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In this study, it is aimed to produce meaningful words in accordance with character-based Turkish grammar rules by using encoder-decoder and attention architecture, which are deep learning algorithms. The results of the developed model are compared with the results of LSTM and GRU models, which are other deep learning algorithms. It is seen that the language models created with LSTM and GRU models give similar results at 100 and 200 epoch values and at different threshold values of the temperature sampling method. Among these models, the GRU model gives the highest success value with 88.40% at 200 epochs and 0.5 temperature threshold value. The encoder-decoder and attention language model developed for this study gives the highest success value of 91.90% at 100 and 200 epoch values and at different threshold values of the temperature sampling method at 200 epoch and 0.5 temperature threshold value. At the end of the experiments, the encoder-decoder and attention architecture model showed an average of 2.83% more success than the LSTM model and an average of 0.19% more success than the GRU model. [ABSTRACT FROM AUTHOR]

Firms in the manufacturing sector need to constantly monitor their performance in order to maintain their development under competitive conditions in the market. In this study, eleven KPIs are determined to measure the production performance by taking into account the factory assets. The proposed system is designed in which the relevant KPIs are obtained via the instantaneous data received from the CNC machines in a production system. The main objective of this study is to measure and estimate production performance. In this way, it is aimed to provide a proactive approach for the assets whose performance is monitored by the decision-makers. LSTM and LightGBM models which are deep learning techniques are proposed for the estimation of performance indicators. The approximately three-month time series OEE (Overall Equipment Effectiveness) values of the sample CNC machine are used for estimation. The estimation performance of methods is compared over performance metrics (MSE, MAE, etc.). The results indicated that LightGBM outperforms LSTM for all performance metrics. [ABSTRACT FROM AUTHOR]

Thanks to the precision agriculture technologies developed in recent years, many processes such as irrigation, fertilization, spraying, weeding and harvesting of agricultural products can be done by autonomous systems. Especially in some plant species such as sunflower, when to apply these processes is largely decided according to the developmental stage of the plant. In this study, deep learning methods were used to classify the developmental stages of sunflower plants. Since the images taken with the drone are high resolution, each of them is divided into 6 equal parts, and then 8 classes are determined and the images belonging to each class are extracted. A data set consisting of 12800 images in total, 1600 in each class, was created. Six different deep learning models, namely AlexNet, InceptionV3, ResNet101, DenseNet121, MobileNet and Xception, were tested with Sgd, Adam and Rmsprop optimization methods and their performances were compared. In order to evaluate the success of the models correctly, the trained models were also tested on a second data set created with images taken from a different terrain and high success rates were obtained. In addition, a 7-class test set was created for images that could not be clearly determined at which stage the plant was in, and the success rates of the models were tested. It was observed that the success rate was very low for the images in the 7-8 intermediate class, and the filters used in the image processing techniques that would increase the success rate for this class were applied to the images, and the models were retrained and the results were evaluated. [ABSTRACT FROM AUTHOR]

After lung cancer, prostate cancer is the most common type of cancer in men. Nowadays, advanced prostate imaging is conducted by multiparametric prostate magnetic resonance imaging (MRI) performed by radiologists. Although many pathologies of the prostate can be visualized, the main purpose is to determine the probability of prostate cancer and to decide whether a biopsy is needed. This process includes the analysis of different series of Magnetic Resonance (MR) images, including T2-weighted images (T2W), diffuse-weighted images (DWI), and dynamic contrast enhanced images (DCE). However, firstly it is necessary to differentiate the prostate zones. Then, lesion scanning is performed in the relevant prostate zones. Finally, there is a need to perform prostate lesion scoring according to PI-RADS v2. Therefore, diagnosis of prostate cancer is a complex and long process. For this reason, decision support systems are needed for the diagnosis of prostate cancer. In this context, the main purpose of the study is to provide automatic segmentation of prostate regions. In the segmentation task, Mask R-CNN algorithm was used with T2W MR images of 15 patients. With the use of the ResNet-50 backbone model, the Mask R-CNN algorithm achieved the segmentation performance with a mAP50 value of 96.040. Finally, the trained model was integrated into the PACS system. Thanks to the integration, a ready-to-use in hospitals artificial intelligence-supported decision support system was developed. Thus, time was saved while reducing the workload on health employees. [ABSTRACT FROM AUTHOR]

Diagnosis of anomalies in the gastrointestinal tract is a current research area. Wireless capsule endoscopy (WCE) for the evaluation of this region is a preferred alternative technology to avoid the risks of traditional endoscopy and to provide a painless process. But this technology, which has many advantages, offers low frame density. This situation affects the quality of the data and causes to a decrease in the diagnostic accuracy rate. In this study, WCE endoscopy images obtained from KID Atlas Dataset 2 were used and a three-stage artificial intelligence-supported diagnostic process was developed for the detection of the inflammatory anomaly, vascular anomaly, polypoid anomaly and normal image categories in the gastrointestinal tract. For the first stage, critical points on the images were clarified using 5 different approaches. These improved images were classified with a region proposal-based object recognition algorithm and performance comparison was made according to the approaches used. In the second stage, the data augmentation technique was applied to the improved images that showed maximum performance in the first stage. Thus, a dataset with a balanced and sufficient number of images was created. In the third stage, this current dataset was classified with five different object recognition algorithms. However, the individual success of each algorithm is different. For this reason, the ensemble learning approach was used to obtain stable outputs for each category and to create a balanced detection process among the categories. Finally, a balanced and stable estimation function was provided between categories with this hybrid structure. [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]

Hydrothermal alteration is an important geological feature used in the exploration stages of precious minerals. This research focuses on two distinct deep-learning network structures created to identify hydrothermal alteration types in microscope images. A dataset of 2500 images, 70% of this data set was used to train, 20% to test, and 10% to measure the validity of the network. Convolutional Neural Network (CNN) and Xception models were trained using Adam, RMSprop and SGD optimization functions and the results are discussed. The Adam and SGD optimization functions for the CNN model performed the most successful classification with 96% accuracy. In the case of the Xception model, the highest accuracy value was 98% for the networks using the Adam and RMSprop optimization functions. Although the Xception model had higher accuracy values, it was observed that the CNN model completed the process significantly faster considering the training time of the network. [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]

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]

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]

Nowadays, deep learning methods are widely used in many fields such as robotics, sound processing, medicine and imagery. In this study, studies on image classification and analysis in the literature were examined in detail. In addition, performance analyzes of deep learning methods were carried out using CNN on 4 different data sets named MNIST, Fashion MNIST, CIFAR-10 and CIFAR-100. The structures of the created deep learning models, the parameter values used for training, the layers used, the complexity matrices obtained for the validation data, accuracy and loss graphs are shown in detail. Our study was carried out with a different network structure consisting of 3 convolution layers, 3 batch normalization layers, 2 maxpooling layers, 1 flatten, 2 droupout, 2 dense layers. In addition, the performance of image classification on different data has been increased with our proposed model. At the end of the test, the accuracy results of our study were compared according to various evaluation metrics. For all the data sets used, the best and worst images were determined. With the proposed CNN model, high accuracy rates were observed for MNIST and Fashion MNIST datasets, with these values being 99.22% and 99.21%, respectively. [ABSTRACT FROM AUTHOR]

Today, the use of face recognition systems and attacks against these systems have increased. Especially with the increasing use of social media, the increase in the sharing of facial images and videos allows attackers to deceive facial recognition systems more easily. For this reason, face spoofing detection has become a very important field of study. Face spoofing attacks are carried out in various types. Generally, performance evaluations are made on scenarios in which all attack types are evaluated together. Therefore, in this study, face spoofing detection performances of deep learning methods in Printed Photo, Digital Photo and Replay Video attack types in the Replay-Attack dataset were evaluated. For this purpose, the face spoofing detection performances of VGG16, DenseNet121 and MobileNet deep network architectures were examined. Then, the change in face spoofing detection performances because of classification of deep features produced by each network with support vector machines (SVM) was examined. Finally, the deep features produced by VGG16, DenseNet121 and MobileNet networks were combined (feature level fusion) and real/fake classification was performed for all attack types with SVM. According to the results, the classification of deep features or their combinations with SVM increases the performance of face spoofing detection according to the attack type. [ABSTRACT FROM AUTHOR]

Nowadays, the Internet is a structure that people can access easily and at the same time produce content easily and without control. In parallel with this situation, the ability of extract information from the raw data that makes up big data has become more complex. The fact that the headlines of the contents contain uncontrolled and misleading elements makes it difficult to reach the right information. The headlines of the contents are important for people to reach the information they want in their limited time. In this study, it is aimed to produce headlines suitable for the content instead of headlines that may be misleading for news. For this purpose, an application that produces headlines for Turkish news with deep learning method has been developed. SuDer news corpus is used as dataset. For the training of the model, it is aimed to obtain more humanoid results in the production of news headlines by using the Transformer architecture, which is frequently preferred in natural language studies today and the abstract summarization method. In this study, in order to compare the performance of the Transformer model, models are prepared and trained with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures. At the end of 25 epochs of training with LSTM, GRU and Transformer architectures on the corpus, the values of loss are 1.03, 0.55 and 2.49 respectively. In the experiments performed on the validation data, measurements are made with ROUGE-1, ROUGE-2 and ROUGE-L metrics. As a result of the measurements, it is observed that the Transformer architecture is partially good, based on the metric values produced. In addition, when the headlines produced with these architectures are examined, it is observed that the headline obtained with the Transformer architecture produce headlines that are partially more suitable for the news content compared to other architectures. [ABSTRACT FROM AUTHOR]

In this study, the Deep Learning technique was combined with the Uncertainty Quantification scale to determine whether patients have cancer or not, and if cancer is present, to classify it as benign or malignant using a dataset of 663,549 lung tomography data. The reliability of this model was also investigated. 80% of the mentioned images were used for training, 10% for testing, and 10% for validation. Two methods were proposed for this purpose. In the first method, measurements were made without using Uncertainty Quantification, and the accuracy rate for the ResNet50 model was found to be 0.965, while the accuracy rate for the AlexNet model was calculated as 0.837. Additionally, the accuracy rate for the Inception v3 model was found to be 0.849, and for the VGG16 model, it was 0.826. Firstly, the accuracy rate of the 4-Layer Convolutional Neural Network model was calculated as 0.971. In the second method, measurements obtained using Uncertainty Quantification (UQ) resulted in an accuracy rate of 0.89 for the Bayesian Neural Networks ResNet50 model. Furthermore, the accuracy rate for the Bayesian Neural Networks AlexNet model was 0.86, for the Inception v3 model it was 0.91, and for the VGG16 model, it was 0.77. The study has innovative features such as the development of a new model called the 4-Layer Convolutional Neural Network and the use of uncertainty quantification methods. These results demonstrate that the performance of specific neural network models can be enhanced by considering uncertainty. [ABSTRACT FROM AUTHOR]

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]

Nowadays, with the increased use of computers, a surge in data production has emerged, making data access more convenient. In this context, a substantial amount of textual data is generated on e-commerce sites, social media, and various electronic platforms. Analyzing and extracting meaningful insights from this amassed data proves valuable for numerous institutions, organizations, and individuals. Sentiment analysis is a commonly employed technique to derive sentiments from textual data, and contemporary sentiment analysis models often leverage the high performance offered by deep learning approaches. Prior to model training, several pre-processing steps are typically applied to the text data. In this study, three distinct deep learning approaches were proposed for sentiment analysis. These models were analyzed on two different datasets: winvoker and Beyazperde. Hyper-parameters and depth of models were optimized using the Bayesian optimization method to enhance the accuracy of model. Additionally, the impact of various pre-processing techniques on model performance were assessed. When non-preprocessed data is utilized, the models trained on the winvoker dataset achieve an accuracy of 94.16%, while those trained on the Beyazperde dataset reach 86.64%. With the application of pre-processing, these accuracies improve to 94.64% for the winvoker dataset and 89.08% for the Beyazperde dataset. Based on these findings, it was concluded that the effect of pre-processing decreased and the accuracy was higher for the winvoker data set with a higher number of samples. [ABSTRACT FROM AUTHOR]

In this study, a study was conducted with the HAM10000 dataset containing skin lesions to see the performance of architectures such as EfficienNetB3, which uses deep learning techniques to detect skin cancer. In this study, applications were made in deep learning to see the effect of the learning rate or learning rate on architectures using convolutional neural networks used in image classification. These applications were made to see the effect of the learning rate, the size and diversity of the data set, and the number of images used in training, as well as the increased number of images, on both the success in classification and the time taken for training. EfficientNetB3 was used as the architecture and HAM1000 dataset, which is open access on the Kaggle platform, was used as the dataset. At the end of the study, without using as many augmented images as possible and with 600 images of each disease class, a learning rate of 0.002 and epoch 15 instead of 10, the highest accuracy performance of 0.8234 was achieved in the fifth application. [ABSTRACT FROM AUTHOR]

As seen in research conducted in the world and in Turkey in recent years, stomach cancer ranks first compared to other types of cancer. It is known as a type of cancer that is very difficult to detect. Gastric dysplasia is when the cells lining the inner surface of the stomach stop growing and developing normally. These cells begin to grow and multiply uncontrollably, then turn into stomach cancer. The main goal is to minimize this disease before it turns into cancer. Gastric dysplasia is divided into two types: low-grade and high-grade dysplasia. High-grade dysplasia is a precursor to cancer. Therefore, early diagnosis is extremely important for the diagnosis and treatment of gastric dysplasia. This study aims to detect dysplasia at an early stage by using deep learning and convolutional neural networks techniques for the detection of gastric dysplasia. In the study, feature extraction was performed on histopathological images using multiple Convolutional Neural Networks (ESA) models. The data, divided into two label classes: patient and healthy, were compared with the help of deep learning architectures and classifiers. [ABSTRACT FROM AUTHOR]

Dental health has a significant impact on overall health and quality of life. Segmentation of impacted teeth is a critical step for early diagnosis and treatment in dentistry. In this study, we investigate the use of deep learning techniques to accurately identify impacted teeth in panoramic dental images. In this context, a Pyramid Scene Segmentation Network (PSPNet) based on ResNet backbone network is developed for embedded tooth segmentation. In the proposed architecture, ResNet18, ResNet34, ResNet50, ResNet101 and ResNet152 versions of the pre-trained ResNet backbone network are adapted. Considering the findings obtained in this study, the ResNet18 model achieved the highest success in segmentation and recognition processes in dental images (92.09% F1 Score, 93.88% Precision, 90.39% Recall, 85.34% IoU Score and 96.89% Dice Coefficient). This research reveals that the rate of successful detection of impacted teeth in adult patients is high as a result of studies on panoramic dental images. These findings emphasize that AI can be an effective tool for dentists and increase confidence in the development of AI in the healthcare sector. [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]

Objective: The aim of this study is to provide a different perspective on the analysis of RNA sequencing data by employing popular machine learning and deep learning methods, rather than classical statistical approaches. Additionally, it aims to provide insights into machine learning and deep learning concepts. Material and Methods: Utilizing machine learning and deep learning techniques, two distinct raw datasets pertaining to asthma and kidney transplantation (GSE85567 and GSE129166) were retrieved from the National Center for Biotechnology Information database and subsequently subjected to requisite quality control and alignment procedures. Random forest (RF), support vector machines (SVM), and deep neural networks (DNN) models were implemented to achieve patient-control differentiation. To prevent overfitting, all data sets were divided into 67.5% training, 10% testing, and 22.5% validation data, and 10-fold cross-validation was employed during the training stages of the models. Python programming language was used for both machine learning and deep learning, and Unix operating (AT&T Bell Laboratories, USA) system was utilized for data processing. Results: In the GSE129166 data set, the RF model obtained an accuracy rate of 0.89 in the validation set. The precision and recall of this model were determined as 0.88 and 0.92, respectively. The SVM model measured an accuracy rate of 0.88 in the validation set, and 0.87 in the test set. For the GSE85567 data set, the accuracy rate of the RF model in the validation set was measured as 0.73. For SVM, the accuracy rate in the validation set was measured as 0.70, while for DNN, it was measured as 0.75. Conclusion: The study conducted on the GSE85567 data set demonstrates that RF and SVM models exhibit high accuracy and performance. The DNN model, on the other hand, has a more balanced precision and recall rate, and is observed to be a significant alternative. Additionally, it is observed that the DNN model shows effective performance on the GSE129166 data set. Particularly, a high accuracy rate and a balanced precision-recall balance were observed in the validation set. It is concluded that all three models are suitable for patient-control classification in RNA-seq data. [ABSTRACT FROM AUTHOR]

Recognition of handwritten digits has recently gained importance and attracted the attention of many scientists, as it is used in many machine learning, deep learning and computer vision applications. Hyperparameter optimization involves determining a set of values aimed at increasing accuracy in both classification and prediction. It is also aimed to optimize the performance in feature selection by regulating the parameters selected by the algorithms more accurately. In this study, a convolutional neural network was used to recognize handwritten digits using the MNIST dataset. There are many open source hyperparameter libraries that deep learning developers can use to determine hyperparameters. In the developed model, hyperparameter optimization techniques were applied using Optuna, HyperOpt and Scikitoptimize libraries and the results were evaluated. Optimization times for hyperparameter libraries and the change in the success rate in recognizing handwritten digits were analyzed. The model trained with randomly given parameters achieved 78.45%, 97.13%, 75.62%, 76.95%, 97.46% and 97.27% accuracy, while the model trained with optimized hyperparameters achieved 99.26% accuracy. [ABSTRACT FROM AUTHOR]

With the rapid advancement of technology, the use of artificial intelligence (AI) is increasing in various sectors such as education, health, security and defense. A critical application of AI is highway management, especially with the rise of autonomous vehicles. The focus of this study is to address the problem of deformations in highway marking lines that pose challenges for autonomous vehicles and affect traffic safety. The research involves using unmanned aerial vehicle (UAV) to create an original image dataset of highway lines. This data set will be processed with image enhancement techniques and deep learning models. The first stage involves cleaning the images from foreign matter. Deep learning models will then identify potential line deformations. These models will be developed and trained for optimum accuracy using various performance metrics. In the study, the mobilenet v3 model, trained with the images in the data set, reached an accuracy rate of 89.58%, the resnet50 v2 model 77.78% and the Convolutional Neural Network model 92.55%. The ultimate goal is to implement a real-time system to accurately detect and report differences in highway lines by combining the UAV with the computer system on the ground. This will ensure timely notification to authorities and help prevent traffic safety problems related to line deformations. This approach demonstrates the practical applications of artificial intelligence in improving road safety and autonomous vehicle navigation. [ABSTRACT FROM AUTHOR]

Cracks in buildings are a source of concern as they may indicate structural problems. Cracks in buildings are one of the major problems with concrete structures, as they affect the appearance of the building, compromise the integrity of the masonry, jeopardize building safety, and reduce the durability of the structure. Cracks in buildings can be a cause for concern and may indicate a potential structural problem that could jeopardize the safety and stability of the building. Understanding the root causes of these cracks is crucial to determining appropriate preventive measures and repair methods. In this study, crack and slope control in buildings was performed with an image processing-based measurement system developed using CNN deep learning algorithms. A dataset of 294 photographs was used for the study. The data were subjected to image processing, preprocessing, and detection with CNN. The accuracy rate is 0.9716 for the VGG-16 architecture and 0.9513 for the Inception-V3 architecture. It was concluded that there is no significant difference between the accuracy rates of the architectures. All vertical and horizontal cracks with a thickness of 2 mm and a continuity of 4 cm were detected. Thus, crack control in buildings can be done quickly and reliably, and building safety will be ensured. [ABSTRACT FROM AUTHOR]

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]

Rice, obtained through the processing of paddy, is one of the most widely consumed food products globally. However, diseases affecting rice plants, particularly those occurring on the rice leaves, pose significant challenges for farmers. The identification of rice plant diseases demands specialized knowledge, making it a complex issue to tackle. Often, due to insufficient understanding, farmers misdiagnose diseases and apply incorrect treatments. Rapid and accurate disease diagnosis plays a pivotal role in enhancing healthy and productive crop cultivation. To address this problem, a deep learning-based model was developed to detect rice plant diseases. The model was trained on a dataset containing four different rice plant diseases and achieved a successful outcome with a loss value of 0.0014. Additionally, four different deep learning algorithms were used to create models through transfer learning with pre-trained ImageNet models, and a comparison of their performance was presented. The most successful model was obtained using the VGG16 transfer learning architecture. Experimental results in this study demonstrate that the proposed transfer learning method can effectively recognize rice leaf diseases, providing a reliable approach for identifying leaf diseases in various plants. [ABSTRACT FROM AUTHOR]

The physical properties of fruit and vegetable products play an important role in their quality classification. With the existing manual methods, disease, pesticide and quality status of agricultural products are checked during the control stages. Manual sorting and classification of products requires expertise and the process is time consuming and labor intensive. Today, with the developing technology, the processing and marketing of products can be realized in optimum time and efficiency with the software techniques used in the fields of agriculture and food. In this study, date palm fruit, which has an important share in the fruit and vegetable market, is considered. The use of image processing and artificial intelligence techniques in classifying the quality of date fruits makes the sales process more consistent and efficient. Within the scope of the study, four different artificial intelligence techniques were used with a uniquely prepared dataset. The dataset consists of three different classes: good, bad and medium quality dates. The dataset specially prepared for the study was trained with MobileNetV2, ResNetV2-50, DenseNet- 201 and Inception-v3 deep learning models. Among the four models used in the study, MobileNetV2 model was determined as the most successful model with 95% accuracy rate. [ABSTRACT FROM AUTHOR]

With the rapid advancement of drone technologies, the use of drones has become of significant importance, particularly raising serious concerns in the areas of security and privacy. Deep learning and transfer learning artificial intelligence techniques hold promise for drone detection. However, to successfully apply these techniques, the need to develop new and efficient solutions for accurately detecting complex aerial conditions, variable speeds, and highly maneuverable drones is inevitable. In this study, the performance of training models using the EfficientNet model for drone detection was compared, and the challenges encountered were discussed, offering a perspective on potential future successes. According to the obtained results, when more layers are frozen in the transfer learning method, the required GPU memory for training decreases, leading to reduced GPU usage. This indicates that models trained with larger image sizes can be trained more quickly. Deep learning methods require more data and GPU resources, which extend the training time. In the conducted experiments, the best success rate achieved by a model trained with the deep learning method was 97.3%, while the model trained using the transfer learning method achieved the highest success rate of 99.7%. This demonstrates that transfer learning achieves higher accuracy with less data. However, the success rate obtained through the deep learning method is also considered quite satisfactory. [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]

Classification studies on image data sets with convolutional neural networks algorithm, which is one of the deep learning approaches, are widely performed successfully in many fields such as medicine and agriculture. However, in cases where the sample numbers of the classes in the image datasets are imbalanced, the classification performance of this algorithm is negatively affected. In general, unlike the majority class, the minority class(es) are not well learned by the convolutional neural network algorithm. In such cases, applying oversampling methods provides successful results. With oversampling methods, the number of minority class(s) samples is increased, making it close to or equal to the sample number of the majority class. In this study, translation, rotation, random erasing, noise injection, mixing of images, kernel filters, generative adversarial networks, flipping, feature space transformation, cropping and color space transformation oversampling methods frequently used in the literature were applied on the Ham10000 dataset. According to the results obtained as a result of the study, oversampling methods have been compared in terms of classification performance. From three different convolutional neural network models, according to the classification results obtained with ResNet50, DenseNet201, MobileNet, in terms of accuracy, the noise injection method in the ResNet50 model was 0.967, the color space transformation method in the DenseNet201 model was 0.965, and in the MobileNet model, the mixing of images method had a classification performance value of 0.974 which was better than the other oversampling methods. [ABSTRACT FROM AUTHOR]

During apple (Malus communis L.) harvesting, physical damage that reduces the quality of the product is inevitable. Early detection and separation of damaged fruits is important in terms of increasing their commercial value. Undetected defective products reduce the production volume as well as food loss, since they affect the quality of intact products. The aim of this study is to detect defects in apples using deep learning techniques on images taken from the "Granny Smith" apple cultivar. A technique that does not require special conditions and that will make classification and defect detection cost-effectively has been researched. In the study, the test accuracy of the InceptionV3 model was 100% after 100 epochs, and the test accuracy of the AlexNet model was 98.33%. A method has been developed that can prevent economic losses that may occur after harvesting by detecting and separating the damages that occur on the fruit during harvesting with deep learning techniques. [ABSTRACT FROM AUTHOR]

Stroke is a cerebrovascular health problem that occurs when blood flow to the brain is slowed or interrupted for various reasons. Since the brain tissue cannot receive enough nutrients and oxygen, brain cells begin to die within minutes and temporary or permanent damage occurs in the functions in the area where the stroke occurred. Brain stroke is a very serious medical condition and requires urgent intervention. Early detection of stroke and segmentation of the stroke site are of great importance in terms of preventing permanent damage. In this study, brain stroke segmentation was performed using U-Net deep learning model with Res2Net backbone. A total of 2223 CT images including 1093 hemorrhagic and 1130 ischemic stroke types were used as dataset. The images were preprocessed with the windowing method and then trained and tested with the proposed model. While the IoU rate was 0.82 in the images used without windowing adjustment, this rate increased to 0.87 after preprocessing, when the data duplication method was added, the average IoU reached 0.92. The test results obtained showed that the use of appropriate window settings in the images increased the segmentation performance. [ABSTRACT FROM AUTHOR]

Detection and tracking of enemy targets is vital to military operations. In this study, the use of deep learning techniques to detect camouflaged tanks is examined. The main purpose of this study is to detect camouflaged tanks hidden in forest areas. In this way, the detection and tracking of enemy targets can be done more effectively and the security of the soldiers can be ensured. The YOLO architecture combines object detection and classification into a single network, resulting in faster and more accurate results. YOLOv5, YOLOv6, YOLOv7 and YOLOv8 architectural designs are compared. The YOLOv6 architectural design outperforms other designs. This design obtained 0,983 mAP50 value, 0,966 precision, 0,935 recall and 0,950 F1 score as a result of performance analysis using 2.234 tank images and 774 car images. The results of this study show that YOLO architectural designs show high performance in object detection and classification. [ABSTRACT FROM AUTHOR]

In this paper, we present a web-based optical character recognition (OCR) system that converts images of Ottoman documents printed with naskh font into text using CNN+RNN-based deep neural network models. For training, three datasets - original, synthetic, and hybrid - were prepared and three different OCR models were created. The original data set consists of 1,000 pages and the synthetic data set consists of 23,000 pages. Hybrid data set contains both. The trained models were compared with Tesseract's Arabic and Persian, Google Docs' Arabic, Abby FineReader's Arabic, and Miletos OCR model/tools with a 21-page test set. The comparison was made with 3 different texts (raw, normalized, and joined) and using 3 different criteria (character, ligature, and word recognition). The Osmanlica.com Hybrid model produced significantly better results than the others with 88.86% raw, 96.12% normalized, and 97.37% joined accuracy in character recognition; 80.48% raw, 91.60% normalized, and 97.37% joined accuracy in ligature recognition; and 44.08% raw and 66.45% normalized accuracy in word recognition. To investigate the effects of the characteristics of the alphabet on OCR, character, ligature, and word frequency analyses of Ottoman was performed. In this analysis, the characters in the alphabet were grouped according to distinctive features such as connectedness, letter body, position and number of dots, type of character, and source language; and frequencies and recognition accuracies were examined for each group. OCR results are also reported for each character. [ABSTRACT FROM AUTHOR]

There are about 466 million hearing and speech disabilities in the world, and there are about 1.5 million hearing and speech disabilities in Turkey. Sign language (SL) is the language that is used by hearing and speech-impaired individuals to communicate among themselves and with other individuals. This language realized with hand, face, and body gestures is a dynamic language that can vary from country to country and from region to region in the countries where it is spoken. Learning sign language at a younger age is important, especially in terms of both cognitive and intellectual development and the academic development of the individual. For this reason, there are a series of studies developed to use technology in sign language learning. The conducted studies reported the successful results of humanoid robots developed in sign language learning. It has been proven that the developed humanoid robots are successfully performed in sign language learning. There is no developed humanoid robot for sign language learning in Türkiye. On the other hand, the current studies in the literature have been carried out with a limited number of ready-made humanoid robots including Turkish sign language expressions. With the humanoid robot we developed within the scope of the study, some Turkish sign language statements were successfully realized. [ABSTRACT FROM AUTHOR]

Fire is a natural disaster that causes ecological, social and economic damage. With global warming and the widespread use of explosive/flammable chemicals, fires have become one of the most important problems for humanity. Early fire detection is critical to minimize destruction. For this reason, studies on fire detection through images have begun to be carried out in order to detect fires early. In recent studies for fire detection from images deep learning algorithms are generally used. These studies focus on the analysis of images taken from cameras with models running on powerful servers. With the developments in mobile devices and internet of things, images can analyzed on edge devices. In the study fire and smoke detection model which requires low processing power has been developed to enable the images to be analyzed on mobile device without transferring them to server. The MobileNet convolutional neural network was revised, the last 3 layers were removed and replaced with a flattening layer and a dense layer consisting of two nodes. Fire and smoke detection model, the method with the highest accuracy was determined among the models developed using revised MobileNet, original MobileNet, MobileNetV2, EfficientNetB0, ShuffleNet, NASNetMobile and PeleeNet convolutional neural networks. To train and test the models, 80% of the dataset consisting of 43,355 images was used for training and 20% for testing. According to test results, highest accuracy rate of 98.37% was achieved in the revised MobileNet. [ABSTRACT FROM AUTHOR]

In recent years, Covid-19, which has become a reality of our lives and has become a pandemic for the whole world, increases the rate of infection and even variants of it begin to appear if the necessary precautions are not strictly followed. As the measures published by WHO and necessary to be taken are taken, the fight against the disease may become easier. Although it is difficult to comply with the measures, if care is taken to comply, the disease is either milder or the disease is not easily caught. One of the most important of these measures is to pay attention to the use of masks in crowded areas. After the importance of mask use was supported by research, inspections for the use of masks in crowded places such as some shopping malls, health institutions and schools began. However, since it is difficult for a human to perform these inspections, mask detection studies have begun to be carried out with deep learning methods, which are frequently used today. In this thesis, it is aimed to perform mask detection using transfer learning based models. DenseNet121, EfficientNetV2M, NasNetMobile, InceptionV3, VGG19 and InceptionResNetV2 deep learning models were used with a total of 906 images with the data set available on the Kaggle website. As a result of the experimental evaluations, it was seen that the best success rate was obtained with the NasNetMobile model, with 99.35% accuracy, 99% precision, 99% recall and 99% f1 scores. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is the most common type of dementia in the world, and the treatment methods currently used are aimed only at preventing the progression of the disease. Brain tissue volume changes due to Alzheimer's disease (AD). With the help of tensor-based morphometry (TBM), changes in brain tissues caused by the disease can be monitored. This study aimed to develop an effective method to differentiate between AD patients and the Cognitive Normal (CN) group subjects. TBM, or small local volume differences, are adopted as classification features. Axial brain image slices with 5-pixel intervals covering the hippocampus and temporal lobe from 3D TBM morphometric images belonging to the AD/CN class were recorded in 2D. Then, 60% of the dataset obtained from each clinical group (AD; CN) was allocated as training, 20% as validation, and 20% as test datasets (training: 480; validation: 120; testing: 120). The model performed AD/CN estimation with validation (92.5%) and testing (89%) accuracy values. The results show that TBM obtained from slices covering the hippocampus and temporal lobe with deep learning can be applied with high accuracy in the diagnosis of AD. [ABSTRACT FROM AUTHOR]

The developments in semiconductor circuit manufacturing technologies make the wafer production processes on which these structures are located more complex and sensitive. Various factors such as environmental conditions associated with manufacturing and material quality directly affect the wafer yield, that is, the amount of space on the wafer that can be considered perfect. Possible patterns showing defective areas on a semiconductor wafer are defined as standard. Classifying the defects on the wafer according to these definitions can provide important information to diagnose the causes of problems that occur in the manufacturing processes. In this study, a decision support method has been developed that automatically performs the wafer defect model classification process up to a certain security value. In this method, the basic classification process is performed by a network structure trained with deep learning methods. Wafers classified with an accuracy above the desired safety value are considered to be correctly classified, only the wafers below this value are subject to the inspection of the human operator. By using the method, the total daily inspection time for an average sized wafer manufacturing facility can be reduced to 10% of the time that would apply if the entire inspection was done by the human operator. In addition, subjective evaluations by the human operator can be avoided to a large extent. [ABSTRACT FROM AUTHOR]

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 deep-learning 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. [ABSTRACT FROM AUTHOR]

Thanks to the opportunities provided by developing technology, there had an increase in the transactions carried out using the internet. This development also led to an increase in data. This situation created the need for new technology for businesses to store, share, control, and manage data securely. One of the current technologies that can be used in this context is the blockchain structure. The blockchain structure is a technology that can be used in many areas, and the most popular usage area today is cryptocurrencies. In this study, it is aimed to estimate Polkadot cryptocurrency, which is one of the essential sub-cryptocurrencies. In the study, the data between 20.08.2020 and 27.02.2023 are used. According to these data, it aimed to estimate the daily average Polkadot value as the output value. Clusters for input values are created in two different ways. In the first input values; number of Polkadot YouTube search, number of Polkadot Google search, and Polkadot volume are used. Unlike the first input values, Ethereum, the leader of the alt cryptocurrencies, is added in the second input value. In this study, which consists of two different input structures, to estimate the daily average values of the Polkadot currency, an estimation study is carried out using multi-layered sensors in artificial neural networks and a long-shortterm memory structure, which is one of the deep learning methods. When the results are examined, it is determined that the values of 4 input sets in the obtained artificial neural networks gave better results with a correlation coefficient of 0.93. [ABSTRACT FROM AUTHOR]

Deep learning is an important discipline in which human-specific problems are solved with the help of machines with advanced hardware power. It is seen this discipline is widely used in the fields of industry, health, defense industry, and sports. In addition, the use of deep learning in the field of horticulture is an important requirement. With the integration of deep learning into horticulture, to do product classification is very important for increasing productivity and production. In this study, a method using ensemble learning is proposed to improve the accuracy of the classification problem for horticultural data. For this method, a new dataset was created, containing a total of 24421 images and 15 crop classes, independent of data augmentation. In order to train this created data set with the help of the proposed method, a hierarchical structure has been designed in which the output of one model is the input of the other model. A total of 7 pre-trained models were used in the experimental studies of the proposed method. Since this method is in an ensemble structure, it is possible to add or remove pre-trained models from the structure. With the help of experimental studies, a performance analysis of the proposed method, which is compared with the traditional CNN method, has been made. As a result of these analyses, it has been observed that the proposed method works 3% more successfully. [ABSTRACT FROM AUTHOR]

Yield estimation is an important field of study in agriculture. Forecasting yields provides producers, consumers, traders and policymakers with important preliminary information and time to take necessary action. Corn is an important product in terms of international trade and is widely used in human and animal nutrition throughout the world. Adana produces the highest amount of corn sown both as main and secondary product in Türkiye. Therefore, in this study, corn yield was tried to be estimated by using various meteorological parameters and plant fertilizer usage amounts. For this purpose, statistical (Auto-ARIMA), machine learning (Random Forest) and deep learning (CNN, LSTM) methods were used. The study findings showed that all models used predicted maize yield highly accurately. However, the highest accuracy LSTM model estimated the yield of first corn crop. [ABSTRACT FROM AUTHOR]

The unregulated and unconscious use of energy sources causes environmental damage. On the other hand the increasing population density, development of industry and technology increase the demand for electrical energy day by day. For this purpose, investments in the energy sector are directed towards renewable energy sources such as wind energy in order to ensure both environment-friendly and supply-demand balance. The amount of energy obtained from wind energy varies depending on regional differences such as wind direction and speed. In this study, a method is proposed to achieve better results in predicting electricity generation from wind energy by capturing the non-linear and non-stationary nature of wind energy using a hybrid approach of deep learning methods, specifically CNN and BLSTM architectures. In the forecasting model, 26280 real-time data measured at hourly frequency are used. In addition, the hyperparameter values used in the model were optimized using the Grid Search algorithm in order to increase the prediction success. The success of the proposed hybrid model is compared with the BLSTM model. As a result, the R² value, which indicates the success rate of the proposed CNN-BLSTM model, was calculated as 0.984. [ABSTRACT FROM AUTHOR]

Blood pressure is the pressure exerted by the blood in the vessels against the vessel walls. If this pressure value is above normal levels, it is known as high blood pressure (HBP) or hypertension (HPT). The diagnosis of this health problem, which negatively affects the quality of life, often causes various damage to organs and can lead to death, is very important. In this study, a method for automatic diagnosis of HPT using ballistocardiography (BCG) signals is proposed. For this, BCG signals are transferred to the time-frequency domain using the continuous wavelet transform filter bank (CWTFB) method. The quality of the conversion has been increased by making some parameter settings in the conversion method used while performing these operations. Then, the obtained images were classified with ResNet18, ResNet50, VGG16 and AlexNet convolutional neural networks and the obtained results were compared. With the proposed method, classification accuracy of 98.92%, 99.34%, 99.22% and 98.07% was obtained for ResNet18, ResNet50, VGG16 and AlexNet architectures, respectively. These high classification results obtained prove that the proposed method can be used for the diagnosis of hypertension. [ABSTRACT FROM AUTHOR]

Music has been a means of emotional expression throughout human history. Different cultures and times have offered unique ways of expressing the emotions experienced by individuals through music. Technological advancements have greatly enhanced our interaction with music, making access easier thanks to digital platforms. While traditional music recommendation systems are based on the user's listening habits, this study aims to provide music recommendations by evaluating the user's immediate emotional state. We utilize a CNN-based emotion recognition system for this purpose. This model predicts one of seven emotions by analyzing facial images. The results of the Confusion Matrix (CM) in the training phase indicate that the model is proficient at classifying emotions. ROC curve analysis shows the model's high capability in emotion prediction. Notably, the model's overall accuracy stands at 92.7%, highlighting its effectiveness. This research demonstrates the potential of emotion-based music recommendation systems in enhancing the user experience. [ABSTRACT FROM AUTHOR]

Strawberry fruits which are rich in vitamin A and carotenoids offer benefits for maintaining healthy epithelial tissues and promoting maturity and growth. The intensive cultivation and swift maturation of strawberries make them susceptible to premature harvesting, leading to spoilage and financial losses for farmers. This underscores the need for an automated detection method to monitor strawberry development and accurately identify growth phases of fruits. To address this challenge, a dataset called Strawberry-DS, comprising 247 images captured in a greenhouse at the Agricultural Research Center in Giza, Egypt, is utilized in this research. The images of the dataset encompass various viewpoints, including top and angled perspectives, and illustrate six distinct growth phases: "green", "red", "white", "turning", "early-turning" and "late-turning". This study employs the Yolo-v7 approach for object detection, enabling the recognition and classification of strawberries in different growth phases. The achieved mAP@.5 values for the growth phases are as follows: 0.37 for "green," 0.335 for "white," 0.505 for "earlyturning," 1.0 for "turning," 0.337 for "late-turning," and 0.804 for "red". The comprehensive performance outcomes across all classes are as follows: precision at 0.792, recall at 0.575, mAP@.5 at 0.558, and mAP@.5:.95 at 0.46. Notably, these results show the efficacy of the proposed research, both in terms of performance evaluation and visual assessment, even when dealing with distracting scenarios involving imbalanced label distributions and unclear labeling of developmental phases of the fruits. This research article yields advantages such as achieving reasonable and reliable identification of strawberries, even when operating in real-time scenarios which also leads to a decrease in expenses associated with human labor. [ABSTRACT FROM AUTHOR]

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]

The high resolution of the image is very important for applications. Publicly available satellite images generally have low resolutions. Since low resolution causes loss of information, the desired performance cannot be achieved depending on the type of problem studied in the field of remote sensing. In such a case, super resolution algorithms are used to render low resolution images high resolution. Super resolution algorithms are used to obtain high resolution images from low resolution images. In studies with satellite images, the use of images enhanced with super resolution is important. Since the resolution of satellite images is low, the success rate in the classification process is low. In this study, super resolution method is proposed to increase the classification performance of satellite images. The attributes of satellite images were extracted using AlexNet, ResNet50, Vgg19 from deep learning architecture. Then the extracted features were then classified into 6 classes by giving input to AlexNet-Softmax, ResNet50-Softmax, Vgg19-Softmax, Support Vector Machine, K-Nearest Neighbor, decision trees and Naive Bayes classification algorithms. Without super resolution and with super resolution feature extraction and classification processes were performed separately. Classification results without super resolution and with super resolution were compared. Improvement in classification performance was observed using super resolution. [ABSTRACT FROM AUTHOR]