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Generative AI-Enabled Quantum Encryption Algorithm for Securing IoT-Based Healthcare Application Using Blockchain

IEEE Internet of Things Journal 2025年

作者： Prajapat, Sunil Kumar, Pankaj Das, Ashok Kumar Muhammad, Ghulam Central University of Himachal Pradesh Srinivasa Ramanujan Department of Mathematics Dharamshala176215 India International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11543 Saudi Arabia

The integration of Artificial Intelligence (AI) with the Internet of Things (IoT) has transformed numerous domains through the AI of Things (AIoT). Nonetheless, AIoT encounters issues related to energy usage and carbon emissions as mobile technology continues to progress. Generative AI (GAI) possesses significant potential to mitigate carbon emissions associated with AIoT, owing to its higher reasoning and generative powers. Conventional security protocols frequently encounter issues with computational efficiency, latency, and overall security comprehensiveness. Blockchain technology, characterized by its decentralized and immutable properties, is a viable approach for improving electronic healthcare data transmission and node authentication in IoT networks. This research examines secure data transmission and node encryption in IoT systems, with a particular emphasis on data management. Conventional approaches encounter constraints in computational efficiency, latency, and comprehensive security. This study presents a novel protocol that combines GAI and blockchain technology with quantum encryption to enhance authentication and ensure secure data transmission. The algorithm comprises multiple consecutive processes, including the encoding and transmission of node requests, followed by the authentication process utilizing hash functions and digital signatures. The authentication approach utilizes a challenge-response technique, guaranteeing that only nodes with authentic credentials can advance. Thereafter, a dynamic key exchange protocol and quantum encryption method provide secure data delivery. The results indicate the procedure’s effectiveness in ensuring secure and regulated access to patient data, underscoring its significance in medical facilities. The system’s functionalities are augmented by a thorough evaluation employing machine learning. The findings indicate that the system exhibits an accuracy of 99.4%, precision of 99.10%, recall of 98.66%, F1-score of

关键词： Quantum computers

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Impact of Expert academic Teaching Quality and its Performance Based on BiLSTM-Deep CNN Network 7

Impact of Expert Academic Teaching Quality and its Performan...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Shukla, Tushar Dhar Acharjee, Purnendu Bikash Chethan, C. Thulasimani, T. Sindhu, M. Sharma, Sanjiv University Institute of Teachers Training & Research Chandigarh University Mohali India CHRIST University Pune India Sri Venkateshwara College of Engineering Department of Information Science and Engineering Bengaluru India Bannari Amman Institute of Technology Department of Mathematics Tamilnadu Sathyamangalam India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Madhav Institute of Technology and Science Department of IT Madhya Pradesh Gwalior India

ISBN: (纸本)9798350340600

Undergraduate and postgraduate students from eight different departments at a UK institution participated in organized conversations about the impact of teachers' research activities on their education. In both samples, positive responses greatly outnumbered negative ones. There was an increase in positive feedback on professors' research when the overall quantity and quality of research in a specific field (as measured by research Assessment Exercise [RAE] ratings) improved. Undergraduate samples with higher RAE scores were more likely to have negative feedback on research than graduate student samples. Both graduate and undergraduate students agreed that lecturers' research increased the instructor's credibility, relevance, and knowledge, as well as piqued and maintained their own interest, engagement, and drive. Data processing, feature selection, and model training are the first steps in the proposed approach. The data are changed from their raw form into a form suitable for academic use during the data pre-processing phase. They are employing Information Gain and Symmetric Uncertainty for feature selection. Following the feature selection process, the models are trained using BiLSTM-CNN. Both the BiLSTM and the CNN methods are inferior to the proposed method. © 2023 IEEE.

关键词： Long short-term memory

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Multi-objective VNF Placement Optimization with NSGA-III 1st

Multi-objective VNF Placement Optimization with NSGA-III

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1st International Conference on Advances in Computing research, ACR’23

作者： Bekhit, Mahmoud Fathalla, Ahmed Eldesouky, Esraa Salah, Ahmad Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Kent Institute Australia Melbourne Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia College of Computers and Informatics Zagazig University Zagazig Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman

ISBN: (纸本)9783031337420

With the rising acceptance of virtual network functions (VNFs) as a replacement for traditional network functions, the optimal placement of VNFs has become a crucial task for ensuring constant performance within constrained resources. This research investigates the challenge of mapping and scheduling VNFs as a multi-objective optimization problem. The state-of-the-art methods have focused on optimizing the mapping or scheduling of VNFs while considering one or two objectives continuously. In this study, we aim to optimize two objectives: minimizing the link capacity cost and maximizing the average resource utilization of each virtual machine. To address this problem, an adaptation of the NSGA-III optimization algorithm is proposed and thoroughly evaluated through a set of experiments. In terms of objective values, the results outlined that the proposed method outperformed the other methods of comparison by a significant margin. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mapping

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Analyzing the behaviors of pedestrians and cyclists in interactions with autonomous systems using controlled experiments: A literature review

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Transportation research Part F: Traffic Psychology and Behaviour 2025年 114卷 270-307页

作者： Danya Li Wencan Mao Francisco C. Pereira Yu Xiao Xiang Su Rico Krueger Department of Technology Management and Economics Technical University of Denmark (DTU) Denmark Information Systems Architecture Science Research Division National Institute of Informatics (NII) Japan Department of Information and Communications Engineering Aalto University Finland Department of Computer Science and Department of Agricultural Sciences University of Helsinki Finland

Urban transportation is set to undergo a profound transformation with the advent of autonomous systems such as autonomous vehicles, automated buses, and sidewalk delivery robots. To promote safe, sustainable, and inclusive urban mobility, understanding and predicting the behaviors of pedestrians and cyclists, including their intentions, decisions, and movements, when they interact with autonomous systems becomes crucial. Gaining a thorough understanding of these complex interactions can not only improve the safety, efficiency, and acceptance of autonomous systems but also enhance the design and implementation of these technologies. Through a comprehensive review of the literature spanning the years 2014 to 2023, we identify 99 articles that empirically investigate the interactions of humans and autonomous systems. Based on our overview of progress and challenges within this field, we further identify five research gaps that future research should address to enhance human-autonomous system interactions, including: (1) scaling up experimental scenarios to multi-user and multi-modal setups to better represent real-world challenges, (2) emphasizing safety-critical scenarios that are difficult to achieve in real-world environments by applying virtual reality, (3) incorporating diverse behavioral data from the human perspective to deepen the understanding of vulnerable road user behavior and decisions, (4) embracing continuous and real-time interaction to better predict dynamic future environments, and (5) enhancing the generalization ability to ensure realism and broad applicability. This review article offers valuable insights for the growing human-autonomous system research community, specifically those interested in leveraging controlled experiments to enhance the understanding and prediction of pedestrians' and cyclists' behaviors in future urban environments.

关键词： Human-autonomous system interaction Human behavioral modeling Autonomous vehicles Virtual reality Controlled experiment

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Detection of Osteoporosis with DEXA Scan Images using Deep Learning Models

Detection of Osteoporosis with DEXA Scan Images using Deep L...

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2022 IEEE International Conference on Advances in Computing, Communication and Applied informatics, ACCAI 2022

作者： Varalakshmi, P. Sathyamoorthy, Smruthi Darshan, V. Ramanujan, V. Rajasekar, Sakthi Jaya Sundar Madras Institute of Technology Anna University Department of Computer Technology Chennai India College of Engineering Guindy Anna University Department of Information Science and Technology India Melmaruvathur Adhiparasakthi Institute of Medical Sciences and Research India

ISBN: (纸本)9781665495295

Osteoporosis is a chronic degenerative disease. Postmenopausal women and people over 50 years of age are at the highest risk of developing osteoporosis. If left untreated, osteoporosis can prove to be debilitating and might require hospitalization. So it is crucial to identify the risk of osteoporosis and take appropriate measures before the disease can negatively impact the quality of human life. Many existing osteoporosis classification models are computationally expensive and suffer from low accuracy due to inefficient processing of scan images. In this paper, the best model for predicting the risk of osteoporosis using DEXA scan images is identified. To improve the accuracy and specificity of the diagnosis, the scan images are pre-processed using denoising, image enhancement, and thresholding. Different CNN and hybrid models are trained, tested and the performance of the models is charted to infer the best model that gives promising results. Experimental results show that the proposed approach of pre-processing the images, data augmentation, sampling with Synthetic Minority Over Sampling Technique (SMOTE) and Inception v3 CNN model achieves the highest accuracy of 92.05%, specificity of 90.37%, the sensitivity of 93.87%, Precision of 89.97%, and F1 score of 91.88%. © 2022 IEEE.

关键词： Image enhancement

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TSAL: Two steps Adversarial learning based domain adaptation 34th

TSAL: Two steps Adversarial learning based domain adaptation

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34th International Florida Artificial Intelligence research Society Conference, FLAIRS-34 2021

作者： Badr, Haidi Wanas, Nayer Fayek, Magda Electronics Research Institute Informatics Dapartment Egypt Cairo university Faculty of Engineering Computer Department Egypt

Since labeled data availability differs greatly across domains, Domain Adaptation focuses on learning in new and unfamiliar domains by reducing distribution divergence. Recent research suggests that the adversarial learning approach could be a promising way to achieve the domain adaptation objective. Adversarial learning is a strategy for learning domain-transferable features in robust deep networks. This paper introduces the TSAL paradigm, a two-step adversarial learning framework. It addresses the real-world problem of text classification, where source domain(s) has labeled data but target domain (s) has only unlabeled data. TSAL utilizes joint adversarial learning with class information and domain alignment deep network architecture to learn both domain-invariant and domain-specific features extractors. It consists of two training steps that are similar to the paradigm, in which pre-trained model weights are used as initialization for training with new data. TSAL’s two training phases, however, are based on the same data, not different data, as is the case with fine-tuning. Furthermore, TSAL only uses the learned domain-invariant feature extractor from the first training as an initialization for its peer in subsequent training. By doubling the training, TSAL can emphasis the leverage of the small unlabeled target domain and learn effectively what to share between various domains. A detailed analysis of many benchmark datasets reveals that our model consistently outperforms the prior art across a wide range of dataset distributions. © 2021by the authors. All rights reserved.

关键词： Network architecture

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Architecture Open Workspace Concept for Prescriptive Model of Digital Twin 9

Architecture Open Workspace Concept for Prescriptive Model o...

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9th International Conference on ICT for Smart Society, ICISS 2022

作者： Supangkat, Suhono Harso Supangkat, Suhono Harso Leksono, Edi Ramelan, Agus Latifah, Ayu School of Electrical and Informatics Engineering Bandung Institute of Technology Bandung Indonesia Engineering Physics Faculty of Industrial Technology Bandung Institute of Technology Bandung Indonesia Electrical Engineering Departement Universitas Sebelas Maret Solo Indonesia Department of Computer Science Institut Teknologi Garut Garut Indonesia

ISBN: (数字)9781665471350

ISBN: (纸本)9781665471350

The workspace is a portion of a structure that can currently apply a smart building system adaption for more ideal design outcomes. Millions of hours are reportedly wasted each year as a result of the harmful effects that poor environmental quality has on workers' productivity and health, according to the findings of several studies. Therefore, a centralized approach is required to be able to give quick and accurate information. This may be accomplished via digital twin technology, which will result in a workspace design that is more ideal in the future. The major goal of this research is to create a digital twin prescriptive model in a physical workspace environment that can create a strategic space design plan and be flexible enough to react to changes in the physical environment, which will improve user comfort and productivity. A life cycle model was employed as the study methodology, which begins with the identification of the model's requirements and ends with its maintenance. This model is employed to offer suggestions for preventative and remedial activities on actual things. © 2022 IEEE.

关键词： Productivity

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Big Five Personality Prediction Based on Indonesian Tweets and Personality Test

Big Five Personality Prediction Based on Indonesian Tweets a...

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International Conference on Electrical engineering and informatics, ICEEI

作者： Made Arbi Parameswara Farrel Satya Putra Mahendra Muhammad Randy Cahya Mardika Ira Puspasari Nugraha Priya Utama School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Computer Engineering Department Dinamika University Surabaya Indonesia

The integration of human resources and artificial intelligence has shown a growing interest in automatically predicting personality traits, particularly in recruitment. To contribute to this field, we propose a new method to assist human resource professionals in Indonesia to identify candidates' personality traits. Biases come from personality tests due to their question format and susceptibility to manipulation on this research, we propose an approach that combines personality tests using the BFI form, which measures OCEAN personality traits, with personality trait predictions derived from Twitter data. This research aims to create a personality prediction model and system that efficiently and effectively screens candidate personality traits in human resources. This research has developed a novel model that combines personality test results with tweets personality prediction with an impressive F1-macro performance of 98%.

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Early Tumor Diagnosis in Brain MR Images via Deep Convolutional Neural Network Model

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computers, Materials & Continua 2021年第8期68卷 2413-2429页

作者： Tapan Kumar Das Pradeep Kumar Roy Mohy Uddin Kathiravan Srinivasan Chuan-Yu Chang Shabbir Syed-Abdul School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Engineering Indian Institute of Information TechnologySurat395007India Research Quality Management Section King Abdullah International Medical Research CenterKing Saud bin Abdulaziz University for Health SciencesMinistry of National Guard-Health AffairsRiyadh11426Kingdom of Saudi Arabia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Graduate Institute of Biomedical Informatics Taipei Medical UniversityTaipeiTaiwan

Machine learning based image analysis for predicting and diagnosing certain diseases has been entirely trustworthy and even as efficient as a domain expert’s ***,the style of non-transparency functioning by a trained machine learning system poses a more significant impediment for seamless knowledge trajectory,clinical mapping,and delusion *** this proposed study,a deep learning based framework that employs deep convolution neural network(Deep-CNN),by utilizing both clinical presentations and conventional magnetic resonance imaging(MRI)investigations,for diagnosing tumors is *** research aims to develop a model that can be used for abnormality detection over MRI data quite efficiently with high *** research is based on deep learning and Deep-CNN was deployed to examine the MR brain image for tracing the *** system runs on Tensor flow and uses a feature extraction module in DeepCNN to elicit the factors of that part of the image from where underlying issues are identified and subsequently succeeded in prediction of the disease in the MR *** results of this study showed that our model did not have any adverse effect on classification,achieved higher accuracy than the peers in recent years,and attained good detection outcomes including case of *** the future work,further improvement can be made by designing models that can drastically reduce the parameter space without affecting classification accuracy.

关键词： Deep learning convolutional neural network brain tumor magnetic resonance imaging

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Blockchain and AI Synergy: The SecNet Approach to CPS Data Challenges

Blockchain and AI Synergy: The SecNet Approach to CPS Data C...

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International Conference on Trends in Electronics and informatics (ICEI)

作者： Sohan Kamble Palash Gourshettiwar Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Sawangi (Meghe) Wardha Maharashtra India Department of Computer Science & Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Sawangi (Meghe) Wardha Maharashtra India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Cyber, physical, and social (CPS) systems have emerged as the driving force behind today's modern information society, where data is defined as the main capital. Information drives almost every technological advancement to amplify industries through artificial intelligence (AI). While data has the potential to enrich an organization, it also presents issues related to privacy, ownership, and security. There are considerable constraints on AI efficiency due to inadequate sensitive sharing frameworks, and the vulnerabilities of individuals' data are not well protected most of the time. This paper presents SecNet, which integrates blockchain and AI to provide a secure mechanism for information exchange between CPSs, thereby enhancing trust, integrity, and productivity. SecNet combines blockchain's distributed ledger with AI's data management skills to protect data and build adequate levels of trust among data users. AI decision-making strength is boosted by SecNet through the provision of high-quality and secure data input, effectiveness in operations, and minimized risks of data sharing. With the help of SecNet, sharing data in CPS systems can become a significant advancement that will stimulate further development and make industries' digital infrastructure more reliable and trustworthy.

关键词： Industries Productivity Smart cities System performance Smart contracts Organizations Blockchains Reliability Artificial intelligence Protection

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