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To fully utilize the potential of solar energy and effectively operate solar energy systems, it is vital to know solar radiation values. Modeling solar radiation data with high variability is a complex problem, and nonlinear methods are needed. This study proposes a hybrid model using CNN and LSTM architectures for solar radiation prediction. The performance and applicability of the proposed model are examined by comparing it with ARIMA statistical method and different machine learning methods such as Random Forest, Decision Tree and K-Nearest Neighbor. The study used hourly solar radiation values measured with a pyranometer on the Afyon Kocatepe University campus. As a result of the experimental studies, the CNNLSTM model is the most successful model in terms of RMSE, MAE, MAPE and r² evaluation criteria, achieving 16.92%, 17.25%, 17.24%, 22.89% more successful results than the Decision Trees model, which produced the most unsuccessful predictions, respectively. [ABSTRACT FROM AUTHOR]

It has become imperative to monitor the data-driven industrial systems of today's technology before potential failures occur. Predictive maintenance predicts these failures before they occur and takes the necessary action to prevent malfunctions from occurring. In this study a comparative predictive maintenance application which is based on machine and deep learning is realized. Logistic Regression, Naive Bayes Classifier, Decision Tree, Support Vector Machine, Random Forest, and K-Nearest Neighborhood are used as the classical machine learning methods while Long Short-Term Memory and Gated Recurrent Unit are used as the deep learning architectures. The performances of the methods are examined on the Predictive Maintenance dataset from UCI Machine Learning Repository for fault type detection and the results are presented comparatively in terms of metrics in detail. In the experimental studies, fault type detection is handled separately in the form of multiple and binary classification problems. In the solution of the multiclassification problem, the highest accuracy among the machine learning methods is obtained by Random Forest method with 98.26%, while the accuracy value obtained with both deep learning architectures is 97.51%. In the solution of the binary classification problem, after the data balancing, the highest accuracy among the machine learning methods is obtained by Random Forest method with 95.03%, while the highest accuracy among the deep learning architectures is obtained by Gated Recurrent Unit architecture as 93.03%. [ABSTRACT FROM AUTHOR]

Sepsis, which is known as multiple organ failure, is the primary cause of mortality for all patients in intensive care units, regardless of their other illnesses. An intensive care unit decision support system that can predict sepsis in intensive care patients early and warns the doctor has been developed. Since the COVID-19 virus, the variant and number of intensive care patients have increased, so this study has been developed as a precaution to worsen the situation with sepsis. A user-friendly interface and system have been designed to help the physician better monitor the patient's sepsis status. It has been developed in order to meet the need for a decision support system that makes sepsis estimation in accordance with the reference intervals of Turkish patients' values. For a better result of predicting sepsis early, it has been concluded how the data obtained and used in a certain period of time should be analyzed and what methods could be used to estimate higher performance. In the study, machine learning (classification and regression), deep learning algorithms have been used for estimation and the results obtained have been compared. As an impact of research, an intensive care sepsis decision support system, which consists of 122400 hourly data of 300 intensive care patients and estimates with approximately between 88% and 94% successful results in accordance with the reference intervals of Turkish patients, has been developed. [ABSTRACT FROM AUTHOR]

Copyright of Viral Hepatitis Journal / Viral Hepatit Dergisi is the property of Galenos Yayinevi Tic. LTD. STI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji is the property of Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With technological advances, humans are constantly generating data through various electronic devices and sensors, and this data is stored in digital environments. A vast amount of data has served as a valuable asset that has facilitated the rise and progression of novel fields, including data science, artificial intelligence (AI), deep learning (DL), and the internet of things (IoT). Effectively managing and analyzing data provides a competitive advantage for modern businesses. The objective of this study is to forecast the stock price of Turkish Airlines (THY), a publicly traded corporation listed on Borsa Istanbul. In order to achieve the intended objective, the utilization of machine learning approaches like SVM and XGBoost, as well as the deep learning algorithm Long Short-Term Memory (LSTM), are used. The models are trained over a time period including daily data from January 4, 2010 to September 5, 2023. The forecast performance of the models is evaluated by comparing the actual and predicted stock prices and the model with the lowest error is identified. The proposed models' performances are assessed using the RMSE, MSE, MAE, and R2 error statistics. According to the results obtained, it is determined that the LSTM model has lower error coefficients than SVM and XGBoost models and gives the best performance. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of InformaticsTechnologies is the property of Institute of Informatics, Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of COMU Journal of Agriculture Faculty / ÇOMÜ Ziraat Fakültesi Dergisi is the property of Canakkale Onsekiz Mart University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Türk Radyoloji Seminerleri is the property of Galenos Yayinevi Tic. LTD. STI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the face of the rapidly increasing world population today, researchers have turned to studies aiming to use existing resources more effectively and efficiently, while also exploring new resources to meet the increasing demands, such as raw materials and nutrients. Hydroponic agriculture has been gaining popularity day by day as one of the alternative methods to fulfill crucial human needs. Its characteristics, including resistance to weather conditions, indoor applicability, and vertical orientation, set hydroponic agriculture apart from traditional methods. The absence of soil in this agricultural approach necessitates more observation and supervision. In this study, the use of machine learning was investigated to overcome the observation and surveillance processes that must be done continuously for the healthy growth of plants. Firstly, we developed a hydroponic prototype to assess this goal. With the developed hydroponic prototype, plant water and wastewater data to be used in the evaluation of the growth and development of plants were obtained by sensors. The data obtained through Arduino was stored in the database and used in the training processes of machine learning algorithms. Our experimental study, which utilized five machine learning and deep learning methods, demonstrated a significant increase in hydroponic agricultural efficiency. Notably, deep learning outperformed other methods with a 99.7% success rate. In conclusion, our study shows that machine learning can be used effectively in hydroponic agriculture by providing observation and surveillance of plants. [ABSTRACT FROM AUTHOR]

Copyright of Journal of University Research / Üniversite Araştırmaları Dergisi is the property of Journal of University Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

İnsanoğlu makinelerin insanlar gibi düşünebildiği ve hareket edebildiği bir çağın başlangıcında bulunuyor. Bu durum her ne kadar ürkütücü görünse de, akademide ilgi gören ve üzerinde artan miktarda çalışmalar gerçekleşmeye başlanan bir konudur. Makine öğrenmesi ve derin öğrenmeyle oluşturulan yapay zeka pek çok alanda olduğu gibi, finans alanında da çokça kullanılmaktadır. Bu çalışmalar içerisinde yurt içinde ve yurt dışında yayınlanan makale, kitap, kitap bölümleri, sempozyum bildirileri olduğu gibi, gerçekleştirilen yüksek lisans ve doktora tezleri de yer almaktadır. Bu tür çalışmalarda gelinen son durumu tespit etmek ve literatürdeki boşlukları ortaya çıkarmak amacıyla sistematik literatür taramaları yapılmaktadır. Bu çalışmada, Türkiye’de gerçekleştirilen ve uygulama bölümlerinde finans alanında makine öğrenmesi ve derin öğrenme tekniklerinin kullanıldığı lisansüstü tezler sistematik literatür taraması ile incelenmektedir. Araştırma, 2018-2023 yılları arasındaki dönemde yapılan çalışmaları kapsamaktadır. Araştırmanın sonucunda, konuyla ilgili yapılan tezlerde makine ve derin öğrenme yöntemlerinin en çok finansal enstrümanların gelecekteki fiyatlarının tahminlemesinde, ardından sırasıyla finansal risklerin tespit edilmesinde, kurumsal finansal başarısızlık ve iflas tahmininde ve ayrıca portföy optimizasyon modellerinde kullanıldığı belirlenmiştir. İlgili dönem boyunca, yapılan tez çalışmalarının sayılarında artan bir grafik bulunmaktadır. Bu çalışmalarda genellikle birden fazla algoritmanın uygulamadaki başarıları karşılaştırılarak en başarılı sonuçlar belirlenmeye çalışılmıştır. En çok çalışılan tez konusunun makine öğrenmesiyle kredi riskinin analizi olduğu, ardından makine öğrenmesiyle hisse senedi fiyat tahmininin geldiği ortaya çıkmıştır. En çok kullanılan algoritmaların karar ormanı, karar ağacı ve uzun-kısa dönem hafıza algoritmaları olduğu tespit edilmiştir. Lisansüstü tez konusu olarak sosyal bilimlerden daha çok, fen bilimleri temel alanında tercih edildiği ve yazılan tezlerin en çok bilgisayar mühendisliği ana bilim dalında hazırlandığı, ardından işletme ana bilim dalında hazırlanan tezlerin geldiği ortaya çıkmıştır.

Trafik yoğunluk tahmini, kullanıcıların daha iyi seyahat kararları verebilmeleri, trafik sıkışıklığının hafifletilmesi, zaman ve yakıt tasarrufu sağlanması ile trafik işlem verimliliğinin arttırılması açısından önemlidir. Akıllı ulaşım sistemlerinin gelişmesi ve yaygınlaşmasıyla birlikte trafik yoğunluğunun tahmin edilmesi giderek daha fazla ilgi görmeye başladı. Trafik yoğunluk tahmini, büyük ölçüde geçmiş ve gerçek zamanlı trafik verilerine bağlıdır. Sensörler, kameralar, mobil cihazlar ve sosyal medya gibi kaynaklarından anlık olarak büyük miktarlarda trafik verileri elde edilmektedir. Giderek artan trafik verileri, trafik yönetimi sorununu çözebilmek amacıyla yapay zekâ teknolojilerinin kullanımını ön plana çıkarmaktadır. Bu çalışmada, trafik yoğunluk tahminine yönelik LSTM tabanlı bir tahmin modeli geliştirilmiştir. Geliştirilen tahmin modeli LR, RF, SVM, MLP, CNN ve Recurrent Neural Network (RNN) ile İstanbul’un trafik verileri kullanılarak test edilmiştir. Deneysel sonuçlar, geliştirilen LSTM tabanlı modelin karşılaştırılan modellere göre daha başarılı sonuçlar ürettiğini ve kavşaktan geçen araç sayısı tahmininde 0,897 R2 değerine, kavşaktan geçen araçların ortalama hızlarının tahmininde ise 0,883 R2 değerine sahip olduğunu göstermiştir.

This study predicts the stock price of Petkim Petrokimya Holding Corp. (PETKM), which is listed in Borsa Istanbul (BIST), using PETKM stock price, US dollar (USD/TRY) price and BIST Chemical, Petroleum Plastic (XKMYA) index price. A time series data set with three inputs and one output is created using these data. Random Forest Regression (RFR), Long-Short Term Memory (LSTM), and Convolutional Neural Network (CNN) algorithms are used in the prediction model. The success of these methods is compared using performance metrics such as MSE, RMSE, MAE, and R2. According to the calculated error metrics, LSTM and RFR algorithms gave better results than CNN with an MSE value less than 0.02. However, the fact that the R2 values of the most successful models created with all three algorithms were greater than 95% revealed that all the algorithms mentioned could be used to estimate this data set.

Copyright of Erzincan University Journal of Science & Technology is the property of Erzincan Binali Yildirim Universitesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The rapid increase in data production, thanks to technological developments and scientific research, leads to the development of Machine Learning (ML) and similar new data analysis tools. It was announced that Amazon Web Services (AWS), a cloud service provider, stored 500EB of data in 2021 [1]. ML is an alternative to traditional engineering methods and does not require field knowledge of the problem to obtain a solution. However, the implementation of ML Algorithms can be complex depending on the content of the data set, and expert knowledge is the most important factor to use these algorithms effectively. Various methods have been developed to find a solution to this problem. There are many different areas and problems that machine learning can be applied to. We have limited our research to problems that can be solved using computer vision and AutoML. We have used AutoML and computer vision-based solutions to solve object classification, detection and segmentation problems. Our goal is to develop a platform that will work without the intervention of any expert. Users can load their datasets, choose the method they want, and train their models according to the problem they choose without any other intervention. After the training process is over, they can use their models in real time by transferring them over the platform in real time with their own hardware. [ABSTRACT FROM AUTHOR]

Small and Medium-sized Enterprises (SMEs) are private sector enterprises whose capital, workforce and assets are below the thresholds determined according to national regulations. SMEs play an important role in the economy of most countries in the world, especially in developing countries. SMEs, which make up approximately 90% of enterprises worldwide, provide more than 50% of employment. Estimating the contribution of SMEs to the economy at the country level is very important in terms of planning and investment. In this study, a deep learning-based model was developed to predict the contribution of SMEs to the economy. The developed LSTM-based deep learning model was compared with RF, SVM, CNN, GRU, MLP and RNN. Experimental results showed that the developed model had a better prediction performance than other models compared with 2.169 MSE, 1.473 RMSE, 1.175 MAE, and 0.959 R2 values. [ABSTRACT FROM AUTHOR]

Airspeed data is so important for an aircraft operation. This study is focused on the estimation of the airspeed data without any additional measurement source such as hardware redundancy. The flight data obtained from a commercial aircraft is processed with a deep learning algorithm, particularly LSTM recurrent neural networks that are developed based on Matrix Laboratory (MATLAB). Correlation analysis is carried out for related data according to a 95% confidence interval for each coefficient in the study to show strong predictor candidates. Data related to the airspeed are processed using Holdout Cross-Validation Technique. According to the results, the designed model achieved an R-squared of 0.9999, a root-mean-squared error of 0.8303 knots, and a standard error of 0.0092 knots. These results show that it is possible to accurately estimate aircraft airspeed data using LSTM recurrent neural network in case of the airspeed data cannot be provided to the flight crew. [ABSTRACT FROM AUTHOR]

Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Türkiye Klinikleri Journal of Ophthalmology is the property of Turkiye Klinikleri and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Itobiad: Journal of the Human & Social Science Researches / İnsan ve Toplum Bilimleri Araştırmaları Dergisi is the property of Itobiad: Journal of the Human & Social Science Researches and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper aims to apply a deep learning algorithm to estimate the prediction of various external financial input variables on the adoption of eco-innovation practices such as renewable energy operations by 5456 SMEs. A Long Short-Term Memory Units (LSTM) is utilized to the data set to assess the performance of different input variables on the adoption of renewable energy. Furthermore, we process the dataset with different machine learning algorithms and compare the results. The findings indicate that LSTM gives the highest performance for all metrics. As a result, some essential theoretical implications for management scholars are given. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Defense Sciences / Savunma Bilmleri Dergisi is the property of Turkish Military Academy Defense Sciences Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

One of the most common problems in engineering is contact problems. In recent years, researchers have turned to alternative methods that can offer effective solutions in a shorter time, instead of solutions containing complex and long mathematical expressions. This study focuses on the estimation of the contact lengths in a homogeneous elastic layer suppressed by two elastic punches with two solution methods. Firstly, a new model was designed for estimation using Deep Learning Neural Network (DNN), one of the deep learning structures. Estimation of contact lengths was provided with the output of the DNN model, which was fed with the homogeneous elastic layer, the ratio of shear modules of the punches and the input parameters of punch radii. The finite element method was used as the second solution method. The problem was modelled in the ANSYS programme, and the solution was made with the same parameters used in DNN modelled. The results obtained from both solutions were compared with the solutions obtained by the theory of elasticity and classical NN in the literature. It had been seen that the results obtained with DNN and ANSYS were compatible with the results obtained with analytical and classical NN and the margin of error was smaller. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of InformaticsTechnologies is the property of Institute of Informatics, Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Skin cancer is one of the most common type of cancer in humans. This type of cancer is produced by skin cells called melanocytes and occurs as a result of division and multiplication of the mentioned cells. The most important symptom of skin cancer is the formation of spots on the skin or the observation of changes in the shape, color, or size of the existing spot. It is necessary to consult a specialist to distinguish the difference between a normal spot and skin cancer. Expert physicians examine and follow up the spots on the skin using skin surface microscopy, called dermatoscopy, or take a sample from the suspicious area and request it to be examined in laboratory environment. This situation increases the cost of the procedure for the diagnosis of skin cancer and also causes it to be treated at a later stage. Therefore, there is a need for a method that can detect skin cancer early. Thanks to machine learning, become popular in recent years, many diseases can be diagnosed with software that helps expert physicians. In this study, VGGNet model structures (VGG-11, VGG13, VGG-16, VGG-19) that quickly classify skin cancer and become a traditional convolutional neural network architecture using deep learning method, a subfield of machine learning, were used. It has been observed that the VGG11 architecture, which is one of the VGGNet model structures, detects skin cancer with superior success accuracy (83%) compared to other model structures [ABSTRACT FROM AUTHOR]

Copyright of International Journal of InformaticsTechnologies is the property of Institute of Informatics, Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Infologica is the property of Acta Infologica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Infologica is the property of Acta Infologica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In parallel with the development of Internet and wireless communication technologies, studies in the field of IoT are also progressing. With the IoT sensors used in the healthcare field, it becomes easier to follow the patients closely. In addition, it is possible to create statistics that will support the treatment process with the data collected from the patients. However, besides the provided facilities, IoT devices that communicate wirelessly and are connected to the internet also have some problems such as security. Serious problems can occur as a result of attacks on different layers of IoT, which is used in healthcare environments. Exposure of sensitive data in the field of Health to any of these attacks can have negative consequences, such as changing the data out of reach of authorized users or capturing it by an attacker. This article attempts to predict duplication, interception, and modification attacks in Message Queuing Telemetry Transport (MQTT) message using artificial intelligence techniques using a complete and labeled IoT dataset containing real world behaviors in IoT networks. On the dataset used, SVM algorithm has Accuracy 85%, f1 98%, Recall 100% values; Naive Bayes (NB) algorithm has Accuracy 89%, f1 86%, Recall 100% values; LSTM has Loss 6.7%, Accuracy 98%, f1 98%, Recall 98%. The LSTM algorithm, which is a deep learning algorithm in detecting abnormal behaviors, has performed better than existing machine learning approaches with low loss and high accuracy data. [ABSTRACT FROM AUTHOR]

Mobile devices, which are becoming more and more widespread today, have turned into hand-held computers thanks to the multimedia communication and applications. Today, the multimedia applications have been supported by traditional mobile phones. With their increasing functionality, mobile devices are used for many purposes such as enriched internet experience, financial transactions, access to social media platforms, sharing, music and video. Performing transactions where sensitive personal data transfers such as banking and shopping are carried out on mobile devices make mobile devices the target of attackers. In this study, a deep learning based malware detection system has been developed based on the interactions of mobile applications on the network. The developed LSTM-based deep learning model has been analysed comparatively with NB, RF, SVM, MLP, CNN, RNN and GRU using accuracy, precision, recall and F-1 metrics. The experimental results showed that the developed LSTM-based deep learning model is more successful in malware detection than others with 95% accuracy. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) analytics, tools, and coding are increasingly used to improve quality in every part of the industry. However, such tools find their place in bugs, vulnerabilities, security points, duplications, and many more. Researchers around the world have conducted many academic studies with AI until today. The increasing development and use in the field in the last decade led to the need to research the publication amount, application areas, and achievements of countries in technology. This article used systematic research and literature review to determine the structured approach to the concepts of AI, machine learning, deep learning, and neural networks that can be searched in the TR Index. Although the results do not have an essential place in the TR Index, they can be evaluated as an accelerated increase of around 70% in the last three years. In the field distribution, engineering ranked first with 673 units. In addition, a decrease in the indexation rate of the subject is also observed. The results of this study may lead to the examination of the indexing of many publications made in our country in the TR Index.

Although air pollution is an important problem for today, reasons such as industrialization, forest fires, exhaust gases, poor quality fuel use confront us with a serious problem that will threaten future generations. Among these reasons, intensive industrialization is one of the most critical factors that play a role in air pollution. Regional industrial development affects air quality in cities. While the amount of some pollutants decreases with the development of the industry, there is an increase in ozone levels. In the coming years, it becomes inevitable to predict air pollution in order not to feel the problems that air pollution will cause more, to manage air quality, and to take precautions against risks. In this study, machine learning and deep learning methods based on time series were applied to predict hourly ozone levels between 2018 and 2021 for the provinces of Kocaeli and Sakarya, where the industry is developed, and Çanakkale, where the industry is not developed much. The applied models were compared using Mean Absolute Error (MAE), Relative Absolute Error (RAE), and R-square (R2) metrics, and it was aimed to determine the most effective method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Polytechnic is the property of Journal of Polytechnic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ankara University Faculty of Medicine / Ankara Üniversitesi Tip Fakültesi Mecmuasi is the property of Galenos Yayinevi Tic. LTD. STI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ankara University Faculty of Medicine / Ankara Üniversitesi Tip Fakültesi Mecmuasi is the property of Galenos Yayinevi Tic. LTD. STI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Electronic retailing (e-retail) is a type of retailing where the retailer and customers communicate via web and mobile applications. In order for e-retail businesses to continue to grow in an increasingly competitive environment and to stand out from competing businesses, they need to be able to respond to changing consumer demands in a timely and accurate manner. A demand forecasting study using Machine Learning and Deep Learning algorithms for the e-retail sector with local supermarkets' data in Turkey (as far as we know) has not been carried out yet. In this study, the demand for a certain category of products was estimated by considering the two-year e-commerce data (website and mobile application) of a local supermarket for the years 2019-2020 and the factors affecting product sales (holidays, CPI value and unemployment rate). Twenty-four different methods of six different artificial intelligence algorithms (Deep Learning, Artificial Neural Networks, Gaussian Process Regression, Regression Tree, Support Vector Regression and Ensemble Learning) were used to obtain the best demand forecasting model. The obtained results were evaluated using correlation coefficient (R), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) criteria. The best result was obtained using Long Short-Term Memory Networks (RMSE= 0.0353; MAE= 0.0164; R = 0.9742). The results obtained will be able to increase the success of e-retail sales by using the product supply in the right quantities, in sales campaigns, and in marketing strategies. [ABSTRACT FROM AUTHOR]

Epidemics have occurred in various time periods throughout history and have caused serious damage to human communities. Today, the modern version of these epidemics, Covid-19, has caused millions of people to die and have health problems as well. The entire world is making incredible efforts to combat the spread of this deadly disease in terms of infrastructure, finance, data resources, protective equipment, life-threatening treatments, and many other resources. Researchers are developing mathematical models to analyze this epidemic situation using data shared across the country. Countries are trying to fight this epidemic depending on the speed of the epidemic. In this study, an LSTM-based prediction model was created to predict the number of cases and deaths in Turkey. Six machine learning methods, including popular deep learning methods, RF, SVM, XGBoost, MLP, CNN, and RNN, were used to measure the prediction success of this model. The LSTM model has been the most successful model in predicting the number of cases, with MSE: 16670823,040 RMSE: 4082,991 MAE: 2543,651 R2: 0.975, and in predicting the number of deaths, MSE: 331,351 RMSE: 18,203 MAE: 14,891 R2: 0.740. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of InformaticsTechnologies is the property of Institute of Informatics, Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Dokuz Eylul University Muhendislik Faculty of Engineering Journal of Science & Engineering / Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi is the property of Dokuz Eylul Universitesi Muhendislik Fakultesi Fen ve Muhendislik Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Machine learning algorithms are used in almost all branches of science today. In particular, classification algorithms are a very popular subject in terms of science and health sciences. Deep learning is one of the machine learning techniques like other algorithms Today, it has become popular again due to the increase in processor speeds. Particularly graphics processor-based calculations have made this subject popular. The aim of this study is to classify the agonist and antiagonist molecules that bind to dopamine receptors, which are well known in the literature, with the data we obtained from chemistry databases, with machine learning algorithms. The aim of the study is also to suggest the use of a deep learning algorithm for an accurate classification when classifying in cases where the number of data is small. Scikit-learn and Tensorflow-Keras from Python libraries were used for training the algorithm. The classification process has been compared with popular machine learning algorithms and the results have been presented as a table. [ABSTRACT FROM AUTHOR]

Today's manufacturing vision necessitates extracting insights from the data collected in real-time from manufacturing processes. Predicting failures with the predictive analysis of the collected process data and preventing these failures by taking necessary actions before they occur is a key factor in ensuring quality at the desired level, increasing productivity, and reducing costs in production systems. In the literature on predictive analysis of process data, machine learning and deep learning methods have attracted considerable attention, especially in recent years. This study has addressed a multi-class failure classification problem in the plastic extrusion process with a real case study. Classification models have been developed based on Long Short-term Memory (LSTM) as a deep learning method and Multilayer Perceptron (MLP) and Logistic Regression (LR) as machine learning methods to predict the failure categories. In the case study, real data taken from the extrusion process of one of the leading insulation companies operated in Izmir has been used. The final dataset includes actual measurements of seven parameters related to temperature and pressure and failure categories as the target variable. Three failure categories have been identified to define Category 0 (No failure), Category 1 (Filter change), and Category 2 (Feeding failures) states, and coded as 0,1 and 2 in the models, respectively. LSTM, MLP, and LR's performance to predict the failure categories have been evaluated and compared based on accuracy, precision, recall, and F1 Score measures. LSTM is the highest performing among the three methods, with 100% prediction accuracy for each failure category. On the other hand, LR and MLP have achieved considerable and close results except for Category 1. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of InformaticsTechnologies is the property of Institute of Informatics, Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of COMU Journal of Agriculture Faculty / ÇOMÜ Ziraat Fakültesi Dergisi is the property of Canakkale Onsekiz Mart University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Erzincan University Journal of Science & Technology is the property of Erzincan Binali Yildirim Universitesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Engineering & Architectural Faculty of Eskisehir Osmangazi University / Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi is the property of Eskisehir Osmangazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)