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7th Workshop on Advances in Argumentation in Artificial Intelligence, AI^3 2023

作者： Ellmauthaler, Stefan Gerlach, Lukas Knowledge-Based Systems Group ScaDS.AI Faculty of Computer Science Cfaed TU Dresden Germany

Abstract Dialectical Frameworks (ADF) are a well known and understood generalisation of Dung's Argumentation frameworks. Multiple approaches to solve the computation and enumeration of the semantics have been proposed over the last decade. One recent approach is to solve the computational hard problems by translating the acceptance condition of a given ADF into reduced ordered binary decision diagrams (roBDD). The use of roBDDs lays a foundation for straightforward graphical visualization of the underlying ADFs and their solutions. In this work, we present ***, a web-service that generates graphical representations of ADFs and for different semantics, allowing their comparison and to spot the influence of yet undecided statements. We propose that this is a first steps towards better explainability and understanding of ADFs. © 2023 CEUR-WS. All rights reserved.

关键词： Web services

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Efficient and Lightweight Framework for Confidential Medical Image Search using Edge Computing 2

Efficient and Lightweight Framework for Confidential Medical...

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2nd International Conference on Computational Intelligence and Network systems, CINS 2024

作者： Rajan, Arun Amaithi Vetriselvi, V. Aishwarya, R. Rajan, Anitha Amaithi Security Research Laboratory Department of Computer Science and Engineering College of Engineering Guindy Anna University Tamil Nadu Chennai600025 India Department of Computer Science and Business Systems Francis Xavier Engineering College Tamil Nadu Tirunelveli627011 India

ISBN: (纸本)9798331504106

In healthcare, medical images are playing a major role in accurate diagnosis. Cloud storage for medical images creates security risks and latency due to some critical vulnerabilities and their distance from data sources. By bringing computation closer to the source, edge computing improves efficiency, still edge servers cannot be trusted blindly, requiring robust security measures. In this article, an efficient and lightweight framework for secure medical image search and retrieval that leverages edge computing is proposed. Here, Medical images are encrypted and similarity-preserving hashcodes are generated locally at the source, which are sent to a trusted master edge server. Before being stored, searchable hashcodes undergo homomorphic encryption, allowing for secure computations on encrypted data. The master edge server generates n shares of the encrypted images by applying a Learning With Errors (LWE) based image sharing scheme and distributing them across nearby edge devices. During retrieval, to ensure resilience against device compromise, m out of n shares are used to reconstruct the image. The master edge server retrieves and reconstructs the top k relevant images in response to the query image. This framework enhances security and efficiency by reducing reliance on cloud storage and enabling secure processing on edge servers, making it ideal for modern healthcare environments. The efficiency and security of the proposed framework are proved theoretically and experimentally. Hence, it proved that the framework is performing better than existing state-of-the-art models. ©2024 IEEE.

关键词： Electronic health record

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Generative AI, IoT, and blockchain in healthcare: application, issues, and solutions

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Discover Internet of Things 2025年第1期5卷 1-23页

作者： Mazhar, Tehseen khan, Sunawar Shahzad, Tariq khan, Muhammad Amir Saeed, Mamoon M. Awotunde, Joseph Bamidele Hamam, Habib School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science International Islamic University Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Science and Information Technology School Education Department Government of Punjab 31200 Layyah Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

This article discusses Blockchain and Generative AI in healthcare, including their uses, difficulties, and solutions. Blockchain technology improves EHR security, privacy, and interoperability, while smart contracts streamline supply chain management and administrative procedures. Blockchain verifies and secures IoT data, improving medical care and treatment, according to case studies. Generative AI systems like ChatGPT have transformed healthcare by personalizing therapy, diagnostics, and predictive analytics. AI systems can examine massive databases to diagnose diseases early, anticipate dangers, and personalize therapies. By providing timely information, boosting treatment adherence, and giving continuous support, AI-powered virtual health assistants have enhanced patient involvement. Generative AI has additionally enhanced medical research and drug development, cutting the time and expense of introducing new medicines. Generative AI and Blockchain provide safe patient data storage, high-quality AI training datasets, and efficient healthcare operations. Scalability, energy usage, and interoperability issues remain. Scalable Blockchain designs and standardized data integration and exchange protocols are suggested by this study. These technologies could improve medical research and therapy by making them safer, more effective, and more individualized. © The Author(s) 2025.

关键词： Electronic health record

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On-Board Intelligent Decision Support System Development for Aviation Complexes Using Machine-Oriented Methods 5

On-Board Intelligent Decision Support System Development for...

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5th International Youth Conference on Radio Electronics, Electrical and Power Engineering, REEPE 2023

作者： Vorobev, Alexander Elesin, Ilya Shevadronov, Alexander Bauman Moscow State Technical University Faculty Computer Science Artificial Intelligence and Control Systems Moscow Russia

ISBN: (纸本)9798350399523

This paper considers the possibility of using an integrated distributed on-board intelligent decision support system development by a CAD-centric integrated pipeline approach. The problems and solutions of developing and integrating such pipeline into aviation complexes are explained. Developed demonstration for making onboard intelligent decision expert system step-by-step. Key features and use cases are listed for CAD pipeline. The need to split a single monolithic system across multiple CADs to meet various requirements is considered. CAD pipeline consists of: CAD "Expert", CAD "Message", CAD "Astra-DB"and CAD "Scenario". In paper explained improvements given by CAD-centric approach, which increases speed of development and its overall quality summarized. © 2023 IEEE.

关键词： computer aided analysis

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A Review of Key Elements for Underwater Wireless Sensor Networks

A Review of Key Elements for Underwater Wireless Sensor Netw...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Al-Dulaimi, Aymen Mohammed Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Aliesawi, Salah A. Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Engineering Techniques Department of Communication Technical Engineering Al-Farahidi University Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq

ISBN: (纸本)9798350369106

Underwater Wireless Sensor Networks (UWSNs) are emerging and have huge prospects since they have various applications in oceanography, environmental monitoring, and underwater communication. These networks contain low-cost submerged sensors that are mostly connected by way of acoustic media. Sensors are relatively new to underwater research, and thanks to recent technological development, they can now function at different depths. In this article, the author explores what UWSNs must do to operate smoothly by exploring the basic features that they must meet for further development. Real-time Underwater communication remains an open problem due to underwater acoustic channels important for undersea data acquisition, undersea navigation, and undersea remote sensing. To mitigate such challenges and in light of the use of new technologies, researchers seek to improve the reliability and reliability of UWSNs. Finally, there is great potential with UWSNs for investigating and exploiting the underwater environment by improving scenarios of different domains in line with subaqueous data and communications. © 2024 IEEE.

关键词： Wireless sensor networks

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A Comprehensive Survey on Computing Services to Detect Stress and Anxiety Using Smart Devices 18

A Comprehensive Survey on Computing Services to Detect Stres...

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18th International Conference on Open Source systems and Technologies, ICOSST 2024

作者： Ahmed, Daniyal Hassan, Muhammad Ammar Gilani, Hassan Khan, Muhammad Sheraz Chaudhry, Atif Imran Afshan, Mehwish University of Central Punjab Faculty of Information Technology & Computer Science Lahore Pakistan School of Systems and Technology University of Management and Technology Lahore Pakistan

ISBN: (纸本)9798331508692

Stress and anxiety are part of a modern person's life, which is why they contribute to severe psychological and somatic disorders. The general objective of this work is to develop a live healthcare platform for stress and anxiety recognition and monitoring using machine learning algorithms from wearable biometric data or phone sensors. It is a scalable and privacy guaranteed solution to monitor continuously by processing real-time data only, through the integration of cloud computing with edge computing. Using machine learning, signals like heart rate variability, electrodermal activity, and accelerometry are analyzed to accurately detect signs of stress and anxiety. It is an app that provides users with personalized feedback and interventions to help enhance their mental health. Our evaluation emphasizes the accuracy of detection, scalability, and the performance of real-time processing on the platform, while also addressing challenges related to privacy and computational efficiency. The findings indicate that the platform can be a valuable resource for managing stress and anxiety in real time, highlighting the growing role of wearable technology in the healthcare sector. © 2024 IEEE.

关键词： Electronic health record

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Deep neural network-based secure healthcare framework

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Neural Computing and Applications 2024年第28期36卷 17467-17482页

作者： Aldaej, Abdulaziz Ahanger, Tariq Ahamed Ullah, Imdad College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia Management Information Systems Department CoBA Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia School of Computer Science Faculty of Engineering The University of Sydney SydneyNSW2006 Australia

Healthcare stands out as a critical domain profoundly impacted by Internet of Things (IoT) technology, generating vast data from sensing devices as IoT applications expand. Addressing security challenges is paramount for a successful IoT healthcare framework, with blockchain technology offering a decentralized structure for robust data protection and secure data exchange within multi-node IoT networks. The research introduces a secure IoT healthcare diagnostic model empowered by deep neural networks, emphasizing encryption, safe transactions, and healthcare diagnostics as key components. Notably, the model incorporates innovative techniques like the orthogonal particle swarm optimization algorithm for sharing medical images and a neighborhood indexing sequence method for hash value encryption. The development of an optimized deep neural network-based classification model for illnesses, validated through extensive trials, demonstrates superior performance metrics compared to existing decision-making techniques, with significant improvements in f-Measure (96.25%), sensitivity (93.26%), specificity (94.26%), and accuracy (93.26%). This study’s scientific contribution lies in its innovative approach to securing IoT-healthcare diagnosis models, validated performance enhancements using real-world datasets, and insightful recommendations for future research directions, fostering advancements in healthcare technology for enhanced patient care and system efficiency. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Internet of things

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Network Traffic Classifiers for Internet of Things Intrusion Detection using Machine Learning Classifiers 3

Network Traffic Classifiers for Internet of Things Intrusion...

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Uniyal, Anita Mishra, Amit Kumar Manushi Pant, Yogesh Computer Science & Engineering GRD IMT Dehradun India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India GRD IMT Department of Computer Science & Engineering Dehradun India Software Business Systems Consultanting India Private Limited India

ISBN: (纸本)9798350303360

In today's era smart devices are integral part of our life and every device becomes smart if it is equipped with sensors and having ability to connect with internet. Without any hindrance we can connect and interact with soundings via smart devices, this is possible because of the technological advancement such as cloud computing and Internet of things. With the latest technologies we are using advance algorithms such as machine learning to make our decision more effective. The importance and need of smart devices in life have more vulnerability attacks so it needs concrete security mechanism and privacy policies to safeguard from cyber security and threats via intrusion detection. The existing IoT security literature is thoroughly analysed in this paper, outlining the many vulnerabilities and possible defences. The paper also looks into the use of machine learning techniques for traffic inflow classification, highlighting comparisons of different classifiers for problems that are special to different domains and different machine learning techniques for early identification of intrusion detection. The paper is concluded with a review of the current problems and potential future directions for IoT security research. © 2023 IEEE.

关键词： Intrusion detection

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A Study on 1D-Point Cloud Using Through-Wall mm Wave Radar for Human Pose Estimation 12

A Study on 1D-Point Cloud Using Through-Wall mm Wave Radar f...

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12th International Electrical Engineering Congress, iEECON 2024

作者： Phaisiri, Kritthee Kittiyanpunya, Chainarong Shutimarrungson, Nattakarn Boonpoonga, Akkarat Akkaraekthalin, Prayoot Kaemarungsi, Kamol King Mongkut's University Of Technology North Bangkok Faculty Of Engineering Department Of Electrical And Computer Engineering Bangkok Thailand Rajamangala University Of Technology Rattanakosin Faculty Of Engineering Department Of Mechatronics Engineering Nakhon Pathom Thailand Mahasarakham Business School Mahasarakham University Department Of Business Computer Maha Sarakham Thailand Faculty Of Engineering Department Of Electrical And Computer Engineering Bangkok Thailand National Electronics And Computer Technology Center National Science And Technology Development Agency Pathum Thani Thailand

ISBN: (纸本)9798350383591

This paper presents human pose estimation using millimeter-wave (mmWave) radar technology capable of penetrating obstacles. Operating within the frequency modulated continuous wave (FMCW) at 60 - 64 GHz, our radar system generates signals conducive to accurate human pose representation. Our data processing framework focuses on producing point cloud data, effectively capturing each human movement. Results are visualized through a 2D graph, with the z-axis representing time. Experimental data, collected from various activities conducted behind different walls, underwent thorough analysis, comparing point cloud positions with actual human postures. The outcomes affirm the effectiveness of mmWave radar systems, positioned behind obstacles, in accurately extracting human postures. © 2024 IEEE.

关键词： Frequency modulation

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Secure Image Encryption using Playfair Cipher with Quadrant-Based Division 3

Secure Image Encryption using Playfair Cipher with Quadrant-...

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Rakhra, Manik Singh, Dalwinder Singh, Arun Kaur, Baljinder Khadimul Islam Zim, M.D. Singh, Janpreet Lovely Professional University Department of Computer Science & Engineering Jalandhar Phagwara India School of Computer Science and Engineering Lovely Professional University Phagwara India Ghent University Master of Science in Business Engineering Faculty of Economic and Business Administration Gent Belgium

ISBN: (纸本)9798350303360

Numerous photo encryption techniques have been presented in the previous decade, thanks to the increased focus on image encryption within data security research. Image encryption is different from textual content encryption due to the intrinsic properties of images, such as their mass records capacity and excessive records redundancy, making it far more challenging to address them utilising standard encryption processes. A new photo encryption add-on that uses Playfair cypher to better securely encode image data. The image is divided into four equal quadrants, which is the method indicated. A different image is shown in each of the four corners. The first quadrant consists of the top left corner, the second of the top right corner, the third of the bottom left corner, and the fourth of the remaining space. In the suggested approach, each quadrant picture functions as a standalone image that is encrypted using a random key. Once the images have been encrypted, they are combined into a single entity, making decryption very challenging. The fundamental goal of our research is to protect images from unauthorised duplication and distribution by encrypting data based on them using a suitable key. © 2023 IEEE.

关键词： Cryptography

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