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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Stefania Loredana Nita Marius Iulian Mihailescu Valentina Marascu Marius Rogobete Department of Computers and Cybersecurity Faculty of Information Systems and Cyber Security Military Technical Academy “Ferdinand I” Bucharest Romania Scientific Research Center in Mathematics and Informatics Faculty of Engineering and Computer Science “Spiru Haret” University Bucharest Romania Low Temperature Plasma Department National Institute for Laser Plasma and Radiation Physics Bucharest Romania Telematics Harman International Bucharest Romania

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

In the rapidly growing interest in digital identity management, there is an important shift toward adopting verifiable credentials, often based on blockchain technology. This transition highlights a growing interest in the concept of Self-Sovereign Identity (SSI), a paradigm that empowers individuals with control over their own digital identities through the use of verifiable credentials. This paper introduces a novel access control framework that leverages the principles of SelfSovereign Identity to significantly enhance the security of system access based on the users’ credentials. By integrating the decentralized and user-centric nature of SSI, our proposed framework aims to address existing vulnerabilities in traditional access control systems. It provides a robust, scalable, and controlled mechanism that ensures higher levels of trust and security in digital interactions. This approach aligns with the broader digital identity trends favoring transparency, user autonomy, and trust.

关键词： Access control computers Privacy Computational modeling computer architecture Market research Blockchains

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AI-Enabled Visual Assistance: Detecting Doors and Stairs to Aid Individuals with Visual Impairments

AI-Enabled Visual Assistance: Detecting Doors and Stairs to ...

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Innovative Trends in Information Technology (ICITIIT), International Conference on

作者： A Prema Kirubakaran Kuljeet Kaur T C Santhosh Kumar Reny Jose Vipul Vekariya Ramya Maranan Department of Information Technology and Information Systems Nile university of Nigeria Abuja FCT Nigeria Department of Management Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Mechanical Engineering BGS Institute of Technology BG Nagar Mandya Karnataka India PG Department of Computer Applications Marian College Kuttikkanam Idukki Kerala India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Post Limda Waghodia Gujarat India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

The study aims to develop a CNN-based system to assist visually impaired individuals in navigating unfamiliar environments confidently. This system utilizes deep learning techniques to process images captured by wearable or mounted cameras. Trained on diverse datasets, the CNN identifies visual patterns for door and stair recognition, considering factors like handles, frames, and textures for doors, and step patterns, handrails, and elevation changes for stairs. Transfer learning and fine-tuning optimize the CNN architecture for robust performance in varied environments. Evaluation metrics such as precision, recall, and F1 score assess the model’s accuracy. Experimental results demonstrate the system’s effectiveness in real-time door and stair identification.

关键词： Measurement Visualization Navigation Visual impairment Transfer learning Stairs Market research

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FastAGEDs: Fast Approximate Graph Entity Dependency Discovery 24th

FastAGEDs: Fast Approximate Graph Entity Dependency Discover...

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24th International Conference on Web Information systems Engineering, WISE 2023

作者： Zhou, Guangtong Kwashie, Selasi Zhang, Yidi Bewong, Michael Nofong, Vincent M. Hu, Junwei Cheng, Debo He, Keqing Liu, Shanmei Feng, Zaiwen College of Informatics Huazhong Agricultural University Hubei Wuhan China AI & Cyber Futures Institute Charles Sturt University Bathurst Australia School of Computing Mathematics & Engineering Charles Sturt University Wagga Wagga Australia Department of Computer Science & Engineering University of Mines & Technology Tarkwa Ghana STEM University of South Australia Adelaide Australia School of Computing Science Wuhan University Hubei Wuhan China Hubei Hongshan Laboratory Huazhong Agricultural University Hubei Wuhan China Macro Agricultural Research Institute Huazhong Agricultural University Hubei Wuhan China

ISBN: (纸本)9789819972531

This paper studies the discovery of approximate rules in property graphs. First, we propose a semantically meaningful measure of error for mining graph entity dependencies (GEDs) that almost hold, to tolerate errors and inconsistencies that exist in real-world graphs. Second, we present a new characterisation of GED satisfaction, and devise a depth-first search strategy to traverse the search space of candidate GEDs efficiently. Further, we perform experiments to demonstrate the feasibility and scalability of our solution, FastAGEDs, with three real-world graphs. The results show FastAGEDs is effective and efficient for mining approximate GEDs in noisy and erroneous real-world graphs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： approximate dependency efficient algorithm graph constraints graph entity dependency

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An Early Warning System to Predict Earthquakes based on WT-ARIMA-ANN Model

An Early Warning System to Predict Earthquakes based on WT-A...

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International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Uppara Raghu Babu Shrinwantu Raha Mujtaba Ali Khan C. Yamini R. Jayadurga Gourav Kalra Department of Civil Engineering Srinivasa Ramanujan Institute of Technology Andhra Pradesh India Department of Geography Bhairab Ganguly College Belgharia Kolkata India Department of Computer Science College of Computing and Informatics University of North Carolina Charlotte Department of Mathematics PSNA College of Engineering and Technology Dindigul Tamil Nadu India Department of MBA Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Mechanical Engineering Maharishi Markandeshwar (Deemed to be University) Mullana India

A devastating earthquake is one of the Earth’s most destructive natural disasters. Real-time seismic monitoring facilities can evolve into Earthquake Early Warning (EEW) systems with the help of modern technology, property damage, reducing the number of casualties, and economic losses. The proposed EEW system’s goal is to supply a precise damage assessment just before an earthquake hits a certain area. The proposed method involves preprocessing, feature extraction, and model training. It is recommended to preprocess the data by first filtering it and then reformatting it. Filtering is essential for getting rid of irrelevant or noisy information. In feature extraction, the Principal Component Analysis (PCA) provides a useful perspective on the data by establishing a new coordinate system, and it also offers a method for reducing the data’s dimensionality. Once the features have been obtained, the models are trained with WT-ARIMA-ANN. The proposed method outperforms two widely used alternatives, ARIMA and ANN.

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Stream Reasoning approach on Human Behavior for Medication Risks Detection using LARS Framework

Stream Reasoning approach on Human Behavior for Medication R...

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International Conference on Pattern Analysis and Intelligent systems (PAIS)

作者： Agti Nadia Sabri Lyazid Kazar Okba Chibani Abdelghani Department of Computer Science University of Mohamed El Bachir El Ibrahimi Bordj Bou Arreridj Algeria LISSI-The Laboratory of Images Signals and Intelligent Systems University Paris-Est Vitry-sur-Seine Paris France Computer Sciences Department LINFI University of Biskra Biskra Algeria College of Information Technology United Arab Emirate University AL Ain United Arab Emirates

Human activity detection and analysis is essential in many areas, including healthcare, smart settings, and surveillance. Traditional techniques fail to efficiently evaluate streaming data, as the need for real-time analysis rises. In this study, we introduce a novel approach to identify potential medical risks by examining the behaviors of an elderly individual. Our approach analyzes human activities using rule-based common sense reasoning over data streams. We utilize LARS (Logic-Based Framework for Analyzing Reasoning over Streams), a system that permits the use of specific time operators, to perform the reasoning. This enables us to more effectively handle and reason about real-time data. It also provides incremental processing of streaming data, which improves results in terms of risk detection. We evaluate the effectiveness of our approach by doing comprehensive testing on synthetic data, which acts as a simulation of real-time recorded human actions, to assess the efficacy of our reasoning model. Our suggested approach provides real-time monitoring of human behavior, demonstrating its usefulness and need and giving an invaluable option for the precise detection of potential medical dangers among elderly people.

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Automated Clinical Coding: What, Why, and Where We Are?

arXiv

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arXiv 2022年

作者： Dong, Hang Falis, Matúš Whiteley, William Alex, Beatrice Matterson, Joshua Ji, Shaoxiong Chen, Jiaoyan Wu, Honghan Centre for Medical Informatics Usher Institute University of Edinburgh Edinburgh United Kingdom School of Informatics University of Edinburgh Edinburgh United Kingdom Centre for Clinical Brain Sciences University of Edinburgh Edinburgh United Kingdom Institute of Health Informatics University College London London United Kingdom Edinburgh Futures Institute Edinburgh United Kingdom Epic Systems Corporation Wisconsin United States University College London Hospitals NHS Foundation Trust Clinical Research Informatics Unit London United Kingdom Department of Computer Science Aalto University Espoo Finland Department of Computer Science University of Oxford Oxford United Kingdom

Clinical coding is the task of transforming medical information in a patient's health records into structured codes so that they can be used for statistical analysis. This is a cognitive and time-consuming task that follows a standard process in order to achieve a high level of consistency. Clinical coding could potentially be supported by an automated system to improve the efficiency and accuracy of the process. We introduce the idea of automated clinical coding and summarise its challenges from the perspective of Artificial Intelligence (AI) and Natural Language Processing (NLP), based on the literature, our project experience over the past two and half years (late 2019 - early 2022), and discussions with clinical coding experts in Scotland and the UK. Our research reveals the gaps between the current deep learning-based approach applied to clinical coding and the need for explainability and consistency in real-world practice. Knowledge-based methods that represent and reason the standard, explainable process of a task may need to be incorporated into deep learning-based methods for clinical coding. Automated clinical coding is a promising task for AI, despite the technical and organisational challenges. Coders are needed to be involved in the development process. There is much to achieve to develop and deploy an AI-based automated system to support coding in the next five years and *** Codes 68T07 (Primary), 68T50 (Secondary) © 2022, CC BY.

关键词： Clinical research

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Gaia Net: Towards robust spectroscopic parameters of stars of all evolutionary stages

arXiv

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arXiv 2025年

作者： Huson, Dylan Cowan, Indiana Sizemore, Logan Kounkel, Marina Hutchinson, Brian Computer Science Department Western Washington University 516 High Street BellinghamWA98225 United States Department of Physics and Astronomy University of North Florida 1 UNF Dr JacksonvilleFL32224 United States Human-Earth Systems & Science Group Pacific Northwest National Laboratory 5825 University Research Court Suite 3500 College ParkMD20740 United States

We present a new processing of XP spectra for 220 million stars released in Gaia DR3. The new data model is capable of handling objects with Teff between 2000 and 50,000 K, and with log g between 0 and 10, including objects of multitude of masses and evolutionary stages. This includes for the first time ever robust processing of spectroscopic parameters for pre-main sequence stars, with log g sensitivity towards their age. Through this analysis we examine the distribution of young low mass stars with ages of up to 20 Myr in the solar neighborhood, and we identify a new massive (> 1000 stars) population, Ophion, which is found east of Sco Cen. This population appears to be fully disrupted, with negligible kinematic coherence. Nonetheless, due its young age it appears to still persist as a spacial overdensity. Through improved determination of ages of the nearby stars, it may be possible to better recover star forming history of the solar neighborhood outside of the moving groups. © 2025, CC BY.

关键词： Giant stars

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Optimized Security Mechanism for publicly Secret Key Sharing over Cloud using Blockchain

Optimized Security Mechanism for publicly Secret Key Sharing...

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2024 International Symposium on Securing Next Generation systems Using Future Artificial Intelligence Technologies, SNSFAIT 2024

作者： Byeon, Haewon Quraishi, Aadam Thuniki, Praveen Keshta, Ismail Soni, Mukesh Shabaz, Mohammad Department of AI and Software Inje University Gimhae50834 Korea Republic of Inje University Medical Big Data Research Center Gimhae50834 Korea Republic of Intervention Treatment Institute HoustonTX United States Sr Program Analyst Georgia CummingGA30040 United States Computer Science and Information Systems Department College of Applied Sciences Al Maarefa University Riyadh Saudi Arabia Dr. D. Y. Patii Vidyapeeth Pune Dr. D. Y. Patii School of Science & Technology Tathawade Pune India Model Institute of Engineering and Technology J&K Jammu India

A publicly verifiable key sharing mechanism based on threshold key sharing is provided to explore the security of users' private keys on the blockchain. Participating nodes check the key fragment after receiving it, effectively preventing it from being abused. The crucial sections of the nodes that participated in the critical splicing are made public during the critical recovery stage to prevent them from performing harmful things during the critical recovery stage. Add IDs to the nodes that participated in the crucial splicing during the key distribution stage;a dynamic threshold system is intended to track and update the status of malicious nodes in real time. When the node that possesses the crucial component fails, the owner of the critical component and the main node relocate a key element to the new participating nodes. To safeguard sensitive information. The experimental results show that this system has a key recovery rate of 80% and threshold qualities such as traceability, enforceability, and recoverability. © 2024 Copyright for this paper by its authors.

关键词： Blockchain Encryption Key sharing mechanism Privacy Secret key Security

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A secure and multi-objective virtual machine placement framework for cloud data centre

arXiv

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arXiv 2021年

作者： Saxena, Deepika Gupta, Ishu Kumar, Jitendra Singh, Ashutosh Kumar Wen, Xiaoqing Department of Computer Applications National Institute of Technology Kurukshetra India Department of Computer Applications Nit Tiruchirappalli Tamilnadu India Department of Creative Informatics Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka8208502 Japan

To facilitate cost-effective and elastic computing benefits to the cloud users, the energy-efficient and secure allocation of virtual machines (VMs) plays a significant role at the data centre. The inefficient VM Placement (VMP) and sharing of common physical machines among multiple users leads to resource wastage, excessive power consumption, increased inter-communication cost and security breaches. To address the aforementioned challenges, a novel secure and multi-objective virtual machine placement (SM-VMP) framework is proposed with an efficient VM migration. The proposed framework ensures an energy-efficient distribution of physical resources among VMs that emphasizes secure and timely execution of user application by reducing inter-communication delay. The VMP is carried out by applying the proposed Whale Optimization Genetic Algorithm (WOGA), inspired by whale evolutionary optimization and nondominated sorting based genetic algorithms. The performance evaluation for static and dynamic VMP and comparison with recent state-of-the-arts observed a notable reduction in shared servers, inter-communication cost, power consumption and execution time up to 28.81%, 25.7%, 35.9% and 82.21%, respectively and increased resource utilization up to 30.21%. © 2021, CC BY.

关键词： Electric power utilization

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Energy Efficiency Maximization in RISs-Assisted UAVs-Based Edge Computing Network Using Deep Reinforcement Learning

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Big Data Mining and Analytics 2024年第4期7卷 1065-1083页

作者： Chuanwen Luo Jian Zhang Jianxiong Guo Yi Hong Zhibo Chen Shuyang Gu School of Information Science and Technology Beijing Forestry UniversityBeijing 100083Chinaand also with the Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland AdministrationBeijing 100083China Advanced Institute of Natural Sciences Beijing Normal UniversityZhuhai 519087Chinaand also with the Guangdong Key Lab of AI and Multi-Modal Data ProcessingBNU-HKBU United International CollegeZhuhai 519087China Department of Computer Information Systems Texas A&M University-Central TexasKilleenTX 76549USA

Edge Computing(EC)pushes computational capability to the Terrestrial Devices(TDs),providing more efficient and faster computing *** Aerial Vehicles(UAVs)equipped with EC servers can be flexibly deployed,even in complex terrains,to provide mobile computing services at all ***,UAVs can establish an air-to-ground line-of-sight link with TDs to improve the quality of communication ***,the UAV-to-TD link may be obstructed by ground obstacles such as buildings or trees,leading to sub-optimal data transmission *** surmount this issue,Reconfigurable Intelligent Surfaces(RISs)emerge as a promising technology capable of intelligently reflecting signals to enhance communication quality between UAVs and *** this paper,we consider the RISs-assisted multi-UAVs collaborative edge Computing Network(RUCN)in urban environment,in which we study the computational offloading *** goal is to maximize the overall energy efficiency of UAVs by jointly optimizing the flight duration and trajectories of UAVs,and the phase shifts of *** is worth noting that this problem has been formally established as ***,we propose the Deep Deterministic Policy Gradients based UAV Trajectory and RIS Phase shift optimization algorithm(UTRP-DDPG)to solve this complex optimization *** results of extensive numerical experiments show that the proposed algorithm outperforms the other benchmark algorithms under various parameter ***,the UTRP-DDPG algorithm improves the UAV energy efficiency by at least 2%compared to DQN algorithm.

关键词： edge computing computing offloading Unmanned Aerial Vehicle(UAV) Reconfigurable Intelligent Surface(RIS) deep reinforcement learning

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