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Improved IDS for Vehicular Ad-Hoc Network using Deep learning Approaches 2

Improved IDS for Vehicular Ad-Hoc Network using Deep Learnin...

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2nd international conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Amutha, S. Ramathilagam, A. Uma Maheswari, G. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India PSR Engineering College Tamil Nadu Sivakasi India RMK College of Engineering and Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350340235

In the contemporary landscape of transportation, Vehicular Adhoc networks (VANETs) have emerged as a pivotal infrastructure, facilitating real-time communication among vehicles and infrastructure to enhance road safety and transportation efficiency. However, the dynamic and open nature of VANETs exposes them to diverse security threats, necessitating robust security measures. Intrusion Detection Systems (IDS) are fundamental for safeguarding VANETs, but their effectiveness in identifying and mitigating intrusions, particularly with evolving attack strategies, remains a challenge. This research focuses on enhancing IDS for VANETs by deploying Convolutional Neural Networks (CNN), a subset of deep learning techniques known for their proficiency in image and signal processing, in intrusion detection. The background underscores the significance of VANETs in modern transportation, emphasizing the growing need for more robust security mechanisms due to increasing connectivity and the advent of Intelligent Transportation Systems (ITS). The primary objective of this research is to assess the performance of CNN-based IDS in the VANET context, specifically in their ability to detect and respond to intrusions effectively and efficiently. The contribution of this research lies in its empirical evaluation of CNN-based IDS for VANETs, offering practical insights into the implementation of advanced security measures in vehicular communication networks. By bridging the gap between theory and application, this research serves as a valuable reference for researchers, practitioners, and policymakers, aiding in the establishment of resilient and secure vehicular communication networks in the era of growing connectivity and sophisticated security challenges. © 2023 IEEE.

关键词： Convolutional Neural Networks Intelligent Transportation Systems Intrusion Detection Systems Vehicular Ad-Hoc Networks

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Credit Card Fraud Detection Using machine learning Techniques

Credit Card Fraud Detection Using Machine Learning Technique...

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international conference on the Paradigm Shifts in Communication, Embedded Systems, machine learning and signal processing (PCEMS)

作者： Indrani Vejalla Sai Preethi Battula Kartheek Kalluri Hemantha Kumar Kalluri VFSTR University India RVR&JC College of Engineering India SRM University AP India

There are many types of fraud in our daily life. One of the frauds occurring these days is credit card fraud. When people around the globe make credit card transactions, there will also be fraudulent transactions. To avoid credit card fraud, we must know the patterns and how the fraud values differ. This paper proposed credit card fraud detection using machine learning based on the labeled data and differentiating the fraudulent and legitimate transactions. The experiment was conducted using supervised machine-learning techniques.

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machine learning Based Approach to Selective Measurements of Hydrogen for Catalytic Gas Sensors 2

Machine Learning Based Approach to Selective Measurements of...

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2nd IEEE international conference on Computation, Communication and Engineering, ICCCE 2022

作者： Spirjakin, Denis Baranov, Alexander Ivanov, Ivan Phong, Cu Thanh Tuan, Nguyen Ngoc Moscow Aviation Institute Moscow Russia Air Defense - Airforce Academy Son Tay Viet Nam

ISBN: (纸本)9781665482486

The results of research to improve hydrogen measurement selectivity of catalytic gas sensors are presented. The method uses temperature modulation to obtain the multidimensional signal, which includes a temperature-dependent sensor response to the presence of different gases in the environment: hydrogen, methane, ethylene, propane, butane, and hexane. The signal processing was performed using machine learning methods. Two models were trained: the linear regression and the neural network. The neural network model showed substantially better accuracy in comparison to the linear one. The absolute mean error for this model is 0.0095 %vol. The level of error is consistent for all gases. The results show that the presented method allows the selective measurements of target gases, hydrogen in particular, and minimize the interference of other gases. In comparison to other methods, the method does not need any changes in the sensor construction and allows selective results even when gases have a strong response to similar temperatures. © 2022 IEEE.

关键词： Hydrogen

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Multi-Class Text Classification Based in Oversampling for Highly Imbalanced Dataset 22

Multi-Class Text Classification Based in Oversampling for Hi...

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22nd IEEE international conference on machine learning and Applications, ICMLA 2023

作者： Dos Santos, Dario P. Da Costa, Joao Paulo J. Da Silva, Daniel A. Mendonca, Fabio Veiga, Carlos De Sousa, Rafael T. Department of Electrical Engineering Brasília Brazil Hamm-Lippstadt University of Applied Sciences Germany Brazil

ISBN: (纸本)9798350345346

Automated document classification through machine learning is crucial for handling extensive modern datasets, as manual analysis becomes impractical due to time and cost constraints. Real-world datasets often grapple with issues like class imbalance and high dimensionality, challenging classification algorithms. Our multi-class classification approach encompasses pre-processing, data balancing, feature engineering, and dimensionality reduction. We conducted experiments on seven datasets, including benchmarks and private data, leveraging natural language processing (NLP) techniques like lemmatization and stemming for corpus preparation. To mitigate feature dimensionality, we applied principal component analysis (PCA) and employed oversampling to tackle data imbalance. Finally, we utilized multilayer perceptron neural networks (MLP), support vector machines (SVM), and random forest (RF) models for clas-sification. Our framework achieved high accuracy, ranging from 77 % to 99 %, contingent on the dataset, while also demonstrating strong performance across additional metrics like precision, F1-score, and recall. © 2023 IEEE.

关键词： Classification Natural Language processing Unbalanced Dataset

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2nd international conference on Advances in Information and Communication Technology, ICTA 2023

2nd International Conference on Advances in Information and ...

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2nd international conference on Advances in Information and Communication Technology, ICTA 2023

ISBN: (纸本)9783031495281

The proceedings contain 35 papers. The special focus in this conference is on Advances in Information and Communication Technology. The topics include: A Real-time Crowd Density Level Detection System;an Architecture for More Fine-Grained Hidden Representation in Named Entity Recognition for Biomedical Texts;application of Deep learning Methods for Forest Fire Intelligent Image processing;choosing Data Points to Label for Semi-supervised learning Based on Neighborhood;classification of Fermentation Levels of Cacao Beans (Theobroma cacao L.) Using Sensing Technology and Neural Networks;DCDynUnet: Deep Supervision Attention Context for Brain Segmentation;enhancing Wildfire Detection Using Semi-supervised Fuzzy Clustering on Satellite Imagery;Evaluating the Performance of Some Deep learning Model for the Problem of Emotion Recognition Based on EEG signal;a Novel Arm Bone Fracture Detection Using Deep learning;feature Reduction for Interpretability of Neuro-Fuzzy Classifier;improved Accuracy of Path System on Creating Intelligence Base;the Vehicle Routing Problem with Drones for Fresh Agricultural Products;ViT-SigNet: Combining Deep CNN and Vision Transformer for Enhanced Signature Verification;combining Local Search and Multi-objective Optimization Algorithm in signalized Intersection Optimization;Comparative Evaluation of PRR and PODA Methods for Model Order Reduction in Electrical Circuits;data-Driven Narratives: Unleashing the Potential of R for Journalistic Storytelling;dynamic Analysis of Capsubot Model in Liquid Environment by Numerical Method;genetic Programming–A Preliminary Study of Knowledge Transfer in Mutation;improvement of Spectral Clustering Method in Social Network Community Detection;a Systematic Review of Artificial Intelligence in Geographic Information Systems;probable Characteristics of Multi-channel Queuing Systems with "Impatient" and "Patient" Claims.

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Designing of VehiNet Using Convolutional Neural Networks and Deep learning Techniques 2

Designing of VehiNet Using Convolutional Neural Networks and...

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2nd international conference on machine learning and Data Engineering, ICMLDE 2023

作者： Kandala, Mahita Kaushik, M. Nambiar, Vaishakh Vignesh, S.G. Jyotsna, C. Singh, Tripty Duraisamy, Prakash Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetam Bengaluru India University of Wisconsin Green BayWI United States

Traffic congestion has become a critical issue in metropolitan areas, inflicting significant hardships on individuals on a daily basis. This adverse situation poses multifaceted challenges, including increased air pollution, heightened fuel consumption, and compromised road safety. Deep learning algorithms, encompassing both Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) have showcased remarkable performance in areas such as computer vision, natural language processing, healthcare, finance, and autonomous systems. This research paper proposes a novel methodology to develop a vehicle detection model - VehiNet, which can subsequently be refined to create an automated traffic signal system. The study explores the effectiveness of various CNN, RNN, and deep learning algorithms and techniques in accomplishing this objective. The dataset picked for the study in addition to being compiled from internet resources, was also painstakingly assembled manually using a variety of methods to collect over 2000 photographs from various angles, lighting conditions, and locations within Bengaluru city. The main motive is to apply CNN, RNN, and other Deep learning classification techniques for object detection and classification of Indian vehicles and see how well these deep learning algorithms and techniques affect prediction efficiency. This research stands out from others and makes its novelty clear thanks to the carefully collected dataset and the detailed analysis of the methodologies. © 2024 Elsevier B.V.. All rights reserved.

关键词： Traffic congestion

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Research on Heartbeat Temporal signal Recognition Based on machine learning and Deep learning

Research on Heartbeat Temporal Signal Recognition Based on M...

引用

Image processing and Computer Applications (ICIPCA), IEEE international conference on

作者： Meng Chen Xidian University Xi'an Shaanxi China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

The rapid development of AI technologies, such as machine learning and deep learning, has provided new directions for medical research. With extensive data mining and learning, AI has demonstrated strong potential in tasks like heartbeat signal recognition and image diagnosis. This study utilizes real heartbeat signal samples from hundreds of thousands of instances to research heartbeat signal prediction using various machine learning and deep learning algorithms, including LightGBM, XGBoost, and CNNs. The results indicate that two CNN models, CNN_1 and CNN_2, achieved accuracies of 99.10% and 99.21% on the test set, significantly outperforming XGBoost's 98.79% and LightGBM's 98.97%. Through further data optimization and model fusion, under a weighted average fusion strategy, the evaluation metric for the CNN models was improved to a maximum of 99.53%. The experimental results of this paper provide data support and a feasible solution path for the auxiliary diagnosis of medical big data.

关键词： Deep learning machine learning algorithms Heart beat Biological system modeling signal processing algorithms Prediction algorithms Data models Classification algorithms Convolutional neural networks Medical diagnostic imaging

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Audio Based Detection of Saw Blade Sharpness Using machine learning 2

Audio Based Detection of Saw Blade Sharpness Using Machine L...

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2nd international conference on signal and Information processing, IConSIP 2022

作者： Bankar, Preeti M Kakade, S.M. Shukla, Piyush Gupta, Abhishek Bagewadi, Milind Kulkarni, R.S. College of Engineering Pune Department of Electrical Engineering Pune India Hella India Pvt.Ltd Pune India

ISBN: (纸本)9781728168852

Any mechanical system like a drill, a cutting machine, vehicle engine can subject to faults during operation. These machines are intended to work within a tolerance range. Therefore, even though some of the faults may not stop the system from working, if these faults persist, they can lead to the breakdown of the system. Primarily, the operating sound of the mechanical machine indicates the status of the machine. Therefore, audio signals can be employed for the detection of any impending fault. The goal of this project is to develop machine learning algorithms for system consists of a circular saw blade used for cutting the printed circuit boards (PCB). This blade gets blunt after every few weeks. As the sharpness of the blade decreases, the quality of the cut deteriorates, and the blade starts to consume more power. When the blade is utilized completely, there is a slight change in the blade sound. Using these sound samples, a machine learning algorithm is developed to classify the audio;either blade is new or has reached the end of its life cycle. The machine learning classifiers are also compared with a classifier programmed by using FFT (Fast Fourier Transform) method. © 2022 IEEE.

关键词： Fault detection

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Application of Multi-channel Impedance Measurement Device in Teaching of "signal Analysis and processing" 2

Application of Multi-channel Impedance Measurement Device in...

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2nd international conference on Artificial Intelligence and Education, ICAIE 2021

作者： Sun, Jiangtao Lu, Xupeng Sun, Shijie Wang, Rui Xie, Yuedong Beihang University School of Instrument and Opto-Electronic Engineering Beijing China

ISBN: (纸本)9781665424929

This article develops a low-cost multi-channel impedance measurement device as a teaching tool based on the electrical impedance tomography (EIT) technique. The device is applied for the teaching of "signal analysis and processing"related courses in the subject of instrumentation science and technology, which can reconstruct images of conductivity distributions within a domain of interest and recognize a variety of human gestures based on machine learning. This auxiliary teaching tool could help students deepen the understanding of core knowledge in the course, such as analog signal processing by operational amplification, signal sampling and discretization, discrete Fourier transform, and data classification through machine learning. The proposed teaching tool is easy to demonstrate during classes, which can help improve teaching efficiency and enhance students' interests towards related knowledge. © 2021 IEEE.

关键词： Electric impedance measurement

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Detecting and Classifying the Vehicles in Traffic signal Using Transfer learning 2nd

Detecting and Classifying the Vehicles in Traffic Signal Usi...

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2nd international conference on signal and Data processing, ICSDP 2022

作者： Valli, Shaik Kumar, K. Nithin Sai Nath, K. Kartheek Prabhakaran, K. Manoj Amrita School of Engineering Chennai Campus Chennai India

ISBN: (纸本)9789819914098

In existing methods, there are different machine learning techniques carried out for detecting and classifying the vehicles. But in real world scenario, their validation performance is not that good. Also, the vehicles are not detected accurately, and detection rate is slow in real time implementation. To overcome this issue, we used a deep learning technique to speed up the process of detecting the vehicles and classifications. Moreover, different deep learning techniques are used for the vehicle’s detection and classifications. Some of the two major techniques include one stage object detector like YOLO V4, SSD, Detect Net, Retina Net, and 2 stage object detectors like RCNN, Fast RCNN, and Faster RCNN. Among all these YOLO V4 is a recent super-fast model and is best for real time applications for object detections. In this paper, we implemented the transfer learning techniques as YOLO V4 for detecting and classifies the huge numbers of vehicles at traffic signals. Using this technique, we achieved 80.01% accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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