Journal of Computer & Electrical and Electronics Engineering Sciences最新文献

Faculty network system implementation using cisco packet tracer 利用cisco数据包跟踪器实现学院网络系统

Journal of Computer & Electrical and Electronics Engineering Sciences Pub Date : 2023-04-28 DOI: 10.51271/jceees-0005

Özge Ersoy Cangir, Mustafa Yıldırım, Nurgül Bostan

引用次数: 0

Convolutional neural network for pothole detection in different road and weather conditions 卷积神经网络在不同道路和天气条件下的坑穴检测

Journal of Computer & Electrical and Electronics Engineering Sciences Pub Date : 2023-04-28 DOI: 10.51271/jceees-0001

Qusai Gazawy, Selim Buyrukoğlu, Yıldıran Yılmaz

引用次数: 0

Prediction of heart attack risk using linear discriminant analysis methods 用线性判别分析方法预测心脏病发作风险

Journal of Computer & Electrical and Electronics Engineering Sciences Pub Date : 2023-04-28 DOI: 10.51271/jceees-0002

Esra Sivari, Selim Sürücü

{"title":"Prediction of heart attack risk using linear discriminant analysis methods","authors":"Esra Sivari, Selim Sürücü","doi":"10.51271/jceees-0002","DOIUrl":"https://doi.org/10.51271/jceees-0002","url":null,"abstract":"Aims: The interruption of blood flow to the heart muscle is called a heart attack. During a heart attack, the risk of permanent damage increases with every second the heart tissue cannot receive enough blood. If early and appropriate intervention is not performed, loss of heart tissue occurs. Causes such as smoking, cholesterol, diabetes, high blood pressure, old age, obesity, genetics, and high levels of certain substances produced in the liver are the main risk factors for heart attack. This study aims to predict the risk of heart attack with machine learning methods using a dataset created by considering risk factors.\u0000Methods: The performances of three types of Linear Discriminant Analysis classifiers, Normal, Ledoit-Wolf, and Oracle Shrinkage Approximating, were compared on the Cleveland dataset.\u0000Results: Normal Linear Discriminant Analysis made the best classification with 83.60% accuracy and performed better than regularized versions.\u0000Conclusion: Linear Discriminant Analysis methods are a promising classifier for heart attack prediction and can be applied in hospitals as an objective and automated system that eases specialists' workload and helps reduce diagnostic costs.\u0000","PeriodicalId":383582,"journal":{"name":"Journal of Computer & Electrical and Electronics Engineering Sciences","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130425286","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

Investigation of financial applications with blockchain technology 区块链技术的金融应用研究

Journal of Computer & Electrical and Electronics Engineering Sciences Pub Date : 2023-04-28 DOI: 10.51271/jceees-0003

Mohammed Ali Mohammed

{"title":"Investigation of financial applications with blockchain technology","authors":"Mohammed Ali Mohammed","doi":"10.51271/jceees-0003","DOIUrl":"https://doi.org/10.51271/jceees-0003","url":null,"abstract":"Aims: This article investigates recent advancements in machine learning and blockchain technology for cryptocurrency price prediction. The study presents a ML system using various techniques applied to six different datasets. The findings highlight that simpler models can outperform complex ones in predicting cryptocurrency prices.\u0000Methods: The methods used in this study include applying diverse ML techniques such as LSTM, CNN, SVM, KNN, XGBoost, Astro ML, LASSO, RIDGE, linear regression, DT, and GP on six cryptocurrency datasets to predict prices.\u0000Results: The research evaluated various machine learning techniques for predicting cryptocurrency prices and reported the following RMSE values: Bitcoin prediction using Nadaraya-Watson kernel regression yielded an RMSE of 0.17, while Dogecoin prediction with linear regression resulted in an RMSE of 0.032. Ethereum price prediction using Gaussian regression achieved an RMSE of 0.02. For USD Coin, a combination of XGBoost, Gaussian regression, and Ridge techniques led to an RMSE of 0.014. Binance Coin price prediction using Gaussian regression had an RMSE of 0.032, and finally, Cardano Coin prediction employing LSTM reached an RMSE of 0.059.\u0000Conclusion: This study demonstrated the effectiveness of various machine learning techniques in predicting cryptocurrency prices. It revealed that simpler models can outperform complex ones in certain cases. The research contributes valuable insights to the field and can guide future work in cryptocurrency price prediction. The proposed model achieved promising results as evaluated by the RMSE metric.","PeriodicalId":383582,"journal":{"name":"Journal of Computer & Electrical and Electronics Engineering Sciences","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128318018","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

Power system fault detection and notification with Wi-Fi 电力系统故障检测和通知与Wi-Fi

Journal of Computer & Electrical and Electronics Engineering Sciences Pub Date : 2023-04-28 DOI: 10.51271/jceees-0004

Ümit Çatalca, Bahattin Emre Metin, Meral Özarslan Yatak, Çağdaş Hisar, Mustafa Teke

引用次数: 0

Journal of Computer &amp; Electrical and Electronics Engineering Sciences最新文献

Journal of Computer & Electrical and Electronics Engineering Sciences最新文献