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Alzheimer’s Disease Detection and Classification using Modified Hybrid CNN-LSTM Network 6

Alzheimer’s Disease Detection and Classification using Modi...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Nisha, A.V. Rajeev, S.K. Soorya, M. Shaheer, S. Dept. of Electronics and Communication Engineering Younus College of Engineering and Technology Kerala Kollam India

ISBN: (纸本)9798331522667

Alzheimer’s Disease (AD) is an escalating neurodegenerative disorder and the leading cause of dementia, with early detection being essential for prompt action and slowing disease progression. However, due to subtle and overlapping brain deterioration patterns, early AD diagnosis is difficult. This study proposes a hybrid deep learning model that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to automatically detect and classify AD from axial brain magnetic resonance imaging (MRI) images. The model uses VGG16, pre-trained on ImageNet, for feature extraction, with an LSTM layer to capture temporal dependencies in MRI slices. The dataset includes three categories: AD, Cognitively Impaired (CI), and Normal control (NC) subjects. To improve generalization, data augmentation techniques such as median blurring and horizontal flipping were used. The model, which was trained with the Adam optimizer, achieved a test accuracy of 0.9942 and a loss of 0.030. High sensitivity and specificity were shown by the performance evaluation which included a classification report and evaluation matrix, particularly in distinguishing AD from CI cases. This hybrid CNN-LSTM model effectively leverages spatial and temporal information for early AD detection, highlighting its potential to improve automated diagnostic tools and enable timely intervention. © 2025 IEEE.

关键词： Convolutional neural networks

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A Review on Machine Learning Techniques for Secure IOT Networks 5

A Review on Machine Learning Techniques for Secure IOT Netwo...

引用

5th IEEE international conference on Advances in computing, communication control and Networking, ICAC3N 2023

作者： Kumar, Abhishek Kushaniya, Pratyush Kumar, Vicky Kumar, Avneesh Galgotias University School Of Computing Science And Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350330861

The Internet of Things (IoT) is a game-changing development of the Fourth Industrial Revolution because it paves the way for previously inconceivable levels of connectivity and collaboration amongst many types of intelligent, autonomous things. The impact of the Internet of Things on our professional, financial, and social lives seems to be considerable. The Internet of Things is allowing 'smart' buildings and 'smart' people to give way to 'smart' communities and even 'smart' planets. Yet, the massive number of interconnected devices poses a challenge to IoT network security because of the many protocols they use. The inability to improve the security of IoT devices is related to their limited computing power. Thus, novel techniques are required to safeguard IoT networks. In the field of security research, ML models have been in the forefront for the last several years. Machine learning (ML) algorithms may provide a practical solution for securing IoT infrastructure considering the massive amounts of data created daily by IoT devices. The major goal of this study is to provide a thorough review of the many studies that have been conducted about ML deployment and IoT security. We briefly covered how security holes in IoT networks might compromise the confidentiality of private data. The advantages, disadvantages, and applications of various ML methods and models on the Internet of Things were then explained. Existing IoT networks' use of machine learning models for user security is also examined. The challenges of using ML models to secure IoT networks were also covered, since they may suggest new research directions. © 2023 IEEE.

关键词： Internet of things

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Traffic Object Detection System Based on YOLOv5 for V2V communications

Traffic Object Detection System Based on YOLOv5 for V2V Comm...

引用

2023 international Russian automation conference, RusAutoCon 2023

作者： Abdulnagimov, Ansaf Klyavlin, Nail Alektorov, Gleb Ufa University of Science and Technology Automated Control and Management Department Ufa Russia Ufa University of Science and Technology Department of High-Performance Computing Technologies and Systems Ufa Russia Ufa University of Science and Technology Department of Computational Mathematics and Cybernetics Ufa Russia

ISBN: (纸本)9798350345551

The deep learning techniques on the base of YOLOv5 architecture have been shown to make a traffic objects detection system for V2V communications to ensure traffic safety and traffic flows prediction. According to the detection data the detector allows to estimate the traffic object speed and the coordinates of movement. To improve the accuracy of calculations, the distortion correction algorithm (in geometric optics), the Chang method and the Kalman filter are used. To set the detector to work in online operation mode, an algorithm has been developed that receives video stream from open city surveillance cameras with the processing of a m3u8 playlist. The developed detector can be used on "connected"vehicles to assess the situation on the roads and ensure safety traffic. © 2023 IEEE.

关键词： Object detection

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Design of Congestion control Algorithm based on Switch Queue Length Change Detection 30

Design of Congestion Control Algorithm based on Switch Queue...

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30th IEEE international conference on Telecommunications, ICT 2024

作者： Li, Wei Wu, Zhaonan Zhang, Bosheng Zheng, Li Zhao, Weibo Zou, Jiaqi Sun, Songlin School of Information and Communication Engineering Beijing University of Posts and Telecommunications China Cloud Computing and Big Data Research Institute China Academy of Information and Communications Technology China

ISBN: (纸本)9798350356694

Remote Direct Memory Access (RDMA) has emerged as the predominant networking technology and plays an important role in the Artificial Intelligence Generated Content (AIGC) industry, which requires effective congestion control algorithms to unleash its performance. Since the Data Center Quantized Congestion Notification (DCQCN) protocol was introduced in 2015, it has quickly become the standard congestion control solution across RDMA networks and an essential component of high-speed clusters and data center networks. However, as a congestion control (CC) algorithm that primarily relies on explicit congestion notification (ECN), DCQCN is challenged by delayed bandwidth convergence and sluggish congestion detection. To address these challenges, this paper proposes an optimized congestion control algorithm solution based on DCQCN. Specifically, we propose more accurate switch queue length monitoring, which is divided into two main parts: a congestion detection phase and a congestion control phase. Our algorithm utilizes the change rate of the switch queue length to refine and identify the degree of congestion and adjust the data sending rate after congestion occurs based on this change rate. Experimental results demonstrate that the proposed algorithm achieves better average flow completion time (FCT) in different scenarios including incast and actual network load, compared with DCQCN and other CC algorithms. © 2024 IEEE.

关键词： Queueing theory

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Battery State Estimation and control System for Mobile Charging Station for Electric Vehicles 2

Battery State Estimation and Control System for Mobile Charg...

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2nd international conference on Sustainable computing and Data communication Systems, ICSCDS 2023

作者： Gopalakrishnan, R. Vidhya, D. Sri Bharath, P. Kaviyan, P. Sivaselvam, P. K. S. Rangasamy College of Technology Department of Electrical and Electronics Engineering Namakkal India

ISBN: (纸本)9781665491990

A drastic development of automobile industry requires a new technology to provide a superior service to people to handle their transportation needs. The raise of Electric Vehicles (EV) attracts many people due to their pollution free transportation support to an environment. Due to the EVs reliance on the battery's finite energy source, real power efficiency plays a major role. For most efficient operation of power in the battery, Battery Management System (BMS) has to be kept tracked and regulated during charging and draining of rechargeable batteries. Optimized BMS monitoring system was employed in the proposed system to keep track of the battery's charge, voltage, current and environmental temperature. The proposed system was tested in an EV with Lithium Ferro Phosphate Battery and Interior Permanent Magnet (IPM) Motor. The proposed system was analysed with the volume of a battery charge, health and its life span. This provides the development of architecture for intelligent BMS. © 2023 IEEE.

关键词： Battery management systems

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Edge computing and 5G Integration for Real-time Analytics in Interoperable Smart Grids

Edge Computing and 5G Integration for Real-time Analytics in...

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2023 international conference on Power Energy, Environment and Intelligent control, PEEIC 2023

作者： Devi, Kskn Venkata Ramana Koithyar, Aravinda Lakhanpal, Sorabh Parmar, Ashish Sethi, Vandana Arora Ftaiet, Adnan Allwi Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India Lloyd Institute of Management and Technology Plot No.-11 Knowledge Park-II Uttar Pradesh Greater Noida201306 India College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350357769

In the rapidly evolving landscape of power and energy systems, the Smart Grid (SG) emerges as a dynamic and adaptive solution to cater to the ever-increasing energy demands while ensuring sustainable growth. The core essence of a Smart Grid is its ability to provide real-time analytics, facilitating efficient energy management and distribution. With the influx of a myriad of heterogeneous devices and systems in the grid, interoperability emerges as a crucial factor to ensure seamless operations. This paper delves deep into the symbiotic relationship between Edge computing (EC) and the fifth generation (5G) mobile networks, illustrating their potential to revolutionize real-time analytics in interoperable Smart Grids. Edge computing, with its decentralized data processing capabilities, caters to the latency-sensitive needs of SG systems. On the other hand, 5G, with its high bandwidth and ultra-reliable low latency communication, offers robust support to EC operations. Their integration promises unprecedented improvements in the efficiency, reliability, and resilience of SGs. Through an exhaustive study, this paper highlights the architectural enhancements, challenges, and potential solutions, casting a vision for the future of intelligent energy systems. © 2023 IEEE.

关键词： 5G Mobile Networks Edge computing Energy Management. Top of Form Real-time Analytics Smart Grid Interoperability

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ADA-Gen: Iterative and Incremental Generation of Full-Stack Apps for Learning Agile/DevOps Software Development Practices 17

ADA-Gen: Iterative and Incremental Generation of Full-Stack ...

引用

17th international conference on Computer Supported Education, CSEDU 2025

作者： Duong, Ta Nguyen Binh School of Computing and Information Systems Singapore Management University Singapore

ISBN: (纸本)9789897587467

To learn Agile/DevOps practices effectively, students need to apply them in an actual software development project. This is challenging if students are mostly from non-computing backgrounds and they do not have time in the curriculum to learn programming and related tools. Therefore, it is important to help students who do not possess programming foundations to develop fully functional software during the process of learning Agile/DevOps concepts. We noted that existing low-code/no-code app development platforms have not been designed to teach Agile/DevOps practices. On the other hand, recent AI-based tools for code generation such as GitHub Copilot have been built mainly for programmers. In this work, we designed and implemented ADA-Gen (Agile/DevOps App Generator), a teaching tool leveraging large language models (LLMs) to generate full-stack web apps following an iterative and incremental development methodology widely practiced in Agile/DevOps circles. ADA-Gen is integrated with Jira, one of the most popular platforms for software project management, so students can use it right away without much further setup. This approach allows non-computing students to experience the complete full-stack software development life cycle. We have conducted extensive evaluations of ADA-Gen using various realistic project scenarios. The evaluations demonstrated the capabilities of ADA-Gen in full-stack web app generation, and in providing plenty of learning opportunities for students to appreciate key Agile/DevOps practices. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Software design

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Fuzzy Impulsive control of Multi-line Re-entrant Manufacturing Systems 6

Fuzzy Impulsive Control of Multi-line Re-entrant Manufacturi...

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6th international conference on Intelligent Autonomous Systems, ICoIAS 2023

作者： Zhang, Kexin Wang, Zhenqian Deng, Yue Gao, Qing Lu, Jinhu School of Cyber Science and Technology Beihang University Beijing100191 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China School of Astronautics Beihang University Beijing100191 China

ISBN: (纸本)9798350371253

This study focuses on the production control problem of the re-entrant manufacturing systems (RMSs) in the presence of product yield loss and couplings of multiple production lines. Initially, to describe and handle the complex dynamics of the RMSs, a nonlinear hyperbolic partial differential equation (PDE) is employed and a Takagi-Sugeno (T-S) fuzzy-model-based impulsive control scheme is proposed. Then, the stability criteria for the RMSs are obtained by leveraging a time-dependent Lyapunov function. Subsequently, we propose a novel robust fuzzy impulsive controller which operates at specific impulsive instants and stages of the plant. Not only can the RMSs respond quickly to market demand when adapting the feeding rate to real-time demand, but also the bandwidth occupation and the communication cost are significantly reduced. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed impulsive control method. © 2023 IEEE.

关键词： Lyapunov functions

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Experimental Investigation into Pressure Regulation for Compensating Acoustic Resonance Using Model Predictive control

Experimental Investigation into Pressure Regulation for Comp...

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Advancements in Electrical, Electronics, communication, computing and automation (ICAECA), international conference on

作者： M Saravanabalaji S Ranganthan V Athappan M Arjun D S Dakshin S Sneha Department of Electronics and Instrumentation Kumaraguru College of Technology Coimbatore India

Pipe bursts in water distribution networks are frequently caused by the noise signal known as acoustic resonance, which is produced frequently. A noise signal was released from inside the pipeline as the turbulent water flow closed contact with the pipe wall. One of the key reasons why pipes shatter with few cracks is the long span of the acoustic. The relationship between pump speed, pressure, and acoustics is well established. Because of the significant variation in pump speed, pressured water pulse oscillations pass inside the pipe. The dynamic pressure of fluid motion in nature generally leads to structural issues. These repetitive oscillations of the flow's contact with the inner pipe wall eventually caused the pipe to rupture or explode. Due to the unpredictable nature of pressure changes, acoustic resonance happens when a high-pressure flow collides with a wall that has strong pulsed oscillations, and the opposite is also true. Technical people generally approach the pressure parameter controlling the pressure-lowering valves. This work's goal is to discuss a better control system for controlling pump speed. An experiment is carried out in order to build the model and simulate it with the best possible control for pump speed regulation. Future implementation of the solution is made easier by the better results.

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Interactive Drone control with Gesture and Posture on the Edge computing Platform

Interactive Drone Control with Gesture and Posture on the Ed...

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2022 international conference on Fuzzy Theory and Its Applications, iFUZZY 2022

作者： Chuang, Cheng-Ta Hsiao, Pei-Shan Cheng, Ping-Feng Huang, Yan-Sheng National Taipei University of Technology Department of Intelligent Automation Engineering Taipei106344 Taiwan

ISBN: (数字)9781665495875

ISBN: (纸本)9781665495875

This paper designs a system that combines sensor and multiple neural network models to achieve the function of interactive drones, such as user tracking, gesture and posture control of the drone, and emergency safety protection. To run on the edge computing platform, Jetson Nano, and control the drone, the neural network models such as object detection, face recognition, and gesture recognition were optimized. The drone could be controlled by the user's gestures and postures successfully. © 2022 IEEE.

关键词： Drones

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