processes, saving energy, detecting faults, and monitoring structures are all hindered by conventional polymer manufacture. This study suggests an AI-IoT system for environmentally friendly production of structural polymer materials to get around these problems. Tools for evaluating system performance and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 169–192 Read article
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Optimization of turning process parameters for Titanium grade 5 using Taguchi method
Abstract: In the contemporary world, the standard of the surface end is most significant demand for several turned work piece due to that maker's square measure seeking to stay competitive in the market. Taguchi parameter style is a powerful tool and efficient methodology for optimizing quality and performance output of the producing method. This paper investigates the parameters poignant the surface roughness manufacture in turning method for material Titanium Grade 5. …
Published in Journal of Experimental & Applied Mechanics · Vol. 10, Issue 2, 2019 · pp. 17–22 Read article
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Review and Opportunities for Thermal examination approaches Used to Investigate the Thermal Properties of Composite Compounds
Abstract: The use of thermal examination approach to assess the thermal quality of energy materials in “China” is concisely described. They are often used to calculate thermal stability, compatibility, and thermophysical constants, as well as to study thermal breakdown kinetics, causes, and interactions. Furthermore, a few studies focused on creativity or advancement, such as analyzing the mechanisms of topochemical reactions, assessing condensed-phase reaction kinetics by tracking the change in functional groups …
Published in Journal of Polymer & Composites · Vol. 12, Issue 6, 2024 · pp. 96–106 Read article
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Dynamic Cost Projection: Enhancing BIM-Integrated Estimation and Budgeting in Construction Projects
Abstract: In the realm of construction project management, the integration of Building Information Modeling (BIM) with dynamic cost projection tools represents a pivotal advancement. This paper explores the synergy between BIM and cost estimation, emphasizing their collective potential to revolutionize project budgeting. By harnessing real-time data and predictive analytics, BIM facilitates accurate and proactive cost projections throughout the project lifecycle. This integration not only enhances cost estimation precision but also fosters …
Published in Journal of Construction Engineering, Technology & Management · Vol. 14, Issue 2, 2024 · pp. 1–10 Read article
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Gene Annotation of Cancer Vaccine for Homo sapiens
Abstract: Objectives: Gene annotation helps us to deduce the structural and functional aspects of a gene that encodes for a functional protein in our body. Thus, by determining the coding sequence and gene location we can derive meaningful insights as to what these genes do in our body. In this study, an unknown gene, cancer vaccine for Homo sapiens has been studied and annotated. Methods: This study was based on a …
Published in International Journal of Molecular Biotechnological Research · Vol. 2, Issue 1, 2024 · pp. 1–14 Read article