2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC)最新文献_第3页

Java Based Software Robot for Automatic E-mail Extraction 基于Java的电子邮件自动提取软件机器人

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10083905

Raghuraj Singh, S. Upadhyay, Neeraj Sagar

引用次数: 0

Single-hand Gesture Recognition of Manipuri Classical Dance of India based on Skeletonization Technique 基于骨架化技术的印度曼尼普尔古典舞单手手势识别

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10083889

M. Devi, Ananya Chakraborty, Alak Roy, Dipanjoy Majumder

{"title":"Single-hand Gesture Recognition of Manipuri Classical Dance of India based on Skeletonization Technique","authors":"M. Devi, Ananya Chakraborty, Alak Roy, Dipanjoy Majumder","doi":"10.1109/ISACC56298.2023.10083889","DOIUrl":"https://doi.org/10.1109/ISACC56298.2023.10083889","url":null,"abstract":"Gesture recognition means recognizing of different expressions by which people can communicate with the outer world. Gestures recognition may be of verbal or non-verbal. In gesture recognition, hand gestures are one of the most common forms of communication, and they can communicate with a wide range of meanings. Dance gestures recognition is one of the challenging tasks in pattern recognition where hand gestures are used. It is a linguistic treatment of human motion through which one can depict dance drama and communicate culturally with people. This paper works with the Manipuri classical dance hand gestures which is among the eight Indian classical dances of India. In Manipuri classical dance, there are 25 single-hand gestures (Asamyukta Mudras) and 12 dual-hand gestures (Samyukta Mudras) are used. Unlike other Indian classical dance forms (eg: Bharatnatyam, Odissi, Kathak), there are no datasets available for Asamyukta mudras of Manipuri classical dance to continue the research work. So, the creation of the Manipuri single-hand gestures dataset is the main motivation behind this work. Here, for this dataset, 1500 mudras were collected from 6 volunteers from different angles of view. This paper also presents a study on various methods for gesture recognition with their applications. Finally, using skeletization technique, four features have been extracted to recognize 25 Asamyukta Mudras, and it can also be possible to identify whether the left or right hand is used to represent the Mudras.","PeriodicalId":211409,"journal":{"name":"2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117046253","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

Comparative analysis of detecting over-claim permissions from android apps 检测android应用程序的过度请求权限的比较分析

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10084321

Raj J Majethiya, Monika Shah

{"title":"Comparative analysis of detecting over-claim permissions from android apps","authors":"Raj J Majethiya, Monika Shah","doi":"10.1109/ISACC56298.2023.10084321","DOIUrl":"https://doi.org/10.1109/ISACC56298.2023.10084321","url":null,"abstract":"Android devices are popular nowadays due to their rich functionality and good performance. Android provides a permission model that supports user privacy by protecting restricted data access and actions on the Android operating system. Based on the app's defined functionality, any app must get users' permission to access different types of restricted data or resources. However, some developers request additional permissions other than the app's functionality, known as over-claim permissions. These over-claim permissions may steal sensitive information from users, Which is one of the leading privacy issues. Looking towards its criticality, many researchers have started working on detecting over-claim permission. This paper conducts a comprehensive survey of various approaches to identify and handle over-claim permissions. It also discusses the limitations of existing techniques and further research scope in this direction, which may help researchers define a path for designing new methods or optimizing existing strategies to resolve over-claim permission problems satisfactorily.","PeriodicalId":211409,"journal":{"name":"2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121187537","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

Applying a machine learning model to forecast the risks to children's online privacy and security 应用机器学习模型预测儿童在线隐私和安全风险

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10084054

Rumel M. S. Rahman Pir, Md. Forhad Rabbi, M. J. Islam

{"title":"Applying a machine learning model to forecast the risks to children's online privacy and security","authors":"Rumel M. S. Rahman Pir, Md. Forhad Rabbi, M. J. Islam","doi":"10.1109/ISACC56298.2023.10084054","DOIUrl":"https://doi.org/10.1109/ISACC56298.2023.10084054","url":null,"abstract":"Children are using digital gadgets at an increasing rate around the world, and this is accompanied by an increase in online privacy and security risks for them. Earlier studies found that children, parents, and teachers can work together to improve children's privacy and security when using digital devices. However, it was also discovered that safety measures can be applied more effectively if it can be predicted earlier which children are at risk and who are not. This research proposes a machine learning model based on 12 data points from children and their parents to predict children at risk of online abuse, cyberbullying, sexting, and stress. The model was created using the interview findings of 42 parents and 30 children as well as separate survey data from 311 parents and 383 children. According to the data study, children suffer the most from abuse, bullying, sexting, and severe mental stress among the various forms of privacy and security violations in the online world and that is why the prediction was done on these 4 situations. In this study, the Random Forest algorithm was used to develop the machine learning model and it was found that the model can somehow predict the privacy and safety of children while using digital devices. Our study contributes to the literature on Computer Supported Cooperative Work (CSCW) research that aims to develop cognitive and conceptual models to make the digital environment safer for children and adolescents.","PeriodicalId":211409,"journal":{"name":"2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126686278","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

AsCul: Annotated Dataset and a Deep Learning based framework for Assamese Cultural Object Detection 阿萨姆文化对象检测的注释数据集和基于深度学习的框架

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10084269

Manash Pratim Lahkar, Abhijit Gogoi, Pankaj Choudhury

引用次数: 0

Transcription of Indian Classical Music using Convolutional Recurrent Neural Network and CTC Loss 使用卷积递归神经网络和CTC损失的印度古典音乐转录

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10083872

Yeshwant Singh, Yatin Gupta, Subham Patar, Anurag Saraswat, Anupam Biswas

{"title":"Transcription of Indian Classical Music using Convolutional Recurrent Neural Network and CTC Loss","authors":"Yeshwant Singh, Yatin Gupta, Subham Patar, Anurag Saraswat, Anupam Biswas","doi":"10.1109/ISACC56298.2023.10083872","DOIUrl":"https://doi.org/10.1109/ISACC56298.2023.10083872","url":null,"abstract":"Swaras (notes) combined with appropriate Alankars (ornamentations) play a vital role in evoking the aesthetic attributes of a Raag in Indian classical music. It adds to the complexity of recognizing Swaras present in a given composition. However, recognizing the employed Swaras is critical for determining many qualities of a Raag, although the process takes time owing to improvisations or ornamentations. In this study, we have curated a sizable transcription dataset containing 25-second segments of music files and their corresponding transcription containing annotations for Swara, Alankar, and silence or noise. We used two features: STFT, CQT, and tonic normalized versions of STFT and CQT, along with melody contour from a music file as input to autoregressive convolution neural network models. All the variants of models are trained using CTC loss. The experiments show that combining convolution layers with recurrent layers using the tonic normalized CQT and melody contour achieves a good performance of 13 % WER.","PeriodicalId":211409,"journal":{"name":"2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128797474","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

An Efficient Consensus Algorithm for Blockchain-Based Federated Learning 基于区块链的联邦学习的高效共识算法

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10083761

Kausthav Pratim Kalita, Debojit Boro, Dhruba Kumar Bhattacharyya

引用次数: 0

Fake news detection using social media data for Khasi language 利用社交媒体数据对卡西语进行假新闻检测

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10083518

Sunita Warjri, Partha Pakray, Saralin Lyngdoh, A. K. Maji

{"title":"Fake news detection using social media data for Khasi language","authors":"Sunita Warjri, Partha Pakray, Saralin Lyngdoh, A. K. Maji","doi":"10.1109/ISACC56298.2023.10083518","DOIUrl":"https://doi.org/10.1109/ISACC56298.2023.10083518","url":null,"abstract":"With the advancement in technology, web technol-ogy in the form of social media is one of the main origins of information worldwide. Web technology has helped people to enhance their ability to know, learn, and gain knowledge about things around them. The benefits that technological advancement offers are boundless. However, apart from these, social media also has major issues related to problems and challenges concerning filtering out the right information from the wrong ones. The sources of information become highly unreliable at times, and it is difficult to differentiate and decipher real news or real information from fake ones. Cybercrime, through fraud mechanisms, is a pervasive menace permeating media technology every single day. Hence, this article reports an attempt to fake news detection in Khasi social media data. To execute this work, the data analyzed are extracted from different Internet platforms mainly from social media articles and posts. The dataset consists of fake news and also real news based on COVID-19, and also other forms of wrong information disseminated throughout the pandemic period. We have manually annotated the assembled Khasi news and the data set consists of 116 news data. We have used three machine learning techniques in our experiment, the Decision Tree, the Logistic Regression, and the Random Forest approach. We have observed in the experimental results that the Decision Tree-based approach yielded accurate results with an accuracy of 87%, whereas the Logistic Regression approach yielded an accuracy of 82% and the Random Forest approach yielded an accuracy of 75%.","PeriodicalId":211409,"journal":{"name":"2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125742792","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 of Various Machine Learning and Deep Learning Techniques for Audio Visual Automatic Speech Recognition 各种用于视听自动语音识别的机器学习和深度学习技术综述

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10084209

Arpita Choudhury, Pinki Roy, Sivaji Bandyopadhyay

{"title":"Review of Various Machine Learning and Deep Learning Techniques for Audio Visual Automatic Speech Recognition","authors":"Arpita Choudhury, Pinki Roy, Sivaji Bandyopadhyay","doi":"10.1109/ISACC56298.2023.10084209","DOIUrl":"https://doi.org/10.1109/ISACC56298.2023.10084209","url":null,"abstract":"The visual cues obtained from the face and mouth region of a speaker provide valuable information for speech per-ception. The idea of audio visual speech recognition is to combine visual information with acoustic speech signals to enhance the intelligibility of speech in the presence of ambient noises. In audio visual speech recognition lip image sequences of speakers are used along with acoustic signals to convert speech into text. Researchers are exploring ways to upgrade the performance of audio visual speech recognition and solve certain real life problems like designing voice dialling systems, highly secured biometric systems for authentication etc. A review of the latest research findings on audio visual automatic speech recognition using traditional machine learning, neural networks and other deep learning techniques is presented in this work. This paper describes future research opportunities through a comparative analysis of the various techniques used in the literature for the different stages of audiovisual speech recognition, including the region of interest detection, audio and visual speech feature extraction and fusion of the modalities.","PeriodicalId":211409,"journal":{"name":"2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134500129","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

Survey on Methodological Model of IoT in Digital Forensic 数字取证中物联网方法论模型研究

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10083537

Ritocheta Roy, D. J

引用次数: 0