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检索条件"任意字段=3rd IEEE International Conference on e-Science and Grid Computing"

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Assessing the Diagnostic Accuracy of Vowel-Based Acoustic Biomarkers in Parkinson's Disease 3

Assessing the Diagnostic Accuracy of Vowel-Based Acoustic Bi...

引用

3rd international conference on electronics and Renewable Systems, ICeARS 2025

作者： Divya, R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Parkinson's disease (PD) is a neurodegenerative disorder of progressive degeneration, with motor and speech impairments. In this study, we investigate the potential of vowel-based acoustic biomarkers for early detection of PD. The study analyses different vocal features including fundamental frequency, jitter, shimmer, and harmonic-to-noise ratio to identify small changes in voice based on the presence of PD. In the research, we use a dataset of voice recordings from PD patients and healthy controls and explore the effectiveness of machine learning models, including traditional feature engineering approaches. It is found that acoustic analysis, in particular explorations through glottal features and hybrid feature extraction techniques, can radically increase the accuracy of PD diagnosis. This kind of approach looks promising to develop affordable and non-invasive diagnostic tools, particularly in remote or resource-limited environments, thus allowing for earlier intervention and tailored treatment intervention for PD patients. © 2025 ieee.

关键词： Neurodegenerative diseases

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Blockchain based File Storage and File Sharing System with Time-Restricted Access using InterPlanetary File System 3

Blockchain based File Storage and File Sharing System with T...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Venkat Prashanth, M. Hemesh Verma, K. Chauhan, Jaykishor Prasad Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (纸本)9798331527549

This paper presents a file storage and sharing system implemented using IPFS Pinata, *** and Hardhat which offer decentralized, effective, and secure access to digital assets. This system also enables the ability to upload files to the IPFS network for distributed storage and the ability to share them with peers . It also implements a time based access control system using smart contracts to easily control access to the files uploaded. This innovation improves security and facilitates timely exchange of information while also eradicating the necessity of having to manage access. This is due to the versatility of blockchain’s unalterable nature together with IPFS distributed file-sharing system which provides a secure file-sharing method while keeping costs low and helps maintain adaptability. © 2025 ieee.

关键词： Blockchain decentralized IPFS secure access Time Based Access Conrol

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Smart Water Ball Launcher: An Arduino-based Fire extinguishing System 3

Smart Water Ball Launcher: An Arduino-based Fire Extinguishi...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sridhar, Govindu L.V.N. Janakiram, M. Vishnu Ram Sai, M. Surya Narayana Reddy, G. Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The proposed work offers the production of an Arduino controlled Fire-RoboCar equipped with a water ball launcher. In a world dominated by human-machine interaction in industry, the efficiency of any task to be done is promoted by this proposed work and delivers solutions to needs like fighting fires through the provision of a productive method. At a time when increased pollution and the expense of fossil fuel are becoming very high, this alternative is environment-friendly. The Fire-RoboCar is a moving robot that can accomplish fire extinguishing tasking in an industrial complex, proving that the system may easily transport water to desired locations. This proposed work advances robotic vehicles’ capabilities and provides innovative solutions to modern industrial challenges. © 2025 ieee.

关键词： Fire extinguishers

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Real-Time Threat Management using Deep Q-Learning and Mininet 3

Real-Time Threat Management using Deep Q-Learning and Minine...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sivakumar, Amrutha Bhavya Reddy, G. Bhanot, Shreya Rajagopal, Shinu M. Prashanth, B.N. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The research combines Deep Q-Learning(DQN) with a Mininet-based network simulation and Scapy intrusions detection system (IDS) for malicious traffic prioritizing. The RL agent continuously learns to act based on real-time traffic data and builds an adaptive model that detects and prioritizes threats such as SYN Flood, UDP Flood, Slowloris, HTTP Flood etc. The RL agent then adjusts its policy via the seamless combination of network simulation and live traffic detection to make sure lossy channels representing high-severity attacks are dealt with first, lessening the damage. The system improves the identification of intrusions by stimulating the intrusion prioritization dynamically with continuous learning and feedback, increasing network security. © 2025 ieee.

关键词： Network intrusion

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Leveraging Sequential Deep Learning Models for Robust Compiler Bug Detection in Software Logs 3

Leveraging Sequential Deep Learning Models for Robust Compil...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mishra, Sahil Tiwari, Utkarsh Kavitha, C.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Compiler bugs critically impact the correctness of software applications, making their detection and resolution vital for improving software quality. This paper proposes a novel approach leveraging sequential deep learning models, including Long short-term memory, Gated recurrent unit, Recurrent neural network, and a stacked ensemble model, to enhance compiler bug identification. By combining the strengths of these architectures, the proposed method improves predictive accuracy and generalization. To ensure model interpretability, we employ LIMe (Local Interpretable Model-agnostic explanations) to identify the features influencing bug detection decisions. experimental results demonstrate that the stacked ensemble outperforms individual models in terms of precision, recall, F1-score, and ROC-AUC. This work advances the state of compiler log analysis and contributes significantly to software quality assurance by integrating robust deep learning techniques with explainable AI. © 2025 ieee.

关键词： Application programs

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environmental Monitoring using LoRa and NodeMCU 3

Environmental Monitoring using LoRa and NodeMCU

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mihir, D.V.S. Pavan, Baramkula Vishnu Datta Aralaguppi, Yogen Avinash Nair, Pranav Biju Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Monitoring of environmental parameters in areas with a sparsely populated and less infrastructure is quite challenging due to issues such as high power consumption, limited range for transmission, and lack of scalability. The innovation of the system proposed here deals with these problems using an IoT-based monitoring system, using LoRa technology and NodeMCU microcontrollers. It can collect and transmit real-time data on temperature, humidity, air quality, and sound levels efficiently. Key outcomes of this research work are reliable range communication up to 2 km under line-of-sight conditions, minimal power consumption, and robust data integrity through sensor calibration and error-checking mechanisms. The solution, which is scalable and low-cost, is proven feasible in real-world applications, particularly in remote and underserved areas, and promises a great future enhancement, including range extension and energy harvesting. © 2025 ieee.

关键词： environmental monitoring

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Understanding Graph Neural Networks Models for Healthcare Fraud Detection in Insurance Claims 3

Understanding Graph Neural Networks Models for Healthcare Fr...

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3rd international conference on Intelligent and Innovative Technologies in computing, electrical and electronics, IITCee 2025

作者： Archana Reddy, P. Jyothi, Divya Varshita, Velumury Akshitha, Chennupati Aiswariya Milan, K. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331515911

This study suggests using graph algorithms to detect healthcare fraud, a serious issue that can lead to significant financial losses and compromise patient care. Health care fraud is a representation of deceitful acts carried out by health providers, patients, or other individuals for the purpose of gaining undue financial profit from the health insurance program. It includes false billing, misrepresentation of diagnosis and procedure, and kickbacks at the expense of substantial financial loss and deterioration of patient care. Our approach models the relationships between patients, healthcare providers, physicians, and procedures as a graph to identify unusual patterns that may indicate fraud. We utilize publicly available tabular datasets, including beneficiary data and claims from inpatient stays and outpatient visits, transforming them into a graph structure to create a unified dataset. The system employs algorithmic analysis and graph construction techniques to identify significant nodes, abnormal clusters, and outliers within the healthcare network. This strategy offers a viable method for reducing healthcare fraud, enhancing the integrity of insurance systems, and ensuring that funds are appropriately allocated to legitimate medical needs. Ultimately, this study aims to decrease the rate of fraud in society. © 2025 ieee.

关键词： Graph neural networks

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Image Compression Algorithms - Comparative Analysis 3

Image Compression Algorithms - Comparative Analysis

引用

3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Niharika Reddy, G. Namrutha, M. Akshaya, Namratha Bhaskaran, Sreebha Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508456

Due to the abundance of the new digital media data, the issue of image quality and volume of data requiring compression has become a significant factor of concern, especially in media storage and transmitting. This work affords a comparative analysis of different image compression techniques with focus on the compression ratio, quality preservation, and complexity. A new hybrid model of Predictive Coding, Run Length Coding and Quantum entropy Coding (QeC) is proposed and shown to exhibit negligible quality loss with substantial space saving. The experimental outcomes show that the proposed method reduces space 80 percent and works better than previous methods for areas requiring high speed and relative accuracy. These insights are timely, as practical computing-communication trade-offs are paramount in the new generation of social networks, medicine, and multimedia streaming. © 2025 ieee.

关键词： Image compression

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A Performance Comparison of Multiple Deep Learning Methodologies in Image to Text Synthesis Tasks 3

A Performance Comparison of Multiple Deep Learning Methodolo...

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3rd international conference on electronics and Renewable Systems, ICeARS 2025

作者： Martinez, Jude Joseph Lamug Winata, edward Kevin Bina Nusantara International University Computer Science Department Faculty of Computing and Media Central Jakarta11480 Indonesia

ISBN: (纸本)9798331509675

Generating textual descriptions from visual inputs has become a critical area of research in the intersection of computer vision and natural language processing called image captioning. This study deep dives towards comparing a few popular image captioning methodologies, going from traditional encoder decoder architectures to more advanced techniques that incorporate attention mechanisms, transformers, and reinforcement learning. We evaluate the performance of these models on publicly available datasets containing logs from the online community Flickr on several dimensions including caption accuracy, diversity and computational efficiency. Captions are evaluated in terms of both quantitative metric(BLeU, ROUGe-N, MeTeOR, CIDer, SPICe) and qualitative analysis(contextual relevance besides creativity) for the caption quality. The results show clear advantages and trade offs of each method, allowing for a holistic assessment of how each approach is suitable for real world problems. Thus, this work sheds important light on the trade offs between model complexity, efficiency and performance in image captioning tasks and provides a useful reference for researchers making similar decisions about model design in similar tasks. © 2025 ieee.

关键词： Deep reinforcement learning

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Improving Robotic Arms Through Natural Language Processing, Computer Vision, and edge computing 3

Improving Robotic Arms Through Natural Language Processing, ...

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3rd international conference on Mechatronics, Control and Robotics, ICMCR 2025

作者： Sikorski, Pascal Yu, Kaleb Billadeau, Lucy esposito, Flavio Aliakbarpour, Hadi Babaias, Madi Saint Louis University Computer Science Department Saint Louis United States Saint Louis University Aerospace & Mechanical Engineering Department Saint Louis United States Saint Louis University Computer Science Department Saint Louis United States Saint Louis University Computer Science Department Saint Louis United States Saint Louis University Aerospace & Mechanical Engineering Department Saint Louis United States

ISBN: (纸本)9798331544546

This paper introduces a prototype for a new approach to assistive robotics, integrating edge computing with Natural Language Processing (NLP) and computer vision to enhance the interaction between humans and robotic systems. Our proof of concept demonstrates the feasibility of using large language models (LLMs) and vision systems in tandem for interpreting and executing complex commands conveyed through natural language. This integration aims to improve the intuitiveness and accessibility of assistive robotic systems, making them more adaptable to the nuanced needs of users with disabilities. By leveraging the capabilities of edge computing, our system has the potential to minimize latency and support offline capability, enhancing the autonomy and responsiveness of assistive robots. experimental results from our implementation on a robotic arm show promising outcomes in terms of accurate intent interpretation and object manipulation based on verbal commands. This research lays the groundwork for future developments in assistive robotics, focusing on creating highly responsive, user-centric systems that can significantly improve the quality of life for individuals with disabilities. © 2025 ieee.

关键词： Robotic arms

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