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Improved Attentive Recurrent Network for Applied Linguistics-Based Offensive Speech Detection

Computer Systems Science & Engineering 2023年第11期47卷 1691-1707页

作者： Manar Ahmed Hamza Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Amira Sayed A.Aziz Mohammad Mahzari Abu Sarwar Zamani Ishfaq Yaseen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech *** ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate *** the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the *** on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and *** these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or *** Learning(ML)techniques have recently been applied to recognize hate speech-related *** current research article introduces a grasshopper optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social *** GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate *** the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding ***,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and *** last,the grasshopper optimization algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition *** depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was *** comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.

关键词： Applied linguistics hate speech offensive language natural language processing deep learning grasshopper optimization algorithm

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Over Frequency Problem Mitigation Using grasshopper Based PID Auxiliary Governors 21

Over Frequency Problem Mitigation Using Grasshopper Based PI...

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21st International Middle East Power Systems Conference (MEPCON)

作者： Abdelnaby, Nesma A. Yakout, Ahmed H. Hasanien, Hany M. Ain Shams Univ Elect Power & Machines Dept Fac Engn Cairo Egypt

ISBN: (纸本)9781728152899

This paper presents the grasshopper optimization algorithm (GOA) method as an instrument for fine-tuning a PID prime mover auxiliary governor to improve the transient frequency response of unintentional formed islands with excess generation. The results obtained are compared to the classical PID design (Ziegler-Nicholas). The study considers different levels of interrupted power starting from 10% to 90% of the studied island generation power. The case study considers a two-area four-machine system. Results are presented in the form of time domain simulation via MATLAB/SIMULINK.

关键词： Islanding PID controller auxiliary governor grasshopper optimization algorithm

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A Gain-improved Broadband Twin-Whip Antenna for High Frequency 5

A Gain-improved Broadband Twin-Whip Antenna for High Frequen...

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IEEE 5th International Conference on Computer and Communication Systems (ICCCS)

作者： Wu, Huaning Wang, Hengfeng Qin, Hua Xie, Xu Naval Univ Engn Coll Elect Engn Wuhan Peoples R China PLA Force 92145 Shanghai Peoples R China

ISBN: (纸本)9781728161365

In this paper, a new type of 10-meters single-loaded broadband twin-whip antenna is designed. The influence of antenna spacing on the performance of twin-whip antenna is analyzed by moment method. The loading position, the parameters of loading and broadband matching network of antenna is optimized and calculated by using algorithm. A 10:1 scaled model antenna has been fabricated, whose VSWR, gain, pattern and non-circularity are simulated and calculated. The measured VSWR is in agreement with the simulation results, which of all frequency points is less than 3 with an average value of 2.15. The maximum gain is greater than -3dB, the maximum is 5.29dB with an average of 3.39dB. The minimum non-circularity of gain is 0.03dB (at 30MHz), and the maximum is 2.74dB (at 230MHz). The efficiency is higher than 16.5%, the maximum was 68.7% with an average of 57.75%.

关键词： twin-whip antenna lumped loading broadband matching short wave grasshopper optimization algorithm

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Performance Improvement of Photovoltaic Emulator Using Lambert W Model and Fractional Order PI Controller 12

Performance Improvement of Photovoltaic Emulator Using Lambe...

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12th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC)

作者： Esfandiari, Sadegh Khaniki, Mohammad Ali Labbaf Montazeri, Seyed Hamid Milimonfared, Jafar Amirkabir Univ Technol Dept Elect Engn Tehran Polytech Tehran Iran KN Toosi Univ Technol Fac Elect Engn Tehran Iran

ISBN: (纸本)9781665403665

A photovoltaic (PV) emulator replicates the exact I-V and P-V output characteristics of a PV module or array. This replication should have occurred in such a way the same output characteristics in different ambient conditions, including different temperature and irradiation levels, mismatching, or partial shading, have been obtained. In this paper, a PV emulator based on a DC-DC converter with a novel control method has been presented. The proposed controller is a fractional PI one optimized with grasshopper optimization algorithm (GOA). It has one degree of freedom more than the conventional PI controller, which is the main advantage of this control approach. Also, a fractional-order lag compensator is used to decrease the output ripple of the converter. On the other hand, the Lambert W function models the PV characteristics which this modeling approach reduces computational burden and time. The simulation results in MATLAB/Simulink validate the performance of the proposed PV emulator.

关键词： DC-DC converter Fractional-order PI controller grasshopper optimization algorithm Lambert W PV emulator

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Intelligent optimization of process parameters to corn straw pretreatment 34

Intelligent optimization of process parameters to corn straw...

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34th Chinese Control and Decision Conference (CCDC)

作者： Guan, Yalin Chen, Juan Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

ISBN: (纸本)9781665478960

For the optimization of pretreatment process parameters of com straw, an intelligent optimization method is used, which combines the prediction model and the optimization algorithm. The support vector machine (SVM) prediction model for biogas production is constructed. And the optimal process parameters are obtained by grasshopper optimization algorithm (GOA) on the prediction model. Due to the difficulty of obtaining experimental data, the problem of small samples during the model training exists. So, the virtual samples are generated by uniform interpolation combined with GOA to improve the model prediction accuracy. Through the experiment of dual-frequency ultrasound combined with alkali pretreatment of corn stover fermentation, the effectiveness of virtual sample generation and the feasibility of intelligent process parameter search are verified.

关键词： Corn Straw optimization of Process Parameters Virtual Sample grasshopper optimization algorithm

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Hybrid GOA-SVR technique for short term load forecasting during periods with substantial weather changes in North-East India 8

Hybrid GOA-SVR technique for short term load forecasting dur...

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8th International Conference on Advances in Computing & Communications (ICACC)

作者： Barman, Mayur Choudhury, Nalin Behari Dev Natl Inst Technol Dept Elect Engn Silchar 788010 Assam India

The power system load demand is extremely prejudiced by thermal inertia caused by weather parameters. This influence even became stronger when there are vast and rapid changes in the weather parameters. Therefore it is necessary to consider the weather changes for a precise short term load forecasting (STLF). This work proposes a hybrid technique for conducting STLF during periods with substantial weather changes. The proposed technique is based on support vector regressing (SVR), grasshopper optimization algorithm (GOA) and a novel Euclidean norm (EN) based data processing unit. Here GOA is utilized for accessing the appropriate SVR parameters. The EN based data processing unit is applied for fixing the training data by assessing some alike days in the recent historicized days. The proposed GOA-SVR technique identifies the substantial changes in weather (temperature) and regulates the training data accordingly. This work is done in North-East India and the proposed technique is tested on the data of load dispatch center of Assam state. Three case studies are conducted and forecasting performances of the proposed technique are compared with the conventional technique of training the system with just recent historicized day's data. The proposed technique found outperformed the conventional technique in all cases. (C) 2018 The Authors. Published by Elsevier B. V.

关键词： Short term load forecasting Substantial weather changes Euclidean norm based data processing unit Support vector regression grasshopper optimization algorithm

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Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

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WORLD JOURNAL OF ENGINEERING 2022年第2期19卷 147-156页

作者： Kaur, Jasleen Rani, Punam Dahiya, Brahm Prakash CT Univ Dept Comp Applicat Sidhwan Khurd India BLM Girls Coll Nawanshahr India

Purpose This paper aim to find optimal cluster head and minimize energy wastage in WSNs. Wireless sensor networks (WSNs) have low power sensor nodes that quickly lose energy. Energy efficiency is most important factor in WSNs, as they incorporate limited sized batteries that would not be recharged or replaced. The energy possessed by the sensor nodes must be optimally used so as to increase the lifespan. The research is proposing hybrid artificial bee colony and glowworm swarm optimization [Hybrid artificial bee colony and glowworm swarm optimization (HABC-GSO)] algorithm to select the cluster heads. Previous research has considered fitness-based glowworm swarm with Fruitfly (FGF) algorithm, but existing research was limited to maximizing network lifetime and energy efficiency. Design/methodology/approach The proposed HABC-GSO algorithm selects global optima and improves convergence ratio. It also performs optimal cluster head selection by balancing between exploitation and exploration phases. The simulation is performed in MATLAB. Findings The HABC-GSO performance is evaluated with existing algorithms such as particle swarm optimization, GSO, Cuckoo Search, Group Search Ant Lion with Levy Flight, Fruitfly optimization algorithm and grasshopper optimization algorithm, a new FGF in the terms of alive nodes, normalized energy, cluster head distance and delay. Originality/value This research work is original.

关键词： Particle swarm optimization Glowworm swarm optimization Cuckoo search grasshopper optimization algorithm

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Harmonic Minimization using grasshopper Optimizer in Five-phase Multilevel Inverter

Harmonic Minimization using Grasshopper Optimizer in Five-ph...

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IEEE International Future Energy Electronics Conference (IFEEC)

作者： Chew, W. T. Yong, W., V Ong, S. L. Sea, Y. W. Ahmad, N. S. Leong, J. H. Univ Malaysia Perlis Fac Elect Engn Technol Perlis Malaysia

ISBN: (纸本)9781665434485

This paper presents the use of grasshopper optimization algorithm based selective harmonic minimization pulse-width modulation technique for optimizing the switching angles of five-phase nine-level multilevel inverter. The proposed algorithm is able to provide optimum switching angles in a wide range of modulation index. Besides, the switching angles obtained by the proposed algorithm can be used to synthesize an output voltage waveform with desired fundamental component and minimized undesired low order harmonics. MATLAB simulation results show the proposed algorithm can provide a wide modulation index range of optimum solutions. Furthermore, both MATLAB simulation and PSIM experimental results obtained from five-phase nine-level cascaded H-bridge multilevel inverter show good agreement, confirming the validity of switching angles calculated using the proposed algorithm.

关键词： multilevel inverter grasshopper optimization algorithm harmonic minimization

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Short-Term EVs Charging Load Prediction Under Low-quality Data Environment Based on GRU-GAN-GOA-MKELM Framework

Short-Term EVs Charging Load Prediction Under Low-quality Da...

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2024 International Seminar on Artificial Intelligence, Computer Technology and Control Engineering, ACTCE 2024

作者： Yang, Ye Wang, Wen Zhong, Yulu Jin, Chunhua Zhao, Chenfang Qiu, Baomin Jia, Wenjie State Grid Smart Internet of Vehicles Co. Ltd Beijing China Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798331521714

The precise forecasting of electric vehicles (EVs) load can provide reference for assessing the influence of EVs on the electricity network and optimizing the operation and allocation of the electricity network. Nevertheless, as the recorded dataset of the EV battery charging stations are difficult to collect, obtaining accurate prediction results with scarce EVs load data is challenging. On the one hand, the missing data exert great influence on forecasting values. On the other hand, a forecasting framework with superior precision should be trained with large amount of data. Aiming at realizing high accuracy EVs load prediction results utilizing scarce data, a data generation approach in terms of a generative adversarial network (GAN) is put forward. This method can generate high quality data from the scarce initial data to enhance prediction precision. Additionally, the prediction framework is established on the basis of the multi-kernel extreme learning machine (MKELM) combining poly and RBF kernel functions, parameters of which are optimally chosen via the grasshopper optimization algorithm (GOA). Through comparing with the five comparison models and three data generation methods, the validity and feasibility of the established EVs load prediction framework is verified. © 2024 IEEE.

关键词： Data generation EV load prediction Generative adversarial networks grasshopper optimization algorithm Multi-kernel extreme learning machine

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Optimal Harmonic Estimation in Renewable Integrated Power System Using grasshopper Optimisation algorithm and Recursive Least Square Approach

Optimal Harmonic Estimation in Renewable Integrated Power Sy...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Bhoi, Ashok Mallick, Ranjan Kumar Nayak, Pravati Mishra, Sairam Panda, Gayadhar Gantyat, Amaresh National Institute of Technology Electrical Engineering Meghalay Shillong India Siksha 'O' Anushandhan deemed to be University Bhubaneswar India Siksha 'O' Anushandhan deemed to be University Bhubaneswar India

ISBN: (纸本)9798350350821

Harmonic estimation in renewable integrated power systems is crucial for maintaining power quality and ensuring the reliability of the grid. This paper presents an innovative approach for optimal harmonic estimation by combining the grasshopper optimization algorithm (GOA) with the Recursive Least Square (RLS) method. RLS is used for estimation of amplitude of harmonics, whereas phase is estimated by GOA. The performance of GOA-RLS is investigated in detail for estimation of power system signals using test signals buried with noise. The integration of GOA, a nature-inspired optimization technique, with RLS, a statistical method, provides a robust framework for accurately identifying harmonics in power systems. The proposed method is tested on a simulated renewable integrated power system, demonstrating superior performance in terms of accuracy, convergence speed. The results highlight the potential of this hybrid approach in enhancing the operational efficiency and stability of modern power grids with high penetration of renewable energy sources. © 2024 IEEE.

关键词： grasshopper optimization algorithm Harmonic Estimation Recursive Least square approach

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