International Journal of Applied Mathematics Electronics and Computers最新文献_第8页

Analysis and detection of Titanic survivors using generalized linear models and decision tree algorithm 基于广义线性模型和决策树算法的泰坦尼克号幸存者分析与检测

International Journal of Applied Mathematics Electronics and Computers Pub Date : 2020-10-09 DOI: 10.18100/ijamec.785297

Burcu Durmuş, Ö. I. Güneri

{"title":"Analysis and detection of Titanic survivors using generalized linear models and decision tree algorithm","authors":"Burcu Durmuş, Ö. I. Güneri","doi":"10.18100/ijamec.785297","DOIUrl":"https://doi.org/10.18100/ijamec.785297","url":null,"abstract":"In the article, it is aimed to investigate the factors affecting survival in today's legendary giant accident with different methods. The analysis aims to find the method that best determines survival. For this purpose, logit and probit models from generalized linear models and random tree algorithm from decision tree methods were used. The study was carried out in two stages. Firstly; in the analysis made with generalized linear models, variables that did not contribute significantly to the model were determined. Classification accuracy was found to be 79.89% for the logit model and 79.04% for the probit model. In the second stage; classification analysis was performed with random tree decision trees. Classification accuracy was determined to be 77.21%. In addition; according to the results obtained from the generalized linear models, the classification analysis was repeated by removing the data that made meaningless contribution to the model. The classification rate increased by 4.36% and reached 81.57%. After all; It was determined that the decision tree analysis made with the variables extracted from the model gave better results than the analysis made with the original variables. These results are thought to be useful for researchers working on classification analysis. In addition, the results can be used for purposes such as data preprocessing, data cleaning.","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116110536","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

Classification of Emg Signals Using Convolution Neural Network 基于卷积神经网络的肌电信号分类

International Journal of Applied Mathematics Electronics and Computers Pub Date : 2020-10-09 DOI: 10.18100/ijamec.795227

Kaan Bakircioğlu, Nalan Özkurt

{"title":"Classification of Emg Signals Using Convolution Neural Network","authors":"Kaan Bakircioğlu, Nalan Özkurt","doi":"10.18100/ijamec.795227","DOIUrl":"https://doi.org/10.18100/ijamec.795227","url":null,"abstract":"An electrical signal is produced by the contraction of the muscles; this electrical signal contains information about the muscles, the recording of these signals called electromyography (EMG). This information is often used in studies such as prosthetic arm, muscle damage detection, and motion detection. Classifiers such as artificial neural networks, support vector machines are generally used for the classification of EMG signals. Despite successful results with such methods the extraction of the features to be given to the classifiers and the selection of the features affect the classification success. In this study, it is aimed to increase the success of the classification of the daily used hand movements using the Convolutional neural networks (CNN). The advantage of the deep learning techniques like CNN is that the relationships in big data are learned by the network. Firstly, the received EMG signals for forearms are windowed to increase the number of data and focus on the contraction points. Then, to compare the success rate, raw signals, Fourier transform of the signal, the root means square, and the Empirical mode decomposition (EMD) is applied to the signal and intrinsic mode functions are obtained. These signals are given to four different CNN. Afterward, to find the most efficient parameters, the results were obtained by splitting data set into three as 70% training set, 15% validation set, and 15% test set. 5 cross-validations have been applied to assess the system’s performance. The best results are obtained from the CNN, which receive the EMD applied signal as input. The result obtained with the cross-validation is 95.90% and the result obtained with the other separation method is 93.70%. When the results were examined, it was seen that CNN is a promising classifier even the raw signal is applied to the classifier. Also, it has been observed that EMD method creates better accuracy of classification. This is an open access article under the CC BY-SA 4.0 license. (https://creativecommons.org/licenses/by-sa/4.0/)","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130829316","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: 3

Control and Monitor of IoT Devices using EOG and Voice Commands 使用EOG和语音命令控制和监控物联网设备

International Journal of Applied Mathematics Electronics and Computers Pub Date : 2020-10-01 DOI: 10.18100/IJAMEC.799507

Ayman A. Wazwaz, Mohammad Ziada, Lubna Awawdeh, M. Tahboub

引用次数: 1

Discovering the same job ads expressed with the different sentences by using hybrid clustering algorithms 利用混合聚类算法发现用不同句子表达的相同招聘广告

International Journal of Applied Mathematics Electronics and Computers Pub Date : 2020-09-30 DOI: 10.18100/IJAMEC.797572

Y. Dogan, Feriştah Dalkılıç, R. A. Kut, K. C. Kara, Uygar Takazoğlu

{"title":"Discovering the same job ads expressed with the different sentences by using hybrid clustering algorithms","authors":"Y. Dogan, Feriştah Dalkılıç, R. A. Kut, K. C. Kara, Uygar Takazoğlu","doi":"10.18100/IJAMEC.797572","DOIUrl":"https://doi.org/10.18100/IJAMEC.797572","url":null,"abstract":"Text mining studies on job ads have become widespread in recent years to determine the qualifications required for each position. It can be said that the researches made for Turkish are limited while a large resource pool is encountered for the English language. Kariyer.Net is the biggest company for the job ads in Turkey and 99% of the ads are Turkish. Therefore, there is a necessity to develop novel Natural Language Processing (NLP) models in Turkish for analysis of this big database. In this study, the job ads of Kariyer.Net have been analyzed, and by using a hybrid clustering algorithm, the hidden associations in this dataset as the big data have been discovered. Firstly, all ads in the form of HTML codes have been transformed into regular sentences by the means of extracting HTML codes to inner texts. Then, these inner texts containing the core ads have been converted into the sub ads by traditional methods. After these NLP steps, hybrid clustering algorithms have been used and the same ads expressed with the different sentences could be managed to be detected. For the analysis, 57 positions about Information Technology sectors with 6,897 ad texts have been focused on. As a result, it can be claimed that the clusters obtained contain useful outcomes and the model proposed can be used to discover common and unique ads for each position.","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126129294","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

Detection and differentiation of COVID-19 using deep learning approach fed by x-rays 利用x射线提供的深度学习方法检测和区分COVID-19

International Journal of Applied Mathematics Electronics and Computers Pub Date : 2020-09-30 DOI: 10.18100/IJAMEC.799651

Ç. Erdaş, Didem Ölçer

{"title":"Detection and differentiation of COVID-19 using deep learning approach fed by x-rays","authors":"Ç. Erdaş, Didem Ölçer","doi":"10.18100/IJAMEC.799651","DOIUrl":"https://doi.org/10.18100/IJAMEC.799651","url":null,"abstract":"The coronavirus, which appeared in China in late 2019, spread over the world and became an epidemic. Although the mortality rate is not very high, it has hampered the lives of people around the world due to the high rate of spread. Moreover, compared to other individuals in the society, the mortality rate in elderly individuals and people with chronic disease is high. The early detection of infected individuals is one of the most effective ways to both fight disease and slow the outbreak. In this study, a deep learning approach, which is alternative and supportive of traditional diagnostic tools and fed with chest x-rays, has been developed. The purpose of this deep learning approach, which has the convolutional neural networks (CNNs) architecture, is (1) to diagnose pneumonia caused by a coronavirus, (2) to find out if a patient with symptoms of pneumonia on chest X-ray is caused by bacteria or coronavirus. For this purpose, a new database has been brought together from various publicly available sources. This dataset includes 50 chest X-rays from people diagnosed with pneumonia caused by a coronavirus, 50 chest X-rays from healthy individuals belonging to the control group, and 50 chest X-rays from people diagnosed with bacterium from pneumonia. Our approach succeeded in terms of accuracy of 92% for corona virus-based pneumonia diagnosis tasks (1) and 81% for the task of finding the origin of pneumonia (2). Besides, achievements for Area Under the ROC Curve (ROC_AUC), Precision, Recall, F1-score, Specificity, and Negative Predictive Value (NPV) metrics are specified in this paper.","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127728571","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: 3

Improved Global Localization and Resampling Techniques for Monte Carlo Localization Algorithm 蒙特卡罗定位算法的改进全局定位和重采样技术

International Journal of Applied Mathematics Electronics and Computers Pub Date : 2020-09-30 DOI: 10.18100/IJAMEC.800166

Humam Abualkebash, H. Ocak

{"title":"Improved Global Localization and Resampling Techniques for Monte Carlo Localization Algorithm","authors":"Humam Abualkebash, H. Ocak","doi":"10.18100/IJAMEC.800166","DOIUrl":"https://doi.org/10.18100/IJAMEC.800166","url":null,"abstract":"Global indoor localization algorithms enable the robot to estimate its pose in pre-mapped environments using sensor measurements when its initial pose is unknown. The conventional Adaptive Monte Carlo Localization (AMCL) is a highly efficient localization algorithm that can successfully cope with global uncertainty. Since the global localization problem is paramount in mobile robots, we propose a novel approach that can significantly reduce the amount of time it takes for the algorithm to converge to true pose. Given the map and initial scan data, the proposed algorithm detects regions with high likelihood based on the observation model. As a result, the suggested sample distribution will expedite the process of localization. In this study, we also present an effective resampling strategy to deal with the kidnapped robot problem that enables the robot to recover quickly when the sample weights drop-down due to unmapped dynamic obstacles within the sensor’s field of view. The proposed approach distributes the random samples within a circular region centered around the robot’s pose by taking into account the prior knowledge about the most recent successful pose estimation. Since the samples are distributed over the region with high probabilities, it will take less time for the samples to converge to the actual pose. The percentage of improvement for the small sample set (500 samples) exceeded 90% over the large maps and played a big role in reducing computational resources. In general, the results demonstrate the localization efficacy of the proposed scheme, even with small sample sets. Consequently, the proposed scheme significantly increases the real-time performance of the algorithm by 85.12% on average in terms of decreasing the computational cost.","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114560115","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

Framingham Risk Score by Data Mining Method 基于数据挖掘方法的Framingham风险评分

International Journal of Applied Mathematics Electronics and Computers Pub Date : 2020-09-30 DOI: 10.18100/IJAMEC.795224

Ş. Kitiş

{"title":"Framingham Risk Score by Data Mining Method","authors":"Ş. Kitiş","doi":"10.18100/IJAMEC.795224","DOIUrl":"https://doi.org/10.18100/IJAMEC.795224","url":null,"abstract":"There are cleaning, integration, reduction, conversion, algorithm implementation and evaluation stages in data mining meaning finding necessary data from a wide variety of variables and data. It is important to create a data warehouse to realize these steps. Data randomly selected from data warehouse is evaluated with certain algorithms. While deaths resulting from heart diseases in our country are 37% according to 2016 data, 420-440 thousand people are diagnosed as heart patients each year and the number of deaths per year can reach 340 thousand people. These values correspond to approximately three times of Europe. In this study, risk of heart attack is calculated by data mining method by taking advantage of Framingham risk score. In order to determine this risk factor; 10-year risk is calculated by looking at sex, age, total cholesterol, HDL cholesterol, blood pressure, diabetes and smoking. While the effects of the ages for men starts -9 points, ends with +13 points and for women starts -7 points, ends with +16 points. While the effects of the total cholesterol for men starts 0 points, ends with +11 points and for women starts 0 points, ends with +13 points. Total scores are between 0-17 and over in men, and scores between 0-25 and over in women. There are risk values ranging from 1% to 30%.","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125105356","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

Vehicle Detection Using Fuzzy C-Means Clustering Algorithm 基于模糊c均值聚类算法的车辆检测

International Journal of Applied Mathematics Electronics and Computers Pub Date : 2020-09-30 DOI: 10.18100/ijamec.799431

Ridvan Saraçoglu, N. Nemati

引用次数: 2

Evaluating the Bank Queuing Systems by Fuzzy Logic 用模糊逻辑评价银行排队系统

International Journal of Applied Mathematics Electronics and Computers Pub Date : 2020-09-29 DOI: 10.18100/IJAMEC.797742

Halil Kilif, I. Ozkan

引用次数: 0

An ANFIS based inverse modeling for pneumatic artificial muscles 基于ANFIS的气动人工肌肉逆建模

International Journal of Applied Mathematics Electronics and Computers Pub Date : 2020-09-28 DOI: 10.18100/IJAMEC.797271

C. V. Baysal

{"title":"An ANFIS based inverse modeling for pneumatic artificial muscles","authors":"C. V. Baysal","doi":"10.18100/IJAMEC.797271","DOIUrl":"https://doi.org/10.18100/IJAMEC.797271","url":null,"abstract":"Pneumatic Artificial Muscles (PAM) are soft actuators with advantages of high force to weight ratio, flexible structure and low cost. On the other hand, their inherent nonlinear characteristics yield difficulties in modeling and control actions, which is an important factor restricting use of PAM. In literature, there are various modeling approaches such as virtual work , empirical and phenomenological models. However, they appear as either much complicated or are approximate ones as a variable stiffness spring for model with nonlinear input-output relationship. In this work, the behaviour of PAM is interpreted as an integrated response to pressure input that results in a simultaneous force and muscle length change. The integrated response behaviour of PAM is not combined effectively in terms of simultaneous resultant force and muscle contraction in many existing models. In order to implement that response, standard identification methods , for instance NNARX, are not suitable for modeling this behaviour. Moreover, an inverse modeling with grey box approach is proposed in order to utilize the model in control applications. Since Neuro-Fuzzy inference systems are universal estimators, the modeling is implemented by an ANFIS structure using the experimental data collected from PAM test bed. According to implementation results, the ANFIS based inverse model has yielded satisfactory performance deducing that it could be a simple and effective solution for PAM modeling and control issue.","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133877536","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