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Assessment of cognitive load using multimedia learning and resting states with deep learning perspective
Published 2019“…The results shows that alpha brain wave produced consistent behavior using for all cognition tasks based on pre-trained CNN models for classification and cognitive load assessment. …”
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Direct approach for mining association rules from structured XML data
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An application of a novel technique for assessing the operating performance of existing cooling systems on a university campus
Published 2018“…Two contributions in this work are: (1) the prediction of a model by using Adaptive Neuro-Fuzzy Inference System (ANFIS)-based Fuzzy Clustering Subtractive (FCS), and (2) the classification and optimization of the predicted models by using an Accelerated Particle Swarm Optimization (APSO) algorithm. …”
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Development of Phasor Measurement Unit Based Fault Detection and Faulty Line Classification in Electrical Power System
Published 2019“…This model implements Non-Recursive Phasor Estimation (NRPE) algorithm. …”
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Application of genetic algorithm and JFugue in an evolutionary music generator
Published 2025“…This project explores the application of Genetic Algorithms (GA) with JFugue, which is a Java-based music programming library to develop an Evolutionary Music Generator. …”
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Cyberbullying detection: a machine learning approach
Published 2022“…Finally, the model was pickled and loaded into the web application. …”
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Daily maximum load forecasting of consecutive national holidays using OSELM-based multi-agents system with weighted average strategy
Published 2023Subjects:Article -
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Development of vehicle detection and counting system for traffic analysis using computer vision
Published 2024“…Initially, the image and video dataset of road traffic is gathered from various online sources and undergoes categorization before loaded into the system for model training. Then two model training process begins, one is for vehicle model detection to learn the feature of a vehicle and the image background, while another training is for binary weather classification for rainy and sunny conditions. …”
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Driver behaviour classification: a research using OBD-II data and machine learning
Published 2024“…The relationship between all features and engine speed is analysed to select the optimal features, which include engine speed, vehicle speed, throttle position, and calculated engine load. Then, the proposed model makes use of the K-Means algorithm to create driving behaviour labels whether belong to safe or aggressive - validated by the safety score criteria. …”
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Teleworking monitoring system using NILM and K-NN algorithms: a strategy for sustainable smart cities
Published 2024“…Together with an event classification method known as K-Nearest Neighbor (k-NN) algorithm, the teleworking event and duration can be identified.The results were presented using classification metrics that consist of confusion matrix andaccuracy score. …”
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Text steganalysis using evolution algorithm approach
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Ganoderma boninense classification based on near-infrared spectral data using machine learning techniques
Published 2022“…High accuracy shows the capability of the classification model to correctly predict the G. boninense detection while high F1-score indicates that the classification is able to validate the detection of G. boninense correctly with low misclassification rate. …”
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Classification System for Heart Disease Using Bayesian Classifier
Published 2007“…This system was mainly developing using java programming. Apache Tom cat was used as a server in order to run the application smoothly. …”
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Biometrics electronic purse
Published 1999“…This paper looked into using biometrics as a mean of authentication, thus requiring a new generation of Smart Card technology to be implemented in banking and multiple applications environment. …”
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Ant colony optimization algorithm for load balancing in grid computing
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An accurate algorithm of PMU-based wide area measurements for fault detection using positive-sequence voltage and unwrapped dynamic angles
Published 2022“…The algorithm includes an index for faulty bus classification based on the positive-sequence voltage measurements of the pre-fault and post-fault conditions, where the bus with a maximum differential percentage is identified as a faulted bus. …”
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