2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)最新文献

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Online Legal Cloud Computing Sharing Application for Smart Medical System Management 智能医疗系统管理的在线法律云计算共享应用
Jiayu Tong, Zhongyuan Li, Zengzheng Qiao
{"title":"Online Legal Cloud Computing Sharing Application for Smart Medical System Management","authors":"Jiayu Tong, Zhongyuan Li, Zengzheng Qiao","doi":"10.1109/ICSCDS53736.2022.9760713","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760713","url":null,"abstract":"This pa per discusses and analyzes the characteristics of cloud computing technology, its application and influence in smart hospital information services, and analyzes the application of online legal cloud computing sharing. This paper comprehensively evaluates the doctor's diagnosis and treatment ability through the analysis of the doctor's pre- and post-operative diagnosis information, and provides a service system for the doctors to summarize and analyze the diagnosis and treatment process, using a cloud computing algorithm based on Boolean matrix operations. Only need to scan the database once, which has the advantages of simple, fast and memory saving. Through the mining and analysis of discharged medicines, combined with the online legal platform, the efficiency of the consultation is increased by 7.3%.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114289493","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}
引用次数: 0
Detection of Abnormalities in Brain using Machine Learning in Medical Image Analysis 医学图像分析中使用机器学习检测大脑异常
A. Sivasangari, Sivakumar, suji helen, S. Deepa, Vignesh, Suja
{"title":"Detection of Abnormalities in Brain using Machine Learning in Medical Image Analysis","authors":"A. Sivasangari, Sivakumar, suji helen, S. Deepa, Vignesh, Suja","doi":"10.1109/ICSCDS53736.2022.9761029","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9761029","url":null,"abstract":"In a variety of medical diagnostic applications, Automatic Defect Detection in clinical imaging has turned into the developing field. Computerized discovery of cancer in MRI which gives the data about the aberrant tissues which is essential for the diagnosis. The traditional technique for Abnormalities detection in Brain is human investigation. This strategy is illogical because of the vast volume of data and the imperfection. Henceforth, trusted and programmed algorithms are preferred to prevent the passing pace of human. In this way, Automated tumor discovery techniques are created as it would save the specialist (radiologist) time and acquire the perfectness. Because of the complexities and diversity of malignancies, MRI brain tumour identification is a difficult task. Machine learning approaches are employed to get over the limitations of traditional classifiers in detecting malignancies in brain scans in this study. MRI scans can be utilised to successfully identify sick cells from healthy ones using machine learning and image classifiers. Convolutional neural network algorithm has been used for classification.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117064591","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}
引用次数: 1
‘You Only Look Once’ Application for Autonomous Driving Vehicles & Cricket Spidercams using Convolutional Neural Network in Deep Learning 在深度学习中使用卷积神经网络的自动驾驶汽车和蟋蟀蜘蛛摄像头的“你只看一次”应用
Ranjith Bhat, R. N.
{"title":"‘You Only Look Once’ Application for Autonomous Driving Vehicles & Cricket Spidercams using Convolutional Neural Network in Deep Learning","authors":"Ranjith Bhat, R. N.","doi":"10.1109/ICSCDS53736.2022.9760926","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760926","url":null,"abstract":"Road safety is a prime concern in this era of high speed and automated driving vehicles. Lot of lives are lost or injured every day due to road accidents. Just understanding where the roads are is not adequate for an autonomous vehicle, obstacles like other vehicles and even less impact-resistant pedestrians and cyclists should be identified and avoided. Moreover, a technology proposed should also be capable to augment itself to provide other applications in the related fields. The proposed method here recognizes and report to the system about the objects such as cars, pedestrians, animals, etc. Once the object is identified, the next time vehicle approaches the similar object, it notifies the driver. And it also tells the system whether the object is moving towards or away from our vehicle. Augmenting this algorithm in applications like that of self-driven vehicle or automobiles/devices using Artificial Intelligence for the blind can be made for better safety. The system developed will be subjected to trials in the real life and correlated with an experimental setup.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116201832","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}
引用次数: 0
Diabetes Prediction using Machine Learning Classification Algorithms 使用机器学习分类算法预测糖尿病
M. Dharani, R. Thamilselvan, Dinesh Komarasamy, U. V., S. G., Soundarya M
{"title":"Diabetes Prediction using Machine Learning Classification Algorithms","authors":"M. Dharani, R. Thamilselvan, Dinesh Komarasamy, U. V., S. G., Soundarya M","doi":"10.1109/ICSCDS53736.2022.9760841","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760841","url":null,"abstract":"Diabetes is one among the chronic diseases or metabolic diseases in which a person's blood glucose levels in the body gets increased. During this phase, the body cells will not respond properly to the insulin present in the body. Diabetes leads to high blood sugar and it is also considered as one of the deadliest diseases across the globe. Diabetes will also result in many problems if left untreated and undiagnosed. Hence, it has to be taken utmost care and a high level of accuracy is required in the diagnostic phase. With the development of the machine learning system, the researchers have gained the flexibility to predict the glucose level with utmost accuracy. In the existing system, various machine learning algorithms such as Support Vector Machine [SVM], Naive Bayes [NB] and Random Forest [RF] have been separately used to predict the blood sugar and they have also achieved a prediction accuracy of up to 75%. In the proposed system, feature extraction has been included and a comparative analysis has been done with support vector machine, naive bayes and random forest algorithms. Then, the performance of the three algorithms is evaluated in various measures such as accuracy, precision, F-measure and recall.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124846217","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}
引用次数: 0
Machine Learning Techniques based Audio Player to Soothe Human Emotions 基于机器学习技术的音频播放器抚慰人类情绪
K. N. Prasanthi, G. S. S. V. S. Tejaswi, M. J. Sai, G. M. Reddy, L. Yasaswini
{"title":"Machine Learning Techniques based Audio Player to Soothe Human Emotions","authors":"K. N. Prasanthi, G. S. S. V. S. Tejaswi, M. J. Sai, G. M. Reddy, L. Yasaswini","doi":"10.1109/ICSCDS53736.2022.9760912","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760912","url":null,"abstract":"People in the current world are suffering from lot of stress related diseases due to various reasons. High stress levels may lead to various health hazards like high blood pressure, heart attack etc. One of the stress relief activities is listening to music. If the music played does not suit the current emotion of the listener, it may aggravate stress of the user further. Emotion based music player is a music player based on machine learning techniques which suggests the songs of the playlist based on person's emotions. This paper proposes an emotion-based music player, which suggests songs based on user's various emotions namely happy, sad, angry and neutral. The application captures the user's photo through web camera and processes the facial image to identify user's emotion using machine learning techniques. Based on the emotion detected, it selects some song to play. The proposed application is more accurate in determining human emotion than existing techniques.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123539948","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}
引用次数: 0
Multimodal and Multilabel Genre Classification of Movie Trailers 电影预告片的多模态和多标签类型分类
Aditya Kishore Jha, Akshat Batra, Akshat Dubey, D. Vishwakarma
{"title":"Multimodal and Multilabel Genre Classification of Movie Trailers","authors":"Aditya Kishore Jha, Akshat Batra, Akshat Dubey, D. Vishwakarma","doi":"10.1109/ICSCDS53736.2022.9760773","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760773","url":null,"abstract":"Movies are a diverse form of art and expressions. Unlike pictures and short clips, movies consist of a story-line which is deliberately made quite complex in order to engage the target audience. This paper presents evaluation of the usefulness of visual, textual and metadata-based functions for predicting the genre of a movie using movie trailers and analyzing it's visual features. The trailers were dissected into individual frames and were evaluated for key characteristics in order to divide them into different genres. Because previous articles employ an impractically large number of parameters to analyse the trailer, this approach aims to keep the number of parameters used to a minimum. The Moviescope dataset has been used which consists of about 5,000 movies with relevant information such as movie trailers, posters, plots and metadata. Linear Regression, KNN (K Nearest Neighbours), Decision Tree, Random Forest and Artificial Neural Networks are just a few of the classification algorithms this research study has used and compared.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"45 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123655762","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}
引用次数: 0
Smart Meet — Facial Recognition-based Conferencing Platform 基于面部识别的智能会议平台
Navneeth C Krishnan, Ashish Eapen Varghese, Viswajith Sankar, Achyuth Jm, A. Ravikumar, Jisha John
{"title":"Smart Meet — Facial Recognition-based Conferencing Platform","authors":"Navneeth C Krishnan, Ashish Eapen Varghese, Viswajith Sankar, Achyuth Jm, A. Ravikumar, Jisha John","doi":"10.1109/ICSCDS53736.2022.9760973","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760973","url":null,"abstract":"The face of a person is his uniqueness or identity. Along with textual data like names and identification numbers, the physical features of one's face are also a very efficient way to identify and preserve individuality. This feature improved accuracy in identifying and distinguishing between specific individuals when utilized with other identification labels. This paper aims to provide a conferencing platform and attendance marking with the help of facial recognition. The traditional method of calling names to mark attendance causes various issues in online classes. The inclusion of facial recognition and other monitoring methods ensures a more accurate and efficient way for attendance marking. In this work, Computer Vision techniques for video monitoring purposes, login tracking, and other features for better and more efficient utility.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113973813","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}
引用次数: 0
Environmental Intelligent Monitoring System based on the Pollution of Toxic Substances in Chemical Production under the Background of Big Data 基于大数据背景下化工生产中有毒物质污染的环境智能监测系统
Xiyan Ji
{"title":"Environmental Intelligent Monitoring System based on the Pollution of Toxic Substances in Chemical Production under the Background of Big Data","authors":"Xiyan Ji","doi":"10.1109/ICSCDS53736.2022.9761018","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9761018","url":null,"abstract":"This paper studies the monitoring system of toxic substance pollution in the production of chemical plants based on big data technology. In order to realize the monitoring of harmful gases in the chemical production process, a data collector is formed with ATmega16 single-chip microcomputer as the core, and a harmful gas intelligent monitoring system is formed through Ethernet. The requirements for clear captured images, sensitive pan/tilt control, the main control room of the explosion-proof monitoring system is built in the chemical industry. The main control software of the digital hard disk video host realizes the monitoring and control of the cameras at each monitoring point. It can also transmit the company's various video signals through broadband or ADSL networks. Pass it on to the management of the company to achieve an active role in the safe production and operation of chemical companies and increase by 12.3%.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122869812","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}
引用次数: 0
Prediction of Air Pollutant PM10 using Various SVM Models 几种支持向量机模型对大气污染物PM10的预测
S. Sunori, P. Negi, Kapil Ghai, Amit Mittal, M. Lohani, M. Manu, P. Juneja
{"title":"Prediction of Air Pollutant PM10 using Various SVM Models","authors":"S. Sunori, P. Negi, Kapil Ghai, Amit Mittal, M. Lohani, M. Manu, P. Juneja","doi":"10.1109/ICSCDS53736.2022.9760965","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760965","url":null,"abstract":"The air pollution is majorly caused by 3 harmful pollutants viz. SO2, NO2 and PM (Particulate Matter). Inside particulate matter, the solid particles are present mixed up with liquid droplets. The PM is of two types viz. PM10 and PM2.5. The diameter of PM10 particles is 10 mm or less, and that of PM2.5 particles is 2.5 mm or less. These particles can cause very harmful consequences on mankind. The PM particles are formed as a result of chemical reaction taking place between SO2 and NO2. So, the knowledge of SO2 and NO2 concentrations can be a base for the prediction of PM. In this article, an effort has been put to predict PM10 component for given SO2 and NO2 pollutant concentrations using SVM (Support Vector Machine) techniques with different kernel functions in MATLAB (R2021a). The prediction performance of all prediction models is evaluated and compared.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124279080","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}
引用次数: 1
Comparison of Different Machine Learning Algorithms for Sentiment Analysis 情感分析中不同机器学习算法的比较
Gagandeep Kaur, Ajay Sharma
{"title":"Comparison of Different Machine Learning Algorithms for Sentiment Analysis","authors":"Gagandeep Kaur, Ajay Sharma","doi":"10.1109/ICSCDS53736.2022.9760846","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760846","url":null,"abstract":"There is an exponential growth in textual content generation every day in today's world. In-app messaging such as Telegram and WhatsApp, social media websites such as Instagram & Facebook, Google searches, news publishing websites, and a variety of additional sources are the possible suppliers. Every instant, all of these sources produce massive amounts of text data. Because of the large amounts of text data, Natural Language Processing (NLP) turns out to be an important tool for interpreting the content. NLP which is a popular subtask of sentiment analysis is the topic of this research. Sentiment Analysis (SA) is a type of textual data mining that discovers and extracts subjective information. It has proven to be an excellent asset for individuals to obtain important data and for organizations to analyze the social outlook of their product, brand or service by observing electronic discussions. The study carried out in this paper primarily focuses on the implementation and performance analysis of various machine learning classification models. The experiment results show that Support Vector Machine (SVM) classifier resulted in the maximum accuracy of 82% for the provided dataset.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126489296","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}
引用次数: 1
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