Search Results - (( java application during algorithm ) OR ( _ classification force algorithm ))
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Classification Algorithms and Feature Selection Techniques for a Hybrid Diabetes Detection System
Published 2021“…The proposed method has three steps: preprocessing, feature selection and classification. Several combinations of Harmony search algorithm, genetic algorithm, and particle swarm optimization algorithm are examined with K-means for feature selection. …”
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Enhancing benthic habitat mapping: A review of integrating satellite and side scan sonar data for improved classification accuracy
Published 2024“…This review not only highlights conceptual discoveries, but also provides recommendations for future research: (1) the need for comparative evaluation of classification algorithms to determine the optimal classifier; and (2) side-scan sonar-derived slope as a forcing factor to improve accuracy. …”
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Classification models for higher learning scholarship award decisions
Published 2018“…A dataset of successful and unsuccessful applicants was taken and processed as training data and testing data used in the modelling process. Five algorithms were employed to develop a classification model in determining the award of the scholarship, namely J48, SVM, NB, ANN and RT algorithms. …”
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SUDOKU HELPER
Published 2015“…In this paper research, author presents an algorithm to provide a tutorial for any Sudoku player who got stuck during the solving process. …”
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Final Year Project -
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A wheelchair sitting posture detection system using pressure sensors
Published 2024“…All the classification algorithms were evaluated by using the k-fold cross validation method. …”
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Financial time series predicting using machine learning algorithms
Published 2013“…In the second proposed framework, Linear Regression Line (LRL) is utilised to identify the trend patterns from historical financial time series, which is supported by ANN and SVM for classification process separately. Subsequently, Dynamic Time Warping (DTW) algorithm is utilised through brute force to predict the trend movement. …”
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Improving automatic forced alignment for phoneme segmentation in Quranic recitation
Published 2024“…These enhancements encompass the adaptation of an acoustic model tailored for Qur’anic recitation as preprocessing and culminate in the development of an algorithm aimed at refining forced alignment based on the phonetic nuances of the Qur’an. …”
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Talkout : Protecting mental health application with a lightweight message encryption
Published 2022“…The investigation of lightweight message encryption algorithms is conducted with systematic quantitative literature and experiment implementation in Java and Android running environment. …”
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Academic Exercise -
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Deep plant: A deep learning approach for plant classification / Lee Sue Han
Published 2018“…Specifically, it can go beyond the regular generic description of a plant, integrating the organ-specific features together with the generic features to explicitly force the designed network to focus on the organ regions during species classification. …”
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CAGDEEP : Mobile malware analysis using force atlas 2 with strong gravity call graph and deep learning
Published 2023“…Afterwards, this study adopts Convolutional Neural Network (CNN) for malware detection and classification algorithm. We compare CAGDeep with a state-of-the-art Android malware detection approach. …”
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Automated classification radiograph of Periodontal bone loss using deep learning
Published 2025“…It also demonstrated higher precision, recall, and F1 scores compared to InceptionV3 and InceptionV4, confirming its effectiveness in OPG classification. Conclusion: The findings indicate that ResNet-50 provides better classification accuracy and reliability than InceptionV3 and InceptionV4 in detecting periodontal bone loss from OPG images. …”
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Digital economy tax compliance model in Malaysia using machine learning approach
Published 2021“…The experimental results show that the ensemble method can improve the single classification model’s accuracy with the highest classification accuracy of 87.94% compared to the best single classification model. …”
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Robust and reliable dual the classification system with error compensation module
Published 2014“…Test using simulated data shows that the algorithm can maintain 100% accuracy when two errors are present. …”
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Handgrip strength evaluation using neuro fuzzy approach
Published 2010“…The results indicate that the classification accuracy of normal and pathological patients are 90 and 75 respectively. …”
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Spectral Estimation And Supervised Classification Technique For Real Time Electromyography Pattern Recognition
Published 2018“…Electromyography (EMG) signal is a biomedical signal which measures physical activity of human muscle.It has been acknowledged to be widely used in rehabilitation or recovery application system assisting physiotherapist to monitor a patient’s physical strength,function,motion and overall well-being by addressing the underlying physical issues.In application system associated with rehabilitation,a signal processing and classification techniques are implemented to classify EMG signal obtained.For real time application in the rehabilitation, the classification is crucial issue.The success of the signal classification depends on the selection of the features that represent a raw EMG signal in the signal processing.Therefore,a robust and resilient denoising method and spectral estimation technique have been acknowledged as necessary to distinguish and detect the EMG pattern.The present study was undertaken to determine the characteristic of EMG features using denoising method and spectral estimation technique for assessing the EMG pattern based on a supervised classification algorithm.In the study,the combination of time-frequency domain (TFD) and time domain (TD) were identified as the preferred denoising method and spectral estimation techniques.In the first part of study, the recorded EMG signal filtered the contaminated noise by using wavelet transform (WT) approach which implemented discrete wavelet transform (DWT) method of the wavelet-denoising signal. …”
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A malware analysis and detection system for mobile devices / Ali Feizollah
Published 2017“…We then used feature selection algorithms and deep learning algorithms to build a detection model. …”
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Thesis