2023 6th International Conference on Information Systems and Computer Networks (ISCON)最新文献

AttnHAR: Human Activity Recognition using Data Collected from Wearable Sensors AttnHAR:使用可穿戴传感器收集的数据进行人类活动识别

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10112183

Monaal Sethi, Manav Yadav, Mayank Singh, P. G. Shambharkar

引用次数: 0

Underwater Image Enhancement using Convolutional Block Attention Module 基于卷积块注意力模块的水下图像增强

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10111974

N. Singh, Aruna Bhat

引用次数: 0

Advanced NLP Framework for Text Processing 用于文本处理的高级NLP框架

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10112058

J. Praveen Gujjar, H. R. Prasanna Kumar, M. S. Guru Prasad

引用次数: 1

Performance Evaluation of Speed Sensitive Handover in Two-Tier Wireless Networks 两层无线网络中速度敏感切换的性能评估

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10111957

Vikas Solanki, Ravi Kumar Sachdeva, V. Lamba, Umesh Solanki, B. K. Sahoo

引用次数: 0

Cyber Security of Smart Metering Infrastructure Using Hybrid Machine Learning Technique 基于混合机器学习技术的智能计量基础设施网络安全

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10112175

Priyamvada Chandel, B. Sawle

引用次数: 0

Analysis of the Use of Gamification Elements in E-commerce Applications as a Purchase Intention Factor Using the Structural Equation Model (SEM) 基于结构方程模型(SEM)的电子商务应用中游戏化元素作为购买意愿因素的分析

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10112151

Fanny Claudia, Viollen Vierrini, Natalia, Natalia Limantara

{"title":"Analysis of the Use of Gamification Elements in E-commerce Applications as a Purchase Intention Factor Using the Structural Equation Model (SEM)","authors":"Fanny Claudia, Viollen Vierrini, Natalia, Natalia Limantara","doi":"10.1109/ISCON57294.2023.10112151","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10112151","url":null,"abstract":"Shopping through e-commerce has become a habit for Indonesians. Amidst many e-commerce, gamification is a method used to increase purchases on their platform. However, the influence of gamification on the intention to buy appears to requires additional research, particularly on Indonesian e-commerce platforms. The purpose of this research is to investigate the application of gamification elements in e-commerce as a purchase intention factor using the Structural Equation Model (SEM). A survey was conducted, and 157 valid questionnaires were collected from Indonesian e-commerce users. The result showed that utilitarian values, hedonic values, and spending less time/energy on gaming all have a notable influence on user intentions toward games play on e-commerce platforms. However, utilitarian values have no notable influence on user’s purchase decisions in e-commerce. Furthermore, social value and perceived enjoyment have a significant impact on the user’s purchase intention in e-commerce, whereas game use intention has no notable influence on the user’s purchase intention.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"270 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122922006","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 Energy-efficient Traffic Scheduling Method based on Slime Mould Algorithm for SDN 基于黏菌算法的SDN节能交通调度方法

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10112055

Zheyuan Wang, Junli Wang, Chungang Yan

引用次数: 0

A Contextual Query Expansion Model using BERT Based Deep Neural Embeddings 基于BERT的深度神经嵌入的上下文查询扩展模型

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10111984

D. Vishwakarma, Suresh Kumar

引用次数: 0

Heart Disease Evaluation with Deep Learning and Machine Learning 用深度学习和机器学习评估心脏病

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10112002

Sachin Upadhyay, Sanjiv Kumar Singh, Jayati Krishna Goswami, Shiv Shanker Singh

{"title":"Heart Disease Evaluation with Deep Learning and Machine Learning","authors":"Sachin Upadhyay, Sanjiv Kumar Singh, Jayati Krishna Goswami, Shiv Shanker Singh","doi":"10.1109/ISCON57294.2023.10112002","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10112002","url":null,"abstract":"All over the world is affected by the disease which is named as heart disease. The main reason behind the heart disease is our busy life, as the person is affected not only by office work but also by personal problems. This mortality rate is too high. We can predict this disease with the help of Machine Learning (ML) and Deep Learning (DL) prediction models. In this paper, to reach the accuracy we worked on three ML & DL models. For this paper we use ML models named as: SVM (Support Vector Machine), LR (Logistic Regression) & Naïve Bayes. DL models named as: CNN (Convolutional Neural Network), RNN (Recurrent Neural Network) & LSTM (Long Short Term Memory). The accuracy obtained in this study is made up of the 85% accuracy of Logistic Regression, the 89% accuracy of SVM, and the 85% accuracy of Naive Bayes. LSTM has an accuracy of 83%, RNN has an accuracy of 91%, and CNN has an accuracy of 83%. The study’s findings indicate that the RNN model is the most accurate, coming in at 90%, and from this we can say that it is the best at predicting heart disease.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128272380","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 Survey of Sarcasm Detection Techniques in Natural Language Processing 自然语言处理中反讽检测技术综述

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10112176

Bhuvanesh Singh, D. Sharma

引用次数: 0