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Digital Twins in the IIoT:Current Practices and Future Directions Toward Industry 5.0

Computers, Materials & Continua 2025年第6期83卷 3675-3712页

作者： Bisni Fahad Mon Mohammad Hayajneh Najah Abu Ali Farman Ullah Hikmat Ullah Shayma Alkobaisi Computer&Network Engineering United Arab Emirates UniversityAl Ain15551United Arab Emirates Big Data Analytics Centre United Arab Emirates UniversityAl Ain15551United Arab Emirates Department of Business Information Technology Liwa CollegeAbu Dhabi41009United Arab Emirates Information System&Security United Arab Emirates UniversityAl Ain15551United Arab Emirates

In this paper,we explore the ever-changing field ofDigital Twins(DT)in the Industrial Internet of Things(IIoT)context,emphasizing their critical role in advancing Industry 4.0 toward the frontiers of Industry *** article explores the applications of DT in several industrial sectors and their smooth integration into the IIoT,focusing on the fundamentals of digital twins and emphasizing the importance of virtual-real *** discusses the emergence of DT,contextualizing its evolution within the framework of *** study categorizes the different types of DT,including prototypes and instances,and provides an in-depth analysis of the enabling technologies such as IoT,Artificial Intelligence(AI),Extended Reality(XR),cloud computing,and the Application Programming Interface(API).The paper demonstrates theDT advantages through the practical integration of real-world case studies,which highlights the technology’s exceptional capacity to improve traceability and fault detection within the context of the *** paper offers a focused,application-driven perspective on DTs in IIoT,specifically highlighting their role in key production phases such as designing,intelligent manufacturing,maintenance,resource management,automation,security,and *** emphasizing their potential to support human-centric,sustainable advancements in Industry 5.0,this study distinguishes itself from existing *** provides valuable insights that connect theoretical advancements with practical implementation,making it a crucial resource for researchers,practitioners,and industry professionals.

关键词： Digital twin in industry integration of DT in IIoT artificial intelligence AI-Driven DT applications

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Multi-Modal data Fusion Approach Using the Structured and Sparse Principal Component Analysis (MMSSCA)

Multi-Modal Data Fusion Approach Using the Structured and Sp...

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Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Jyoti Kandpal A Jonitha Janaki Sivakumar Rejon Kumar Ray School of Computing Graphic Era Hill University Dehradun Uttarakhand India Faculty of Data Science & Computing Universiti Malaysia Kelantan Malaysia Computer Science and Creative Technologies Global College of Engineering and Technology Muscat Oman Department of Business analytics Gannon University Erie PA USA

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

There are many different sorts of data that can be gathered and analyzed, including pictures, videos, texts, speeches, music, and other noises, Video content, for example, generally includes minimum some types of audible sounds or any type of visual image, and often also useful text data that can be of some specific type of titles or subtitles, which may consist of some unique type text language which is different type of speech. “Modality” is basically defines each different type of acquisition strategy. As natural phenomena tend to be quite complex, it is unusual for a single modality to provide exhaustive understanding of the phenomenon of interest. The process of combining and interpreting the useful data between the same modal or in different modal, and also interaction with the human beings with multi-modal data fusion &the accurate and deep interpretation and understanding of data obtained from the combining process from it, are central to the research challenge on multimodal data analysis. We contend that many of these challenges are shared by researchers in different fields. Both the “why” and the “how” of data fusion are addressed in this paper. Various examples from the realms of science and technology serve as inspiration for the first problem, which is then suggested by a various types of mathematical based model framework demonstrating the utility of data fusion. To solve the second problem, we introduce the idea of “diversity” as a central concept and discuss several data-driven solutions based on matrix and tensor decompositions, highlighting how they take into account diversity in the underlying data sets to address the aforementioned issues, this research suggests a novel approach for modeling multi data fusion implemented using sparse technique and structural analysis.

关键词： Visualization Tensors Diversity reception Noise data integration Music data models

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Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection 39

Look Inside for More: Internal Spatial Modality Perception f...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)157735897X

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Feature extraction

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An Evaluation and Implementation of Pretty Good Privacy (PGP) in Wireless Network Security

An Evaluation and Implementation of Pretty Good Privacy (PGP...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Arvind Hans M. Vigenesh Yuvaraj S Suvarna R. Bhagwat Hamza Zaki Chiging Yamang Faculty Of Business Mangagement & Commerce Usha Martin University Ranchi India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department Of Computer Science Mangalayatan University Aligarh India Department of Bussiness & Management Himalayan University Itanagar India

Top privateers (PGP) is an encryption protocol that shields statistics saved, exchanged, or transmitted over wireless networks. PGP provides a green manner to comfy WiFi networks and communications using symmetric and publickey cryptography. This paper evaluates and examines the utility of PGP in wireless network protection. The paper critiques the layout and implementation of PGP on specific varieties of Wi-Fi networks, which include the partnerships to be had for implementing PGP in pre-present and destiny wireless infrastructures. Similarly to the numerous partnerships, the paper explores PGP's unique protocols and technologies and how they can be integrated inside Wi-Fi networks based on their extraordinary capabilities and functions. The paper additionally discusses the commercial advertising of PGP, its effect on wireless community protection, and the packages of PGP, in addition to a number of the demanding situations and threats related to its use. Moreover, the paper highlights the advantages of implementing PGP in wireless community safety. It gives a diffusion of trying out and implementation strategies that can be used to ensure a successful implementation. The paper concludes with a dialogue about the possible destiny directions of PGP and how it could be used to ease and protect wireless networks.

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Machine Learning Heart Disease Prediction Using KNN and RTC Algorithm

Machine Learning Heart Disease Prediction Using KNN and RTC ...

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International Conference on Power, Energy, Control and Transmission systems (ICPECTS)

作者： Senthil G. A R. Prabha M Razmah T. Veeramakali Sridevi S Yashini R Dept of Information Technology Agni College of Technology Chennai India Dept of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Dept of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Sri Ramachandra Engineering and Technology Chennai India

ISBN: (纸本)9781665462761

Heart Disease or cardiovascular disease refers to the range of heart conditions like cardiac arrest, coronary artery disease. Heart disease can be very well hindered through certain lifestyle changes. There is a significant increase in the mortality rate recently due to the distinctive heart diseases. Machine learning uses mathematical models to work efficiently with the enormous amount of data. It plays a crucial role in medical science in the prediction of distinctive diseases. Cardiologists inspects the heart functionality using electrocardiography, computed tomography. These tests are quite expensive for a common man. Recent times, the life span of a human is guaranteed only with the support of medications. As prevention is better than the cure, machine learning helps to predict the vulnerability of a heart disease with few elemental symptoms and health factors. It is been fed by the basic data of the patients like age, sex. Machine learning helps to predict the vulnerability in advance which provides the cardiologists with great acumen for the adaption of the treatment. Machine learning algorithms have proven to produce reliable and accurate output with the help of the inputs. The algorithms used in the article include KNN and decision tree classifier which is compared to yield the desired and efficient output.

关键词： Heart Machine learning algorithms Predictive models Prediction algorithms data models Real-time systems Classification algorithms

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RETRACTED ARTICLE: A systematic Survey on Implementation of Fuzzy Regression Models for Real Life Applications (Archives of Computational Methods in engineering, (2024), 31, 1, (291-311), 10.1007/s11831-023-09978-x)

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Archives of Computational Methods in engineering 2025年

作者： Khan, Mufala Kumar, Rakesh Aledaily, Arwa N. Kariri, Elham Viriyasitavat, Wattana Yadav, Kusum Dhiman, Gaurav Kaur, Amandeep Sharma, Ashutosh Vimal, S. Department of Mathematics Lovely Professional University Punjab Phagwara144411 India Department of Computer Science Government Bikram College of Commerce Punjab Patiala147001 India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al‑Kharj Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Mohali Gharuan140413 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India Division of Research and Development Lovely Professional University Phagwara India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Tamil Nadu Rajaplayam India

The Editors-in-Chief have retracted this article. After publication, concerns were raised about the originality of the study reported. An investigation conducted by the Publisher confirmed that the article contains material that substantially overlaps with a previous study by different authors [1]. In particular, the research methodology, research questions, numerous formulations, and discussion are almost identical with those in [1]. The Editors-in-Chief therefore consider this article redundant. None of the authors have responded to correspondence from the Publisher about this retraction. © The Author(s) under exclusive licence to International Center for Numerical Methods in engineering (CIMNE) 2025.

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Explainable deep stacking ensemble model for accurate and transparent brain tumor diagnosis

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Computers in Biology and Medicine 2025年 191卷 110166-110166页

作者： Haque, Rezaul Khan, Mahbub Alam Rahman, Hamdadur Khan, Shakil Siddiqui, Md Ismail Hossain Limon, Zishad Hossain Swapno, S M Masfequier Rahman Appaji, Abhishek Department of Computer Science and Engineering East West University A 2 Jahurul Islam Ave Dhaka1212 Bangladesh Department of Management Information System Pacific State University 3424 Wilshire Blvd. 12th Floor Los Angeles CA90010 United States Department of Management of Information System International American University 3440 Wilshire Blvd STE 1000 Los AngelesCA90010 United States Department of Business Analytics International American University 3440 Wilshire Blvd STE 1000 Los AngelesCA90010 United States Department of Engineering/Industrial Management Westcliff University IrvineCA92614 United States Department of Computer Science Westcliff University IrvineCA92614 United States Department Of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur-2 Dhaka1216 Bangladesh Department of Medical Electronics Engineering B.M.S. College of Engineering Bangalore560019 India Maastricht University University Eye Clinic Maastricht Maastricht Netherlands

Early detection of brain tumors in MRI images is vital for improving treatment results. However, deep learning models face challenges like limited dataset diversity, class imbalance, and insufficient interpretability. Most studies rely on small, single-source datasets and do not combine different feature extraction techniques for better classification. To address these challenges, we propose a robust and explainable stacking ensemble model for multiclass brain tumor classification. To address these challenges, we propose a stacking ensemble model that combines EfficientNetB0, MobileNetV2, GoogleNet, and Multi-level CapsuleNet, using CatBoost as the meta-learner for improved feature aggregation and classification accuracy. This ensemble approach captures complex tumor characteristics while enhancing robustness and interpretability. The proposed model integrates EfficientNetB0, MobileNetV2, GoogleNet, and a Multi-level CapsuleNet within a stacking framework, utilizing CatBoost as the meta-learner to improve feature aggregation and classification accuracy. We created two large MRI datasets by merging data from four sources: BraTS, Msoud, Br35H, and SARTAJ. To tackle class imbalance, we applied Borderline-SMOTE and data augmentation. We also utilized feature extraction methods, along with PCA and Gray Wolf Optimization (GWO). Our model was validated through confidence interval analysis and statistical tests, demonstrating superior performance. Error analysis revealed misclassification trends, and we assessed computational efficiency regarding inference speed and resource usage. The proposed ensemble achieved 97.81% F1 score and 98.75% PR AUC on M1, and 98.32% F1 score with 99.34% PR AUC on M2. Moreover, the model consistently surpassed state-of-the-art CNNs, Vision Transformers, and other ensemble methods in classifying brain tumors across individual four datasets. Finally, we developed a web-based diagnostic tool that enables clinicians to interact with the proposed mo

关键词： Magnetic resonance imaging

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EdgeDis: Enabling Fast, Economical, and Reliable data Dissemination for Mobile Edge Computing

arXiv

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arXiv 2023年

作者： Li, Bo He, Qiang Chen, Feifei Lyu, Lingjuan Bouguettaya, Athman Yang, Yun The College of Arts Business Law Education and Information Technology Victoria University MelbourneVIC3122 Australia The National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab. Cluster and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China The Department of Computing Technologies Swinburne University of Technology MelbourneVIC3122 Australia The School of Information Technology Deakin University Geelong Australia SONY AI Inc. Tokyo108-0075 Japan The School of Computer Science University of Sydney CamperdownNSW2006 Australia

Mobile edge computing (MEC) enables web data caching in close geographic proximity to end users. Popular data can be cached on edge servers located less than hundreds of meters away from end users. This ensures bounded latency guarantees for various latency-sensitive web applications. However, transmitting a large volume of data out of the cloud onto many geographically-distributed web servers individually can be expensive. In addition, web content dissemination may be interrupted by various intentional and accidental events in the volatile MEC environment, which undermines dissemination efficiency and subsequently incurs extra transmission costs. To tackle the above challenges, we present a novel scheme named EdgeDis that coordinates data dissemination by distributed consensus among those servers. We analyze EdgeDis’s validity theoretically and evaluate its performance experimentally. Results demonstrate that compared with baseline and state-of-the-art schemes, EdgeDis: 1) is 5.97x - 7.52x faster;2) reduces dissemination costs by 48.21% to 91.87%;and 3) reduces performance loss caused by dissemination failures by up to 97.30% in time and 96.35% in costs. © 2023, CC BY-NC-SA.

关键词： Efficiency

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