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27 articles for “Real Life Datasets”
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A Comparison among Twenty-Seven Normality Tests
Abstract: This paper studies and compares the power of 27 normality tests via the Monte Carlo simulation of sample data generated from symmetric three short-tailed and three long-tailed, asymmetric three short-tailed and three long-tailed distributions under different sample sizes by using R codes. Our simulation results showed that for symmetric short-tailed Uniform (0, 1), t (10) and Beta (4, 4) distributions showed that the Agostino-Pearson K2, Jarque Bera and Kurtosis tests …
Published in Research & Reviews : Journal of Statistics · Vol. 8, Issue 3, 2019 · pp. 41–59 Read article
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A Review of Code Defect Likelihood Using ML Methods
Abstract: AbstractSoftware begets defects and defects are inevitable in software design and coding. This paper focused on five machine learning analysis models (MLAM); Support Vector Machine, Random Forest, K-Nearest Neighbor, Classification and Regression Tree, and Linear Discriminant Analysis. These models are trained to detect software defects using the software defect dataset from Promise data repository. The collected dataset is preprocessed to reduce the amount of redundant features using a dimension reduction …
Published in Journal of Open Source Developments · Vol. 7, Issue 3, 2020 · pp. 1–6 Read article
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Realtime Life Cycle Assessment, Synthesizing Life Cycle Inventory with Business Process Management, Sustainable Growth in the Fourth Industrial Revolution
Abstract: A prevalent methodology for evaluating the ecological impacts of processes and products throughout their entire life cycle is known as Life Cycle Assessment (LCA). This approach involves scrutinizing each phase, encompassing the extraction of raw materials, manufacturing, utilization, and disposal. Nevertheless, conventional LCA methods often fail to account for the dynamic nature of industrial processes and are deficient in real-time data. The Life Cycle Inventory (LCI), a pivotal component of …
Published in International Journal of Industrial and Product Design Engineering · Vol. 2, Issue 2, 2024 · pp. 1–9 Read article
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A IoT-Enabled Predictive Intelligence for Real-Time Failure and Damage Evolution Monitoring of Polymer Composites
Abstract: Damage assessment of carbon-fibre-reinforced polymer composites is still challenging since the damage occurs as a combination of matrix cracking, interfacial debonding, delamination and fibre fracture. The present work proposes a framework for predictive-intelligence based on IoT for multiaxial fatigue and compression-after-impact (CAI) CFRP experiments, employing publicly available acoustic-emission (AE) datasets. A causal CNN–GRU attention model is developed by integrating time-domain, spectral, wavelet, loading-history and trend features to estimate the damage …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 Read article
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Material-Integrated Energy Management: Role of Advanced Polymers and Composites in Smart Homes
Abstract: The article discusses the possibility of improving the performance as well as the sustainability of smart-home-based energy management systems (EMS) by applying advanced polymeric and composite materials. Smart homes today are placing greater requirements on lightweight, thermally stable, high durability, and electrically conducting compounds in such important aspects of the devices as batteries, substrate layers in solar panels, thermally resistant enclosures, and high efficiency thermal insulation on intelligent appliances. Such …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1361–1374 Read article
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A Review on Applications of Artificial Intelligence (AI) in Parkinsons’s Disease Diagnosis and Treatment and Its Future Challenges
Abstract: Parkinson’s disease (PD) is a long-term, progressive neurodegenerative disorder that mainly occurs in people older than 60 years, affecting nearly 1% of this population. It is chiefly marked by the loss of dopaminergic neurons in the substantia nigra, a crucial brain region responsible for controlling motor functions. The resultant dopamine deficiency significantly disrupts motor control, manifesting in clinical symptoms such as tremors, bradykinesia, muscle rigidity, and postural instability. While PD …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 15, Issue 3, 2025 · pp. 1–15 Read article
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Industrial Prognostics via Ensemble Machine Learning: An Uncertainty Aware Framework for RUL Estimation on NASA FD004 Telemetry
Abstract: Estimating the Remaining Useful Life (RUL) of industrial machinery in real-time is now vital for both operational safety and smart resource management. In the aviation industry, turbofan engines deal with constantly shifting flight conditions, making traditional, scheduled maintenance both expensive and prone to error. This paper addresses the flaws in common “point-prediction” AI models, which offer a single failure date without any margin for error, by introducing a new, uncertainty-aware …
Published in Journal of Aerospace Engineering & Technology · Vol. 16, Issue 2, 2026 Read article
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Comparative Analysis of MCNN and RCNN for Speech Emotion Recognition Using Gender Information
Abstract: Speech emotion recognition is a speech processing task and a computer-based approach designed to identify and classify the emotions conveyed in audio signals. The aim of this system is to evaluate a speaker's emotional state, such as happiness, anger, sadness, or frustration, by analyzing their speech patterns, which include prosodic features like pitch, frequency, and rhythm. Speech emotion recognition is used in various real-life scenarios that include Customer Service, Healthcare, …
Published in Journal of Communication Engineering & Systems · Vol. 15, Issue 1, 2025 · pp. 1–10 Read article
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Continuous Learning in Language Models: A Survey of Streaming Data Processing Techniques
Abstract: The integration of continual learning with Large Language Models (LLMs) and Natural Language Processing (NLP) represents a transformative step toward creating adaptive, intelligent systems capable of functioning effectively in ever-changing environments. Traditional LLMs are typically trained on large, pre-collected datasets, which limits their ability to evolve as new information emerges. Continual learning, in contrast, enables models to acquire new knowledge incrementally without the need for complete retraining, thereby supporting long-term …
Published in Recent Trends in Programming languages · Vol. 12, Issue 3, 2025 · pp. 23–34 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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A Review on AI and Machine Learning for Predictive Maintenance and FDD in RAC Systems
Abstract: The paper reviews the existing AI/ML methods first in the general context of predictive maintenance and FDD of RAC systems, then specifically focusing on granular cooling appliances. Perspectives and insights are provided on the reasons why potentially valuable models do not make it into practice more often, and where future research and development should be headed. New emerging topics for decision support systems to include domain knowledge and physics-based modeling …
Published in Journal of Refrigeration, Air conditioning, Heating and ventilation · Vol. 13, Issue 1, 2026 · pp. 15–25 Read article
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Prediction of Crops Driven by Machine Learning
Abstract: Agriculture has made its own way to make every living being healthy, survive in this universe. In India, we all realize that agriculture is the foundation of the country. Smart things (IoT) have brought modification in each and every area/field of common man’s life by making everything intelligent and smart. The factors that have impacted the crop significantly yield water, UV, pesticides. This paper is for or shows that a …
Published in Journal of Advancements in Robotics · Vol. 8, Issue 2, 2021 · pp. 17–21 Read article
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Predicting Multiple Diseases Using Machine Learning: A Data-Driven Approach
Abstract: The increasing prevalence of chronic and life-threatening diseases highlights the need for innovative healthcare solutions that enable early detection and proactive management. The Multiple Disease Prediction Platform is a web-based system utilizing machine learning (ML) and deep learning (DL) algorithms to analyze user-inputted health data, generating real-time predictions of potential health risks. By leveraging Python’s Streamlit library, the platform provides an interactive and accessible diagnostic experience, eliminating the need for …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 16–35 Read article
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A Web Application for Predicting Diabetes Using Machine Learning Methods
Abstract: Diabetes is a long-term disease caused by high glucose quantity in the blood. It has the potential to result in serious health complications like heart disease, hypertension, and ocular damage. It is good to identify any health issues as early as possible to get the right medical treatment and make necessary lifestyle adjustments. One makes use of machine learning techniques to predict diabetes and develop treatment options using actual cases. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 3, 2024 · pp. 92–102 Read article
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IoT-Based Emergency SOS System for Post-Accident Assistance
Abstract: The increase in road accidents poses significant challenges for timely medical response, often leading to life-threatening delays. This project proposes an IoT-based accident wound detection system that utilizes a night vision camera mounted on either the interior or exterior of a vehicle. The system aims to detect injuries sustained by individuals during a collision and promptly alert emergency services. By employing a night vision camera, the system can operate effectively …
Published in Journal of Microelectronics and Solid State Devices · Vol. 12, Issue 2, 2025 · pp. 11–19 Read article
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AI Approaches in Gait and Posture Analysis: A Review
Abstract: This review synthesizes current research on the application of artificial intelligence (AI) in gait and posture analysis, focusing on methodologies, algorithms, and clinical applications. It examines the use of machine learning (ML) and deep learning (DL) techniques to extract relevant features from sensorderived data, offering objective, and automated assessments that surpass traditional methods. A systematic literature review was conducted, analyzing studies that utilized AI for gait and posture analysis with …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 3, 2025 · pp. 1–3 Read article
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Comparative Study of BERT Variants for Sentiment Analysis with Error Analysis
Abstract: The use of media is going up fast in India, and this has led to the rise of Hinglish. Hinglish is an informal blend of Hindi and English that people commonly use in everyday conversations, especially across social media platforms such as Twitter, Facebook, and WhatsApp. People use Hinglish to talk to each other in a way that is not very formal. Hinglish blends English vocabulary with informal usage, often …
Published in International Journal of Computer Science Languages · Vol. 4, Issue 1, 2026 · pp. 39–48 Read article
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An Analysis of Machine Learning Models for Early Cardiac Risk Stratification
Abstract: The paper shows an in-depth study of machine learning and artificial intelligence solutions to early cardiac risk stratification which has a crucial necessity because cardiovascular disease (CVD) prediction remains a significant issue that needs to be improved beyond the conventional risk score. Since CVD is the most serious disease killer in the world, claiming 17.9 million deaths every year, there is a strong need to get the most sophisticated predictive …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 13, Issue 2, 2026 Read article
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Cardiovascular Illness Detection and Categorization with Innovative Neural Networks
Abstract: Health-related problems are increasingly prevalent in modern-day societies and are significantly shaped by a multitude of factors encountered in everyday life. Among these, cardiovascular diseases have emerged as one of the primary causes of death on a global scale, posing serious challenges to public health systems. In response to this growing concern, the present study proposes a machine learning-based framework that is not only highly effective but also reliable and …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 21–30 Read article
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Interpretable Skin Cancer Detection via Optimized CNN Models for Smart Healthcare Solutions
Abstract: Skin cancer is a common and potentially life-threatening condition, highlighting the importance of reliable and efficient diagnostic techniques. Recently, convolutional neural networks (CNNs) have demonstrated significant potential in automating the classification of skin cancer using thermoscopic images. Despite these advancements, the lack of interpretability in these models poses a barrier to their widespread use in clinical settings. In this study, we propose an interpretable CNN architecture optimized for skin cancer …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 12, Issue 1, 2025 · pp. 41–45 Read article