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

{"title":"Municipality Water Management System using IoT","authors":"B. Perumal, P. Nagaraj, S. Esakki Raja, S. Jaya Sunthari, S. Keerthana, M. V. Muthukumar","doi":"10.1109/ICACRS55517.2022.10029181","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029181","url":null,"abstract":"Over the decades, associate degree bumper variety of articles and approaches was taking to boost the water management system. This Water Management System (WAS) is employed to cut back water loss, work force usage, and waste matter utilization. It halts the barriers behind the municipality’s water management. This water management drawback was facing major problems in additional over the countries within the world. Some machine learning and self-learning algorithms facilitate to resolve such issues quickly. Some ancient approaches assist infrastructural pressure management, leak management, and sensible water distribution system. This proposed work introduces some sensors to acknowledge leak identification and water supply required identification. A sensible water flow detector helps to spot the distributed water level at this time, exploitation the web of things transfers messages to alert the sufficiency and deficiency of the actual space. Using RFID, the municipality workplace identifies the distribution of water regional-wise. This IoT-based water management system makes the water management system more adept and fewer wastage.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"13 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":"122163793","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":"Smart Cervical Band for Detection of Cervical Spondylosis using IoT Technologies","authors":"M. Arunkumar, K. Pugalendhi, S. Agalya","doi":"10.1109/ICACRS55517.2022.10029175","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029175","url":null,"abstract":"Wireless Body Area Networks, also known as WBANs, have the potential to become a new centre of research activity that provides exciting development prospects. This may happen if more people started using these networks, which are also known as WBANs. The frequency signal that is supported by non-contact sensing will give a solution to the problems of private comfort and privacy that have been an issue in recent years. The identification of changes in cervical mobility as well as changes in cervical strain over the course of time is an important part of both the diagnosis and the prevention of cervical spondylosis. In this article, we make use of channel state information to achieve sensible perception and observation, as well as the quick and cost-effective detection of various postures and inappropriate bending of the neck. These goals were accomplished by utilising channel state information. It provides a strategy that is both cost-effective and effective for protecting cervical health, and it also provides some assistance to medical professionals in learning the factors that contribute to cervical os sickness in a very timely manner. Both of these benefits are important in ensuring that cervical health is maintained. Caretakers and medical professionals pay particular attention to the warning indications of neck discomfort and cervical spondylosis (CS), two disorders that are common in individuals of all ages but are especially common in elderly people and small children. On the other hand, etiological variables such as hazardous posture, anxiety, depression, neck strain, and sports or activity activities that may develop moderate neck discomfort into serious surgical difficulties are typically not diagnosed in the early stages of the condition. These variables can develop relatively modest pain in the neck into more significant conditions that necessitate surgical treatment. In light of these concerns, a solution has been proposed that makes use of machine learning and is backed by the internet of things in order to lessen complications relating to neck pain and metallic components. In order to determine the position of the neck and send that information to a Google cloud server so that it may be recorded, the detector that is incorporated into a neckband has a gyro metre as well as a measuring instrument. In addition, alerts are connected to the user and sent to the user in the event that the user’s neck is in a position that is regarded to be abnormal. The accuracy of the system may be improved by employing machine learning algorithms that are in sync with the system that is advised.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"312 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114102841","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 Survey on Application of Metaheuristics Techniques for Ensemble Feature Selection (EFS)","authors":"S. Vijayalakshmi, V. Venkatesan","doi":"10.1109/ICACRS55517.2022.10029177","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029177","url":null,"abstract":"Unprecedented increase in the quantum of data generated through online social, financial, business and learning transaction have necessitated the incorporation of indigenous techniques to intelligently preprocess/trim the data to a level that suits the learning algorithm in all dimensions with improved classification accuracy and performance. Intelligent Feature Preprocessing techniques are deployed to address the issue of \"curse of dimensionality\" that is persistent in the current voluminous databases. Individual feature selection techniques when applied to the dataset may face several deficiencies viz. may get stagnated in the local optima of the complex feature search space, tends to yield unstable feature subsets missing the feature interaction etc. These challenges have encouraged the implementation of Ensemble Feature Selection (EFS) techniques where multiple feature selection models (Base Feature Selectors) are integrated to form a composite model (stronger version) to yield an optimal and robust feature subset. Eventual application of these features to the learning/classification algorithm produces better results with high accuracy and efficiency. High quantum of feature subsets generated through EFS necessitates the replacement of conventional feature searching/ranking/combining strategies with stochastic metaheuristic techniques exclusively designed for tackling high dimensional data issues to generate a global optimal solution. The voluminous intermediate feature subsets from the EFS mandates the deployment of Population based multi-objective Metaheuristics algorithm viz. Minimizing the number of features, classification error, minimizing the total misclassification cost and maximizing the classification accuracy. Population based Metaheuristics techniques serves as correct fit for seamlessly finding optimal feature subsets that sieves through multiple feature subsets generated through EFS. This paper aims to present a survey encompassing the adoption of metaheuristics techniques for efficient feature selection with an aim to improve the detection rate of various attack classification accuracy and efficiency.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"280 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":"120959795","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":"Efficiency Enhancement using Least Significant Bits Method in Image Steganography","authors":"P. Subhasri, S. Santhoshkumar, A. Sumathi, C. Balakrishnan, F. Kurus Malai Selvi","doi":"10.1109/ICACRS55517.2022.10029071","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029071","url":null,"abstract":"Over the years, there has been a tremendous growth in the field of steganography. Steganography is a technique of hidden message passing i.e. transferring a message which is not visible to human eyes, through some media such as an image, music, games etc. In this particular article we focus on Image steganography which has its own advantages and has undergone a lot of improvements in the past years. The most basic image steganography can be achieved by changing the LSBs (Least Significant Bits) of the image pixels. These bits can usually be called the redundant bits. However, changing a large numbers of LSBs of an image can distort the image to an extent where it would be easily noticeable that the image maybe carrying a hidden message rendering it useless. These LSBs are changed according to the message bits allowing the person to hide their data which can be decoded later by reading the LSBs of image pixels. This paper introduces and explains a method to improve the efficiency of LSB method.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"50 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":"121151435","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":"Heart Disease Prediction and Classification using Machine Learning and Transfer Learning Model","authors":"R. Sivaprasad, M. Hema, Bharati N Ganar, D. Sunil, Vaishali Mehta, M. Fahlevi","doi":"10.1109/ICACRS55517.2022.10029279","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029279","url":null,"abstract":"Heart disease is a dangerous condition that can lead to a fatal condition due to cardiac arrest. Recent studies have revealed various facts for analyzing cardiac data by sensing, monitoring, and learning data in IoT to predict early diagnosis and treatment. Through machine learning based feature analysis, accurate disease detection has been implemented. However, the dominant methods do not accurately predict the result since the incorrect features contain non-related support values to select the features to perform training validation and produce prediction inaccuracy. To overcome this limitation, a Machine Learning and Transfer Learning Model (TLM) is proposed to perform heart disease prediction. Initially, pre-processing has been carried out to reduce dimension, and the scaling factor was also used to calculate the margin rate. To increasing the prediction accuracy Disease Prone Impact Rate (DPIR) intends to find the support values. To select the labeled features, Relative Feature Margin Selection (RFMS) is used to select and train the model by Multilayer perception neural network (MLPNN). This classifier selects the margin weights to predict the heart disease risk level based on the class. This predicts higher impact of cardiac deficiency rate by attaining the relevant features based deep feature data learning model, which produce higher precision rate to increase the prediction accuracy than other methods.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"186 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":"121730546","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":"Usage of Classifier Ensemble for Security Enrichment in IDS","authors":"Ch. Phaneendra Varma, G. Babu, P. K. Sree, N. Sai","doi":"10.1109/ICACRS55517.2022.10029251","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029251","url":null,"abstract":"The success of the web and the consequent rise in data sharing have made network security a challenge. Attackers from all around the world target PC installations. When an attack is successful, an electronic device's security is jeopardised. The intrusion implicitly includes any sort of behaviours that purport to think twice about the respectability, secrecy, or accessibility of an asset. Information is shielded from unauthorised clients' scrutiny by the integrity of a certain foundation. Accessibility refers to the framework that gives users of the framework true access to information. The word \"classification\" implies that data within a given frame is shielded from unauthorised access and public display. Consequently, a PC network is considered to be fully completed if the primary objectives of these three standards have been satisfactorily met. To assist in achieving these objectives, Intrusion Detection Systems have been developed with the fundamental purpose of scanning incoming traffic on computer networks for malicious intrusions.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"33 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":"121466676","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":"Design and Development of Automatic Tomato Sorting Intelligent System using Image Processing","authors":"Baluprithviraj K.N, D. M, Dharanidharan D, Gokulkrishnan K, J. S, M. M","doi":"10.1109/ICACRS55517.2022.10029117","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029117","url":null,"abstract":"Exports of goods play an important role in the economic growth of all countries. When exporting, it is essential to separate the goods by quality and quantity. The main idea of the proposed research work is tomato colour sorting by using ATMEGA 328P. This helps in processing tomatoes for export to different countries. In order to effectively sort quality tomatoes for export, this system is sufficient to sort tomatoes accurately and effectively. This also helps our economic growth. For this purpose, this study recommends using the MATLAB software with integrated controller. Large quantities of tomatoes are delivered to large supermarkets and shopping malls every day. Usually, the people who work there can manually sort these tomatoes into different sizes, shapes, etc. Tomatoes of different sizes, shapes, etc. may not be sorted correctly. Therefore, it is possible to solve those problems through available techniques for effectively sorting tomatoes based on their colour with the help of continuous monitoring and reporting capabilities available in the proposed system.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"49 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":"122483540","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 Study of Genealogy and Tracking Systems for Automotive Manufacturing Industries","authors":"A. Thomas, A. R, A. Geetha","doi":"10.1109/ICACRS55517.2022.10029278","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029278","url":null,"abstract":"Automotive manufacturing involves aggregation of large quantity of parts into a single product. Genealogy, tracking and traceability are vital due to the complexity in handling the quantum of parts which runs into thousands. Understanding the nuances of Genealogy, tracking and traceability is of vital importance. Moreover, vehicle recall policy mandates automotive industries to have a robust traceability system in place. This paper focuses on the study of various genealogy and tracking systems in use globally for automotive manufacturing, the standards governing them and recommends the use of IT based systems as optimal for cost control and operations friendliness.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"102 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":"122521923","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":"Recent Advancement and Comparative Turnouts of 1Ø Grid Tied Non-Isolated Inverters Topologies","authors":"Rushikesh S. Shahakar, K. Thakur, Nutan S. Thakare","doi":"10.1109/ICACRS55517.2022.10029237","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029237","url":null,"abstract":"Renewable Energy (RE) is environmentally friendly, abundant, economically feasible and has become a prime interest as well as a potential substitute to conventional fossil fuels. One of the fastest growing RE source is Photo-Voltaic (PV) has gained a lot of momentum these days which can be hooked to the grid distribution network. Power electronics interface plays a vital role in power generation. Converter topologies have quite a substantial impact on the efficiency of a generation system. This research examines the current advancements in non-isolated Grid-connected PV (GPV) inverter systems along with their historical overview. Several transformerless inverters have been presented using various ways such as decoupling the DC from the AC side and/or clamping the common mode or using common ground configurations. In addition, various trailblazing inverter topologies are elaborated in a systematic manner with the foreseeable major hurdles in developing a smart and efficient system. Furthermore, different performance metrics are appropriately analyzed and reported in a table. This paper aims to comprehensively review and classify various transformerless inverters with detailed analytical comparisons in order to offer a clear understanding on the development of optimal transformerless inverters for the rising generation GPV systems.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"44 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":"127766064","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":"Hybrid Machine Learning based False Data Injection Attack Detection and Mitigation Model for Waste Water Treatment Plant","authors":"A. Parvathy, G. Leela Kasyap, D. Venkata Abhinav, A.N.V. Surya Sai, R. Sriranjani, N. Hemavathi","doi":"10.1109/ICACRS55517.2022.10029198","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029198","url":null,"abstract":"The industries are deploying Internet of Things (IoT) for complex applications in their respective domains which employ different sensors to collect data from different plants and pass it over signal conditioning units to obtain desired results. Now-a-days, these plants are vulnerable to cyber-attacks and one of prominent attacks is False Data Injection Attack (FDIA) which means injecting false data into the sensor readings, which has considerable losses that may include loss of human lives, equipment, etc. Hence, the proposal aims to develop a Machine Learning Algorithm which can protect the clean data from corrupted data and are useful for Anomaly Detection and Data Cleaning. False data injection is detected through three machine learning algorithms namely, Linear Regression with Least squares method (OLS) and Gradient descent and K-Means clustering and the suitable detection model is obtained through comparison of performance indices. Further, Auto Encoder neural network is applied to reconstruct the true data from the false data.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"2 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":"134356375","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}