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Copyright of University of Health Sciences Journal of Nursing is the property of Saglik Bilimleri 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.)

Normalization is utilized to remove outliers from the dataset and address network bias. In this research, MeanVariance-Softmax-Rescale (MVSR) and Min-Max normalizations are employed in various combinations for the diagnosis of COVID-19 using a Convolutional Neural Network (CNN)-based Deep Learning (DL) model, aimed at enhancing network accuracy. To accomplish this, the CNN model is developed within the Google Colab environment and trained using a publicly available dataset consisting of chest X-ray images related to COVID-19. The dataset is normalized using different combinations of the MVSR and Min-Max normalization algorithms to compare model accuracy. Each normalized dataset is used for model training, and subsequently, each trained model has been saved as a .h5 file and loaded into the Kria KV260 Vision AI Starter Kit FPGA for the testing phase. The most accurate results are obtained when MVSR and Min-Max normalizations are applied simultaneously. This highperforming scenario is re-evaluated with COVID-19 and normal X-ray images on FPGA configuration. Experimentally, the highest accuracy is achieved in realtime with the MVSR+Min-Max scenario, reaching 93%. The model's precision, recall, and F1-Score values are determined as 0.91, 0.96, and 0.93, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Karaelmas Science & Engineering Journal / Karaelmas Fen ve Mühendislik Dergisi is the property of Karaelmas Science & Engineering Journal 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.)

Dijital dönüşüm ile tedarik zinciri yönetiminde büyük veri analitiğinin önemi gün geçtikçe artmaktadır. Özellikle müşteri taleplerinin hızlı ve doğru tahmin edilmesinde büyük verinin kullanımı firmalara rekabet avantajı sağlamaktadır. Bu doğrultuda, yapay zekâ tekniklerinden biri olan derin öğrenme modelleri büyük verideki karmaşık örüntülerin keşfedilmesinde öne çıkmaktadır. Son yıllarda literatürde çok sayıda derin öğrenme yöntemi önerilmiştir. Bu çalışmada, satış tahmini problemi için derin öğrenme yöntemlerinin performansları karşılaştırılmıştır. Bu kapsamda derin sinir ağı (DNN), derin otokodlayıcı (Deep AE), evrişimli sinir ağı (CNN), tekrarlayan sinir ağı (RNN), uzun kısa-süreli bellek (LSTM) ağı, çift yönlü LSTM (Bi-LSTM) ağı, kapılı tekrarlayan birim (GRU), CNN-LSTM ve evrişimli LSTM (ConvLSTM) yöntemleri uygulanmıştır. Çeşitli sektörlere ait satış verileri kullanılarak deneysel çalışmalar gerçekleştirilmiştir. Hiperparametre optimizasyonu ardından ele alınan yöntemlerin performansları tahmin doğruluğu ve eğitim süreleri açısından karşılaştırılarak sonuçların istatistiksel anlamlılığı değerlendirilmiştir. Sonuç olarak, LSTM ve GRU modellerinin tahmin doğruluğunda başarılı sonuçlar verdiği, CNN modelinin ise eğitim süresini kısalttığı görülmüştür.

Küresel gıda talebindeki hızlı artış nedeniyle yüksek kaliteli hayvansal ürün üretiminin artırılması gerekliliği, modern hayvancılık uygulamalarında teknoloji kullanımı ihtiyacını beraberinde getirmiştir. Özellikle ekstansif koşullarda küçükbaş hayvan yetiştiriciliğinde hayvanların otomatik olarak izlenmesi ve yönetilmesi, verimliliğin artırılması açısından büyük öneme sahiptir. Bu noktada, insansız hava araçlarından elde edilen yüksek çözünürlüklü görüntüler ile derin öğrenme algoritmalarının birleştirilmesi, sürülerin uzaktan takip edilmesinde etkili çözümler sunma potansiyeli taşımaktadır. Bu çalışmada, insansız hava araçlarından (İHA) elde edilen yüksek çözünürlüklü görüntüler üzerinde derin öğrenme algoritmaları kullanılarak kıl keçilerinin otomatik olarak tespit edilmesi, takip edilmesi ve sayılması amaçlanmıştır. Bu kapsamda, en güncel You Only Look Once (YOLOv8) mimari varyasyonlarından YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l ve YOLOv8x olmak üzere beş farklı model gerçek hayvan izleme uçuşlarından elde edilen İHA görüntüleri üzerinde eğitilmiştir. Elde edilen bulgulara göre, 0,95 F1 skoru ve 0,99 mAP50 değeri ile hem sınırlayıcı kutu tespiti hem de segmentasyon performansı açısından en yüksek başarımı YOLOv8s mimarisi göstermiştir. Sonuç olarak, önerilen derin öğrenme tabanlı yaklaşımın, İHA destekli hassas hayvancılık uygulamalarında etkili, düşük maliyetli ve sürdürülebilir bir çözüm olabileceği öngörülmektedir.

As the amount of the available information continues to grow, finding the relevant information has become increasingly challenging. As a solution, text summarization has emerged as a vital method for extracting essential information from lengthy documents. There are various techniques available for filtering documents and extracting the pertinent information. In this study, a comparative analysis is conducted to evaluate traditional approaches and state-of-the-art methods on the BBC News and CNN/DailyMail datasets. This study offers valuable insights for researchers to advance their research and helps practitioners in selecting the most suitable techniques for their specific use cases.

Ensuring security in speaker recognition systems is crucial. In the past years, it has been demonstrated that spoofing attacks can fool these systems. In order to deal with this issue, spoof speech detection systems have been developed. While these systems have served with a good performance, their effectiveness tends to degrade under noise. Traditional speech enhancement methods are not efficient for improving performance, they even make it worse. In this research paper, performance of the noise mask obtained via a convolutional neural network structure for reducing the noise effects was investigated. The mask is used to suppress noisy regions of spectrograms in order to extract robust i-vectors. The proposed system is tested on the ASVspoof 2015 database with three different noise types and accomplished superior performance compared to the traditional systems. However, there is a loss of performance in noise types that are not encountered during training phase.

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 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 International Journal of Engineering Research & Development (IJERAD) is the property of International Journal of Engineering Research & Development 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 International Journal of Engineering Research & Development (IJERAD) is the property of International Journal of Engineering Research & Development 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 Kafkas University, Journal of Economics & Administrative Sciences Faculty / Kafkas Üniversitesi Iktisadi ve Idari Bilimler Fakültesi Dergisi is the property of University of Kafkas, Faculty of Economics & Administrative Sciences 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 study aims to investigate the effectiveness of deep learning and machine learning algorithms on consumer price index (CPI) forecasting using monthly consumer price index data and 5 independent variables (employment rate, average dollar rate, producer price index, Brent oil prices, and consumer loan interest rate) for the period January 2005–June 2023. To this end, different deep learning and machine learning models such as Long and Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Random Forest, Artificial Neural Network, and K-Nearest Neighbors are used for CPI forecasting, and their forecasting performance is evaluated with RMSE, MSE, MAE, MAPE, and R² error statistics. The results show that the GRU model outperforms the LSTM, Random Forest, Neural Network, and K-Nearest Neighbors models. The GRU model outperformed the other four models in RMSE, MSE, MAE, MAPE, and R² values. In addition, in CPI forecasting, it was observed that the GRU deep learning model can be used effectively in the inflation domain. These results provide an effective way to forecast CPI, which is an important component of economic forecasting and inflation management. Moreover, the SHAP (Shapley Additive Explanations) analysis applied for the training and testing phases of the GRU model clarified the contribution of each feature in the GRU model's forecasts and allowed us to understand the effects of variables on the model's forecasting ability. This analysis helped to better understand the success of the GRU model and the economic dynamics underlying the forecasts. This study demonstrates the applicability of the GRU deep learning model in economics and finance and provides a valuable tool for economic and financial decisionmakers. [ABSTRACT FROM AUTHOR]

The internet holds a significant role in all aspects of our lives, and its importance continues to grow each day. Therefore, the usability of the Internet holds great significance. Low data quality and disinformation severely impact the usability of the internet. Consequently, people face challenges in obtaining accurate and clear information. In the present day, websites predominantly feature image-based content like pictures and videos, as opposed to text-based content. The classification of such content holds immense importance for search engines. As a result, the classification of web pages stands as a crucial research area for scholars. This study focuses on the classification of image-based web pages. A deep learning-based approach is proposed to categorize web pages into four main groups: tourism, machinery, music, and sports. The suggested method yielded the most favourable outcomes when utilizing the Stochastic Gradient Descent (SGD) optimization method, achieving an accuracy of 0.9737, a recall of0.9474, an Fl score of 0.9474, and an Area Under the ROC Curve (AUC) value of 0.9649. Furthermore, the utilization of Deep Learning (DL) led to achieving the most advanced results in web page classification within the existing literature, particularly on the WebScreenshots dataset. [ABSTRACT FROM AUTHOR]

In the study, the rates of impact energy absorption of Acrylonitrile Butadiene Styrene (ABS) fractures produced by the Fused Deposition Modeling (FDM) method were examined. Charpy impact test results were determined using layer thickness, printing speed, support angle, build orientation, notch type, and unfill type. Box-behnken experimental design design in the study. Notch impact samples are produced on an ABS Three-dimensional Printer (3DP). Then, charpy impact tests were performed on the impact test device. Data were evaluated using the Minitab 21 program. Later, Deep Learning (DL) and Extreme Learning Machines (ELM) file models were created based on this development. The best results were obtained as 0.844 kJ/m2 with a layer thickness of 0.09 mm. At 60 mm/s printing speed and 30° support angle, the impact energy absorption is 0.803 kJ/m2. The extinction edge of the highest impact energy is 0.841 kj/m2. The most effective impact absorption was obtained as 0.827 kJ/m2 in the U notch type. In the full infill type, impact energy absorption is obtained as 0.777 kJ/m2. In DL, man is the programming and tanh is the activation function. DL, Mean Squared Error (MSE) value was calculated as 0.000923, r2 was calculated as 0.97427. In ELM, the activation function is sigmoidal at the input and linear at the output. [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 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 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 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.)

Bağların kontrolünün, takibinin ve bakımının zamanında doğru bir şekilde yapılması çok önemlidir. Bağ zararlılarıyla mücadelede aşırı pestisit kullanımı, insan sağlığını tehlikeye atmakta ve çevre kirliliğine yol açmaktadır. Ayrıca aşırı pestisit kullanımı ekonomik açıdan düşünüldüğünde işletme giderlerinin artmasına sebep olmaktadır. Bu nedenle bağdaki zararlıların ve hasarlarının zamanında teşhisi çok önemlidir. Zamanında tespiti sağlamaya yardımcı olan yöntemlerden biri derin öğrenmedir. Bu çalışmada, bir derin öğrenme nesne algılama algoritması olan YOLOv8x modeli ile bazı bağ zararlılarının (Salkım güvesi, Trips, Bağ yaprak uyuzu ve İki noktalı kırmızı örümcek) yaprak ve meyve kısmında oluşturduğu hasarın tespitine yönelik çalışma gerçekleştirilmiştir. 7 farklı sınıftan ve 3500 görüntüden meydana gelen veri seti oluşturulmuştur. Oluşturulan veri seti; YOLOv8(n/s/m/l/x) modelleri ile eğitilmiştir. Eğitim sonucunda, YOLOv8x modeli performans değerleri sırayla; mAP0,5, mAP0,5-0,95, Kesinlik (Precision), Duyarlılık (Recall), 0,926, 0,648, 0,892 ve 0,903 şeklinde sonuçlar vermiştir. Aynı veri seti, YOLOv7, DETR ve RTMDet modelleriyle de eğitilerek YOLOv8x modeliyle performans karşılaştırmaları yapılmıştır. Karşılaştırma sonucunda bağlarda belirtilen zararlıların oluşturduğu hasarı en iyi tespit eden YOLOv8x modeli olmuştur.

Türkiye is a country on the Alpine-Himalayan earthquake zone and needs an effective disaster management plan, with its geography experiencing severe seismic activities. In this respect, natural disaster risks can be reduced by using developing artificial intelligence technology and deep learning applications in the mitigation, preparedness, response, and recovery phases that constitute the disaster management plan. This study examines deep learning models, application areas, deep learning layers and libraries used, and how deep learning can be used in the four stages of disaster management through study examples in the literature. The study aims to examine the use of deep learning in architecture and disaster management phases based on the earthquake factor as a result of the literature review. As a result, when studies on deep learning are examined, disaster management studies closely related to the discipline of architecture are mainly in the response phase. However, the discipline of architecture plays an important role at every stage of disaster management. In this respect, as holistic studies and applications related to deep learning, architectural science, and effective disaster management increase, the loss of life and property due to disasters, especially earthquakes, will decrease. The study carried out is thought to be an important guide for future research.

Traffic sign detection has attracted a lot of attention in recent years among object recognition applications. Accurate and fast detection of traffic signs will also eliminate an important technical problem in autonomous vehicles. With the developing artificial intelligency technology, deep learning applications can distinguish objects with high perception and accurate detection. New applications are being tested in this area for the detection of traffic signs using artificial intelligence technology. In this context, this article has an important place in correctly detecting traffic signs with deep learning algorithms. In this study, three model of (You Only Look Once) YOLOv5, an up-to-date algorithm for detecting traffic signs, were used. A system that uses deep learning models to detect traffic signs is proposed. In the proposed study, real-time plate detection was also performed. When the precision, recall and mAP50 values of the models were compared, the highest results were obtained as 99.3, 95% and 98.1%, respectively. Experimental results have supported that YOLOv5 architectures are an accurate method for object detection with both image and video. It has been seen that YOLOv5 algorithms are quite successful in detecting traffic signs and average precession.

Copyright of Istanbul Commerce University Journal of Social Sciences / İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi is the property of Istanbul Commerce University Journal of Social Sciences 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 Firat University Journal of Experimental & Computational Engineering (FUJECE) is the property of Firat University Journal of Experimental & Computational Engineering (FUJECE) 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 Firat University Journal of Experimental & Computational Engineering (FUJECE) is the property of Firat University Journal of Experimental & Computational Engineering (FUJECE) 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.)

Tuberculosis, a contagious lung ailment, stands as a prominent global mortality factor. Its significant impact on public health in Nigeria necessitates comprehensive intervention strategies. Detecting, preventing, and treating this disease remains imperative. Chest X-ray (CXR) images hold a pivotal role among diagnostic tools. Recent strides in deep learning have notably improved medical image analysis. In this research, we harnessed publicly available and proprietary CXR image datasets to construct robust models. Leveraging pre-trained deep neural networks, we aimed to enhance tuberculosis detection. Impressively, our experimentation yielded remarkable outcomes. Notably, f1-scores of 98% and 86% were attained on the respective public and private datasets. These results underscore the potency of deep neural networks in effectively identifying tuberculosis from CXR images. The study emphasizes the promise of this technology in combating the disease's spread and impact. [ABSTRACT FROM AUTHOR]

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]

Ensuring security in speaker recognition systems is crucial. In the past years, it has been demonstrated that spoofing attacks can fool these systems. In order to deal with this issue, spoof speech detection systems have been developed. While these systems have served with a good performance, their effectiveness tends to degrade under noise. Traditional speech enhancement methods are not efficient for improving performance, they even make it worse. In this research paper, performance of the noise mask obtained via a convolutional neural network structure for reducing the noise effects was investigated. The mask is used to suppress noisy regions of spectrograms in order to extract robust i-vectors. The proposed system is tested on the ASVspoof 2015 database with three different noise types and accomplished superior performance compared to the traditional systems. However, there is a loss of performance in noise types that are not encountered during training phase. [ABSTRACT FROM AUTHOR]

With escalating environmental concerns worldwide, the shift towards second-hand car markets has emerged as an eco-friendly alternative to reduce the carbon footprint associated with manufacturing new vehicles. However, the lack of accurate and efficient price prediction mechanisms may impede the growth and efficiency of these markets. This study, focusing on the Turkish second-hand car market, contributes towards addressing this gap by introducing a unique, comprehensive dataset gathered from various online markets across Turkey, thereby offering a broad spectrum of data pertaining to different vehicle types, specifications, and resale conditions. The study employs both classical machine learning methods, specifically decision trees, and deep learning models to predict used car prices. This comparative analysis aims to assess the potential of these methods in improving the predictability and transparency of resale price determination. Despite the superior performance of decision tree models, the study found that deep learning techniques achieved comparable results, indicating their potential for further optimization and enhancement. The accurate prediction of resale prices could streamline the operations of second-hand car markets, increasing their appeal to potential buyers and sellers. This could also contribute to environmental sustainability by significantly reducing the demand for new cars. [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 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 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 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 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 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.)

Amaç Teknolojideki son gelişmeler ve veri setlerindeki artış tıbbi görüntülerde yapay zekanın en temel yaklaşımlarından biri olan derin öğrenme algoritmasının popülaritesini arttırmaktadır. Bu nedenle, yapılan çalışmada derin öğrenme algoritmasının kullanımına ilişkin yayınları araştırmak ve derin öğrenmenin kullanımına dikkat çekmek için bibliyometrik analiz yapılması amaçlanmıştır. Yöntem "Deep Learning" OR "DL" AND "Medical Imaging" AND “Radiology” anahtar kelimeleri kullanılarak 2019 ile 2022 yıllarında yayınlanan veriler Web of Science Core Collection (WOSCC) veritabanından elde edildi. WOS veritabanında araştırma alanı (Research areas) “Radiology Nuclear medicine medical imaging” ve ülke (Region/Country) alanı “Turkey” ve doküman tipi (Document type) “article” olanlar çalışmaya dahil edildi. Bulgular Yapılan çalışmada araştırılan konu ile ilgili toplam 259 yazardan en az 1 yayını ve 1 atıfı olacak şekilde seçim yapıldığında 211 yazar elde edildi. Yazarlar tarafından en az 1 kez kullanılan 195 anahtar kelime elde edildi. Elde edilen anahtar kelimeler arasında en sık kullanılan anahtar kelimelerden “deep learning” ve “artificial intelligence” olduğu görüldü. Ayrıca yapay zekayla ilgili olan “Transfer learning” ve “Machine learning” anahtar kelimelerinin de diğer anahtar kelimelere göre daha sık kullanıldığı görüldü. Dergiler arasında en çok atıfın 133 atıf ile 2021’de “Medical Image Analysis” dergisinde yayınlanan makaleye yapıldığı görüldü. Ayrıca “Medical image analysis” dergisinin 268 atıf ve 8 doküman ile ilk sırada yer aldığı görüldü. Bu derginin ortalama yayın yılının 2021’de fazla olduğu görüldü. Sonuç Derin öğrenme algoritmalarının görüntü segmentasyonu, görsel hesaplama, algılama ve sınıflandırma gibi farklı görevlerinin yanı sıra radyasyon dozunun azaltılmasına yardımcı olma gibi avantajları bulunmaktadır. Dolayısıyla derin öğrenme algoritmasının kullanımının tıbbi görüntüleme alanında gittikçe artması kaçınılmazdır. Yapılan çalışma özellikle derin öğrenmenin tıbbi görüntülemede kullanılması ile ilgili verilerin bibliyometrik analizinin yapılmasının farkındalık oluşturacağını ve yararlı olacağını umuyoruz.

Yarı iletken devre elemanı üretim teknolojilerinde gerçekleşen gelişimler, bu elemanların üzerinde yer aldığı yonga plakası üretim süreçlerini daha karmaşık ve hassas hale getirmektedir. Üretim ile ilişkili çevresel koşullar, malzeme kalitesi gibi çeşitli faktörler, yonga plakası üzerinde kusursuz olarak nitelendirilebilecek alan miktarını yani verimi doğrudan etkilemektedir. Bir yarı iletken yonga plakası üzerindeki kusurlu alanların oluşturabileceği desenler standart olarak tanımlanmış durumdadır. İncelenen bir yonga plakası yüzeyindeki kusurların bu tanımlara göre sınıflandırılması, üretim süreçlerinde oluşan problemlerin kaynaklarının belirlenmesi için önemli bilgiler sağlayabilmektedir. Bu çalışmada, mevcut uygulamalarda her yarı iletken yonga levhası için insan operatörler tarafından yapılan kusur deseni sınıflandırma işlemini belirli bir güvenlik değerine kadar otomatik olarak gerçekleştiren ve böylece toplam işlem süresini azaltan bir karar destek yöntemi geliştirilmiştir. Bu yöntemde temel sınıflandırma işlemi için derin öğrenme metotlarıyla eğitilmiş bir ağ yapısı kullanılmaktadır. İstenilen güvenlik değerinin üzerinde bir doğrulukla sınıflandırılan yonga plakaları doğru sınıflandırılmış olarak kabul edilmekte, bu değerin altında kalan yonga plakaları ise insan operatörün incelemesine tabi tutulmaktadır. Yöntemin kullanılması ile; ortalama büyüklükte bir yonga plakası üretim tesisi için geçerli günlük toplam inceleme süresi, tüm incelemenin insan operatör tarafından yapıldığı durumda geçerli sürenin %10’una indirilebilmekte, ayrıca insan operatörün yapabileceği öznel değerlendirmelerin de önüne geçilebilmektedir.

Plant disease classification is the use of machine learning techniques for determining the type of disease from the input leaf images of the plants based on certain features. It is an important research area since early identification and treatment of plant disease is critical for saving crops, preventing agricultural disasters, and improving productivity in agriculture. This study proposes a new convolutional neural network model that accurately classifies the diseases on the plant leaves for the agriculture sectors. It especially works on the classification of plant diseases for grape leaves from images by designing a deeplearning architecture. A web application was also implemented to help the agricultural workers. The experiments carried out on real-world images showed that a significant improvement (8.7%) on average was achieved by the proposed model (98.53%) against the state-of-the-art models (89.84%) in terms of accuracy.

Mikroskobik sistemlerde var olan odaklama derinliğinden dolayı numunenin tüm alanının odaklandığı görüntü elde etmek imkânsız olabilmektedir. Bu durum, mikroskobik sistemlerde görüntü işleme ve yapay zekâ algoritmaları kullanılarak gerçekleştirilen sınıflandırma, bölütleme, hizalama (registration), panoramik birleştirme (stitching) gibi uygulamalarının başarılarını olumsuz yönde etkilemektedir. Literatürde numunenin tüm alanının odaklandığı görüntü elde etmek için odaklama derinliğinin artırılması yaklaşımları geliştirilmektedir. Literatür çalışmaları, bu yaklaşımların, görüntülerdeki eğrilerin ve kenarların düşük kesinlikte karakterizasyonu, daha yüksek koşma süresi ve incelenen numuneye ve kullanılan mikroskoba göre performans değişimi gibi çeşitli kısıtlamalara sahip olduklarını ortaya koymaktadır. Ek olarak, bu yaklaşımlar odaklama bilgilerini genelde görüntülerin gri seviye değerlerini kullanarak hesaplamaktadırlar. Bu çalışmada bu kısıtlamaları minimize etmek için yeni bir odaklama derinliğinin artırılması yaklaşımı geliştirilmekte ve odaklama derinliğinin artırılmasında derin özelliklerin odaklama değerlerinin çıkarılmasındaki etkileri incelenmektedir. Çalışmada elde edilen sonuçlar derin özelliklerin piksellerin odaklama değerlerini hesaplamada gri seviye değerlerine göre daha etkin olduğunu göstermektedir.

One of the most important technological developments in the field of medicine is Computed Tomography and Magnetic Resonance imaging techniques. This technique allows the size and shape of tumor areas in body tissues to be determined, making it easier for specialists to determine the type of tumor as well as whether it is benign or malignant. Various deep learning-based computer software have been developed to accurately detect tumor areas in tissue. Due to the lack of image data used in deep learning studies, a limitation naturally arises in studies in this field. In order to eliminate the lack of image data in these studies, image augmentation can be performed using deep learning methods as well as data augmentation methods using various image processing techniques. In this study, Generative Adversarial Networks, a deep learning technique, were employed to duplicate brain MR images and generate synthetic images. After the resulting MR images were made usable by undergoing various pre-processing, similarity rates to real images were calculated using metrics such as Peak Signal-to-Noise Ratio, Structural similarity index and Mean Square Error, and by looking at these rates, realistic images were added to the data set and the data set was expanded. [ABSTRACT FROM AUTHOR]

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 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 Instructional Technology & Lifelong Learning is the property of Instructional Technology & Lifelong Learning 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 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.)

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.

Robotik hasat sistemlerinde lahana ve brokolinin derin öğrenme kullanılarak sınıflandırılması oldukça önemlidir. Derin öğrenme, yapay sinir ağları ve büyük veri setleri kullanılarak karmaşık modellerin öğrenilmesine olanak sağlayan bir makine öğrenme yöntemidir. Bu yöntem yardımıyla bitki sınıflandırmasında ve görsel tanıma problemlerinde etkili bir şekilde kullanılabilir. Lahana ve brokoli gibi bitkilerin sınıflandırılması için öncelikle bir derin öğrenme modeli oluşturulması gerekmektedir. Bu nedenle yapılan çalışmada derin öğrenme yöntemlerinden olan Inception_v3 görüntü tanıma ve sınıflandırma modellemesi kullanılmıştır. Çalışma oluşturulan 2 sınıf üzerinden yürütülmüştür. Oluşturulan sınıflar lahana ve brokoli’dir. Modelin eğitimi için Google Colab’ın sağladığı tpu donanım hızlandırıcısı kullanılmıştır. Eğitim döngüsü (epoch) sayısı 10’dur.Eğitim parametreleri olarak öğrenme hızı 0,001 tespit edilmiştir. Bu sonuçlara göre brokoli ve lahana data setin eğitimi için Inception_v3 modelinin başarılı olduğu sonucuna varılmıştır. Eğitim sürecinde modelin kayıp değeri giderek düşmüş ve doğruluk değeri artmıştır. Son aşama olan doğrulama aşamasında kayıp değeri 0,0005, doğruluk değeri 1,0000 olarak gözlenmiştir.