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Fault detection and classification in hybrid energy-based multi-area grid-connected microgrid clusters using discrete wavelet transform with deep neural networks
Published 2024“…Nowadays, deep learning algorithms are essential for ensuring the reliable, safe, and efficient operation of these complex energy systems. …”
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A systematic review of text classification research based on deep learning models in Arabic language
Published 2020“…The evaluation criteria used in the algorithms of different neural network types and how they play a large role in the highly accurate classification of Arabic texts are discussed. …”
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Soft Sensor for NOx Emission using Dynamical Neural Network
Published 2020“…Lag space for the model is found by using genetic algorithm to find the best time delayed model. Lag space obtained from the linear model is then used for constriction of the dynamical neural network. …”
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Intelligent Rapidly-Exploring Random Tree Star Algorithm
Published 2024Get full text
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Efficient convex-elastic net algorithm to solve the Euclideantraveling salesman problem
Published 1998“…This paper describes a hybrid algorithm that combines an adaptive-type neural network algorithm and a nondeterministic iterative algorithm to solve the Euclidean traveling salesman problem (E-TSP). …”
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A Hybrid Deep Learning Model Using CNN and K-Mean Clustering for Energy Efficient Modelling in Mobile EdgeIoT
Published 2023“…This research proposed a hybrid model for energy-efficient cluster formation and a head selection (E-CFSA) algorithm based on convolutional neural networks (CNNs) and a modified k-mean clustering (MKM) method for MEC. …”
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PROVOKE: Toxicity trigger detection in conversations from the top 100 subreddits
Published 2022“…Therefore, in this study, we find the turning points (i.e., toxicity triggers) making conversations toxic. …”
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Developing a UAE-Based Disputes Prediction Model using Machine Learning
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The unified effect of data encoding, ansatz expressibility and entanglement on the trainability of HQNNs
Published 2023“…One of the prominent challenges lies in the presence of barren plateaus (BP) in QML algorithms, particularly in quantum neural networks (QNNs). …”
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A Survey of Deep Learning Approaches for the Monitoring and Classification of Seagrass
Published 2025“…This study not only examines the well-known challenges such as limited availability of data but provides a novel, structured taxonomy of deep learning techniques tailored for the monitoring of seagrass, highlighting their unique advantages and limitations within diverse marine environments. By synthesizing findings across various data sources and model architectures, we offer critical insights into the selection of context-aware algorithms and identify key research gaps, an essential step for advancing the reliability and applicability of AI-driven seagrass conservation efforts.…”