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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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2
Making programmer effective for software development teams: An extended study
Published 2017“…In the same way, male programmer can work in a good way with male leaders or other way around for females. …”
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3
Automatic Classification of Cervix Type
Published 2019“…In a developed country like United States, cervical cancer rate has been improved by 70% in the last 40 years due to a good screening programmes implemented in their country. …”
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Final Year Project -
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VHDL modeling of EMG signal classification using artificial neural network
Published 2012“…A back-propagation neural network with Levenberg-Marquardt training algorithm has been used for the classification of EMG signals. …”
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5
Web-based expert system for material selection of natural fiber- reinforced polymer composites
Published 2015“…Finally, the developed expert system was deployed over the internet with central interactive interface from the server as a web-based application. As Java is platform independent and easy to be deployed in web based application and accessible through the World Wide Web (www), this expert system can be one stop application for materials selection.…”
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6
A Machine Learning Classification Application to Identify Inefficient Novice Programmers
Published 2023Conference Paper -
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A machine learning approach of predicting high potential archers by means of physical fitness indicators
Published 2019“…The present study classified and predicted high and low potential archers from a set of physical fitness variables trained on a variation of k-NN algorithms and logistic regression. 50 youth archers with the mean age and standard deviation of (17.0 ± 0.56) years drawn from various archery programmes completed a one end archery shooting score test. …”
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A machine learning approach of predicting high potential archers by means of physical fitness indicators
Published 2019“…The present study classified and predicted high and low potential archers from a set of physical fitness variables trained on a variation of k-NN algorithms and logistic regression. 50 youth archers with the mean age and standard deviation of (17.0 ± 0.56) years drawn from various archery programmes completed a one end archery shooting score test. …”
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The Identification of High Potential Archers Based on Fitness and Motor Ability Variables: A Support Vector Machine Approach
Published 2018“…The present study classified and predicted high and low-potential archers from a set of fitness and motor ability variables trained on different SVMs kernel algorithms. 50 youth archers with the mean age and standard deviation of 17.0 ± 0.6 years drawn from various archery programmes completed a six arrows shooting score test. …”
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10
Leaf condition analysis using convolutional neural network and vision transformer
Published 2024“…Besides, existing leaf disease detection programs do not provide an optimized user’s experience. As a result, although customers may receive an excellent interactive features programme, the backend algorithm is not optimized. …”
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The identification of high potential archers based on relative psychological coping skills variables: a support vector machine approach
Published 2018“…Support Vector Machine (SVM) has been revealed to be a powerful learning algorithm for classification and prediction. …”
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Implementation of Hybrid Indexing, Clustering and Classification Methods to Enhance Rural Development Programme in South Sulawesi
Published 2024“…This shows that the accuracy status indicates a high percentage of correct predictions, and then the F1 Score of 0.9 indicates a well-balanced trade-off between precision and recall, demonstrating the model's overall effectiveness. …”
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Ensemble learning for multidimensional poverty classification
Published 2020“…Analysis of this study showed that Per Capita Income, State, Ethnic, Strata, Religion, Occupation and Education were found to be the most important variables in the classification of poverty at a rate of 99% accuracy confidence using Random Forest algorithm.…”
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Register Transfer Level Implementation Of Pooling - Based Feature Extraction For Finger Vein Identification
Published 2017“…The algorithm was mainly inspired by spatial pyramid pooling in generic image classification combined with PCA. …”
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15
A step towards the development of VHDL model for ANN based EMG signal classifier
Published 2012“…A feed-forward ANN with back-propagation learning algorithm is used for the classification of EMG signals. …”
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Proceeding Paper -
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The identification of high potential archers based on relative psychological coping skills variables: A Support Vector Machine approach
Published 2018“…Support Vector Machine (SVM) has been revealed to be a powerful learning algorithm for classification and prediction. …”
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Magnetic resonance imaging sense reconstruction system using FPGA / Muhammad Faisal Siddiqui
Published 2016“…This thesis aimed to investigate and develop a novel parameterized architecture design for SENSE algorithm. …”
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Thesis -
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Fpga-Based Accelerator For The Identification Of Finger Vein Pattern Via K-Nearest Centroid Neighbors
Published 2016“…In order to increase the processing, this thesis introduces an architecture for finger vein recognition based on K-Nearest Centroid Neighbors (KNCN) classifier implemented on Field Programmable Gate Array (FPGA). KNCN is a type of classification technique in image processing which involves calculation of centroid and sorting of K-nearest centroid neighbors. …”
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19
Development of a steady state visual evoked potential (SSVEP)-based brain computer interface (BCI) system
Published 2007“…The system includes a programmable visual stimulator, EEG amplifier with filter system, data acquisition card, and signal processing and classification algorithms. …”
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Conference or Workshop Item -
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An efficient low-cost real-time brain computer interface system based on SSVEP
Published 2010“…The system includes a frequency-programmable visual stimulator, EEG-band amplifier and filter, 16-bit data acquisition card, and signal processing and classification algorithms. …”
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