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Enhancing Arabic Cyberbullying Detection with End-to-End Transformer Model

computer Modeling in engineering & sciences 2024年第11期141卷 1651-1671页

作者： Mohamed A.Mahdi Suliman Mohamed Fati Mohamed A.G.Hazber Shahanawaj Ahamad Sawsan A.Saad Information and Computer Science Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Information Systems Department College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Software Engineering Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Computer Engineering Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia

Cyberbullying,a critical concern for digital safety,necessitates effective linguistic analysis tools that can navigate the complexities of language use in online *** tackle this challenge,our study introduces a new approach employing Bidirectional Encoder Representations from the Transformers(BERT)base model(cased),originally pretrained in *** model is uniquely adapted to recognize the intricate nuances of Arabic online communication,a key aspect often overlooked in conventional cyberbullying detection *** model is an end-to-end solution that has been fine-tuned on a diverse dataset of Arabic social media(SM)tweets showing a notable increase in detection accuracy and sensitivity compared to existing *** results on a diverse Arabic dataset collected from the‘X platform’demonstrate a notable increase in detection accuracy and sensitivity compared to existing methods.E-BERT shows a substantial improvement in performance,evidenced by an accuracy of 98.45%,precision of 99.17%,recall of 99.10%,and an F1 score of 99.14%.The proposed E-BERT not only addresses a critical gap in cyberbullying detection in Arabic online forums but also sets a precedent for applying cross-lingual pretrained models in regional language applications,offering a scalable and effective framework for enhancing online safety across Arabic-speaking communities.

关键词： Cyberbullying offensive detection Bidirectional Encoder Representations from the Transformers(BERT) continuous bag of words Social Media natural language processing

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Enhancing Predictive Capabilities for Cyber Physical Systems Through Supervised Learning

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第16期49卷 77-86页

作者： Dhanalakshmi, B. Tamije Selvy, P. Department of Computer Science and Engineering Dr.N.G. P Institute of technology India Department of Computer Science and Engineering Hindusthan College of Engineering and Technology India

The rapid advancement and proliferation of Cyber-Physical Systems (CPS) have led to an exponential increase in the volume of data generated continuously. Efficient classification of this streaming data is crucial for predicting system behaviors and enabling proactive decision-making. This research aims to extract actionable knowledge from the continuous data streams of CPS and predict their behavior using advanced supervised learning algorithms. The predictions facilitate timely interventions and necessary actions within the interconnected physical network. The background of this work lies in the intersection of CPS, machine learning, and data stream mining. Traditional batch processing methods are inadequate for real-time analysis of CPS data due to their inherent latency and computational inefficiency. This research employs state-of-the-art techniques for real-time data processing, including incremental learning, sliding window models, and ensemble methods tailored for streaming data. Our approach differs from existing works by focusing on a comprehensive framework that integrates real-time data ingestion, preprocessing, feature extraction, and model updating in a seamless pipeline. Unlike previous studies that often rely on static datasets and offline analysis, our method ensures continuous learning and adaptation to evolving data patterns. Comparative analysis with existing techniques demonstrates superior performance in terms of accuracy, latency, and scalability. Specifically, our models achieved an average classification accuracy of 92%, with a precision of 90%, recall of 89%, and an F1 score of 89.5%. These metrics indicate significant improvements over traditional batch processing methods, which typically lag in responsiveness and adaptability. This research provides a robust and efficient solution for the real-time classification of streaming data from CPS, enhancing the system's ability to predict behaviors and take necessary actions promptly. © 2025 Slov

关键词： Self-supervised learning

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Bengali reduplication generation with finite-state transducers (FSTs)

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International Journal of Speech Technology 2024年第3期27卷 729-737页

作者： Barman, Abhijit Saha, Diganta Pal, Alok Ranjan Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Department of Computer Science and Engineering College of Engineering and Management Kolaghat721171 India

Reduplication is a highly productive process in Bengali word formation, with significant implications for various natural language processing (NLP) applications, such as parts-of-speech tagging and sentiment analysis. Despite its importance, this area has not been extensively explored in computational linguistics, especially for low-resource languages like Bengali. This study first demonstrates that a two-way finite-state transducer (FST) can effectively capture complete reduplication generation processes in Bengali. Second, it is shown that the formation of partial reduplication requires a set of 2-way FSTs due to the diverse patterns involved in Bengali partial reduplications. Third, the research highlights the utility of the reduplication generation process in identifying Bengali reduplication instances, achieving a commendable F1-Score of 88.11%. This method outperforms current state-of-the-art methods for identifying reduplicated expressions in Bengali text. This research contributes valuable insights into the computational representation of reduplication in Bengali, offering potential enhancements for NLP tasks in low-resource language scenarios. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Computational linguistics

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Blockchain Enabled Metaheuristic Cluster Based Routing Model for Wireless Networks

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computer Systems science & engineering 2023年第2期44卷 1233-1250页

作者： R.M.Bhavadharini S.Karthik Department of Computer Science and Engineering Easwari Engineering CollegeChennai600089India Department of Computer Science and Engineering SNS College of TechnologyCoimbatore641035India

With recent advancements made in wireless communication techniques,wireless sensors have become an essential component in both data collection as well as tracking *** Sensor Network(WSN)is an integral part of Internet of Things(IoT)and it encounters different kinds of security *** is designed as a game changer for highly secure and effective digital ***,the current research paper focuses on the design of Metaheuristic-based Clustering with Routing Protocol for Blockchain-enabled WSN abbreviated as *** proposed MCRP-BWSN technique aims at deriving a shared memory scheme using blockchain technology and determine the optimal paths to reach the destination in clustered *** MCRP-BWSN technique,Chimp Optimization Algorithm(COA)-based clustering technique is designed to elect a proper set of Cluster Heads(CHs)and organize the selected *** addition,Horse Optimization Algorithm(HOA)-based routing technique is also presented to optimally select the routes based onﬁtness ***,HOA-based routing technique utilizes blockchain technology to avail the shared mem-ory among nodes in the *** nodes are treated as coins whereas the ownership handles the sensor nodes and Base Station(BS).In order to validate the enhanced performance of the proposed MCRP-BWSN technique,a wide range of simulations was conducted and the results were examined under different *** on the performance exhibited in simulation outcomes,the pro-posed MCRP-BWSN technique has been established as a promising candidate over other existing techniques.

关键词： Wireless networks blockchain routing protocol clustering security metaheuristics

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Robust Transmission Design for Federated Learning Through Over-the-Air Computation

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China Communications 2025年第3期22卷 65-75页

作者： Hamideh Zamanpour Abyaneh Saba Asaad Amir Masoud Rabiei School of Electrical and Computer Engineering College of EngineeringUniversity of TehranTehranIran Department of Electrical Engineering and Computer Science York UniversityCanada

Over-the-air computation(AirComp)enables federated learning(FL)to rapidly aggregate local models at the central server using waveform superposition property of wireless *** this paper,a robust transmission scheme for an AirCompbased FL system with imperfect channel state information(CSI)is *** model CSI uncertainty,an expectation-based error model is *** main objective is to maximize the number of selected devices that meet mean-squared error(MSE)requirements for model broadcast and model *** problem is formulated as a combinatorial optimization problem and is solved in two ***,the priority order of devices is determined by a sparsity-inducing ***,a feasibility detection scheme is used to select the maximum number of devices to guarantee that the MSE requirements are *** alternating optimization(AO)scheme is used to transform the resulting nonconvex problem into two convex *** results illustrate the effectiveness and robustness of the proposed scheme.

关键词： federated learning imperfect CSI optimization over-the-air computing robust design

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Bifurcation analysis and control study of improved full-speed differential model in connected vehicle environment

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Chinese Physics B 2024年第7期33卷 245-266页

作者：艾文欢雷正清李丹洋方栋梁刘大为 College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China College of Electrical Engineering Lanzhou Institute of TechnologyLanzhou 730050China

In recent years, the traffic congestion problem has become more and more serious, and the research on traffic system control has become a new hot spot. Studying the bifurcation characteristics of traffic flow systems and designing control schemes for unstable pivots can alleviate the traffic congestion problem from a new perspective. In this work, the full-speed differential model considering the vehicle network environment is improved in order to adjust the traffic flow from the perspective of bifurcation control, the existence conditions of Hopf bifurcation and saddle-node bifurcation in the model are proved theoretically, and the stability mutation point for the stability of the transportation system is found. For the unstable bifurcation point, a nonlinear system feedback controller is designed by using Chebyshev polynomial approximation and stochastic feedback control method. The advancement, postponement, and elimination of Hopf bifurcation are achieved without changing the system equilibrium point, and the mutation behavior of the transportation system is controlled so as to alleviate the traffic congestion. The changes in the stability of complex traffic systems are explained through the bifurcation analysis, which can better capture the characteristics of the traffic flow. By adjusting the control parameters in the feedback controllers, the influence of the boundary conditions on the stability of the traffic system is adequately described, and the effects of the unstable focuses and saddle points on the system are suppressed to slow down the traffic flow. In addition, the unstable bifurcation points can be eliminated and the Hopf bifurcation can be controlled to advance, delay, and disappear,so as to realize the control of the stability behavior of the traffic system, which can help to alleviate the traffic congestion and describe the actual traffic phenomena as well.

关键词： bifurcation analysis vehicle queuing bifurcation control Hopf bifurcation

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Online Sales Prediction in E-Commerce Market Using Machine Learning 4

Online Sales Prediction in E-Commerce Market Using Machine L...

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4th International Conference on Signal Processing and Communication, ICSPC 2023

作者： Ajaykrishna, S. Suganya, T.S. Rao, Beema Pughazendi, N. Computer Science and Engineering Panimalar Engineering College India Srm Institute of Science and Technology Ramapuram Campus India

ISBN: (纸本)9798350300772

Product sales anticipation assumes a key part in upgrading idealness of item conveyance in E-Commerce. Among numerous heterogeneous provisions pertinent to sales estimating, advancement crusades held in E-Commerce and contending connection amid substitutable items would significantly muddle the substance. Unlikely, these variables are typically neglected in the current research;since the regular time-series investigation-based procedures principally believe the business records unaided. In this article, we make use of the previous data in online trade market to build up the framework to anticipate sales. As stated by the qualities of diverse data, 3-sorts of expectation techniques are: Incentive-Auto-Regressive-Integrated-Moving-Average (I-ARIMA), Long-Short-Term-Memory (LSTM) and Artificial-Neural-Network (ANN). These 3-techniques can handle the concern with diverse exactness necessities and various data types. Broad investigations are led more than two genuine datasets in diverse areas from E-Commerce. Outcomes reveal that the LSTM gets munificent implementation attain over others and modern intense learning choices as far as anticipating precision. This research considers the benefits and inconveniences of the three kinds of strategies on diverse informational repositories and gives considerable rules to traders on their advertising methodologies. © 2023 IEEE.

关键词： Long short-term memory

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Smart contract and blockchain-based secured approach for storing and sharing electronic health records

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Multimedia Tools and Applications 2025年第16期84卷 16883-16907页

作者： Dutta, Jhuma Barman, Subhas Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal Jalpaiguri735102 India

Electronic Health Records (EHRs) are important data for doctors, patients, and researchers in medical science. EHRs have great medical value in the healthcare industry to improve healthcare services. Therefore, digital health records should be collected, stored, and maintained securely. Smart contract-based blockchain technology provides the most protective way of storing and sharing data. Stored records in blockchain are immutable and are corruption-free results accurate analysis. In this paper, a model is proposed for capturing, storing, and protecting health records in an e-healthcare system through blockchain technology. Transactions are executed in the Ethereum platform through smart contracts written in the Solidity programming language. Health records are stored in a decentralized, distributed digital ledger. The Interplanetary File System (IPFS) is incorporated with the proposed system to store large medical reports in the cloud storage. Transaction deployment cost and information retrieval cost have been calculated. The average transaction costs for doctor and patient registration are 0.00394 ETH and 0.00438 ETH respectively. The performance of the proposed model has been compared with the existing health record systems and shows better performance in terms of cost. Blockchain technology provides better security during health records sharing between patients and medical communities. The model provides smart contract and blockchain-based solutions to ensure immutability, transparency, and traceability of health data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Smart contract

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Performance Evaluation of a Multi-input Interleaved Boost Converter with a Tuned Proportional-integral Controller

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Chinese Journal of Electrical engineering 2024年第4期10卷 119-128页

作者： Sucharita Pal Dola Sinha Mou Das Mahapatra Saibal Majumder Sovan Bhattacharya Chan Byopadhyay Department of Electrical Engineering Asansol Engineering CollegeAsansol 713305India Department of Electrical Engineering Dr.B.C.Roy Engineering CollegeDurgapur 713206India Department of Computer Science and Engineering(Data Science) Dr.B.C.Roy Engineering CollegeDurgapur 713206India Department of Computer Science and Engineering University of BremenBremen 28359Germany

The need for renewable energy access has led to the use of variable input converter approaches because renewable energy sources often generate electricity in an unpredictable manner. A high-performance multi-input boost converter is developed to provide the necessary output voltage and power while accommodating variations in input sources. This converter is specifically designed for the efficient usage of renewable energy. The proposed architecture integrates three separate unidirectional input power sources: photovoltaics, fuel cells, and storage system batteries. The architecture has five switches, and the implementation of each switch in the converter is achieved by applying the calculated duty ratios in various operating states. The closed-loop response of the converter with a proportional-integral (PI) controller-based switching system is examined by analyzing the Matlab-Simulink model utilizing a proportional-integral derivative (PID) tuner. The controller can deliver the desired output voltage of 400 V and an average power of 2 kW while exhibiting low switching transient effects. Therefore, the proposed multi-input interleaved boost converter demonstrates robust results for real-time applications by effectively harnessing renewable power sources.

关键词： Multi-input converter interleaved boost converter pulse-width modulation proportional-integral controller

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Text-driven clothed human image synthesis with 3D human model estimation for assistance in shopping

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Multimedia Tools and Applications 2025年第1期84卷 167-200页

作者： Karkuzhali, S. Aasim, A. Syed StalinRaj, A. Department of Computer Science and Engineering Mepco Schlenk Engineering College Tamil Nadu Sivakasi India

Online shopping has become an integral part of modern consumer culture. Yet, it is plagued by challenges in visualizing clothing items based on textual descriptions and estimating their fit on individual body types. In this work, we present an innovative solution to address these challenges through text-driven clothed human image synthesis with 3D human model estimation, leveraging the power of Vector Quantized Variational AutoEncoder (VQ-VAE). Creating diverse and high-quality human images is a crucial yet difficult undertaking in vision and graphics. With the wide variety of clothing designs and textures, existing generative models are often not sufficient for the end user. In this proposed work, we introduce a solution that is provided by various datasets passed through several models so the optimized solution can be provided along with high-quality images with a range of postures. We use two distinct procedures to create full-body 2D human photographs starting from a predetermined human posture. 1) The provided human pose is first converted to a human parsing map with some sentences that describe the shapes of clothing. 2) The model developed is then given further information about the textures of clothing as an input to produce the final human image. The model is split into two different sections the first one being a codebook at a coarse level that deals with overall results and a fine-level codebook that deals with minute detailing. As mentioned previously at fine level concentrates on the minutiae of textures, whereas the codebook at the coarse level covers the depictions of textures in structures. The decoder trained together with hierarchical codebooks converts the anticipated indices at various levels to human images. The created image can be dependent on the fine-grained text input thanks to the utilization of a blend of experts. The quality of clothing textures is refined by the forecast for finer-level indexes. Implementing these strategies can result

关键词： Vector quantization

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