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Heart Disease Prediction Using Different Machine Learning Algorithms 使用不同的机器学习算法预测心脏病
2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848885
Arkadeep Bhowmick, Kapil Dev Mahato, Chandrashekhar Azad, U. Kumar
{"title":"Heart Disease Prediction Using Different Machine Learning Algorithms","authors":"Arkadeep Bhowmick, Kapil Dev Mahato, Chandrashekhar Azad, U. Kumar","doi":"10.1109/AIC55036.2022.9848885","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848885","url":null,"abstract":"Heart disease (HD) cases are increasing rapidly every day, so it is very crucial to detect them beforehand. In recent times, machine learning algorithms (MLA) are trending for heart or cardiovascular disease prediction in the healthcare field. Data mining techniques such as reinforcement, unsupervised, and supervised play a crucial role in examining the enormous amount of data in the medical field industry. The available dataset of HD individuals from the Cleveland database of the UCI repository is employed to test and verify the performance of MLA. This article makes an early prediction of HD by executing different MLA, for example, decision tree (DT), random forest (RF), and logistic regression (LR). After the comparative study of three algorithms, we found that the DT is the most efficient algorithm with the highest accuracy of 94.7 percent. This value is higher than the recently reported value of 83.87 percent.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124309445","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
A Survey on Computational Approaches for Raga Identification 拉格识别的计算方法综述
2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848984
Surekha Patil, R. Bhavsar, B. Pawar
{"title":"A Survey on Computational Approaches for Raga Identification","authors":"Surekha Patil, R. Bhavsar, B. Pawar","doi":"10.1109/AIC55036.2022.9848984","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848984","url":null,"abstract":"Music is the most fascinating and popular art forms in the society. In Indian context, ragas are the main pillars of different music types and genres. They have a strong academic foundation. Light music is most widely heard and appreciated form of music worldwide. Every light music composition has its roots somewhere in the core classical music and ragas. Hence the raga identification of an Indian classical raga is an academically attractive research problem in computational musicology which has attracted attention of various research scholars. A computational approach for automatic raga identification can enable raga-based Music Information Retrieval and popularization of Indian Classical Music. This paper presents a survey of computational approaches attempted till date. The approaches used are also discussed in detail.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122580200","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
Performance Analysis of Soft Computing ANN Based MPPT Technique for Solar PV Generator 基于软计算神经网络的太阳能光伏发电MPPT技术性能分析
2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848876
Sunita Chahar, D. K. Yadav
{"title":"Performance Analysis of Soft Computing ANN Based MPPT Technique for Solar PV Generator","authors":"Sunita Chahar, D. K. Yadav","doi":"10.1109/AIC55036.2022.9848876","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848876","url":null,"abstract":"This paper proposes the solution to finalize a way to use the soft computing artificial neural network (ANN) based MPPT (maximum power point tracking) scheme for a solar photovoltaic (PV) generator to work efficiently and produce maximum power. A better state-of-the-art technique based on soft computing ANN to take out maximum power for self-identified and given dynamic irradiation conditions is used. The comparison of the performance of the proposed methodology for given data pattern and generated data pattern is presented in terms of computational epoch point of view and mean square error. The reported simulation results in the case of self-generated data patterns found superior performance for the proposed technique for Solar PV generator configuration.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122636048","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: 4
Revivify: A Depression Detection and Control System using Tweets and Automated Chatbot revivive:一个使用tweet和自动聊天机器人的抑郁症检测和控制系统
2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848978
Riddhi Hakani, Samiksha Patil, Sakshi Patil, Siddhi Jhunjhunwala, Khushali Deulkar
{"title":"Revivify: A Depression Detection and Control System using Tweets and Automated Chatbot","authors":"Riddhi Hakani, Samiksha Patil, Sakshi Patil, Siddhi Jhunjhunwala, Khushali Deulkar","doi":"10.1109/AIC55036.2022.9848978","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848978","url":null,"abstract":"Mental Health is a stigma in India and on a global scale. Ignorance of mental health on our part has created a world where those suffering from it cannot talk about it openly and often feel uncomfortable disclosing it to others or even professional therapists. To address this problem, we propose a digital system that detects signs of anxiety and also suggests methods for depression control. Revivify performs a comprehensive analysis of a user’s mental state using different techniques. We have used tweets, patient health questionnaires, depression anxiety stress scale (DASS), and personalized responses as our dataset. Our system uses Feed Forward Neural Networks, Latent Dirichlet Allocation, and Random Forest Classifier algorithm to classify the user responses and tweets into one of the nine levels of anxiety and depression. Random Forest Classifier gives the highest accuracy. Further, the chatbot also suggests various blogs and provides helpline numbers for damage control. This system is a cost-effective solution to detect depression.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129944884","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
Performance Analysis of Weighted Low-Rank Approximation Models for Robust Face Recognition 加权低秩近似模型鲁棒人脸识别性能分析
2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848957
K. V. Sridhar, Praneeth Madugula
{"title":"Performance Analysis of Weighted Low-Rank Approximation Models for Robust Face Recognition","authors":"K. V. Sridhar, Praneeth Madugula","doi":"10.1109/AIC55036.2022.9848957","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848957","url":null,"abstract":"Face recognition has become a research hotspot in the fields of computer vision, pattern recognition, and machine learning. The accuracy of recognizing faces with varying expressions and illumination as well as occlusions and noise, on the other hand, presents a unique challenge in face recognition systems. Facial images must be preprocessed for improved recognition accuracy. A major issue with the existing approaches is that they have limited capacity that cannot handle large-scale occlusion and noise situations adequately. In this paper, we present Low -rank matrix approximation (LRMA) models like Robust principal component analysis (RPCA), Weighted Nuclear Norm Minimization (WNNM), and Weighted Schatten p-norm (WSNM) for Robust face recognition. A confusion matrix is used for calculating the accuracy of face recognition. Experiments are conducted and the performance of these LRMA models is compared using the Yale database with facial occlusions, poor illumination, expressions, and noise. The results show, both intuitively and numerically, that WSNM outperforms RPCA in removing facial occlusions, resulting in restored low-rank images with greater PSNR and SSIM.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130614138","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
A Review on Virtual Reality for 3D Virtual Trial Room 三维虚拟审判室的虚拟现实技术综述
2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848914
Debangana Ram, Bholanath Roy, Vaibhav Soni
{"title":"A Review on Virtual Reality for 3D Virtual Trial Room","authors":"Debangana Ram, Bholanath Roy, Vaibhav Soni","doi":"10.1109/AIC55036.2022.9848914","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848914","url":null,"abstract":"With the improvement of technology, internet shopping has expanded tremendously across the world. Customers may now buy products from anywhere and at any time due to the online shopping revolution. Virtual Trial Room is a 2D implementation of an e-commerce shopping experience in which the customer may try on clothing in front of the camera before purchasing it. It creates a virtual system in which ecommerce consumers may try on clothing items before making purchases without having to put them on in person. During the pandemic, online purchasing has just seen a significant increase. When all offline purchasing came to a standstill. The virtual trial room is the most recent innovation that has enhanced the customer's hopping experience to a larger extent. Customers imagine themselves when trying on several outfits. It provides a clearer picture of the clothing the customer wants to purchase, resulting in higher customer satisfaction.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132760916","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
An Improved Technique to Identify Fake News on Social Media Network using Supervised Machine Learning Concepts 使用监督机器学习概念识别社交媒体网络上假新闻的改进技术
2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848967
K. Sreedhar, Syed Thouheed Ahmed, Greeshma Sreejesh
{"title":"An Improved Technique to Identify Fake News on Social Media Network using Supervised Machine Learning Concepts","authors":"K. Sreedhar, Syed Thouheed Ahmed, Greeshma Sreejesh","doi":"10.1109/AIC55036.2022.9848967","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848967","url":null,"abstract":"This paper presents an improved social media news separation system called unstructured Fake News Detection (UFND) system and it aims to identify the unstructured social media news data that belongs into fake or real class based on probability and improved naive bayes techniques. The proposed UFND consists of four phases likely pre-processing, training, matching and validation respectively. The technique identifies matching phrases over each individual data element based on predetermined key words model and ignore the irrelevant words in the respective document. In the later phase, the proposed system has train the pre-processed data set through the process of separating the data set into two classes namely fake and real based on probability technique. Further the system has identified the given test news document belongs to existing class label over the training data set based on improved Naive Bayes technique. The UFND system evaluates the performance over the result of previous stage. The UFNDS experimental results have demonstrated an outperforming results in segregating and identifying the fake news pattern over the unstructured social media news data set with good accuracy based on supervised probability methods.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"257 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132536397","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
Social Image Content Analysis for Fashion using Deep Learning 基于深度学习的时尚社交图像内容分析
2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848904
Seema V. Wazarkar, K. Kotecha, S. Patil, Nidhi Kalra
{"title":"Social Image Content Analysis for Fashion using Deep Learning","authors":"Seema V. Wazarkar, K. Kotecha, S. Patil, Nidhi Kalra","doi":"10.1109/AIC55036.2022.9848904","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848904","url":null,"abstract":"Social image content analysis is one of the important tasks for fashion analysis. Use of proposed system in fashion industries will uplift their business as social visual perception is very useful for the decision making in fashion industries. It supports growth in business and helps in minimizing loss or provides prior knowledge of risks. Analysis of social content data is a challenging task due to the nature of social data. Social content data is unstructured and full of ambiguity. But, this source of data is very important because it keeps updating continuously so that current data is being available for analysis. There is a necessity of current data for applications related to fashion as fashion trends keep changing. Therefore, in this paper convolutional neural networks is applied along with our machine learning approaches to find optimal fashion analyzing approach where social media is utilized to predict fashion style. Deep learning approach with Softsign and Softplus function performed well.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130030042","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
A Study on Human-Computer Interaction based on Surveillance Tasks 基于监控任务的人机交互研究
2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848839
Ruchi Jayaswal, M. Dixit
{"title":"A Study on Human-Computer Interaction based on Surveillance Tasks","authors":"Ruchi Jayaswal, M. Dixit","doi":"10.1109/AIC55036.2022.9848839","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848839","url":null,"abstract":"The article presents a brief overview of the field of Human Computer Interaction (HCI). It discusses the multidisciplinary field of HCI and attempts to explain that the computer vision applications such as HCI, virtual reality, security, video surveillance, people monitoring is highly correlated to intelligent human video surveillance tasks. The roots and origin of HCI will be explained and how it evolved with the generations. Subsequently, the fidelity prototyping of HCI will be discussed, then later examines the human computer interaction related with intelligent video surveillance system through vision and movement mode generates the output according to the application requirement. The study aims to present an analysis of the surveillance application framework which is solved by the amalgamation of computer vision and HCI.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130093802","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
Review on Deployment, Coverage and Connectivity in Wireless Sensor Networks 无线传感器网络的部署、覆盖和连通性研究进展
2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848920
Jahanvi Sharma, Arshnoor Bhutani
{"title":"Review on Deployment, Coverage and Connectivity in Wireless Sensor Networks","authors":"Jahanvi Sharma, Arshnoor Bhutani","doi":"10.1109/AIC55036.2022.9848920","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848920","url":null,"abstract":"Recently wireless sensor networks (WSNs) have received extensive attention from the researchers’ community due to their vital role in diverse applications. Both the sensing coverage and networks connectivity are the prime issues that have a significant impact on WSNs performance. Therefore, the deployment of minimal nodes while ensuring both coverage and connectivity is of immense importance. This paper classifies different coverage techniques i.e., grid-based, force-based, and computational geometry-based with a detailed analysis of their merits and demerits. Moreover, different sensing models are discussed for a better perception of concepts. Finally, we signify the major findings and open research problems in this area.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114208912","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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