Journal of Intelligent Systems with Applications最新文献_第3页

EEG based Spatial Attention Shifts Detection using Time-Frequency features on Empirical Wavelet Transform 基于经验小波变换时频特征的脑电空间注意转移检测

Journal of Intelligent Systems with Applications Pub Date : 2021-12-27 DOI: 10.54856/10.54856/jiswa.202112181

Gokhan Altan, Gulcin Inat

引用次数: 5

A Comparison of Different Strategies for The Modification of Quartz Tuning Forks Based Mass Sensitive Sensors Using Natural Melanin Nanoparticles 天然黑色素纳米粒子修饰石英音叉质敏传感器的不同策略比较

Journal of Intelligent Systems with Applications Pub Date : 2021-12-27 DOI: 10.54856/jiswa.202112177

Derya Demir, Sude Gundogdu, Seyda Kilic, Tugce Kartallioglu, Yusuf Alkan, Engin Baysoy, Gizem Kaleli Can

引用次数: 4

Forecasting Model to Predict the Spreading of the COVID-19 Outbreak in Turkey 预测2019冠状病毒病在土耳其蔓延的预测模型

Journal of Intelligent Systems with Applications Pub Date : 2021-12-27 DOI: 10.54856/jiswa.202112165

Ceyhun Bereketoglu, Nermin Ozcan, T. Kıran, M. L. Yola

{"title":"Forecasting Model to Predict the Spreading of the COVID-19 Outbreak in Turkey","authors":"Ceyhun Bereketoglu, Nermin Ozcan, T. Kıran, M. L. Yola","doi":"10.54856/jiswa.202112165","DOIUrl":"https://doi.org/10.54856/jiswa.202112165","url":null,"abstract":"This study aimed to forecast the future of the COVID-19 outbreak parameters such as spreading, case fatality, and case recovery values based on the publicly available epidemiological data for Turkey. We first performed different forecasting methods including Facebook's Prophet, ARIMA and Decision Tree. Based on the metrics of MAPE and MAE, Facebook's Prophet has the most effective forecasting model. Then, using Facebook's Prophet, we generated a forecast model for the evolution of the outbreak in Turkey fifteen-days-ahead. Based on the reported confirmed cases, the simulations suggest that the total number of infected people could reach 4328083 (with lower and upper bounds of 3854261 and 4888611, respectively) by April 23, 2021. Simulation forecast shows that death toll could reach 35656 with lower and upper bounds of 34806 and 36246, respectively. Besides, our findings suggest that although more than 86.38% growth in recovered cases might be possible, the future active cases will also significantly increase compared to the current active cases. This time series analysis indicates an increase trend of the COVID-19 outbreak in Turkey in the near future. Altogether, the present study highlights the importance of an efficient data-driven forecast model analysis for the simulation of the pandemic transmission and hence for further implementation of essential interventions for COVID-19 outbreak.","PeriodicalId":112412,"journal":{"name":"Journal of Intelligent Systems with Applications","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132778282","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

Interpolation-Based Smart Video Stabilization 基于插值的智能视频稳定

Journal of Intelligent Systems with Applications Pub Date : 2021-12-27 DOI: 10.54856/jiswa.202112185

Semiha Dervişoğlu, Mehmet Sarıgül, Levent Karacan

引用次数: 1

Manufacturing Multicolor LED-Based Phototherapy Device with a Novel 3D Design 一种新型3D设计的多色led光疗装置的制造

Journal of Intelligent Systems with Applications Pub Date : 2021-12-27 DOI: 10.54856/jiswa.202112174

Büşra Yaşar, Y. Isler, Nermin Topaloğlu Avşar

引用次数: 1

Pulse-Galvanic Skin Response Analysis with Multiple Sensor Device Design 多传感器器件设计的脉冲-电皮肤响应分析

Journal of Intelligent Systems with Applications Pub Date : 2021-12-27 DOI: 10.54856/jiswa.202112170

Mehmet Ali Dincer, Kubra Evren Sahin, Savaş Şahin

引用次数: 0

Detecting Abnormalities in Heart Sounds 检测心音异常

Journal of Intelligent Systems with Applications Pub Date : 2021-12-27 DOI: 10.54856/jiswa.202112180

Muhammed Telceken, Y. Kutlu

引用次数: 2

Ovarian Cancer Prediction Using PCA, K-PCA, ICA and Random Forest 基于PCA、K-PCA、ICA和随机森林的卵巢癌预测

Journal of Intelligent Systems with Applications Pub Date : 2021-12-27 DOI: 10.54856/jiswa.202112168

Asiye Şahin, Nermin Ozcan, G. Nur

{"title":"Ovarian Cancer Prediction Using PCA, K-PCA, ICA and Random Forest","authors":"Asiye Şahin, Nermin Ozcan, G. Nur","doi":"10.54856/jiswa.202112168","DOIUrl":"https://doi.org/10.54856/jiswa.202112168","url":null,"abstract":"Ovarian cancer, which is the most common in women and occurs mostly in the post-menopausal period, develops with the uncontrolled proliferation of the cells in the ovaries and the formation of tumors. Early diagnosis is very difficult and in most cases, it is a type of cancer that is in advanced stages when first diagnosed. While it tends to be treated successfully in the early stages where it is confined to the ovary, it is more difficult to treat in the advanced stages and is often fatal. For this reason, it has been focused on studies that predict whether people have ovarian cancer. In our study, we designed a RF-based ovarian cancer prediction model using a data set consisting of 49 features including blood routine tests, general chemistry tests and tumor marker data of 349 real patients. Since the data set containing too many dimensions will increase the time and resources that need to be spent, we reduced the dimension of the data with PCA, K-PCA and ICA methods and examined its effect on the result and time saving. The best result was obtained with a score of 0.895 F1 by using the new smaller-sized data obtained by the PCA method, in which the dimension was reduced from 49 to 6, in the RF method, and the training of the model took 18.191 seconds. This result was both better as a success and more economical in terms of time spent during model training compared to the prediction made over larger data with 49 features, where no dimension reduction method was used. The study has shown that in predictions made with machine learning models over large-scale medical data, dimension reduction methods will provide advantages in terms of time and resources by improving the prediction results.","PeriodicalId":112412,"journal":{"name":"Journal of Intelligent Systems with Applications","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133352594","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

Regional Signal Recognition of Body Sounds 人体声音的区域信号识别

Journal of Intelligent Systems with Applications Pub Date : 2021-12-27 DOI: 10.54856/jiswa.202112187

Osman Balli, Y. Kutlu

引用次数: 2

Diabetes Prediction Using Machine Learning Techniques 使用机器学习技术预测糖尿病

Journal of Intelligent Systems with Applications Pub Date : 2021-12-27 DOI: 10.54856/10.54856/jiswa.202112183

Seyma Kiziltas Koc, M. Yeniad

{"title":"Diabetes Prediction Using Machine Learning Techniques","authors":"Seyma Kiziltas Koc, M. Yeniad","doi":"10.54856/10.54856/jiswa.202112183","DOIUrl":"https://doi.org/10.54856/10.54856/jiswa.202112183","url":null,"abstract":"Technologies which are used in the healthcare industry are changing rapidly because the technology is evolving to improve people's lifestyles constantly. For instance, different technological devices are used for the diagnosis and treatment of diseases. It has been revealed that diagnosis of disease can be made by computer systems with developing technology.Machine learning algorithms are frequently used tools because of their high performance in the field of health as well as many field. The aim of this study is to investigate different machine learning classification algorithms that can be used in the diagnosis of diabetes and to make comparative analyzes according to the metrics in the literature. In the study, seven classification algorithms were used in the literature. These algorithms are Logistic Regression, K-Nearest Neighbor, Multilayer Perceptron, Random Forest, Decision Trees, Support Vector Machine and Naive Bayes. Firstly, classification performance of algorithms are compared. These comparisons are based on accuracy, sensitivity, precision, and F1-score. The results obtained showed that support vector machine algorithm had the highest accuracy with 78.65%.","PeriodicalId":112412,"journal":{"name":"Journal of Intelligent Systems with Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130555834","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: 2