2023 International Conference on Computer Communication and Informatics (ICCCI)最新文献

{"title":"Genetic Based Fuzzy Logic Control Of SEDC Motor","authors":"N. Aarthi, P. Anbarasu, D. Nagarajan, A. Sajitha Banu, M. Vinosh","doi":"10.1109/ICCCI56745.2023.10128371","DOIUrl":"https://doi.org/10.1109/ICCCI56745.2023.10128371","url":null,"abstract":"This thesis primarily aims at offering an efficient technique of speed control for the small, independently excited SEDC motors utilised in a variety of applications, including industrial, commercial, and medical. The major objective of this work is to suggest a practical approach for controlling the speed of these weak motors. The natural optimization technique known as the genetic algorithm is employed in the suggested way to enhance the speed-controlled operation of the SEDC motor. The goal of this thesis work is to improve the values of several Performance parameters, such as rising time, time taken to settle, time taken to fall, peak overshoot, and steady state error, in order to regulate the motor in an efficient manner. The motor is operated using both the conventional PI controller and GA optimized controller MATLAB version R2013a was used to generate the SIMUINK MODEL for both controller operations. In terms of SEDC motor control, the proposed GA-optimized controller performs the best.","PeriodicalId":205683,"journal":{"name":"2023 International Conference on Computer Communication and Informatics (ICCCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133009588","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":"Text Recognition Based On Encoder And Decoder Framework","authors":"Subhashini Peneti, Thulasi Chitra","doi":"10.1109/ICCCI56745.2023.10128599","DOIUrl":"https://doi.org/10.1109/ICCCI56745.2023.10128599","url":null,"abstract":"Now-a-days we all are using digital technologies in all sections. Handwriting textbook recognition is an active and utmost exploration areas in the field of image processing and pattern recognition but, still we’re using Handwriting clones converted into electronic clones to communicate and store electronically.Through the textbook, we reuse the supplied image, rooting features, and feting it. The training of the system to fete and classify objects takes place, as well as the creation of a bracket schema. The system is trained using this system. Handwriting textbook recognition refers to detecting the computer digital comprehensible. Handwriting textbook input for Handwriting sources similar as photos, paper documents, and other sources. Occasionally it’s complex to understand the mortal hand jotting as cursive handwriting, Poor quality document/ image, different individualities have different handwriting styles and other coffers.The main end of this design is to develop a Handwriting textbook recognition system which is used to read scholars and lectures handwritten notes, croakers conventions, Research and Development labs etc. A handwriting recognition system handles formatting, performs correct segmentation into characters, and find correct presumptive of words. The use of neural networks for feting handwriting textbook is more effective and robust. The end is to ameliorate the effectiveness of neural networks for Handwriting textbook recognition. Keywords - Presumptive, Pattern, Neural","PeriodicalId":205683,"journal":{"name":"2023 International Conference on Computer Communication and Informatics (ICCCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130251986","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":"Unsupervised Online Video Object Segmentation","authors":"R. Mahima, M. Maheswari, E. Priyanka, C. Praiselin, K. Sanjitha","doi":"10.1109/ICCCI56745.2023.10128359","DOIUrl":"https://doi.org/10.1109/ICCCI56745.2023.10128359","url":null,"abstract":"Video segmentation refers to reading video photos and segmenting them into areas of interest. The unsupervised video segmentation performs critical position in huge style of packages from item identity to compression. The unsupervised online video object segmentation structure is proposed with the aid of using implementing the movement property, transferring in a concordance with a standard item for segmented areas. By incorporating notable movement item proposals and detection, a pixel smart fusion policy is advanced efficiently to locate and do away with noise which include dynamic heritage and desk bound objects. Furthermore, with the aid of using leveraging the received segmentation from without delay previous frames, an ahead propagation set of rules with hired to address unreliable movement detection and object proposals.","PeriodicalId":205683,"journal":{"name":"2023 International Conference on Computer Communication and Informatics (ICCCI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127987374","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 of secure QR payment system using Visual Cryptography method","authors":"Praveena Narayanan, Yadlapalli Lokesh, P. Charitha, G.Chethan Teja Kumar, B. Bhavya, S. Hemalatha","doi":"10.1109/ICCCI56745.2023.10128604","DOIUrl":"https://doi.org/10.1109/ICCCI56745.2023.10128604","url":null,"abstract":"In this paper, we will discuss how a secure link-sharing system based on QR codes was created and put into operation. In recent years, QR codes have grown in popularity since they expedite link sharing and provide users with the greatest level of ease. QR-based online systems, as easy as they may appear, are subject to a variety of assaults. As a result,link sharing must be safe enough to ensure each process, integrity and secrecy. The link sharing system must also guarantee each transaction’s sender and receiver’s legitimacy. The suggested QR-based system in this paper is secured using visual cryptography. Thevisual cryptography is carried out using a web application that is part of the proposed system. Users can exchange URLs through QR code using a simple and user-friendly interface provided by the app.","PeriodicalId":205683,"journal":{"name":"2023 International Conference on Computer Communication and Informatics (ICCCI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125399537","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":"Identification of Pneumonia Symptoms in Covid19 patients using Transfer Learning Approach","authors":"P M Ebin, B. Athira","doi":"10.1109/ICCCI56745.2023.10128630","DOIUrl":"https://doi.org/10.1109/ICCCI56745.2023.10128630","url":null,"abstract":"Over 1 million individuals were impacted globally by the COVID 19 epidemic, which also claimed over 10 lakh lives. As a result of the Covid 19 infection, pneumonia might develop, putting the patient in danger of serious illness or even death. Therefore, it is crucial to recognize the signs of pneumonia and its existence in Covid 19 patients. The VGG16 architecture is a Deep Learning architecture that was the first runner-up in the 2014 visual recognition challenge. The researchers are applying transfer-learning to detect the presence of pneumonia in this case. Chest X-ray scans from kaggle, a publicly accessible open dataset, served as the study’s data set. The model’s accuracy was 95.83%, and a comparison with various other models was also presented.","PeriodicalId":205683,"journal":{"name":"2023 International Conference on Computer Communication and Informatics (ICCCI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125588564","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":"Diabetes Prediction using Machine Learning","authors":"G. Parimala, R. Kayalvizhi, S. Nithiya","doi":"10.1109/ICCCI56745.2023.10128216","DOIUrl":"https://doi.org/10.1109/ICCCI56745.2023.10128216","url":null,"abstract":"Diabetes is considered to be one of the worst illnesses in the world. Diabetes is caused by a combination of variables, including obesity, excessive blood glucose levels, and other causes. It does this by altering the insulin hormone, which in turn causes an irregular metabolism in the crab and raises its blood sugar levels. This program’s primary objective is to lessen the risk that people may acquire diabetes by making forecasts for them and urging them to take more care of their diet and lifestyle in the years to come. The key goals of this research were to develop and execute a method for predicting diabetes using machine learning techniques, as well as investigate the strategies that would be used to achieve success in this Endeavour. The suggested technique makes use of a wide variety of classification and ensemble learning algorithms, some examples of which include Knn, Label Encoder, and train test split. The results of the research may provide information that will help medical professionals make more accurate early predictions and judgments in order to better manage diabetes and save lives. The method first extracts information from a dataset, such as certain symptoms that may be utilized to gain further knowledge about diabetes, and then validates that information using other data. This paper objective was to build classification models for the diabetes data set, develop models that can determine whether or not a person is sick, and get the greatest possible validation scores in the models that were developed. Massive datasets may be found in the healthcare business. By investigating enormous datasets in this manner, we may uncover previously unknown information and trends, which will enable us to draw conclusions based on the data and make accurate forecasts. We categorize the dataset using random techniques since our major goal in doing this research is to determine the method that is the most accurate for predicting diabetes. This will be accomplished by integrating machine learning, data visualization, and data interpretation. The use of machine learning, which is becoming more important in the modern healthcare sector, will be the focus of this research. Massive datasets may be found in the healthcare business.","PeriodicalId":205683,"journal":{"name":"2023 International Conference on Computer Communication and Informatics (ICCCI)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126896527","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 Approach For Cervical and Breast Cancer Classification Using Deep Learning: A Comprehensive Survey","authors":"G. S. P. Ghantasala, Bui Thanh Hung, P. Chakrabarti","doi":"10.1109/ICCCI56745.2023.10128454","DOIUrl":"https://doi.org/10.1109/ICCCI56745.2023.10128454","url":null,"abstract":"The most common malignancies in women worldwide are breast & cervical, but very few researchers have examined how gender expectations affect diagnostic adherence provided sexual implications and physical contact. Considering these perceptions is essential to enhancing diagnostic accuracy and services since they may be a major factor in decision-making. As cervical and breast treatments for cancer to be effective, accurate early detection is essential. Machine learning and deep learning are being used by an expanding population and businesses to analyze vast volumes of data and provide useful insights. It has become quite frequent in clinical practices to use ML-based techniques to predict the initial stages of major illnesses like cancer, renal failure, and cardiovascular diseases. Several of the most prevalent diseases in women include cervical cancer, and early detection could help reduce mortality and morbidity. The paper provides a comprehensive analysis of the techniques and research issues widely employed now in the area. The causes, as well as mortality statistics of cancer, are also covered in this. The goal of the paper is to present a deep learning-centric system for earlier and more accurate breast and cervical cancer prediction. The Convolutional neural networks (CNN) were utilized in the progress of deep learning models. This research analyzes the effectiveness of the CNN model employing transfer learning with a model that has already been trained (VGG16). Considering the results, deep learning algorithms have the capacity to anticipate disease somewhere at the earliest stage at which fatality rates can be reduced.","PeriodicalId":205683,"journal":{"name":"2023 International Conference on Computer Communication and Informatics (ICCCI)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123377400","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":"Predictive Analytics based Modeling of the purchase intention of electric vehicles, and understanding the drivers and risks in their adoption for the people of Tamil Nadu in India","authors":"Gaurav Nagpal, Ankita Nagpal, N. Jasti","doi":"10.1109/ICCCI56745.2023.10128324","DOIUrl":"https://doi.org/10.1109/ICCCI56745.2023.10128324","url":null,"abstract":"While the adoption of electric vehicles by the World is very important to address the issues of climate change, the rate of adoption of EVs is substantially low due to some of the barriers in their adoption by the population. While several studies have been done in the past using different theories to measure the perception of people towards the electric vehicles in the context of several countries including India, there is no such study for a specific Indian state. Since India is the land of diversity where people with different lifestyles and values co-exist, the state-specific studies can generate more insights for the policy makers, manufacturers and marketers of EVs. Therefore, this study measures the perception of people of Tamil Nadu, one of the prominent automotive manufacturing hubs in the country, towards the adoption of electric vehicles through a structured primary survey. A logistic regression model has also been developed to measure the purchase intention of the potential adopters. The important findings of the study and managerial implications have also been discussed.","PeriodicalId":205683,"journal":{"name":"2023 International Conference on Computer Communication and Informatics (ICCCI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121559052","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":"Detection and Segmentation of Retinopathy Diseases using EAD-Net with Fundus Images","authors":"G. Sivapriya, V. Praveen, S. Saranya, R. Surya, S. Sweetha","doi":"10.1109/ICCCI56745.2023.10128343","DOIUrl":"https://doi.org/10.1109/ICCCI56745.2023.10128343","url":null,"abstract":"Diabetic retinopathy is one of the major concerns affecting most of the population. It causes the injury in the blood vessels of the retina which is very sensitive to light and is located at the back of the eye, causes this. In the early stages, it may did not cause any symptoms or it will be only minor vision problems. When blood vessels are damaged, they can leak, causing dark spots to appear in our vision. The Diabetic Retinopathy (DR) can be recognized by presence of Hard Exudate, Soft Exudates, Microaneurysms and Haemorrhages. The most important aspect is accurate detection of diseases at an early stage. Manually annotating these scratches is a significant task in clinical survey. This method is completely based on the convolutional neural network and further that can be classified into three modules attention module, encoder module and decoder module. The fundus images were normalised and augmented before sent to the EAD-Net for pixel-wise label forecast and for Self-operating feature extraction. After pre-processing, the image is sent into the EAD Net for training which is followed by testing of an image and finally the segmentation of the image will be done. optimizer is used here is Adam and categorical CE as the loss function. This EAD-Net is the novel method for diagnosing different stages of DR. It produces fitting results with an accuracy of 95 percentage when segmenting 4 different lesions. These active segmentations have significant clinical implications in the monitoring and in the diagnosis of DR.","PeriodicalId":205683,"journal":{"name":"2023 International Conference on Computer Communication and Informatics (ICCCI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122209795","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":"Analysis of Dynamic Scheduling for Edge Cloud Computing","authors":"Charan Sai Reddy Vanipenta, Sai Ram Bhukya, Trivikramarka Reddy Koppula, Chandra Shekar Nelavelli, T. Vignesh, Suneetha Bulla","doi":"10.1109/ICCCI56745.2023.10128455","DOIUrl":"https://doi.org/10.1109/ICCCI56745.2023.10128455","url":null,"abstract":"This paper presents a comprehensive analysis of dynamic scheduling for edge cloud computing. The purpose of this paper is to explore how dynamic scheduling can be utilized to improve the performance of edge cloud computing systems. First, this paper introduces edge cloud computing, its applications, and the importance of scheduling. Next, this paper discusses the different types of scheduling algorithms and how they can be used in edge cloud computing. The paper then investigates the challenges associated with dynamic scheduling and presents a survey of existing dynamic scheduling solutions. Finally, this paper highlights the benefits of dynamic scheduling for edge cloud computing and provides some concluding remarks.","PeriodicalId":205683,"journal":{"name":"2023 International Conference on Computer Communication and Informatics (ICCCI)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116128384","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}