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Towards Aspect Based Components Integration Framework for Cyber-Physical System

computers, Materials & Continua 2022年第1期70卷 653-668页

作者： Sadia Ali Yaser Hafeez Muhammad Bilal Saqib Saeed Kyung Sup Kwak University Institute of Information Technology Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindi46000Pakistan Department of Computer Engineering Hankuk University of Foreign StudiesYongin-si17035Korea Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982DammamSaudi Arabia Department of Information and Communication Engineering Inha UniversityIncheon22212Korea

Cyber-Physical Systems(CPS)comprise interactive computation,networking,and physical *** integrative environment of CPS enables the smart systems to be aware of the surrounding physical *** systems,such as smart health care systems,smart homes,smart transportation,and smart cities,are made up of complex and dynamic *** components integration development approach should be based on the divide and conquer *** way multiple interactive components can reduce the development complexity *** reusability enhances efficiency and consistency in CPS,encapsulation of component functionalities and a well-designed user interface is vital for the better end-user’s Quality of Experience(QoE).Thus,incorrect interaction of interfaces in the cyber-physical system causes system ***,interface failures occur due to false,and ambiguous requirements analysis and ***,to resolve this issue semantic analysis is required for different stakeholders’viewpoint analysis during requirement specification and components *** work proposes a framework to improve the CPS component integration process,starting from requirement specification to prioritization of components for *** semantic analysis and assessing the reusability of specifications,the framework uses text mining and case-based reasoning *** framework has been tested experimentally,and the results show a significant reduction in ambiguity,redundancy,and irrelevancy,as well as increasing accuracy of interface interactions,component selection,and higher user satisfaction.

关键词： Cyber-physical systems component-based development casebased reasoning prioritization requirement management specification text mining

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Video Action Recognition Method Based on Personalized Federated Learning and Spatiotemporal Features

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computers, Materials & Continua 2025年第6期83卷 4961-4978页

作者： Rongsen Wu Jie Xu Yuhang Zhang Changming Zhao Yiweng Xie Zelei Wu Yunji Li Jinhong Guo Shiyang Tang School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Computer Science Chengdu University of Information TechnologyChengdu610225China Shanghai Key Lab of Intelligent Information Processing School of CSFudan UniversityShanghai200433China School of Sensing Science and Engineering Shanghai Jiao Tong UniversityShanghai200240China School of Mechanical and Manufacturing Engineering University of New South WalesSydney2052Australia School of Electronics and Computer Science University of SouthamptonSouthamptonSO171BJUK

With the rapid development of artificial intelligence and Internet of Things technologies,video action recognition technology is widely applied in various scenarios,such as personal life and industrial ***,while enjoying the convenience brought by this technology,it is crucial to effectively protect the privacy of users’video ***,this paper proposes a video action recognition method based on personalized federated learning and spatiotemporal *** the framework of federated learning,a video action recognition method leveraging spatiotemporal features is *** the local spatiotemporal features of the video,a new differential information extraction scheme is proposed to extract differential features with a single RGB frame as the center,and a spatialtemporal module based on local information is designed to improve the effectiveness of local feature extraction;for the global temporal features,a method of extracting action rhythm features using differential technology is proposed,and a timemodule based on global information is *** translational strides are used in the module to obtain bidirectional differential features under different action ***,to address user data privacy issues,the method divides model parameters into local private parameters and public parameters based on the structure of the video action recognition *** approach enhancesmodel training performance and ensures the security of video *** experimental results show that under personalized federated learning conditions,an average accuracy of 97.792%was achieved on the UCF-101 dataset,which is non-independent and identically distributed(non-IID).This research provides technical support for privacy protection in video action recognition.

关键词： Video action recognition personalized federated learning spatiotemporal features data privacy

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Energy-Efficient Shop Scheduling Using Space-Cooperation Multi-Objective Optimization

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 601-615页

作者： Ding, Jiepin Xia, Jun Yang, Yaning Zhou, Junlong Chen, Mingsong Li, Keqin East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai200062 China Ningxia Normal University School of Physics and Electronic Information Engineering Guyuan756500 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China State University of New York Department of Computer Science New PaltzNY12561 United States

Since Industry 5.0 emphasizes that manufacturing enterprises should raise awareness of social contribution to achieve sustainable development, more and more meta-heuristic algorithms are investigated to save energy in manufacturing systems. Although non-dominated sorting-based meta-heuristics have been recognized as promising multi-objective optimization methods for solving the energy-efficient flexible job shop scheduling problem (EFJSP), it is hard to guarantee the quality of the Pareto front (e.g., total energy consumption, makespan) due to the lack of population diversity. This is mainly because an improper individual comparison inevitably reduces population diversity, thus limiting exploration and exploitation abilities during population updates. To achieve efficient population evolution, this paper introduces a novel space-cooperation multi-objective optimization (SCMO) method that can effectively solve EFJSP to obtain scheduling schemes with better trade-offs. By cooperatively evaluating the similarity among individuals in both the decision space and objective space, we propose a space-cooperation population update method based on a three-vector representation that can accurately eliminate repetitive individuals to derive higher-quality Pareto solutions. To further improve search efficiency, we propose a difference-driven local search, which selectively changes the positions of operations with higher differences to search for neighbors effectively. Based on the Taguchi method, we conduct experiments to obtain a suitable parameter combination of SCMO. Comprehensive experimental results show that, compared to state-of-the-art methods, our SCMO method achieves the highest HV and NR and the lowest IGD, with an average of 0.990, 0.952, and 0.001, respectively. Meanwhile, compared to traditional local search approaches, our difference-driven local search obtains twice the HV on instance Mk12 and reduces the solving time from 1521s to 475s. © 2016 IEEE.

关键词： Job shop scheduling

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LeXNet++: Layer-wise eXplainable ResUNet++ framework for segmentation of colorectal polyp cancer images

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Neural Computing and Applications 2025年第1期37卷 213-229页

作者： Das, Surajit Khan, Soumya Suvra Sengupta, Diganta Debashis, De Dept. of Information Technology Meghnad Saha Institute of Technology Behind Urbana Complex Near Ruby General Hospital Anandapur Rd Uchhepota Kolkata700150 India Dept. of Computer Science & Engineering Meghnad Saha Institute of Technology Behind Urbana Complex Near Ruby General Hospital Anandapur Rd Uchhepota Kolkata700150 India Dept. of Computer Science & Engineering Heritage Institute of Technology Chowbaga Road West Bengal Anandapur Kolkata700107 India Dept. of Computer Science & Engineering Maulana Abul Kalam Azad University of Technology West Bengal NH-12 Simhat Haringhata West Bengal Nadia741249 India

Colorectal polyps are benign lesions that develop in the colon and can progress to cancer if left untreated. Clinical observations from medical images are often preferred over computational results due to the lack of trust in the machine learning models, thereby posing serious challenge for the explainability of the results. In order to computationally diagnose colorectal polyps from cancerous images and explain the results, we propose a Layer-wise eXplainable ResUNet++ (LeXNet++) framework for segmentation of the cancerous images, followed by layer-wise explanation of the results. We utilize a publicly accessible dataset that contains of 612 raw images with a resolution of 256×256×3 and an additional 612 clinically annotated and labeled images with a resolution of 256×256×1, which includes the infected region. The LeXNet++ framework comprises of three components—encoder, decoder and the bridge. The encoder and the decoder components each comprise of four layers. Each of the four layers in the encoder and the decoder comprises of 14 and 11 internal sub-layers, respectively. Among the sub-layers of the encoder and the decoder, there are three 3×3 convolutional layers with an additional 3×3 convolution-transpose layer in the decoder. The output of each of the sub-layers has been explained through heatmap generation after each iteration which have been further explained. The encoder and the decoder are connected by the bridge which comprises of three sub-layers. The results obtained from these three sub-layers have also been explained to inculcate trust in the findings. In this study, we have used three models to segment the images, namely UNet, ResUNet, and proposed LeXNet++. LeXNet++ exhibited the best result among the three models in terms of performance;hence, only LeXNet++ was explained layer-wise. Apart from explanation of the results fetched in this study, the performance of the proposed explainable model has been observed to be 2% greater than the existing poly

关键词： Image segmentation

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An effective object detection via BS2ResNet and LTK-Bi-LSTM

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Multimedia Tools and Applications 2025年 1-20页

作者： V, Arulalan V, Premanand Kumar, Dhananjay Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu District Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering Vellore Institute Technology Chennai Campus Tamil Nadu Chennai India Department of Information Technology Anna University MIT Campus Tamil Nadu Chennai India

Video analytics faces complex challenges in object detection and classification. Deep learning based approaches have achieved remarkable success in past decade. However, existing object identification models that utilize backbone’s core features still present challenges due to their lack of semantic information. To address these issues, a novel object detection and classification framework utilizing Batch normalization and Softswish activation adapted ResNet (BS2ResNet) and Logistic Tanh Kaiming Bi-directional Long Short Term Memory (LTK-Bi-LSTM) techniques was proposed. The framework employs frame conversion, noise removal, and contrast elevation during frame pre-processing, followed by background subtraction using the Supreme Distance-centered Fuzzy C-Means (SD-FCM) clustering algorithm, and edge detection using the Hyperbolic Tangent Kernel Canny Edge Detector (HTKCED). BS2ResNet is then used for object detection, and features are extracted and passed to the LTK-Bi-LSTM neural network for object classification. The proposed system was found to improve object detection and classification accuracy, outperforming existing techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Long short-term memory

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Enhancing Cyber Security through Automated Machine Learning Techniques 1

Enhancing Cyber Security through Automated Machine Learning ...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Kirti, H. Das, Neeraj Zaidi, Taskeen Singh Dar, Sukhvinder Kumar Jain, Sandeep Rajan, T. Surulivel Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Department of Computer Science and Information Technology Karnataka Bangalore India Symbiosis Law School Nagpur Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350359756

The growing prevalence of digitized statistics and virtual structures has made cyber safety a vital pillar for present day companies. Cyber security threats will have devastating results on businesses, from revenue loss to logo reputation damage. To defend against those ever-evolving threats, groups need to proactively screen their virtual environment and use automated machine getting to know techniques to successfully hit upon anomalous conduct that could sign a capability breach. computerized machine studying gives an effective way to scale a business enterprise's security posture even as lowering the strain on human security sources. This technical summary outlines the modern nation of automatic gadget gaining knowledge of strategies for cyber protection and how automatic device getting to know can be used to enhance the security of a digital environment. This abstract additionally explores the demanding situations that groups might also face in leveraging computerized gadget learning strategies for cyber security, and descriptions strategies for effectively integrating automatic gadget getting to know into an employer's safety posture. © 2023 IEEE.

关键词： Cybersecurity

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Connectivity-Driven Pseudo-Labeling Makes Stronger Cross-Domain Segmenters 38

Connectivity-Driven Pseudo-Labeling Makes Stronger Cross-Dom...

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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Zhao, Dong Zang, Qi Wang, Shuang Sebe, Nicu Zhong, Zhun School of Artificial Intelligence Xidian University Shaanxi China Department of Information Engineering and Computer Science University of Trento Italy School of Computer Science and Information Engineering Hefei University of Technology China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

Presently, pseudo-labeling stands as a prevailing approach in cross-domain semantic segmentation, enhancing model efficacy by training with pixels assigned with reliable pseudo-labels. However, we identify two key limitations within this paradigm: (1) under relatively severe domain shifts, most selected reliable pixels appear speckled and remain noisy. (2) when dealing with wild data, some pixels belonging to the open-set class may exhibit high confidence and also appear speckled. These two points make it difficult for the pixel-level selection mechanism to identify and correct these speckled close- and open-set noises. As a result, error accumulation is continuously introduced into subsequent self-training, leading to inefficiencies in pseudo-labeling. To address these limitations, we propose a novel method called Semantic Connectivity-driven Pseudo-labeling (SeCo). SeCo formulates pseudo-labels at the connectivity level, which makes it easier to locate and correct closed and open set noise. Specifically, SeCo comprises two key components: Pixel Semantic Aggregation (PSA) and Semantic Connectivity Correction (SCC). Initially, PSA categorizes semantics into "stuff" and "things" categories and aggregates speckled pseudo-labels into semantic connectivity through efficient interaction with the Segment Anything Model (SAM). This enables us not only to obtain accurate boundaries but also simplifies noise localization. Subsequently, SCC introduces a simple connectivity classification task, which enables us to locate and correct connectivity noise with the guidance of loss distribution. Extensive experiments demonstrate that SeCo can be flexibly applied to various cross-domain semantic segmentation tasks, i.e. domain generalization and domain adaptation, even including source-free, and black-box domain adaptation, significantly improving the performance of existing state-of-the-art methods. The code is available at https://***/DZhaoXd/SeCo. © 2024 Neural information

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Smart City Survey on AIoT Using Machine Learning, Deep Learning, and Its Computing Tools 5th

Smart City Survey on AIoT Using Machine Learning, Deep Lear...

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Priakanth, P. Jothimani, K. Gopikrishnan, S. Linga Reddy, C. Department of Computer Science and Design Kongu Engineering College Erode India School of Computer Science and Engineering VIT-AP University Amaravati India Department of Information and Communication Technology University of Agder Kristiansand Norway

ISBN: (纸本)9789819735228

Urban-city administrators are proceeding on smart applications with the fully integrated IoT-artificial intelligence analytics explication assists intelligent-safer workstation, immediate happenstance response, disaster control management including smart electricity, trash, air and water allowing them to produce more prominent levels of flexibility, yielding, sustainability and profitability. Though smart city notion is gradually developing and yet not established all over the world owing to economical, technological, and administrating blockades. This perspective paper aims to accomplish a review of IoT-enabled artificial intelligence regarding the usage of machine learning and deep learning for the development of smarter cities in addition to different computing tools to employ methods, platforms, architectures, and its real-world enactment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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MetaCo-Distillation: A Robust Collaborative Distillation Framework for Multivariate Knowledge 22

MetaCo-Distillation: A Robust Collaborative Distillation Fra...

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22nd IEEE International Conference on Smart City, SmartCity 2024

作者： Wang, Jianing Liu, Jiqiang Zhang, Tao Wang, Jian Tang, Xiangyun Sun, Geng Yang, Zhaohui Han, Zhu Niyato, Dusit School of Cyberspace Science and Technology Beijing Jiaotong University Beijing China School of Information Engineering Minzu University of China Beijing China College of Computer Science and Technology Jilin University Changchun China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Electrical and Computer Engineering University of Houston HoustonTX United States School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798331540487

Federated Learning (FL), as a rapidly evolving privacy-preserving collaborative machine learning paradigm, has shown great potential in the emerging Metaverse areas. Although numerous studies have theoretically demonstrated the feasibility of FL, practical applications in the Metaverse face challenges such as learning forgetting due to non-independent and identically distributed (non-i.i.d.) data, high communication overhead from model parameter transmission, and the inability to accommodate heterogeneous models. To address these challenges, this paper proposes a collaborative distillation framework called Co-distillation. In this distributed environment, each device independently trains its local model using private data, communicates predictions on an unlabeled public dataset, and generates surrogate labels for the public dataset based on received predictions. These surrogate labels, along with the received predictions, serve as teacher logits for local model distillation. This approach is more adaptable to heterogeneous streaming data continuously generated by industrial devices, offers higher communication efficiency than traditional methods, allow for heterogeneous models. Experiments have been conducted, demonstrating the superior performance of the proposed framework. © 2024 IEEE.

关键词： Federated learning

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Cardiac Arrest Prediction Model Using Ensemble Technique 4

Cardiac Arrest Prediction Model Using Ensemble Technique

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Sinha, Dibakar More, Sarla Jeyakrishnan, V. Mishra, Durgesh Manipal University Jaipur Department of Computer Science Engineering Rajasthan Jaipur India Symbiosis University of Applied Sciences School of Computer Science and Information Technology MP Indore India

ISBN: (纸本)9798331518981

The physician employs a diverse range of laboratory diagnostics, physical examinations, and on occasion, even invasive procedures to ascertain the presence of diseases. It is necessary to have a physician that possesses exceptional training, expertise and skill in the specific field, for detection of the disease in its initial phases. Despite the widespread use of ensemble techniques in disease prediction, here is a notable absence of comprehensive evaluation of regularly utilized ensemble algorithms for highly studied diseases. Consumers typically resort to seeking medical assistance from a doctor or other medical expert only after exhausting all other home remedies without finding relief for their ailments. This is probably a result of insufficient comprehension, as well as other factors related to behavior. This paper introduces a conventional ensemble LSTM model that incorporates data preparation and feature engineering. The model proposed in this research represents major gains in numerous statistical parameters like precision, f score, accuracy and ROC curve in comparison to the other models. © 2024 IEEE.

关键词： Diseases

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