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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Bandari, Sheethal Rangdal, Aakanksha Rukmanna Manisha, Budhi Shailaja, M. Sreenidhi Institute of Science And Technology Department of Electronics And Computer Engineering Hyderabad India

ISBN: (纸本)9798350365092

Today, we find almost everybody using internet and with increase in demand of computer networking, Hackers are taking advantage of this situation and trying to intrude into the networks and disturb the networks thus by injecting malwares into it. It is very important to identify whether the network is free from malware or not in order to ensure secrecy of valuable data. Intrusion Detection Systems (IDS) are some of the most prominent technologies for administrating and looking after security issues in the network. With rapidly changing network traffic data, one classifier is not enough to fend off modern network intruders. In this work, ensemble classifiers are used to classify data based on binary and multiclass. We took this as a major challenge to increase accuracy rates of detection for individual attack types and all types of attacks. This allows us to identify attacks and specific attack categories. We've chosen to contrast other works by opting for Artificial Networks as well. Python being the most versatile programming language in recent times, we chose IDS-ML an open source code repository in order to develop IDS from public network traffic records that can protect modern networks with high efficiency and accuracy. © 2024 IEEE.

关键词： Malware

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Hybrid Image Compression Using DCT and Autoencoder 7

Hybrid Image Compression Using DCT and Autoencoder

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Nayak, Dibyalekha Kar, Tejaswini Ray, Kananbala Ravindra, J.V.R. Nandan Mohanty, Sachi Shah and Anchor Kutchi Engineering College Electronics and Computer Science Engineering Mumbai India Kiit Deemed to Be University School of Electronics Engineering Bhubaneswar India School of Electronics and Communication Engineering Telengana Hydrabad India VIT-AP University School of Computer Science and Engineering Amaravati India

ISBN: (纸本)9798331527822

Medical image processing is an emerging area that has an effect on recognition, analysis and treatment techniques of the diseases. In medical image processing compression is required to reduce the bandwidth and storage related issues. In this method DCT and Autoencoder based image compression technique is used for the better compression of the medical brain images. To get the separate Region of Interest(ROI) and non-ROI based region Otsu method is considered. DCT and Autoencoder based hybrid compression is achieved on ROI and non-ROI regions of an image respectively. The evaluation parameter of this method are Peak Signal to Noise Ratio (PSNR), Structure Similarity Index matrix (SSIM), Feature Similarity Index Matrix (FSIM), Compression Ratio (CR) and Space saving(SS) percentage. For the medical brain images proposed method has PSNR of 44.45db, SSIM of 0.97, FSIM of 0.99, CR 9.26 and SS of 89.9(%). © 2024 IEEE.

关键词： Image compression

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A Coherent Analytical Model to Strengthen Vehicular Communication in VANET Using Droptail Priority Queue

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Journal of The Institution of Engineers (India): Series B 2025年 1-12页

作者： Kumar, Satyam Sinha, Bam Bahadur Khare, Priyank Electronics and Communication Engineering Indian Institute of Information Technology Ranchi Jharkhand Ranchi834004 India Computer Science and Engineering National Institute of Technology Sikkim Sikkim Ravangla737139 India

People’s demand for vehicles has been increasing day by day over the last few decades. A survey tells us that over 50,000 vehicles run on the roads per day. Such a large number of vehicles causes traffic. A survey tells us that over 1200 road crashes occur everyday in India out of which 25% result in deaths. Most of these accidents occur due to a lack of communication between the vehicles. This has led to the attraction of researchers towards the Vehicular Ad Hoc Network (VANET) ensuring road safety by establishing an Intelligent Transportation System (Malhi in Comput Secur 89:101664, 2020). In VANET, due to the frequent movement of vehicles and rapid variations in topology, the communication link between vehicles may break. This paper analyzes these problems and comes up with a novel approach to overcome these and maintain the performance of the protocol. In order to assess the performance metrics for the proposed protocol, an analytical model has been created. Simulations have been carried out to validate it and have been compared against similar state of comparative methods. © The Institution of Engineers (India) 2025.

关键词： Vehicular ad hoc networks

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Real-Time Multi-objects Detection Using YOLOv7 for Advanced Driving Assistant Systems 2nd

Real-Time Multi-objects Detection Using YOLOv7 for Advanced ...

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2nd International Conference on Innovations in Data Analytics, ICIDA 2023

作者： Solunke, Babruvan R. Gengaje, Sachin R. Computer Science and Engineering Walchand Institute of Technology Solapur India Electronics and Computer Engineering Walchand Institute of Technology Solapur India

ISBN: (纸本)9789819734658

Accurate and efficient multi-object localization and categorization is one of the key needs for applications of robotic vision, intelligent military surveillance systems, security, and ADAS. It is a significant and complex issue with computer vision that has received a lot of attention. Due to the development of self-driving cars, smart video monitoring, face recognition, and several people tracking services, there is a great demand for quick and precise object detection systems. These methods locate each object by drawing bounding box around it in addition to identify and classify every object in an image or video frame. One of the basic needs for driverless vehicles and many modern driving aid technologies is the ability to recognize and interpret all stationary and moving objects surrounding a vehicle under varying driving and weather circumstances. Convolutional neural network (CNN) technology can provide safety in modern vehicles. This paper analyzes the recent deep learning-based object detection methods, challenges and presents an improved general framework for real-time multi-object detections in ADAS-based on YLOv7 and CNN. We compared the YOLOv7 approach with earlier versions in the YOLO family, and it was found that YOLOv7 outperforms all earlier real-time object detectors to obtain a reliable speed between 5 and 160 frames per second and improved accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object detection

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Optimal Deep Convolutional Neural Network with Pose Estimation for Human Activity Recognition

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computer Systems science & engineering 2023年第2期44卷 1719-1733页

作者： S.Nandagopal G.Karthy A.Sheryl Oliver M.Subha Department of Computer Science and Engineering Nandha College of TechnologyErode638052TamilnaduIndia Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science and Engineering St.Joseph's College of EngineeringChennai600119TamilnaduIndia Department of Electronics and Communication Engineering University College of Engineering NagercoilNagercoil629004TamilnaduIndia

Human Action Recognition(HAR)and pose estimation from videos have gained signiﬁcant attention among research communities due to its applica-tion in several areas namely intelligent surveillance,human robot interaction,robot vision,*** considerable improvements have been made in recent days,design of an effective and accurate action recognition model is yet a difﬁcult process owing to the existence of different obstacles such as variations in camera angle,occlusion,background,movement speed,and so *** the literature,it is observed that hard to deal with the temporal dimension in the action recognition *** neural network(CNN)models could be used widely to solve *** this motivation,this study designs a novel key point extraction with deep convolutional neural networks based pose estimation(KPE-DCNN)model for activity *** KPE-DCNN technique initially converts the input video into a sequence of frames followed by a three stage process namely key point extraction,hyperparameter tuning,and pose *** the keypoint extraction process an OpenPose model is designed to compute the accurate key-points in the human ***,an optimal DCNN model is developed to classify the human activities label based on the extracted key *** improving the training process of the DCNN technique,RMSProp optimizer is used to optimally adjust the hyperparameters such as learning rate,batch size,and epoch *** experimental results tested using benchmark dataset like UCF sports dataset showed that KPE-DCNN technique is able to achieve good results compared with benchmark algorithms like CNN,DBN,SVM,STAL,T-CNN and so on.

关键词： Human activity recognition pose estimation key point extraction classiﬁcation deep learning RMSProp

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Signal Conducting System with Effective Optimization Using Deep Learning for Schizophrenia Classification

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computer Systems science & engineering 2023年第5期45卷 1869-1886页

作者： V.Divya S.Sendil Kumar V.Gokula Krishnan Manoj Kumar School of Electrical and Electronics Engineering Sathyabama Institute of Science and TechnologyChennaiTamilnaduIndia Department of Electrical and Electronics Engineering S A Engineering CollegeChennaiTamilnaduIndia Department of Computer Science and Information Technology CVR College of EngineeringMangalpallyHyderabadTelanganaIndia School of Computer Science University of Petroleum and Energy StudiesDehradunIndia

Signal processing based research was adopted with Electroencephalogram(EEG)for predicting the abnormality and cerebral *** proposed research work is intended to provide an automatic diagnostic system to determine the EEG signal in order to classify the brain function which shows whether a person is affected with schizophrenia or *** detection and intervention are vital for better ***,the diagnosis of schizophrenia still depends on clinical observation to *** reliable biomarkers,schizophrenia is difficult to detect in its early phase and hence we have proposed this *** this work,the EEG signal series are divided into non-linear feature mining,classification and validation,and t-test integrated feature selection *** this work,19-channel EEG signals are utilized from schizophrenia class and normal ***,the datasets initially execute the splitting process based on raw 19-channel EEG into 6250 sample point’s *** this process,1142 features of normal and schizophrenia class patterns can be *** other hand,157 features from each EEG patterns are utilized based on Non-linear feature extraction process where 14 principal features can be identified in terms of considering the essential *** last,the Deep Learning(DL)technique incorporated with an effective optimization technique is adopted for classification process called a Deep Convolutional Neural Network(DCNN)with mayfly optimization *** proposed technique is implemented into the platform of MATLAB in order to obtain better results and is analyzed based on the performance analysis framework such as accuracy,Signal to Noise Ratio(SNR),Mean Square Error,Normalized Mean Square Error(NMSE)and *** comparison,the proposed technique is proved to a better technique than other existing techniques.

关键词： Deep learning optimization algorithm signal conducting system schizophrenia convolutional neural network mayfly optimization algorithm

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Deep learning based video surveillance for predicting vehicle density in real time scenario

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4371-4383页

作者： Priyanka, G. Senthil Kumar, J. Veena, S.T. Department of Computer Science and Engineering Mepco Schlenk Engineering College Sivakasi India Department of Electronics and Communication Engineering Mepco Schlenk Engineering College Sivakasi India

Video surveillance systems are often used for traffic monitoring and to characterize traffic load. However, most of the surveillance videos are low frame rated and extracting the right motion feature from them is a challenging task. Deep learning is a growing technology that extracts each object’s feature without human intervention. This paper proposes two different Convolutional Neural Networks (CNN) named Multi Scale Convolutional Neural networks (MSCNN) and Single Scale Convolutional Neural networks (SSCNN) to extract the features and prediction of traffic density. The experimental evaluation of the proposed models was carried out on the UCSD traffic dataset. Our method exploits the benefits of the redundant nature of traffic scenes that are captured from a top-down perspective showing vehicles on a road encircled by background objects. Though a variety of objects is present in each frame, our proposed method attempts to acquire information only about specific object features which helps to predict traffic density better in real-time scenarios. Based on the extracted features, each traffic scene is categorized into three different classes low, medium, and high. Also, the performance of the proposed architecture exhibits 12% more accuracy compared with the existing deep neural networks. Furthermore, in comparison with the MAE score, the proposed system is able to reduce the error from 1 to 0.3 than the existing models. In addition, the training and testing phases of the proposed approach consume considerably less time and optimized usage of CPU resources. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

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Image-based Plant Diseases Detection using Deep Learning

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Materials Today: Proceedings 2023年 80卷 3500-3506页

作者： Panchal, Adesh V. Patel, Subhash Chandra Bagyalakshmi, K. Kumar, Pankaj Khan, Ihtiram Raza Soni, Mukesh Department of Computer Science & Engineering GEC Patan India School of Computer Science and Engineering VIT Bhopal University Sehore MP Bhopal India Electrical and Electronics Engineering SRI Ranganathar Institute of Engineering and Technology Coimbatore India Department of Computer Science & Engineering Institute of Engineering & Technology Uttar Pradesh Greater Noida India Jamia Hamdard Computer Science Department Delhi India Jagran Lakecity University Bhopal India

Agriculture plays a major role in developing countries like India, however the food security still remains a vital issue. Most of the crops get wasted due to lack of storage facility, transportation, and plant diseases. More than 15% of the crops get wasted in India due to diseases and hence it has become one of the major concern to be resolved. There is a need of automatic system that can identify these diseases and help farmers to take appropriate steps to get rid of crop loss. Farmers have followed the conventional method of identifying the plant disease with their naked eyes, and it not possible for all the farmers to identify these diseases the same way. With the advance in Artificial Intelligence, there is a need to incorporate the facilities of the computer vision in the field of agriculture. Deep Learning rich libraries and user as well as developer friendly environment to work with, all these qualities make Deep Learning as the favorable method to get started with this problem. In this paper we have used Deep Learning because of the advantages it offers to work with images especially in image classification to get improvised results. The methodology includes taking leaves of infected crops and label them as per the disease pattern. The images of infected leaves are processed pixel based operations are applied to improve the information from the image. As a next step feature extraction is done followed by image segmentation and at the last classification of crop diseases based on the patterns extracted from the diseased leaves. The CNN (Convolutional Neural Network) is used for the classification of diseases, for the demonstration purpose the public dataset is used consisting of around 87 K images (RGB type images) including healthy as well as diseased leaves. © 2021

关键词： Crops

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Energy Measurement by Smart Metering using Blockchain Technology 10

Energy Measurement by Smart Metering using Blockchain Techno...

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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Aishwarya, R. Chandrakala, D. Jeyanthi Yegammai, S. Varrshini, V. Kanaga Suba Raja, S. Vishnu Prasad, V. Easwari Engineering College Electrical & Electronics Engineering Chennai India SRM Institute of Science and Technology Computer Science & Engineering Tiruchirappalli India

ISBN: (数字)9798350353778

ISBN: (纸本)9798350353778

The development of demand-side management with controlled loads has received a lot of attention as a result of the smart grid's ongoing growth and the energy market's volatility. The large number of household loads in the residential sector has generated a great deal of interest in managing the energy consumption of individual residences. This paper suggests simulating the monitoring of several SHEMS (smart home energy management system) characteristics. We have concentrated on the functionalities of the suggested design that gather data and analyse it, and we'll apply them to common home loads like electric water heaters and charging stations for electric cars. Overall, we discovered that the modelling of the loads results in considerable financial savings for consumers when a SHEMS is put into place. Here, we'll use blockchain technology to transfer data securely. Every piece of energy data has a timestamp and a hash value. Since blockchain technology operates through code and encrypts all data, it is becoming more and more difficult for hackers to have a field day. Additionally, since all records are linked to one another via time stamps and hash codes, any change would have a significant impact on the entire database. © 2024 IEEE.

关键词： Energy utilization

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Medical Reports Simplification Using Large Language Models 23th

Medical Reports Simplification Using Large Language Models

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Almezhghwi, Khaled Hassan, Morad Ali Ghadedo, Adel Belhaj, Fairouz Shwehdi, Rabei Electrical and Electronics Engineering College of Electronics Technology Tripoli Tripoli Libya Computer Science Bani Waleed University Bani Waleed Libya Computer Science College of Electronics Technology Tripoli Tripoli Libya Computer Information Systems Higher Institute of Science and Technology Tripoli Libya Electrical and Electronics Engineering College of Electronics Technology Tripoli Tripoli Libya

ISBN: (纸本)9783031648120

Making medical reports easily understandable for a wider audience is a significant endeavor, and the recent advancements in deep learning and large language models offer a promising solution. In our research, we have introduced a fine-tuned Text-to-Text Transformer (T5) model to efficiently summarize these medical reports. Our model is trained and assessed on the publicly accessible Indiana Dataset. To gauge its effectiveness, we employ the ROUGE metrics. The outcomes we've achieved hold promise for the potential of this approach in enhancing medical report summarization. By harnessing the power of cutting-edge technology, we aim to make medical information more accessible and comprehensible to everyone. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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