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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Liao, Yunming Xu, Yang Xu, Hongli Wang, Lun Qian, Chen University of Science and Technology of China School of Computer Science and Technology China University of Science and Technology of China Suzhou Institute for Advanced Study China University of California Jack Baskin School of Engineering Department of Computer Science and Engineering Santa Cru United States

ISBN: (纸本)9798350334142

Data generated at the network edge can be processed locally by leveraging the paradigm of edge computing (EC). Aided by EC, decentralized federated learning (DFL), which overcomes the single-point-of-failure problem in the parameter server (PS) based federated learning, is becoming a practical and popular approach for machine learning over distributed data. However, DFL faces two critical challenges, i.e., system heterogeneity and statistical heterogeneity introduced by edge devices. To ensure fast convergence with the existence of slow edge devices, we present an efficient DFL method, termed FedHP, which integrates adaptive control of both local updating frequency and network topology to better support the heterogeneous participants. We establish a theoretical relationship between local updating frequency and network topology regarding model training performance and obtain a convergence upper bound. Upon this, we propose an optimization algorithm, that adaptively determines local updating frequencies and constructs the network topology, so as to speed up convergence and improve the model accuracy. Evaluation results show that the proposed FedHP can reduce the completion time by about 51% and improve model accuracy by at least 5% in heterogeneous scenarios, compared with the baselines. © 2023 IEEE.

关键词： Edge computing

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Privacy‐preserving remote sensing images recognition based on limited visual cryptography

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1166-1177页

作者： Denghui Zhang Muhammad Shafiq Liguo Wang Gautam Srivastava Shoulin Yin Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhouChina Department of New Networks Peng Cheng LaboratoryShenzhenChina College of Information and Communications Engineering Dalian Minzu UniversityDalianChina Department of Mathematics and Computer Science Brandon UniversityBrandonManitobaCanada Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Math Lebanese American UniversityBeirutLebanon College of Information and Communications Engineering Harbin Engineering UniversityHarbinChina

With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing *** fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image ***,due to the complex architecture of IoT and the lack of a unified security protection mechanism,devices in remote sensing are vulnerable to privacy leaks when sharing *** is necessary to design a security scheme suitable for computation‐limited devices in IoT,since traditional encryption methods are based on computational *** Cryptography(VC)is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted *** stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in *** this study,the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is *** diffusing the error between the encryption block and the original block to adjacent blocks,the degradation of quality in recovery images is *** fine‐tuning the pre‐trained model from large‐scale datasets,we improve the recognition performance of small encryption datasets for remote sensing *** experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.

关键词： activity recognition feature extraction image classification KNN privacy protection remote monitoring remote sensing

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Recognition of Fire Situation Using Graph Convolutional Network Model 15th

Recognition of Fire Situation Using Graph Convolutional Netw...

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15th International Conference on computer science and its Applications, CSA 2023 and 17th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2023

作者： Kim, Si Jin Park, Ji Su Kang, Jungho Shon, Jin Gon Advanced Convergence Technology Laboratory Korea National Open University Seoul Korea Republic of Department of Computer Science and Engineering Jeonju University Jeonju-Si Korea Republic of Department of Software Engineering Baewha Woman University Seoul Korea Republic of Department of Computer Science Korea National Open University Seoul Korea Republic of

ISBN: (纸本)9789819724468

Fires cause a lot of casualties and economic losses. In order to prevent fire accidents in advance, it is necessary to find out the cause of the fire. Existing fire alarm systems detected fires with temperature, smoke, and flame sensors, but could not distinguish the cause of the fire. In this paper, to solve this problem, the recognition of fire situation was tested using the GCN model. The fire dataset used in the experiment is a fire dataset composed of causes such as boxes, clothing, and electricity. The accuracy of 93.44% was confirmed as a result of the learning experiment with the GCN model, a graph-based deep learning model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Premixed flames

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AI-Enhanced Child Handwriting Analysis: A Framework for the Early Screening of Dyslexia and Dysgraphia

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SN computer science 2025年第5期6卷 1-26页

作者： Rangasrinivasan, Sahana Sumi Suresh, M.S. Olszewski, Abbie Setlur, Srirangaraj Jayaraman, Bharat Govindaraju, Venu Department of Computer Science and Engineering University at Buffalo Buffalo 14260 NY United States Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kerala Kollam 690525 India College of Education and Human Development University of Nevada-Reno Reno 89557 NV United States Amrita Institute of Advanced Research Elburn 60119 IL United States

Dyslexia and dysgraphia are two specific learning disabilities (SLDs) that are prevalent among children. To minimize the negative impact these SLDs have on a child’s academic and social-emotional development, it is crucial to identify dyslexia and dysgraphia at an early age, enabling timely and effective intervention. The first step in this process is screening, which helps determine if a child requires further instruction or a more in-depth assessment. Current screening tools are expensive, require additional administration time beyond regular classroom activities, and are designed to screen exclusively for one condition, not for both dyslexia and dysgraphia, which often share some common behavioral characteristics. Most dyslexia screeners focus on speech and oral tasks and exclude writing activities. However, analyzing children’s writing samples for behavioral signs of dyslexia and dysgraphia can offer valuable insights into the screening process, which can be time-consuming. As a solution, we propose a co-designed framework for building artificial intelligence (AI) tools that could boost the efficiency of screening and aid practitioners such as speech-language pathologists (SLPs), occupational therapists, general educators, and special educators by simplifying their tasks. This paper reviews current screening methods employed by practitioners, the use of AI-based systems in identifying dyslexia and dysgraphia, and the handwriting datasets available to train such systems. The paper also outlines a framework for developing an AI-integrated screening tool that can identify writing-based behavioral indicators of dyslexia and dysgraphia in children’s handwriting. This framework can be used in conjunction with current screening tools like the Dysgraphia and Dyslexia Behavioral Indicator Checklist (DDBIC). The paper also proposes a methodology for collecting children’s offline and online handwriting samples to build a valuable dataset for developing AI solutions. The pr

关键词： Artificial intelligence Child handwriting Dysgraphia Dyslexia Early screening

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Unraveling Dementia Severity: A Deep Learning Approach for Brain MR Image-Based Prediction and Misclassification Analysis

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Procedia computer science 2025年 258卷 1199-1208页

作者： Rekha R Nair Tina Babu Tripty Singh Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

Alzheimer’s disease is the most prevalent cause of dementia, and its early diagnosis is crucial to prevent the progression to severe stages where cognitive abilities are severely impaired. This research paper presents an innovative approach to predict the severity of dementia through classification and grading. The research introduces an innovative adaptation of the DEMNET model, referred to as the DEMENtia network model. The research implements a novel methodology leveraging Convolutional Neural Networks (CNNs) to identify significant patterns within unorganized web-based data collections. The investigation employs a dataset com- prising four categories, obtained from the Kaggle platform. The developed model demonstrates exceptional performance, achieving 99.9% accuracy during training, 97.4% accuracy in testing, and an overall precision of 0.975. The DEMENtia network model suc- cessfully categorizes individuals into four groups: those without dementia, and those with moderate, mild, or very mild dementia. The model achieves a remarkable accuracy of 99.20% in classifying the Moderate demented class, a significant advantage over existing approaches. To understand this behavior, conducted an in-depth analysis by visualizing the pixel intensity distribution over the space. The proposed model validity has been confirmed through validation by a team of neurologists, ensuring its potential for real-world clinical applications. By accurately predicting dementia severity, the proposed model can aid in early diagnosis and treatment planning, contributing to improved patient care and management.

关键词： Alzheimer’s disease Dementia DEMENtia network model Convolutional Neural Network (CNN)

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Deep Learning-Based Image Analysis to Track Vehicles 2

Deep Learning-Based Image Analysis to Track Vehicles

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Prabakaran, P. Jayasudha, R. Sandhu, Mukta Gupta, Sandeep School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Department of Computer Applications Chennai India Dr.N.G.P.Institute of Technology Department of Mathematics Coimbatore India Shri Vishwakarma Skill University Skill Department of Computer Science and Engineering Haryana Palwal India Department of Electrical Engineering Dehradun India

ISBN: (纸本)9798350371055

There are many ways to track the traffic conditions on roads. With the rise of AI-based image processing technology, there has been a surge in interest in developing traffic monitoring systems that rely on camera vision data. An improved road monitoring system may be derived from the data collected in this research by placing a camera at a junction. The technique detects and classifies vehicles using a deep-learning-based methodology (YOLOv4). By comparing the positions of the vehicle with the map HD, lane-by-lane trajectories of the vehicle may be approximated. The predicted vehicle trajectories are used to calculate each lane's wait lengths and traffic volumes, broken down by travel direction. This technique was evaluated using five distinct metrics: vehicle detection rates, trajectory prediction, type of vehicle categorization, traffic volume estimate, and queue length assessment. Thousands of samples were used to assess its performance. Based on the findings, it is acceptable to assume that the system can identify vehicles with a 98% success rate and an error rate of less than 25% regarding lane-by-lane trip volume estimation and vehicle type classification. Therefore, this study's approach reveals the viability of collecting comprehensive traffic information from a camera set up at a junction. Current traffic monitoring schemes have a reasonable likelihood of being developed by this study's significant contribution, which is a strategy that combines AI with HD map approaches. © 2024 IEEE.

关键词： Artificial Intelligence HD Map Traffic Monitoring System Vehicle Type Classification YOLOv4

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Secure RFID-Assisted Authentication Protocol for Vehicular Cloud Computing Environment

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12528-12537页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Tariq, Tayyaba Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

Vehicular network technology has made substantial advancements in recent years in the field of Intelligent Transportation Systems. Vehicular Cloud Computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol's security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol's security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes. © 2024 IEEE.

关键词： Authentication

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Information-Centric IoT-Based Smart Farming with Dynamic Data Optimization

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computers, Materials & Continua 2023年第2期74卷 3865-3880页

作者： Souvik Pal Hannah VijayKumar D.Akila N.Z.Jhanjhi Omar A.Darwish Fathi Amsaad Department of Computer Science and Engineering Sister Nivedita UniversityKolkataIndia Post Doctoral Researcher Sambalpur UniversitySambalpurIndia Department of Computer Science Anna Adarsh College for WomenChennaiIndia Department of Information Technology Vels Institute of ScienceTechnology and Advanced StudiesChennaiIndia School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya47500SelangorMalaysia FITTaylor’s UniversitySubang Jaya47500SelangorMalaysia Department of Information Security and Applied Computing Eastern Michigan UniversityUSA School of Information Security and Applied Computing Eastern Michigan UniversityUSA

Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies,including big data,the cloud,and the Internet of Things(IoT).Many researchers try to integrate IoT-based smart farming on cloud platforms *** define various frameworks on smart farming and monitoring system and still lacks to define effective data management *** IoT-cloud systems involve massive structured and unstructured data,data optimization comes into the ***,this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization(ICISF-DDO),which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved ***,a conceptual framework of the proposed scheme and statistical design model has beenwell *** information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data *** simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%,reliability ratio of 98.63%,a coverage ratio of 99.67%,least sensor error rate of 8.96%,and efficient energy consumption ratio of 4.84%.

关键词： Smart farming data optimization information management internet of things cloud

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Harvesting Insights: Predicting Vegetable Prices with Hybrid CLA-Conv BiGRU Techniques 3

Harvesting Insights: Predicting Vegetable Prices with Hybrid...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Lal Ambashtha, Kanahaiya Praveen Kumar, Chatakunta Pandey, Vivekanand Sakthivel Padaiyatchi, S. Bharathi, A. Navaz, K. Gnsu Faculty of Information Technology Bihar Sasaram India Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad India Amity University Bihar Patna India Nehru Institute of Engineering and Technology Department of Electrical and Electronics Engineering TN Coimbatore India Vels Institute of Science Technology and Advanced Studies Department of Information Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350381931

The system delves into the important topic of agricultural market pricing, with a specific emphasis on the ever-changing realm of vegetable supply and prices. Stabilizing the supply and prices of vegetables becomes an enormous task when consider that the system are grown outdoors and that weather variables significantly impact harvests. Such unpredictability has far-reaching consequences for the country's economy, highlighting the critical need to develop efficient methods of precise forecasting. The government has made multiple attempts to stabilize vegetable prices and supplies, but these have all been unsuccessful due to the unpredictable weather patterns of late. This system uses state-of-the-art deep learning methods, notably the Hybrid CLA-Conv BiGRU model, which incorporates a novel preprocessing strategy based on the HP Filter, to conquer this obstacle. A strong prediction model for vegetable prices is constructed using the process, which includes feature extraction, scoring, and selection. © 2024 IEEE.

关键词： Vegetables

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An IoT based Voice Assisted Smart Residential Appliance Control Mechanism using Intelligent Node MCU Controller 5

An IoT based Voice Assisted Smart Residential Appliance Cont...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Cherian, Aswathy K. Mercy, H. Reshma, R. Sivakami, K. Krishnamoorthy, R. Thiagarajan, R. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Chennai Kattankulathur India Jerusalem College of Engineering Department of Computer Science and Engineering Chennai India Justice Basheer Ahmed Sayeed College for Women Dept of Computer Science Chennai India Nehru Institute of Engineering and Technology Department of Ece Coimbatore India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Chennai India Prathyusha Engineering College Department of It Tamilnadu Chennai India

ISBN: (纸本)9798350379945

The idea of a gadget operated by a remote or switch is today considered antiquated. This is the technology of the future. The article presents a novel approach of managing smart appliances with voice commands, which is referred to as Voice Assisted Smart Appliance Control (VASAP). In order to assess the efficacy and performance of the suggested system, it is compared against the traditional GPRS Assisted Smart Appliance Control (GPRSSAC). An Android app requires the user to speak commands. Voice instructions may be recognized and transmitted to the system using this program. These commands are decoded by a receiver and decoder, which then send them over a serial connection to the Arduino controllers. The system's software and hardware are developed in accordance with this plan, and the system's wireless communication technique, which integrates Wi-Fi and RF (radio frequency), is also established. The systems functional needs inform the design of two sets of control schemes-one for voice control and one for button control-with the goal of making the system more compassionate. For the voice control solution to work, the user must first read out from the smart terminal the control instructions that have been pre-programmed into the system's voice library. Once the recognition is confirmed, the user will be able to command their household appliances. Under the key control scheme, smart terminal keys are used to operate household appliances. In order to collect and store audio, verify family members, and recognize voice commands, the upper computer uses an algorithm that was studied in this study. The lower computer collects environmental information in real-time and wirelessly controls home facilities. Upon startup, family members are asked to confirm their voice commands. Following this, the system uses real-time environmental information and the results of voice command recognition to formulate a control command. Through the serial port, the command to operate the ass

关键词： Domestic appliances

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