Journal of Intelligent Systems with Applications最新文献

{"title":"Machine Learning Based Electric Energy Consumption Prediction of a Large-Scaled Production Plant with Small-Scaled Data","authors":"Volkan Ozdemir, Anil Çaliskan, A. Yiğit","doi":"10.54856/jiswa.202012124","DOIUrl":"https://doi.org/10.54856/jiswa.202012124","url":null,"abstract":"This report covers the statistical approach to predict consumed energy for a tire production plant. The reasons behind this study are also to optimize the energy consumption budget and to follow the production area wised KPIs which is also vital for ISO 50001 Energy management system standard. In order to make it happen, writers clarify the main problem, then start to apply the steps of the cross industry standard process for data mining (CRISP-DM) [1] methodology. The most important point of this study was that although the historical data is small scaled, the parameters have a higher dimension according to input examples. Hence, the data to be used as input could be explained with simple variables to be used in the budget period. The study introduces data preparation steps based on the production area, grid search for best regression algorithm, comparison of models, and seven-month validation results.","PeriodicalId":112412,"journal":{"name":"Journal of Intelligent Systems with Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129224535","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}

{"title":"Usage of Machine Learning Algorithms on Precision Agriculture Applications","authors":"Yekta Can Yildirim, M. Yeniad","doi":"10.54856/jiswa.202012129","DOIUrl":"https://doi.org/10.54856/jiswa.202012129","url":null,"abstract":"Agricultural monitoring and analysis of data to be used in management decisions to increase the quality, profitability, sufficiency, continuity and efficiency of agricultural production is called Precision Agriculture.[1]Precision Agriculture technologies aim to help the farmers with the decision making process by providing them information and control over their land, crop status and environment using remote sensing systems. Remote sensing systems use multispectral cameras to gather information, which filter different wavelengths of light in separate bands. Vegetation indices derived from the spectral bands of the remote sensing systems carry useful information about crop characteristics such as nitrogen content, chlorophyll content and water stress which supports the farmers to plan irrigation and pesticide spraying processes without the need of manual examination, providing a cost and time-efficient solution. This study aims to explore three specific Precision Agriculture applications, such as crop segmentation, illness detection and yield prediction on olive trees in Manisa, Turkey by using machine learning algorithms. Using the spectral band information gathered from an Orange-Cyan-NIR (OCN) camera embedded UAV system, vegetation health index was calculated and the data was preprocessed by segmentating the tree pixels from background based on those values using MiniBatchKMeans algorithm. Optimal features were selected based on accuracy comparison for yield and disease predictions. A Decision Tree Regressor (DTR) model was trained for yield prediction while a Random Forest Classifier (RFC) model was trained for disease prediction. The results showed that crop segmentation had an accuracy rate of 0.85-0.95, while DTR and RFC models had an R2 score of 0.99 and accuracy rate of 0.98 respectively, which displayed the importance and usefulness of vegetation indices.","PeriodicalId":112412,"journal":{"name":"Journal of Intelligent Systems with Applications","volume":"5 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116743552","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}

{"title":"Classification of Phonocardiography Signals Using Imbalanced Machine Learning Techniques","authors":"M. B. Selek, Sude Pehlivan, Y. Isler","doi":"10.54856/jiswa.202012128","DOIUrl":"https://doi.org/10.54856/jiswa.202012128","url":null,"abstract":"Cardiovascular diseases, which involve heart and blood vessel dysfunctions, cause a higher number of deaths than any other disease in the world. Throughout history, many approaches have been developed to analyze cardiovascular health by diagnosing such conditions. One of the methodologies is recording and analyzing heart sounds to distinguish normal and abnormal functioning of the heart, which is called Phonocardiography. With the emergence of machine learning applications in healthcare, this process can be automated via the extraction of various features from phonocardiography signals and performing classification with several machine learning algorithms. Many studies have been conducted to extract time and frequency domain features from the phonocardiography signals by segmenting them first into individual heart cycles, and then by classifying them using different machine learning and deep learning approaches. In this study, various time and frequency domain features have been extracted using the complete signal rather than just segments of it. Random Forest algorithm was found to be the most successful algorithm in terms of accuracy as well as recall and precision.","PeriodicalId":112412,"journal":{"name":"Journal of Intelligent Systems with Applications","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127012923","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}

{"title":"Process Analysis in Production of Desired Quality Steel in Ladle Furnaces in Iron and Steel Industry","authors":"Gokce Ozdes, Y. Kutlu","doi":"10.54856/jiswa.202012131","DOIUrl":"https://doi.org/10.54856/jiswa.202012131","url":null,"abstract":"Iron production in the iron and steel industry is a process that starts with the melting of scrap in electric arc furnaces or iron ore in basic oxygen furnaces. The proportions of the alloys in the liquid steel obtained from the liquid steel obtained by melting scrap are of great importance in order to produce the desired quality iron. In steel production, it is necessary to reduce the carbon rate to the desired level, to reduce the proportions of manganese, silicon and other chemicals to the values prescribed in the prescription, and to remove sulfur from liquid steel as much as possible. Therefore, alloys are added (FeSiMnPOTP, AltelPOTP, GrnKrbnPOTP, FeMnOrtCPOTP, KirecPOTP, FeSiPOTP, AlPOTP, FlşptPOTP etc.). Each alloy added has a chemical that acts. For example; If it is desired to change the aluminum ratio of liquid steel, AltelPOTP alloy is added. In the analysis results, it is observed that the aluminum ratios have changed. The liquid steel transferred to the ladle furnace is analyzed at certain intervals and the addition of chemical alloys continues until the required ratios are obtained. Chemical alloys added to liquid steel should not be less or more than they should be, in terms of both material and quality standards. Because the mentioned alloys are serious cost items when purchased in dollars and spread over a long term. For this reason, the rates should be adjusted very accurately. All these metallurgical processes are complex, multivariate systems. Looking at the examinations made, it is seen that while the alloys to be added to the liquid steel in the ladle furnace are rehearsed for an average of 4 times in a casting, this process is repeated at least 2 and at most 6 times. Taking samples from the liquid steel in the ladle furnace, sending the sample for chemical analysis, obtaining the result of chemical analysis and repeating these processes if the desired quality standards are not obtained, the average time is 45 minutes. These periods cause serious waste of time. For this reason, the time of the next casting has to be started later than the planned time. This causes delay in the subsequent processes (pouring liquid steel into molds in continuous casting, forming in the rolling mill, passing through quality tests, etc.). Today, with the advancement of technology, the use of artificial intelligence in the iron and steel industry will be a mandatory approach to minimize the number of proofs and minimize the loss of material and temporal labor.","PeriodicalId":112412,"journal":{"name":"Journal of Intelligent Systems with Applications","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114558923","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}

{"title":"Patient Survival Prediction with Machine Learning Algorithms","authors":"M. B. Selek, S. S. Egeli, Y. Isler","doi":"10.54856/jiswa.202012126","DOIUrl":"https://doi.org/10.54856/jiswa.202012126","url":null,"abstract":"In this study, the intensive care unit patient survival is predicted by machine learning algorithms according to the examinations performed in the first 24 hours. The data of intensive care patients collected from approximately two hundred hospitals over a period of one year were used. Algorithms are run in Python environment. Machine learning models were compared with the Cross-Validation method, and the random forest algorithm is used. The model made the prediction with 92,53% accuracy rate.","PeriodicalId":112412,"journal":{"name":"Journal of Intelligent Systems with Applications","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122045849","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}

{"title":"Predictive Maintenance Studies Applied to an Industrial Press Machine Using Machine Learning","authors":"Erkut Yiğit, M. Z. Bilgin, Ahmet Erdem Oner","doi":"10.54856/jiswa.202012117","DOIUrl":"https://doi.org/10.54856/jiswa.202012117","url":null,"abstract":"The main purpose of Industry 4.0 applications is to provide maximum uptime throughout the production chain, to reduce production costs and to increase productivity. Thanks to Big Data, Internet of Things (IoT) and Machine Learning (ML), which are among the Industry 4.0 technologies, Predictive Maintenance (PdM) studies have gained speed. Implementing Predictive Maintenance in the industry reduces the number of breakdowns with long maintenance and repair times, and minimizes production losses and costs. With the use of machine learning, equipment malfunctions and equipment maintenance needs can be predicted for unknown reasons. A large amount of data is needed to train the machine learning algorithm, as well as adequate analytical method selection suitable for the problem. The important thing is to get the valuable signal by cleaning the data from noise with data processing. In order to create prediction models with machine learning, it is necessary to collect accurate information and to use many data from different systems. The existence of large amounts of data related to predictive maintenance and the need to monitor this data in real time, delays in data collection, network and server problems are major difficulties in this process. Another important issue concerns the use of artificial intelligence. For example, obtaining training data, dealing with variable environmental conditions, choosing the ML algorithm better suited to a specific scenario, necessity of information sensitive to operational conditions and production environment are of great importance for analysis. In this study, predictive maintenance studies for the transfer press machine used in the automotive industry, which can predict the maintenance need time and give warning messages to the relevant people when abnormal situations approach, are examined. First of all, various sensors have been placed in the machine for the detection of past malfunctions and it has been determined which data will be collected from these sensors. Then, machine learning algorithms used to detect anomalies with the collected data and model past failures were created and an application was made in a factory that produces automotive parts.","PeriodicalId":112412,"journal":{"name":"Journal of Intelligent Systems with Applications","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129434058","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}

{"title":"Classification of Epileptic and Normal EEG Signals Using Power Spectrum of Sub-bands","authors":"Sude Pehlivan, Y. Isler","doi":"10.54856/jiswa.202005095","DOIUrl":"https://doi.org/10.54856/jiswa.202005095","url":null,"abstract":"The early diagnosis of epilepsy, which affects the lives of many people worldwide, is the first step of treatment to help patients to continue their lives efficiently. Experts have to spend a lot of time and energy to make this diagnosis as quickly and accuratelyaspossible.The aimofthisstudywasto investigatethe capacity of machine learning algorithms to distinguish epileptic and normal signals to develop a system that can automatically diagnose seizures. LabVIEW was used to obtain the sum of EEG sub-band powers which were used as an attribute for both epileptic and normal records. These attributes were classified with different classifiers using Matlab and as a result of the classification, it was concluded that the sub-band power sum can be used as a meaningful attribute in the classification of epileptic and normal EEG signals.","PeriodicalId":112412,"journal":{"name":"Journal of Intelligent Systems with Applications","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116524945","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}

{"title":"Electronic Stethoscope Design","authors":"M. B. Selek, Mert Can Duyar, Y. Isler","doi":"10.54856/jiswa.202005098","DOIUrl":"https://doi.org/10.54856/jiswa.202005098","url":null,"abstract":"Nowadays, despite the developing technology lots of patients lost their lives because of wrong and late diagnosis. With early diagnosis, most diseases and negative effects of the diseases for the patient can be prevented. Early diagnosis can also prevent cardiological diseases. Although auscultation of the chest with a stethoscope is an effective and basic method, a stethoscope isn't enough for the diagnosis of some diseases. One example of these diseases is heart valve malfunctions when the valves do not work as desired heart murmurs occur. The main goal of this project is to develop an electronic stethoscope and observing obtained signals as a graphic. The main difficulty while auscultation of chest with a stethoscope is, this procedure needs lots of experience and also even tough physician have enough experience, it's very hard to diagnose grade 1 and 2 heart murmurs. Furthermore, while auscultation tachycardia patients, generally it's very hard to decide where the first heart (S1) sound and second heart sound (S2) begins. In this project, it is planned to demonstrate heart sounds as a graphic. This method provides physicians to diagnose all kinds of chest sounds easily even the sounds which they cannot diagnose or recognize with their ears by stethoscope. Moreover, as the chest sounds are obtained as digital data, these data can be sent as desired. When a physician needs to get someone else's idea, these recordings can be sent to another professional.","PeriodicalId":112412,"journal":{"name":"Journal of Intelligent Systems with Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126172633","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}

{"title":"Determination of Tumor Boundaries in FLAIR Sequence MR Image with Different Image Segmentation Algorithms","authors":"Muhammet Usame Ozic, Cansu Gunes, Ahmet Avci","doi":"10.54856/jiswa.202005112","DOIUrl":"https://doi.org/10.54856/jiswa.202005112","url":null,"abstract":"Tumors are undesired tissue disorders that occur in many different parts of the body. These disorders can be either benign or malignant depending on their type. Brain tumors are non-brain structures that are frequently encountered in neurology. These structures negatively affect daily life by disrupting the functional centers of the person with respect to their region in the brain. Determining certain boundaries of tumor areas in radiology is an important parameter for treatment and diagnosis. In this study, segmentation of the tumor region on FLAIR sequence MR image taken from the BRATS database has been tried with seven different image processing algorithms. Segmentation performances of algorithms have been determined by using dice and jaccard indexes.","PeriodicalId":112412,"journal":{"name":"Journal of Intelligent Systems with Applications","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116361510","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}

{"title":"Design and Implementation of Digital Filters for ECG Data Based on Field Programmable Gate Array and MATLAB","authors":"Emre Cancioglu, Gokberk Cakiroglu, Alkim Gokcen, Yilmaz Sefa Altanay","doi":"10.54856/jiswa.202005102","DOIUrl":"https://doi.org/10.54856/jiswa.202005102","url":null,"abstract":"This study provides design and implementation of four digital filters (low pass, high pass, band pass and band stop) for ECG (electrocardiogram) data on FPGA with MATLAB by a serial communication. The study is conducted with using ECG data which is obtained from PhysioBank Database platform. SysGen (System Generator for DSP) which is a toolbox for MATLAB is used for designing and implementing the digital filters. The aim of the study is to perform four different digital filters with various blocks on the SysGen Toolbox. The study then examines the results of four different digital filters.","PeriodicalId":112412,"journal":{"name":"Journal of Intelligent Systems with Applications","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123241181","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}