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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Jebaraj, Bharath Singh Nasreen, M.Ayeesha Sushmitha, S. Azarudeen, A. Subha, T.D. Vivekrabinson, V. Kalasalingam Academy of Research and Educatuion Department of Computer Science and Engineeing Sriviliputhur India R.M.D. Engineering College Department of Electronics and Communication Engineering Thiruvallur India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India R.M.K. Engineering College Thiruvallur Department of Electronics and Communication Engineering Thiruvallur India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350367171

The automotive industry's growth, fueled by technology and job opportunities, has heightened global automobile sales. Despite this progress, road safety remains a concern, with numerous lives lost in car accidents annually. To address this, Vehicle-to-Vehicle (V2V) communication emerges, enhancing road safety through real-time information exchange. Roadside communication Units (RCUs) provide vital services, offering traffic updates, weather notifications, and immediate alerts about incidents. Intelligent tollgate systems in vehicles streamline transactions, and speed control systems ensure safer driving. Leveraging Zigbee technology enables seamless communication with toll plazas, improving traffic flow. This study aims to enhance road safety through intelligent tollgate systems, V2V communication, and innovative technologies, emphasizing their potential to optimize traffic flow and provide a more comfortable driving experience. Identified are future research directions and development opportunities in this dynamic field. © 2024 IEEE.

关键词： Zigbee

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Data Analytics Approach for Enhanced Sales Forecasting (DAAESF): Feature Selection and Classifier Integration Analysis

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SN computer science 2024年第8期5卷 1158页

作者： Kaur, Gagandeep Kaur, Harpreet Goyal, Sonia Department of Computer Science and Engineering Punjabi University Punjab Patiala 147002 India Department of Electronics and Communication Engineering Punjabi University Punjab Patiala 147002 India

Accurately forecasting sales has significant ramifications for producers, distributors, and investors. Sales forecasting accuracy enables businesses to enhance their manufacturing, distribution, and promotional activities. The current research intends to examine the implications of feature selection methods on enhancing the accuracy of seasonal sales forecasts. The author evaluates different feature selection methods in combination with predictive models, aiming to determine their impact on the effectiveness of predictions. Within this research, a diverse set of eight classifiers has been utilized: namely Naïve Bayes, Logistic Regression, Neural Network, Random Forest, J48, IBK, SVM, and K Star. Alongside these classifiers, four distinct feature selection techniques-namely Gainratio, Infogain, Relief, and CFS have also been employed. The effectiveness of these strategies was evaluated individually as well as collaboratively. The outcome of the proposed novel methodology DAAESF engendered a notable advancement in accuracy rates. Combining feature selection techniques with Neural Network led to a 32% accuracy enhancement compared to other classifiers for cement sales prediction, while Naïve Bayes experienced a decline in performance from 19.55% to 32.55% due to its distinct functions. Additionally, feature selection notably improved prediction accuracy across classifiers, with Neural Network achieving up to 22.68% improvement using CFS, and SVM showing gains of 26.3% with Infogain, highlighting the critical role of feature selection in model optimization. Naïve Bayes and J48 exhibited mixed results across datasets and feature selection methods. Additionally, to substantiate the robustness and validity of the observed outcomes, the Friedman test was judiciously applied. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Data analytics Feature selection techniques Friedman test Logistics Machine learning Predictive models Sales forecasting Seasonal sales

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IoT-Driven Revolution: Transforming Elderly Healthcare Through Dynamic Heart Health Monitoring and Analysis

IoT-Driven Revolution: Transforming Elderly Healthcare Throu...

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

作者： Mani, Kamlesh Singh, Kamlesh Kumar Litoriya, Ratnesh Amity University Department of Computer Science and Engineering Lucknow India Amity University Department of Electronics and Communication Engineering Lucknow India Medi-Caps University Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

This research works to analyze the practical implementation of IoT technology in revamping the elderly health care system, with cardio care and heart health monitoring and analysis in mind. The study goes to great lengths not just to depict but also to seek solutions for the increasing problems of an aging population. As a result, new strategies for the provision of healthcare to the elderly are among the priorities. At its core, the research team will exploit Internet of Things (IoT) devices, sensors, and advanced data analytics to come up with a way of dynamic, continuous, and real-time monitoring of heart health. The paper starts by outlining whether changing demographics related to elderly people should be a top priority in healthcare services and, mainly, how much can be crucial in tackling heart health issues. After setting goals, the study will proceed by formulating, testing, and assessing an efficient Internet of Things-based healthcare approach for monitoring the heart health of elderly people. Regarding details of a method that involves picking up IoT gadgets and sensors, data collection procedures and strategies for participant recruitment will be provided. This article is focused on such technologies as architecture, heart disease monitoring, and algorithms for data processing. Findings unveiled some basic facts about the data collected from the elderly, for instance, their cardiac-related research. The implementation of the research findings in practice is mentioned, stressing patient-oriented projects such as personalized care plans and the interventions done at the right time by the Internet of Things-driven healthy lifestyle system. The last paragraph outlines the future directions for the technology-proficient healthcare assistants of tomorrow, including their refinement and mass use, ultimately ending up with the affirmation of the revolutionized elderly care practices of the future brought forth by IoT technologies. Firstly, the study exemplifies

关键词： Electronic health record

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A Deep Learning Approach for Detecting Malware Using Autoencoder

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第8期51卷 1051-1059页

作者： Panchagnula, Venu Madhav Satya Keerthi, N.V.L.Ch. Surekha, S. Sujatha, R. Veeraiah, Duggineni Ramesh, Eluri Lakshmi, B. Electronics and Communication Engineering Department Prasad V Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada India Computer Science and Engineering Department Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India AIML Department Vignana Bharathi Institute of Technology Ghatkesar Hyderabad India Information Technology Department M. Kumarasamy College of Engineering Karur India Computer Science and Engineering Department Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India Computer Science and Engineering Department R.V.R & J. C. College of Engineering Guntur India Electronics and Communication Engineering Department Rajiv Gandhi University of Knowledge Technologies Andhra Pradesh Nuzvid India

Research in the field of malware detection is currently leaning toward methods based on artificial intelligence algorithms due to the increasing limitations of traditional detection methods and the increasing accuracy of these methods. This study therefore introduces a novel approach to malware detection. This method integrates a deep learning model's autoencoder network with a grayscale graphic depiction of malware. By looking at the autoencoder's reconstruction error, we may assess if the grey-scale picture approach is feasible to use for malware detection. Additionally, it differentiates between malicious and safe software by utilizing the autoencoder's dimensionality reduction capabilities. Using the Android dataset, our newly introduced detection model attained a 95% accuracy and maintained a consistently high F-score of approximately 95%. This outperformed more conventional methods of machine learning detection. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Malware

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Optimization of Memory Usage in High-Speed Cameras using FPGA 3

Optimization of Memory Usage in High-Speed Cameras using FPG...

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3rd International Conference on electronics and Renewable Systems, ICEARS 2025

作者： Kavin Kumar, K. Kumar, Neelam Sanjeev Senthilkumar, C. Velmurugan, V. Sangar, G. Chandrasekaran, Gokul Kongu Engineering College Department of Electronics and Communication Engineering Erode638060 India Vadapalani Chennai India Simats University Simats Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai602 105 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani Chennai India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798331509675

High-speed cameras generate large amounts of data, making memory optimization difficult for real-time processing. This project minimizes data size by converting RGBA (Red, Green, Blue, Alpha) images to RGB, eliminating the Alpha channel to reduce memory usage. The captured images are provided as input to Verilog code in hexadecimal format, with the conversion done by MATLAB. Bilinear Interpolation is applied to reduce the potential quantization errors during the conversion, using the values of four surrounding pixels to smooth the image and maintain quality. After processing, the image is reconstructed using MATLAB, ensuring the integrity of the output. By utilizing the parallel processing capabilities of FPGAs, this method ensures real-time performance while optimizing memory usage. It is ideal for high-speed imaging applications where both efficiency and image quality are crucial. © 2025 IEEE.

关键词： MATLAB

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Implementation of Adaptive Traffic Inundation Detection Technique in RPL-Based IoT Networks

Implementation of Adaptive Traffic Inundation Detection Tech...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Sowndarya, K. K. Devi Rajganesh, N. Dmi College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Tamil Nadu Sriperumbudur India

ISBN: (纸本)9798331508685

Recently, the Internet of Things (IoT) technology facilitates reliable communication for several mission-critical applications. These applications require secure as well as consistent data transmission. However, the Traffic Inundation (TI) attack occupies an internal intrusion device which disseminates misinterpreted data to instigate unnecessary transmissions of the Destination Information Object (DIO) control packets. This damage makes the network incompatible and intensifies a message overhead problem. To mitigate this, a new Adaptive Traffic Inundation Detection (ATID) technique is proposed for Low power and Lossy Networks (LLN). The suggested ATID modifies the traditional Routing protocol for Low Power and Lossy Networks (RPL) function to establish robust verification of new updates. After receiving any updates, the proposed ATID shifted to a legitimacy check phase in order to conclude how to interpret the new updates. Once the TI attacks are identified, it shifts to the attack mitigation phase. By involving these two phases, the proposed ATID achieves maximum detection accuracy rate of 98.6%. © 2025 IEEE.

关键词： Internet of things

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Denoising of MRI and Brain Tumor Classification using Local Binary Pattern and SVM Classifier 3

Denoising of MRI and Brain Tumor Classification using Local ...

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3rd International Conference on electronics and Renewable Systems, ICEARS 2025

作者： Kavin Kumar, K. Kumar, Neelam Sanjeev Senthilkumar, C. Nanmaran, R. Sangar, G. Chandrasekaran, Gokul Kongu Engineering College Department of Electronics and Communication Engineering Erode India Vadapalani Chennai India Simats University Simats Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai602 105 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani Chennai India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798331509675

All data collection systems, including magnetic resonance imaging, are susceptible to noise. Modern high-field scanners can achieve high signal-to-noise ratios;however, noise becomes a significant issue that requires careful management when signals become extremely faint due to specific acquisition processes. The proposed scheme consists of steps which include denoising, feature extraction and classification. For denoising, the Non-Local Means filtering approach is used, and the performance of the denoising method is analyzed using PSNR values. The major objective here is to study texture classification methods to distinguish between various types of brain cancers, such as gliomas, meningiomas, and pituitary tumors. We then evaluate its performance on an additional dataset. The obtained photos serve both educational and evaluative purposes. The accuracy of two methods is computed and compared. The feature extraction performance is compared between two separate classifiers using standard deviation, variance, and mean as performance measures. Among the methods tested, SVM achieved the best accuracy rate of 96.67%. © 2025 IEEE.

关键词： KNN LBP MDC NLM SVM

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Realtime Object Distance Measurement Using Stereo Vision Image Processing 2nd

Realtime Object Distance Measurement Using Stereo Vision Ima...

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2nd International Conference on Big Data, Machine Learning, and Applications, BigDML 2021

作者： Arunakumari, B.N. Shashidhar, R. Naziya Farheen, H.S. Roopa, M. Department of Computer Science and Engineering BMS Institute of Technology and Management Karnataka Bengaluru560064 India Department of Electronics and Communication Engineering JSS Science and Technology University Karnataka Mysuru570006 India Department of Electronics and Communication Engineering Navkis College of Engineering Hassan Karnataka Hassan573217 India Department of Electronics and Communication Engineering Dayananda Sagar College of Engineering Bengaluru560078 India

ISBN: (纸本)9789819934805

In recent years, great progress has been made on 2D and 3D image understanding tasks, such as object detection and instance segmentation. The recent trends in technology driverless cars are making a difference in daily life. The basic principle in these driverless cars is object detection and localization using multiple video cameras and LIDAR and it is one of the current trends in research and development, so attempts to achieve the same on small scale using the available resources. In the proposed method, firstly the stereo images are captured in a dual-lens camera, and secondly, converting the RGB image into a grayscale image. The third step is to apply a global threshold to separate the background, to get the same size of the image using morphological operation. Blob detection is used to detect the points and regions in the image. The fourth step is to detect the object distance and size measurement using the pinhole camera formula. Further, in the proposed work, an effort is made to determine the linear space between the camera and the object from the pictures taken from the camera. Typically, stereo images are used for computation. Binocular stereopsis, or stereo vision, is the capability to derive information about how far left the objects are, grounded uniquely on the comparative places of the object in the two eyes. It depends on both sensory and motor capabilities, using the similar principle the human brain employs, taking two images of the same object taken from two different linearly separated distances. The frame rate of the system can go a maximum of up to 15 frames per second. 15 frames per second can be considered as acceptable for most autonomous systems, and it will work in realtime. Effective convolutional matching technique between embeddings are used for localization that leads LIDAR to increase centimeter level accuracy by about 97%. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Pinhole cameras

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Multi-scale network for single image deblurring based on ensemble learning module

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Multimedia Tools and Applications 2025年第11期84卷 9045-9064页

作者： Wu, WenBo Pan, Yun Su, Na Wang, JingTao Wu, ShaoChuan Xu, ZeKun Yu, YouJian Liu, YaPeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Anhui University Hefei230601 China

In this paper, we have identified two primary issues with current multi-scale image deblurring methods. On the one hand, the blurring scale is ignored. On the other hand, the context information of images is not fully utilized, and the spatial detail information of images is lost. To better solve the above problems, on top of that, this paper proposes a multi-scale network for single image deblurring. The designed network contains two parts: 1) Ensemble Learning Model: The Weak Learning Model in this module connects multiple weak learners in series to increase network depth. Moreover, through multi-scale feature ensemble module, the output features and intermediate features of different weak learner are integrated, and finally strong learner is used to enhance the features with semantic information at different scales;2) Spatial Feature Enhancement Module: This module can preserve spatial detail information of images and compensate for the disadvantages of losing image spatial details due to continuous downsampling in the Ensemble Learning Model structure. Our proposed deblurring model has been extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to the most advanced deblurring methods. In particular, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values are 33.57 dB and 0.965 on the GoPro dataset, respectively, with PSNR and SSIM improving by 0.49 dB and 0.002 over NAFNet, and 0.65 dB and 0.004 over Restormer. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Steering Control of Self-Driving Car Using Super-Twisting Sliding Mode Control 5

Steering Control of Self-Driving Car Using Super-Twisting Sl...

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5th International Conference on Advances in Computational Tools for engineering Applications, ACTEA 2023

作者： Dika, Mohammad Owayjan, Michel Achkar, Roger Departments of Mechatronics Enginering and Computer and Communication Engineering American University of Science and Technology Ashrafiyeh Lebanon

ISBN: (纸本)9798350309959

Super-Twisting is an algorithm implemented on the typical Sliding Mode Control (SMC) that offers a different method of controlling complex systems, and commonly found in advanced robotic systems such as industrial robotic manipulators and mobile robotics. This paper presents a review of the steering control of a self-driving car using Super-Twisting algorithm. The algorithm offers optimal performance and speed when compared to other common controls such as the PID control due to its chattering resistant nature and speed. The algorithm is tested using a real-world servo motor applied on a real car body on the 2021 NXP Racetrack ©, using a set amount of time expected to complete the track at a certain speed. The Super-Twisting algorithm is applied on the motor with the required steering angles for the racetrack with respect to time as the reference input, and it is compared to an implemented PID Control. The results show the superior performance of the Super-Twisting SMC in this application compared to the PID control, offering the best option of control resulting in better line following and hence better overall time on the circuit.

关键词： Sliding mode control

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