2020 International Conference on Data Science and Engineering (ICDSE)最新文献

ICDSE 2020 Copyright Page ICDSE 2020版权页面

2020 International Conference on Data Science and Engineering (ICDSE) Pub Date : 2020-12-03 DOI: 10.1109/icdse50459.2020.9310153

引用次数: 0

Semantic Rule-based Automatic Code conversion System 基于语义规则的自动代码转换系统

2020 International Conference on Data Science and Engineering (ICDSE) Pub Date : 2020-12-03 DOI: 10.1109/ICDSE50459.2020.9310169

S. Gollapudi, S. Sasi

引用次数: 0

Cockpit Display Graphics Symbol Detection for Software Verification Using Deep Learning 基于深度学习的驾驶舱显示图形符号检测软件验证

2020 International Conference on Data Science and Engineering (ICDSE) Pub Date : 2020-12-03 DOI: 10.1109/ICDSE50459.2020.9310145

Debabrata Pal, Abhishek Alladi, Yashwanth Pothireddy, George Koilpillai

{"title":"Cockpit Display Graphics Symbol Detection for Software Verification Using Deep Learning","authors":"Debabrata Pal, Abhishek Alladi, Yashwanth Pothireddy, George Koilpillai","doi":"10.1109/ICDSE50459.2020.9310145","DOIUrl":"https://doi.org/10.1109/ICDSE50459.2020.9310145","url":null,"abstract":"In Software Development Life-cycle, Verification and Validation plays a very important role, especially in the case of Safety-Critical Industries like Aerospace. Display dashboard consists of multiple static and dynamic objects having affine transformation, graphics overlap, shadows and less inter symbol discriminative features compared to natural images. Manual Software graphics verification is an error-prone and time-consuming activity. In this paper, we propose a novel software graphics verification pipeline to verify graphics symbols and alphanumeric objects as per Software requirements. To the best of our knowledge, our proposed approach is the first study on deep learning-based graphics symbol detection from complex synthetic background which requires high model accuracy. We experiment using Single-shot Multibox Detector (SSD) and You Only Look Once (YOLO v2) to detect different Graphical symbols from display simulator real-time captured video frames. These detected objects are further classified based on their nature. Objects containing alphanumeric digits can be recognized using Optical Character Recognition and dynamic symbols are detected using object detection to infer other properties. Finally, all the extracted properties can be compared with test expectations to verify their correctness. The result shows superior accuracy of the SSD algorithm over other state-of-the-art object detection algorithms for detecting real-time graphics symbols.","PeriodicalId":233107,"journal":{"name":"2020 International Conference on Data Science and Engineering (ICDSE)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132189789","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

BiLSTM-Autoencoder Architecture for Stance Prediction 姿态预测的自编码器结构

2020 International Conference on Data Science and Engineering (ICDSE) Pub Date : 2020-12-03 DOI: 10.1109/ICDSE50459.2020.9310133

S Meena Padnekar, G Santhosh Kumar, P. Deepak

{"title":"BiLSTM-Autoencoder Architecture for Stance Prediction","authors":"S Meena Padnekar, G Santhosh Kumar, P. Deepak","doi":"10.1109/ICDSE50459.2020.9310133","DOIUrl":"https://doi.org/10.1109/ICDSE50459.2020.9310133","url":null,"abstract":"The recent surge in the abundance of fake news appearing on social media and news websites poses a potential threat to high-quality journalism. Misinformation hurts people, society, science, and democracy. This reason has led many researchers to develop techniques to identify fake news. In this paper, we discuss a stance prediction technique using the Deep Learning approach, which can be used as a factor to determine the authenticity of news articles. The Fake News Stance Prediction is the process of automatically classifying the stance of a news article towards a target into one of the following classes: Agree, Disagree, Discuss, Unrelated. The stance prediction task’s input is the news articles containing a pair: a headline as the target and a body as a claim. This paper proposes a deep learning architecture using Bi-directional Long Short Term Memory and Autoencoder for stance prediction. We illustrate, through empirical studies, that the method is reasonably accurate at predicting stance, achieving a classification accuracy as high as 94%. The proposed stance detection method would be useful for assessing the credibility of news articles.","PeriodicalId":233107,"journal":{"name":"2020 International Conference on Data Science and Engineering (ICDSE)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133291294","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: 6

Detection of Melanoma from Skin Lesion Images using Deep Learning Techniques 利用深度学习技术从皮肤病变图像中检测黑色素瘤

2020 International Conference on Data Science and Engineering (ICDSE) Pub Date : 2020-12-03 DOI: 10.1109/ICDSE50459.2020.9310131

Vimal Shah, Pratik Autee, Pankaj Sonawane

引用次数: 6

Enhanced Predictive Modeling of Cricket Game Duration Using Multiple Machine Learning Algorithms 使用多种机器学习算法增强的板球比赛持续时间预测建模

2020 International Conference on Data Science and Engineering (ICDSE) Pub Date : 2020-12-03 DOI: 10.1109/ICDSE50459.2020.9310081

Shivam Tyagi, R. Kumari, Sarath Chandra Makkena, S. Mishra, Vishnu S. Pendyala

{"title":"Enhanced Predictive Modeling of Cricket Game Duration Using Multiple Machine Learning Algorithms","authors":"Shivam Tyagi, R. Kumari, Sarath Chandra Makkena, S. Mishra, Vishnu S. Pendyala","doi":"10.1109/ICDSE50459.2020.9310081","DOIUrl":"https://doi.org/10.1109/ICDSE50459.2020.9310081","url":null,"abstract":"Cricket has the second-largest fan-base after football. Interest in any game is a factor of quality of the game which in turn depends on the quality of players. It is therefore important to have good players and that they are paid well. Sports industry largely relies on the advertising sector for sponsorship and financing of games. Advertisement companies spend a fortune to acquire the best slots during a game to catch the maximum viewership. This implies that advertising companies have a lot of interest in the duration of a match. Indian Premier League (IPL) has a huge fan-base and is one of the major events where companies spend a large amount of money to advertise their products. Due to this, a short game, which ends prior than expected, results in loss of opportunity in terms of time-slots lost and hence revenue and fan interest. The prediction of duration of a game will be beneficial for both sport and advertisement industry. In this paper, we use machine learning algorithms to predict the duration of a match in terms of the number of balls expected to be delivered in the match. The work introduces four different approaches, using historical data, to predict the number of balls in a match.","PeriodicalId":233107,"journal":{"name":"2020 International Conference on Data Science and Engineering (ICDSE)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134357776","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

Real-Time Image Processing: Face Recognition based Automated Attendance System in-built with “Two-Tier Authentication” Method 实时图像处理:基于人脸识别的内置“两层认证”方法的自动考勤系统

2020 International Conference on Data Science and Engineering (ICDSE) Pub Date : 2020-12-03 DOI: 10.1109/ICDSE50459.2020.9310090

R. Mehta, Sidh Satam, Maaz Ansari, S. Samantaray

{"title":"Real-Time Image Processing: Face Recognition based Automated Attendance System in-built with “Two-Tier Authentication” Method","authors":"R. Mehta, Sidh Satam, Maaz Ansari, S. Samantaray","doi":"10.1109/ICDSE50459.2020.9310090","DOIUrl":"https://doi.org/10.1109/ICDSE50459.2020.9310090","url":null,"abstract":"With the advent of computer vision, there has been significant growth in the research and development of facial recognition based automated attendance systems. Although current systems have been successful in alleviating human interaction and manual efforts, there still exist several challenges such as severe misclassifications, undetectable face angles, and different lighting conditions which result in a drastic drop in the accuracy. The system introduced in this paper has achieved an overall accuracy of 93.33%. A concept termed the “two-tier authentication” method has been developed to improve the overall accuracy of the system and to integrate a mechanism of time allowance for students. This method facilitates granting attendance to students based on the number of recognized faces as well as the probability of each prediction allowing for a more robust method of marking attendance to students. The novelty of this approach is to introduce an accurate statistical sequence for the execution of a proxy-free automated attendance system that employs state-of-the-art algorithms. Composed of 3 distinct parts, every sub-system performs a specific task namely, face detection, generation of face embeddings (FaceNet), and face classification. A comparative study was carried out to select the most appropriate detection and classification algorithms where Faster R-CNN and Support Vector Classifier outperformed their corresponding competitors respectively.","PeriodicalId":233107,"journal":{"name":"2020 International Conference on Data Science and Engineering (ICDSE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122354777","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

ICDSE 2020 Breaker Page ICDSE 2020断路器页

2020 International Conference on Data Science and Engineering (ICDSE) Pub Date : 2020-12-03 DOI: 10.1109/ICDSE50459.2020.9310126

引用次数: 0