2022 IEEE Pune Section International Conference (PuneCon)最新文献_第6页

Chief Guests 主要的客人

2022 IEEE Pune Section International Conference (PuneCon) Pub Date : 2022-12-15 DOI: 10.1109/punecon55413.2022.10014748

引用次数: 0

A Forensic Methodology for the Analysis of Twitter Application in Android Devices Android设备上Twitter应用分析的取证方法

2022 IEEE Pune Section International Conference (PuneCon) Pub Date : 2022-12-15 DOI: 10.1109/PuneCon55413.2022.10014960

Priyanka V S, Satheesh Kumar S

引用次数: 0

Search-based Feature Selection for Cross-Project Fault Prediction 基于搜索的跨项目故障预测特征选择

2022 IEEE Pune Section International Conference (PuneCon) Pub Date : 2022-12-15 DOI: 10.1109/PuneCon55413.2022.10014936

Yogita Khatri, S. Singh

引用次数: 0

Vision-based Monitoring of Student Attentiveness in an E-Learning Environment 电子学习环境中基于视觉的学生注意力监测

2022 IEEE Pune Section International Conference (PuneCon) Pub Date : 2022-12-15 DOI: 10.1109/PuneCon55413.2022.10014782

Jyoti Madake, Sandesh Shende, S. Bhatlawande, Rohit Shinde, Shripad Govekar, S. Shilaskar

引用次数: 0

Human Emotion Recognition Using Gabor Variance Features with Back Propagation Neural Network Classifier 基于Gabor方差特征和反向传播神经网络分类器的人类情绪识别

2022 IEEE Pune Section International Conference (PuneCon) Pub Date : 2022-12-15 DOI: 10.1109/PuneCon55413.2022.10014743

Kanchan S. Vaidya, Pradeep M. Patil, Mukil Alagirisamy, B. Pansambal

{"title":"Human Emotion Recognition Using Gabor Variance Features with Back Propagation Neural Network Classifier","authors":"Kanchan S. Vaidya, Pradeep M. Patil, Mukil Alagirisamy, B. Pansambal","doi":"10.1109/PuneCon55413.2022.10014743","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014743","url":null,"abstract":"It is natural as stimuli-response humans always express their feelings & reaction to any certain event automatically appears on the 'face,. Facial expression is an important medium for human communication as it express human thinking, feelings and his or her current mental situation, thus it is being used in many application areas. This paper aims to introduce a novel method for human emotion recognition using average variance as the feature vectors obtained from the Gabor filter convolved 'n, images which helps in classifying those emotions. Based on the Gabor variance features, a three layer back propagation neural network (BPNN) has been used as a classifier. The BPNN architecture used in the experimentation work contains 210 input units in the input layer, which corresponds to the displacement information of the Gabor variance feature vectors. There are 6 units in the output layer and one hidden layer of 256 units. According to the JAFFE database, the average accuracy of the proposed emotion recognition algorithm was the highest with 94.66%. Timing analysis using the same database shows that the template response time is lower because the BPNN is only 3-tier architecture which requires a single training as emphasis is on recall time. Because network training is only needed once, recall time is more important than training time.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128885975","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 Web-based Application for Snake Species Identification using Vision Transformer and CNN-based Ensemble Meta Classifier 基于视觉变换和cnn集成元分类器的基于web的蛇类识别应用

2022 IEEE Pune Section International Conference (PuneCon) Pub Date : 2022-12-15 DOI: 10.1109/PuneCon55413.2022.10014812

D.D.K.R.W. Dandeniya, B.C.T. Wickramasinghe, C. Dasanayaka

引用次数: 0

Effective utilization of Machine Learning Techniques to Classify Breast Cancer Tumors 有效利用机器学习技术对乳腺癌肿瘤进行分类

2022 IEEE Pune Section International Conference (PuneCon) Pub Date : 2022-12-15 DOI: 10.1109/PuneCon55413.2022.10014940

Gauri Kamath, A. Phadke

{"title":"Effective utilization of Machine Learning Techniques to Classify Breast Cancer Tumors","authors":"Gauri Kamath, A. Phadke","doi":"10.1109/PuneCon55413.2022.10014940","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014940","url":null,"abstract":"Breast Cancer occurs when alterations called mutations to take place in the genes that cause anomalous cell advancement in the breast. One of the ways to achieve success in this field of cancer is by digging deep into machine learning techniques to diagnose the disease better as well as attempt to cure it. This paper aims at identifying breast cancer tumors fast and efficiently. The system suggested in the research uses the Wisconsin Breast Cancer Dataset, which was downloaded from the UCI repository, and allows binary classification, classifying tumors as malignant or benign. Techniques used to implement classification are Support Vector Machines and Random Forest. To comprehend the trends and patterns in the Wisconsin Breast Cancer Dataset, a thorough data visualization of the dataset has been conducted. The system employs data processing techniques to retrieve useful data, followed by Principal Component Analysis to carry out feature extraction. For SVM, to reiterate through the predefined hyperparameters, Grid Search CV has been implemented. For the Random Forest algorithm, k-fold cross-validation has been applied to achieve a unique set of results. The highest accuracy achieved using the random forest algorithm is 99.7% and the same for SVM is 98.2%. The following algorithms have been highlighted since their implementation has helped to retrieve significant accuracy levels. The models have been evaluated by computing the precision, recall score, f1 score, and confusion matrix. Models have also been compared using truepositive rate, true negative rate, false positive rate, and false negative rate.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130284779","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

Detection of Malicious Activities and Connections for Network Security using Deep Learning 利用深度学习检测网络安全中的恶意活动和连接

2022 IEEE Pune Section International Conference (PuneCon) Pub Date : 2022-12-15 DOI: 10.1109/PuneCon55413.2022.10014736

M. Rokade, Sunil S. Khatal

{"title":"Detection of Malicious Activities and Connections for Network Security using Deep Learning","authors":"M. Rokade, Sunil S. Khatal","doi":"10.1109/PuneCon55413.2022.10014736","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014736","url":null,"abstract":"Computer attacks are growing in both number and diversity as a result of the ongoing growth of the Internet: ransomware is more prevalent than ever before, and zero-day vulnerabilities are gaining so much importance that they are attracting media attention. Antivirus software and firewalls are no longer sufficient to safeguard a company's network; instead, many layers of security are required. One of the most important layers, an intrusion detection system, is designed to protect its target from any potential attack by continually monitoring the system (IDS). IDSs may currently be classified into two basic categories: anomaly detection and signature-based detection. For signature-based detection, the IDS compares the data it is watching to known attack patterns. Although this method has gained popularity because to tools like Snort, it has a serious drawback: it can only detect known threats that have already been described in a database. On the other hand, anomaly detection builds a model of the system's typical behaviour before searching for anomalies in the observed data. As a consequence, while it often generates a great deal of false alarms, this approach may reveal undiscovered risks.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"80 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134445644","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

Education 4.0: Case Study on Selection of Digital Learning Platform and Communication Tools for Future Education 4.0 in India 教育4.0:印度未来教育4.0数字化学习平台与交流工具选择案例研究

2022 IEEE Pune Section International Conference (PuneCon) Pub Date : 2022-12-15 DOI: 10.1109/PuneCon55413.2022.10014956

Rajkamal Sangole, Darshana Desai, Anand Jain

{"title":"Education 4.0: Case Study on Selection of Digital Learning Platform and Communication Tools for Future Education 4.0 in India","authors":"Rajkamal Sangole, Darshana Desai, Anand Jain","doi":"10.1109/PuneCon55413.2022.10014956","DOIUrl":"https://doi.org/10.1109/PuneCon55413.2022.10014956","url":null,"abstract":"This Industrial Revolution 4.0 is in boom across the globe; which is an indication for Universities and Colleges to adopt the future i.e. Education 4.0. Since the Last Decade, there has been a drastic change in the field of education. It has changed from physical learning to digital learning due to Covid-19. As digital learning came into the picture many developments have happened in using industrial revolution 4.0. This transformation has taught all of us how to use ICT effectively for conducting the lectures for the students remotely and smoothly. ICT has enabled new ways of student learning with the challenge of the availability of good learning platforms and tools. Industrial Revolution 4.0 and Education 4.0 work hand in hand for bringing new ways of learning. Industry 4.0 is majorly working on Robotic Process Automation, Industrial Internet of Things, Artificial Intelligence, and Smart Technologies. All the Technologies which are associated with Industry 4.0 have a massive impact on individuals' everyday lifestyles. To improve education, learning skills, and personal skills in students, universities and colleges need to adopt Education 4.0 which is an evolution in the Education System. Teaching students digital technologies in their current syllabus will change the learning habits of the students which will in turn improve universities and colleges. Future of Education will be completely based on Education 4.0 which will restructure the current education system. This case study will be focusing on the selection of Digital Learning Platform and Communication Tools for Future Education 4.0 in India on the basis of different parameters.","PeriodicalId":258640,"journal":{"name":"2022 IEEE Pune Section International Conference (PuneCon)","volume":"265 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115663518","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 of Deep Learning Application in Oral Cancer Prognosis 深度学习在口腔癌预后中的应用综述

2022 IEEE Pune Section International Conference (PuneCon) Pub Date : 2022-12-15 DOI: 10.1109/PuneCon55413.2022.10014937

Sayyada Hajera Begum, P. Vidyullatha

引用次数: 1