2021 International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA)最新文献_第2页

CoNTESA 2021 Knowledge Partners CoNTESA 2021知识合作伙伴

2021 International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA) Pub Date : 2021-12-09 DOI: 10.1109/contesa52813.2021.9657118

引用次数: 0

Lightweight Blockchain of Things (BCoT) Architecture for Enhanced Security: A Literature Review 用于增强安全性的轻量级物链(BCoT)架构:文献综述

2021 International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA) Pub Date : 2021-12-09 DOI: 10.1109/contesa52813.2021.9657112

Aofan Liu, Mst. Surma Khatun, Han Liu, Mahdi H. Miraz

{"title":"Lightweight Blockchain of Things (BCoT) Architecture for Enhanced Security: A Literature Review","authors":"Aofan Liu, Mst. Surma Khatun, Han Liu, Mahdi H. Miraz","doi":"10.1109/contesa52813.2021.9657112","DOIUrl":"https://doi.org/10.1109/contesa52813.2021.9657112","url":null,"abstract":"Both the internet of things (IoT) and distributed ledger technology (DLT), more commonly known as the blockchain, are two popular emerging technologies of this era. While blockchain offers strengthened security, along with other benefits, it requires peer-to-peer (P2P) nodes for its consensus process. On the contrary, IoT ecosystems inherently consist of many P2P nodes but it is highly critiqued for its lack of security measures. Therefore, the fusion of these complementary duos, known as the blockchain of things (BCoT), has become a recent research trend. While the fit is good and the benefits such consolidation can offer are obvious, a lot of challenges are yet to be addressed. Therefore, we have conducted a comprehensive literature review, covering 33 research articles, spanning over the last six years (2016–2021), to report the state-of-the-art research in this domain. We have synthesised the existing literature by comparing, contrasting, resembling as well as critically evaluating them and thus, deduced the current challenges and future research directions, particularly with regards to lightweightness.","PeriodicalId":323624,"journal":{"name":"2021 International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131273776","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

Credit Approval System Using Machine Learning: Challenges and Future Directions 使用机器学习的信用审批系统:挑战和未来方向

2021 International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA) Pub Date : 2021-12-09 DOI: 10.1109/contesa52813.2021.9657153

Mohammad Fahim Faisal, Mohammad Neyamath Ullah Saqlain, M. Bhuiyan, Mahdi H. Miraz, M. Patwary

引用次数: 5

CoNTESA 2021 Author Index CoNTESA 2021作者索引

2021 International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA) Pub Date : 2021-12-09 DOI: 10.1109/contesa52813.2021.9657145

引用次数: 0

CoNTESA Technical Cosponsors CoNTESA技术共同发起人

2021 International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA) Pub Date : 2021-12-09 DOI: 10.1109/contesa52813.2021.9657134

引用次数: 0

A Novel Oversampling Technique to Solve Class Imbalance Problem: A Case Study of Students’ Grades Evaluation 一种解决班级失衡问题的过采样新方法——以学生成绩评价为例

2021 International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA) Pub Date : 2021-12-09 DOI: 10.1109/contesa52813.2021.9657151

Dilshad Jahin, Israt Jahan Emu, Subrina Akter, M. Patwary, Mohammad Arif Sobhan Bhuiyan, Mahdi H. Miraz

{"title":"A Novel Oversampling Technique to Solve Class Imbalance Problem: A Case Study of Students’ Grades Evaluation","authors":"Dilshad Jahin, Israt Jahan Emu, Subrina Akter, M. Patwary, Mohammad Arif Sobhan Bhuiyan, Mahdi H. Miraz","doi":"10.1109/contesa52813.2021.9657151","DOIUrl":"https://doi.org/10.1109/contesa52813.2021.9657151","url":null,"abstract":"The academic performance of the students is one of the critical aspects in ranking educational institutions, particularly at the secondary level. If the student’s performance is not appropriately defined, then the institution’s reputation is at risk. Therefore, data mining could be used for this purpose, to attain high accuracy. However, the data being incomplete, inaccurate and/or noisy, or with an imbalance class label in the dataset, is highly likely to affect the accuracy of the data mining model. This paper proposes a semi-supervised oversampling method to first prepare a balanced dataset and then to classify the students’ grades into a binary class with overall performance in any given course. The student performance dataset from the UCI machine learning repository is used, which contains student performance related data of two different courses. A detailed validation result shows that the decision tree algorithm performs better with the balanced dataset compared to the imbalanced one.","PeriodicalId":323624,"journal":{"name":"2021 International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA)","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122495155","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: 4

Analysis and Performance Evaluation of E-Commerce Implementation 电子商务实施的分析与绩效评价

2021 International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA) Pub Date : 2021-12-09 DOI: 10.1109/contesa52813.2021.9657111

M. Fetaji, B. Fetaji, Maaruf Ali, I. Zeqiri, Halil Snopçe, M. Ebibi

引用次数: 0

Recommender System based on Deep Neural Network and Long Short Term Memory 基于深度神经网络和长短期记忆的推荐系统

2021 International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA) Pub Date : 2021-12-09 DOI: 10.1109/contesa52813.2021.9657131

Sandeep Kumar Rachamadugu, Jayanarayana Reddy Dwaram, Kiran Rao Patike

{"title":"Recommender System based on Deep Neural Network and Long Short Term Memory","authors":"Sandeep Kumar Rachamadugu, Jayanarayana Reddy Dwaram, Kiran Rao Patike","doi":"10.1109/contesa52813.2021.9657131","DOIUrl":"https://doi.org/10.1109/contesa52813.2021.9657131","url":null,"abstract":"To provide relevant recommendations for clients, a recommendation system is essential in online commerce, streaming services, and news article websites. Existing methods in recommendation systems are limited by the cold start problem. The Deep Neural Network (DNN) – Long Short-Term Memory (LSTM) technique is developed in this study to improve the efficiency of recommendation systems. The DNN method is used to predict new user ratings based on prior user ratings, while the LSTM method is used to recommend a relevant movie to the user. The user-item similarity was calculated and used in the LSTM algorithm to offer the relevant recommendation. The LSTM approach has the advantage of storing relevant information over time and making appropriate recommendations. The proposed DNN-LSTM (Deep Neural Network-Long Short-Term Memory) technique in the recommendation system is evaluated using the MovieLens 100k and 1M datasets. In the MovieLens 100k dataset, the proposed DNN-LSTM approach has an RMSE of 0.431, while the existing HCBCF (Hellinger Coefficient Based Collaborative Filtering) method has an RMSE of 0.871.","PeriodicalId":323624,"journal":{"name":"2021 International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127360083","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

CoNTESA 2021 TOC

2021 International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA) Pub Date : 2021-12-09 DOI: 10.1109/contesa52813.2021.9657123

引用次数: 0

Exploration of the Attacking Web Vectors 攻击Web向量的探索

2021 International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA) Pub Date : 2021-12-09 DOI: 10.1109/contesa52813.2021.9657129

Tea Osmëni, Maaruf Ali

引用次数: 0