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Predicting the Likelihood of a Recurrent Febrile Seizure Using Thermographic Images and Deep Learning with Rules Generation 1

Predicting the Likelihood of a Recurrent Febrile Seizure Usi...

引用

1st International Conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Patil, Pratima Deshpande, Deepa S. MGM University Jawaharlal Nehru Engineering College Dept. of Computer Science and Engineering Chhatrapati Sambhajinagar431003 India

ISBN: (纸本)9798350355611

Recurrent febrile seizures are a common concern in children, often triggered by rapid changes in body temperature during fever episodes. For rapid action and care, early and accurate seizure recurrence prediction is crucial. Limited techniques were developed for predicting the likelihood of a Recurrent Febrile Seizure using a Thermographic image. Several drawbacks were considered for recurrent febrile seizure prediction such as lower performance, high time consumption, and error rate. Hence, the proposed technique is introduced to provide an efficient Deep Learning (DL)-based technique. Initially, the data is gathered from datasets, and the Intra-Attentional Convolutional ResNet (InAtt-CRNet) with the Yolov5-Face model detects the face and reduces the processing time by tacking the face with a bounding box. The patient's forehead or nose temperature is monitored using the Region of Interest (ROI) finding approach following face detection and tracking in each frame. Then, the temperature is predicted using the Enhanced Convolutional Capsule Network (EC2Net). The body temperature, age, duration of fever, and family history are used to construct the clinical decision rule. The Twin-directional Gated Long Short-term memory (TdG-LSTM) is used to predict the likelihood of a subsequent febrile seizure. The proposed technique achieves 98.16% accuracy, 98.45% precision, 0.12 RMSE, and 29 seconds as processing time. © 2025 IEEE.

关键词： Long short-term memory

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A Pipeline for Immersive data Visualization in CAVE Systems

A Pipeline for Immersive Data Visualization in CAVE Systems

引用

2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Challa, Koundinya Jaiswal, Chandra Pendem, Abhinav Gokaraju, Balakrishna North Carolina A&T State University Dept. Computational Data Science and Engineering Greensboro United States

ISBN: (纸本)9798331504847

Virtual Reality (VR) has transformed data visualization by enabling immersive and interactive experiences. CAVE (Cave Automatic Virtual Environment) systems provide a unique platform for exploring complex datasets in an intuitive, spatially immersive manner. However, a standardized pipeline for developing VR scripts tailored to CAVE-based data visualization remains underexplored. This paper presents a structured pipeline for creating and deploying immersive data visualization in CAVE systems, specifically using the WorldViz Prism CAVE environment. Our approach facilitates seamless integration of multi-dimensional datasets, enabling real-time interaction with 27 visual graphs within a single VR scene. The system allows dynamic graph manipulation, interactive exploration, and enhanced user engagement. By streamlining VR script development, this pipeline improves accessibility for researchers and practitioners, fostering a more intuitive understanding of complex datasets. Experimental results demonstrate the effectiveness of this approach in enhancing data interpretability and decision-making. © 2025 IEEE.

关键词： Video analysis

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Electric Vehicle Charging System Using RFID and IoT for Enhanced User Experience 5

Electric Vehicle Charging System Using RFID and IoT for Enha...

引用

5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Hassain, Md Mehedi Haque Jamil, Injamul Hoque, Md Mahmudul Hasan, Md. Jahid International Islamic University Chittagong Dept. of Electrical and Electronics Engineering Chittagong Bangladesh University of Greenwich Dept. of Computing and Mathematical Sciences London United Kingdom CCN University of Science and Technology Dept. of Computer Science and Engineering Cumilla Bangladesh Comilla University Dept. of Information and Communication Technology Cumilla Bangladesh

ISBN: (纸本)9798331519957

Electric vehicles (EVs) are surging in popularity globally, offering a greener and more energy-efficient alternative to traditional cars. Our research aims to develop an RFID based system designed to automate EV charging with minimal user input. This system integrates RFID readers, tags, a microcontroller, and a relay module to manage and monitor charging sessions efficiently. An IoT platform supports real time monitoring and remote control, while a secure payment mechanism handles transactions seamlessly. RFID tags identify vehicles for automatic charging initiation, and the relay module prevents overcharging by disconnecting power after a set time. The system uses MQTT for data transmission and offers real-Time insights into energy use and account balances. All information is updated and displayed in real-Time on the server. MySQL ensures scalability, enabling effective handling of large volumes of data. The mobile application dashboard provides a view of the charging and discharging processes for user monitoring, and an email notification is sent after the recharge. EV battery charging status is monitored through visual graphs. Key benefits include a streamlined user experience, improved charging management, and support for EV adoption. This innovation advances sustainable mobility with a user friendly, efficient charging solution. © 2025 IEEE.

关键词： Radio frequency identification (RFID)

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The Medical Segmentation Decathlon without a Doctorate

The Medical Segmentation Decathlon without a Doctorate

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Medical Imaging 2025: Clinical and Biomedical Imaging

作者： Samir, Jessica Ramadass, Karthik Saunders, Adam M. Krishnan, Aravind R. Remedios, Lucas W. McMaster, Elyssa M. Landman, Bennett A. Dept. of Psychological Science and Neuroscience Belmont University NashvilleTN United States Dept. of Computer Science NashvilleTN United States Dept. of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center NashvilleTN United States

ISBN: (纸本)9781510685987

Segmentation of anatomical structures leverages medical imaging technology for accurate diagnosis and treatment planning. Recent advances in active learning offer the opportunity for the interactive creation of artificial intelligence (AI) segmentation algorithms. Although active learning has been extensively investigated retrospectively, prospective studies have not been widely reported. This study aimed to determine whether a novice in medical imaging could independently label anatomical structures using Radiopaedia, 20% of clinician-generated labels for education of the novice, and AI guidance. Given the pancreas and spleen challenges from the Medical Segmentation Decathlon (MSD), a novice employed the MONAI Label extension on 3D Slicer with active learning (AL) techniques to perform segmentation. This study also explored the use of MONAI (Medical Open Network for AI) Label, a user-friendly framework for label submission and model training. Our segmentation results yielded Dice scores comparable to the range of those from the 2018 segmentation challenges of the MSD. We achieved Dice scores of 0.209 for the pancreas task using 70 images and 0.831 for the spleen using 34 images. The pancreas results do not fall within MSD’s reported ranges for the L1 region which is 0.48-0.80. However, the spleen results fall within the MSD’s reported range of 0.83-0.97. The interactive AI framework by MONAI Label effectively segments as we reached reasonable performance in less than a month with one person without clinical expertise. © 2025 SPIE.

关键词： Medical education

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Explainable AI-based Detection and Interpretation of Abnormalities in Chest X-rays 3

Explainable AI-based Detection and Interpretation of Abnorma...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Muthukumar, Sai Smrithi Gandham, Bhavika Vijayekkumaran, Trisha Jyotsna, C. Aiswariya Milan, K. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331523893

Chest X-ray images are widely used in diagnosing medical conditions, however, due to radiologist fatigue and shortage of resources the possibility of spotting peculiarities increases. This work proposes an explainable AI system to identify, quantify, and explain the pathology found in chest x-ray images. It builds credibility, enhances medical judgment, and reflects efficient utilization of radiology assets. This study used ResNet for the chest X-ray image classification and compared it with other related models like EfficientNetB0, DenseNet, VGGNet, and MobileNet. ResNet had the best AUC value of 0.826 thereby garnering it the best classification accuracy hence validating its selection as the main model. Moreover, ResNet adapted explainability to provide visual and textual interpretations and to classify diseases where such actionable insights could improve the reliability of diagnosis and patient outcomes. © 2025 IEEE.

关键词： CheXpert Explainable AI Grad-CAM ResNet X-ray masks X-rays

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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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An Optimized Steganographic Technique for 24-Bit Digital Images using Slab Partitioning

An Optimized Steganographic Technique for 24-Bit Digital Ima...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Pinaki Pratim Acharjya Jayeeta Majumdar Subhabrata Barman Jayashri Deb Sinha Ramsundar Ghorai Arpan Ghosh Dept. of Computer Science and Engineering Haldia Institute of Technology Haldia West Bengal India Dept. of Computer Science and Engineering (Data Science) Haldia Institute of Technology Haldia West Bengal India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Based on image slabs and sheets, a novel kind of image steganography method for digital images has been proposed in this research work. Using the binary representation of the pixel, this approach splits the cover image into 24 fixed-size sheets. The secret message's binary counterpart is then incorporated into the cover image. It looks through each sheet's rows and columns to locate the one that most closely matches the secret message's binary counterpart. The row or column's position and how it differs from the message it stores in the LSB slab. Compared to previous ways, the proposed technique somewhat modifies the cover image.

关键词： Image quality Steganography Visualization Digital images Control systems Robustness Slabs

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Securing Networks: Advanced Detection and Prevention of Address Resolution Protocol Cache Spoofing Attack 3

Securing Networks: Advanced Detection and Prevention of Addr...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Sriharsha, V.V.S. Daule, Viraj Kisan Surya, R. Thangam, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508456

As the technology advances, an increase in the attacks to manipulate or extract data illegally has also risen. This also includes one such attack known as Address Resolution protocol cache spoofing attack. The Address Resolution protocol Spoofer conducts an in-dept. exploration into one of the most significant security vulnerabilities identified in local networks, namely Address resolution protocol cache poisoning. The study implements a system which uses a Python based tool which is able to simulate Address Resolution protocol spoofing attacks on the network traffic of two hosts through a Python library known as the Scapy library. This tool allows the execution of Man In The Middle attacks by forging Address Resolution protocol replies and changing the stream of traffic without setting off any alarm by the user. The tool also has a detection mechanism that alerts the users as soon as it detects attempted spoofing;which hence identifies both the offender and the defensive capabilities in Address Resolution protocol spoofing. Additionally, another layer of security is added by user-defined encryption and decryption of the message being delivered. The study has resulted in a secure network, protecting the data from ARP spoofing. © 2025 IEEE.

关键词： HTTP

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Optimizing Drone Delivery Management: An Algorithmic Framework for Collision Detection and Avoidance with Resource Management 3

Optimizing Drone Delivery Management: An Algorithmic Framewo...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Sriharsha, Paramkusam Gowtham, Yelipe Chowdary, Geda Tejesh Bhaskaran, Sreebha Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508456

The revolutionary impact of drones on delivery services encounters major challenges, such as collision with other obstacles and other drones when operating inside crowded areas. This research introduces a Collision Detection and Avoidance (CDA) algorithm which performs effective path identification together with altitude reservation to prevent mid-air collisions. The system improves operational safety together with generating effective delivery routes that deliver both operational safety and efficiency. The system optimizes resource allocation through its ability to arrange drones for the purpose of meeting delivery requirements without excessive congestion. Performance measurement facilitates continuous system improvement. The research implements Dijkstra, A∗ and Bellman Ford shortest path algorithms to the CDA system for evaluating their transport safety and productivity and resource utilization performance. The analysis checks appropriate drone usage in diverse drone applications. The system proves its capability in the incorporation of real-time data for improving drone operations that leads to effective resource use in challenging delivery settings. © 2025 IEEE.

关键词： Resource allocation

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Forecasting Cryptocurrency Prices in the DeFi Ecosystem Using Predictive Models

Forecasting Cryptocurrency Prices in the DeFi Ecosystem Usin...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Sruthi, Srigiri Saravanan, Deivanai Jyotsna, C. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331538538

In the rapidly evolving cryptocurrency markets, accurate price prediction is crucial for investors and traders. This task investigates the efficacy of advanced machine learning and deep learning for forecasting cryptocurrency prices. The proposed idea utilizes Autoregressive Integrated Moving Average (ARIMA) models, Bidirectional Long Short-Term Memory (Bi- LSTM) networks, Gated Recurrent Units (GRU), and Long Short-Term Memory networks improved with Self-Attention (LSTM-Self-Attention). Numerous studies were conducted using the historical data available. Root Mean Squared Error (RMSE) were used to evaluate the models. The study tries to show the impact of outside factors such as economic data and market belief in purchase to improve the precision of the forecast. This research offers investors and policy makers insight that demonstrates the ability of sophisticated machine learning models to navigate the intricacies of cryptocurrency marketplaces. Incorporating these approaches into a single predictive framework enables accurate forecasting and the identification of anomalies in DeFi markets. The findings contribute to the decision making that is strategic DeFi investments, providing tools for better prediction and analysis, and laying the groundwork for future advancements in DeFi analytics. © 2025 IEEE.

关键词： Prediction models

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