Leukemia is a fatal cancer and has two main types: Acute and chronic. Each type has two more subtypes: Lymphoid and myeloid. Hence, in total, there are four subtypes of leukemia. This study proposes a new approach for diagnosis of all subtypes of leukemia from microscopic blood cell images using convolutional neural networks (CNN), which requires a large training data set. Therefore, we also investigated the effects of data augmentation for an increasing number of training samples synthetically. We used two publicly available leukemia data sources: ALL-IDB and ASH Image Bank. Next, we applied seven different image transformation techniques as data augmentation. We designed a CNN architecture capable of recognizing all subtypes of leukemia. Besides, we also explored other well-known machine learning algorithms such as naive Bayes, support vector machine, k-nearest neighbor, and decision tree. To evaluate our approach, we set up a set of experiments and used 5-fold cross-validation. The results we obtained from experiments showed that our CNN model performance has 88.25% and 81.74% accuracy, in leukemia versus healthy and multi-class classification of all subtypes, respectively. Finally, we also showed that the CNN model has a better performance than other well-known machine learning algorithms.

Text data analysis of social media is becoming more and more important since it includes the most recent information on what people think about. Likewise, emotion is one of the most valuable parts of human communication, emotion analysis is a type of information extraction process which identifies the emotional states of a given text. In this study, we investigated the performance of deep neural networks on emotion analysis from Turkish tweets. For this, we examined three different deep learning architectures including artificial neural network (ANN), convolutional neural network (CNN) and recurrent neural network (RNN) with long short-term memory (LSTM). Besides, we curated a dataset of Turkish tweets and annotated each tweet automatically for six emotion categories using a lexicon-based approach. For the evaluation, we conducted a set of experiments for each architecture. The results showed that the lexicon-based automatic annotation of tweets is valid. Secondly, ANN produced the worst result as expected, and CNN resulted in the highest score of 0.74 in terms of accuracy measure. Experiments also showed that our proposed approach for emotion analysis of tweets in Turkish performs better than state-of-the-art in this topic.

This study presents a new dataset to be used in emotion extraction studies in Turkish text. We consider emotion extraction as a supervised text classification problem, which thereby requires a dataset for the training process. To satisfy this requirement, we aim to create a new dataset containing data for the six emotion categories: happiness, fear, anger, sadness, disgust and surprise. To gather this dataset, we conducted a survey and collected 27,350 entries from 4709 individuals. In the next step, we performed a validation process in which annotators validated each entry one by one by assigning a related emotion category. As a result of this process, we obtained two datasets, one raw and the other validated. Subsequently, we generated four versions of these two datasets using two different stemming methods and then modelled them using a vector space model. Then, we ran machine learning algorithms, including complement naive Bayes (CNB), random forest (RF), decision tree C4.5 (J48) and an updated version of support vector machines (SVMs), on the models to calculate the accuracy, precision, recall and F-measure values. Based on the results we obtained, we concluded that the SVM classifier yielded the highest performance value and that the models trained with a validated dataset provide more accurate results than the models trained with a non-validated dataset.

Bu makale, öncelikle sağlık alanında yapay zekâ (YZ) kul- lanımıyla ortaya çıkabilecek potansiyel riskleri inceleyerek mevcut yasal ve etik değerler üzerinden durumunu inceler. Ardından sağlık sektöründe gelecekteki YZ kullanımının etki- lerini ve bu etkilerin giderilmesi için açıklanabilir YZ’nin kilit rolünü ele alır. Hasta memnuniyetini arttırmak, sağlık profes- yonellerine destek olmak ve genel sağlık sonuçlarını iyileştir- mek için YZ’nin potansiyel risklerini en aza indirgeyerek nasıl kullanılabileceğini tartışır.