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19 articles for “k-NN”
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Empowering Communication: A Review of Sign Language Translation Systems Powered by Machine Learning
Abstract: This research study offers a fresh solution to the communication gap between the hearing population and the deaf and hard-of-hearing community: the creation of a machine learning-based sign language translator. By utilizing cutting-edge K Nearest Neighbour (K-NN), the system effectively converts sign language motions into text and vice versa, facilitating smooth communication between sign language users and well-read people. The basis of the project is thorough data collection and careful …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 1, 2024 · pp. 25–31 Read article
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Identification of Brain Stroke Using Artificial Intelligence
Abstract: Globally, strokes are the primary cause of disability and mortality. Recently, machine learning (ML) and deep learning (DL) have been employed by artificial intelligence algorithms as effective stroke diagnosing techniques. These days, machine learning and data mining technologies are used in the construction of the main models. We have used five machine learning algorithms to determine if a stroke has occurred or is likely to occur based on a patient’s …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 2, 2024 · pp. 15–22 Read article
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An Overview on AI-Driven IoT Based Decision Making in Climate change Study: KSK approach in Climate Change Study
Abstract: As the Earth’s climate enters a state of unprecedented volatility, the traditional methods of ecological observation—characterized by delayed reporting and fragmented data—are no longer sufficient. This study investigates the paradigm shift toward AI-driven IoT (KSK Approach)-based decision-making frameworks as the primary frontier in climate science. By deploying a "planetary nervous system" of interconnected sensors—measuring everything from soil moisture in the Sahel to glacial melt rates in the Arctic—we generate a …
Published in International Journal of Climate Conditions · Vol. 3, Issue 1, 2026 · pp. 1–10 Read article
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Advancing Healthcare Systems: A Machine Learning Approach to Multi-Disease Prediction
Abstract: The integration of machine learning algorithms in healthcare has revolutionized the way we approach disease prediction and diagnosis. An attempt to employ machine learning techniques to forecast numerous diseases is presented in this study. A diverse dataset containing patient records, medical history, and relevant features for various diseases was used to develop predictive models. Feature selection and normalization were among the preprocessing methods used to clean and prepare the data. …
Published in Journal of Electronic Design Technology · Vol. 16, Issue 1, 2025 · pp. 1–6 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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Multiple Disease Prediction Using Machine Learning Algorithms
Abstract: The incorporation of machine learning algorithms into healthcare has transformed disease prediction and diagnosis. This research introduces a method for predicting various diseases using machine learning techniques. A comprehensive dataset, consisting of patient records, medical histories, and key disease-related features, was utilized to build predictive models. Data preprocessing methods, including feature selection and normalization, were implemented to clean and prepare the dataset. Several machine learning algorithms, such as Decision Trees, …
Published in Research and Reviews : A Journal of Immunology · Vol. 14, Issue 3, 2024 · pp. 34–38 Read article
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Deep Learning Based Detection and Classification of Brain Tumors Using MRI Images
Abstract: Brain tumor detection using magnetic resonance imaging (MRI) is a critical task in the early detection and treatment of brain tumors. Manual analysis of brain tumor detection using MRI is a tedious task that requires expertise in the field. Therefore, this study proposes a deep learning-based approach for brain tumor detection and classification using Convolutional Neural Networks (CNN). The proposed approach preprocesses the MRI image using normalization, resizing, and noise …
Published in International Journal of Brain Sciences · Vol. 3, Issue 2, 2026 Read article
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Potential of Particle Size Mix Ratios of Plantain Ogoni Red with Clay Soil: The Integrity of Adsorbent Performance in AGO Treatment in Fresh Water Environment
Abstract: The research is focus on monitoring the performance of various formulated adsorbent mix ratio of clay soil with some agro-based materials in treatment of contaminated water environment. The agro-based material used was Plantain Ogoni Red (POR) and fresh water environment was used for this research. The agro-based material was processed into different particle sizes of 150 𝜇m, 300 𝜇m, 600 𝜇m and 1.18 mm and the clay soil into fine …
Published in Journal of Modern Chemistry & Chemical Technology · Vol. 15, Issue 3, 2024 · pp. 12–25 Read article
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An Insight for Visually Impaired using AI Techniques
Abstract: We know that the life of blind people is very risky. They always need an assistance or another person for helping them.In this project we introduce AI spectacles for blinds, which will help them to find what is happening in front of them and they will be able to find their own things without any help. In this proposed system,weareusing a real time object detection using YOLOv3 model.‘You only look …
Published in Journal of Artificial Intelligence Research & Advances Read article
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Machine Learning-Based Approach for Heart Disease Prediction
Abstract: Heart disease is a significant global health challenge, with early diagnosis and prediction being essential for reducing mortality rates. Machine Learning (ML), an efficiently developing field within Artificial Intelligence, provides innovative methods for analyzing complex clinical data to predict heart disease. This review examines the basic machine learning techniques, data, and metrics used in cardiovascular disease prediction. It explores the role of supervised learning, such as decision trees and logistic …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 64–73 Read article
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Experimental investigation of thermally grown Indium film’s structural and tribological properties
Abstract: Indium thin film growth found numerous applications, such as cold welding of infrared detectors and readout-integrated circuits (ROIC) to form sensor chips. In film is grown on the Si substrate by thermal evaporation. The XRD results indicated the tetragonal bcc phase in the (101) preferential plane. Scanning electron microscopy (SEM) indicated an uniform, continuous film covering the whole surface. The EDS examination confirmed the pure In film. The atomic force …
Published in Journal of Polymer & Composites · Vol. 11, Issue 12, 2023 · pp. 58–66 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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Adaptive Routing Protocol to Optimize the Quality of Service of MANET Using Neural Network
Abstract: A MANET is a group of mobile nodes that create a temporary network without relying on centralized administration or standard supporting devices, often functioning as a conventional network. These dynamic environments present significant challenges for traditional routing and switching protocols, particularly in delivering Quality of Service (QoS) benchmarks such as bandwidth, latency, packet delivery ratio, and robustness. This study proposes an Adaptive Routing Protocol (ARP) leveraging Neural Network (NN) techniques …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 12, Issue 3, 2025 · pp. 01–09 Read article
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Evaluation of Agro-based Adsorbents for Oil Spill Remediation in Freshwater and Saltwater Environments: Kinetic and Adsorption Model Analysis
Abstract: Environmental pollution caused by oil spills poses significant risks to both human health and ecosystems, particularly in regions like Nigeria’s Niger Delta, where oil spills are frequent. Conventional methods for oil spill cleanup have limitations, which has prompted research into alternative, more effective techniques. This study investigates the use of agro-based materials, specifically plantain and banana species, combined with clay soil as adsorbents for oil removal in freshwater and saltwater …
Published in Journal of Petroleum Engineering & Technology · Vol. 15, Issue 2, 2025 · pp. 22–32 Read article
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Hybrid Techniques in Mango Leaf Disease Identification: Evaluating Neural Networks and Support Vector Machines
Abstract: Mango leaf diseases pose a significant threat to mango production, impacting both yield and fruit quality. Early and accurate detection of these diseases is crucial for effective management. This paper evaluates the use of hybrid techniques, specifically the integration of neural networks (NNs) and support vector machines (SVM), in the identification and classification of mango leaf diseases. NN excel in extracting complex features from images, while SVMs are robust classifiers, …
Published in Trends in Opto-electro & Optical Communication · Vol. 14, Issue 3, 2024 · pp. 19–27 Read article
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A Comprehensive Analysis of Machine Learning Models for Credit Card Fraud Detection
Abstract: This paper presents an indepth comparison of various machine learning models—Logistic Regression, Support Vector Classification (SVC), and Neural Networks (NN)—in the context of credit card fraud detection. The analysis spans multiple performance metrics, including accuracy, F1 score, precision, recall, and computational efficiency. Logistic Regression demonstrates competitive performance in terms of accuracy, but its poor precision renders it unsuitable for fraud detection tasks. Conversely, the Neural Network exhibits balanced precision and …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 Read article
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Development of Fast and Power Efficient Elevator
Abstract: The rapid urbanization and vertical expansion of cities have led to a surge in the construction of high-rise buildings, creating a growing demand for elevator systems that are not only fast but also energy efficient. As population density in metropolitan areas increases, the pressure on vertical transportation systems intensifies, highlighting the need for solutions that can handle high passenger traffic without compromising performance or sustainability. This study presents the development …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 3, Issue 2, 2025 · pp. 32–41 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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Screening Some Sorghum (Sorghum Bicolor L. Moench) Genotypes for Yield and Resistance Against Stem Borer (Sesamia Calamistis) in Nigeria
Abstract: Sorghum is a staple crop with global significance, scientifically it’s known as Sorghum Bicolor, stands as one of the most important cereal crops globally, particularly in regions with arid and semi-arid climates. Sorghum remains a cornerstone of agricultural economies worldwide with its widespread cultivation and diverse applications; however, its production is often hindered by various biotic stresses, with stem borer (Sesamia calamistis) infestation being a significant concern. The stem borer …
Published in Research and Reviews : A Journal of Biotechnology · Vol. 15, Issue 1, 2025 · pp. 50–55 Read article