2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)最新文献_第3页

An Analytical Study on Consumer Perception for a Product against its Social Media Imprint 基于社交媒体烙印的产品消费者感知分析研究

2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) Pub Date : 2021-12-16 DOI: 10.1109/CSITSS54238.2021.9682783

Ivy Baroi, Suman De

{"title":"An Analytical Study on Consumer Perception for a Product against its Social Media Imprint","authors":"Ivy Baroi, Suman De","doi":"10.1109/CSITSS54238.2021.9682783","DOIUrl":"https://doi.org/10.1109/CSITSS54238.2021.9682783","url":null,"abstract":"Data is the key to create insights, and with the expansion of social media in the last decade, the quantity of data generated about human behavior has increased multifold. Billions of opinions are floated around the social media platforms of Twitter, Facebook, Instagram, among others covering topics ranging from politics, sports, entertainment, products, and so on. Every post has a sentiment that can be measured and processed to form insights about various products. Consumer Behavior and Insights is benefited mainly from such practices and leverages the use of public Application Programming Interfaces (APIs) and Analytics tools to cleanse and crunch unstructured data to extract meaning out of it. This paper is a study of generic perception formulated towards a brand and how it is reflected through social media. We also look at Kaggle, which also serves as a platform to correlate with data uploaded by other Data Science enthusiasts. It covers the usage of Twitter APIs, Analytics through R Language and presents a business scenario of how marketers can benefit from the use of these technology solutions.","PeriodicalId":252628,"journal":{"name":"2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122276772","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

Implementation of Single Camera Markerless Facial Motion Capture using Blendshapes 使用Blendshapes实现单相机无标记面部动作捕捉

2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) Pub Date : 2021-12-16 DOI: 10.1109/CSITSS54238.2021.9683460

Meghana Rao Somepalli, M. Charan, S. Shruthi, Suja Palaniswamy

{"title":"Implementation of Single Camera Markerless Facial Motion Capture using Blendshapes","authors":"Meghana Rao Somepalli, M. Charan, S. Shruthi, Suja Palaniswamy","doi":"10.1109/CSITSS54238.2021.9683460","DOIUrl":"https://doi.org/10.1109/CSITSS54238.2021.9683460","url":null,"abstract":"Facial motion capture is the process of digitizing the facial motion of an actor by locating several facial landmarks of the actor’s face and using the relative coordinates of these landmarks to drive the facial structure of a 3D character in software like Blender. Recent advances have enabled markerless technology to track the desired facial features from frame to frame. In this work, the input from a single front-facing camera is used and the face is located using face detection algorithms. Its output is then used to find the relative coordinates of facial landmarks like lip corners, upper eyelids, and eyebrows etc., using a facial landmark detector. To achieve comparable levels of accuracy without the depth or 3D information that would be captured from a multi-camera setup, morph targets have been used to add constraints to the animation to avoid unnatural positions of the virtual character. The distance between a referential landmark that has minimal movement and the driving landmark determines the influence of the corresponding morph target. To establish an orientation invariant landmark detection, geometric normalization and face size normalization have been deployed.","PeriodicalId":252628,"journal":{"name":"2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)","volume":"255 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116326628","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 Community Based Study for Early Detection of Postpartum Depression using Improved Data Mining Techniques 基于社区的产后抑郁症早期检测改进数据挖掘技术研究

2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) Pub Date : 2021-12-16 DOI: 10.1109/CSITSS54238.2021.9682941

Priyanka Mazumder, S. Baruah

{"title":"A Community Based Study for Early Detection of Postpartum Depression using Improved Data Mining Techniques","authors":"Priyanka Mazumder, S. Baruah","doi":"10.1109/CSITSS54238.2021.9682941","DOIUrl":"https://doi.org/10.1109/CSITSS54238.2021.9682941","url":null,"abstract":"Pregnancy for women is one of the most beautiful feelings that exist in world. But this pregnancy leads women to various hormonal, physical and mental changes which affect their life, family, child and many more. Immediate after delivery the women had to overcome the rapid slowdown of hormones and initial Postpartum Blues. Today it has been observed that Postpartum Blue when exist for more month and year are predict to be suffering from Postpartum Depression or Postpartum Psychosis. The study tried to generate the most possible condition on which the women will suffer from Postpartum Depression by taking Survey of 96 participants. The study tried to develop a predictive model which can help to predict the Postpartum Depression among women. The Predictive model development is done using Data Mining Algorithms-J48, Random Tree, Random Forest and Reduce Error Pruning (REP) Tree. These four algorithms are further collaborated with Adaptive Boosting and Bagging. The development of class model in dataset is done by Edinburgh Postpartum Depression Scale which help to justify the exact observation of suffering from Postpartum Depression. The development of model is done using WEKA application tool.","PeriodicalId":252628,"journal":{"name":"2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132987735","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

Solving the Lunar Lander Problem using Reinforcement Learning 用强化学习解决月球着陆器问题

2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) Pub Date : 2021-12-16 DOI: 10.1109/CSITSS54238.2021.9682970

Rohit Sachin Sadavarte, Rishab Raj, B. Sathish Babu

引用次数: 1

Detection of Plant Leaf-based Diseases Using Machine Learning Approach 基于机器学习方法的植物叶片病害检测

2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) Pub Date : 2021-12-16 DOI: 10.1109/CSITSS54238.2021.9683020

P. Chaitanya Reddy, Rachakulla Mahesh Sarat Chandra, P. Vadiraj, M. Ayyappa Reddy, T. Mahesh, G. Sindhu Madhuri

{"title":"Detection of Plant Leaf-based Diseases Using Machine Learning Approach","authors":"P. Chaitanya Reddy, Rachakulla Mahesh Sarat Chandra, P. Vadiraj, M. Ayyappa Reddy, T. Mahesh, G. Sindhu Madhuri","doi":"10.1109/CSITSS54238.2021.9683020","DOIUrl":"https://doi.org/10.1109/CSITSS54238.2021.9683020","url":null,"abstract":"Agriculture productivity is increasing day-by-day based on recent advances and research growth in technology. Detection of plant leaf-based diseases and for improving the quality of plant leaf-based is very essential in agriculture. Detecting various plant leaf-based diseases with human sight, many laboratory-based approaches like polymerase chain reaction, decrease in food production, pest management, hyper spectral techniques are identified for detection of diseases but they are very high time consuming and high cost to the farmers. Identification of recent advanced techniques and various systematic models using Machine Learning (ML) approaches may increase the agriculture productivity. Researchers worked on modern approaches in ML algorithms for detection of leaf diseases for increasing the accuracy results. Every approach has its importance and is focused towards the direction of ML applications and is also based on issues faced by the farmers. In this research paper, detection of leaf-based diseases is analyzed using Support Vector Machine (SVM), Random Forest algorithms. The performance metrics like Root Mean Square Error (RMSE), Peak Signal Noise Ratio (PSNR), Disease affected area of the leaf by using Euclidian Distance method and Accuracy results are compared to benefit the farmers with less time, low cost and increase our agriculture productivity.","PeriodicalId":252628,"journal":{"name":"2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133714474","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: 13

Big Data Analytics Tools: A Comparative Study 大数据分析工具:比较研究

2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) Pub Date : 2021-12-16 DOI: 10.1109/CSITSS54238.2021.9683711

N. Pavithra, C. Manasa

引用次数: 2

Detection of autistic individuals using facial images and deep learning 使用面部图像和深度学习来检测自闭症患者

2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) Pub Date : 2021-12-16 DOI: 10.1109/CSITSS54238.2021.9683205

Yu Khosla, Prerana Ramachandra, N. Chaitra

{"title":"Detection of autistic individuals using facial images and deep learning","authors":"Yu Khosla, Prerana Ramachandra, N. Chaitra","doi":"10.1109/CSITSS54238.2021.9683205","DOIUrl":"https://doi.org/10.1109/CSITSS54238.2021.9683205","url":null,"abstract":"Autism spectrum disorder(ASD) is a medical condition that causes major impairments to the neurology of the autistic individual. An autistic child has difficulty responding to their name, avoids maintaining eye contact, and lacks the ability to show emotions. Humans are social animals and the limitations brought about by ASD mars an individual’s overall development. ASD is normally diagnosed using brain images in childhood. However, this proves to be very expensive and takes a large amount of time. Recent studies have shown that ASD can be detected by making use of facial images. In this paper, deep learning models are pre-trained to classify facial images of children as either healthy or potentially autistic. Features such as eyes, nose, and lip distance in a child’s image and its arrangement can be an indicator of autism. Unlike the previous methods used to detect autism, the proposed method performs extensive pre-processing by removing the duplicate images, thereby, making it suitable for real-world applications. On training, the MobileNet model on facial images gave a maximum of 87% testing accuracy.","PeriodicalId":252628,"journal":{"name":"2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127042832","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: 7

Multi thoracic disease classifier using Convolutional Neural Networks 基于卷积神经网络的多胸椎疾病分类器

2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) Pub Date : 2021-12-16 DOI: 10.1109/CSITSS54238.2021.9682868

Chetan, B. Veerappa

{"title":"Multi thoracic disease classifier using Convolutional Neural Networks","authors":"Chetan, B. Veerappa","doi":"10.1109/CSITSS54238.2021.9682868","DOIUrl":"https://doi.org/10.1109/CSITSS54238.2021.9682868","url":null,"abstract":"India faces acute shortage of radiologists. As per NCBI (National Center for Biotechnology Information), USA, India has one radiologist per 1,00,000 people. In past two years we have seen an unprecedented COVID-19 pandemic which has posed a huge burden on our health care infrastructure and health care professionals. The rural parts are hit worst struggling to provide lifesaving health care access causing millions of Indians to lose their lives. In this regard our paper focuses on developing an Artificial Intelligence (AI) based web application which may reduce the burden on healthcare professionals and help in timely diagnosis of chest x-ray findings without delays and also with precision. This will help to treat patients with utmost care, can avoid unnecessary surgeries and save lives. In the recent years AI empowered systems have proven to be dominant in all domains. AI which encompasses all the industries has been proven to be vital in healthcare by helping healthcare professionals in taking decisions and also in diagnosis and detection of several critical ailments like cancer and others. In this paper we have leveraged the transfer learning as benchmark to obtain the models for our task of chest image classification. We have run the experiment through the various standard models available retaining the identical experimental conditions and did the comparative analysis to evaluate them and to pick the best one among them. The results achieved show that Densenet-169 provided the best results with 95.56 percentage validation accuracy during model training which has been used for making predictions in the web application.","PeriodicalId":252628,"journal":{"name":"2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126465538","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

Multipurpose Vertical Plotter Machine-MVPM 多用途垂直绘图仪- mvpm

2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) Pub Date : 2021-12-16 DOI: 10.1109/CSITSS54238.2021.9683516

Sunilkumar Hattaraki, U. Dixit, S. Padaganur, S. R. Purohit, Abhilash C Patil, Sujay Kulkarni

引用次数: 0

An Analysis of Ambiguity Detection Techniques for Software Requirement Specification 软件需求说明书的模糊检测技术分析

2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) Pub Date : 2021-12-16 DOI: 10.1109/CSITSS54238.2021.9683497

Sahana Raikar, Nagaraj G Cholli

引用次数: 1