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Improved Dynamic Johnson Sequencing Algorithm(DJS)in Cloud Computing Environment for Efficient Resource Scheduling for Distributed Overloading

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Journal of Systems science and Systems engineering 2024年第4期33卷 391-424页

作者： Anurag Sinha Pallab Banerjee Sharmistha Roy Nitasha Rathore Narendra Pratap Singh Mueen Uddin Maha Abdelhaq Raed Alsaqour Department of Computer Science Engineering ICFAI Tech SchoolICFAI UniversityJharkhandRanchi834004India Department of Computer Science Engineering Amity UniversityJharkhandRanchi835303India Department of Computing and Information Technology Usha Martin UniversityRanchi835103India Department of Information Technology Bharti Vidyapeeth’s College of EngineeringPaschim ViharNew DelhiIndia Department of CSE Delhi UniversityNew DelhiIndia College of Computing and Information Technology University of Doha for Science and TechnologyDoha24449Qatar Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdurrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic University93499RiyadhSaudi Arabia

This study introduces an innovative approach to optimize cloud computing job distribution using the Improved Dynamic Johnson Sequencing Algorithm(DJS).Emphasizing on-demand resource sharing,typical to Cloud Service Providers(CSPs),the research focuses on minimizing job completion delays through efficient task *** Johnson’s rule from operations research,the study addresses the challenge of resource availability post-task *** advocates for queuing models with multiple servers and finite capacity to improve job scheduling models,subsequently reducing wait times and queue *** Dynamic Johnson Sequencing Algorithm and the M/M/c/K queuing model are applied to optimize task sequences,showcasing their efficacy through comparative *** research evaluates the impact of makespan calculation on data file transfer times and assesses vital performance indicators,ultimately positioning the proposed technique as superior to existing approaches,offering a robust framework for enhanced task scheduling and resource allocation in cloud computing.

关键词： First come first served(FCFS)scheduling round robin task scheduling queuing model Johnson scheduling algorithm

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Criminal behavior detection using the LSTM model and tweet dataset 1

Criminal behavior detection using the LSTM model and tweet d...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Gaikwad, Mayuri Diliprao Dutonde, Abhimanyu Tulsiramji Gaikwad-Patil College of Engineering & Technology Departement of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350360707

The science of digital forensics is crucial in detecting and pursuing criminals since criminal behaviour depends more and more on digital technology. A model for behaviour detection is provided in this paper based on research into the most recent work in the field. when measured against models used in cutting-edge research. This study will help authorities working to prevent cybercrime and cybersecurity identify profiles with specific behaviours. The research focuses on identifying illegal behaviour from social media by evaluating the leading-edge work and suggesting an improved methodology. To get a better-trained model for this study, first collect the "Tweets Dataset for Detection of Cyber-Trolls"dataset from Kaggle. After that perform the preprocessing data for word cloud. the implementation work is complete on the python programming language and jupyer notebook. The results showed that the proposed LSTM model, achieved high accuracy. The suggested model outperformed with a 1.00% accuracy rate. This study will be very helpful to organization that deal with cybercrime and cyber security in identifying and eliminating profiles that exhibit specific behaviours. © 2024 IEEE.

关键词： Tweets

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Analysis of Encryption Techniques for Secure Data in Cloud Environment 1

Analysis of Encryption Techniques for Secure Data in Cloud E...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Kumar, M. Ravi Jeya, I. Jasmine Selvakumari Athira, S. Hindusthan College of Engineering and Technology Department of Computer Science And Engineering Coimbatore India Vit Bhopal University School of Computing Science and Engineering Sehore India Hindusthan College of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798331529246

Over the years, Cloud computing is becoming increasingly popular due to the continually changing technology. The primary goal of the cloud computing network is to offer consumers pay-per-use usage of on-demand processing and storage resources, enabling low-cost, efficient infrastructure utilization for smaller businesses. However, because of the feature of cloud resource sharing, data security and privacy create issues, and prevent smart systems from continuously transferring produced data to the cloud computing resources. Many encryption methods were presented to address data security concerns. This article presents the processes of several data protection and encryption systems currently in use and critically analyzes them based on common factors. The purpose of this survey is to compile data encryption methods now in use, often discussed in the survey of smart systems for data security when applied to the cloud platforms. © 2025 IEEE.

关键词： Cloud security

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Deep Reinforcement Learning-Based Strategies for Truck Platooning at Highway on-Ramps

Deep Reinforcement Learning-Based Strategies for Truck Plato...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, An Qi, Liang Luan, Wenjing Liu, Kun Guo, Xiwang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer and Communication Engineering Liaoning Petrochemical University Fushun113001 China

ISBN: (纸本)9781665410205

The development of Connected and Automated Trucks (CATs) provides a new opportunity for freight industry to enhance fuel efficiency, increase traffic flow, and improve safety through platooning. Particularly at highway on-ramps, how to effectively form CAT platoons is a key research topic. In the process of CAT platooning, the timing, location, and speed of CAT merging significantly impact safety and energy consumption. Thus, this study proposes a hierarchical merging strategy, aimed at achieving effective autonomous CAT platooning at highway on-ramps by considering the interference of human-driven vehicles (HDVs). Specifically, we employ a model-free deep reinforcement learning method that guides CAT merging process by exploring optimal driving behaviors. It ensures the safety and efficiency of the CAT merging process. In addition, we use the real vehicle dynamics model in simulation. The proposed strategy can handle the variation of the CATs' initial positions and speeds at on-ramps, as well as interference caused by HDVs at highway mainline. The effectiveness of the proposed strategy has been validated through simulations. The results show that the proposed strategy can effectively coordinate CAT platooning at highway on-ramps. © 2024 IEEE.

关键词： Deep reinforcement learning

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Evolutionary Multi-Tasking Optimization for High-Efficiency Time Series Data Clustering

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Tsinghua science and technology 2024年第2期29卷 343-355页

作者： Rui Wang Wenhua Li Kaili Shen Tao Zhang Xiangke Liao Xiangjiang Laboratory Changsha 410205China College of Systems Engineering National University of Defense TechnologyChangsha 410073China Ant Group Co. Ltd.Hangzhou 310000China College of Computer Science and Technology NUDTChangsha 410073China

Time series clustering is a challenging problem due to the large-volume,high-dimensional,and warping characteristics of time series *** clustering methods often use a single criterion or distance measure,which may not capture all the features of the *** paper proposes a novel method for time series clustering based on evolutionary multi-tasking optimization,termed i-MFEA,which uses an improved multifactorial evolutionary algorithm to optimize multiple clustering tasks simultaneously,each with a different validity index or distance ***,i-MFEA can produce diverse and robust clustering solutions that satisfy various preferences of *** on two artificial datasets show that i-MFEA outperforms single-objective evolutionary algorithms and traditional clustering methods in terms of convergence speed and clustering *** paper also discusses how i-MFEA can address two long-standing issues in time series clustering:the choice of appropriate similarity measure and the number of clusters.

关键词： time series clustering evolutionary multi-tasking multifactorial optimization clustering validity index distance measure

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The Impact of Domain Name Server(DNS)over Hypertext Transfer Protocol Secure(HTTPS)on Cyber Security:Limitations,Challenges,and Detection Techniques

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computers, Materials & Continua 2024年第9期80卷 4513-4542页

作者： Muhammad Dawood Shanshan Tu Chuangbai Xiao Muhammad Haris Hisham Alasmary Muhammad Waqas Sadaqat Ur Rehman Faculty of Information Technology Beijing University of TechnologyBeijing100124China National Center of Robotics and Automation University of Engineering and TechnologyPeshawar25000Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia School of Computing and Engineering Sciences Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUnited Kingdom School of Engineering Edith Cowan UniversityPerthWA 6027Australia School of Computer Science and Engineering University of SalfordManchesterM54BRUnited Kingdom

The DNS over HTTPS(Hypertext Transfer Protocol Secure)(DoH)is a new technology that encrypts DNS traffic,enhancing the privacy and security of ***,the adoption of DoH is still facing several research challenges,such as ensuring security,compatibility,standardization,performance,privacy,and increasing user *** significantly impacts network security,including better end-user privacy and security,challenges for network security professionals,increasing usage of encrypted malware communication,and difficulty adapting DNS-based security ***,it is important to understand the impact of DoH on network security and develop newprivacy-preserving techniques to allowthe analysis of DoH traffic without compromising user *** paper provides an in-depth analysis of the effects of DoH on *** discuss various techniques for detecting DoH tunneling and identify essential research challenges that need to be addressed in future security ***,this paper highlights the need for continued research and development to ensure the effectiveness of DoH as a tool for improving privacy and security.

关键词： DNS DNS over HTTPS cybersecurity machine learning

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Privacy BERT-LSTM: a novel NLP algorithm for sensitive information detection in textual documents

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Neural Computing and Applications 2024年第25期36卷 15439-15454页

作者： Muralitharan, Janani Arumugam, Chandrasekar Department of Information Technology St.Joseph’s College of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering St.Joseph’s College of Engineering Tamil Nadu Chennai India

In this modern digital era, the increasing volume of textual data and the widespread adoption of natural language processing (NLP) techniques have presented a critical challenge in safeguarding sensitive privacy information. As a result, there is a pressing demand to design robust and accurate NLP-based techniques to perform efficient sensitive information detection in textual data. This research paper focuses on the detection and classification of sensitive privacy information in textual documents using NLP by proposing a novel algorithm named Privacy BERT-LSTM. The proposed Privacy BERT-LSTM algorithm employs BERT for obtaining contextual embeddings and LSTM for sequential information processing, facilitating efficient sensitive information detection in textual documents. The BERT with its bidirectional characteristics captures the nuances and meaning of the textual documents, while the LSTM derives the long-range dependencies in the textual data. Moreover, the proposed Privacy BERT-LSTM algorithm with its attention mechanism highlights the important regions of the textual documents, contributing to efficient sensitive information detection. The comprehensive performance evaluation is conducted by employing the SMS Spam Collection dataset in terms of standard performance metrics and comparing it with different state-of-the-art techniques, namely, CASSED, PRIVAFRAME, CNN-LSTM, Conv-FFD, GCSA, TSIIP, and, C-PIIM. The experimental outcomes clearly illustrate that the Privacy BERT-LSTM algorithm demonstrates superior performance in identifying various types of sensitive information by achieving an accuracy of 92.50%, F1-score of 85.02%, and Precision of 89.36%. The proposed algorithm outperforms existing baseline models, providing valuable advancements in sensitive information detection using NLP. Therefore, this research contributes to the advancement of privacy protection in NLP applications and opens avenues for future investigations in the domain of sensitive info

关键词： Classification (of information)

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Ultimately Bounded Output Feedback Control for Networked Nonlinear Systems With Unreliable Communication Channel: A Buffer-Aided Strategy

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1566-1578页

作者： Yuhan Zhang Zidong Wang Lei Zou Yun Chen Guoping Lu the College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China IEEE the Department of Computer Science Brunel UniversityUB83PHUnited Kingdom the College of Information Science and Technology Donghua UniversityShanghai 201620 the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China the School of Automation Hangzhou Dianzi UniversityHangzhou 310018China the School of Electrical Engineering Nantong UniversityNantong 226019China

This paper concerns ultimately bounded output-feedback control problems for networked systems with unknown nonlinear dynamics. Sensor-to-observer signal transmission is facilitated over networks that has communication *** transmissions are carried out over an unreliable communication channel. In order to enhance the utilization rate of measurement data, a buffer-aided strategy is novelly employed to store historical measurements when communication networks are inaccessible. Using the neural network technique, a novel observer-based controller is introduced to address effects of signal transmission behaviors and unknown nonlinear *** the application of stochastic analysis and Lyapunov stability, a joint framework is constructed for analyzing resultant system performance under the introduced controller. Subsequently, existence conditions for the desired output-feedback controller are delineated. The required parameters for the observerbased controller are then determined by resolving some specific matrix inequalities. Finally, a simulation example is showcased to confirm method efficacy.

关键词： Buffer-aided strategy neural networks nonlinear control output-feedback control unreliable communication channel

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Developing Nex Waveforms for Multiuser VLC Networks employing Deep Learning 2

Developing Nex Waveforms for Multiuser VLC Networks employin...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Manikanta, R.V. Ramakrishna, B. Pandarinath, P. Durga Devi, Chegondi Vasarao, P. Srinu Rao Polamuri, Subba Swarnandhra College of Engineering & Technology India Aditya University Department of Computer Science and Engineering India

ISBN: (纸本)9798350364699

The increasing complexity and power requirements of communication systems make them impractical for widespread usage. Theoretical systems have been superbly applied to the actual world by artificial intelligence (AI), with less complexity producing greater outcomes. Here, they contrast two kinds of detectors - one based on deep learning and the other on the more conventional maximum likelihood (ML) decoder - and two methods for multiuser Visible Light Communication (VLC) systems: Successive Interference Cancellation (SIC) and Non-Orthogonal Multiple Access (NOMA). Examine the differences and similarities between Index Modulation (IM), Orthogonal Frequency Division Multiplexing (OFDM), and the new Orbital Angular Momentum (OAM) multiplexing. Simulated results for a variety of signal-to-noise ratios (SNRs) reveal that, for both users, deep learning-based structures provide superior decryption at the receiver end, mainly for advanced SNR values, when compared to ML-based systems. By using deep learning, the detection error may be reduced to about 20% for low SNR values and roughly 30% for high SNR values. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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Hybrid Color Texture Features Classification Through ANN for Melanoma

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Intelligent Automation & Soft Computing 2023年第2期35卷 2205-2218页

作者： Saleem Mustafa Arfan Jaffar Muhammad Waseem Iqbal Asma Abubakar Abdullah S.Alshahrani Ahmed Alghamdi Faculty of Computer Science&Information Technology Superior UniversityLahore54600Pakistan Department of Software Engineering Superior UniversityLahore54600Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of Jeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia

Melanoma is of the lethal and rare types of skin *** is curable at an initial stage and the patient can survive *** is very difﬁcult to screen all skin lesion patients due to costly *** are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of *** challenges are required an automated system to classify the clinical features of melanoma and non-melanoma *** trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reﬂections,air bubbles,and oils on almost all *** contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular *** entropy and morphology-based automated mask selection is pro-posed for the active contour *** proposed method can improve the overall segmentation along with the boundary of melanoma *** this study,features have been extracted to perform the classiﬁcation on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been ***,hybrid fused texture features;such as local,color feature as global,shape features,and Artiﬁcial neural network(ANN)as classiﬁers have been proposed for the categorization of the malignant and *** had been carried out on datasets Dermis,DermQuest,and *** results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.

关键词： Gray level co-occurrence matrix local binary pattern artiﬁcial neural networks support vector machines color skin cancer dermoscopic

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