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Efficient Joint Key Authentication Model in E-Healthcare

computers, Materials & Continua 2022年第5期71卷 2739-2753页

作者： Muhammad Sajjad Tauqeer Safdar Malik Shahzada Khurram Akber Abid Gardezi Fawaz Alassery Habib Hamam Omar Cheikhrouhou Muhammad Shafiq Department of Computer Science Air University Multan CampusMultan60000Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur 63100Pakistan Department of Computer Science COMSATS University IslamabadIslamabadPakistan Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Faculty of Engineering Moncton UniversityNBE1A3E9Canada CES Laboratory National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Many patients have begun to use mobile applications to handle different health needs because they can better access high-speed Internet and *** devices and mobile applications are now increasingly used and integrated through the medical Internet of Things(mIoT).mIoT is an important part of the digital transformation of healthcare,because it can introduce new business models and allow efficiency improvements,cost control and improve patient *** the mIoT system,when migrating from traditional medical services to electronic medical services,patient protection and privacy are the priorities of each ***,it is recommended to use different user authentication and authorization methods to improve security and *** this paper,our prosed model involves a shared identity verification process with different situations in the e-health *** aim to reduce the strict and formal specification of the joint key authentication *** use the AVISPA tool to verify through the wellknown HLPSL specification language to develop user authentication and smart card use cases in a user-friendly *** model has economic and strategic advantages for healthcare organizations and healthcare *** medical staff can increase their knowledge and ability to analyze medical data more *** model can continuously track health indicators to automatically manage treatments and monitor health data in real ***,it can help customers prevent chronic diseases with the enhanced cognitive functions *** necessity for efficient identity verification in e-health care is even more crucial for cognitive mitigation because we increasingly rely on mIoT systems.

关键词： E-health systems joint key authentication mutual authentication

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A Hybrid Model Integrating Multi-Omic and Topological Information of PPI Network for Drug Synergism Prediction

A Hybrid Model Integrating Multi-Omic and Topological Inform...

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2023 RIVF International Conference on Computing and communication Technologies, RIVF 2023

作者： Nguyen, Giang T.T. Phuong, Kien T. Nguyen-Trong, Khanh Le, Duc-Hau Hanoi University of Science and Technology Network Information Center Hanoi Viet Nam School of Information and Communication Technology Hanoi University of Science and Technology Hanoi Viet Nam Institute of Technology Faculty of Information Technology Telecommunications Hanoi Viet Nam Thuyloi University Faculty of Computer Science and Engineering Hanoi Viet Nam

ISBN: (纸本)9798350315844

Combination therapies in precision medicine promise to enhance treatment efficacy and combat drug resistance. Current drug combination prediction methods struggle to extract valuable insights from multi-omics cell line data and integrate them with protein-protein interaction (PPI) network information. To address these challenges, we introduce AE-XGBSynergy, a novel approach. AE-XGBSynergy integrates multi-omics cell line data and drug-cell line features derived from the PPI network's topological characteristics to predict drug synergy combinations. It employs the struc2vec algorithm to extract features for drugs and cell lines based on the PPI network's topological characteristics. Additionally, a pre-trained encoder captures abstract representations of high-dimensional cell line data. These representations of drug pairs and cell lines serve as input for an extreme gradient-boosting algorithm to predict drug combinations for cancer cell lines. We assessed our model's performance using two datasets: the O'Neil and DrugCombDB datasets, which provide extensive drug combination information. Our experimental results show that AE-XGBSynergy excels at integrating multi-omics data to predict drug synergies, with genomic data playing a particularly significant role. Notably, when compared to NEXGB, a hybrid model that includes drug and cell line features using a PPI network, our approach outperforms in terms of accuracy (ACC), AVC-ROC, AUC-PR, Precision, and F1-score. © 2023 IEEE.

关键词： Forecasting

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Air Purification Robotics using Cloud and Deep Q Networks for Autonomous Systems 1

Air Purification Robotics using Cloud and Deep Q Networks fo...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Reddy, Elangovan Guruva Damodhar, T. S. Balaji Yuvaraj, S. Vathani, B. Santha Sivakumar, S. Malathi, N. Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh Vaddeswaram India Madanapalle Institute of Technology and Science Department of Electrical and Electronics Engineering Andhra Pradesh Madanapalle India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur India Mahalashmi Women's College of Arts and Science Department of Management Studies Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Perambalur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350384246

Rising urban air pollution poses serious health dangers. Through robots, cloud computing, and deep reinforcement learning, this research proposes a new air purification method. The suggested autonomous system analyzes real-time air quality parameters and optimizes air purification tactics using cloud-based data processing. Advanced sensors and purification processes allow robotic agents to intelligently adapt to shifting pollution patterns in dynamic settings. Robots use Deep Q Networks (DQN) to learn and change their purifying tactics based on past data and environmental input. Cloud computing and deep learning improve air purification efficiency and enable real-time pollution response decision-making. The autonomous system is scalable and adaptable to varied urban environments since it requires little human involvement. The cloud-based design lets autonomous agents communicate and coordinate to improve air quality. Experimental findings show that the suggested strategy improves air purification over previous approaches. This system advances environmental robotics and lays the groundwork for intelligent, autonomous systems that handle urban air quality and pollution issues. © 2024 IEEE.

关键词： Robotics

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Privacy-Secure and Decentralized Biometric Authentication Models Using Federated Learning Frameworks

Privacy-Secure and Decentralized Biometric Authentication Mo...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： Mathivanan. P K. Mahalakshmi Faculty of Information and Communication Engineering Anna university Chennai Department of Computer Science and Engineering KIT - Kalaignarkarunanidhi Institute of Technology Coimbatore India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

A biometric identifier, in contrast to other identifiers used for authentication, is a quantitative evaluation of an individual's physical attributes that is successfully used to confirm or validate the identification. Because of its remarkable and consistent texture variation, iris recognition is thought to be the most dependable biometric recognition. Concerns about security, authentication, and identification are growing in importance across all domains as technology advances daily. High-security applications use these unique patterns for iris recognition. To increase the security of biometric applications without jeopardizing individual privacy, this research suggests a novel FL technique for privacy-preserving iris detection. Traditional biometric systems rely on centralised data storage and processing, which threatens data security and misuse. Federated Learning: decentralized model training that allows multiple federated clients (like mobile devices or edge servers) to collaboratively train a global model without sharing their raw iris data. We explore how this architecture can be applied to iris detection systems, maintaining accuracy while mitigating privacy risks. Our method ensures that iris features are learned locally on individual devices, and only encrypted model updates are shared with the central server. To strengthen privacy, we incorporate differential privacy and secure aggregation techniques to prevent data leakage during model training. Experimental results demonstrate the effectiveness of the proposed framework in maintaining high detection accuracy, low communication overhead, and robust privacy guarantees.

关键词： Training Privacy Differential privacy Accuracy Federated learning Biological system modeling Data models Object recognition Servers Iris recognition

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Optimizing Resource Utilization Using Vector Databases in Green Internet of Things

Optimizing Resource Utilization Using Vector Databases in Gr...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Kumari, Rani Sah, Dinesh Kumar Cengiz, Korhan Nauman, Ali Ivković, Nikola Mihaljević, Ivan Birla Institutue of Technology Department of Computer Science and Engineering Ranchi India Siksha 'o' Anusandhan Department of Computer Science and Engineering Bhubaneswar India Istinye University Department of Computer Engineering Istanbul Turkey University of Hradec Kralove Faculty of Informatics and Management Department of Information Technologies Kralove50003 Czech Republic Yeungnam University Department of Information and Communication Engineering 280 Daehak-Ro Gyeongsan Gyeongbuk38541 Korea Republic of University of Zagreb Faculty of Organization and Informatics Pavlinska 2 Varaždin42000 Croatia

ISBN: (纸本)9798350370218

With the rapid proliferation of Internet of Things (IoT) devices and the ever-increasing volume of sensor data, optimizing resource utilization has become crucial for building sustainable and efficient IoT systems. In this study, we propose a novel approach for optimizing resource utilization in Green IoT through efficient storage and retrieval in vector databases. Our approach leverages specialized data structures, including k-d trees and ball trees, to achieve improved storage efficiency and accelerated retrieval performance for high-dimensional sensor data. We conducted extensive experiments to evaluate the effectiveness of our proposal, comparing it with traditional database approaches. The results demonstrate significant improvements in storage efficiency, with vector databases requiring considerably less storage space compared to traditional databases. Moreover, our approach enables fast and accurate retrieval of high-dimensional sensor data, reducing query times and enhancing real-time data analysis and decision-making capabilities. The technical achievements of our proposal offer promising prospects for the development of sustainable and efficient IoT systems in various domains, such as environmental monitoring, healthcare, and smart cities. Our work contributes to advancing the field of Green IoT by addressing the challenges of resource utilization and query performance through efficient storage and retrieval in vector databases. © 2023 IEEE.

关键词： Green IoT Indexing Techniques Resource Utilization Specialized Data Structures Vector Databases

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The Contribution of Pancasila Education and Civic Education on Value Education for The Students During COVID 19 Pandemic in Digital Era

The Contribution of Pancasila Education and Civic Education ...

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2022 IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2022

作者： Benawa, Arcadius Lusia, Erma Alwino, Alfensius Irawan, Iwan Witono, Petrus Hepi Faculty of Economics and Communication Bina Nusantara University Character Building Development Center Mass Communication Program Communication Department Jakarta Indonesia Faculty of Economics and Communication Bina Nusantara University Tourism Department Jakarta Indonesia Bina Nusantara University School of Computer Science Character Building Development Center Computer Science Department Jakarta Indonesia Bina Nusantara University School of Industrial Engineering Industrial Engineering Department Jakarta Indonesia

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

When all nations including Indonesia are hit by COVID-19 pandemic in era digital, we are compelled to conduct research with the main purpose, namely how much the contribution or influence of Pancasila Education and Civic Education on Value Education for students during the Covid-19 pandemic. The method used in this research is a quantitative method. The data was obtained by asking the students who were respondents to fill out a questionnaire. 150 students who have attended Pancasila and Citizenship courses were chosen by random sampling. Then the data obtained from the responses of the respondents were processed by the SPSS 22 program, and analyzed by path analysis. From the data processing and path analysis, the results of the research are obtained which prove that Pancasila Education has a significant contribution or influence on Value Education. Civic Education has no significant contribution or influence on Value Education. Based on these findings from this research, it is necessary to increase more intensive and serious efforts in delivering Pancasila Education, especially Civic Education in order to increase the contribution of both education to value education for the students, so that the students as the younger generation do not only have an understanding about Pancasila Education and Civic, but also be able to live the values, especially SPIRIT (Striving for the excellence, Perseverance, Integrity, Respect, Innovation, and Team Work) which are the core values at Bina Nusantara University. © 2022 IEEE.

关键词： Students

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Personalized Care for Multiple Sclerosis with IoT Wearables and Deep Learning

Personalized Care for Multiple Sclerosis with IoT Wearables ...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Vetriselvi, D. Sampathrajan, Rajeshkumar Deepa, A. Sujatha, V. Deepa, P. Pandi Murugan, S. Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India McKinsey & Company Fort WorthTX United States Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Electronics and Communication Engineering Shree Sathyam College of Engineering and Technology Tamil Nadu Salem India Department of Artificial Intelligence and Data Science Agni College of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350374933

This research work presents a new method for improving individualized treatment for patients with Multiple Sclerosis (MS) by combining Internet of Things (IoT) wearables with deep learning (DL) methods, namely Long Short-Term Memory (LSTM) networks. MS is a long-term autoimmune disorder that affects the central nervous system. It is marked by unexpected episodes of worsening symptoms and periods of improvement, highlighting the need for tailored therapy. The widespread use of IoT devices presents unique possibilities for the ongoing monitoring of many physiological and activity measurements important to the development and symptoms of MS. Using LSTM networks, which are very proficient in analyzing temporal sequences, create a prediction model that may anticipate exacerbations of MS symptoms and customize therapies appropriately. Through the analysis of longitudinal data obtained from IoT devices, our model acquires knowledge about unique patterns of illness development and treatment response. This knowledge allows us to create customized care plans for individuals. These paths include proactive activities such as adjusting medication, making lifestyle changes, and seeking early medical advice. The goal is to reduce the worsening of symptoms, improve the quality of life, and optimize treatment results for individuals with MS. The proposed method shows potential for transforming the treatment of MS by offering proactive and patient-centered therapy adaptable to individual requirements and the dynamics of the illness. © 2024 IEEE.

关键词： Patient treatment

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Exploring Large Scale Docker Image Vulnerability and Storage Performance Analysis Using High Performance Container-Based Docker Environment

Exploring Large Scale Docker Image Vulnerability and Storage...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： Kavya Sri. B K. Mahalakshmi Faculty of Information and Communication Engineering Anna university Chennai Department of Computer Science and Engineering KIT - Kalaignarkarunanidhi Institute of Technology Coimbatore India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

In modern cloud environments, Docker containers have become a cornerstone for efficient application deployment, enabling microservices architectures and seamless scalability. However, large-scale Docker environments present significant challenges, particularly in terms of security vulnerabilities and storage performance. This paper explores vulnerability analysis and storage performance optimization in high-performance, container-based Docker environments. Docker swarm is a container orchestration platform which could be very useful for maintaining the docker containers at scale even at distributed environment, these tools also provide us a great features for managing the rolling updates and health check in the lifecycle of containers. We perform a comprehensive evaluation of Docker image vulnerabilities using automated scanning tools, assessing common vulnerabilities and exposures (CVEs) across large image repositories. Additionally, we investigate storage performance bottlenecks and analyse the impact of various storage drivers on container efficiency. Our findings offer critical insights into balancing performance and security in high-scale Docker deployments, proposing strategies for mitigating risks and enhancing resource management. The results guide container orchestration decisions for enterprises aiming to deploy secure, high-performance systems.

关键词： Performance evaluation Runtime Scalability Containers Software Security Image storage Resource management Optimization Secure storage

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Parallel fault diagnosis using hierarchical fuzzy Petri net by reversible and dynamic decomposition mechanism

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Frontiers of Information Technology & Electronic engineering 2025年第1期26卷 93-108页

作者： Yinhong XIANG Kaiqing ZHOU Arezoo SARKHEYLI-HÄGELE Yusliza YUSOFF Diwen KANG Azlan Mohd ZAIN School of Communication and Electronics Engineering Jishou UniversityJishou 416000China Internet of Things and People Research Center Department of Computer Science and Media TechnologyMalmöUniversityMalmö20506Sweden Faculty of Computing Universiti Teknologi MalaysiaSkudai 81310Malaysia

The state space explosion,a challenge analogous to that encountered in a Petri net(PN),has constrained the extensivestudy of fuzzy Petri nets(FPNs).Current reasoning algorithms employing FPNs,which operate through forward,backward,andbidirectional mechanisms,are *** algorithms streamline the inference process by eliminating irrelevant componentsof the ***,as the scale of the FPN grows,the complexity of these algorithms escalates sharply,posing a significantchallenge for practical *** address the state explosion issue,this work introduces a parallel bidirectional reasoningalgorithm for an FPN that utilizes reverse and decomposition strategies to optimize the implementation *** algorithminvolves hierarchically dividing a large-scale FPN into two sub-FPNs,followed by a converse operation to generate the reversalsub-FPN for the *** detailed mapping between the original and reversed FPNs is thoroughly *** operations are then conducted on the left-sub-FPN and the resulting reversal right-sub-FPN,with the final resultderived by computing the Euclidean distance between the outcomes from the output places of the two sub-FPNs.A case study ispresented to illustrate the implementation process,demonstrating the algorithm’s significant enhancement of inference efficiencyand substantial reduction in execution time.

关键词： Fuzzy Petri net(FPN) State explosion Decomposition Parallel Bidirectional reasoning

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Quantum Attacks on Public Cryptosystems 6

Quantum Attacks on Public Cryptosystems

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Singh, Puneet Hasabnis, Amey R. Rajpoot, Subhadra Singh, Pankaj College of Telecommunication Engineering Faculty of Communication Engineering Military MP Mhow India Amiuty University Department of Applied Sciences Uttar Pradesh Greater Noida India BITS Pilani Pilani India Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

Quantum Key Distribution (QKD) networks have emerged as a revolutionary technology in ensuring secure communication in the digital age. By leveraging the principles of quantum mechanics, QKD allows two parties to establish a secret key securely, which can subsequently be used for encrypting and decrypting sensitive information. While QKD holds immense promise for enhancing cybersecurity, it also presents several challenges that necessitate further research to achieve its full potential. The research presented here examines the development of QKD networks, their real-world use, as well as the general QKD network architecture and the most recent advancements in QKD network deployments. © 2023 IEEE.

关键词： Quantum Key Distribution (QKD Networks

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