2022 IEEE International Conference on Data Science and Information System (ICDSIS)最新文献_第7页

Predicting the Stock Price Using Natural Language Processing and Random Forest Regressor 基于自然语言处理和随机森林回归的股票价格预测

2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915940

E. Naresh, Babu J Ananda, K. Keerthi, M. R. Tejonidhi

{"title":"Predicting the Stock Price Using Natural Language Processing and Random Forest Regressor","authors":"E. Naresh, Babu J Ananda, K. Keerthi, M. R. Tejonidhi","doi":"10.1109/ICDSIS55133.2022.9915940","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915940","url":null,"abstract":"Together with data mining, artificial intelligence and machine learning techniques have been used to rectify a multitude of real-world problems. Such methods have found to be completely successful, resulting in full accuracy with reduced monetary expenditure and preserving massive amounts of time, too. Sentiment analysis is frequently implemented to customer voice components like evaluations and review reactions, web and digital media, and health system components for applications ranging from marketing to customer support to clinical research. Social media is a framework commonly used by individuals to share their opinions and demonstrate sentiments on various occasions. Stock market index forecasting is a tedious task; this is purely since stock data series starts behaving as a similar to arbitrary-walk. The businesses have to hire investment specialists who would take excessively high profits in order to advise on investment choices. Such investment professionals offer an easy approach, which can be used by anyone with an internet connection and a computer. The main objective is to build a reliable, inexpensive and sustainable framework for forecasting the stock market value by implementing sentiment classification to twitter data. The real time twitter data is pre-processed to remove unwanted data and tokenization is applied. The sentiment analysis is applied followed by Random Forest classifier and the graph plots are obtained. X-axis in the resultant graph represents time series and Y-axis represents the closing price.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116074601","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

Preserving the Privacy of Medical Data using Homomorphic Encryption and Prediction of Heart Disease using K-Nearest Neighbor 基于同态加密的医疗数据隐私保护和基于k -近邻的心脏病预测

2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915983

Sagarika Behera, B. Rekha, Pragya Pandey, B. Vidya, Jhansi Rani Prathuri

引用次数: 1

ECG Beat Classification using CNN 使用CNN进行心电拍分类

2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9916004

H. A. Deepak, T. Vijaykumar

引用次数: 1

HDVis: An Interactive Heart Disease Analysis Through Visualization System HDVis:一种基于可视化系统的交互式心脏病分析

2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915972

Md. Maidul Islam, Tanzina Nasrin Tania, Sharmin Akter

{"title":"HDVis: An Interactive Heart Disease Analysis Through Visualization System","authors":"Md. Maidul Islam, Tanzina Nasrin Tania, Sharmin Akter","doi":"10.1109/ICDSIS55133.2022.9915972","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915972","url":null,"abstract":"Heart disease is a disorder in which blood arteries get clogged and the heart ceases to beat. According to several research, this disorder has overtaken cancer as such top cause of death. The fact that anomalies can only be noticed and acknowledged at the end of the process is alarming. It is, however, treatable if the sickness is detected early. The purpose of this study is to develop an interactive visual way (Vis) to analyze heart disease (HD) and its components. First, the data on heart illness was analyzed using a case study method. Second, we look at how user-centered technology may help us address these issues, and we create a Vis called “HDVis” to analyze and display raw data as visualizations like graphs, and a variety more interaction options. This aids in the extraction of important data as well as the portrayal of that data clearly and understandably. Furthermore, we emphasize several crucial findings of this research that might benefit the inquiry of the healthcare community. To test our solution, we conducted 20-person user research. By allowing iterative study and adjustment of the material on such a platform with multiple points of view, the HDVis technology enhances the workflow of visual interpretation. The findings indicate that perhaps the medical community should concentrate too much on establishing proper legislative measures to reduce the incidence of HD.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130352925","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

LUPOnto-An Ontology fpr Smart Land Use and Planning in Mauritian Local Councils 毛里求斯地方议会智能土地利用和规划的本体

2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915964

B. Gobin-Rahimbux

引用次数: 0

Wi-fi Controlled Smart Home Automation Wi-fi控制智能家居自动化

2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915976

N. K, Jyothsna K Gowda, H. S., Hemashree S T, Joel P John

{"title":"Wi-fi Controlled Smart Home Automation","authors":"N. K, Jyothsna K Gowda, H. S., Hemashree S T, Joel P John","doi":"10.1109/ICDSIS55133.2022.9915976","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915976","url":null,"abstract":"All the devices inside the house are connected, by the virtue of developing automation trade and wireless property. Today’s World is moving to digitalization wherever everything is formed simple and comfy for humans i.e., young youth also as adult. Some of the home appliances which are smart utilize IOT (Internet of Things) and can control any home appliances using the app like Turning ON and turning OFF of electric bulbs, Fan, AC, Water pump, Water for agriculture. Anyone can control the home appliances with the help of Internet Connection, Node MCU ESP8266, Android Application. Here in this paper, it includes working of node esp8266 which are used for controlling the house application like fan, light bulb, sensors, motion detection, and water motor with the help of Blynk app and coding. In this era majority of the devices are controlled with mobile application and with Wi-Fi connectivity these devices are controlled as per user requirement. This paper is making clear that devices are monitored with Node MCU and control these devices using Blynk App. In line with demand of want one will connect multiple devices like sensors, appliance, and plenty of additional until-8.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129736518","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: 2

Recurrent Neural Network based Prediction of Transition to Mild Cognitive Impairment Using Unobtrusive Sensor Data 基于递归神经网络的轻度认知障碍过渡预测

2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915966

Rajaram Narasimhan, G. Muthukumaran, Kingshuk Dey, Anantha Ramakrishnan

{"title":"Recurrent Neural Network based Prediction of Transition to Mild Cognitive Impairment Using Unobtrusive Sensor Data","authors":"Rajaram Narasimhan, G. Muthukumaran, Kingshuk Dey, Anantha Ramakrishnan","doi":"10.1109/ICDSIS55133.2022.9915966","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915966","url":null,"abstract":"Alzheimer’s disease (AD) is the most common neurodegenerative disease among older adults. Mild Cognitive Impairment (MCI) is a transitional stage where older adults begin to exhibit symptoms that could be precursors to AD progression. Detecting the MCI stage early can help prevent or delay the onset of an advanced stage in AD and thus enhance the quality of life among older adults. This neurodegeneration process resonates with the inherent temporal nature of this disease progression. Patterns/trends in daily activities/routine measured over time are better indicators of the disease progression and help detect the transitional stage, MCI. This paper aims to leverage the activity patterns derived through unobtrusive sensors at historical time points and investigate the effect of these activity trends in predicting the progression at a future time point. This study proposes a prediction model leveraging Long short-term memory recurrent neural networks (RNN). From the daily activity/routine standpoint, walk and sleep-related measures are used as input features to the model along with the diagnostic label derived from neuropsychological assessment data, and the transition to MCI is predicted at a future time point. The initial experiment and results show that the study approach proposed in this paper can predict the progression yielding an 82 percent overall prediction accuracy and 90 percent accuracy in predicting degenerating cases. These results encourage future experiments with other extended activity features and further fine-tuned RNN model.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128979929","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

Smart Translation for Physically Challenged People Using Machine Learning 使用机器学习为残疾人提供智能翻译

2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915882

Ramakrishna Hegde, R. M. Chitrashree, N. Dimple, Harshitha G Shet, J. Deekshitha, S. M. Soumyasri

{"title":"Smart Translation for Physically Challenged People Using Machine Learning","authors":"Ramakrishna Hegde, R. M. Chitrashree, N. Dimple, Harshitha G Shet, J. Deekshitha, S. M. Soumyasri","doi":"10.1109/ICDSIS55133.2022.9915882","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915882","url":null,"abstract":"In sign language, hand motions are one of the nonverbal communication modalities employed. It is most typically used by deaf and hard of hearing persons who have hearing or speech impairments. Difficulties communicating with one another or with regular people folks. Several sign language systems have been created by There are numerous producers all throughout the world, but they are neither versatile nor adaptable, end-user-friendly in terms of price. Our project's goal is to create a feasible system for communication for deaf people. This project is divided into two sections. (1) It translates an audio message into sign language, and (2) it translates images/video into text/speech. The first category we'll take input as audio, turns the audio recorded message into text message, and later displays predetermined Indian Sign Language(ISL) visuals or GIFs. The use of this technique facilitates communication between hearing and deaf persons. In the second category, we will gather photographs and train images with CNN and present the results.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129089996","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: 2

Performance Analysis of Vegetation Area Classifications in Satellite Images Using Machine and Deep Learning Approaches 基于机器和深度学习方法的卫星图像植被面积分类性能分析

2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915859

S. Vijayalakshmi, S. M. Kumar

{"title":"Performance Analysis of Vegetation Area Classifications in Satellite Images Using Machine and Deep Learning Approaches","authors":"S. Vijayalakshmi, S. M. Kumar","doi":"10.1109/ICDSIS55133.2022.9915859","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915859","url":null,"abstract":"At present, the vegetation area around the world is shrinking due to the development of construction area in both urban and rural areas. It is very important to expand the present vegetation area to meet the food requirements of all people in world. In order to cope with this aspect, the present vegetation areas should be detected. In this paper, the vegetation areas in remote satellite images are detected and segmented using machine learning and deep learning algorithms. The machine learning algorithm Support Vector Machine (SVM) consists of preprocessing, feature extraction and classification modules where the deep learning algorithm consists of data augmentation and Convolutional Neural Networks (CNN) classification module. In this paper, the conventional CNN architecture is modified in this paper as the novelty in order to improve the classification accuracy of the proposed satellite image system. The segmented vegetation area is compared with manually segmented images in order to evaluate the performance of the proposed system. The developed CNN architecture produces features itself in each Convolutional layers. The CNN based vegetation area segmentation method achieves 96.03% of SEN, 98.12% of SPE and 98.07% of ACC and SVM based vegetation area segmentation method achieves 94.12% of SEN, 96.67% of SPE and 97.01% of ACC.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127834863","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

Text-Based Cyberbullying Prevention using Toxicity Filtering Mobile Chat Application and API 基于文本的网络欺凌预防毒性过滤移动聊天应用程序和API

2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915958

Varun Sreedhar, Sanjana Kumar, Srikrishna Veturi, A. Khade

{"title":"Text-Based Cyberbullying Prevention using Toxicity Filtering Mobile Chat Application and API","authors":"Varun Sreedhar, Sanjana Kumar, Srikrishna Veturi, A. Khade","doi":"10.1109/ICDSIS55133.2022.9915958","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915958","url":null,"abstract":"The internet becoming a ubiquitous thing for most people has both positive and negative consequences. One negative consequence is that everyone’s profile or contact info on any social media is made available to anyone on the internet. With the ever-growing userbase of social media platforms, the risk of being cyberbullied is very high. Since most communication on any social media platform is done through chats, an attempt has been made to curb the cyberbullying on these social media platforms in textual form. This will be done by providing an API (Application Programming Interface) that can receive an input text and respond with an annotation if the text is predicted to be offensive or not and a framework for supporting the same algorithm and running the artificially intelligent model that can understand natural language on mobile devices locally to offer a complex service to the end-users directly without having to depend on the internet and compromising on privacy. Finally, making this available in form of a mobile application would give a lot of user’s access to an extremely useful and helpful system.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132465477","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