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1732 articles for “Predicting”
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Harnessing Deep Learning to Explore Microbial Community Structure and Carbon Storage Capacity in Mangrove Ecosystems: A Framework for Computationally
Abstract: Mangrove ecosystems represent one of the most efficient natural carbon sinks on Earth, functioning as critical blue carbon habitats that sustain diverse microbial communities responsible for biogeochemical cycling and long-term carbon storage. Despite their global ecological significance, accurately quantifying and predicting carbon sequestration in mangrove systems remains challenging due to the complex interactions between microbial diversity, sediment chemistry, and environmental drivers. This study presents a comprehensive and sustainable artificial intelligence …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 · pp. 41–49 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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GenChrome-ML: A Machine Learning Framework for Early Detection of Chromosomal Disorders Using Genomic Data
Abstract: The increasing burden of chronic disease and cancer demands innovative, more rapid and effective diagnostic tools in the field of healthcare. The majority of current diagnostic tools are dependent upon clinical symptomology and manual evaluation, leading to delays in early detection and treatment. The development of artificial intelligence (AI) and machine learning (ML), in recent years, has offered opportunities for the enhancement of disease prediction, diagnosis and personalization of treatment …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article
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Enhancing Power Conversion Efficiency in Tandem Solar Cells with Temporal Dynamic Graph Neural Network
Abstract: In modern homes, people want good comfort and also less electricity bill, so managing heating load and cooling load become very important. Heating Load (HL) and Cooling Load (CL) depend on many things like wall material, window size, sunlight, ventilation, and weather. Because of this many factors, calculation and optimization of HL and CL is little difficult and many time normal formulas give wrong or not perfect results. So in …
Published in Journal of Semiconductor Devices and Circuits · Vol. 13, Issue 2, 2026 · pp. 12–19 Read article
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Effects of loading rates on durability life of a part for random loads
Abstract: In the current scenario of fierce competition, manufacturers are under tremendous pressure to launch defect free products at the earliest. Cutting down time at every stage of product development is the need of the hour and testing is no exception. Testing and validation in the automotive domain is of immense importance as any small failure can also lead to catastrophic consequences. Continuous emphasis is in place to accelerate the tests …
Published in Journal of Automobile Engineering and Applications · Vol. 5, Issue 3, 2018 · pp. 1–10 Read article
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“Automated Real-Time Transformer Health Monitoring System Using IoT”
Abstract: Transformers play a crucial role in the architecture of electricity production and distribution. To guarantee that a transformer performs successfully and efficiently, performance must be closely monitored. Manual and time-consuming procedures are used to monitor transformer characteristics such oil level, temperature, and pressure. To solve this issue, an IoT-based transformer parameter monitoring system may be a good choice. Designing and implementing an IoT-based transformer parameter monitoring system that enables real-time …
Published in Journal of Semiconductor Devices and Circuits · Vol. 10, Issue 1, 2024 · pp. 16–23 Read article
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Detection of Cancer using Machine Learning Algorithms
Abstract: Cancer is a group of diseases characterized by uncontrolled growth and spread of abnormal cells. There are over 100 types of cancer. And any part of the body can be affected. Cancer has become 2nd leading cause of death. Some hospitals offer cancer screening tests; the test results need to be evaluated by an oncologist. The cancer screening test are very expensive and not available in all of the hospital. …
Published in Trends in Machine design · Vol. 7, Issue 3, 2020 · pp. 9–16 Read article
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Artificial Neural Network Model for Stock Market Forecasting
Abstract: AbstractIn recent years, many attempts have been made to predict the behavior of bonds, currencies, stocks or stock markets. Neural networks, as an intelligent data mining method, have been used in many different challenging pattern recognition problems such as stock market prediction. The aim of this paper is to predict stock market using artificial neural networks (ANNs). The authors used feed forward neural network trained by back-propagation algorithm to make …
Published in Journal of Computer Technology & Applications · Vol. 5, Issue 1, 2014 · pp. 7–12 Read article
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Drug Discovery and Design: Focus on computational approaches used in the discovery and design of new drugs
Abstract: Computational approaches have revolutionized the field of drug discovery and design, offering efficient and cost-effective strategies for identifying and developing new therapeutics. This review article explores the application of computational methods in drug discovery, with a focus on virtual screening techniques, molecular docking, molecular dynamics simulations, and quantitative structure-activity relationship (QSAR) models. Virtual screening plays a crucial role in narrowing down large chemical libraries by utilizing ligand-based and structure-based approaches. …
Published in Research and Reviews : Journal of Computational Biology · Vol. 12, Issue 1, 2023 · pp. 26–30 Read article
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Physics-Adaptive Digital Twin with Neural-Operator Reduced-Order Modelling
Abstract: This study proposes a novel Physics-Adaptive Digital Twin with Neural-Operator Reduced-Order Modelling (PADT-NO) framework for predictive modelling of complex, nonlinear, and multiscale fluid flows. The proposed mathematical framework integrates fundamental conservation laws, Navier–Stokes dynamics, physics-constrained neural operators, adaptive reduced-order modelling, and uncertainty-aware state estimation within a unified computational architecture. Unlike conventional computational fluid dynamics and purely data-driven approaches, the proposed model dynamically couples high-fidelity physical information with a low-dimensional latent …
Published in Recent Trends in Fluid Mechanics · Vol. 13, Issue 2, 2026 · pp. 89–103 Read article
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Student Academic Achievement Forecast Based on Emotional Intelligence, Personality, Demographic Characteristics and Attitude Towards Education and Future Career
Abstract: This study was aimed at predicting students' academic achievement based on emotional intelligence of personality traits, attitudes to education and future career. The present study was a correlational-analytical study. The statistical population of Zanjan University students in the academic year of 1395-1395 was the sample of 489 people selected by cluster random sampling method. Academic Resilience Scale (ARI) and Schotte Emotional Intelligence Questionnaire and Researcher Attitude Questionnaire were used to …
Published in International Journal of Education Sciences · Vol. 1, Issue 2, 2024 · pp. 25–36 Read article
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Development of a Machine Learning and Artificial Intelligence Based Model Aimed at Forecasting the Prognostic Impact of C-Reactive Protein in Myocarditis
Abstract: The specific role of inflammation markers in myocarditis remains uncertain. We investigated the diagnostic and prognostic significance of C-reactive protein (CRP) levels at the initial diagnosis among myocarditis patients. Our retrospective study enrolled patients clinically suspected (CS) or biopsy-proven (BP) with myocarditis, with available CRP data at diagnosis. We collected patient information, including clinical, laboratory, and imaging findings at diagnosis and follow-up visits. We utilized machine learning methods, specifically random …
Published in Research and Reviews: A Journal of Health Professions · Vol. 14, Issue 2, 2024 · pp. 12–24 Read article
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The Evaluation of the Light Parameters and Pleural Fluid Cholesterol to Determine the Differences Between Exudative and Transudative Pleural Discharge
Abstract: Background: Pleural effusion occurs when an imbalance between pleural fluid production and absorption leads to the accumulation of excess fluid in the pleural cavity. Pleural effusions are commonly classified into two types: transudative or exudative, depending on the underlying mechanism of fluid formation. While transudates typically result from systemic factors like heart failure or liver cirrhosis, exudates are usually caused by local factors such as infection, malignancy, or inflammation. Differentiating …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 14, Issue 3, 2024 · pp. 30–35 Read article
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Evaluating Advancements and Identifying Research Gaps in Automotive Spare Parts Demand Forecasting
Abstract: The automotive industry, a key driver of global economic activity, relies heavily on the effective management of spare parts to ensure vehicle longevity and reliability. Accurate prediction of demand for these components is imperative to uphold ideal stock levels, minimize expenditures, and elevate customer contentment. This review of literature assesses recent progressions in demand prediction methodologies for automotive spare parts, with a specific emphasis on conventional statistical methods and contemporary …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 3, 2024 · pp. 47–58 Read article
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Mathematical Modeling Analysis of India's Accident &Use of Fly Ash and Polymers in Road Safety
Abstract: Accident predicting models (APMs) are exceptionally strong tools for adaptation and mitigation strategies because they have the ability to predict both the severity and frequency of crashes. Road accidents are a major problem all throughout the world, especially in developing countries. Understanding the key variables that contribute can assist in reducing the frequency of traffic collisions. This study also discovered recent developments on fly ash, green composites, other polymer materials …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 488–499 Read article
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Computational Simulations in Drug Discovery: Modeling Protein Folding and Drug Binding
Abstract: Computational simulations have become essential tools in drug discovery, offering unprecedented insights into molecular behavior at the atomic level. These simulations, particularly in the domains of protein folding and drug binding, allow for the exploration of complex biological systems that are often difficult to study experimentally. Protein folding, a critical aspect of drug discovery, involves the transition of a polypeptide chain from an unfolded to a biologically active structure. Understanding …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 1, 2025 · pp. 23–29 Read article
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Integrating Atmospheric Science: Understanding Greenhouse Gases, Aerosols, and Air Quality Dynamics
Abstract: Atmospheric science investigates the Earth’s atmospheric systems to understand their composition, dynamics, and the implications for climate, weather, and air quality. This review explores five primary areas within the field: atmospheric composition, atmospheric modeling, remote sensing, air pollution, and boundary layer dynamics, highlighting critical challenges and advancements. Rising levels of greenhouse gases (GHGs), including carbon dioxide and methane, continue to drive global warming, while feedback mechanisms—like cloud interactions and surface …
Published in International Journal of Atmosphere · Vol. 1, Issue 1, 2024 · pp. 32–35 Read article
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Exploring Antimalarial Activity of Chalcone Derivatives through QSAR
Abstract: Background: The core structure of chalcones contains a reactive α,β-unsaturated system within the aromatic rings, which plays a key role in mediating various biological effects. These effects include enzyme inhibition, anticancer activity, anti-inflammatory properties, as well as antibacterial, antifungal, antimalarial, antiprotozoal, and anti-filarial actions.Modifying the structure by introducing substituent groups to the aromatic ring can enhance potency, reduce toxicity, and expand their range of pharmacological actions.Methods: A total of twenty-seven …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 3, Issue 2, 2025 · pp. 1–6 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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A Review of Machine and Deep Learning Techniques for Cyber Security
Abstract: Nowadays in the digital landscape, cyber threats and attacks are increasing in an exponential manner, posing server risks to organizations and critical infrastructures. Data breaches often result from sophisticated threat models that exploit vulnerabilities in networks, systems and user behaviors. Cyber solutions are increasingly incorporating machine learning and deep learning to prevent and mitigate these security issues. These technologies have the potential to detect anomalies, classify threats and predict potential …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 · pp. 01–07 Read article