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Euromicro Symposium on Digital System Design

作者： Nicolas Sklavos Maria Pantopoulou Francisco Florez-Revuelta Computer Engineering and Informatics Department (CEID) SCYTALE Group University of Patras Hellas School of Nuclear Engineering Purdue University West Lafayette Indiana USA Department of Computing Technology University of Alicante Alicante Spain

ISBN: (数字)9781665474047

ISBN: (纸本)9781665474054

Active and Assisted Living (AAL) systems have a purpose to improve the lives of older or impaired people in various aspects. However, the use of equipment for data acquisition in these systems can be considered intrusive in some cases. Although de-identification may provide the needed protection to some extent, it is not always preferred, as it could affect the quality and utility of any obtained data. It is therefore crucial to establish methodologies for protecting the privacy of those monitored and thus affected by AAL systems. The purpose of GoodBrother is to a) analyze any issues arising from the use of monitoring AAL systems, regarding the users' privacy; b) establish proper guidelines for the use of these systems; c) develop privacy-aware methodologies for data handling; d) increase the systems' robustness and reliability; e) create databases to use towards benchmarking. Each one of these objectives are handled by separate interdisciplinary working groups.

关键词： Data privacy Privacy Ethics Digital systems Cameras Robustness Assisted living

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Analysis of Drug Data Mining with Clustering Technique Using K-Means Algorithm 1

Analysis of Drug Data Mining with Clustering Technique Using...

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1st International Conference on Innovation and Application of Science and Technology, ICIASTECH 2019

作者： Lia Hananto, April Assiroj, Priati Priyatna, Bayu Nurhayati Fauzi, Ahmad Yuniar Rahman, Aviv Shofiah Hilabi, Shofa Faculty of Computing Universiti Teknologi Malaysia Skudai Johor Malaysia Faculty of Technology and Computer Science Universitas Buana Perjuangan Karawang Indonesia Master of Computer Science Universitas Budi Luhur Jakarta Indonesia Department of Informatics Engineering Universitas Widyagama Malang Indonesia

Data processing is very important in the development of information technology. Almost all fields of work have information data. Data can be used to help analysis in work. At present, health information data is very important to be processed in order to help medical personnel to make decisions. So that the results of the right decision to help patients. Lately, drug data has been misused for information eliminating a depressed patient without a doctor's prescription with a total data of 53766. The results shown are very large. So it requires very much attention from the government. As a result of the deviation of information and applied to the patient will result in death. Therefore, research needs to be conducted to group data on drug data. The source of research data is obtained from the UCI Machine Learning Repository Education website. The method proposed in this research is data mining. This solution can help researchers in the analysis of these data. One technique in data mining with clustering is using the K-means algorithm. The variables used are drug name, condition, useful count. The first research results can classify three categories consisting of using the highest drugs, using medium drugs and using lace drugs. Then the accuracy of the data is obtained with condition 99.45% valid records 53471, drug name 100% with valid records 53766, useful count 100% with valid records 53766. © Published under licence by IOP Publishing Ltd.

关键词： Data mining

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AI-Enabled Predictive Analytics for Proactive Maintenance in IoT Systems

AI-Enabled Predictive Analytics for Proactive Maintenance in...

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International Conference on Contemporary computing and informatics (IC3I)

作者： Harpreet Singh Chawla K. Veerasamy Sunilkumar R Patil V. Sivakumar B. Shunmugapriya C. Balarama Krishna Department of Computer Science & Engineering IFTM University Moradabad U.P Department of Computer Science Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Mechanical Engineering Bharati Vidyapeeth's College of Engineering Pune India Department of Information Technology School of Computing (SoC) Kalasalingam Academy for Research and Education (KARE) Krishnankoil Virudhunagar Dist TN Department of Computer Science and Engineering National engineering College Kovilpatti Tamil Nadu India School of Science SR University Warangal Telangana India

Wind turbine operations and maintenance expenditures have always been a significant burden, with the majority of money spent on unforeseen unorganized failures, fixes, and downtime expenses. Commercial Internet of Things (IoTs) and artificial intelligence (AI) technological advances have achieved breakthroughs and are truly revolutionary innovations with the ability to monitor, forecast, and avoid catastrophic breakdowns. A Sensor-oriented IoT system (SOIS) will aid in the monitoring of critical characteristics of wind turbines that govern their operating circumstances, such as wind pace, motion, temperature, and power generated. For proactive maintenance of wind turbines, an integrated strategy using Machine Learning (ML) approaches for SOIS was applied. The system's diagnostic component receives sensor data and does predictive analytics in cloud environment. Five ML techniques are used, and the outcomes are summarized for prediction. The techniques' outputs are contrasted for the accuracy of the detected statistics, and it is discovered that the Optimized Artificial Neural Network performs better for proactive maintenance and predictive analytics of the power of wind turbines.

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A Testbed Implementation of a Biometric Identity-Based Encryption for IoMT-enabled Healthcare System 2021

A Testbed Implementation of a Biometric Identity-Based Encry...

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The 5th International Conference on Future Networks & Distributed Systems

作者： Mahima Aggarwal Mohammed Zubair Devrim Unal Abdulla Al-Ali Thomas Reimann Guillaume Alinier KINDI Center for Computing Research College of Engineering Qatar University Qatar Department of Computer Science and Engineering College of Engineering Qatar University Qatar Ambulance Services Hamad Medical Corporation Qatar

ISBN: (纸本)9781450387347

Historically, health data was stored locally on the hospital’s server as patient health records, and we had to rely on the planned check-up during doctor’s visits. Now, we can monitor health continuously and in real-time. Remote healthcare monitoring can also be helpful in setting up alerts, allowing early intervention and treatment. In this way, the patient gets timely treatment, avoids doctor visits or hospitalization hassles, and reduces healthcare expenses. Additionally, in an ambulance, real-time patient data can be sent to the physician via wireless communication which can save the life of a critical patient. The critical problem that arises in Internet of Medical Things (IoMT) is how securely the health vitals can be transmitted to the physician. There are numerous ways to transmit data securely to the physician, however Identity-based cryptography is the most popular these days to provide solutions for secure transmission. Identity-based cryptography is the public key cryptography in which an arbitrary string representing an individual is used as a public key. Biometric information based public key generation by identity based encryption is a potential candidate for providing a solution to such a problem. In this work, we propose using facial biometric information of the clinicians to create a public key for encrypting health vitals of a patient.

关键词： User authentication Internet of Medical Things Type-3 pairing Biometric identity based encryption Fuzzy Identity based Encryption

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Privacy-Preserving and Hardware Acceleration-Based Authentication Scheme for Data Collection in E-Health Applications

Privacy-Preserving and Hardware Acceleration-Based Authentic...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Kasem Khalil Ahmed Sherif Mohammad M.R. K. Mamun Mohamed Elsersy Ahmad Abdel-Aliem Imam Muhammad Hataba Mohamed Mahmoud Electrical and Computer Engineering Department University of Mississippi MS USA School of Computing Sciences and Computer Engineering University of Southern Mississippi MS USA Electrical and Computer Engineering Department Tennessee Technological University TN USA Higher Colleges of Technology Al Ain Abu Dhabi UAE William Carey University College of Osteopathic Medicine Mississippi MS USA

E-health applications have a variety of solutions that employ digital technology in healthcare to enhance how medical treatment is delivered. E-health applications should use a privacy-aware authentication approach for data collection to ensure the security and privacy of patients' data. Lightweight cryptographic solutions are required to authenticate data sources while incurring low computation and communication overheads. This paper introduces a privacy-preserving authentication approach for validating each data source before accepting its data. Our proposed cryptographic scheme authenticates the data source without revealing sensitive identity information. Because of the vast volume of data sources in E-health applications (patient, doctor, nurse, hospital, etc.), a hardware-accelerated authentication mechanism is a suitable option. Our proposed authentication method is implemented using VHDL and three field-programmable gate arrays (FPGAs) with different capabilities to show the hardware utilization of our scheme. Also, we provided two different security levels by using two different key sizes to provide the tradeoff between security and hardware costs. The simulation result shows that our proposed scheme consumes 0.091 mW and has a latency of 98 ms with a data source identification (ID) size of 64 bits and a key length of 8192 bits. Also, the implementation is tested using an ID size of 128 bits and a key length of 32,768 bits.

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A Dementia Diagnosis Technique Based on AI and Hardware Acceleration

A Dementia Diagnosis Technique Based on AI and Hardware Acce...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Kasem Khalil Mohammad M. R. Khan Mamun Ahmed Sherif Mohamed Elsersy Ahmad Abdel-Aliem Imam Kamal Abouzaid Electrical and Computer Engineering Department University of Mississippi MS USA Electrical and Computer Engineering Department Tennessee Technological University TN USA School of Computing Sciences and Computer Engineering University of Southern Mississippi MS USA Computer and Information Systems Dept. Higher Colleges of Technology AlAin Abu Dhabi UAE College of Osteopathic Medicine William Carey University Mississippi MS USA

The effects of Alzheimer’s disease (AD) are devastating, both personally and within the patient’s family, as the disease progresses slowly over many years. It could significantly affect illness consequences and outcomes if detected early and accurately. Non-invasive medical testing often uses blood bio-samples because they are easy to collect and relatively cheap to analyze. This paper proposes a federated learning (FL)-based diagnostic model for AD using blood bio-samples. We have used blood bio-sample data sets downloaded from the ADNI website to test and compare the efficacy of our models. According to the enormous data collected for early AD detection, we used a hardware acceleration scheme to implement our FL model to fasten the training and testing operations. The hardware-accelerator method is implemented using VHDL and Altera 10 GX FPGA. The simulation results show the proposed algorithms reach an accuracy and sensitivity of 89% and 87%, respectively, for early detection while taking less time to train than other state-of-the-art algorithms. The proposed algorithms also consume 52-172 mW, which makes them suitable for constrained devices.

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QoE Maximization for Multiple-UAV-Assisted Multi-Access Edge computing: An Online Joint Optimization Approach

arXiv

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arXiv 2024年

作者： He, Long Sun, Geng Sun, Zemin Wu, Qingqing Kang, Jiawen Niyato, Dusit Han, Zhu Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China The School of Automation Guangdong University of Technology Guangzhou510006 China The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446701 Korea Republic of The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

In disaster scenarios, conventional terrestrial multi-access edge computing (MEC) paradigms, which rely on fixed infrastructure, may become unavailable due to infrastructure damage. With high-probability line-of-sight (LoS) communication, flexible mobility, and low cost, unmanned aerial vehicle (UAV)-assisted MEC is emerging as a new promising paradigm to provide edge computing services for ground user devices (UDs) in disaster-stricken areas. However, the limited battery capacity, computing resources, and spectrum resources also pose serious challenges for UAV-assisted MEC, which can potentially shorten the service time of UAVs and degrade the quality of experience (QoE) of UDs without an effective control approach. To this end, in this work, we first present a hierarchical architecture of multiple-UAV-assisted MEC networks that enables the coordinated provision of edge computing services by multiple UAVs. Then, we formulate a joint task offloading, resource allocation, and UAV trajectory planning optimization problem (JTRTOP) to maximize the QoE of UDs while considering the energy consumption constraints of UAVs. Since the problem is proven to be a future-dependent and NP-hard problem, we propose a novel online joint task offloading, resource allocation, and UAV trajectory planning approach (OJTRTA) to solve the problem. Specifically, the JTRTOP is first transformed into a per-slot real-time optimization problem (PROP) using the Lyapunov optimization framework. Then, a two-stage optimization method based on game theory and convex optimization is proposed to solve the PROP. Simulation results provide empirical evidence supporting the superior system performance of the proposed OJTRTA in comparison to alternative approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

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CGATracker: Correlation-Aware Graph Alignment for Referring Multi-Object Tracking

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Zhuang, Siping Li, Guangyao Wu, Qiangqiang Lu, Yang Hu, Hai-Miao Wang, Hanzi Xiamen University Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen 361005 China City University of Hong Kong Department of Computer Science Hong Kong Beihang University Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beijing 100191 China

Referring multi-object tracking (RMOT) aims to identify specific targets based on sentence descriptions. To enhance multi-modal learning, previous works typically relied on a simple fusion module at early or late stages. However, those methods frequently underutilize textual semantics and struggle to model the relationships between region-level features and word-level features. To address these limitations, we propose CGATracker, a correlation-aware graph alignment method for RMOT, which facilitates precise relationship modeling through relational scoring. Specifically, we design a Language-driven Relational Alignment (LRA) module, which establishes two connection graphs to generate positive and negative samples for the visual-textual alignment. Additionally, to effectively leverage referring information, we introduce a Semantic Clarify Booster (SCBooster) module based on a semantic infusion mechanism and a bias-aware verification mechanism for interactions with different modalities. Moreover, by designing a Multi-level Crossmodal Fusion (MCF) module, our method aggregates contextual features at multiple depths to enable the creation of the enriched correlation-aware graph. Extensive experiments conducted on the Refer-KITTI and Refer-KITTI-V2 datasets demonstrate the effectiveness of CGATracker. © 1991-2012 IEEE.

关键词： end-to-end tracking multi-modal learning Referring multi-object tracking

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Toward Agentic AI: Generative Information Retrieval Inspired Intelligent Communications and Networking

arXiv

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arXiv 2025年

作者： Zhang, Ruichen Tang, Shunpu Liu, Yinqiu Niyato, Dusit Xiong, Zehui Sun, Sumei Mao, Shiwen Han, Zhu College of Computing and Data Science Nanyang Technological University Singapore Computer Science and Design Pillar University of Technology and Design Singapore Institute for Infocomm Research Agency for Science Technology and Research Singapore Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446701 Korea Republic of

—The increasing complexity and scale of modern telecommunications networks demand intelligent automation to enhance efficiency, adaptability, and resilience. Agentic AI has emerged as a key paradigm for intelligent communications and networking, enabling AI-driven agents to perceive, reason, decide, and act within dynamic networking environments. However, effective decision-making in telecom applications, such as network planning, management, and resource allocation, requires integrating retrieval mechanisms that support multi-hop reasoning, historical cross-referencing, and compliance with evolving 3GPP standards. This article presents a forward-looking perspective on generative information retrieval-inspired intelligent communications and networking, emphasizing the role of knowledge acquisition, processing, and retrieval in agentic AI for telecom systems. We first provide a comprehensive review of generative information retrieval strategies, including traditional retrieval, hybrid retrieval, semantic retrieval, knowledge-based retrieval, and agentic contextual retrieval. We then analyze their advantages, limitations, and suitability for various networking scenarios. Next, we present a survey about their applications in communications and networking. Additionally, we introduce an agentic contextual retrieval framework to enhance telecom-specific planning by integrating multi-source retrieval, structured reasoning, and self-reflective validation. Experimental results demonstrate that our framework significantly improves answer accuracy, explanation consistency, and retrieval efficiency compared to traditional and semantic retrieval methods. Finally, we outline future research directions. © 2025, CC BY.

关键词： Generative adversarial networks

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CSCL-DTI: predicting drug-target interaction through cross-view and self-supervised contrastive learning

CSCL-DTI: predicting drug-target interaction through cross-v...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xuan Lin Xi Zhang Zu-Guo Yu Yahui Long Xiangxiang Zeng Philip S. Yu College of Computer Science Xiangtan University China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University China Singapore Immunology Network (SIgN) Agency for Science Technology and Research (A*STAR) Singapore College of Information Science and Engineering Hunan University China Department of Computer Science University of Illinois Chicago USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Accurately predicting drug-target interactions (DTI) is a critical step in drug discovery. Existing methods of DTI prediction primarily employ Simplified Molecular-Input Line-Entry System (SMILES) sequences or molecular graphs to learn drug representations. However, the features learned by such single-view approach is prone to incomplete. While some multiview methods that consider the views of both SMILES sequences and molecular graphs have been developed, these methods often fall in short in capturing potential interactions between views. In this work, we propose a novel dual contrastive learning framework CSCL-DTI for DTI prediction. First, we design a contrastive-enhanced cross-view representation learning (CVRL) to learn representations for drugs. In this module, Transformer-based and graph convolutional network (GCN)-based encoders are separately adopted to learn view-specific representations, followed by contrastive learning to enrich the representations by accounting for the potential interplay between local chemical context and topological structure. Second, we combine Transformer with self-supervised contrastive learning (SSCL) to learn representations for targets by modelling protein amino acids sequences. The scheme allows to effectively preserve the intrinsic characteristics of the sequences. Finally, we introduce a bilinear attention network to obtain an integrated representation by adaptively incorporating drug and target representations. Benchmarking experiments on two datasets demonstrated that CSCL-DTI 1 outperforms six state-of-the-art methods.

关键词： Drugs Proteins Representation learning Protein engineering Adaptation models Contrastive learning Predictive models Transformers Amino acids Diffusion tensor imaging

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