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Governing AI-Driven Health Research: Are IRBs Up to the Task?

Ethics & human research 2021年第2期43卷 35-42页

作者： Friesen, Phoebe Douglas-Jones, Rachel Marks, Mason Pierce, Robin Fletcher, Katherine Mishra, Abhishek Lorimer, Jessica Véliz, Carissa Hallowell, Nina Graham, Mackenzie Chan, Mei Sum Davies, Huw Sallamuddin, Taj Assistant professor in the Biomedical Ethics Unit and the Department of Social Studies of Medicine at McGill University Associate professor of anthropological approaches to data and infrastructure head of the Technologies in Practice research group the codirector of the ETHOS Lab at the IT University of Copenhagen Assistant professor of law at Gonzaga University and the Edmond J. Safra/Petrie-Flom Centers Joint Fellow-in-Residence at Harvard University Technology and Society at the Tilburg Law School at Tilburg University Coordinator of Cyber Security Oxford and the founding administrator of the Computer Science Department Research Ethics Committee in the Department of Computer Science at the University of Oxford DPhil candidate at the Uehiro Centre for Practical Ethics at the University of Oxford DPhil candidate on the NEUROSEC team in the Department of Psychiatry at the University of Oxford Associate professor in the Faculty of Philosophy at the Institute for Ethics in AI as well as a tutorial fellow at Hertford College at the University of Oxford Codirector of the EPSRC Centre for Doctoral Training in Health Data Science and a professor at the Ethox Centre and Wellcome Centre for Ethics and Humanities at the University of Oxford Senior research fellow in data ethics at the Wellcome Centre for Ethics and Humanities in the Nuffield Department of Population Health at the University of Oxford DPhil student in the Nuffield Department of Population Health at the University of Oxford and a research fellow in the Department of Applied Health Research at University College London Lecturer in education at the School of Education and Sport at the University of Edinburgh Data protection and information lawyer

Many are calling for concrete mechanisms of oversight for health research involving artificial intelligence (AI). In response, institutional review boards (IRBs) are being turned to as a familiar model of governance. Here, we examine the IRB model as a form of ethics oversight for health research that uses AI. We consider the model's origins, analyze the challenges IRBs are facing in the contexts of both industry and academia, and offer concrete recommendations for how these committees might be adapted in order to provide an effective mechanism of oversight for health-related AI research. © 2021 by The Hastings Center. All rights reserved.

关键词： AI research oversight artificial intelligence ethics committees health-related AI research institutional review boards research ethics

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The Burn Grafting Image Reclamation Redefined with the Peak-Valley Approach

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Critical Reviews™ in Biomedical engineering 2025年第2期53卷 21-35页

作者： B. P. Pradeep Kumar E. Naresh (Corresponding author A. Ashwitha Kadiri Thirupal Reddy N. N. Srinidhi Department of Computer Science and Design Atria Institute of Technology Bangalore VTU Karnataka India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Assistant Professor Department of CSE Malla Reddy Engineering College for Women Hyderabad Telangana Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

Burn injuries constitute a significant public health challenge, often necessitating the expertise of medical professionals for diagnosis. However, in scenarios where specialized facilities are unavailable, the utility of automated burn assessment tools becomes evident. Factors such as burn area, depth, and location play a pivotal role in determining burn severity. In this study, we present a classification model for burn diagnosis, leveraging automated machine learning techniques. Our approach includes an image reclamation system that incorporates the peak and valley algorithm, ensuring the removal of noise while consistently delivering high-quality results. By using skewness and kurtosis, we demonstrate substantial improvements in diagnostic accuracy. Our proposed system sources key features from enhanced grafting samples using peak valley transformation, enabling the computation of BQs and a unique bin analysis to enhance image reclamation. Our experimental results highlight efficiency gains, notably growing the matching features of graft samples for 14 matching images. The intended work involves the creation of a burn classification reclamation model. The proposed approach utilizes a support vector machine (SVM). The evaluation of the model will be conducted using an untrained catalogue, with a specific focus on its effectiveness in reclaiming images that necessitate grafts and distinguishing them from those that do not. Our approach holds promise in grafting sample reclamation in emergency settings, thereby expediting more accurate diagnoses and treatments for acute burn injuries. This work has the latent to save lives and improve patient upshots in burn traumas.

关键词： burn grafting image reclamation peak-valley approach LUV color space support vector machine

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Multipath routing mechanism using embedded architecture of interfaced circuit

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Microprocessors and Microsystems 2020年 77卷 103188-103188页

作者： Alappat, Valanto Prathap, Joe Research scholar Faculty of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Associate Professor Department of IT RMD Engineering College Chennai India

Mobile Adhoc Network (MANET) is a group of remote compact hubs confining a short term framework without any guidance of any settled communication establishment. In view of confined resources, visit organizes packets and surprising topological changes, proactive gathering designs get high overheads in this condition. To keep the security of compact uniquely selected frameworks various wellbeing endeavors are arranged, for instance, encryption estimations, firewalls et cetera. However there is some degree of threatening exercises. In this work, a Modified IDEA-HMAC based secure disseminated grouping calculation for MAODV routing protocol is proposed for secure message transmission, routing through Cluster based Intrusion Detection System in MANET. This algorithm for Intrusion recognition frameworks are proposed to distinguish any intruder in the system and its vindictive exercises. It is additionally utilized for cluster arrangement, upkeep and purging activities and grouping data for speedy course disclosure, supports and cluster conveyance. The Modified IDEA is utilized for encryption, it is one of the safe and most generally utilized square figures and the cryptographic quality of Modified IDEA depends on a blend of three inconsistent gathering tasks – XOR, expansion and particular duplication and it is secure against powerless key assaults. © 2020

关键词： Cryptography

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Advancing knowledge management in machine-to-machine communication - A comparative analysis of stacked ensembles for IoT device classification mechanisms

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AIP Conference Proceedings 2024年第1期3214卷

作者： Ganesh S. Pise Sachin D. Babar Parikshit N. Mahalle Smt. Kashibai Navale College of Engineering Pune Assistant Professor in Pune Institute of Computer Technology Pune India Department of Computer Engineering Sinhgad Institute of Technology Lonavala Research Guide Smt. Kashibai Navale College of Engineering Pune India Department of Artificial Intelligent and Data Science Vishwakarma Institute of Information Technology Pune India

The Machine-to-Machine (M2M) connection in the context of the Internet of Things (IoT), with the main goal of improving information management through advanced classification methods. As the number of IoT devices has grown, strong methods for efficiently classifying them are needed to make contact smooth and make the best use of resources. This study compares two cutting-edge machine learning techniques called Stacked Ensembles to see which one is better at making IoT device classification more accurate and reliable. The paper research looks at different classification models within Stacked Ensembles and compares how well they work to more standard methods. The study system has a large collection with a lot of different IoT devices and different communication methods and network situations. The paper focused is to find the best Stacked Ensemble setups for accurate and efficient device classification in M2M communication by carefully testing and evaluating them. The study adds to the fields of machine learning and the Internet of Things, but it also looks at information management in M2M networks, which is a very important topic. We expect that system effectiveness, resource usage, and general network performance will all get better by using advanced sorting methods. It is hoped that the study results will help people create and use strong knowledge management strategies. This will make the M2M communication setting smarter and more flexible as the IoT world changes.

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Comprehensive Analysis of Lightweight Cryptographic Algorithms for Battery-Limited Internet of Things Devices

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International Journal of Distributed Sensor Networks 2025年第1期2025卷

作者： Nahom Gebeyehu Zinabu Yihenew Wondie Marye Kula Kekeba Tune Samuel Asferaw Demilew Department of Software Engineering College of Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia aastu.edu.et Associate Professor in School of Electrical and Computer Engineering (SECE) Addis Ababa Institute of Technology (AAiT) Addis Ababa University (AAU) Addis Ababa Ethiopia aau.edu.et Department of Information Technology College of Computing Debre Berhan University Debre Berhan Ethiopia dbu.edu.et

The growth of billions of devices functioning in resource-constrained situations in the Internet of Things (IoT) era poses serious security issues. As a result of their limited processing power efficiency, memory, and battery life, traditional cryptographic algorithms like elliptic curve cryptography (ECC), Rivest–Shamir–Adleman (RSA), and Advanced Encryption Standard (AES) are sometimes too resource-intensive for these devices. Lightweight cryptography has become a crucial field as a remedy, created especially to maintain security while maximizing energy efficiency and reducing resource usage. With an emphasis on contrasting well-known lightweight block ciphers and pointing out areas for future research, this paper examines the most recent developments in lightweight cryptographic methods. In order to synthesize insights into present issues and future goals, it explores cutting-edge methods that improve performance, security, and energy efficiency. Among the main trends that were covered were the trade-offs between resource limitations and security strength, hardware–software co-optimization, block and stream cipher optimization, and hybrid encryption techniques. The study’s conclusion emphasizes how urgently more research is needed to create cryptographic solutions that can sufficiently safeguard IoT devices.

关键词： battery-limited devices block cipher algorithm embedded system security energy-efficient cryptography IoT security lightweight cryptography low-power cryptographic algorithms and performance analysis

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Feeling Recognition by Facial Expression Using Deep Learning

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Journal of Physics: Conference Series 2021年第1期1717卷

作者： Dr. Shalini Gupta Dr. Shubha Jain Associate Professor Computer Science and Engineering Department Kanpur Institute of Technology KANPUR Associate Professor Computer Science and Engineering Department Axis Institute of Technology and Management KANPUR

The visible appearance of the emotion state, personality, purpose, psychological feature activity and psychopathology of an individual is the Facial expression. This plays an outgoing role in social affairs. Automatic recognition of facial expressions will be a vital part of natural human-machine interfaces. It should even be applied in activity science and in clinical apply. Fellows in nurturing automatic facial features Recognition system must perform detection and placement of faces during a disordered scene, facial feature extraction and facial features classification. Emotion recognition by facial features utilizes a Deep Learning system, which is enforced victimization Convolution Neural Network (CNN). The CNN model of the project relies on LeNet design. Kaggle facial features dataset with seven facial features labels as fear, anger, happy, surprise, sad, neutral and disgust is employed during this project. The system achieved 60.37 accuracy.

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Mitigating post-harvest losses through IoT-based machine learning algorithms in smart farming

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AIP Conference Proceedings 2024年第1期3161卷

作者： R. Rajesh Kanna T. Mohana Priya V. Sivakumar Chandrasekharan Nataraj Jishamol Thomas Assistant Professor Department of Computer Science CHRIST (Deemed to be University) Bangalore India Associate Professor School of Computing Faculty of Computing Engineering and Technology Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia Senior Lecturer School of Engineering Faculty of Computing Engineering and Technology Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia Research Scholar Department of Sociology and Social Work CHRIST (Deemed to be University) Bangalore India

This research paper explores the transformative potential of Internet of Things (IoT) technology in mitigating the longstanding issue of post-harvest losses within the agriculture sector. These losses, which encompass both quantitative and qualitative deterioration of food commodities from harvest to consumption, have posed persistent challenges, resulting in economic losses and food wastage. By delving into the current landscape of post-harvest losses and the application of IoT technology, the paper offers valuable insights into how IoT can be harnessed to reduce these losses effectively. It not only highlights the benefits and existing IoT solutions but also addresses the inherent challenges, providing recommendations for their resolution. Moreover, the research introduces a machine learning-based model, specifically Random Forest ML, to identify and prevent losses in tandem with IoT devices, empowering farmers with timely alert messages for informed decision-making, thus fostering a more sustainable and efficient agricultural ecosystem.

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A hybrid machine learning strategy for aquatic plant surveillance in sustainable aqua-ecosystems using IoT attributes

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Aquaculture 2025年 609卷

作者： D. Palanikkumar T. Anuradha Jothi Ramalingam S.S. Sivaraju Department of Computer and Science Engineering Dr. NGP Institute of Technology Coimbatore 641048 Tamil Nadu India Department of Electrical and Electronics Engineering KCG College of Technology Chennai 600097 Tamil Nadu India Associate Professor Department of Mathematical and Computational Sciences National Institute of Technology Surathkal Mangalore 575 025 Karnataka India Department of Electrical and Electronics Engineering R V S College of Engineering and Technology Kannampalayam Coimbatore 641402 Tamil Nadu India

Using a hybridized machine learning framework combined with IoT technology, this research proposes a unique way of monitoring and maintaining the optimal condition of aquatic plants in ecosystems. Our method integrates state-of-the-art convolutional neural networks for plant health data analysis, time series analysis for water quality temporal variation interpretation, and clustering algorithms for ecological data pattern identification. We used an extensive open-source dataset that included various characteristics of IoT platforms, such as water temperature, pH, turbidity, dissolved oxygen levels, and particular metrics for plant development. Because of its critical role in maintaining ecological harmony in our research region, a certain aqua-plant is the primary focus of the dataset. Our Hybrid Machine Learning Strategy (HMLS) demonstrated outstanding performance in forecasting aquatic plant health and growth patterns with a Graph Neural Network (GNN), obtaining a 94 % accuracy rate in plant health data assessments and categorization. In addition, the contour-based clustering technique was employed to effectively group comparable ecological circumstances with a 93.5 % accuracy rate, while time series analysis identified temporal changes with a 94.22 % forecast accuracy. These results prove that our integrated strategy serves a purpose for aqua-ecosystem sustainability management by offering predicted insights and real-time monitoring. This study substantially contributes to environmental monitoring by using modern machine learning algorithms and IoT technologies to provide a scalable and economical solution for aquatic ecosystem conservation.

关键词： Accuracy Aqua parameters Clustering Contour Learning strategy Plant health Predictive mechanism Sensors Temporal

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Security and privacy for 6G: A survey on prospective technologies and challenges

arXiv

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arXiv 2021年

作者： Nguyen, Van-Linh Lin, Po-Ching Cheng, Bo-Chao Hwang, Ren-Hung Lin, Ying-Dar The Department of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan The Department of Communications Engineering National Chung Cheng University Chiayi Taiwan The Department of Information Technology Thai Nguyen University of Information and Communication Technology Thai Nguyen Viet Nam Advanced Institute of Manufacturing with High-tech Innovations National Chung Cheng University and A Jointly Appointed Professor of AI College National Yang Ming Chiao Tung University Taiwan The Department of Computer Science National Yang Ming Chiao Tung University Taiwan

Sixth-generation (6G) mobile networks will have to cope with diverse threats on a space-air-ground integrated network environment, novel technologies, and an accessible user information explosion. However, for now, security and privacy issues for 6G remain largely in concept. This survey provides a systematic overview of security and privacy issues based on prospective technologies for 6G in the physical, connection, and service layers, as well as through lessons learned from the failures of existing security architectures and state-of-the-art defenses. Two key lessons learned are as follows. First, other than inheriting vulnerabilities from the previous generations, 6G has new threat vectors from new radio technologies, such as the exposed location of radio stripes in ultra-massive MIMO systems at Terahertz bands and attacks against pervasive intelligence. Second, physical layer protection, deep network slicing, quantum-safe communications, artificial intelligence (AI) security, platform-agnostic security, real-time adaptive security, and novel data protection mechanisms such as distributed ledgers and differential privacy are the top promising techniques to mitigate the attack magnitude and personal data breaches substantially. © 2021, CC BY-NC-ND.

关键词： Surveys

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SAMS: Smart Agriculture Management System Using Emerging Technologies IoT, AI -A Study

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IOP Conference Series: Materials science and engineering 2021年第1期1074卷

作者： B. Srinivas Kumar S.G. Santhi K. Kranthi Kumar Research Scholar Department of Computer Science and Engineering Annamalai University. Associate Professor Department of Computer Science and Engineering Annamalai University. Assistant Professor Department of Information Technology Gudlavalleru Engineering College Gudlavalleru

Farming or Agriculture is the science and art of cultivating plants. It is the vital enrichment in the growth of human living. It creates food and the other related things by farming with domestic animals and will enable people to live long and healthy life. There are two agricultural categories, one is traditional Agriculture and the second one is Modern Agriculture. In Traditional Agriculture Farmers had faced many difficulties to Farm, but with the modern Agriculture Farmers can be able to see many improvements in Framing due to the agronomy, use of agricultural chemicals such as pesticides and fertilizers, Plant breeding and technological growth, these enrichments in Farming would yield good crop.(IoT)Internet of Things very often called as a system it can able to connect real physical Things, Implanted sensors, Embedded software's along with different technologies to connect and to transfer data amongst various diverse devices over the Internet. It is the hopeful expertise or technology which gives accurate and most reliable solutions on the way to too many domains. There is an extensive set of wide range of real time applications of IoT devices such as Industrial, Consumer, Commercial, Infrastructure may made IoT is the one of the best technology to see enhancements in many domains. Solutions which are given by IoT would reduce human efforts in Farming and may provide sound of crop. In this paper a small survey will be presented to see how Internet of Things and its equivalent or other represent technologies AI, Cloud Computing would be useful to provide SMART AGRICULTURE along with Modern agricultural improvements will also be presented.

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