2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)最新文献_第3页

Deep And Machine Learning Towards Pneumonia And Asthma Detection 用于肺炎和哮喘检测的深度和机器学习

2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) Pub Date : 2021-09-29 DOI: 10.1109/3ICT53449.2021.9581963

Amani Yahyaoui, N. Yumusak

引用次数: 2

Socio-technical Challenges in the Implementation of Smart City 智慧城市实施中的社会技术挑战

2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) Pub Date : 2021-09-29 DOI: 10.1109/3ICT53449.2021.9581905

Hitesh Mohapatra

引用次数: 3

On the Implementation of Access Control in Ethereum Blockchain 以太坊区块链中访问控制的实现

2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) Pub Date : 2021-09-29 DOI: 10.1109/3ICT53449.2021.9581591

Insaf Achour, S. Ayed, H. Idoudi

引用次数: 0

Development of COVID-19 mRNA Vaccine Degradation Prediction System 新型冠状病毒mRNA疫苗降解预测系统的研制

2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) Pub Date : 2021-09-29 DOI: 10.1109/3ICT53449.2021.9582052

Soon Hwai Ing, A. Abdullah, Shigehiko Kanaya

{"title":"Development of COVID-19 mRNA Vaccine Degradation Prediction System","authors":"Soon Hwai Ing, A. Abdullah, Shigehiko Kanaya","doi":"10.1109/3ICT53449.2021.9582052","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9582052","url":null,"abstract":"The threatening Coronavirus which was assigned as the global pandemic concussed not only the public health but society, economy and every walks of life. Some measurements are taken to stifle the spread and one of the best ways is to carry out some precautions to prevent the contagion of SARS-CoV-2 virus to uninfected populaces. Injecting prevention vaccines is one of the precaution steps under the grandiose blueprint. Among all vaccines, it is found that mRNA vaccine which shows no side effect with marvelous effectiveness is the most preferable candidates to be considered. However, degradation had become its biggest drawback to be implemented. Hereby, this study is held with desideratum to develop prediction models specifically to predict the degradation rate of mRNA vaccine for COVID-19.3 machine learning algorithms, which are, Linear Regression (LR), Light Gradient Boosting Machine (LGBM) and Random Forest (RF) are proposed for 12 models development. Dataset comprises of thousands of RNA molecules that holds degradation rates at each position from Eterna platform is extracted, pre-processed and encoded with label encoding before loaded into algorithms. The results show that the LGBM-based model which is trained along with auxiliary bpps features and encoded with method 1 label encoding performs the best (RMSE = 0.24466), followed by the same criteria LGBM-based model but encoded with label encoding method 2, with a difference in 0.00003 in tow the topnotch model. The RF-based model with applaudable performance (RMSE = 0.25302) even without the ubieties of the riddled bpps features in contradistinction to the training and encoding criteria of the superb mellowed LGBM-based model is worth being further cultivated for the prediction study on COVID-19 mRNA vaccines' degradation rate.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123502102","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

COCOSO-based Network Interface Selection Algorithm for Heterogeneous Wireless Networks 基于cocoso的异构无线网络接口选择算法

2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) Pub Date : 2021-09-29 DOI: 10.1109/3ICT53449.2021.9581462

Brahim Mefgouda, H. Idoudi

引用次数: 1

Defeating the Credit Card Scams Through Machine Learning Algorithms 通过机器学习算法击败信用卡诈骗

2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) Pub Date : 2021-09-29 DOI: 10.1109/3ICT53449.2021.9582060

Kameron Bains, Adebamigbe Fasanmade, J. Morden, A. Al-Bayatti, M. S. Sharif, A. S. Alfakeeh

{"title":"Defeating the Credit Card Scams Through Machine Learning Algorithms","authors":"Kameron Bains, Adebamigbe Fasanmade, J. Morden, A. Al-Bayatti, M. S. Sharif, A. S. Alfakeeh","doi":"10.1109/3ICT53449.2021.9582060","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9582060","url":null,"abstract":"Credit card fraud is a significant problem that is not going to go away. It is a growing problem and surged during the Covid-19 pandemic since more transactions are done without cash in hand now. Credit card frauds are complicated to distinguish as the characteristics of legitimate and fraudulent transactions are very similar. The performance evaluation of various Machine Learning (ML)-based credit card fraud recognition schemes are significantly pretentious due to data processing, including collecting variables and corresponding ML mechanism being used. One possible way to counter this problem is to apply ML algorithms such as Support Vector Machine (SVM), K nearest neighbor (KNN), Naive Bayes, and logistic regression. This research work aims to compare the ML as mentioned earlier models and its impact on credit card scam detection, especially in situations with imbalanced datasets. Moreover, we have proposed state of the art data balancing algorithm to solve data unbalancing problems in such situations. Our experiments show that the logistic regression has an accuracy of 99.91%, and naive bays have an accuracy of 97.65%. K nearest neighbor has an accuracy is 99.92%, support vector machine has an accuracy of 99.95%. The precision and accuracy comparison of our proposed approach shows that our model is state of the art.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"231 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123060860","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

Support Vector Machine and Decision Tree-Based Elective Course Suggestion System: A Case Study 基于支持向量机与决策树的选修课建议系统案例研究

2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) Pub Date : 2021-09-29 DOI: 10.1109/3ICT53449.2021.9581846

M. F. Adak, S. Ercan

引用次数: 0

A New Approach for Labelling XML Data 标记XML数据的新方法

2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) Pub Date : 2021-09-29 DOI: 10.1109/3ICT53449.2021.9581352

Alhadi A. Klaib, A. Milad, Mustafa Almahdi Algaet

引用次数: 2

Future Micro Hydro Power: Generation of Hydroelectricity and IoT based Monitoring System 未来微型水电:水力发电和基于物联网的监测系统

2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) Pub Date : 2021-09-29 DOI: 10.1109/3ICT53449.2021.9581590

A. Akib, S. Mahmud, M. Mridha

{"title":"Future Micro Hydro Power: Generation of Hydroelectricity and IoT based Monitoring System","authors":"A. Akib, S. Mahmud, M. Mridha","doi":"10.1109/3ICT53449.2021.9581590","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581590","url":null,"abstract":"The paper focuses on the Future Micro Hydro Power: generation of hydroelectricity and its monitoring system. The world is moving towards technological advancement day by day. For this reason, the energy need will surge further in the coming days. But we could not yet ensure the proper electricity needs in the poor or developing country. Now it's an essential needy thing to survive this 4.0 industry's time. This ‘Future Micro Hydro Power’ device will generate energy by exploiting the small water sources (i.e., Washroom, Kitchen, Etc.) in the multi steroid buildings. A massive amount of water is used in the house every day. Water taps are used not only in homes but in all modern buildings. We have demonstrated how hydropower will generate from these tiny water sources and how this power can run a house. Here the user will be able to monitor the amount of energy produced and use it if desired. The cost of the devices will be much lower, and their performance will be much higher. After the experimental installation, we got some data that proves its outstanding efficiency.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128631077","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

A new approach to detect next generation of malware based on machine learning 基于机器学习的下一代恶意软件检测新方法

2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) Pub Date : 2021-09-29 DOI: 10.1109/3ICT53449.2021.9581625

Ikram Ben abdel ouahab, Lotfi Elaachak, Yasser A. Alluhaidan, M. Bouhorma

{"title":"A new approach to detect next generation of malware based on machine learning","authors":"Ikram Ben abdel ouahab, Lotfi Elaachak, Yasser A. Alluhaidan, M. Bouhorma","doi":"10.1109/3ICT53449.2021.9581625","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581625","url":null,"abstract":"In these days, malware attacks target different kinds of devices as IoT, mobiles, servers even the cloud. It causes several hardware damages and financial losses especially for big companies. Malware attacks represent a serious issue to cybersecurity specialists. In this paper, we propose a new approach to detect unknown malware families based on machine learning classification and visualization technique. A malware binary is converted to grayscale image, then for each image a GIST descriptor is used as input to the machine learning model. For the malware classification part we use 3 machine learning algorithms. These classifiers are so efficient where the highest precision reach 98%. Once we train, test and evaluate models we move to simulate 2 new malware families. We do not expect a good prediction since the model did not know the family; however our goal is to analyze the behavior of our classifiers in the case of new family. Finally, we propose an approach using a filter to know either the classification is normal or it's a zero-day malware.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130642630","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