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1733 articles for “Predicting”
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Enhanced Multimodal Disease Prediction Using Hybrid Ensemble Learning and AutoML Techniques
Abstract: The integration of hybrid ensemble learning and automated machine learning (AutoML) is revolutionizing disease prediction by addressing the complexity, imbalance, and high dimensionality inherent in medical datasets. This paper proposes an advanced pipeline that combines diverse ensemble learning models with AutoML-based optimization to predict chronic diseases such as kidney diseas-e, Parkinson’s disease, and lung cancer. Publicly available datasets from UCI and PhysioNet repositories were preprocessed using outlier removal, normalization, and …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 · pp. 10–20 Read article
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SpecForesight: A Predictive Analytics Pipeline for Laptop Price Forecasting
Abstract: This paper frames laptop pricing as a supervised predictive analytics problem, transforming product specifications into feature-rich signals to forecast price with calibrated regression models and operational guardrails against drift. A structured pipeline ingests tabular listings, performs data cleaning, and engineers domain-informed features (e.g., central processing unit (CPU) family and clocks, graphics processing unit (GPU) tiering, memory/storage density, display, and touch capabilities), followed by encoding and normalization to optimize model learnability. …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 · pp. 61–71 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. 55–66 Read article
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AI-Driven Lightning Strike Prediction Using Polymer-Integrated Sensor Platforms for Climate-Resilient Energy Systems in India
Abstract: Lightning strikes are a major climate-related threat to India, resulting in severe human injuries as well as regular damages to the power transmission network and renewable energy infrastructure. This research aims to introduce the concept of an AI-based lightning strike prediction and mitigation system with the integration of polymers for making climate-resilient energy infrastructure. Multidata are collected based on satellite images, climate variables, as well as surface-based sensing modules, and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 234–242 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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Machine Learning Assisted Timing Violation Prediction in Sub-7nm VLSI Physical Design
Abstract: The continuous scaling of semiconductor technology into the sub-7nm regime has introduced significant challenges in timing closure due to process variability, interconnect delay, power density, and manufacturing uncertainties. Conventional static timing analysis techniques often require extensive computational resources and iterative optimization cycles, resulting in increased design complexity and longer turnaround time. This research proposes a Machine Learning Assisted Timing Violation Prediction framework for sub-7nm VLSI physical design to improve early-stage …
Published in International Journal of VLSI Circuit Design & Technology · Vol. 4, Issue 1, 2026 Read article
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Machine Learning-Based Quantification of Polymer Structure Property Relationships for Predictive Material Design
Abstract: Polymer structures exhibit complex, hierarchical arrangements that strongly influence macroscopic properties, yet consistent quantification remains challenging due to nonlinear interactions and limited unified modeling strategies. Existing approaches inadequately capture generalized structure–property mappings across diverse polymer systems. This research aims to establish a machine learning-based quantification model for polymer structure–property relationships to support predictive material design. A Polymer Structure Property Dataset of 5,000 polymer samples includes structural descriptors and experimentally measured …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 737–754 Read article
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Diabetes Risk Prediction from Survey Data Using Machine Learning Algorithms
Abstract: Diabetes mellitus represents one of the most significant global health challenges, affecting millions worldwide and leading to severe complications if left undiagnosed or poorly managed. Early detection and risk assessment are crucial for preventing the progression of this chronic condition. This research presents a comprehensive machine learning approach for predicting diabetes risk using survey-based health parameters. The study implements and compares four prominent classification algorithms: Logistic Regression, K-Nearest Neighbors (KNN), …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article
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Mass Spectrometry–Based Phosphoproteomic Markers to Predict Kinase Inhibitor Response in Solid Tumors
Abstract: Mass spectrometry-based phosphoproteomics has emerged as a powerful tool for predicting kinase inhibitor responses in solid tumors, offering direct functional insights into signaling pathways that surpass traditional genomic profiling by capturing dynamic kinase activities and adaptive resistance mechanisms. Technological breakthroughs, including data- independent acquisition (DIA), trapped ion mobility spectrometry (timsTOF), and efficient enrichment methods like TiO2 or IMAC, now enable comprehensive profiling of over 40,000 phosphorylation sites from limited clinical …
Published in International Journal of Cell Biology and Cellular Functions · Vol. 4, Issue 2, 2026 Read article
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A Lightweight Cost-Sensitive Explainable Ensemble Framework for Early Heart Disease Risk Prediction
Abstract: Cardiovascular disease is still one of the leading causes of death, and hence, the early prediction of risk is a very important task in preventive medicine. Although recent studies have shown encouraging results in the application of machine learning algorithms to the prediction of heart disease, it has been noticed that most of the algorithms are more concerned with accuracy-driven optimization than the concerns of safety and false negatives. In …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 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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Analyzing and Predicting Academic Behavior from Peer Pressure Indicators Using Machine Learning
Abstract: The academic achievement of a student is determined by their capability, but also by the companions with whom they associate. Friends can have a positive impact on students' motivation for school, and at times friends are distractions leading to a lack of attention on their school assignments. This particular study focuses on the number and quality of companions students associate with and to what extent that could be used as …
Published in International Journal of Education Sciences · Vol. 3, Issue 2, 2026 · pp. 1–7 Read article
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PREDICTING THE COMPRESSIVE STRENGTH OF CONCRETE USING ANN IN MATLAB
Abstract: This research work focuses on the production of Artificial Neural Networks ( ANNs) in the prediction of concrete compressive strength after 28 days. The measurement of the compressive strength of concrete in situ by means of cores cut from hardened concrete is recognised as the most common technique, but the compressive strength of concrete is very difficult to predict because several factors affect it.In this paper , in order to …
Published in Journal of Construction Engineering, Technology & Management · Vol. 10, Issue 3, 2020 · pp. 46–52 Read article
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DISEASE PREDICTION SYSTEM USING MACHINE LEARNING
Abstract: Machine learning (ML) is a rising field. It already plays an important role in many fields, with projects of all sizes showing its positive impact. One such use of machine learning algorithms is in the healthcare field. Medical facilities need to be improved in order to make better decisions about patient diagnosis and treatment options. In this paper, we attempt to use the performance of Machine learning equipment in health …
Published in Journal of Control & Instrumentation · Vol. 12, Issue 2, 2021 · pp. 14–18 Read article
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Using Convolutional Neural Networks (CNN) for Age and Gender Prediction
Abstract: The network, security, and care have all become more dependent on age and gender identification. It's commonly used for children's access to age-appropriate content. To expand its reach, social media uses it to provide layered adverts and marketing. Face recognition has progressed to the point where we need to map it out further in order to achieve more usable results using various methodologies. In this study, we suggest using deep …
Published in Journal of Instrumentation Technology & Innovations · Vol. 12, Issue 1, 2022 · pp. 27–32 Read article
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Uncertainty Prediction in Brain Tumour Segmentation
Abstract: Gliomas are one of the most common brain tumour at different levels of the province, with Magnetic Resonance Imaging (MRI) used for diagnosis. In this project, It was asked to try to find uncertainty in the Brain Tumour Segmentation on MRI images using the BraTs19 Dataset and to look at how machine learning algorithms can work with these MRI images. Since these tissues are so large in shape and appearance, …
Published in Journal of Instrumentation Technology & Innovations · Vol. 13, Issue 2, 2023 · pp. 40–52 Read article
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Early Heart Disease Prediction Using Hybrid Machine Learning Techniques
Abstract: In the contemporary era, cardiovascular disease is one in all the most causes of death within the world. Estimating Heart problems i.e cardiopathy is a crucial challenge within the area of clinical data analysis. Large volumes of data produced by the healthcare sector have been proved to be useful for helping with decision-making and speculation, thanks to machine learning (ML).. Various studies help us to review and supply glimpse into …
Published in Journal of Microcontroller Engineering and Applications · Vol. 9, Issue 2, 2022 · pp. 35–41 Read article
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Comparison of RSM and ANN Modeling Approaches in Predicting the Laser Phase Transformation Hardening Parameters on the Heat Input and Hardened-Bead Profile Quality of Unalloyed Titanium
Abstract: In the present work, laser transformation hardening (LTH) of unalloyed titanium, nearer to ASTM Grade 3 of chemical composition was investigated using CW 2kW, Nd: YAG laser. The laser process variables such as laser power, scanning speed, and focused position play a major role in deciding the laser hardened bead quality. Two methods, Response Surface Methodology (RSM) and Artificial Neural Network (ANN) were used to predict the heat input and …
Published in Journal of Materials & Metallurgical Engineering · Vol. 5, Issue 1, 2015 · pp. 36–59 Read article
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Machine Learning Techniques for Early Detection of Heart Disease
Abstract: Cases of heart disease are increasing rapidly, thus it's important and concerning to be aware of any potential ailment beforehand. This diagnosis is a difficult task that must be completed fast and precisely. The primary goal of this study is to determine which patient, based on different medical features, has a higher chance of having heart disease. We created a heart disease prediction algorithm based on the patient's medical history …
Published in Journal of Microelectronics and Solid State Devices · Vol. 10, Issue 3, 2023 · pp. 16–21 Read article
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Human Activity Recognition Using For Smartphone Sensors To Predict The Best Accuracy Based On Machine Learning Algorithms
Abstract: Human activity recognition requires predicting the action of a person based on sensor-generated data. Due to the enormous number of applications possible by modern ubiquitous computing devices, it has sparked a lot of attention in recent years. It categorizes data into actions such as walking, sitting, standing, and lying. The accelerometer and gyroscope were used to generate the sensor data, and the sensor signals were pre-processed with noise filters. The …
Published in Recent Trends in Sensor Research & Technology · Vol. 9, Issue 1, 2022 · pp. 12–23 Read article