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121
An Efficient Prediction System for Diabetes Disease Based on Deep Neural Network
Published 2021“…In this work, an efficient medical decision system for diabetes prediction based on Deep Neural Network (DNN) is presented. Such algorithms are state‐of‐the‐art in computer vision, language processing, and image analysis, and when applied in healthcare for prediction and diagnosis purposes, these algorithms can produce highly accurate results. …”
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122
Assessment of four dose calculation algorithms using IAEA-TECDOC-1583 with medium dependency correction factor (K<sub>med</sub>) application
Published 2024“…K<sub>med</sub> is calculated for D<sub>m.m</sub> and D<sub>w.w </sub>algorithm types in bone and lung media for both photon beams. …”
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Development of an Optimization Scheme for A Fixed-Wing UAV Long Endurance with PEMFC and Battery
Published 2018Get full text
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A Framework for Predictive Modeling in Sustainable Projects
Published 2012Get full text
doctoralThesis -
127
Simultaneous stabilisation of power systems using geneticalgorithms
Published 1997“…The paper considers the simultaneous stabilisation of a power system over a wide range of operating conditions via a single power system stabiliser using genetic algorithms. A power system operating at various load levels is treated as a finite set of plants. …”
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128
A multi-pretraining U-Net architecture for semantic segmentation
Published 2025“…In this research, we propose and evaluate a modified version of a deep learning algorithm called U-Net architecture for partitioning histopathological images. …”
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129
A Survey of Data Clustering Techniques
Published 2023“…This survey examines seven widely recognized clustering techniques, namely k-means, G-means, DBSCAN, Agglomerative hierarchical clustering, Two-stage density (DBSCAN and k-means) algorithm, Two-levels (DBSCAN and hierarchical) clustering algorithm, and Two-stage MeanShift and K-means clustering algorithm and compares them over a real dataset - The Blockchain dataset, including prominent cryptocurrencies like Binance, Bitcoin, Doge, and Ethereum, under several metrics such as silhouette coefficient, Calinski-Harabasz, Davies-Bouldin Index, time complexity, and entropy.…”
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130
Efficient Seismic Volume Compression using the Lifting Scheme
Published 2000“…A separable 3-D discrete wavelet transform (DWT) using long biorthogonal filters is used. The computation efficiency of the DWT is improved by factoring the wavelet filters using the lifting scheme. …”
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131
Cyberbullying Detection in Arabic Text using Deep Learning
Published 2023“…Therefore, this study aims to evaluate several versions of Recurrent Neural Networks (RNNs) and Feedforward Neural Networks (FNNs) for detecting cyberbullying in the Arabic language. Although these algorithms are widely used in text classification and outperform the performance of classical classifiers, many have been extensively investigated in other domains such as sentiment analysis and dialect identification, as well as cyberbullying detection in English text. …”
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132
A method for data path synthesis using neural networks
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Modulation With Metaheuristic Approach for Cascaded-MPUC49 Asymmetrical Inverter With Boosted Output
Published 2020“…For the calculation of optimum angles, a meta-heuristic based Genetic Algorithm (GA) technique is employed. The generation of 49-level output requires 24 transitions in one quarter of a cycle. …”
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FPGA-Based Network Traffic Classification Using Machine Learning
Published 2020“…Classification approaches based on machine learning techniques have shown promising results with high levels of accuracy. In this paper, the suitability of packet-level and flow-level features is validated using stepwise regression and random forest feature selection. …”
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138
Data Embedding in HEVC Video by Modifying the Partitioning of Coding Units
Published 2019Get full text
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139
An efficient approach for textual data classification using deep learning
Published 2022“…<p dir="ltr">Text categorization is an effective activity that can be accomplished using a variety of classification algorithms. In machine learning, the classifier is built by learning the features of categories from a set of preset training data. …”
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140
FPGA-Based Network Traffic Classification Using Machine Learning
Published 2019Get full text
doctoralThesis