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1733 articles for “Predicting”
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Utilizing Machine Learning to Predict the Dimensional Variation of Shafts Printed using Fused Deposition Modeling
Abstract: With the onset of the fourth industrial paradigm, additive manufacturing techniques are coming to the forefront in mechanical engineering domain. The technological burgeoning of additive manufacturing, particularly 3-D printing, has observed substantial growth in rapid prototyping, functional part manufacturing, and tooling because it has significantly reduced the manufacturing costs and processing time. One of the most commonly used techniques of additive manufacturing is Fused Deposition Modelling (FDM), examining and controlling …
Published in Trends in Mechanical Engineering & Technology · Vol. 11, Issue 2, 2021 · pp. 41–46 Read article
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Predicting Eye Blindness by Detecting Exudates in the Retina of Human Eye
Abstract: Diabetic retinopathy is a condition where a person suffering from diabetes starts to loosen his vision slowly as the severity of the disease increases gradually. We can diagnose this condition by the fundus image of the retina of the human eye; although it is very complicated for doctors to predict the conditions just by seeing the fundus images. By detecting diabetic retinopathy at the earliest, we can protect patients from …
Published in Journal of Computer Technology & Applications · Vol. 12, Issue 1, 2021 · pp. 17–23 Read article
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Machine Learning Approach to Predict the Mental Health Issues of Employees in Tech Companies
Abstract: Mental disorders are conditions that have an impact on your emotions, thoughts, and behaviour. It may happen infrequently or have a persistent effect. The topic of mental health has been major and challenging, especially for working professionals. Over time, the urbanized living and workload put a strain on people, making them more vulnerable to mental disorders like anxiety and mental disorders. Working professionals therefore seem to be at an elevated …
Published in Journal of Computer Technology & Applications · Vol. 13, Issue 3, 2022 · pp. 1–10 Read article
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Predictive Modeling System for Automated Skin Lesion Classification Using Deep Neural Networks and Voting Ensembles
Abstract: Skin cancer is one of the most prevalent cancers globally. Early and accurate diagnosis is critical for timely treatment and improved prognosis. This study presents a predictive modeling system for automated classification of skin lesions from dermoscopic images using deep neural networks and voting ensemble techniques. A customized 16-layer convolutional neural network architecture is developed for feature learning from lesion images. The concept of horizontal voting ensemble is implemented by …
Published in Journal of Computer Technology & Applications · Vol. 14, Issue 2, 2023 · pp. 29–35 Read article
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In silico Prediction of Two New Conformations of Aβ42 Peptide and Possible Mechanism of Their Aggregation
Abstract: Alzheimer’s disease is one of the most studied neurodegenerative diseases. The cause for most cases of this disease is unknown. However, there are many hypotheses regarding the progression of this disease. One of the hypotheses is the amyloid hypothesis. Amyloid precursor protein present on chromosome 21 is cleaved by β and γ sectretases. The cleaved fragments of this protein, nearly 36–43 residues long aggregates and are deposited as plaque in …
Published in Research and Reviews : Journal of Computational Biology · Vol. 8, Issue 1, 2019 · pp. 1–7 Read article
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Prevalence and Predictors of Goiter among 6–12 years Old Children in Eastern Tigray Region, Northern Ethiopia: A Community-based Cross-sectional Study
Abstract: Iodine deficiency disorder (IDD) is the collective name of endemic goiter and endemic cretinism. Goiter is the main preventable major public health problem worldwide during pregnancy and childhood in an environment where iodine is deficient. The aim of this study was to assess the prevalence rate and associated factors of goiter among 6–12 years old children in eastern Tigray region, Northern Ethiopia. We used community-based cross-sectional survey. The study population …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 6, Issue 2, 2017 · pp. 52–60 Read article
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Digital Twin Assisted Intelligent Prediction of Polymer Composite Degradation Under Environmental Exposure
Abstract: Polymer matrix composites (PMCs) deployed in aerospace, marine, automotive, and renewable-energy structures are continuously subjected to coupled environmental stressors — ultraviolet (UV) radiation, moisture ingress, thermal cycling, and mechanical loading — that progressively degrade their mechanical performance. Conventional accelerated ageing tests and empirical lifetime models are time-consuming, destructive, and poorly suited to in-service, asset-specific degradation forecasting. This paper proposes a Digital Twin (DT) assisted intelligent prediction framework that fuses a …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Implement Artificial Intelligence and Machine Learning for Engineering Design, Predictive Modeling, and Optimizing Polymer Nanocomposites
Abstract: Polymer nanocomposites are high performance engineered materials obtained by inclusion of nano-sized fillers into the polymer matrix to enhance mechanical, thermal, electrical, barrier and functional properties. However, the complex and non-linear interactions among polymer chemistry, nanofiller characteristics, filler concentration, dispersion, interfacial bonding and processing conditions make it challenging to anticipate and maximize their properties. Artificial intelligence (AI) and machine learning (ML) offer powerful data-driven solutions to these difficulties by establishing …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Model to Predict a Split Range Control of Hot and Cold Water System
Abstract: Mathematical model was developed to monitor, predict, and stimulate hot and cold water system using split range control application. Computer program language of MATLAB software of ODE function was applied to monitor the trend of temperature parameter in the system. Result obtained revealed decrease in temperature value of the hot water and increase in the temperature value of cold water until equilibrium was attained at 132°F at > 1.0 h. …
Published in Journal of Water Pollution & Purification Research Read article
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Predicting and Prohibiting the Risk of Heart Failure Using Machine Learning
Abstract: It is challenging to estimate the likelihood of complex chronic disease while treating conditions like heart failure. The application of machine learning, an area of artificial intelligence, in cardiovascular care is growing quickly. In essence, it defines how computers classify and understand data, or choose a task with or without human intervention. The theoretical underpinnings of machine learning are models that accept input data (such as images or text) and …
Published in International Journal of Computer Science Languages · Vol. 1, Issue 1, 2023 · pp. 15–20 Read article
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Conceptualization of An Intelligent Decision Framework for Control Factors and Weld Quality Prediction
Abstract: To improve the robot's welding quality, control welding precision, optimize welding parameters, realize continuous welding quality database optimization, and increase welding defect detection, a fuzzy neural network-based intelligent decision-making system must be built. This study demonstrates how fuzzy control theory and BP neural networks may be used to identify welding issues and enhance process variables. The experimental findings indicate that, with seam classification accuracy close to 90%, enhancing welding parameters …
Published in Journal of Polymer & Composites · Vol. 11, Issue 6, 2023 · pp. 10–19 Read article
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Pharmacophore mapping, 3D QSAR, docking, and ADME prediction studies of novel Benzothiazinone derivatives
Abstract: Background Tuberculosis is a major public health concern worldwide which is caused by Mycobacterium tuberculosis. DprE1 (Decaprenyl Phosphoryl Ribose 2’- Epimerase) is the most challenging target for development of novel anti- tubercular agents because it is a small protein and located into cytoplasmic membrane. So, novel anti-TB drugs did not bound effectively with it. DprE1 catalyzes the oxidation of the 2’ hydroxyl group of DPR (Decaprenyl Phosphoryl D- Ribose) …
Published in International Journal of Antibiotics · Vol. 1, Issue 1, 2024 · pp. 59–82 Read article
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A Review on Loan Approval Prediction Based on Machine Learning Techniques
Abstract: The banking industry has also benefited greatly from technological advancements. An increasing number of individuals are submitting loan applications on a daily basis. When deciding which loan applicants to approve, the bank must take certain rules into account. The bank needs to choose the best one for approval based on certain characteristics. The process of carefully verifying every person and recommending them for loan approval is laborious and fraught with …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 2, 2024 · pp. 1–11 Read article
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Prediction of Mobile Phone Price Using Machine Learning Classifiers
Abstract: One cannot imagine one's life without mobile phones; in today's digital era, mobile phones have become a necessity for everyone to fulfil their various demands like messaging, communication, entertainment, productivity, research, shopping and many more. In a thriving market of mobile phones where new smartphones are launched every year with new advanced features and various designs, determining the expense of a mobile can be a trouble-some tasks for consumers. In …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 2, 2024 · pp. 101–108 Read article
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Cutting-edge Deep Learning Methods for Predicting and Detecting Cardiovascular Diseases
Abstract: Cardiovascular diseases (CVDs) remain a major global health issue, highlighting the need for improved early detection and risk assessment methods. This research investigates the efficacy of both deep learning and traditional machine learning methods in forecasting cardiovascular diseases (CVDs). We evaluate a variety of models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Multilayer Perceptrons (MLPs), Long Short-Term Memory (LSTM) networks, as well as Logistic Regression (LR), Decision Trees …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 2, 2024 · pp. 36–42 Read article
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Automatic Stroke Recovery Rate Prediction System Based on Movement Analysis During Computer Interaction
Abstract: Stroke is a major health concern worldwide, often resulting in impaired motor functions and affecting the quality of life for affected individuals. This research introduces an innovative approach for predicting stroke recovery rates by leveraging movement analysis during computer interaction. The proposed system aims to provide a non-invasive and automated solution to assess the rehabilitation progress of stroke survivors. The system utilizes advanced motion tracking technologies to capture and analyze …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 2, 2024 · pp. 76–82 Read article
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Customer Churn Prediction Using ML Algorithms
Abstract: Comprehending customer churn is essential for businesses aiming to enhance and sustain customer relationships. This study introduces a machine learning approach aimed at forecasting customer churn by leveraging demographic and behavioral data. Our research involved developing predictive models using support vector machines (SVM), random forests, and decision trees, evaluating their efficacy using real-world data from the telecom industry. Our findings underscore that random forests consistently outperform SVM and decision trees …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 2, 2024 · pp. 70–75 Read article
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An Investigation of Model Predictive Control in Self-driving Vehicles
Abstract: Autonomous vehicles, which are often known as self-driving automobiles or driverless cars, are vehicles that can navigate and operate without human intervention. They require efficient controllers capable of handling complexities, with reduced computational costs, and should handle multiple inputs and outputs simultaneously. Model predictive control (MPC) possesses all these characteristics which means it can be utilized effectively for the same purpose. MPC for autonomous vehicles proposes various ways of achieving …
Published in Trends in Electrical Engineering · Vol. 14, Issue 1, 2024 · pp. 40–50 Read article
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Crime Prediction and Criminal Identification System Using Machine Learning
Abstract: Advanced machine learning and data analytics-driven crime prediction and criminal identification systems have become game-changing instruments for contemporary law enforcement. Utilizing past crime statistics, surveillance footage, and additional resources, these systems forecast criminal activity, manage resources efficiently, and improve investigation capacities. With an emphasis on their importance in enhancing public safety and lowering crime rates, this paper presents an overview of criminal identification and prediction systems. Examining the technologies and …
Published in International Journal of Electronics Automation · Vol. 2, Issue 1, 2024 · pp. 28–34 Read article
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Diabetic Risk Prediction Using Machine Learning
Abstract: The global prevalence of Type 2 diabetes has risen dramatically in recent years, posing a serious public health risk. Long-term risk prediction is an important technique for evaluating who is most likely to develop type 2 diabetes. Early detection and response can lead to better management and prevention of diabetes complications. Developing a user-friendly Windows program for long-term Type 2 diabetes risk prediction could revolutionize preventive healthcare due to technological …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 1, 2024 · pp. 11–17 Read article