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Hybrid Flow Shop with Setup Times Scheduling Problem

computer Systems science & engineering 2023年第1期44卷 563-577页

作者： Mahdi Jemmali Lotfi Hidri Department of Computer Science and Information College of Science at ZulfiMajmaah UniversityMajmaah11952Saudi Arabia Mars Laboratory University of SousseSousseTunisia Department of Computer Science Higher Institute of Computer Science and Mathematics of MonastirMonastir UniversityMonastir5000Tunisia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The two-stage hybridflow shop problem under setup times is addressed in this *** problem is *** the other hand,the studied problem is modeling different real-life applications especially in manufacturing and high *** this kind of problem requires the development of adapted *** this context,a metaheuristic using the genetic algorithm and three heuristics are proposed in this *** approximate solutions are using the optimal solution of the parallel machines under release and delivery ***,these solutions are iterative procedures focusing each time on a particular stage where a parallel machines problem is called to be *** general solution is then a concatenation of all the solutions in each *** addition,three lower bounds based on the relaxation method are *** lower bounds present a means to evaluate the efficiency of the developed algorithms throughout the measurement of the relative *** experimental result is discussed to evaluate the performance of the developed *** total,8960 instances are implemented and tested to show the results given by the proposed lower bounds and *** indicators are given to compare between *** results illustrated in this paper show the performance of the developed algorithms in terms of gap and running time.

关键词： Hybridﬂow shop genetic algorithm setup times heuristics lower bound

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BengaliHateCB: A Hybrid Deep Learning Model to Identify Bengali Hate Speech Detection from Online Platform 6

BengaliHateCB: A Hybrid Deep Learning Model to Identify Beng...

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6th International Conference on Electrical engineering and information and Communication technology, ICEEICT 2024

作者： Saha, Sagor Kumar Mim, Afrina Akter Akter, Sanzida Hosen, Md. Mehraz Shihab, Arman Habib Mehedi, Md Humaion Kabir Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka Bangladesh Brac University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

Online issues including hate speech, abusive communications, and harassment have been exacerbated by the rising number of Internet users. People in Bangladesh often face online harassment and threats expressed in Bengali on various social media platforms. Also, there has not been nearly enough investigation into the possibility of Offensive language in Bengali literature. Although finding realistic ways to reduce hate speech in Bengali texts is urgently needed, there is a notable lack of study in the area of Bengali abusive speech detection, despite the widespread detrimental impacts of abusive text on people's well-being. The results of this research provide a method for spotting bad hateful comments in Bengali online profiles. This research provides a methodology to identify potentially manipulative hate speech in Bengali social media postings. The BERT architecture is used to gather characteristics of Bengali texts. The next step in hate speech classification is to use a Convolutional Neural Network (CNN) model including a softMax activation function. We propose a new model, BERT-CNN, that combines both models. On the Bengali Hate Speech from Social Platforms (BD-SHS) dataset, the BERT-CNN model outperformed most baseline architectures, with accuracy, precision, recall, and F1-scores of 95.67%, 93.55%,92.67%, and 94.44%, respectively. According to our research, the method we suggested for spotting hate speech in Bengali writings posted on social networking sites works well, which can lessen online hate comments and foster a more civilized online community. © 2024 IEEE.

关键词： Speech recognition

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Android Malware Detection Using Machine Learning and Neural Network: A Hybrid Approach with Federated Learning 7

Android Malware Detection Using Machine Learning and Neural ...

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7th International Conference on Advanced Communication Technologies and Networking, CommNet 2024

作者： Purkayastha, Bishal Shyam Rahman, Md. Musfiqur Shahpasand, Maryam University of Staffordshire Department of Computer Science London United Kingdom Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798350367027

Android is the most popular mobile operating system, which makes it a major target for malware attacks. This paper focuses on the growing problem of malware targeting Android devices and presents a new approach for detecting such threats. The proposed method combines machine learning, neural network and Federated Learning to improve malware detection. A majority voting technique is used to select the most important features from the dataset, which helps identify harmful behaviors more effectively. Various machine learning algorithms, including Random Forest and XGBoost, and neural network architectures, such as CNN-LSTM, are evaluated on an Android malware dataset. Federated Learning ensures data privacy during training, addressing key cybersecurity concerns. The study emphasizes the importance of advanced techniques for adaptive malware detection and provides a detailed analysis of performance across multiple models. Results indicate high accuracy, underscoring the efficacy of the proposed methods in combating Android-specific malware. © 2024 IEEE.

关键词： Adversarial machine learning

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The Multi-Way Path Over the Blockchain: The Development of the Security Algorithm RB-24-1 6

The Multi-Way Path Over the Blockchain: The Development of t...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Rajaprakash, S. Bagath Basha, C. Rathish Babu, T.K.S. Karthik, S. Subapriya, V. Kavitha, S. Department of Computer Science & Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science & Engineering Kommuri Pratap Reddy Institute of Technology Telagana Hyderabad India Department of Computer Science & Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai India Department of Computer Science & Engineering Aarupadai Veedu Institute of Technology Vinayaka Missions Research Foundation Tamil Nadu Chennai India Department of Computer Science & Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

In the current world, a lot of new technologies are becoming famous. The Block Chain is one of these technologies. A very high amount of safety is offered by the technology in question;it is extremely robust. Users are not privy to a great deal of information regarding Block Chain;however, Protecting the data that is sent in multiple ways is what its security features are for. The "SalSa and RBJ25 algorithms" were still being used by this person. We are going to talk about the new "security mechanism" we have named RB-24-1. This method is comprised of these four different techniques. The procedure begins with the "Latin Alphabet code" being assigned to T. This is the first step in the process. To proceed to the next step, use Equation (1) to perform four times the multiplication of the number by itself. "Exchanging the values of the cells" is what the third step does., and it is carried out with the help of encrypted data. Putting the "SalSa" algorithm into action within the matrix constitutes the fourth step throughout the process. Finally, the plain text is converted into the encrypted text format. This has been a long time coming. There is a higher level of security offered by the method that has been suggested in compare and contrast with the method that is being used at the moment. © 2025 IEEE.

关键词： Encryption algorithms

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Critical Analysis of Life Span Improvement Techniques in Energy Constraints Edge IoT Devices

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SN computer science 2023年第3期4卷 221页

作者： Padmini, M.S. Kuzhalvaimozhi, S. Department of Computer Science and Engineering The National Institute of Engineering Mysuru India Department of Information Science and Engineering The National Institute of Engineering Mysuru India

IoT edge computing facilitates data to be processed at a location closer to the place where it is generated. Placing computing closer allows for faster and more reliable service to the users. It also benefits latency-sensitive applications. These benefits are overshadowed by the risk of energy drain in edge IoT devices. Many works have been proposed to improve the life span of edge IoT devices. These works target either computation or communication overhead reduction to minimize energy consumption. This survey does a critical analysis of existing solutions and presents the research gap. In addition to the identification of research gaps, the survey also presents possible solutions at a higher level to address these gaps. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Communication strategies Computation techniques Energy Internet of things (IOT) Optimization

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Federated Learning With Massive Random Access

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IEEE Transactions on Wireless Communications 2024年第10期23卷 13856-13871页

作者： Xia, Shuhao Shi, Yuanming Zhou, Yong Wu, Youlong Yang, Lin F. Letaief, Khaled B. The School of Information Science and Technology ShanghaiTech University Shanghai201210 China The Department of Electrical and Computer Engineering University of California at Los Angeles Los AngelesCA90095 United States Hong Kong

In this paper, we propose an online federated learning framework with massive random access, aiming to learn a sequence of global models using local data that are sequentially collected by massive edge devices. As only a subset of devices is capable of collecting data and performing local model update at any specific moment, the communication pattern between the edge server and devices is random and sporadic, which is referred to as sporadic local updates. This motivates us to adopt a two-phase grant-free random access scheme that consists of the activity detection and model transmission phases to facilitate efficient communication between the edge server and devices. We first provide the regret analysis for online federated learning, and derive the optimality gap in terms of successful transmission probabilities. Then, we characterize the achievable transmission rate of each active device using random matrix theory and establish the relationship between the pilot length and the outage probability. Furthermore, we propose an optimal pilot length design by minimizing the optimality gap. To validate our scheme, we provide comprehensive experimental results that demonstrate the superiority of the proposed scheme over traditional schemes in various online tasks. © 2002-2012 IEEE.

关键词： Wireless sensor networks

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DeepSurNet-NSGA II:Deep Surrogate Model-Assisted Multi-Objective Evolutionary Algorithm for Enhancing Leg Linkage in Walking Robots

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computers, Materials & Continua 2024年第10期81卷 229-249页

作者： Sayat Ibrayev Batyrkhan Omarov Arman Ibrayeva Zeinel Momynkulov Joldasbekov Institute of Mechanics and Engineering Almaty050000Kazakhstan Department of Mathematical and Computer Modeling International Information Technology UniversityAlmaty050000Kazakhstan Department of Information Systems Al-Farabi Kazakh National UniversityAlmaty050000Kazakhstan

This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II(Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II)for solving complex multiobjective optimization problems,with a particular focus on robotic leg-linkage *** study introduces an innovative approach that integrates deep learning-based surrogate models with the robust Non-dominated Sorting Genetic Algorithm II,aiming to enhance the efficiency and precision of the optimization *** a series of empirical experiments and algorithmic analyses,the paper demonstrates a high degree of correlation between solutions generated by the DeepSurNet-NSGA II and those obtained from direct experimental methods,underscoring the algorithm’s capability to accurately approximate the Pareto-optimal frontier while significantly reducing computational *** methodology encompasses a detailed exploration of the algorithm’s configuration,the experimental setup,and the criteria for performance evaluation,ensuring the reproducibility of results and facilitating future advancements in the *** findings of this study not only confirm the practical applicability and theoretical soundness of the DeepSurNet-NSGA II in navigating the intricacies of multi-objective optimization but also highlight its potential as a transformative tool in engineering and design *** bridging the gap between complex optimization challenges and achievable solutions,this research contributes valuable insights into the optimization domain,offering a promising direction for future inquiries and technological innovations.

关键词： Multi-objective optimization genetic algorithm surrogate model deep learning walking robots

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OIDS: A Smart Framework for Intrusion Detection in 5G WSN's by using Ontology

OIDS: A Smart Framework for Intrusion Detection in 5G WSN's ...

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2023 International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2023

作者： Bisht, Sover Singh Rajput, Anuj Singh Mandal, Sanjeev Kumar Noida Institute of Engineering and Technology Department of Data Science Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350344387

The increasing depth and applications of 5G wireless sensor networks also raise the possibility of network intrusions. In this research, a network intrusion detection system based on the ontology notion is proposed. As it creates ontologies and maintains the relationships among sensor nodes in a network, it is referred upon as a smart system. This proposed system finding is able to secure data in 5G networks. The current detection technique's parameters such as patrol nodes, live nodes, hop, sensor nodes, and energy (in joules) are used to gauge how well the suggested system performs. Attack estimation rate (AER) and precision are the types of metrics in use. © 2023 IEEE.

关键词： Intrusion detection system Ontology patrol nodes and wireless networks Sensor nodes

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Implementing Robust Color and Multispectral Image Recognition with Deep Learning

Implementing Robust Color and Multispectral Image Recognitio...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Pawar, Rahul Kumar, Prafull Anand, Kul Bhushan Department of Computer Science and Information Technology Bangalore India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

this technical abstract describes the implementation of strong shade and multispectral image popularity using a deep knowledge of techniques. Via know-how, the standards, and strategies of deep getting to know, the method affords solutions to the problems of function extraction and multiclass type for green evaluation of imagery information. Particularly, the paper introduces an approach of deep studying for image recognition that combines shade and multispectral imaging. It additionally discusses the advantages of this method, which include advanced type accuracy and robustness in opposition to noise in comparison to standard procedures. The paper then outlines the architecture of the proposed model, which incorporates convolutional neural networks (CNNs), and analyzes the overall performance of the model at the Indian Pines photo database. In the end, the paper concludes with a dialogue on the implications of the proposed model and gives directions for future research. © 2024 IEEE.

关键词： Accuracy Implementation Incorporates Multispectral Research Shade Technical

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Cyber Security Intrusion Detection Using Deep Learning Approaches, Datasets, Bot-IOT Dataset 4

Cyber Security Intrusion Detection Using Deep Learning Appro...

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4th International Conference on Advancements in Computational sciences, ICACS 2023

作者： Manan, Iram Rehman, Faisal Sharif, Hanan Ali, Chaudhry Nouman Ali, Rana Rashid Liaqat, Amiad Khawaja Fareed University of Engineering and Information Technology Department of Information Technology Rahim Yar Khan Pakistan Lahore Leads University Department of Computer Science & Information Technology Lahore Pakistan

ISBN: (纸本)9781665463775

Cyber Security is a crucial point of the current world;it is used to analyze, defend, and detect network intrusion systems. An intrusion detection system has been designed using Deep learning techniques, which helps the network user to detect malicious intentions. The dataset plays a crucial part in intrusion detection. As a result, we describe various well-known cyber datasets. Mainly we have analysed the IoT traffic-based dataset with some other datasets. We have also analyzed deep learning DL models, including Feed forward deep neural network (FDNN), deep auto-encoder, De-noising auto-encoder, deep migration, stacked de-noising auto-encoders, Replicator Neural networks, and Self-Taught Learning. We observe the effectiveness of models individually in two different types (multiclass and binary) through real-world traffic datasets, such as Bot-IoT dataset. Moreover, we evaluate the effectiveness of various methods based on the most critical key performance indicators, namely correctness, rate of false alarms and detection rate. © 2023 IEEE.

关键词： Intrusion detection

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