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1126 articles for “predictive”
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Advancing Grid Integration: Maximum Power Point Tracking-Based Model Predictive Current Control Technique for Photovoltaic Systems
Abstract: Tracking a PV system's maximum power point is necessary as demand for grid-connected solar systems rises, regardless of utility demand. Since the PV system's output is D.C., a converter serving as a middleman between the PV system and the D.C. link capacitor is necessary to track the maximum power at all loads. Since the firing angle of the IGBT in the boost converter is between 0 and 1 at maximum …
Published in International Journal of Electrical Machine Analysis and Design · Vol. 1, Issue 2, 2023 · pp. 31–35 Read article
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A Study and Prediction of Psychological Disorders Through Machine Learning
Abstract: Physical illness is very much visible but not psychological illness therefore, it requires more attention and care. Psychological disorders also known as psychiatric disorders refer to a wide range of conditions affecting a person’s thought process, leading to significant changes in the behavior of an individual. The most prevalent psychological disorders include depression, anxiety disorders, and post-traumatic stress disorder (PTSD). Symptoms of psychological disorders vary greatly but include common symptoms …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 2, Issue 2, 2024 · pp. 32–38 Read article
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Integrative Machine Learning Approaches for Predicting the Rheological Behaviour of Soft Magnetorheological Elastomers
Abstract: Magnetorheological Elastomers (MREs) are advanced composite materials known for their ability to alter mechanical properties under external magnetic fields, making them highly valuable in adaptive damping systems, vibration control, and smart devices. The accurate prediction of rheological behavior in soft MREs remains a significant challenge due to the complex interplay between material composition and magnetic fields. To address this challenge, this study employs a multi-pronged approach that integrates traditional material …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 1083–1096 Read article
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The Integrity of Eadic-Hofstee Plot Model to Predict the kinetic Parameters of Crude Oil Degradation using Vernonia amygdalina Stem
Abstract: The integrity of Eadic-Hofstee concept was tested for the determination of the functional coefficients and parameters of crude oil degradation kinetics. The techniques enhanced the relationship between the substrate divided by the specific rate of the substrate degradation against substrate concentration (TPH). The investigation reveals the values of the biokinetic parameters of maximum specific rate of substrate degradation (Vmax) and the equilibrium constant values of the substrate degradation (Ks). The …
Published in Journal of Petroleum Engineering & Technology · Vol. 14, Issue 2, 2024 · pp. 37–47 Read article
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A Review on Predicting Wear and Friction of PTFE Composites - Fillers to Machine Learning Models
Abstract: Polytetrafluoroethylene (PTFE) composites, a self-lubricating material with low friction, became an indispensable material in engineering applications where load carrying capacity and wear are crucial. The pure PTFE has poor mechanical strength and wear resistance which can be enhanced by the addition of fillers in appropriate volume fraction. The wear performance is dependent on various factors such as fillers, operating parameters, environmental conditions as well as manufacturing attributes. This makes the …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 114–128 Read article
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Algorithm for the prediction of cardiovascular disease (CVD)
Abstract: cardiovascular diseases (CVD) still claim a significant number of deaths globally and remain the number one killer with an annual death toll of nearly 17.9 million. While several medical advancements have been made, an early diagnosis is still hard to obtain, which often leads to worsening conditions and intricate treatment options. With the advancement of modern technology, Machine learning has demonstrated to be a miraculous tool which can greatly impact …
Published in Research and Reviews : A Journal of Immunology · Vol. 15, Issue 2, 2025 Read article
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Machine Learning Approach to Predict the Performability and Emissions of Diesel Engine Fueled with Doped Biodiesel Blend
Abstract: Enhancing the performability and emission characteristics of diesel engines has been a difficult task in light of growing concerns about global warming and other negative effects, as diesel accounts for 70% of global energy demand. In this study, engine performance and exhaust emissions for various fuel blends were thoroughly evaluated using machine learning techniques to predict engine emission and performance behavior. We focused on biodiesel blend and nanoparticle additive concentration …
Published in International Journal of Mechanical Dynamics and Systems Analysis · Vol. 3, Issue 1, 2025 · pp. 1–12 Read article
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Sustainable Concrete Solutions: Chemically Predictive Modeling of Geopolymer Versus Ordinary Concrete in a Circular Economy
Abstract: Concrete is a fundamental structural material widely used in civil engineering projects. It plays a key role in various types of building structures, with its quality directly influencing the durability of the construction. This research focuses on assessing the current state of concrete construction, highlighting critical issues and technical aspects in the process, while emphasizing the importance of improving construction quality and management practices. Geopolymer concrete (GPC) presents a viable …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 170–192 Read article
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Harnessing Machine Learning for Stock Movement Prediction: A Review of Current Approaches
Abstract: Stock price prediction is a crucial task in financial analysis, aiding investors and traders in making informed decisions. This study investigates the use of deep learning methods, particularly Long Short-Term Memory (LSTM) networks, for predicting stock prices based on historical market data. The dataset, sourced from Yahoo Finance, consists of time-series stock price data, which is preprocessed, feature-engineered, and visualized to improve prediction accuracy. The model's performance is assessed using …
Published in Journal of Advances in Shell Programming · Vol. 12, Issue 2, 2025 · pp. 29–40 Read article
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Accuracy Improvement for Propeller Cavitation Noise Prediction Using UDF
Abstract: Recently, there has been an increase in demand for propulsion systems with higher hydrodynamic performance and lower underwater-radiated noise, as environmental issues are gaining more attention in addition to the traditional military necessity. It is important to reduce cavitation noise when designing propellers of the ships, especially for oceanographic research vessels because they use acoustic instruments and cavitation noise can interfere with their operation. It is well known that, when …
Published in Journal of Experimental & Applied Mechanics · Vol. 16, Issue 2, 2025 · pp. 18–26 Read article
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Optimized Machine Learning Framework for Battery State Prediction in Smart Charging Systems
Abstract: Good estimation of battery states, including State-of-Charge (SoC), State-of-Health (SoH), and Remaining Useful Life (RUL), are important in managing energy wisely and controlling the adaptive charging. This work introduces a streamlined machine learning model based on the ability to use multi-dimensional sensor measurements in terms of voltage, current, temperature, and cycle number to forecast battery conditions with high accuracy. Decent preprocessing, such as noise elimination, feature scaling, and calculated features, …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 11–23 Read article
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Stock Market Prediction Using Machine Learning: Techniques, Challenges, and Future Directions
Abstract: The continuous advancement of machine learning (ML) technologies has significantly transformed the field of financial forecasting, particularly in the area of stock market prediction. The ability to accurately forecast stock price movements and market trends plays a crucial role in supporting informed investment strategies and effective risk management. This paper provides a comprehensive review of recent developments in the application of ML techniques for predicting stock market behavior. It classifies …
Published in E-Commerce for Future & Trends · Vol. 13, Issue 1, 2026 · pp. 10–16 Read article
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NutriHeart with Chatbot
Abstract: Heart disease stands as one of the world's principal reasons for human deaths since it causes major preventable fatalities each year. Healthcare institutions currently explore machine learning (ML) integration for establishing new approaches toward predicting, and acting ahead of healthcare developments. NutriHeart presents an AI-based platform that accomplishes cardiovascular risk detection early and extends heart wellness by delivering customized nutritional and lifestyle recommendations. Using Support Vector Machines (SVM) along with …
Published in Recent Trends in Parallel Computing · Vol. 12, Issue 3, 2025 · pp. 22–34 Read article
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Prediction and Comparative Analysis of Thermal Conductivity of Jatropha Oil-based Hybrid Nanofluid by Multivariable Regression and ANN
Abstract: In the present study, a multivariable regression (MR) and artificial neural network (ANN) method was used to predict the thermal conductivity of Jatropha oil-based ZnO-Ag hybrid nanofluid. Firstly, the ZnO-Ag hybrid nanoparticles were synthesized and mixed in the jatropha oil to prepare various nanofluids at different volume concentrations (F) ranging from 0.05 to 0.20%. The stability and thermal conductivity of the prepared nanofluids were investigated. Wide ranges of temperature and …
Published in Journal of Polymer & Composites · Vol. 11, Issue 8, 2023 · pp. 32–39 Read article
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Machine Learning Based Early Cataract Detection: A Predictive Modeling Approach
Abstract: Cataracts, characterized by dense cloudy areas in the eye’s lens, afflict more than 50% of elderly individuals, leading to impaired vision and potential blindness. Detecting cataracts at an early stage is crucial to facilitate simpler treatments, as neglecting the condition may necessitate complex eye surgery. To address this issue, we are creating a predictive system that identifies cataract disease by analyzing user-provided eye features. To achieve this, we leverage OpenCV, …
Published in International Journal of Computer Science Languages · Vol. 1, Issue 2, 2023 · pp. 1–8 Read article
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Application of Artificial intelligence in Single Point Incremental Forming for Surface Roughness Prediction
Abstract: The sheet metal forming industries always try to find an emerging trend to form sheet-metal in a cost-effective manner. In this regard, a forming technique is trending termed as single point incremental forming (SPIF) in which a simple forming tool having hemispherical end rod is moving and simultaneously deforming the clamped metal sheet according to predetermined toolpath command and forms a complete shape. The achievement of required surface quality is …
Published in Journal of Polymer & Composites · Vol. 12, Issue 1, 2024 · pp. 237–246 Read article
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Enzyme Stability Prediction using BERT and CNN-A Deep Learning Approach for Enhanced Biocatalysis
Abstract: An important factor in determining the efficacy of industrial enzymes used in various biotechnological applications is their stability. The goal of this study is to develop a predictive model for industrial enzyme stability, which is essential to the efficiency of these enzymes in biotechnological applications. The research takes a comprehensive strategy to comprehend the parameters affecting enzyme stability by combining statistical analysis, deep learning algorithms (BERT and CNN), and molecular …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 14, Issue 2, 2024 · pp. 19–35 Read article
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Unlocking The Bioactivity Potential: Molecular Insights and Predictions of Salen, Salophen, Allicin, Curcumin, and Piperine
Abstract: In this work, the prediction of the biological activity of several significant molecules, including salen, salophen, allicin, curcumin, and piperine, are discussed. Using Molinspiration software, the molecular properties of these compounds were calculated. These molecules are highly significant due to their extensive potential in medical applications. Salen and salophen, for instance, play crucial roles in cancer chemotherapy and act as inhibitors of angiogenesis. Curcumin is renowned for its antioxidant properties, …
Published in International Journal of Cheminformatics · Vol. 1, Issue 2, 2023 · pp. 14–21 Read article
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AI-Driven Innovation in Biomaterials: Predictive Modeling and Design for the Future
Abstract: The integration of artificial intelligence (AI) is revolutionizing the field of biomaterials, paving the way for innovative approaches in their development and production. This paper examines the connection between AI and biomaterials, emphasizing the substantial impact of predictive modeling on the evolution of the field. By examining recent research and cutting-edge uses, the document shows how AI-powered predictive modeling has revolutionized biomaterial design, marking a period of unparalleled precision and …
Published in Trends in Machine design · Vol. 11, Issue 3, 2024 · pp. 25–35 Read article
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Prediction of Customer Churn Using Machine Learning Classification Models
Abstract: Customer churn prediction is a critical task in both the telecommunication and medical industries, where retaining customers or patients is essential for ensuring long-term profitability and maintaining high-quality service. To address this, a range of machine learning models—including logistic regression, decision trees, random forests, gradient boosting machines, and support vector machines—were employed to accurately forecast churn behavior. Prior to model training, the dataset underwent thorough preprocessing, which included handling missing …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 86–92 Read article