Search Results - (( evolution classification modelling algorithm ) OR ( program segmentation using algorithm ))
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Classification of Immunosignature Using Random Forests for Cancer Diagnosis
Published 2015“…In this work, we will develop a robust classification model that can be utilized in cancer diagnosis using immunofingerprint data. …”
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Proceeding Paper -
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Development Of Semi-Automatic Liver Segmentation Method For Three-Dimensional Computed Tomography Dataset
Published 2017“…In post-processing, the contour of liver is smooth by binary Gaussian filter. The liver segmentation program with proposed algorithm is evaluated with CT datasets obtained from SLIVER07 to prove its effectiveness in liver segmentation. …”
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Monograph -
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Deep learning detector for pests and plant disease recognition
Published 2020“…Meanwhile, evolution in deep convolutional neural networks for image classification has rapidly improved the accuracy of object detection, classification and system recognition. …”
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Final Year Project / Dissertation / Thesis -
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Y-type Random 2-satisfiability In Discrete Hopfield Neural Network
Published 2024“…Finally, a new logic mining model namely Y-Type Random 2-Satisfiability Reverse Analysis was proposed, which showed optimal performances in terms of several metrics as compared to the existing classification models. …”
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Thesis -
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Class binarization with self-adaptive algorithm to improve human activity recognition
Published 2018“…However, the learning complexity of classification is increased due to the expansion number of learning model. …”
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Thesis -
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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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Stock market turning points rule-based prediction / Lersak Photong … [et al.]
Published 2021“…Feature selection was used to sort out key features for further classification. News classification into factors affecting stock market turning point was done using Naïve Bayes, Deep Learning, Generalized Linear Model (GLM) and Support Vector Machine (SVM). …”
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Book Section -
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Recognition of isolated elements picture using backpropagation neural network / Melati Sabtu
Published 2005“…The project used Back-propagation Neural Network for the algorithm to classified images. …”
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Thesis -
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Classification with degree of importance of attributes for stock market data mining
Published 2004“…The SVM is a training algorithm for learning classification and regression rules from data [7]. …”
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Word segmentation of output response for sign language devices
Published 2020“…The proposed text segmentation method in this work is by using the dynamic programming and back-off algorithm, together with the probability score using word matching with an English language text corpus. …”
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ios Mobile Malware Deep Analysis And Classification For Social Media And Online Banking Exploitation
Published 2024thesis::master thesis -
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Supervised retinal vessel segmentation based on neural network using broader aging dataset
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Proceeding Paper -
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Interactive blood vessel segmentation from retinal fundus image based on canny edge detector
Published 2021“…The program will draw out the vessel edges. After that, those vessel edges will be segmented to bring focus on its details or detect the abnormal vessel. …”
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Accuracy of advanced deep learning with tensorflow and keras for classifying teeth developmental stages in digital panoramic imaging
Published 2022“…Results: Image segmentation using the DP-AC algorithm enhanced the visibility of the image features in the region of interest while suppressing the image’s background noise. …”
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Fast recovery of unknown coefficients in DCT-transformed images
Published 2017“…In this paper, we propose a fast hierarchical DCT coefficients recovery method by combining image segmentation and linear programming. In theory the proposed method can reduce the overall time complexity by a linear factor which is the number of image segments used. …”
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