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Blockchain-assisted Puncturable Signcryption for Cloud and Fog-Hosted Industrial Cyber-Physical Systems

IEEE Network 2025年

作者： Ma, Qingru Shen, Jian Pandi, Vijayakumar Gupta, Brij B. Arya, Varsha Mahalinghum, Maheswari Zhejiang Sci-Tech University School of Computer Science and Technology School of Artificial Intelligence Hangzhou310018 China Zhejiang Sci-Tech University School of Information Science and Engineering School of Cyber Science and Technology Zhejiang Key Laboratory of Digital Fashion and Data Governance Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Hangzhou310018 China Department of Computer Science and Engineering Tamil Nadu Tiruchirappalli620009 India Asia University Department of Computer Science and Information Engineering Taichung413 Taiwan China Medical University Hospital China Medical University Department of Medical Research Taichung Taiwan Pune412115 India School of Cybersecurity Korea University Seoul Korea Republic of Kyung Hee University Seoul02447 Korea Republic of Lebanese American University Beirut1102 Lebanon Center for Interdisciplinary Research Dehradun India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India

The widespread adoption of emerging technologies, such as interconnected sensors and advanced automation systems, has led to rapid advancements in smart industrial environments. While industrial cyber-physical systems (ICPSs) bring efficiency and innovation to industrial processes, the security risks are also of great concern. The existing work provides many methods for data security and authentication for ICPSs. However, since the authentication status of ICPSs is changeable, the up-to-date methods can hardly meet the requirements for rapid authentication and secure data transmission. What’s more, largescale message revocation incurs significant communication and computational costs. This paper proposes a blockchain-assisted identity-based puncturable signcryption (BC-IBPSC) scheme for cloud and fog-hosted ICPSs. First, based on the concepts of puncturable encryption and signature, a novel identity-based puncturable signcryption method is presented to achieve efficient verifiable data encryption. Second, the blockchain is utilized to record all valid and punctured messages, to achieve evidence storage and traceability. The results of the analysis indicate that the scheme ensures security and operates efficiently. In addition, experiments show that the size of the signcrypted files does not affect the value of Gas consumption on the blockchain when uploading proofs. © 2025 IEEE. All rights reserved.

关键词： Authentication

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Dynamic Lane Management with IoT for Real-time Lane Configuration and Traffic Flow

Dynamic Lane Management with IoT for Real-time Lane Configur...

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Computer, Communication and Control (IC4), International Conference on

作者： Mohammed Rehaman Pasha S. Latha R. Usha V. Bini Marin M.V Ishwarya M. Muthulekshmi Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India Department of Department of Biotechnology Vinayaka Mission’s Kirupananda Variyar Engineering College Vinayaka Mission’s Research Foundation(DU) Salem Tamil Nadu India Department of Mathematics S. A. Engineering College Chennai Tamil Nadu India Faculty of Management Studies SRM Institute of Science and Technology Ramapuram Chennai Tamil Nadu India Artificial intelligence and Data science Department Agni College of Technology Thazhambur Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Traffic may be reduced and transportation efficiency improved by properly controlling traffic flow in urban areas. This research provides a new method for managing lanes in real-time by using IoT connectivity. To optimize traffic flow in response to shifting demand patterns, the suggested system prioritizes making lane design changes in real time depending on the current traffic conditions. Using information collected from Internet of Things (IoT) sensors and linked automobiles, the system may dynamically allot lanes in response to variations in traffic volumes and congestion. It describes developing and deploying a dynamic lane control system based on the Internet of Things, stressing the technology’s potential to improve traffic flow in congested metropolitan areas. The simulation results show its efficiency in decreasing traffic and increasing throughput. It demonstrates the potential of intelligent transportation systems for creating smarter and more efficient cities by combining IoT, real-time data processing, and traffic control.

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Optimizing Supply Chain Security Using ACO and Blockchain: Integrating Root of Trust, Unclonable Functions, and SBOM for Cybersecurity

Optimizing Supply Chain Security Using ACO and Blockchain: I...

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2025 International Conference on Visual Analytics and data Visualization, ICVADV 2025

作者： Beevi, L Sherin Bhama, Ponsy R K Sathia Vijayan, J Albert Joe Prathap, P.M. Dani, W Vinil Premalatha, J. R.M.D. Engineering College Department of Computer Science and Engineering Kavaraipettai India Madras Institute of Technology Anna University Department of Computer Technology Chromepet India Amaala Utilities Ppp Larsen & Toubro Limited Saudi Arabia St. Peter's College of Engg.and Tech. Department of Eee Chennai India Dhanalakshmi Srinivasan College of Engg. and Tech. Department of Artificial Intelligence and Data Science Mamallapuram India

ISBN: (纸本)9798331521394

Proposed work describes an optimal framework for improving supply chain security and delivery efficiency by combining Ant Colony Optimization (ACO) and blockchain technology. Inconvenient path design is a primary trouble in last-mile delivery, causing delays and wasting resources. This study seeks to address the above issue by optimizing delivery schedules, guaranteeing that many purchasers in the same geographic area are handled by a single delivery person, hence eliminating redundant trips and delivery times. Proposed technique is driven by real-world inefficiencies seen in e-commerce systems, such as the case of two independent things bought to the same address but delivered at different times by different couriers on the same day. ACO is used to find the best delivery route, guaranteeing that all consumers in a particular area receive their items simultaneously. The framework incorporates blockchain technology to improve security, transparency, and traceability during the delivery process. Furthermore, advanced cybersecurity measures such as Root of Trust (RoT), Physically Unclonable Functions (PUFs), and Software Bill of Materials (SBOM) are used to secure data integrity and enable third-party monitoring. By combining ACO for route optimization with blockchain for safe transaction processing, this system not only improves shipping time as well as asset efficiency, but it also protects supply chain cybersecurity. The suggested approach provides a scalable and reliable framework for future smart logistics and e-commerce applications, assuring operational efficiency and security. © 2025 IEEE.

关键词： Ant colony optimization

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The Potential of Large Language Models in Supply Chain Management: Advancing Decision-Making, Efficiency, and Innovation

arXiv

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arXiv 2025年

作者： Aghaei, Raha Kiaei, Ali A. Boush, Mahnaz Vahidi, Javad Barzegar, Zeynab Rofoosheh, Mahan School of Mathematics and Computer Science Iran University of Science & Technology Tehran Iran Department of Computer engineering Sharif University of Technology Tehran Iran Department of Artificial Intelligence in Medicine Faculty of Advanced Technologies in Medicine Iran University of Medical Sciences Tehran Iran Cellular and Molecular Biology Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Computer engineering group Alborz Vocational Technical University Alborz Iran

The integration of large language models (LLMs) into supply chain management (SCM) is revolutionizing the industry by improving decision-making, predictive analytics, and operational efficiency. This white paper explores the transformative impact of LLMs on various SCM functions, including demand forecasting, inventory management, supplier relationship management, and logistics optimization. By leveraging advanced data analytics and real-time insights, LLMs enable organizations to optimize resources, reduce costs, and improve responsiveness to market changes. Key findings highlight the benefits of integrating LLMs with emerging technologies such as IoT, blockchain, and robotics, which together create smarter and more autonomous supply chains. Ethical considerations, including bias mitigation and data protection, are taken into account to ensure fair and transparent AI practices. In addition, the paper discusses the need to educate the workforce on how to manage new AI-driven processes and the long-term strategic benefits of adopting LLMs. Strategic recommendations for SCM professionals include investing in high-quality data management, promoting cross-functional collaboration, and aligning LLM initiatives with overall business goals. The findings highlight the potential of LLMs to drive innovation, sustainability, and competitive advantage in the ever-changing supply chain management landscape. Copyright © 2025, The Authors. All rights reserved.

关键词： Competition

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AI in Medicine: CNN Techniques for Pneumonia and Pandemic Pathogen Identification

AI in Medicine: CNN Techniques for Pneumonia and Pandemic Pa...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Ganji Ramanjaiah Nagendar Yamsani Smritilekha Das Balajee Maram Ananda Babu Rudrubati Katakam Venkateswara Rao Dept. of Computer Science & Engineering (Data Science) R VR & JC College of Engineering Chowdavaram Guntur Andhra Pradesh India School of Computer Science and Artificial Intelligence SR University Warangal Telangana Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Dept. Of Computer Science & Engineering (AI & DS) Kallam Haranadhareddy Institute of Technology Chowdavaram Guntur Andhra Pradesh India Dept of Computer Science and Engineering Koneru lakshmaiah Education Foundation Vaddeswaram Guntur DT Andhra Pradesh India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

The current paper deals with the applicability of Convolutional Neural Networks in the detection of pneumonia and pandemic pathogens using medical imaging data. Leveraging the transformative power of AI in our study, we have placed a focus on the development and evaluation of a CNN model that will be trained on a heterogeneous dataset of chest X-rays and CT scans. This paper will propose a method for developing a CNN to classify images from a diverse dataset of chest X-rays and CT scans, ensuring diagnostic accuracy and speed to meet the critical challenges involved in disease diagnosis in a timely manner. Our results show that the CNN model has high accuracy, sensitivity, and specificity for the identification of pneumonia and differentiation of pandemic pathogens from others, surpassing conventional diagnostic techniques. What was more important to underline in this study was that the model demonstrated strong performance across multiple datasets and, more importantly, can easily be transplanted to clinical workflows. Improvement in the early detection capacity made this research emblematic of the huge role AI is expected to play in redefining health care, especially with respiratory diseases and pandemic preparedness. Future work will extend to the augmentation of the dataset and the fine-tuning of the model for its full clinical application.

关键词： Pathogens Pneumonia Accuracy Pandemics Computed tomography Sensitivity and specificity Convolutional neural networks artificial intelligence Medical diagnostic imaging X-ray imaging

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Multi-granularity prior networks for uncertainty-informed patient-specific quality assurance

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Computers in Biology and Medicine 2024年 179卷 108925页

作者： Zeng, Xiaoyang Zhu, Qizhen Ahmed, Awais Hanif, Muhammad Hou, Mengshu Jie, Qiu Xi, Rui Shah, Syed Attique School of Computer Science and Engineering University of Electronic Science and Technology of China - UESTC Sichuan611731 China Department of Radiation Oncology Peking Union Medical College Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China School of Big Data and Artificial Intelligence Chengdu Technological University Sichuan611730 China School of Computing and Digital Technology Birmingham City University STEAMhouse BirminghamB4 7RQ United Kingdom

Deep Learning Automated Patient-Specific Quality Assurance (PSQA) aims to reduce clinical resource requirements. It is vital to ensure the safety and effectiveness of radiation therapy by predicting the dose difference metric (Gamma passing rate) and its distribution. However, current research overlooks uncertainty quantification in model predictions, limiting their trustworthiness in real clinical environments. This paper proposes a Multi-granularity Uncertainty Quantification (MGUQ) framework. A Bayesian framework that quantifies uncertainties at multiple granularities for multi-task PSQA, specifically Gamma Passing Rate (GPR) prediction and Dose Difference Prediction (DDP), integrates visualization-based interactive components. Using Bayesian theory, we derive a comprehensive multi-granularity loss function that comprises granularity-specific loss and coherence loss components. Additionally, we proposed Multi-granularity Prior Networks, a dual-stream network architecture, to infer the distributions of DDP (modeled as t-distributions) and GPR (modeled as Gaussian distributions) under specific statistical assumptions. Comprehensive evaluations are conducted on a dataset from ‘‘Peeking Union Medical College Hospital’’, and results show that our proposed method achieves a minimum MAE loss of 0.864 with a 2%/3 mm criterion and realizes the uncertainty visualization of dose difference. Further, it also achieves 100% Clinical Accuracy (CA) with a workload of 67.2%. Experiments demonstrate that the proposed framework can enhance the trustworthiness of deep learning applications in PSQA. © 2024

关键词： Forecasting

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SPOT (Sales Production based On Time-Series): A Comprehensive Approach to Sales Forecasting using Contextually-tailored Time Series Analysis

SPOT (Sales Production based On Time-Series): A Comprehensiv...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Mohammed Zama Aditya Jotawar Jiakai Du Fahim Islam Anik Nafisa Anjum Bilash Saha Nazmus Sakib Hossain Shahriar Institute for Artificial Intelligence and Data Science University at Buffalo SUNY New York United States Department of Mechanical Engineering Khulna University of Engineering & Technology Khulna Bangladesh Department of Information Technology Kennesaw State University Georgia United States

Unit sales in retail chain stores exhibit a significant degree of variance, affected by seasonality and special events. Individual products and categories also display a considerable difference in sales based on the geography of individual stores. In various businesses, a myriad of products is moved each day, and accurately predicted knowledge of sales facilitates the planning of logistics, warehousing, and procurement, besides other executive decision-making processes. Software developed for this purpose are undergoing continuous improvements. SPOT (Sales Production based On Time-Series) is an application to produce sales forecasts for customized input parameters – such as an individual store, state in which store is located, a particular product, category/sub-category of products, which will prove to be immensely useful for decision-making processes across a variety of concerned business units. SPOT delivers the results in the form of graphical summarization of selected data, along with its time-series forecasts. More incisive sales predictions can be exported as a tabular numeric file. For the purpose of demonstrating the entire process, data from Walmart was considered; however, this can be extended to any related application fields as well.

关键词： Procurement Time series analysis Decision making Warehousing Production Organizations Software

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Cloud-Supported Bayesian Networks for Predicting Plant Stress in Adaptive Agricultural Practices

Cloud-Supported Bayesian Networks for Predicting Plant Stres...

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Multi-Agent Systems for Collaborative intelligence (ICMSCI), International Conference on

作者： Pramod Pandey R. Aiyshwariya Devi Sree Southry S K. Balasubadra M. Rajmohan M. Muthulekshmi Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Maharashtra India Department of Artificial Intelligence and Data Science R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sona College of Technology Salem Tamil Nadu India Department of Information Technology R.M.D. Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

To maximize yields and optimize resource allocation in modern agriculture, planning for and managing plant stress is crucial. An innovative approach to plant stress prediction in adaptive agriculture is presented in this paper, which makes use of cloud-supported Bayesian networks (BNs). It combines real-time data from agricultural sensors with past records and environmental variables to build probabilistic models that can predict possible stress factors affecting plant health. The continually evolving features of agricultural ecosystems may be accommodated by using cloud infrastructure to train models and process data scalable. This technique successfully predicts degrees of plant stress, as shown in a thorough case study. This framework makes it easier to apply adaptive agricultural techniques by providing farmers with practical information based on predictive analytics. Resource-efficient practices appropriate to crop varieties and environmental circumstances also help make agricultural operations more sustainable. To overcome the challenges of modern agriculture, make informed decisions, and enhance agricultural systems.

关键词： Cloud computing Plants (biology) Scalability Biological system modeling Nutrients Real-time systems data models Sustainable development Stress Farming

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Intelligent Approach Integrating Multiagent Systems and Case-Based Reasoning in Brain-Computer Interface

Intelligent Approach Integrating Multiagent Systems and Case...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Kiran Malik Jagjit Singh Dhatterwal Kuldeep Singh Kaswan Manish Gupta Jyoti Thakur Department of Computer Science & Engineering MRIEM Rohtak Haryana India Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Guntur AP India School of Computer Science & Engineering Galgotias University Greater Noida UP India Director IQAC Lovely Professional University Punjab India Department of Information Technology GL Bajaj Institute of Technology and Management Greater Noida UP India

In this research paper investigates the integration of Multiagent Systems (MAS) and Case-Based Reasoning (CBR) within Brain-Computer Interfaces (BCls). The aim is to enhance decision-making and adaptation in BCls, addressing limitations of standalone approaches. The problem statement revolves around the need for improved BCI performance and adaptability. Methodology involves designing a hybrid MAS-CBR framework and evaluating its performance against traditional BCI systems. MAS enables collaborative decision-making among agents, while CBR facilitates learning from past cases. Integration is achieved by using MAS for agent coordination and CBR for case adaptation. Key findings indicate that the hybrid approach yields improved decision accuracy and adaptation compared to individual systems. The collaborative nature of MAS and the knowledge reuse of CBR synergize effectively, resulting in enhanced BCI performance. This integration showcases the potential to advance BCls beyond their current limitations. Implications of this research are significant, as it offers a novel approach to overcome BCI challenges. The hybrid MAS-CBR framework can be further refined to optimize real-time decision-making and adaptability in various application domains. Ultimately, this research contributes to the evolution of BCls by leveraging the strengths of both MAS and CBR, leading to more efficient and robust brain-computer interaction paradigms.

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Enhanced Prediction of Early-Stage Autism Using Supervised Learning with Optimized Feature engineering

Enhanced Prediction of Early-Stage Autism Using Supervised L...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Amritpal Sidhu Kiran K S P.D. More K.S. Bhuvaneshwari Prateek Aggarwal M. Sindhu Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Engineering and Technology Physics JAIN (Deemed to be University) Bangalore Karnataka India Computer Engineering Vishwakarma Institute of Technology Pune India Department of Artificial Intelligence and Data Science Karpagam College of Engineering Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of CSE Prince Shri Venkateshwara Padmavathy Engineering College Chennai India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

An autism spectrum disease (ASD) affects a person's brain development. Autism spectrum disorder is detected in children and young adults. For an early diagnosis of the disease, parental observation is crucial. That is why it is possible to provide children with effective therapy or treatment when they are young. Misdiagnosis is common due to the wide range of symptoms and levels of autism range syndrome. The datasets on children and toddlers with ASD that were identified at an early age were subjected to a limited number of Feature Transformation methods. The next step was to determine which classification methods performed best by applying them to various ASD datasets. Log, Power-Box Cox, and Yeo-Johnson Transformations for toddler datasets and Log transformations for children’s datasets were the FTs that followed the best prediction. The next step in using these altered datasets for early autism prediction is to use characteristic Selection Techniques in order to identify the most important ASD risk characteristic. In this comparison, the proposed method is the top machine learning technique with an incredible accuracy. Decision Tree, Random Forest (RF), K-Nearest Neighbours(KNN), Support Vector Machine(SVM), and Gradient Descent(GD) are some of the other models that perform a decent job, but they can't compare to the outstanding results that proposed method obtained. With an accuracy rate of 98.99%, proposed method stands out as the most reliable and efficient alternative among these methods.

关键词： Radio frequency Support vector machines Autism Pediatrics Accuracy Supervised learning Vectors Reliability Random forests Diseases

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