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Intelligent Methods, systems, and Applications (IMSA)

作者： Alaa Sheta Malik Braik Ertan Öznergiz Walaa H. Elashmawi Peter Rausch Emad S. Othman Department of Computer Science Southern Connecticut State University New Haven CT USA Computer Science Department Al-Balqa Applied University Salt Jordan Mechanical Engineering Faculty Istanbul Technical University Istanbul Turkey Dept of Computer Science Misr International University Cairo Egypt Computer Science Department George Simon Ohm Univ. of Applied Sciences Nuremberg Germany Management Information System Dept. AL-Shorouk Academy Cairo Egypt

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

This research introduces the application of an innovative bio-inspired metaheuristic technique, termed the Crow Search Algorithm (CSA), to model a crucial industrial process - hot rolling manufacturing. Inspired by the foraging patterns of crows, the CSA algorithm has demonstrated its prowess in solving diverse optimization challenges. In the context of this study, the CSA algorithm is harnessed to fine-tune the parameters of a simulation model focused on predicting the force exerted during a hot rolling procedure. The proposed model takes into consideration a range of influential factors, including the initial temperature (Ti), width (W s ), carbon equivalent (C e ), gauge (h i ), draft (i), and roll diameter (R). The findings underscore the CSA's capability to deliver an exceptional modeling performance characterized by swift convergence and high solution quality. By getting along very well with the proposed model with the CSA algorithm, a robust and efficient avenue to optimize the hot rolling process emerges, with the potential for expansion into other manufacturing domains. The computational and simulation results demonstrated that the proposed approach-based CSA outperformed different meta-heuristic search algorithms, such as the Salp Swarm Algorithm (SSA), Dandelion Optimizer (DO), Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), and Moth-Flame Optimization (MFO), in all test cases. The CSA has achieved the highest coefficient of determination (R 2 ), equal to 0.97244, and the lowest mean squared error (MSE), equal to 1904.97, compared to its opponent algorithms.

关键词： Cutting tools Temperature distribution Computational modeling Simulation Biological system modeling Metaheuristics Force

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Semantic Literature Review on Non-Fungible Token: Expansion Area of Usage & Trends

Semantic Literature Review on Non-Fungible Token: Expansion ...

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International Conference on information Management and Technology (ICIMTech)

作者： Gede Indra Raditya Martha Harco Leslie Hendric Spits Warnars Harjanto Prabowo Meyliana Achmad Nizar Hidayanto Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Faculty of Computer Science University of Indonesia Depok Indonesia

Blockchain, Metaverse & NFT are technologies that were booming during the Pandemic. As a derivative product of blockchain, the Non-Fungible Token (NFT) is one of the technologies that has attracted the most interest from investors and the public because of its potential benefits and has always been closely associated with the Metaverse as a main purposes research of the NFTs. This paper is to explore the possibilities and provide an overview of the current use of NFT technology so that it can provide insight for further research & gap with the big picture that it is trying to present in this paper. However, after exploring further related to NFT in this research, it was found that the problem that most people are trying to solve through NFT is related to the topic of Decentralization & Web3 not as a Metaverse main backbone.

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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Towards an Intelligent Industrial Environment: Blockchain-Empowering Human-Centered Artificial Intelligence for Risk Assessment and Strategic Evaluation

SSRN

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SSRN 2023年

作者： Khan, Abdullah Ayub Chen, Yen-Lin Hajjej, Fahima Shaikh, Aftab Ahmed Yang, Jing Ku, Chin Soon Por, Lip Yee Department of Computer Science and Information Technology Benazir Bhutto Shaheed University Lyari Sindh Karachi75660 Pakistan Department of Computer Science Sindh Madressatul Islam University Sindh Karachi74000 Pakistan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei106344 Taiwan Department of Software Engineering College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O.Box 84428 Riyadh11671 Saudi Arabia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Department of Computer Science Universiti Tunku Abdul Rahman Kampar31900 Malaysia

With the recent introduction of high-level human control and high-level IoT control in intelligent transportation systems, their association with blockchain increases the performance of the Internet of Vehicles (IoVs) and E-Healthcare, which leads to wider adaptation in the future as well. In this paper, the contributions of this systematic article are divided into three different folds: first, this paper evaluates more than a hundred previously published articles and highlights the gaps in futuristic concerns. Second, the paper presents a novel and secure human-centered AI-enabled risk assessment framework for the IoVs with blockchain distributed ledger technology (DLT). Third, a distributed public permissionless network with two operational channels of inter-communication for vehicle-to-vehicle (V2V) is proposed. It is considered an extension in the Vehicle-to-Everything (V2X) environment. Along with that, the pseudo-development of chaincodes to automate human-centered IoV node transactions. In addition, a Practical Byzantine Fault Tolerance (PBFT) consensus protocol and related controlled executions are programmed for fast scheduling, offloading, managing, and optimizing with reliability. © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Online Learning Experience Assessment Survey During the Covid-19 Pandemic 7

Online Learning Experience Assessment Survey During the Covi...

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7th International Conference on information Management and Technology, ICIMTech 2022

作者： Hassan, Rosilah Murad, Dina Fitria Wahi, Wahiza Wijanarko, Bambang Dwi Asmak Ismail, Nurul Halimatul Awwad, Samer A. B. Center for Cyber Security Faculty of Information Science and Technology Selangor Bangi Malaysia BINUS Online Learning Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Selangor Bangi Malaysia BINUS Online Learning Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Applied College Princess Nourah Bint Abdulrahman University Department of Computer Science and Information System Riyadh Saudi Arabia Deanship of Information and Communication Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

Due to the Covid-19 epidemic, the education system worldwide has changed profoundly. This has caused many lecturers to transform face-to-face teaching and learning to online learning. In this regard, this study focuses on students' experiences in dealing with online learning. A survey method using a questionnaire is used to gather data from students, and 46 respondents answered the survey. The survey is divided into three sections, which comprise of Online Learning (Assigning Tasks), Online Learning (Quizzes), and Online Learning (Online Meetings). The results showed that the online learning application that was mostly used for assigned tasks was Google Classroom, which was 78.3%. The online learning application that is mostly used for quizzes was Kahoot, 97.8% and for online meetings was Zoom, 63.2%. Overall, most respondents, 45.7% chose a combination of online learning and face-to-face classes. © 2022 IEEE.

关键词： Surveys

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Intrusion Detection using Deep Neural Network Algorithm on the Internet of Things 11

Intrusion Detection using Deep Neural Network Algorithm on t...

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11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Ikhwan, Syariful Wibowo, Adi Warsito, Budi School of Postgraduate Studies Diponegoro University Doctoral Program of Information System Semarang Indonesia Institut Teknologi Telkom Purwokerto D3 Telecommunication Engineering Banyumas Indonesia Diponegoro University Computer Science Informatics Department of Science and Mathematics Faculty Semarang Indonesia Diponegoro University Statistics Department of Science and Mathematics Faculty Semarang Indonesia

ISBN: (纸本)9781665460309

The increasing use of IoT devices on future networks is very helpful for humans in their lives. However, the increase in devices connected to IoT networks also increases the potential for attacks against those networks. Vulnerabilities in Internet of Things (IoT) networks can be exposed at any time. Artificial intelligence can be used to protect the IoT network by being able to detect attacks on the network so that they can be prevented. In this study, network detection was carried out using the Deep Neural Network (DNN) algorithm. The test was carried out using the UNSW Bot-IoT dataset with a comparison of training data of 75% of the overall data. The results obtained show the ability of the algorithm to detect attacks on average with 99.999% accuracy. The validation loss and training loss look very small. In this study, there is a validation loss that still occurs in overfitting, but the difference is very small. © 2022 IEEE.

关键词： Internet of things

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Blockchain-Based Model and Architecture Public Health system

SSRN

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SSRN 2024年

作者： Fernando, Erick Winanti, Winanti Prabowo, Yulius Denny Johan, Monika Evelin Tjahjana, David Department of Information Systems Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia Department of Information Systems Universitas Insan Pembangunan Indonesia Computer Science Department Binus Online Learning Bina Nusantara University Jakarta11480 Indonesia STMIK Insan Pembangunan Indonesia

Blockchain has been identified as one of the technologies that can be used in the healthcare system, especially in the issues of data security, privacy, and interoperability. This article explores the application of blockchain in various medical processes ranging from patient registration, visit registration, initial examination, patient examination, diagnostic input, and drug preparation. This study was conducted at a healthcare facility that utilizes blockchain as a technology to manage patient data, medical records, and drug prescriptions. Through a mixed-method approach, this study evaluates the impact of blockchain on operational efficiency, data security, and system reliability. The results show that blockchain not only improves the security and privacy of patient data but also accelerates operational processes in the healthcare chain. An in-depth discussion of the benefits, challenges, and future potential of blockchain in healthcare is outlined to provide insights for blockchain technology developers and healthcare practitioners. Model validation was conducted through Focus Group Discussion (FGD) with domain experts in the health sector. From the experiments conducted, this study concludes that blockchain can be a transformational solution in addressing major problems in the healthcare sector, although cross-sector collaboration is needed to ensure successful implementation. © 2024, The Authors. All rights reserved.

关键词： Electronic health record

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The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption 30

The Power of Bamboo: On the Post-Compromise Security for Sea...

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30th Annual Network and Distributed system Security Symposium, NDSS 2023

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)1891562835

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2023 30th Annual Network and Distributed system Security Symposium, NDSS 2023. All Rights Reserved.

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Evaluation of the Application of Hospital Management information system (SIMRS) in RSUD Dr. Kanujoso Djatiwibowo Using the HOT-Fit Method 3

Evaluation of the Application of Hospital Management Informa...

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3rd Borneo International Conference on Applied Mathematics and Engineering 2020, BICAME 2020

作者： Setiorini, Adani Natasia, Sri R Wiranti, Yuyun Tri Ramadhan, Dean A Department of Information System Majoring in Mathematics and Information Technology Kalimantan Institute of Technology Balikpapan Indonesia Department of Computer Science Faculty of Mathematics and Natural Sciences IPB University Bogor Indonesia

Hospital Management information system (SIMRS) is a system that can process and integrate the entire hospital service process flow in the form of a network of coordination, reporting and administrative procedures to obtain accurate data and information. At the RSUD Dr. Kanujoso Djatiwibowo (RSKD) of Balikpapan City, the application of SIMRS experienced problems such as the high number of troubleshooting cases and the redundancy of patient medical record numbers. This case is consideredto inhibit the performance of hospital services related to efficiency. Based on this problem, the evaluation of SIMRS in the RSKD was carried out by using the constructed Human, Organization, Technology-Fit (HOT-Fit) model. This model involves eight variables which consists of system Quality, information Quality, and Service Quality, system Use, User Satisfaction, Structure, and Environment, and Net Benefits. The results indicate that the proposed model either directly or indirectly affectsthe benefits obtained from implementing the SIMRS. Further implications of the results toward improvement of the system are provided. © Published under licence by IOP Publishing Ltd.

关键词： information management

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SVFormer: A Direct Training Spiking Transformer for Efficient Video Action Recognition

arXiv

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arXiv 2024年

作者： Yu, Liutao Huang, Liwei Zhou, Chenlin Zhang, Han Ma, Zhengyu Zhou, Huihui Tian, Yonghong AI Department Peng Cheng Laboratory Shenzhen China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China Faculty of Computing Harbin Institute of Technology Harbin China

Video action recognition (VAR) plays crucial roles in various domains such as surveillance, healthcare, and industrial automation, making it highly significant for the society. Consequently, it has long been a research spot in the computer vision field. As artificial neural networks (ANNs) are flourishing, convolution neural networks (CNNs), including 2D-CNNs and 3D-CNNs, as well as variants of the vision transformer (ViT), have shown impressive performance on VAR. However, they usually demand huge computational cost due to the large data volume and heavy information redundancy introduced by the temporal dimension. To address this challenge, some researchers have turned to brain-inspired spiking neural networks (SNNs), such as recurrent SNNs and ANN-converted SNNs, leveraging their inherent temporal dynamics and energy efficiency. Yet, current SNNs for VAR also encounter limitations, such as nontrivial input preprocessing, intricate network construction/training, and the need for repetitive processing of the same video clip, hindering their practical deployment. In this study, we innovatively propose the directly trained SVFormer (Spiking Video transFormer) for VAR. SVFormer integrates local feature extraction, global self-attention, and the intrinsic dynamics, sparsity, and spike-driven nature of SNNs, to efficiently and effectively extract spatio-temporal features. We evaluate SVFormer on two RGB datasets (UCF101, NTU-RGBD60) and one neuromorphic dataset (DVS128-Gesture), demonstrating comparable performance to the mainstream models in a more efficient way. Notably, SVFormer achieves a top-1 accuracy of 84.03% with ultra-low power consumption (21 mJ/video) on UCF101, which is state-of-the-art among directly trained deep SNNs, showcasing significant advantages over prior models. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy efficiency

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