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IoT-Enabled Asthma Trackers for Children with KNN for Improved Breathing control 5

IoT-Enabled Asthma Trackers for Children with KNN for Improv...

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5th IEEE international conference on advances in Electrical, computing, communication and Sustainable Technologies, ICAECT 2025

作者： Sathyanathan, P. Balachander, K. Pandey, Pramod Srinivasan, V. Prasanna Ganeshbabu, T.R. Muthulekshmi, M. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Nagpur Campus Maharashtra Pune India R.M.D. Engineering College Department of Information Technology Tamil Nadu Chennai India Department of Electronics and Communication Tamil Nadu Namakkal India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350356236

Managing asthma in children is a complex task that needs constant vigilance and personalized treatments to get the best potential control. For better respiratory management, an Internet of Things (IoT)-enabled pediatric asthma tracking system that uses machine learning (ML) methods, particularly for K-nearest neighbors (KNN), is presented. The system's integration of wearable sensors, which continuously monitor vital signs and environmental conditions, provides a comprehensive view of the child's respiratory health. Asthma exacerbation causes, and trends may be discovered by analyzing the obtained data using KNN algorithms. Analysis allows for the generation of real-time warnings and feedback, allowing caregivers to take proactive steps in reducing risks and optimizing treatment procedures. Our IoT-enabled method for improving respiratory control and decreasing asthma-related consequences in children is feasible and effective in pilot research, including children with asthma. Our research highlights the promise of IoT devices and ML algorithms to transform how pediatric asthma is treated by providing tailored, evidence-based treatments that lead to better health outcomes. © 2025 IEEE.

关键词： Pediatrics

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Enhanced Security Through Honeywords and Video CAPTCHA: An Innovative Authentication and Tracking Mechanism 5

Enhanced Security Through Honeywords and Video CAPTCHA: An I...

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5th IEEE international conference on advances in Electrical, computing, communication and Sustainable Technologies, ICAECT 2025

作者： Gurale, Madhuri Borate, Komal Sangitrao, Rachana R. Tiwari, Garima D. Y. Patil College of Engineering Information Technology Department Akurdi Pune412101 India D. Y. Patil College of Engineering Artifical Intelligency and Data Science Department Akurdi Pune412101 India D. Y. Patil College of Engineering Department of Instrumentation and Control Akurdi Pune412101 India D. Y. Patil College of Engineering Department of Electronics & Communication Akurdi Pune412101 India

ISBN: (纸本)9798350356236

In recent years, activities across countries have increasingly been carried out digitally, with information being shared over networks to boost speed and efficiency. However, this digital transformation poses security threats. Cybercrime has consistently evolved, necessitating improved network security measures to combat digital scams. To maintain security, users are assigned unique identification passwords, which they are instructed to keep secret. Despite this, attackers often manage to steal passwords. To counter these threats, we propose using honeywords, which are decoy passwords created from the existing user password. If an attacker inputs a honeyword, an alert is activated for the administrator. Moreover, we implement IP and location monitoring for users and introduce a approach known as the video click-based CAPTCHA system. This scheme authenticates users by distinguishing between humans and bots, addressing issues in traditional graphic password CAPTCHA schemes. This architecture significantly enhances protection and security of data and applications over online networks. © 2025 IEEE.

关键词： Malware

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Achievable Rates for Finite Alphabet Constellation Over Non-Gaussian Noise Channel

Achievable Rates for Finite Alphabet Constellation Over Non...

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international conference on 6G communications networking and Signal Processing, SGCNSP 2023

作者： Hussain, Aadil Saqib, Najam-Us Mushtaq, Asrar Mehraj Shah, Shahid Communication Control and Learning Lab Department of Electronics and Communication Engineering National Institute of Technology Srinagar Srinagar India

ISBN: (纸本)9789819778751

Under the assumption of receiver noise characteristics, the Gaussian noise models are well-studied in the literature. There are several scenarios where the Gaussian Noise assumption is not valid;common examples include electromagnetic interference channels, underwater acoustic channels, powerline communication channels, and NAND flash memory channels. In this paper, we study non-Gaussian noise models, specifically Laplacian noise (a well-known model for impulsive noise). Diverting from the usual assumption of Gaussian inputs, we take a more practical approach and consider finite alphabet constellations as input and compute achievable rates (by computing mutual information (MI)) for various modulation schemes. It is a well-known result that MI computed for finite constellation does not have any closed form;hence, we use the famous LogSumExp inequality to find the upper bound for MI. Simulation results are compared with the obtained bound, and remarkable accuracy is found at high SNR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gaussian noise (electronic)

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Design of Orthogonal Polyphase Codes for MIMO Radar Using an Enhanced Salp Swarm Algorithm 1

Design of Orthogonal Polyphase Codes for MIMO Radar Using an...

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1st international conference on Intelligent Manufacturing and Cloud computing, ICIMCC 2024

作者： Pang, Yu Zhao, Runhui Wen, Hong Wang, Yongfeng Feng, Xuewei Tang, Tao Zhao, Ziang Ma, Wendi School of Aeronautics and Astronautics University of Elec.Science and Tech. of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Lab. of Sichuan Province UESTC 611731 China Intelligent IoT Communication Technology Engineering Research Center of Sichuan Province UESTC 611731 China

ISBN: (纸本)9781643685779

This paper presents the design of highly orthogonal waveforms focusing on minimizing autocorrelation sidelobes and cross-correlation peaks. An enhanced Salp Swarm Algorithm is proposed to improve global search capabilities through refined population initialization and leader position updates. The feasibility and benefits of this approach are demonstrated through practical implementation. © 2025 The Authors.

关键词： MIMO radar

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Improving Data Encoding for Enhanced AI Performance in Complex Datasets via Quantum Feature Space Mapping: Harnessing Quantum Algorithms

Improving Data Encoding for Enhanced AI Performance in Compl...

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2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Walia, Gurpreet Singh Sathiya Priya, S. Karthikeya, K.V. Suresh, D. Sudheer, P. Syambabu, V. NetApp Inc. Lead Enterprise Security and Networking Architect DurhamNC27709 United States Panimalar Engineering College Department of Ece Chennai India Sattva Knowledge City At&t Hyderabad India St. Joseph's Institute of Technology Department of Ece Chennai India India Mlr Institute of Technology Department of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798331508685

The purpose of this study is to investigate the potential for quantum feature space mapping to be utilized in the enhancement of data encoding approaches for artificial intelligence (AI) systems that are capable of managing complicated datasets. Traditional artificial intelligence models frequently face challenges when confronted with the high-dimensionality and non-linearity of such datasets. This can hinder their performance in areas such as pattern recognition, optimization, and predictive modeling. Quantum computing, which is able to make use of quantum superposition and entanglement, provides a method that is revolutionary in nature by means of quantum feature space mapping. By encoding data into quantum states, this technology enables the construction of feature spaces that are tenfold bigger than those that would be possible in classical conditions. This makes it much simpler to differentiate between intricate patterns that would otherwise be indistinguishable. The purpose of this research is to investigate the application of quantum algorithms, such as Quantum Support Vector Machines (QSVM) and Variational Quantum Classifiers (VQC), with the intention of enhancing the capacity of artificial intelligence to learn from complex and high-dimensional data. We demonstrate that quantum feature space mapping leads to more efficient and accurate data classification, regression, and clustering tasks by comparing the performance of quantum-enhanced artificial intelligence models to that of their classical counterparts on difficult datasets. Additionally, the research addresses important difficulties, such as the constraints of the quantum hardware that is now available and the requirement for quantum circuits that are scalable. Quantum computing has the potential to improve the performance of artificial intelligence in the management of complicated, high-dimensional data, which may ultimately pave the way for advances in domains such as genetics, finance, and cryptogra

关键词： Support vector machines

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Robust Multi-agent Federated Reinforcement Learning for Task Offloading 7th

Robust Multi-agent Federated Reinforcement Learning for Task...

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7th international conference on Wireless communications, networking and Applications, WCNA 2023

作者： Yan, Dibao Wang, Yongfeng Hou, Wenjing Song, Huanhuan Wen, Hong Ma, Wendi Sun, Fan School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China Sichuan Intelligent IoT Communication Technology Engineering Research Center UESTC Chengdu China

ISBN: (纸本)9789819624089

With the proliferation of various mobile smart devices, edge computing and computational offloading technologies have emerged as pivotal support mechanisms, enhancing the service quality of these devices. To facilitate the learning of task offloading strategies across diverse and complex scenarios, this study introduces a federated learning strategy algorithm based on multi-agent deep reinforcement learning. This algorithm aims to aggregate the training strategies of multiple edge computing devices, thereby enabling the synthesis of superior task offloading strategies tailored to a wide array of environments while also providing safeguards against malicious nodes. This paper specifically examines the reward inversion attack, illustrating the algorithm’s capability to identify and counteract such malicious threats effectively. Experimental results validate that the proposed algorithm not only robustly defends against these attacks but also adeptly learns the task offloading strategies pertinent to each node. © The Author(s) 2025.

关键词： Federated learning

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Tuberculosis Diagnosis from Chest X-Ray Image Using Deep Learning Techniques 5

Tuberculosis Diagnosis from Chest X-Ray Image Using Deep Lea...

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5th IEEE international conference on advances in Electrical, computing, communication and Sustainable Technologies, ICAECT 2025

作者： Shams, Karib Saha, Ratul Rodela, Mahmuda Islam Simran, Sanjana Rashid Khan, Musharrat East West University Computer Science and Engineering Department Dhaka Bangladesh

ISBN: (纸本)9798350356236

Tuberculosis (TB) is a significant global health challenge, necessitating precise and efficient diagnostic tools. This work investigates the effectiveness of various deep learning models such as EfficientNetB3, DenseNet121, VGG19, DenseNet201, and ResNet50 in detecting TB from chest X-ray images. Utilizing the TB Chest X-ray Database, we evaluated pre-trained models, developed custom architectures, and employed the k-fold validation method. The EfficientNetB3, with a customized design, demonstrated superior performance, while the DenseNet201 excelled in the k-fold validation setup. Our findings highlight the potential of the EfficientNetB3 and DenseNet201 in advancing TB diagnostics, offering significant improvements in speed and accuracy. We got 100% accuracy in EfficientnetB3. This study not only contributes to the growing field of AI in medical imaging but also underscores the ethical considerations required in deploying AI for healthcare, ensuring patient privacy and equitable algorithmic decisions. The outcomes of this study hold promise for improving real-time healthcare through more effective TB detection and patient care. © 2025 IEEE.

关键词： Disease control

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Integrating Instruction-based Learning with Bidirectional LSTM for Autonomous Vehicle Pedestrian Detection

Integrating Instruction-based Learning with Bidirectional LS...

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2025 international conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Shoba, B. Raghunathan, T. Peter, S.Sam Karthick, K. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Psn College of Engineering and Technology Department of Electronics and Communication Engineering Tirunelveli India Dr.NG.p.Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sona College of Technology Department of Information Technology Salem India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

Pedestrian detection is a crucial aspect of autonomous vehicle (A V) safety, as it enables accurate identification and tracking of individuals in an A V environment. Effective pedestrian detection supports essential A V functions like braking, steering, and speed control to prevent accidents in urban settings. Traditional computer vision methods, such as Support Vector Machines and Random Forests, rely on pre-designed features like the Histogram of Oriented Gradients. While the existing techniques offer some performance, it face significant challenges in dynamic environments, especially under varying lighting conditions, occlusions, and diverse pedestrian poses. Traditional models also require extensive manual feature engineering and tuning, which limits scalability and adaptability. Recent advances in deep learning, particularly with Convolutional Neural Networks (CNNs), have allowed for automatic feature learning, yielding practical improvements over classical methods. However, CNNs can struggle in low light and when facing adversarial attacks, as they mainly capture spatial features. To address these limitations, this research proposes a new methodology combining CNNs for spatial feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) networks to capture temporal dependencies. This hybrid approach enhances detection capabilities by learning both static and dynamic patterns. Our proposed CNN-BiLSTM model achieves a 92 % detection accuracy and an F1 score of 88.5% across diverse conditions, outperforming existing models by up to 15%. By integrating temporal features, the model reduces missed detections and false positives by up to 20%, providing a more reliable and adaptable solution for pedestrian detection in autonomous vehicles. © 2025 IEEE.

关键词： Convolutional neural networks

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Comprehensive Security Analysis and Threat Mitigation Strategies for Software-Defined networking (SDN) Environments 3

Comprehensive Security Analysis and Threat Mitigation Strate...

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3rd international conference on Intelligent Systems, Advanced computing, and communication, ISACC 2025

作者： Karki, Ajit Deb, Raktim The ICFAI University Sikkim School of Information Technology Gangtok India The ICFAI University Tripura School of Science and Technology Agartala India

ISBN: (纸本)9798331523893

Software Defined networking (SDN) is a new networking standard that significantly reduces the complexity of network administration tasks. SDN is considered a networking breakthrough. Additionally, it provides a flexible interface that allows for network innovation by controlling the network's general behavior. However, for many years, traditional IP networks were difficult to maintain, prone to errors, and difficult to grow with new features. The idea is derived from the separation and abstraction of the data plane and the control of conventional network devices. Better segmentation, scalability, programmability, faster delivery, and increased service and experience quality are all advantages of this separation for network operations. To make system administrators' tasks more at ease, SDN has been suggested as a prospective standard for a symbolized and rationally integrated network administration plane. By separating the control and data planes, SDN enables network configuration through programmability. This paper provides a comprehensive explanation of SDN, covering its numerous levels and their purposes, to keep up with the new technologies accelerated development. © 2025 IEEE.

关键词： control plane Data plane IP Software defined networking

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Distributed Event Triggered Secondary control of Islanded Microgrid System

Distributed Event Triggered Secondary Control of Islanded Mi...

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advances in Electrical, computing, communication and Sustainable Technologies (ICAECT), international conference on

作者： B.Chiranjeev Rao Shimpy Ralhan Department of Electrical Engineering Shri Shankaracharya Technical Campus Bhilai India Department of Electrical & Electronics Engineering Shri Shankaracharya Technical Campus Bhilai India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

The demand for decentralized control techniques in DC microgrids has increased due to their ability to enhance scalability, reduce communication requirements, and improve overall system efficiency. This paper presents a distributed event-triggered secondary control strategy for DC microgrids, aiming to regulate voltage and manage power sharing in a cost-effective and scalable manner. By implementing event-triggered mechanisms, communication between distributed energy resources (DERs) is minimized, leading to reduced overhead while ensuring system stability. This article investigates an event-triggered mechanism that incorporates event detection and adaptive control strategies to maintain frequency, voltage, proportional current and powersharing stability, while avoiding unnecessary controller interactions.

关键词： Reactive power Event detection Scalability Microgrids Power system stability Generators Energy efficiency Distributed power generation Voltage control Frequency control

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