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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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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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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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AI-Enhanced Farming: A Real-Time Monitoring App powered by IoT 5

AI-Enhanced Farming: A Real-Time Monitoring App powered by I...

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5th International Conference on Smart electronics and communication, ICOSEC 2024

作者： Gayathri, R. Raaj, R. Sathesh Balaanand, M. Kumar, J. Arun Sasikala, N. Rajesh, K. Psna College of Engineering and Technology Department of Computer Science and Engineering Dindigul India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Tagore Institute of Engineering & Technology Department of Computer Science and Engineering Salem India Dayananda Sagar University Department of Computer Science and Engineering Karnataka Harohalli India Ssm Institute of Science and Technology Department of Electronics and Communication Engineering Dindigul India

ISBN: (纸本)9798331504403

The Constant challenge faced by the farmers is to maintain their crops and enhance productivity. Despite various methods and solution evolving, still farmers can't upgrade to it. Farmers are not aware of the modern agricultural methods. The major cause is that the software and solution developed are of higher complexity. To upgrade them, technology is the foremost important tool to stand up in this modern world. To help them with their problem and adapt to the modern world, the development of software with inbuilt AI and user-friendly features (in agriculture) is needed. This study proposes a novel Agri-tech app, which is a IoT based app used by the farmers for weather forecasting, soil moisture and nutrient content analysis. The developed application also recommends the crop type that can be cultivated in the soil by using the database of soil moisture and nutrient content. IoT sensors fixed in the field are used to detect the moisture level in the soil and notify the farmers to irrigate the land. It recommends the farmer to add manures and fertilizers to soil by calculating the amount of nitrogen, phosphorous, and oxygen contents present in the soil. Using these technical innovation farmers can monitor their fields over a system or smart phone. Moreover, the proposed approach can promote crop diversity and rotation for healthier soil, encourage sustainable practices like organic farming. © 2024 IEEE.

关键词： Soil moisture

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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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Towards Safer Digital Spaces: Automated Detection of Cyberbullying Through Multi-Modal Learning 5

Towards Safer Digital Spaces: Automated Detection of Cyberbu...

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5th International Conference on Sustainable communication Networks and Application, ICSCNA 2024

作者： Karthikeyan, M. Saravana Francis Raj, J. Relin Krishnan, R. Santhana Kumari, D. Abitha Murali, S. Joshua, K. Paul Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India RMK Engineering College Department of Computer Science and Engineering Thiruvallur India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India

ISBN: (纸本)9798331530013

Cyberbullying and online harassment present significant challenges to digital safety, demanding robust detection systems capable of identifying abusive content across multiple formats. This work proposes a multi-modal framework that leverages CLIP and RoBERTa embeddings to detect and categorize cyberbullying and harassment in both text and image-based posts. By utilizing two curated datasets, HarassDetect-TextImage and CyberGuard-MultiModal, the model is trained on a diverse range of offensive and non-offensive content. Text data is preprocessed through advanced techniques, while CLIP and RoBERTa generate embeddings capturing the context of both textual and visual inputs. For sequential analysis of threaded posts, a Transformer layer further enhances the model's contextual awareness, enabling it to recognize harassment patterns that unfold over multiple messages. A Support Vector Machine (SVM) classifier then categorizes content into Non-Cyberbullying, Minor, Moderate, and Severe classes, each associated with confidence levels guiding automated actions. High-confidence classifications trigger immediate responses, such as content restriction or user warnings, while moderate-confidence cases are flagged for manual review. This adaptive framework not only improves accuracy but also allows for a continuous feedback loop, updating the model to address evolving patterns of cyberbullying. Evaluation results demonstrate the efficacy of this approach in creating safer online environments through automated and contextually aware cyberbullying detection. © 2024 IEEE.

关键词： Support vector machines

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Wideband Tunable Transmission Type Polarization Converter Metasurface using Graphene for Terahertz Frequency Regime 4

Wideband Tunable Transmission Type Polarization Converter Me...

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4th IEEE International Conference on Mobile Networks and Wireless communications, ICMNWC 2024

作者： Mistri, Hiranmay Ghosh, Anumoy Sardar, Abdur Rahaman Ramkrishna Mahato Government Engineering College Department of Electronics and Communication Engineering Purulia India National Institute of Technology Mizoram Department of Electronics and Communication Engineering Aizwal India Ramkrishna Mahato Government Engineering College Depatment of Computer Science and Engineering Purulia India

ISBN: (纸本)9798350352931

This article represents a broadband transmission type linear-to-circular polarization converter (LTCPC) realized using a graphene metasurface for the terahertz (THz) frequency regime. The composition of the unit cell includes a lossy-SiO2 substrate with a graphene-based sub-wavelength mono-layer structure on the top and a similar but very thin gold sub-wavelength structure at the back side to cover it partially. This metasurface can be used to transform waves with linear polarization into circularly polarized waves from 9.15 THz to 13.0 THz, i.e., about 3.85 THz bandwidth and about 35 percent of fractional bandwidth (FBW). The chemical potential (CP) and relaxation time (RT) can be varied to tune the graphene metasurface over a vast range of frequencies. All physical measurements of the unit cell geometry are optimized for performance optimization. The proposed lumped parameter circuit for the unit cell is also incorporated. The proposed unit cell can perform invariably up to 40° incident angle. © 2024 IEEE.

关键词： Terahertz waves

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Intrusion Detection System for Big Data Analytics in IoT Environment

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computer Systems science & engineering 2022年第10期43卷 381-396页

作者： M.Anuradha G.Mani T.Shanthi N.R.Nagarajan P.Suresh C.Bharatiraja Department of Computer Science and Engineering St.Joseph’s College of EngineeringChennai600119India Department of Computer Science and Engineering University College of Engineering ArniThatchur632326India Department of Electronics&Communication Engineering Kings College of EngineeringPudukkottai613303India Department of Electronics&Communication Engineering K.Ramakrishnan College of EngineeringTiruchirapalli621112India Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India

In the digital area,Internet of Things(IoT)and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal *** IoT networks are popular and widely employed in real world applications,security in IoT networks remains a challenging *** intrusion detection systems(IDS)cannot be employed in IoT networks owing to the limitations in resources and ***,this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning(IMFSDL)based classification model,called IMFSDL-IDS for IoT *** proposed IMFSDL-IDS model involves data collection as the primary process utilizing the IoT devices and is preprocessed in two stages:data transformation and data *** manage big data,Hadoop ecosystem is ***,the IMFSDL-IDS model includes a hill climbing with moth flame optimization(HCMFO)for feature subset selection to reduce the complexity and increase the overall detection ***,the beetle antenna search(BAS)with variational autoencoder(VAE),called BAS-VAE technique is applied for the detection of intrusions in the feature reduced *** BAS algorithm is integrated into the VAE to properly tune the parameters involved in it and thereby raises the classification *** validate the intrusion detection performance of the IMFSDL-IDS system,a set of experimentations were carried out on the standard IDS dataset and the results are investigated under distinct *** resultant experimental values pointed out the betterment of the IMFSDL-IDS model over the compared models with the maximum accuracy 95.25%and 97.39%on the applied NSL-KDD and UNSW-NB15 dataset correspondingly.

关键词： Big data cybersecurity IoT networks intrusion detection deep learning metaheuristics intelligent systems

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A smart recommender model based on learning method for sentiment classification

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Multimedia Tools and Applications 2024年 1-21页

作者： Chiranjeevi, Phaneendra Rajaram, A. Research Scholar Department of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam611002 India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam611002 India

The problem of classifying the text is not only the topic since it contains the sentiment. Large amount of information are availed but determining the positive and negative thoughts is a major task. The existing method uses various classification technique and machine learning techniques for data processing. Naïve Bayes, entropy classification, linear regression, etc., are the existing algorithm. These algorithms are not performing well with time-consuming analysis. The proposed method uses the Structured Support Vector Machine Learning Algorithm for classification. Here the multifaceted text classification dataset is taken for the analysis. This proposed research work recognizes the favorable and unfavorable sentiments towards specific subjects. The techniques for giving the sentences and terms in a document their semantic significance so they can be used more effectively than existing possibilities. Based on the sentiment keyword from the user review, we can rank the positive and negative weights. By this application, the user will get to know which is best and suitable related to their requirement since they can access their requirement in efficient way. Entire survey should be model in MATLAB 2018a suite. The accuracy of this proposed analysis been increased when compared to the existing analysis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Adversarial machine learning

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A Hierarchical Aggregation Network with Edge Guidance for Sturdy Medical Image Segmentation 2

A Hierarchical Aggregation Network with Edge Guidance for St...

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2nd International Conference on Advances in Computation, communication and Information Technology, ICAICCIT 2024

作者： Rasheedha, A. Reethika, A. Rajeshwari, R. Remya, R. Kumar, M. Suresh Saranya, M.D. Department of Biomedical Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Sri Ranganathar Institute of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798331541217

Convolutional neural networks (CNNs) are essential for medical picture segmentation in clinical analysis and illness detection. On the other hand, noise levels and intraclass variability are considerable in medical photos, but interclass differences are negligible. It is difficult to extract strong contextual information and combine discriminative characteristics for fine-grained segmentation. Current techniques are unable to produce precise mask edges, resulting in fuzzy borders and decreased segmentation precision. In this research, a new A Hierarchical Aggregation Network with Edge Guidance that maintains precise spatial features and gathers rich contextual information is presented. The Efficient Fusion Attention Module is used by the proposed model to highlight important spatial and channel information, the Adaptive Hierarchical Feature Aggregation Module to maximize feature fusion, and the Inter-layer Edge-aware Module to improve edge prediction accuracy. Experimentally and qualitatively, the proposed method will perform better than six other cutting-edge segmentation techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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