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8th International Conference on Data Management, Analytics and Innovation, ICDMAI 2024

作者： Niha, K. Surendiran, B. Amutha, S. School of Computer Science and Engineering VIT Vellore Campus Tamil Nadu Vellore India Department of Computer Science and Engineering National Institute of Technology Puducherry India School of Computer Science and Engineering VIT Chennai Campus Chennai India

ISBN: (纸本)9789819732449

The IoMT is a device that works with different healthcare systems to help develop networking technologies. It reduces the unnecessary burden of visiting the hospital. Instead, it enables the transfer of medical related data with physicians through a secured network. Smart devices, such as wearable and medical monitors are used for healthcare use in the IoMT network. Telehealth and other services are adapted in homes and hospitals in other real-time locations. Approximately in the globe, 80% of healthcare have implemented IoT (Internet of Things) technologies and 20% are expected to do the same in the future. This paper focuses on four topics: (1) recent advances on Internet of medical things, (2) highlights the emerging data fusion process for data preparation, (3) discusses the life cycle of sensor devices and their protocols, and (4) processing data with different techniques and theories. In a nutshell, this paper gives a comprehensive overview of IoMT research advancement, application, data management, and analytics techniques along with the current research challenges. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic health record

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A ROS-2 based Path Planning and Maze Solving of Autonomous Robots

A ROS-2 based Path Planning and Maze Solving of Autonomous R...

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2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Nuhel, Ahsan Kabir Hossain, Sazzad Hassan, Md.Tonim Haider, Iftekhar Saha, Ankur Taha, Adnan Sabith Ony, Asma-UI Husna Electrical and Electronics Engineering Bangladesh University of Hertfordshire Computer Networks and Systems Security United Kingdom Electrical and Electronics Engineering Bangladesh Computer Science and Engineering Bangladesh University of Information Technology and Sciences Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350350289

The goal of this study is to build a self-driving robot that can effectively navigate mazes by utilizing sophisticated computer vision algorithms with ROS2. Fusion 360 is used to create the robot model, and ROS2 launch files are used to initialize the sensor and control nodes. Attaining optimal path planning and accurate localization are the main objectives. The combination of novel motion planning based on the orientation of the robot with graph search algorithms like DFS, A∗, and Dijkstra's leads to effective maze discovery and traversal. Thorough testing shows that the system can successfully navigate unfamiliar maze configurations, demonstrating the dependability of ROS2 and modern robotics techniques. By completing this study, a foundation for future advancements in perception and planning to manage more complex settings is laid. © 2024 IEEE.

关键词： computer vision

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Optimizing Deepfake Detection with Transfer Learning Methods 1

Optimizing Deepfake Detection with Transfer Learning Methods

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Kingsy Grace, R. Ragavan, M. Karthikeyan, N. Prakalathan, M. Saveetha, V. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Computer Science & Business Systems Coimbatore India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

The goal of the proposed system is to identify whether a video has endured software manipulation or not. This system specially deals with identifying deepfake videos from real ones. As new techniques emerged to make deepfake, a handful of photos and fake videos are spreading at an alarmingly increasing rate, which is indistinguishable from the real ones to the naked eye. The proposed system uses CNN with transfer learning to predict the degree of manipulation involved. The user uploads the video that they want to check;the video is saved on the server, and then they experience the process of deepfake detection. Then the user is presented with a plot showing the degree of the fakeness of the video and the accuracy of the prediction. The user can then assume based on the plot, as it shows the frames where manipulation might be involved. © 2024 IEEE.

关键词： Convolutional neural networks

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Blockchain Approach for Analyzing the Issues of IoT Systems 5th

Blockchain Approach for Analyzing the Issues of IoT Systems

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Rawat, Abhishek Verma, Rajat Suryavanshi, Raghuraj Singh Department of Computer Science and Engineering Pranveer Singh Institute of Technology UttarPradesh Kanpur India

ISBN: (纸本)9789819771776

The Internet of Things combined with blockchain technology has emerged as an innovative platform to better safeguard data, save costs, and pale, achieve strength. Combining blockchain and IoT means that the distributed ledger with sophisticated cryptographic mechanisms, consensus mechanisms, and some other processes may transmit data and information in a secure and open approach among devices across long distances. This paper begins with an introduction to the IoT and blockchain technology platforms before conducting a study of how their merger can help improve the platforms. The paper will additionally emphasize critical criteria before integrating the blockchain in IoT systems. Finally, the paper offers practical examples from blockchain-supported IoT project. Overall, combining blockchain technology into IoT might transform, how perceive and communicate technical advancements, paving the path for a more secure, connected, but decentralized future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Distributed ledger

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Joint trust-based detection and signature-based authentication technique for secure localization in underwater wireless sensor network

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Multimedia Tools and Applications 2025年 1-20页

作者： Kumar, Manni Goyal, Nitin Qaisi, Ramy Mohammed Aiesh Najim, Mohd Gupta, Sachin Kumar Department of Computer Science & Engineering Chandigarh University Gharuan Punjab India Department of Computer Science Engineering School of Engineering and Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Electrical and Electronic Engineering College of Engineering University of Jeddah Jeddah21589 Saudi Arabia Department of Electronics and Communication Engineering Central University of Jammu UT of J&K Jammu181143 India

Ensuring secure and accurate node localization in Underwater Wireless Sensor Networks (UWSN) is a significant challenge, as conventional methods tend to neglect the security risks associated with malicious node interference. This research addresses this issue by proposing a Joint Trust-based Detection and Signature-based Authentication (JTDSA) technique, aimed at improving both the security and accuracy of node localization in UWSNs. The technique utilizes Intrusion Detection System (IDS) methods to calculate the trust values of secondary-anchor nodes (SNs), ensuring only the most trusted SNs are selected. Furthermore, a digital signature algorithm secures the Received Signal Strength (RSS) data from tampering. The method incorporates a hybrid approach combining Angle of Arrival (AoA) and RSS ranging techniques to achieve robust localization. Simulation results demonstrate that JTDSA improves localization accuracy by 4% compared to existing methods, achieving an overall accuracy of 99.2%. The technique also reduces packet drop by 35% and enhances attack detection accuracy by 3%. These results indicate that the proposed JTDSA technique offers an effective solution for secure and reliable localization in UWSNs. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Sensor nodes

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Leveraging Large Language Models to Identify the Values Behind Arguments 2nd

Leveraging Large Language Models to Identify the Values Be...

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2nd International Workshop on Value engineering in Artificial Intelligence, VALE 2024

作者： Senthilkumar, Rithik Appachi Homayounirad, Amir Siebert, Luciano Cavalcante Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

ISBN: (纸本)9783031854620

Human values capture what people and societies perceive as desirable, transcend specific situations and serve as guiding principles for action. People’s value systems motivate their positions on issues concerning the economy, society and politics among others, influencing the arguments they make. Identifying the values behind arguments can therefore help us find common ground in discourse and uncover the core reasons behind disagreements. Transformer-based large language models (LLMs) have exhibited remarkable performance across language generation and analysis. However, leveraging LLMs in sociotechnical systems that assist with discourse and argumentation necessitates systematically evaluating their ability to analyse and identify the values behind arguments, an under-explored research direction. Using a multi-level human value taxonomy inspired by the Schwartz Theory of Basic Human Values, we present a systematic and critical evaluation of GPT-3.5-turbo in human value identification from a dataset of multi-cultural arguments, across the zero-shot, few-shot and chain-of-thought prompting strategies, carrying forward from prior research on this task which leveraged a fine-tuned BERT model. We observe that prompting strategies exhibit performance levels close to, but still behind fine-tuning for value classification. We also detail some challenges associated with value classification with LLMs, offering potential directions for future research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Human Values Large Language Models Prompting

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Insights on Improving Reasoning and Increasing Capabilities in Large Language Models 6th

Insights on Improving Reasoning and Increasing Capabilities ...

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6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Krisshna, S. Varun Sendhil, R. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9789819780891

Large Language models (LLMs) based on the Transformer architecture are designed to understand and generate human-like text by learning patterns and relationships from vast amounts of textual data. These models have been reported to have "sparks" of artificial general intelligence owing to their very attractive properties such as high generalization and performance in unseen data. These models have demonstrated strong capabilities in generalization and performance on novel data, leading some to speculate they may contain early sparks of artificial general intelligence. However, true AGI remains an unachieved goal requiring breakthroughs beyond current techniques. LLMs have poor mathematical reasoning capabilities, inherit biases present in training data, and can hallucinate and deliver false information. In this chapter, we discuss several methods that improve the current state-of-the-art models through the use of external tools such as web browsing, better prompting techniques, scaling, using Reinforcement learning (RL) and tree-based search algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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Context Sensitive Dynamic Slicing of Multi-Agent System 20

Context Sensitive Dynamic Slicing of Multi-Agent System

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20th IEEE India Council International Conference, INDICON 2023

作者： Mishra, Alina Meher, S.K. Behera, H.S. Mohapatra, D.P. Veer Surendra Sai University of Technology Department of Computer Science and Engineering India National Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350305593

Dynamic slicing is a method for finding and fixing bugs and testing various software systems based on a particular execution. In case of multi agent systems, we use agents as basic run-time entity to execute the functionality. The slicing criterion is used to identify the statements in a code that modify the values of variables at a specific location in a program, either directly or indirectly. Static slicing, on the other hand, considers all possible program executions instead of a specific execution. In this research work, we designed a method for dynamic slicing Multi-Agent Systems. The first step in our method is to generate a Multi-Agent System software execution trace, which records the order of events and interactions among agents during one execution. We use this trace to create a dependency graph that shows the connections between the statements in the program. Using this dependency graph, we apply a context-sensitive approach that takes into account the context in which statements are executed to produce more precise slices. Our aim is to provide complete debugging and testing methods for Multi-Agent Systems that allow developers to easily locate and isolate program parts that are related to a certain problem or behaviour. © 2023 IEEE.

关键词： Agent Behaviors Agent-oriented Dependence Graph (AoDG) Agents Java Agent Development Framework (JADE) Message Passing Multi-Agent Systems (MAS)

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An Extensive Analysis of Neuromorphic Computing

An Extensive Analysis of Neuromorphic Computing

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2024 International Conference on Advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Vajpayee, Amit Kaur, Palak Preet Sharma, Ankit Varshney, Santosh Parul Institute of Engineering and Technology Department of Computer Science and Engineering Vadodara India Apex Institute of Technology Chandigarh University Department of Bigdata Analytics Mohali India Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Neuromorphic computing is a new paradigm that emerges from the structure and function of the human brain and aims to revolutionize computing. The technology is designed to simulate the high speed, low power consumption and large contraction of the brain. Neuromorphic systems provide great potential for intelligence and big data by using special algorithms and special designs to simulate neural networks. Despite encouraging progress from technology companies such as Intel and IBM, the development of neural computing chips still faces major challenges. This approach brings significant benefits to artificial intelligence, machine learning and big data. This technology is still in its infancy but has great potential to revolutionize intelligence. By mimicking the brain's performance and learning potential, neuromorphic computing could pave the way for smarter, faster, and more flexible systems that work well on water, performing tasks such as complex data analysis and on-the-fly decision making. © 2024 IEEE.

关键词： Adversarial machine learning

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Single-Frame Image Based Multi-variant Bird Species Identification and Classification 5th

Single-Frame Image Based Multi-variant Bird Species Identifi...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Adhikari, Nilanjana Bhattacharya, Suman Sultana, Mahamuda Department of Computer Science and Engineering Guru Nanak Institute of Technology Kolkata700114 India

ISBN: (纸本)9789819771776

The increasing demand for advanced ornithological tools and techniques aimed at accurately identifying and classifying multiple bird species within a single-frame image has led us to develop a framework for researchers, environmentalists, and bird enthusiasts. Traditional methods often struggle to handle the complexity of diverse avian variants present in each scene, particularly when capturing images in real-world, uncontrolled environments. Single-frame images, such as those obtained through wildlife monitoring cameras or handheld devices, offer a practical and common means of avian observation. Our work centered on the automated detection and classification of a group of multi-variant bird species using a Deep Learning (DL) framework. The You Only Look Once (YOLO) version 5 networks were deployed in this study. Each object within the image frame was detected, annotated, and captured in a bounded box. Subsequently, each bounded object underwent scrutiny and recognition. The manually collected dataset comprises five mutually exclusive local bird species native to India. The developed model demonstrated satisfactory performance in terms of Precision, Recall, mAP, and F1 values, achieving scores of 0.87, 0.81, 0.82, and 0.826, respectively. This system can work as an automated bird species detection, recognition and classification tool for numerous individuals encountering difficulty recognizing multiple birds within a single image frame. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Birds

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