In this work, a high dynamic range (HDR) image generation method using a single input image is presented. The proposed approach generates over-and under-exposed images by making use of a novel adaptive histogram separation scheme. Thus, it becomes possible to eliminate ghosting effects which generally occur when several input image containing camera/object motion are utilized in HDR imaging. Additionally, it is proposed to utilize a fuzzy logic based approach at the fusion stage which takes visibility of the inputs pixels into account. Since the proposed approach is computationally light-weight, it is possible to implement it on mobile devices such as smart phones and compact cameras. Experimental results show that the proposed approach is able to provide ghost-free and improved HDR performance compared to the existing methods 1 .

To forecast the movement directions of stocks, exchange rates, and stock markets are significant and an active research area for investors, analysts, and researchers. In this paper, word embedding and deep learning-based direction prediction of Istanbul Stock Exchange (BIST 100) is proposed by analyzing nine banking stocks with high volume in BIST 100. Though English news articles have been employed for forecasting of market direction previously, to the best of our knowledge, Turkish news articles and user comments from social media and different platforms have not been utilized with the combination of deep learning techniques and word embedding methods to predict the direction of Turkish stocks and market. For this objective, long short-term memory networks, recurrent neural networks, convolutional neural networks as deep learning algorithms and Word2Vec, GloVe, and FastText as word embedding models are evaluated. To demonstrate the effectiveness of proposed model, four different sources of Turkish news are collected. The news articles about stocks from Public Disclosure Platform (KAP), text-based technical analysis of each stock from Bigpara, user comments from both Twitter and Mynet Finans platforms are gathered. Experiment results demonstrate that the combination of deep learning techniques and word embedding methods have a great potential to predict the direction of BIST 100. INDEX TERMS Deep learning techniques, financial sentiment analysis, stock market prediction, word embedding methods This work is licensed under a Creative Commons Attribution 4.

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Robots and artificial intelligence technologies have become very important in the health applications as in many other fields. The proposed system in this work aims to provide detailed analysis of pre-op and post-op stage of FUE hair transplant procedures to enable surgeon to plan and assess success of the operations. In order to achieve this target, a robotic and vision-based system imaging and AI based analysis approach is developed. The proposed system performs analyses in three main stages: initialization, scanning, and analysis. At the initialization stage, 3D model of the patient's head generated at first by locating a depth camera in various positions around the patient by the help of a collaborative robot. At the second stage, where high resolution image capturing is performed in a loop with the usage of the 3D model, raw images are processed by a deep learning based object detection algorithm where follicles in pre-op and extracted follicle positions (i.e. holes) and placed grafts in post-op is detected. At the last stage, thickness of each hair is computed at the detected hair follicle positions using another deep learning-based segmentation approach. These data are combined to obtain objective evaluation criteria to generate patient report. Experimental results show that the developed system can be used successfully in hair transplantation operations.