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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis

Procedia computer science 2025年 253卷 84-93页

作者： Anak Agung Ayu Putri Ardyanti Zhiqiang Xie Andrea Stevens Karnyoto Bens Pardamean Ni Made Estiyanti School of Computer Science and Technology Harbin University of Science and Technology Harbin 150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Doctoral School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, computer science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions.

关键词： integrated scheduling assembly job shop scheduling heuristic algorithm genetic algorithm bibliometric analysis

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Intelligent Target Cell Selection Algorithm for Low Latency 5G Networks 2nd

Intelligent Target Cell Selection Algorithm for Low Latency ...

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2nd EAI International Conference on Computational Intelligence and Communications, CICom 2021

作者： Nyangaresi, Vincent Omollo Al Sibahee, Mustafa A. Abduljabbar, Zaid Ameen Alhassani, Abdulhadi Abduljaleel, Iman Qays Abood, Enas Wahab Faculty of Biological & Physical Sciences Tom Mboya University College Homabay Kenya College of Big Data and Internet Shenzhen Technology University Shenzhen China Computer Technology Engineering Department Iraq University College Basrah Iraq Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen China Communications Engineering Department Iraq University College Basrah Iraq Department of Computer Science College of Computer Science and Information Technology University of Basrah Basrah Iraq Department of Mathematics College of Science University of Basrah Basrah Iraq

ISBN: (纸本)9783031195228

Security and efficiency during the handover process are critical challenges in 5G heterogeneous networks owing to its strict low latency and security requirements. The 5G networks support many services and communication paradigms. Examples of these applications include as Internet of Things (IoT), vehicular ad hoc networks (VANETs), device-to-device communication (D2D), among others. As such, security lapses in the core network can easily escalate to the supported communication devices and networks and vice versa. Unfortunately, legacy cellular networks deploy the received signal strength indicator (RSSI) as the only parameter during the handover process. This results in increased handover latencies, packet losses, and high handover failure probability. Consequently, other protocols have been introduced that deploy additional handover parameters such as data rates, service costs, battery power, bandwidth, and energy consumption. However, these schemes only deal with efficiency of the handoff process but rarely consider security and privacy issues. Conversely, security and privacy protocols rarely address efficiency aspects of the handover process. In this paper, an algorithm that leverages on artificial neural networks coupled with fuzzy logic for target cell selection is presented. Based on the obtained results, there is a 56.1% reduction in handover latency and a 38.8% reduction in packet losses when the proposed scheme is deployed. In terms of security, it upholds both backward and forward key secrecy. In addition, desynchronization and replay and attacks are effectively thwarted in the proposed algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： 5G mobile communication systems

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IoT for Water Quality Categorization 5

IoT for Water Quality Categorization

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Sastrohartono, Hermantoro Krisdiarto, Andreas Wahyu Uktoro, Arief Ika Rahutomo, Reza Suparyanto, Teddy Pardamean, Bens Institute of Agriculture Stiper Faculty of Agricultural Technology Agricultural Engineering Department Yogyakarta Indonesia Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results. © 2023 IEEE.

关键词： Internet of things

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Securing MIMO Wiretap Channel With Learning-Based Friendly Jamming Under Imperfect CSI

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IEEE Internet of Things Journal 2025年第11期12卷 16009-16022页

作者： Minh Tuan, Bui Nguyen, Diep N. Linh Trung, Nguyen Nguyen, Van-Dinh Van Huynh, Nguyen Thai Hoang, Dinh Krunz, Marwan Dutkiewicz, Eryk University of Technology Sydney School of Electrical and Data Engineering SydneyNSW2007 Australia University of Engineering and Technology Vietnam National University Faculty of Electronics and Telecommunications Hanoi100000 Viet Nam College of Engineering and Computer Science VinUniversity Hanoi100000 Viet Nam University of Liverpool Department of Electrical Engineering and Electronics LiverpoolL69 3GJ United Kingdom The University of Arizona Department of Electrical and Computer Engineering TucsonAZ85721 United States

Wireless communications are particularly vulnerable to eavesdropping attacks due to their broadcast nature. To effectively deal with eavesdroppers, existing security techniques usually require accurate channel state information (CSI), e.g., for friendly jamming (FJ), and/or additional computing resources at transceivers, e.g., cryptography-based solutions, which unfortunately may not be feasible in practice. This challenge is even more acute in low-end IoT devices. We thus introduce a novel deep learning-based FJ framework that can effectively defeat eavesdropping attacks with imperfect CSI and even without CSI of legitimate channels. In particular, we first develop an autoencoder-based communication architecture with FJ, namely, AEFJ, to jointly maximize the secrecy rate and minimize the block error rate (BLER) at the receiver without requiring perfect CSI of the legitimate channels. In addition, to deal with the case without CSI, we leverage the mutual information neural estimation (MINE) concept and design a MINE-based FJ scheme that can achieve comparable security performance to the conventional FJ methods that require perfect CSI. Extensive simulations in a multiple-input-multiple-output (MIMO) system demonstrate that our proposed solution can effectively deal with eavesdropping attacks in various settings. Moreover, the proposed framework can seamlessly integrate MIMO security and detection tasks into a unified end-to-end learning process. This integrated approach can significantly maximize the throughput and minimize the BLER, offering a good solution for enhancing communication security in wireless communication systems. © 2014 IEEE.

关键词： Jamming

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Posterior Consistency for Missing data in Variational Autoencoders

arXiv

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arXiv 2023年

作者： Sudak, Timur Tschiatschek, Sebastian University of Vienna Faculty of Computer Science Vienna Austria University of Vienna Research Network Data Science Vienna Austria

We consider the problem of learning Variational Autoencoders (VAEs), i.e., a type of deep generative model, from data with missing values. Such data is omnipresent in real-world applications of machine learning because complete data is often impossible or too costly to obtain. We particularly focus on improving a VAE’s amortized posterior inference, i.e., the encoder, which in the case of missing data can be susceptible to learning inconsistent posterior distributions regarding the missingness. To this end, we provide a formal definition of posterior consistency and propose an approach for regularizing an encoder’s posterior distribution which promotes this consistency. We observe that the proposed regularization suggests a different training objective than that typically considered in the literature when facing missing values. Furthermore, we empirically demonstrate that our regularization leads to improved performance in missing value settings in terms of reconstruction quality and downstream tasks utilizing uncertainty in the latent space. This improved performance can be observed for many classes of VAEs including VAEs equipped with normalizing flows. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Advanced Lung Cancer Diagnosis and Treatment with Artificial Intelligence: Applications, Methods, and Future Directions

Advanced Lung Cancer Diagnosis and Treatment with Artificial...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Pawan Dilip Chaudhari Amit Gudadhe Artificial Intelligence and Data Science Faculty of Engineering and Technology(DMIHER) Sawangi(M) Wardha Maharashtra India Computer Science and Design Faculty of Engineering and Technology(DMIHER)Sawangi(M) Wardha Maharashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

AI technology revolutionizes the treatment of lung cancer and other related lung cancer diagnoses, hence becoming the game-changing technology in the diagnosis and treatment of this serious disease. As one of the most prevalent and deadliest cancers globally, early identification, precise diagnosis, and efficient planning for the treatment of lung cancer have always been difficult. The therapy using AI-based methods is introducing another novel way of addressing the problems concerning lung cancer via advanced imaging analysis, predictive modeling, and tailored plans of treatment, the most important among which is of course, imaging analysis. What is certainly most exciting among the applications of AI in lung cancer treatment is in the realm of imaging analysis. AI has been seen to be extremely clever in the interpretation of images rather than using machine learning or deep learning but has been much faster and more accurate than a human being while doing it. Besides, these systems also increase the general accurateness of diagnosis through a reduction of the error in diagnosis and consistent results. Alluring promises are made for AI soon for the treatment of lung cancer. In this paper, we discuss how AI has been transformed into imaging, diagnosis, and treatment planning and how this will improve patient outcomes and completely transform the way healthcare is delivered. We also consider barriers still to be cleared for realizing fully the potential benefits of AI. These include problems such as the quality of available data used in model interpretation and generalizability across different healthcare settings. Therefore, we end and suggest some future research on collaboration by emphasizing the safe, effective, and ethical entry of AI into normal clinical practice.

关键词： Analytical models Renewable energy sources Ethics Lung cancer Imaging Medical treatment Transforms Predictive models Planning Artificial intelligence

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Robust Audio Anti-Spoofing with Fusion-Reconstruction Learning on Multi-Order Spectrograms

arXiv

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arXiv 2023年

作者： Wen, Penghui Hu, Kun Yue, Wenxi Zhang, Sen Zhou, Wanlei Wang, Zhiyong School of Computer Science The University of Sydney NSW Australia Faculty of Data Science City University of Macau Macao China

Robust audio anti-spoofing has been increasingly challenging due to the recent advancements on deepfake techniques. While spectrograms have demonstrated their capability for antispoofing, complementary information presented in multi-order spectral patterns have not been well explored, which limits their effectiveness for varying spoofing attacks. Therefore, we propose a novel deep learning method with a spectral fusion-reconstruction strategy, namely S2pecNet, to utilise multi-order spectral patterns for robust audio anti-spoofing representations. Specifically, spectral patterns up to second-order are fused in a coarse-to-fine manner and two branches are designed for the fine-level fusion from the spectral and temporal contexts. A reconstruction from the fused representation to the input spectrograms further reduces the potential fused information loss. Our method achieved the state-of-the-art performance with an EER of 0.77% on a widely used dataset - ASVspoof2019 LA Challenge. © 2023, CC BY.

关键词： Deep learning

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Effectiveness of generative adversarial networks in denoising medical imaging (CT/MRI images)

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Multimedia Tools and Applications 2024年 1-25页

作者： Ramadass, Sudhir Narayanan, Sreekumar Kumar, Rajiv K, Thilagavathi Data Analytics Sterck Systems Private Limited Chennai Nungambakkam India Network and Infrastructure Management Faculty of Engineering and Technology Botho University Gaborone Botswana School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Department of Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India

For clinical analysis or medical intrusion, imaging technology is a potent tool for producing visual images of a body's inside. However, several existing methods include restricted access to high-quality and diverse datasets, overfitting, a lack of interpretability, high computational costs, varying medical imaging data, and ethical issues concerning patient privacy, consent, and bias. To overcome these issues, the effectiveness of generative adversarial networks in denoising medical imaging (CT/MRI images) is proposed. The input CT images are first gathered and preprocessed to reduce undesired noise using Altered Phase Preserving Dynamic Range Compression (APPDRC). The General Adversarial Network (GAN) is then given these preprocessed images to cope with the noisy areas of CT/MRI while also complying with maintaining image structures such as boundary details while preventing over-smoothing, and the weight parameters for the GAN are optimized using the Golden Eagle Optimization (GEO) technique. General Adversarial Network (GAN) has piqued the interest of academics studying medical image-denoising mechanisms and developing sophisticated medical image-denoising algorithms based on deep learning. In light of reducing the difficulty in feature learning and improving final diagnosis accuracy, data preprocessing is necessary and crucial in gan-based medical image denoising methods. This work focuses on GAN-based effective denoising approaches in CT/MRI. The potential challenges and research objectives are in the context of data preprocessing in medical image denoising. Because of how CT/MRI images are acquired, it was observed that noise in CT/MRI data follows the distribution. Generative Adversarial Networks (GANs) for denoising medical imaging, mainly CT and MRI images, have yielded promising results of 99% accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Diagnosis of Malignant Lymphoma Cancer Using Hybrid Optimized Techniques Based on Dense Neural Networks

Diagnosis of Malignant Lymphoma Cancer Using Hybrid Optimize...

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International Conference on computer and Applications (ICCA)

作者： Salah A. Aly Ali Bakhiet Mazen Balat Faculty of Computing & Data Science Badya University Giza Egypt Computer Science Dept. Faculty of Science Fayoum University Fayoum Egypt Computer Science Dept. Culture & Science October 6th City Giza Egypt CS & IT Dept. E-Japanese University of Science & Tech. Alexandria Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Lymphoma diagnosis, particularly distinguishing between subtypes, is critical for effective treatment but remains challenging due to the subtle morphological differences in histopathological images. This study presents a novel hybrid deep learning framework that combines DenseNet201 for feature extraction with a Dense Neural Network (DNN) for classification, optimized using the Harris Hawks Optimization (HHO) algorithm. The model was trained on a dataset of 15,000 biopsy images, spanning three lymphoma subtypes: Chronic Lymphocytic Leukemia (CLL), Follicular Lymphoma (FL), and Mantle Cell Lymphoma (MCL). Our approach achieved a testing accuracy of 99.33%, demonstrating significant improvements in both accuracy and model interpretability. Comprehensive evaluation using precision, recall, F1-score, and ROC-AUC underscores the model's robustness and potential for clinical adoption. This framework offers a scalable solution for improving diagnostic accuracy and efficiency in oncology.

关键词： Deep learning Accuracy Biological system modeling Neural networks Transfer learning Leukemia Oncology Robustness Optimization Testing

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A Distributed Privacy-Preserving Framework for Deep Learning with Edge-Cloud Computing 15

A Distributed Privacy-Preserving Framework for Deep Learning...

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart data, Smartdata 2022

作者： Dai, Fei Liu, Guozhi Huang, Bi Xu, Xiaolong Chen, Chaochao Zhou, Zhangbing Zhou, Xiaokang Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China Nanjing University of Information Science and Technology School of Computer and Software Nanjing China Zhejiang University College of Computer Science and Technology Zhejiang China China University of Geosciences School of Information Engineering Beijing China Shiga University Faculty of Data Science Shiga Japan

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

Industrial Internet of Things (IIoT) systems can leverage deep learning (DL) models to provide intelligent applications. However, the training process of such DL models tends to leak privacy when the training data contain sensitive operational and management information. Most studies about privacy-preserving DL require a trusted data collector and thus are not suitable in an untrusted environment. In this paper, we propose a distributed privacy-preserving framework for DL with edge-cloud computing, where direct privacy leakage and indirect privacy leakage of training data are both considered. First, a pre-trained convolutional neural network (CNN) is partitioned into two parts for limiting direct privacy leakage of raw data, namely the feature module and the classification module. The feature module consisting of the lower layers of the CNN is deployed on an edge server, which is used to attract the feature of raw data. The feature data is fed to the classification module consisting of the higher layers of the CNN, which is deployed on the cloud server to compute image classification tasks. Second, a perturbation module between the feature module and the classification module is designed for limiting indirect privacy leakage during feature data transmission. The perturbation module uses local differential privacy (LDP) to produce noise in the feature data. Third, to further improve the accuracy, we retrain the classification module with the randomized feature data from edge servers. Experimental results show that the proposed framework achieves high accuracy under low privacy budgets. © 2022 IEEE.

关键词： Classification (of information)

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