Pub Date : 2022-10-12DOI: 10.34110/forecasting.1173063
A. Özdemir, Aytuğ Onan, Vildan ÇINARLI ERGENE
In this study, the performance of the proposed sample selection method was evaluated on some basic classifiers by conducting a basic literature review on the use of topic modelling methods by considering the online evaluations of the employees in order to determine and analyze the job satisfaction factors. In addition, the effectiveness of different representation structures are evaluated in order to represent the data sets effectively and the main results are obtained regarding the use of classification ensemble methods in the field of text mining. In this work it was emphasized that machine learning methods can achieve high performance in classification and work effectively and scalably with large data sets. The dataset used in this study was obtained from www.kaggle.com. A total of 67529 comments collected from people working at Google, Amazon, Netflix, Facebook, Apple and Microsoft were evaluated. Within the scope of this study, a text mining and artificial intelligence-based method will be developed and a solution will be brought to text mining with artificial intelligence methods.
{"title":"Topic Modelling and Artificial Intelligence Based Method Using Online Employee Assessments to Analyze Job Satisfaction","authors":"A. Özdemir, Aytuğ Onan, Vildan ÇINARLI ERGENE","doi":"10.34110/forecasting.1173063","DOIUrl":"https://doi.org/10.34110/forecasting.1173063","url":null,"abstract":"In this study, the performance of the proposed sample selection method was evaluated on some basic classifiers by conducting a basic literature review on the use of topic modelling methods by considering the online evaluations of the employees in order to determine and analyze the job satisfaction factors. In addition, the effectiveness of different representation structures are evaluated in order to represent the data sets effectively and the main results are obtained regarding the use of classification ensemble methods in the field of text mining. In this work it was emphasized that machine learning methods can achieve high performance in classification and work effectively and scalably with large data sets. The dataset used in this study was obtained from www.kaggle.com. A total of 67529 comments collected from people working at Google, Amazon, Netflix, Facebook, Apple and Microsoft were evaluated. Within the scope of this study, a text mining and artificial intelligence-based method will be developed and a solution will be brought to text mining with artificial intelligence methods.","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122344182","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}
Pub Date : 2022-08-25DOI: 10.34110/forecasting.1145381
Zeynep Nur Duman, Müzeyyen Büşra Çulcu, Oğuzhan Katar
Traffic is the situation and movement of pedestrians, animals, and vehicles on highways. The regulation of these movements and situations is also a basic problem of traffic engineering. It is necessary to collect data about traffic in order to produce suitable solutions to problems by traffic engineers. Traffic data can be collected with equipment such as cameras and sensors. However, these data need to be analyzed in order to transform them into meaningful information. For a difficult task such as calculating and optimizing traffic density, traffic engineers need information on the number of vehicles to be obtained from the image data they have collected. In this process, artificial intelligence-based computer systems can help researchers. This study proposes a deep learning-based system to detect vehicle objects using YOLOv5 model. A public dataset containing 15,474 high-resolution UAV images was used in the training of the model. Dataset samples were cropped to 640×640px sub-images, and sub-images that did not contain vehicle objects were filtered out. The filtered dataset samples were divided into 70% training, 20% validation, and 10% testing. The YOLOv5 model reached 99.66% precision, 99.44% recall, 99.66% mAP@0.5, and 89.35% mAP@0.5-0.95% during the training phase. When the determinations made by the model on the images reserved for the test phase are examined, it is seen that it has achieved quite successful results. By using the proposed approach in daily life, the detection of vehicle objects from high-resolution images can be automated with high success rates.
{"title":"YOLOv5-based Vehicle Objects Detection Using UAV Images","authors":"Zeynep Nur Duman, Müzeyyen Büşra Çulcu, Oğuzhan Katar","doi":"10.34110/forecasting.1145381","DOIUrl":"https://doi.org/10.34110/forecasting.1145381","url":null,"abstract":"Traffic is the situation and movement of pedestrians, animals, and vehicles on highways. The regulation of these movements and situations is also a basic problem of traffic engineering. It is necessary to collect data about traffic in order to produce suitable solutions to problems by traffic engineers. Traffic data can be collected with equipment such as cameras and sensors. However, these data need to be analyzed in order to transform them into meaningful information. For a difficult task such as calculating and optimizing traffic density, traffic engineers need information on the number of vehicles to be obtained from the image data they have collected. In this process, artificial intelligence-based computer systems can help researchers. This study proposes a deep learning-based system to detect vehicle objects using YOLOv5 model. A public dataset containing 15,474 high-resolution UAV images was used in the training of the model. Dataset samples were cropped to 640×640px sub-images, and sub-images that did not contain vehicle objects were filtered out. The filtered dataset samples were divided into 70% training, 20% validation, and 10% testing. The YOLOv5 model reached 99.66% precision, 99.44% recall, 99.66% mAP@0.5, and 89.35% mAP@0.5-0.95% during the training phase. When the determinations made by the model on the images reserved for the test phase are examined, it is seen that it has achieved quite successful results. By using the proposed approach in daily life, the detection of vehicle objects from high-resolution images can be automated with high success rates.","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"211 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133785332","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}
Pub Date : 2022-06-21DOI: 10.34110/forecasting.1126184
M. Öz, Serkan Budak, Ender Kurnaz, Akif Durdu
The use of unmanned aerial vehicles (UAV) systems has increased in recent years. Therefore,studies on UAVs have increased today. In this direction, the production of UAV systems with domestic resources has gained importance. In this study, it is desired to develop a domestic and national flight control card and software. In the flight control board designed for the UAV, it is aimed to keep the vehicle in balance in the air. Accurate measurement of platform orientation plays an important role in many applications such as aerospace, robotics, navigation, marine, machine interaction [1]. Inertial Measurement Unit (IMU) sensor was used to accurately measure the orientation of the UAV. IMU sensor is widely used in UAVs due to its light weight and low energy consumption. In this direction, the need for a filter has emerged in the IMU sensor, which is used to accurately measure the orientation of the unmanned aerial vehicle. In this study, a complementary filter was applied on the IMU sensor. Thanks to this filter, it has been observed that the accuracy of the data received from the IMU sensor has increased. Based on the data obtained, a Proportional Integral Derivative (PID) algorithm was developed, and the vehicle was kept in balance. In this study, ARMCortex-M4 based STM32F407VG microcontroller and MPU6050 as IMU sensor were used. Keil-uVision5 compiler is preferred for software. As a result, high accuracy in the orientation detection of unmanned aerial vehicles was obtained by applying a complementary filter on the IMU sensor.
{"title":"Orientation Determination in IMU Sensor with Complementary Filter","authors":"M. Öz, Serkan Budak, Ender Kurnaz, Akif Durdu","doi":"10.34110/forecasting.1126184","DOIUrl":"https://doi.org/10.34110/forecasting.1126184","url":null,"abstract":"The use of unmanned aerial vehicles (UAV) systems has increased in recent years. Therefore,studies on UAVs have increased today. In this direction, the production of UAV systems with domestic resources has gained importance. In this study, it is desired to develop a domestic and national flight control card and software. In the flight control board designed for the UAV, it is aimed to keep the vehicle in balance in the air. Accurate measurement of platform orientation plays an important role in many applications such as aerospace, robotics, navigation, marine, machine interaction [1]. Inertial Measurement Unit (IMU) sensor was used to accurately measure the orientation of the UAV. IMU sensor is widely used in UAVs due to its light weight and low energy consumption. In this direction, the need for a filter has emerged in the IMU sensor, which is used to accurately measure the orientation of the unmanned aerial vehicle. In this study, a complementary filter was applied on the IMU sensor. Thanks to this filter, it has been observed that the accuracy of the data received from the IMU sensor has increased. Based on the data obtained, a Proportional Integral Derivative (PID) algorithm was developed, and the vehicle was kept in balance. In this study, ARMCortex-M4 based STM32F407VG microcontroller and MPU6050 as IMU sensor were used. Keil-uVision5 compiler is preferred for software. As a result, high accuracy in the orientation detection of unmanned aerial vehicles was obtained by applying a complementary filter on the IMU sensor.","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131283393","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}
Pub Date : 2022-06-09DOI: 10.34110/forecasting.1125559
Serkan Budak, Muhammet Tekin, Akif Durdu, Cemil Sungur
Unmanned underwater vehicles (ROV/AUV) are autonomous or remotely controlled robotic systems that can move underwater at any desired angle. Unmanned underwater vehicles; It is used in areas such as underwater image taking, ship maintenance and repair, coast guard, examination of shipwrecks, underwater cleaning. In this study, the software design of the balance control of underwater vehicles was carried out using the PID algorithm. For the PID algorithm trial, a two-motor test setup with an IMU sensor was prepared. After the data from the sensor were recorded in MATLAB using the Kalman filter, the transfer function of the system was obtained using the System Identification Toolbox. With the obtained transfer function, the stable operation of the system is provided in real time. As a result of the researches on software and hardware integration, microcontroller ARM-based STM32 was used.
{"title":"Kalman Filter and PID Application on Underwater Vehicles","authors":"Serkan Budak, Muhammet Tekin, Akif Durdu, Cemil Sungur","doi":"10.34110/forecasting.1125559","DOIUrl":"https://doi.org/10.34110/forecasting.1125559","url":null,"abstract":"Unmanned underwater vehicles (ROV/AUV) are autonomous or remotely controlled robotic systems that can move underwater at any desired angle. Unmanned underwater vehicles; It is used in areas such as underwater image taking, ship maintenance and repair, coast guard, examination of shipwrecks, underwater cleaning. In this study, the software design of the balance control of underwater vehicles was carried out using the PID algorithm. For the PID algorithm trial, a two-motor test setup with an IMU sensor was prepared. After the data from the sensor were recorded in MATLAB using the Kalman filter, the transfer function of the system was obtained using the System Identification Toolbox. With the obtained transfer function, the stable operation of the system is provided in real time. As a result of the researches on software and hardware integration, microcontroller ARM-based STM32 was used.","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127891935","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}
Pub Date : 2022-04-22DOI: 10.34110/forecasting.1104066
P. Bhattacharjee
{"title":"Enhancing the Yearly Profit of a Wind Farm Using a Novel Transfer Function for Binary Particle Swarm Optimization Algorithm","authors":"P. Bhattacharjee","doi":"10.34110/forecasting.1104066","DOIUrl":"https://doi.org/10.34110/forecasting.1104066","url":null,"abstract":"","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126594900","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}
Pub Date : 2022-04-19DOI: 10.34110/forecasting.1055932
Cansın Kemal Can
{"title":"Probabilistic Approach to the Future Course of Fiscal Stability in Turkey: 1958 – 2025","authors":"Cansın Kemal Can","doi":"10.34110/forecasting.1055932","DOIUrl":"https://doi.org/10.34110/forecasting.1055932","url":null,"abstract":"","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126309062","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}
Pub Date : 2022-03-30DOI: 10.34110/forecasting.1077416
O. Dalkılıç, Naime Demirtaş
{"title":"A mathematical analysis of the relationship between the vaccination rate and COVID-19 pandemic in Turkey","authors":"O. Dalkılıç, Naime Demirtaş","doi":"10.34110/forecasting.1077416","DOIUrl":"https://doi.org/10.34110/forecasting.1077416","url":null,"abstract":"","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114159670","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}
Pub Date : 2021-12-28DOI: 10.34110/forecasting.1035912
Fatih Cemrek, Özge Güneş
{"title":"Estimating CO2 Emission Time Series with Support Vector, Artificial Neural Networks and Classic Time Series Analysis","authors":"Fatih Cemrek, Özge Güneş","doi":"10.34110/forecasting.1035912","DOIUrl":"https://doi.org/10.34110/forecasting.1035912","url":null,"abstract":"","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126593993","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}
Pub Date : 2021-09-06DOI: 10.34110/forecasting.957494
F. Yiğit, Şakir Esnaf, Bahar Yalcin
{"title":"A Poisson-Regression, Support Vector Machine and Grey Prediction Based Combined Forecasting Model Proposal: A Case Study in Distribution Business","authors":"F. Yiğit, Şakir Esnaf, Bahar Yalcin","doi":"10.34110/forecasting.957494","DOIUrl":"https://doi.org/10.34110/forecasting.957494","url":null,"abstract":"","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114370624","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}
Pub Date : 2021-06-07DOI: 10.34110/forecasting.917300
Uğurcan Ayik, Gökhan Erkal
{"title":"Forecasting of Unemployment and Economic Growth for Turkey: ARIMA Model Application","authors":"Uğurcan Ayik, Gökhan Erkal","doi":"10.34110/forecasting.917300","DOIUrl":"https://doi.org/10.34110/forecasting.917300","url":null,"abstract":"","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114699123","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}
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