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375 articles for “classification machine learning”
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Bearing Fault Diagnosis Using ANN and SVM with the Help of Wavelet Transform Based Features
Abstract: Many researchers use vibration signals as fault characteristics of any rotating bearing for fault detection and use various optimization techniques available for fault of bearing classification. The method of fault classification uses few neural network (NN) geometry and parameters necessary for the formulation. There is no derived formulation which can be used to select the optimal values for the network parameters. The parameters which are required to be calculated, impacts …
Published in Recent Trends in Electronics Communication Systems · Vol. 4, Issue 2, 2017 · pp. 26–34 Read article
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Data Handling Algorithms for the Healthcare System for the Prediction of Diabetes in Health Data Science (HDS): A Review Report
Abstract: In recent years, diabetes has become the biggest disease in different countries around the world. This disease is caused by adulteration in food ingredients, unhealthy food habits, a lack of physical exercise, and changing the lifestyle every time without a routine chart. The main objective of this review paper is to provide a proper understanding of the machine learning algorithm used in the healthcare system to handle diabetic patients' data. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 2, 2024 · pp. 1–10 Read article
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AI-Based Machine Learning Web Application Firewall (ML-WAF)
Abstract: This research investigates the use of deep learning techniques for the real-time detection of malicious activities in web traffic and proposes an intelligent, AI-driven Web Application Firewall (WAF) designed to provide automated and adaptive security. The system analyzes diverse components of HTTP requests, including request methods, URLs, headers, cookies, and payload content, to accurately identify and classify malicious behavior. The proposed model targets a wide range of common and critical …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 Read article
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Fraudulent Data Detection of Credit Card by Using Random Forest Method to Calculate ROC and Confusion Matrix
Abstract: Credit card fraud defaults in trillions of dollars online merchants. Researchers have more and more complex methods of detecting fraud using algorithms for machine learning, but there are rare reports of hands-on implementations. We describe the implementation of an extortion discovery framework in a huge e-tail retailer. The paper looks at the combination of manual and robotized classification and offers experiences into the whole handle of advancement. The paper can …
Published in Journal of Advanced Database Management & Systems · Vol. 7, Issue 1, 2020 · pp. 15–22 Read article
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An Efficient CNN Model for Automated Cotton Leaf
Abstract: Timely and accurate identification of cotton leaf diseases are essential for maintaining healthy crop production and minimizing agricultural losses. Early detection allows farmers to take preventive or corrective measures, reducing the risk of disease spread and improving overall yield. In this study, we propose a Convolutional Neural Network (CNN) based model for the automated classification of cotton leaf diseases using image-based detection techniques. The model is trained on a diverse …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 14, Issue 3, 2025 · pp. 01–10 Read article
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Assessing the Effectiveness of Machine Learning Algorithms in Simulating Malware Detection Processes
Abstract: On the information system comprehends, computer virus attacks play a very important role and, by undermining regionally and globally, are considered one of the most critical threats. The traditional malware detection methods, more varied, being based on signature, are incapable of providing sufficient coverage over malicious programs in a short time. This is due to the speed of evolution and growing complexity of malware variants. This situation, in turn, requires …
Published in Journal of Electronic Design Technology · Vol. 14, Issue 3, 2023 · pp. 11–20 Read article
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Crop Disease Prediction Using Image Processing
Abstract: For any country in the world, its livelihood depends on agriculture. However, crop diseases affect the production and food supply of any country because we are unable to detect crop diseases. This paper presents a machine learning CNN (convolutional neural network) model, which uses images of crops to detect diseases. This model detects the diseases in the early stage and provides us with a solution to the crop diseases. It …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 15, Issue 2, 2026 · pp. 9–16 Read article
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Next-Gen Agriculture: Deep Learning Algorithms for Real-Time Plant Disease Detection via IoT
Abstract: In addition to providing high-quality food, the agriculture industry plays a critical role in supporting expanding people and economies. Plant diseases can have a detrimental effect on biodiversity and result in significant losses in food production. Automated methods for early and precise identification of plant diseases can reduce financial losses and enhance the quality of food produced. Deep learning has significantly improved object detection and picture classification accuracy in recent …
Published in Recent Trends in Sensor Research & Technology · Vol. 11, Issue 1, 2024 · pp. 18–23 Read article
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Diabetes Prediction Using ML Techniques
Abstract: Diabetes mellitus, commonly referred to as diabetes, denotes a cluster of prevalent endocrine disorders characterized by persistent elevated levels of blood sugar. Diabetes is classified into two main types: type 1 and type 2. Type 1 diabetes arises when the body is unable to produce insulin, while type 2 diabetes involves either insulin resistance or insufficient insulin production. Early detection and intervention are essential to reduce its harmful impacts. The …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 11, Issue 3, 2024 · pp. 1–9 Read article
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Accelerating Unsupervised Feature Learning: Parallelized Training of Denoising Autoencoders
Abstract: Unsupervised representation learning has become a cornerstone of contemporary machine learning, enabling algorithms to extract informative features from un-labelled, high-dimensional data. This work investigates the efficacy of stacked denoising autoencoders (SDAEs) trained via parallelized stochastic gradient descent (SGD) as a scalable approach to feature extraction. By strategically leveraging multi-threaded computation, our study systematically examines the trade-offs between increased parallelism, training efficiency, and the preservation of model accuracy. Experiments on the …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 3, 2025 · pp. 56–64 Read article
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Fuzzy Algorithm Based Sentiment Analysis: Enhancing Accuracy and Interpretability
Abstract: Sentiment analysis is the technique of determining the point of view, polarity, and intensity of a text. A poll found that 93% of consumers read online evaluations before making a decision. Manually analysing these judgements is challenging. To determine the review's overall sentiment or thinking polarity, sentiment analysis may be employed. This study's goal is to classify product assessment qualities as positive, negative, or neutral. Polarity labelling, feature extraction, and …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 10, Issue 2, 2023 · pp. 1–7 Read article
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Alzheimer’s Disease Detection Using ML Algorithm
Abstract: A degenerative neurological state of affairs, Alzheimer's disease (AD) gradually impairs cognitive and functional capacities, especially in people over 65. Early AD detection is crucial for efficient management and treatment prep. This study delves into novel approaches for the early detection of AD using non-invasive methods. We've implemented a blend of neuroimaging data analysis and machine learning algorithms to pinpoint markers indicative of the disease during its initial phases. Our …
Published in Journal of Experimental & Applied Mechanics · Vol. 15, Issue 3, 2024 · pp. 53–57 Read article
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Advances in Polymer-Modified Concrete using XAI
Abstract: Industry 4.0 technologies are being quickly adopted by the construction sector, opening new avenues for enduring operational and environmental issues. This sector looks at how explainable AI can forecast air and enhance the quality of building materials. XAI, AI, ML, and big data drive a new paradigm in polymeric material development. The effective XAI and ML-assisted design creates innovative, high-performance polymeric materials. It covers building a database and representing structures, …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 133–144 Read article
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Retinal Disease Detection Using Deep CNN
Abstract: Age-related macular degeneration, glaucoma, and diabetic retinopathy are the three main causes of blindness in the globe. To avoid visual loss, early identification and treatment of these disorders are essential. The goal of this research is to create an automated method for detecting retinal diseases by analyzing retinal fundus pictures with machine learning techniques. Python and the Tkinter package for the graphical user interface are used in the construction of …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 13, Issue 2, 2024 · pp. 46–50 Read article
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Fuzzy Variable Frame Analysis for Speech Recognition
Abstract: AbstractRecent works in machine learning has focused on models such as support vector machine (SVM), artificial neural network (ANN) and long short-term memory (LSTM), for automatically controlling the generalization and parameterization of the optimization process. This paper presents a fuzzy interpretation frame analysis procedure using LSTM classifier for noisy speech at word level using thresholding and local maxima procedure at framing level for the recognition process. Front end MFCC procedure …
Published in Current Trends in Signal Processing · Vol. 9, Issue 3, 2019 · pp. 9–18 Read article
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Automated Machine Learning System for Model Selection and Hyperparameter Optimization
Abstract: The proliferation of machine learning applications in various scientific and industrial domains has given rise to an urgent need for developing principled, automated techniques for optimal architecture selection and hyperparameter tuning for machine learning models without human expert intervention. In this paper, we introduce the Automated Machine Learning System for Model selection and hyperparameter Optimization (AMLSMO)—a state-of-the-art, all-encompassing AutoML system that combines the power of meta-learning-based warm-starting, Bayesian Optimization with …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 15–23 Read article
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Solitary Identification and Analysis of Considerate Nutritional Consumption and Identifying Malnutrition
Abstract: The food supply chain is in danger from various factors, including climate threats, such as heat waves, floods, droughts, and other extreme occurrences such as intensive livestock farming, demand for animal protein, practices for antimicrobial resistance and various other factors leading to serious food shortages and an increase in food prices. Malnutrition is a major issue, with an estimated 663 million individuals affected. Poor nutrition is a serious problem that …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 10, Issue 2, 2023 · pp. 46–62 Read article
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Speech Emotion Recognition using Convolutional Neural Networks
Abstract: This research work presents an assortment of techniques in Speech Emotion Recognition with the help of spectrograms and handcrafted Deep learning architecture: Convolutional Neural Networks (CNN). Emotional kingdom detection is a crucial part of human-device interplay studies. To make interaction between man and machine more natural, many milestones are conquered in speech emotion recognition, but still this process requires more up-to-the-mark results. To make an attempt for the same, this …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 8, Issue 3, 2021 · pp. 26–32 Read article
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Design and Development of Agrobot Using Feed Forward Network for Smart Farming Agriculture
Abstract: The Agrobot framework is a form of natural language processing that requires training to understand human language and meet user needs accordingly. The development of a chatbot tailored for the agricultural industry represents a significant advancement in agricultural technology. This chatbot serves as an interactive tool to assist farmers in various aspects of agricultural practices, including crop management, pest control, weather forecasting, and market information. The main objective of this …
Published in Journal of Advancements in Robotics · Vol. 11, Issue 1, 2024 · pp. 6–14 Read article
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Optimizing Heart Disease Prediction: Comparative Analysis of Machine Learning Algorithm for Early Detection
Abstract: The expanding realm of data analysis holds considerable importance in healthcare, particularly in the medical sector where forecasting heart disease is considered a complex endeavor. Early prediction of serious health conditions can be the determining factor between survival and fatality, with heart disease being one such critical health issue. Over the past decade, the main reason for death has been heart disease. Heart disorders come in many different forms, and …
Published in International Journal of Computer Science Languages · Vol. 2, Issue 1, 2024 · pp. 1–10 Read article