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Scheduling Bi-Objective Lot-Streaming Hybrid Flow Shops with Consistent Sublots via an Enhanced Artificial Bee Colony Algorithm

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Complex System Modeling and Simulation 2025年第1期5卷 46-67页

作者： Benxue Lu Kaizhou Gao Peiyong Duan Adam Slowik School of Computer Science Liaocheng UniversityLiaocheng 252000China Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Information and Automation Engineering Qilu University of TechnologyJinan 250353China Department of Electronics and Computer Science Koszalin University of TechnologyKoszalin 75-453Poland

This work addresses bi-objective hybrid flow shop scheduling problems considering consistent sublots(Bi-HFSP_CS).The objectives are to minimize the makespan and total energy ***,the Bi-HFSP_CS is formalized,followed by the establishment of a mathematical ***,enhanced version of the artificial bee colony(ABC)algorithms is proposed for tackling the Bi-HFSP_***,fourteen local search operators are employed to search for better *** different Q-learning tactics are developed to embed into the ABC algorithm to guide the selection of operators throughout the iteration ***,the proposed tactics are assessed for their efficacy through a comparison of the ABC algorithm,its three variants,and three effective algorithms in resolving 95 instances of 35 different *** experimental results and analysis showcase that the enhanced ABC algorithm combined with Q-learning(QABC1)demonstrates as the top performer for solving concerned *** study introduces a novel approach to solve the Bi-HFSP_CS and illustrates its efficacy and superior competitive strength,offering beneficial perspectives for exploration and research in relevant domains.

关键词： hybrid flow shop scheduling Q-learning lot streaming local search

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A Summary on Privacy and Security in Cloud Data using Various Approaches 4

A Summary on Privacy and Security in Cloud Data using Variou...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Jayaram, B. Sethukarasi, T. Sindhu, M. Jeyamohan, H. Rmk Engineering College Department of Computer Science and Business Systems 601206 India Rmk Engineering College Department of Computer Science and Engineering 601206 India Velammal Engineering College Department of Computer Science and Engineering Chennai600066 India

ISBN: (纸本)9798350300888

Nowadays online users are prone to lot of security related issues in protecting their data. In order to achieve this privacy preservation in cloud plays a major role. For this purpose various technologies related to cyber security was proposed according to application in which the security has to be implemented. Some of these techniques include machine learning, deep learning, federated learning natural language processing, block chain etc. This paper analyses various approaches proposed by researches. This paper is divided into 7 sections such as role of cyber security in privacy preservation, privacy preservation in cloud using cyber security, what is a hybrid model and how to build it to implement features of security, Malicious node deduction in wireless sensor network using cyber security, relation between cyber-security and block-chain, federated learning and differential privacy. © 2023 IEEE.

关键词： Cybersecurity

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Secure Authentication and Authorization with MAC Address and Cryptography-Based Multi-Factor Algorithm 2

Secure Authentication and Authorization with MAC Address and...

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2nd International Conference on Informatics, ICI 2023

作者： Abualkas, Yaser M.A. Lalitha Bhaskari, D. Andhra University Visakhapatnam Department of Computer Science and Systems Engineering India

ISBN: (纸本)9798350343830

The aim of this paper is to propose an MFA (Multi-Factor Authentication) algorithm using MAC address that achieves authentication and authorization in a secure and efficient manner. At first aim to achieve authentication by algorithm involves a user registration process, generating a random MAC address, and storing user data in an encoded format using map charset. During the login process, a QR code is generated containing user details, and the MAC address is checked against the local MAC address stored in the user device. If the MAC addresses match, the user is granted access to the account. In case of a mismatch, an alert is sent to the user. This algorithm provides an additional layer of security and prevents unauthorized access to user accounts for the second aim to achieve authorization by The Cryptography algorithm by MAC address provides a high level of security and authorization. The algorithm involves converting the target device's MAC address and plain text to ASCII code, adding a random number, and applying the RSA algorithm. The output is then stored in an n*n matrix, which is reordered for added security. The encrypted text is then sent to the target device, ensuring secure communication. This algorithm prevents unauthorized access to sensitive information and provides an additional layer of security. This paper highlights the importance of cryptography in ensuring data security and proposes a new algorithm that provides a high level of security and authorization. © 2023 IEEE.

关键词： Matrix algebra

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Multi-task System for Multiple Languages Translation Using Transformers 4th

Multi-task System for Multiple Languages Translation Using T...

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4th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2023

作者： Nunna, Bhargava Satya Department of Computer Science and Systems Engineering Andhra University Vishakapatnam India

ISBN: (纸本)9789819912025

In this work, we explore the problem of the multi-task Neural Machine Translation (NMT) model which can simultaneously translate given sentences from multiple source languages to a single target language. Our solution is supported by a recently proposed transformer model, where we extended the neural machine translation to a multi-task system that shares the common target language translation and separates the modeling of different source language representations. The multi-task system could be helpful in scenarios where insufficient data or imbalanced data exists. As the multi task system shares the parameters of different language pairs, the language pair with inadequate data could utilize the resources from data-abundant language pairs and can improve the translation quality. Our approach is also considerably faster learning when compared to separately trained NMTs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural machine translation

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A Weighted Optimal Scheduling Scheme for Congestion Control in Cloud Data Center Networks

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IEEE Transactions on Services Computing 2023年第4期16卷 1-9页

作者： Li Y. Jian S. Hsieh S. Chung W. Zomaya A.Y. Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Systems The University of Sydney Australia

Cloud services are experiencing a remarkable increase in the number of users and the resource required over the past few years. Thus, it has become a great challenge for the internet vendors to make a robust framework to serve the customers with low cost and delay. Congestion control is one of the essential topics of routing algorithms in cloud data center networks. In this paper, we propose a weighted optimal scheduling scheme WSPR for congestion control in cloud data center networks which prevents the congestion in advance with the global view so that it can make good use of vacant network resources. We choose BCube as our network model and modify the network topology to fit software-defined networks so as to have a full view of the topology. First, we design the SP graph which contains all shortest paths between a source server and a destination server. Second, we propose WSPR to allocate the most appropriate path to each flow for congestion control. We implement a system to simulate a data center, and evaluate our proposed scheme WSPR by comparing WSPR with other classical methods. The experimental results demonstrate that our proposed scheme WSPR has the best performance in terms of the maximum delay, average delay, and throughput among all compared methods. IEEE

关键词： Cloud computing Cloud data center networks congestion control Data centers Network topology Optical switches Routing Servers software-defined networks Topology

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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Security Metrics and Authentication-based RouTing (SMART) Protocol for Vehicular IoT Networks

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SN computer science 2024年第2期5卷 1-14页

作者： Kumar, Rakesh Singh, Sunil K. Lobiyal, D.K. Kumar, Sudhakar Jawla, Shashi Department of Applied Computational Sciences & Engineering GLBITM Greater Uttar Pradesh Noida India Department of Computer Science & Engineering CCET Panjab University Chandigarh India School of Computer & Systems Sciences Jawaharlal Nehru University New Delhi India Department of Computer Science & Engineering SVIET Mohali India

Routing protocols, responsible for determining optimal paths, fall into two main categories: reactive and proactive protocols. In the realm of reactive routing protocols, exemplified by Ad hoc On-demand Distance Vector (AODV), routes are created only when there is an actual data transmission requirement. In contrast, proactive routing protocols maintain pre-computed paths to all potential destinations, resulting in reduced resource utilization within reactive protocols and continuous route maintenance within proactive ones. Reactive routing protocols are resource efficient as they establish routes as needed, while proactive counterparts maintain routing tables for all possible destinations, ensuring constant route availability regardless of data transmission demands. This paper primarily concentrates on the reactive routing protocol category, focusing on real-time path optimization and routing information updates. In the context of Vehicular Internet of Things (VIoT) networks, where malicious entities might attempt to flood, mislead, or impersonate routing packets, it is imperative to ensure robust security measures within the routing protocol. Unfortunately, secure routing protocols in VIoT networks, including AODV, SAODV, and SGHRP, often exhibit inefficiencies and impose a high overhead. To address these challenges, this research paper introduces the Security Metrics and Authentication-based RouTing (SMART) protocol for VIoT networks, with a focus on enhancing security while minimizing overhead. The SMART protocol utilizes the Merkle tree for hash (digest) generation, which is then encrypted using Elliptic Curve Cryptography (ECC) to reduce overhead. This proposed protocol enhances security by authenticating the source and incorporating security metrics into the routing information. To assess the performance of the SMART protocol, simulations were conducted using Network Simulator-2 (NS₂). The results demonstrated an improved packet delivery ratio, red

关键词： AODV Authentication ECC Hash digest Merkle Tree OBU Routing Protocol SAODV VIoT Networks

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A decision-making model for self-driving vehicles based on GPT-4V, federated reinforcement learning, and blockchain

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Neural Computing and Applications 2024年第34期36卷 21545-21560页

作者： Alam, Tanweer Gupta, Ruchi Ahamed, N. Nasurudeen Ullah, Arif Department of Computer Science Faculty of Computer and Information Systems Islamic University of Madinah P.O.Box. 170 Madinah42351 Saudi Arabia Department of Information Technology Ajay Kumar Garg Engineering College Ghaziabad Abdul Kalam Technical University Lucknow India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Department of Computer Science Faculty of Computing and Artificial Intelligence Air University Islamabad Pakistan

Decision-making is crucial in fully autonomous vehicle operations and is expected to greatly influence future transportation systems. Observing the current driving status of autonomous vehicles is vital for its decision-making process. The autonomous connected vehicles on the road send significant data about their movements to the server to maintain continuous training. With the Proof of Authority (PoA) consensus process, blockchain technology provides a valid, decentralised and secure option to improve transactions throughput and minimise delay. The limited computational capacity of vehicles poses a challenge in achieving high accuracy and low latency while training self-driving algorithms. GPT-4V surpassed challenging autonomous systems in scene interpretation and causal thinking. GPT-4V has ability to navigate circumstances without access to database, interpret intentions, and make sound decisions in real-world driving scenarios. The reward function and different driving conditions are organised to allow an optimal search to find the most efficient driving style while ensuring safety. The consequences of the Blockchain-enabled decision-making model (DMM) for Self-Driving Vehicles (SDV) primarily based on GPT-4V and Federated Reinforcement Learning (FRL) would, likely, upgrades in decision-making accuracy, operational performance, statistics integrity, and potentially enhanced learning skills in SDV. Integrating blockchain technology, superior language modelling GPT-4V and FRL may lead to multiplied safety, reliability, and decision-making ability in SDV. This study utilised the Simulation of Urban MObility (SUMO) simulator to assess the ability of SDV to maintain its desired speed consistently and securely in a highway setting using proposed DMM. This study indicates that the suggested DMM, utilising the driving state evaluation approach for SDV, can help these vehicles operate safely and effectively. The performance of the proposed model, such as CPU utilisation

关键词： Reinforcement learning

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Prediction of Web Service Execution QoS Parameters with Utilization of Machine Learning Methods 29th

Prediction of Web Service Execution QoS Parameters with Util...

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29th International Conference on systems engineering, ICSEng 2022

作者： Falas, Lukasz Sztukowski, Adam Warzyński, Arkadiusz Schauer, Patryk Department of Computer Science and Systems Engineering Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9783031274695

With ever-increasing advancement of ICT (Information and Communications Technology) new challenges and requirements resulting from societal expectations emerge. Modern Cloud and Internet of Things feature functionalities that often involve not only data manipulation, but also mission critical tasks requiring ICT systems to strictly comply with defined nonfunctional requirements, especially in terms of web service execution time. These applications may range from solutions developed for e-Health, Industry 4.0, Smart Cities to applications related to fields like Smart Home or autonomous vehicles. Often, the QoS (Quality of Service) parameters are strongly correlated with user provided input and computational resources allocated to services that are handling the requests. To meet the needs of modern systems, new methods for QoS prediction are required, which can be later used by middleware responsible for resource allocation in service-based systems. This paper focuses on research related to QoS prediction methods. It discusses the general web service QoS parameters estimation problem and related challenges. Then a proposition of machine learning based approach for prediction of service QoS parameters based on the request data is presented, followed by experimentation aimed at evaluation of the proposed solution on real-word web services in a simulated environment. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Quality of service

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Fundamental Limits of Two-Hop MIMO Channels: An Asymptotic Approach

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IEEE Transactions on Information Theory 2025年第6期71卷 4069-4111页

作者： Zhuang, Zeyan Zhang, Xin Xu, Dongfang Song, Shenghui The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong The Hong Kong University of Science and Technology Division of Integrative Systems and Design Hong Kong Hong Kong

Multi-antenna relays and intelligent reflecting surfaces (IRSs) have been utilized to construct favorable channels to improve the performance of wireless systems. A common feature between relay systems and IRS-aided systems is the two-hop multiple-input multiple-output (MIMO) channel. As a result, the mutual information (MI) of two-hop MIMO channels has been widely investigated with very engaging results. However, a rigorous investigation on the fundamental limits of two-hop MIMO channels, i.e., the first and second-order analysis, is not yet available in the literature, due to the difficulties caused by the two-hop (product) channel and the noise introduced by the relay (active IRS). In this paper, we employ large random matrix theory, specifically Gaussian tools, to derive the closed form deterministic approximation for the mean and variance of the MI. Additionally, we determine the convergence rate for the mean, variance and the characteristic function of the MI, and prove the asymptotic Gaussianity. Furthermore, we also investigate the analytical properties of the fundamental equations that describe the closed-form approximation and prove the existence and uniqueness of the solution. An iterative algorithm is then proposed to obtain the solutions for the fundamental equations. Numerical results validate the accuracy of the theoretical analysis. © 1963-2012 IEEE.

关键词： Iterative methods

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