In this paper, efficient machine learning technique is introduced to develop efficient machine learning model for hate speech recognition from the tweet data. Initially, the tweet data is gathered from the open-source...
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Remote sensing (RS) has developed significantly with the progress of the Internet of Things (IoT) which is allowed the cheap and fast acquisition of data in millions and billions of interrelated devices utilized throu...
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Remote sensing (RS) has developed significantly with the progress of the Internet of Things (IoT) which is allowed the cheap and fast acquisition of data in millions and billions of interrelated devices utilized throughout the whole world. RS scene classifier that purposes for classifying scene types for RS images has wide applications in several domains like urban planning, national defence security, environmental monitoring, and natural hazard detection. State-of-the-art deep learning (DL) successes are performed in a novel wave of RS scene classification applications, but it is the absence of explainability and trustworthiness. An intrusion detection system (IDS) plays a vital role to ensure security in the RS-based IoT environment. In this aspect, this study presents an ebola optimization algorithm with deep learning-based scene classification and intrusion detection (EOADL-SCID) technique on IoT-enabled remote sensing images. The aim of the EOADL-SCID system lies in the effectual scene classification of remote sensing images and intrusion detection. It involves a two-stage procedure. In the initial stage, the EOADL-SCID algorithm involves a modified DarkNet-53 feature extractor, EOA-based hyperparameter tuning, and graph convolution network (GCN) based classification. Next, in the second stage, the intrusion detection process takes place via two subprocesses namely variational autoencoder (VAE) based intrusion detection and skill optimizationalgorithm (SOA) based parameter tuning. The simulation outcomes of the EOADL-SCID approach are tested utilizing two benchmark databases and the experimental outcomes highlighted the improved performance of the EOADL-SCID algorithm on scene classification and intrusion classification processes.& COPY;2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).
Modular multilevel converter (MMC) plays a significant part in high voltage power electronic industries due to its wide range of benefits such as modularity and reliability. The circulating component exists between th...
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Modular multilevel converter (MMC) plays a significant part in high voltage power electronic industries due to its wide range of benefits such as modularity and reliability. The circulating component exists between three-phase half-bridge MMC causing certain issues in the design of MMC such as system loss, voltage instability, current disturbance, etc. To handle such problematical factors, a novel circulating current control mechanism named 'hybrid ebola gannet based generalized type 2 fuzzy controller (HEG-GT2FC)' is proposed in this paper. Here, the main controlling parameters of MMC namely harmonic distortion, current ripple, and circulating current settling time are adjusted or minimized using the generalized type 2 fuzzy controller. The hybrid ebola gannet algorithm solves the high voltage conversion problem by determining the optimal solution. The proposed HEG-GT2FC mechanism is evaluated using the MATLAB/Simulink tool. The performance of the proposed HEG-GT2FC mechanism is investigated in terms of measures such as current ripple, total harmonic distortion, and circulating current settling time. The simulation results illustrate that the proposed HEG-GT2FC mechanism achieves a very low percentage of current ripple (0.09%), total harmonic distortion (4.98%), and circulating current settling time (0.8 sec) than other compared state of art techniques.
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