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5th International Conference on computer and Applications, ICCA 2023

作者： Alnuaimi, Lolwa Hassan Jaam, Jihad Al John Moores University School of Computing and Data Sciences Dept. of Computer Science Oryx Universal College in Partnership with Liverpool Doha Qatar

ISBN: (纸本)9798350303254

Electronic ticketing (e-ticket) systems employing QR codes or barcodes are essential in the digital era but come with cybersecurity risks like fraud, counterfeiting, and ticket scalping. This study explores the cybersecurity implications of large-scale event e-ticketing, with a focus on comprehending these technologies and improving security. It introduces the Ticket_Events application, which combines online registration and the secure generation of QR-coded electronic tickets. The app mandates QR code scanning at the event venue and encourages the use of location services for enhanced verification, preventing ticket swapping and potential fraud. The primary objective is to create a dependable e-ticketing application centered on ownership verification. In conclusion, the study delves into the intricacies of e-ticketing and barcode scanning, pinpoints challenges, and proposes solutions to fortify the security of digital ticket delivery. © 2023 IEEE.

关键词： Bar codes

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Multi Small Object Detection and Prioritized Tracking for Navy Operations using Deep Learning Techniques

Multi Small Object Detection and Prioritized Tracking for Na...

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2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Nithya, B. Subash, Neethu Sivapriya, K. Devadharshini, R. National Institute of Technology Dept. of Computer Science and Engg. Tiruchirappalli India St.Joseph's College Department of Data Science Tiruchirappalli India

ISBN: (纸本)9798350314946

Real-time Unmanned Aerial Vehicle (UAV) tracking with deep learning improves situational awareness and surveillance capabilities. There is a significant requirement for prioritized tracking and multi-small item detection in the context of Navy operations. To address this, this paper aims to develop an efficient algorithm for locating and tracking small objects, such as ships and people, in extensive maritime surveillance. It proposes an efficient Multi Small Object Detection and Prioritized Tracking (MSOD-PT) approach. This approach aims to develop a reliable and expandable system that enhances situational awareness and helps naval personnel decide by rigorously prioritizing options in actual Navy operation scenarios. The proposed MSOD-PT adopts YOLOv8 for extremely rapid and accurate object identification, allowing for the simultaneous accurate bounding box localization of several small objects. The allocation of computer resources for significant targets is then optimized through the use of DeepSORT, which is utilized to track using specified criteria and give different IDs to things that have been discovered. The targets are then prioritized according to the user's monitoring needs. The simulation results, which use the AFO dataset, show that the proposed approach is practical and can react to prioritized targets that are not similar. For the custom YOLOv8 model with a confidence score of 0.6, the accuracy is 0.56 for daytime and 0.33 for Night time, but the precision is 0.8 for daytime and 0.6 for Night time. © 2023 IEEE.

关键词： Object detection

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AI-Powered Resilience: Addressing the Mental Health Impact of Mass Layoffs in the Digital Age 2

AI-Powered Resilience: Addressing the Mental Health Impact o...

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2nd International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Avik, Suranjeet Chowdhury Chowdhury, Abdullahi Moriwam Maruf, Md Abdullah Al Naha, Ranesh Ahammad, Imtiaz Bangladesh University of Textiles Dept. of Textile Engineering Dhaka Bangladesh School of Comp. and Mathematical Sciences The University of Adelaide Adelaide Australia University of Bangladesh Faculty of Computer Science & Eng. World Dhaka Bangladesh East West University Bangladesh Dept. of Computer Science & Engineering Dhaka Bangladesh Centre for Smart Analytics Federation University Australia Churchill Australia

ISBN: (纸本)9798350328202

In recent years, the technology sector has witnessed substantial disruptions, leading to major layoffs within toptier tech conglomerates. As the momentum of the digital age continues, there's an emerging concern regarding the psycho-logical impact of these layoffs on individual employees. This research delves deep into understanding the repercussions of these widespread job losses on mental health, emphasizing the increased intensity of symptoms like depression and anxiety. Utilizing the Beck Depression Inventory (BDI) for its methodology, the study scrupulously evaluates the dept. of these mental health symptoms before and subsequent to the implementation of AI-driven mental health solutions. The conclusive data robustly establishes that when AI-centric interventions are combined with holistic mental health practices, there's a marked reduction in depression and anxiety symptoms, particularly those originating from job-related insecurities. As the world grapples with the swift changes in employment dynamics in this digital age, the study accentuates the significance of AI. Tapping into its vast potential to enhance mental resilience can be seen as an avant-garde strategy, presenting a pragmatic way to address the looming mental health challenges arising from unpredictable economic transitions. © 2024 IEEE.

关键词： Employment

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RadChain Connect: Integration Between Blockchain and FreeRADIUS for Secure Authentication in Wi-Fi Networks/Web Environment 39

RadChain Connect: Integration Between Blockchain and FreeRAD...

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39th International Conference on Information Networking, ICOIN 2025

作者： Hoffmann, Eduardo Freitas Machado, Caciano Westphall, Carla Merkle Federal University of Santa Catarina Dept. of Computer Science and Statistics Santa Catarina Brazil Federal University of Rio Grande do Sul Data Processing Center Porto Alegre Brazil

ISBN: (纸本)9798331506940

User authentication in web environments and Wi-Fi networks is crucial due to the increase in fraud and invasions. Traditional methods, such as WPA2-PSK, have vulnerabilities, making the use of more secure solutions necessary. RadChain Connect is a proposal that combines blockchain with FreeRADIUS and self-sovereign identity, aiming to provide an efficient authentication system. Initially, it focuses on web authentication in universities, enhancing security at login. In the future, there are plans to expand the solution to Wi-Fi networks, leveraging the versatility of FreeRADIUS and the security of the EAP protocol. © 2025 IEEE.

关键词： Wi-Fi

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Comparative Study of Inductive Graph Neural Network Models for Text Classification

Comparative Study of Inductive Graph Neural Network Models f...

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2022 International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2022

作者： Pandian, Saran Chaurasia, Uttkarsh Ranjan, Shudhanshu Saxena, Shefali Daiict Data Science Gandhinagar India Nit Dept. of Computer Science and Engineering Hamirpur India Presidency University Dept. of Computer Science and Engineering Banglore India Nit Dept. of Electronics and Communication Hamirpur India

ISBN: (纸本)9781665461092

Graph neural networks(GNN) are a special variant of neural networks which help in dealing with unstructured data such as graph data. The advent of the GNN has helped in dealing with problems in different domains, especially in the domain of Natural Language Processing(NLP). In NLP, GNNs are used to implement tasks such as text classification which has a wide variety of applications. There are two ways to represent the text data using GNN namely, Inductive and transductive. In this paper, we apply the approach of the inductive model using different variants of GNN. We observed that the GAT variant gave better performance compared to other variants. Moreover, we observed that the complexity of the model and the dataset size influences the entropy of the output. © 2022 IEEE.

关键词： Entropy

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Design and Implementation of a Mobile Application for Intelligent Attendance Tracking 1

Design and Implementation of a Mobile Application for Intell...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, Priyanka Nadhiya, S. Jain, Deepak Verma, Anupama Kumar, Saurabh Bhavadharani, S. Chandigarh University Dept. of Computer Science Engineering Chandigarh India Dhanalakshmi Srinivasan College Dept. of AI & Data Science Coimbatore India IIM Indore Dept. of Management Indore India Usha Martin University Dept. of Business Management & Comm Jharkhand India Mangalayatan University Dept. of Business Management & Commerce Aligarh India A.H. Edu. & Research Dept. of Commerce Meenakshi Chennai India

ISBN: (纸本)9798350356816

Taking attendance is an important task in schools, universities, and other institutions. However, traditional attendance taking methods such as paper-based attendance sheets or manual entry into an electronic system are time-consuming and prone to errors. In recent years, the emergence of facial recognition technology has opened up new possibilities for attendance management systems. This research paper discusses the development and implementation of a Smart Attendance System using Facial Recognition technology using OpenCV in educational institutions. The study explores the benefits, limitations, and ethical considerations associated with this technology and its implications for the future of attendance management systems. This model takes use of LBPH and gja, exfilaface recognition to increase accuracy over conventional models. After this, the connection of recognized faces is compared with the kdatabase containing student's faces. This model will be an effective method for controlling student attendance. © 2024 IEEE.

关键词： Students

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Smart City Solutions Leveraging Internet of Things (IoT) Technology 1

Smart City Solutions Leveraging Internet of Things (IoT) Tec...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kumawat, Bhawesh Shalini, E. Tomar, Vinay Upadhyay, Rajesh Kumar Gupta, Divyanshu Geetha, P.V. Madhav University Dept. of CSE Pindwara India Meenakshi A.H. Edu. & Research Dept. of Computer Science Chennai India Jiwaji University Dept. of Chemistry Gwalior India Usha Martin University Dept. of EEE Jharkhand India J.S. University Dept. Electronics & Comm. Engineering Shikohabad India Dhanalakshmi Srinivasan College Dept. of AI & Data Science Coimbatore India

ISBN: (纸本)9798350356816

The widespread adoption of the Internet of Things (IoT) is facilitating the development of Smart City initiatives globally. Everyday objects are increasingly equipped with electronic gadgets and connected through various protocol suites, becoming part of a vast network. According to a recent Gartner study, by 2020, smart cities are projected to deploy 50 billion connected devices. This advancement is expected to make urban environments more adaptive and resilient to future changes. This paper explores current and future trends in smart cities and IoT, highlighting the technologies and applications driving their growth. We examine how the expansion of IoT directly impacts the evolution of smart cities and discuss the interplay between the two, as well as the factors motivating their development. Smart cities have the potential to reduce manual labor, mitigate disaster risks, and streamline daily life. Lastly, we address some of the challenges associated with IoT in the context of smart cities and propose potential solutions to these issues. © 2024 IEEE.

关键词： Smart city

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Personalized Federated Learning for Smart Healthcare Using SDN-Driven Edge Computing

Personalized Federated Learning for Smart Healthcare Using S...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Basu, Deborsi Chaudhary, Ritu Ghosh, Uttam Datta, Raja G. S. Sanyal School of Telecommunications Indian Institute of Technology Kharagpur India Indian Institute of Technology Dept. of Electronics and Electrical Communication Engineering Kharagpur India School of Applied Computational Sciences Dept. of Computer Science and Data Science Mmc TN United States

ISBN: (纸本)9798331504847

The advancement in distributed Federated Learning (FL) techniques is transforming next-generation edge computing, especially in e-Helathcare systems where data integrity and security are critical concerns. Collecting, processing and analyzing such substantial medical data is highly challenging due to their non-identical and versatile nature. Conventional FL approaches often show unsatisfactory and inaccurate results while operating on those data. Traditional FL methods also lack optimal dynamic management mechanisms for edge devices in ehealthcare, which is crucial to ensure that only authorized devices participate in handling any system failures and new registrations. To tackle such important aspects, in this work, we propose a personalized FL technique to handle abrupt label shifts among clients' data. The proposed approach further targets to achieve personalized models for local edge clients. A statistical batch normalization is proposed on customized edge servers, keeping the data specificity intact. To make the proposed system more secure and robust, a Software Defined Network-based (SDN-based) centralized server is used that allows only authorized and efficient edge clients to participate. The proposed comprehensive experiments on two healthcare datasets demonstrate better accuracy than traditional FL methods with enhanced security and privacy. © 2025 IEEE.

关键词： mHealth

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Differentially Private Multi-Label Learning Is Harder Than You'd Think

Differentially Private Multi-Label Learning Is Harder Than Y...

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IEEE European Symposium on Security and Privacy Workshops

作者： Anika Hannemann Benjamin Friedl Erik Buchmann Dept. of Computer Science Leipzig University Center for Scalable Data Analytics and Artificial Intelligence (ScaDS. AI) Dresden/Leipzig Germany Center for Scalable Data Analytics and Artificial Intelligence (ScaDS. AI) Dresden/Leipzig Germany

ISBN: (数字)9798350367294

ISBN: (纸本)9798350367324

Machine Learning is key in modern data analysis. However, privacy concerns related to sharing sensitive data often hinder the full potential of Machine Learning. One of the popular techniques to ensure privacy is the Private Ag-gregation of Teacher Ensembles (PATE) framework. PATE trains an ensemble of teacher models on private data and transfers the knowledge to a student model, with rigorous privacy guarantees derived using Differential Privacy. So far, PATE has been mainly applied to binary and multi-class classifications. However, such problem types often diverge from real-world scenarios. In medicine, patients often have numerous diseases, and in healthcare multiple treatments may be relevant, not just one. In multi-label problems, the objective is to identify not just a single label, but rather a set of relevant labels that characterizes the data point accurately. This paper explores the intricacies of applying PATE to multi-label learning and identifies the challenges involved. Since the original aggregation algorithms can only handle binary and multi-class problems, we generalize them and call the variants GNThreshold, Confident GNThreshold and Inter-active GNThreshold. We provide a formalized description and privacy analysis. We conduct comparative experiments on image and numeric data sets and evaluate model quality and privacy bounds. Finally, we summarize our findings, analyze challenges and discuss possible optimizations for multi-label PATE. data and Code Availability We experiment with the publicly available NUS-WIDE data set [6] and multiple synthetically generated data sets [26]. data description and processing details are in section 5. Our code can be found at https//***/BonJamon/MultiLabeIPate

关键词： Privacy Differential privacy Codes Machine learning Medical services data models Numerical models

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Next Generation Intelligent Traffic Signal Control: Empowering Electronics Consumers with Edge-AIoT Capabilities

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 1926-1934页

作者： Chavhan, Suresh Doswada, Rohit Gupta, Deepak Gunjal, Saymam Rodrigues, Joel J.P.C. School of Data Sciences Indian Institute of Science Education and Research Kerala Thiruvananthapuram India C-DoT Karnataka Bangalore India Dept. of Computer Science and Engineering Maharaja Agrasen Institute of Technology Delhi India Centre for Research Impact and Outcome Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering University of Visvesvaraya College of Engineering Karnataka Bangalore India Federal University of Piaui PI Teresina Brazil

Traffic congestion has become a major issue that is being faced by the majority of road users. The increasing vehicle usage, and the lack of space and funds to construct new transport infrastructure, further complicates the issue. In this scenario, it is important to come up with an intelligent and economical solution that improves the quality of road users' service. The problem with the traffic handling framework is signal timings are fixed which is not adaptive to the density of vehicles. To address this issue we propose an Edge-Augument Artificial Intelligence of Things (AIoT) road user cooperation for traffic management. The proposed system efficiently utilizes electronic devices to learn and adapt to changing traffic conditions in real-time. By optimizing the traffic signal timings based on the actual traffic conditions, adaptive systems reduce delay, improve traffic flow, reduce fuel consumption and pollution, and improve the electronics consumers' and road users' experiences. The proposed system has been tested with real-time experiments by integrating Electronic devices like cameras, smartphones, and AGX Xavier (edge device) with Cloud (ThingSpeak). The proposed system is verified by simulating the proposed system in the SUMO traffic simulator and its reliability is concluded by comparing the waiting time, depart delay, running, and halt time with the existing traditional method. © 2025 IEEE.

关键词： Traffic congestion

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