2021 Grace Hopper Celebration India (GHCI)最新文献

Performance Analysis of Machine Learning Algorithms for Disease Prediction 疾病预测机器学习算法的性能分析

2021 Grace Hopper Celebration India (GHCI) Pub Date : 2021-02-19 DOI: 10.1109/GHCI50508.2021.9514000

B. Priya, C. Chaitra, K. Reddy

{"title":"Performance Analysis of Machine Learning Algorithms for Disease Prediction","authors":"B. Priya, C. Chaitra, K. Reddy","doi":"10.1109/GHCI50508.2021.9514000","DOIUrl":"https://doi.org/10.1109/GHCI50508.2021.9514000","url":null,"abstract":"With the recent technological advances in microelectronics, wireless communication, machine learning (ML), and decision-making process, Wireless Body Area Network (WBAN) has become the most promising technology. As we all know that we are in global pandemic due to Covid-19 situation now, hence, there is a demand occurring in health care services and continuous monitoring. Moreover, prediction of abnormalities at an early stage will be crucial for a person in diagnosis. Hence, in this paper we have developed and compared the performance of three machine learning algorithms such as Decision Tree Classifier (DTC), K-Nearest Neighbor (KNN), and Random Forest (RF). Each algorithm is tested with datasets of 100, 200, 500 & 1000 users respectively. Further, threshold values have been identified by consulting with doctors for accurate disease prediction based on the vital signals collected by various sensors. The three algorithms used are based on supervised learning, where the output is predicted based on the training of the developed classifier. From the results, it is observed that the accuracy in disease prediction using RF is 0.99 & outperformed when compared with state of the art for datasets of 1000 users.","PeriodicalId":378325,"journal":{"name":"2021 Grace Hopper Celebration India (GHCI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126898554","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

Handwritten Documents Text Recognition with Novel Pre-processing and Deep Learning 基于新型预处理和深度学习的手写文档文本识别

2021 Grace Hopper Celebration India (GHCI) Pub Date : 2021-02-19 DOI: 10.1109/GHCI50508.2021.9514054

Nidhi, D. Ghosh, Dharmendra Chaurasia, Saurav Mondal, Asmita Mahajan

{"title":"Handwritten Documents Text Recognition with Novel Pre-processing and Deep Learning","authors":"Nidhi, D. Ghosh, Dharmendra Chaurasia, Saurav Mondal, Asmita Mahajan","doi":"10.1109/GHCI50508.2021.9514054","DOIUrl":"https://doi.org/10.1109/GHCI50508.2021.9514054","url":null,"abstract":"Data being the most valuable resource on earth today, there is a pressing need to transform all data digitally. The world has been historically and still in multiple sectors operating based on handwritten text. For example, Handwritten taxation data and calculations, claim forms, doctor’s prescription, legal documents, resumes, financial documents, accounts sheets, and many more. Handwritten data can be very easily misinterpreted even by human personnel. Hence, the most critical challenge that remains is the transformation of handwritten documents. Researches from different authors present an application wherein they take in word crop images generated from a document manually and extract the text from it. In our thesis, we present an end to end solution wherein a text image is uploaded, and handwritten text from the entire image is extracted as-is. The three main contributions of our thesis are 1. Improved text localizer trained on the various handwritten and printed dataset, 2. Novel classification model to segregate printed and handwritten words, 3. Effective image preprocessing techniques applied to handwritten word crops to make them eligible to be fed to the deep learning model for improved overall accuracy.","PeriodicalId":378325,"journal":{"name":"2021 Grace Hopper Celebration India (GHCI)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124414385","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

Stress Analysis for Students in Online Classes 网络课堂学生压力分析

2021 Grace Hopper Celebration India (GHCI) Pub Date : 2021-02-19 DOI: 10.1109/GHCI50508.2021.9514059

Chhavi Sharma, Pranjal Saxena

引用次数: 2

Template-based NLG for tabular data using BERT 使用BERT的基于模板的表格数据NLG

2021 Grace Hopper Celebration India (GHCI) Pub Date : 2021-02-19 DOI: 10.1109/GHCI50508.2021.9514032

Srushti Gajbhiye, M. Lopes

引用次数: 1

Automated Test Scheduling using Adaptive Hybrid Prediction Technique 使用自适应混合预测技术的自动化测试调度

2021 Grace Hopper Celebration India (GHCI) Pub Date : 2021-02-19 DOI: 10.1109/GHCI50508.2021.9514046

Sarmishta Sarangarajan, B. Sai Shruthi

{"title":"Automated Test Scheduling using Adaptive Hybrid Prediction Technique","authors":"Sarmishta Sarangarajan, B. Sai Shruthi","doi":"10.1109/GHCI50508.2021.9514046","DOIUrl":"https://doi.org/10.1109/GHCI50508.2021.9514046","url":null,"abstract":"In a typical data center, there is always an ongoing need to isolate faulty components. Diagnostic and Regression tests are generally tending towards automation. However, the diagnostic tools also put the underlying hardware to various levels of stress. It is challenging to select appropriate test tools and schedule them in such a way that they can help uncover maximum defects and ensure minimal disturbance to the live customer setup. In this paper, we propose a technique to automatically schedule tests on a target system with minimal disturbance to the workload using a real-time adaptive hybrid predictive model. The models we use are trained to predict resource utilization in a fast and accurate manner. This solution enhances the critical decision-making ability of an admin by scheduling the regression or diagnostic tests accurately. Schedules are recommended based on actual resource utilization and are spaced out at intervals when low resource utilization is predicted. This ensures minimal downtime for maintenance and helps meet customer SLA. We also propose a technique to automate the selection process of the best fit time-series model based on analysis of data, which in due course would reduce the prediction overhead by half. This solution can work alongside any existing management framework or can be designed as a standalone tool.","PeriodicalId":378325,"journal":{"name":"2021 Grace Hopper Celebration India (GHCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130008170","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

[Copyright notice] (版权)

2021 Grace Hopper Celebration India (GHCI) Pub Date : 2021-02-19 DOI: 10.1109/ghci50508.2021.9514016

引用次数: 0

Music genre classification using multi-modal deep learning based fusion 基于多模态深度学习融合的音乐类型分类

2021 Grace Hopper Celebration India (GHCI) Pub Date : 2021-02-19 DOI: 10.1109/GHCI50508.2021.9514020

Laisha Wadhwa, Prerana Mukherjee

{"title":"Music genre classification using multi-modal deep learning based fusion","authors":"Laisha Wadhwa, Prerana Mukherjee","doi":"10.1109/GHCI50508.2021.9514020","DOIUrl":"https://doi.org/10.1109/GHCI50508.2021.9514020","url":null,"abstract":"Music genre classification is extensively used in almost all music streaming applications and websites. Most of them use it either to recommend playlists to their customers (such as Spotify, Soundcloud) or simply as a product (e.g. Shazam and MusixMatch). In this paper, we present a novel approach to classify a given song by encoding both textual and music features. The contribution of this work is twofold, i) We propose a multi modal fusion network approach which enables music genre classification utilizing both the textual features (lyrics) and musical features (mel spectrogram) achieving an accuracy of 90.4%. ii) We also propose a multiframe convolutional recurrent neural network (CRNN) based classifier that uses K-nearest neighbor approach over the predictions of every frame to predict the genre of a given song. In multi-modal fusion approach, we utilize co-attention between the textual and musical features for training classification network. The advantage of CRNN based multi frame approach is that it not only enriches the classification process but also enables to generate more training data from a smaller number of music files and thus helps in data augmentation. Our models and code are available on https://github.com/laishawadhwa/Multi-modal-music-genre-classification.","PeriodicalId":378325,"journal":{"name":"2021 Grace Hopper Celebration India (GHCI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134603328","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: 3

Implementation and Analysis of Supervised Learning methods for Bugs Classification bug分类的监督学习方法的实现与分析

2021 Grace Hopper Celebration India (GHCI) Pub Date : 2021-02-19 DOI: 10.1109/GHCI50508.2021.9513994

Bhagyashree M Katti

{"title":"Implementation and Analysis of Supervised Learning methods for Bugs Classification","authors":"Bhagyashree M Katti","doi":"10.1109/GHCI50508.2021.9513994","DOIUrl":"https://doi.org/10.1109/GHCI50508.2021.9513994","url":null,"abstract":"Classification of bugs or logs is a vital aspect in the development of high-quality products. Early detection of errors, frequent patterns showing anomalies and, timely rectification of errors reduces the risk of developing faulty software. The aim of proposed system is to integrate a machine learning based intelligent layer to the existing Automation framework. The focus of this work is to classify the logs by extracting the underlying error messages in logs and hence ease the work of developers to save time invested in analyzing the logs. The data set includes logs collected from the Automation framework that were reported during automation runs in the last few months. This paper aims at finding the optimal algorithm to classify the bugs. In this experiment, we examine Naïve Bayes, Multilayer perceptron, CNN, and a hybrid model with a combination of CNN + Naïve Bayes Algorithm along with feature extraction techniques. It is observed from the experiment, that the Multilayer perceptron network has the highest accuracy of 91 percent. This experiment shows that our proposed system can classify logs effectively into different class of failure-types.","PeriodicalId":378325,"journal":{"name":"2021 Grace Hopper Celebration India (GHCI)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116070301","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

YOLO as a Region Proposal Network for Diagnosing Breast Cancer YOLO作为乳腺癌诊断的区域性建议网络

2021 Grace Hopper Celebration India (GHCI) Pub Date : 2021-02-19 DOI: 10.1109/GHCI50508.2021.9513988

Ananya Bal, M. Das, Shashank Mouli Satapathy

引用次数: 0

[GHCI 2021 Front cover] [GHCI 2021封面]

2021 Grace Hopper Celebration India (GHCI) Pub Date : 2021-02-19 DOI: 10.1109/ghci50508.2021.9514014

引用次数: 0