2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)最新文献

{"title":"Real Time Road Lane Detection using Computer Vision Techniques in Python","authors":"Salna Joy, Mamta B S, Tejas B Mukesh, Mohammed Muzammil Ahmed, Uday Kiran","doi":"10.1109/ICACRS55517.2022.10029238","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029238","url":null,"abstract":"Autonomous driving car is one of the most disruptive Artificial Intelligence developments. Lane detection System (LDS), which is an early phase in the training of a self-driving car, is one of the numerous steps involved. With the growth in urban traffic, road safety becomes more and more essential. The majority of accidents on the roads are caused by drivers leaving the lane without obeying the correct laws. Lane departure warning system and Adaptive Cruise Control (ACC) are sophisticated driver assist systems scenarios that can help the vehicles analyse their present working atmosphere and give the proper input for defensive driving or sound the alert in perilous circumstances. The difficulty in predicting the road traffic environment, however, is a bottleneck in the development of this system. The primary perceptual signals in a complicated traffic condition are street border and carriageway delineation, in addition to pavement colouring separation and roughness identification. The application of machine learning (ML) in intelligent driving systems (IDS) has made lane identification and computer vision a hot issue. The vehicle’s position and trajectory in respect to the lane are precisely predicted by the lane detection system using data collected from lane markers in a complicated environment. This work focuses on a series of images taken from a real-time input feed. After RGB image is converted to grayscale Gaussian filter is used to perform noise removal. The Canny Edge Detector detects the edges with significant variations in intensity. The canny image is masked except for region of interest and Hough Line Transform (HLT) detects the extremes of the detected lines in the image. Thus, the findings from the Hough transform function is a value of pixels that is superimposed into the original image, as detected lane lines.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"1983 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131235050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Automated System for Arrhythmia Detection using ECG records from MITDB","authors":"G. Roland, D. J, S. Padhi, S. Kayalvili, S. Cloudin, Ashok Kumar","doi":"10.1109/ICACRS55517.2022.10029289","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029289","url":null,"abstract":"Electrocardiogram (ECG) is a common non-invasive diagnostic technique used to detect cardiac disease. Several cardiac abnormalities can be uncovered by analysing the heart's electrical impulses or the combination of action impulse waveforms generated by several types of specialized cardiac tissues. Recently, cardiovascular disease has become the most prevalent disease and demands more healthcare professional to evaluate the ECG data. This has led to the widespread adoption of Machine Learning (ML)-based methods for detecting the ECG features. This research study has collected and cleaned the ECG signal data obtained from the MIT-BIH Arrhythmia database (MITB). In this work, a range of processing steps such as augmentation using SMOTE, normalization, denoising, and feature extraction using Local Binary Pattern (LBP) are used. The ML techniques employed in this study include Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Neural Network (NN). Further, the experimental findings are utilized to establish the best model. The NN approach has been demonstrated to detect arrhythmias more accurately (92.01%) than the other three examined methods. Even when prior knowledge is inadequate, the NN approach can aid in the diagnosis of arrhythmia.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133984900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning based Botnet Detection in Large-Scale Network","authors":"Alan Shine Manuel, E. Daniel, S. Durga, I. Beulah Rani","doi":"10.1109/ICACRS55517.2022.10029304","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029304","url":null,"abstract":"Botnets is a term given to a network of computational devices which are affected with malware which is used to bring them under the control of an attacker. These computers enslaved by the attacker are used for various attacks such as DoS attack, phishing attack, stealing of data, Crypto-Jacking etc. The attacker does so with the help of a command-and-control server. This command-and-control server gives the necessary commands to bots which are the enslaved computational devices. The Internet of Things has become an undeniable part of today’s computing world. Recently IoT devices are extensively affected by the botnets as they have low security compared to the normal computing devices. Hence, the security of IoT devices must be ensured. A study of the recent works in the field leads us to the conclusion that most of the works are specified to the IoT devices only. Today’s internet, the normal computational devices as well as the IoT devices are becoming so much connected. So along with the IoT the security of normal computational devices must also be addressed. The network traffic is becoming so large. The classification of such a large network will demand a large processing power which may not be affordable by all. So, a more affordable approach in this field must be devised. This paper proposes a Hadoop Distributed File System based system, which incorporates the power of multiple systems for the classification of network traffic in a very large network. This study considers the recent works in the field. On the insight of the recent works, the system uses CTU-13 dataset for training the model. The classification algorithm used is the Random Forest algorithm.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131586591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robotic Arm Aided Thermal Screening System","authors":"Usha Rani C M, K. Shailaja, Raksha C, Harisha H L, P. P","doi":"10.1109/ICACRS55517.2022.10029258","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029258","url":null,"abstract":"With the onset of the Covid-19 pandemic, the health of people has become more of a concern. With this, temperature measurement has gained even more significance. Non-contact thermometers give the advantage of being used in extreme infectious environments, lightweight, repeatability, and many more. Thermal screening helps in identifying people with a high body temperature who are potentially at risk. This research work focuses on the non-contact human body temperature measurement with the assistance of a robotic arm. The robotic arm is used to dispense the power of mobility to the system. The robotic arm, interfaced with Raspberry PI, is used to dispense the power of mobility to the system. Non-contact infrared temperature sensor, MLX90614, is interfaced with Arduino Nano and is used to measure human body temperature. The temperature obtained from the thermal gun is fed to the serial monitor app in the mobile that is connected through the USB cable to Arduino Nano. The temperature sensor’s data is displayed on a mobile phone in Celsius unit. The format in which the sensor data is displayed is programmed using Arduino IDE.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127657985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparative Study on Machine Learning based Cross Layer Security in Internet of Things (IoT)","authors":"K. Saranya, Dr. A. Valarmathi","doi":"10.1109/ICACRS55517.2022.10029035","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029035","url":null,"abstract":"The Internet of Things is a developing technology that converts physical objects into virtual objects connected to the internet using wired and wireless network architecture. Use of cross-layer techniques in the internet of things is primarily driven by the high heterogeneity of hardware and software capabilities. Although traditional layered architecture has been effective for a while, cross-layer protocols have the potential to greatly improve a number of wireless network characteristics, including bandwidth and energy usage. Also, one of the main concerns with the internet of things is security, and machine learning (ML) techniques are thought to be the most cuttingedge and viable approach. This has led to a plethora of new research directions for tackling IoT's growing security issues. In the proposed study, a number of cross-layer approaches based on machine learning techniques that have been offered in the past to address issues and challenges brought on by the variety of IoT are in-depth examined. Additionally, the main issues are mentioned and analyzed, including those related to scalability, interoperability, security, privacy, mobility, and energy utilization.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127689240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling of Chicken Swarm Optimization with Hybrid Networks for Visual Places Recognition","authors":"S. Senthamizhselvi, A. Saravanan","doi":"10.1109/ICACRS55517.2022.10029265","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029265","url":null,"abstract":"Visual Place Recognition (VPR) is procedure of appropriately recognizing previously visited places under variable viewpoint conditions. VPR becomes a stimulating process because of the difference in lighting conditions (daytime or night), shadow, weather conditions, view points, or even diverse seasons. So, it is needed to design effectual VPR models to manage the variations in the visual appearances. With this motivation, this article develops a chicken swarm optimization with hybrid network for VPR (CSOHN-VPR) model. The presented CSOHN-VPR model aims to recognize the visual places effectively and accurately. To accomplish this, the presented CSOHN-VPR model allows for automatically extracting the features using the HybridNet technique. For optimal hyperparameter tuning process, the CSO algorithm is exploited to it which helps in improving the recognition performance. At last, city block similarity measure is used to recognize the visual places. To demonstrate the enhanced place recognition outcomes of the presented CSOHN-VPR method, a detailed experimentation process is made. The obtained values pointed out the improvements of the CSOHN-VPR model over other models. Therefore, the CSOHN-VRP model is able to recognize places regardless of varying illuminations, seasons, and viewpoints.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127962363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Liver Tumor Grade Detection using CNN Based LSTM Model with Corelated Feature Set from CT Images","authors":"Venkateswarlu Gavini, G. Lakshmi","doi":"10.1109/ICACRS55517.2022.10029307","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029307","url":null,"abstract":"Every year, liver cancer claims the lives of more than 1.5 million people around the world. Liver cancer can be detected early with Computed Tomography (CT). This could save millions of lives per year. Additionally, there is a pressing need for a computerized system to consistently, quickly, and accurately identify and evaluate CT scans. Because of their small size, shape, intensity, and low contrast, it is extremely difficult to accurately segment minute tumours from the surrounding liver tissue. Deep learning algorithms are utilized to conduct a study of several ways for the prior detection of liver cancer in abdominal imaging. In a computer-aided liver disease diagnostic and liver surgery planning system, such as a liver transplantation system, the ability to accurately and automatically segment the liver parenchyma is essential. However, it is particularly difficult to delineate the liver on CT pictures. A more difficult task in computer-aided diagnosis, stage classification of liver tumours was addressed in this research. It is now being proposed that a modified computer-aided diagnosis can lessen the radiologists' heavy workloads and second opinions. A Convolution Neural Network (CNN) based long short-term memory (LSTM) with correlated feature set (CNN-LSTM-CFS) model is proposed in this paper in order to develop an automated method for liver tumor stage detection. The proposed approach makes use of correlated features from feature set using labeled feature set. The relevance of features appeared to represent the most important imaging criteria for each class, and feature maps have indeed been consistent with the original image features. The difference between cancerous and non-cancerous lesions must be made in addition to the CT-based lesion-stage description in order to arrive at an accurate diagnosis and treatment plan. It necessitates a high level of knowledge, resources, and previous experience. Colorectal cancer metastases in the liver can be distinguished from benign cysts in abdominal CT scans of the liver using a deep end-to-end learning technique. The proposed model is contrasted with the existing methods and the results indicate that the proposed model performance is superior to traditional methods.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128531096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Social Distancing Monitoring and Alerting System using YOLO Deep Learning Algorithm","authors":"P. Dharani, M. Thirukumaran, B. Balaji","doi":"10.1109/ICACRS55517.2022.10029311","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029311","url":null,"abstract":"The Covid-19 disease is caused by the severe acute respiratory (SAR) syndrome coronavirus-2 and becomes the reason for the Global Pandemic since 2019. Until July 2022, the total reported cases were 572 million and reported deaths were 6.38 million around the world. In many countries the infections caused severe damages. It not only took the precious lives but also caused few other national damages like economic crisis. The only solution to stop this pandemic is to increase the vaccination and reducing the spreads. The covid 19 virus is an airborne disease and spread when people breathe virus contaminated air. The WHO and all the nations were insisting to maintain social distance to control the virus spreading. But maintaining the social distance in public places is very hard. In this project we developed a method for detecting social distance. The system uses Raspberry Pi processor to detect the distance between two people from the live video stream. The YOLOv3 technique is used to detect the object from single frame of the video.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129063555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Extensive Review of Machine Learning Techniques for EEG Signal Processing","authors":"M. Anita, A. Meena Kowshalya, B. Maheswari, A. Muthuram","doi":"10.1109/ICACRS55517.2022.10029003","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029003","url":null,"abstract":"Electrical brain activity is detected by signals in an Electroencephalogram (EEG). Based on their frequencies, EEG signals are usually put into one of five groups: delta, theta, alpha, beta, and gamma. These signals help find a pattern that can be used to predict when a person will have a seizure. Classifying a seizure is a very important job for a doctor, as it helps them figure out what kind of seizure it is and if there will be any other problems. The goal of seizure classification is to learn as much as possible about the EEG signals. Literature shows that there are a lot of EEG signal pre-processing techniques, selection methods, feature extraction from EEG signals, and classification algorithms that can be used to find out if someone is having a seizure. The most important thing about pre-processing EEG signals is that it improves the quality of raw experimental data, which leads to better datasets, better classifications, and better accuracy. This study gives an overview of recent EEG pre-processing methods, datasets that can be used for experiments, and EEG classification techniques that will help a beginner researcher build on and use the right techniques.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116818692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence based Smart Cosmetics Suggestion System based on Skin Condition","authors":"Kavyashree N, R. V, Prasanna R. Rasal, Rahul Jadhav, M. Saidireddy, K. Kharade","doi":"10.1109/ICACRS55517.2022.10029120","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029120","url":null,"abstract":"Today, cosmetics have a big impact on how people look. Customers can access a range of products and conduct e-commerce on the website. Choosing the best product for our skin might be challenging. It offers a prediction approach so that individuals can select the goods that are suitable for their skin type. Configuration concepts perform better than conventional methods. It uses deep learning technology to streamline the complex procedures in the IT industry for cosmetics and beauty care. Along with its clients and product offerings, the beauty sector has grown over time. With the expansion of products and consumers, selecting the right cosmetics becomes more and more important. Due to the substantial influence that cosmetics have on a person’s appearance, the client should choose the best cosmetics for themselves based on personal factors (skin type). Given that every person has a distinct type of skin, choosing cosmetics that suit the person may be difficult. The composition depends on whether the client has oily, dry, or neutral skin. Even the product the user is looking for can have an extremely difficult skin condition; AI algorithms are perfectly suited to address this problem since they can evaluate massive amounts of unstructured data and generate insightful results.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115372422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}