(1) Background: According to the World Health Organization (WHO), 6.3 million intrauterine fetal deaths occur every year. The most common method of diagnosing perinatal death and taking early precautions for maternal and fetal health is a nonstress test (NST). Data on the fetal heart rate and uterus contractions from an NST device are interpreted based on a trace printer’s output, allowing for a diagnosis of fetal health to be made by an expert. (2) Methods: in this study, a predictive method based on ensemble learning is proposed for the classification of fetal health (normal, suspicious, pathology) using a cardiotocography dataset of fetal movements and fetal heart rate acceleration from NST tests. (3) Results: the proposed predictor achieved an accuracy level above 99.5% on the test dataset. (4) Conclusions: from the experimental results, it was observed that a fetal health diagnosis can be made during NST using machine learning.

Detecting the rail surface faults is one of the most important components of railway inspection process which should be performed periodically. Today, the railway inspection process is commonly performed using computer vision. Performing railway inspection based on image processing can lead to false-positive results. The fact that the oil and dust residues occurring on railway surfaces can be detected as an error by the image processing software can lead to loss of time and additional costs in the railway maintenance process. In this study, a hardware and software architecture are presented to perform railway surface inspection using 3D laser cameras. The use of 3D laser cameras in railway inspection process provides high accuracy rates in real time. The reading rate of laser cameras to read up to 25.000 profiles per second is another important advantage provided in real time railway inspection. Consequently, a computer vision-based approach in which 3D laser cameras that could allow for contactless and fast detection of the railway surface and lateral defects such as fracture, scouring and wear with high accuracy are used in the railway inspection process was proposed in the study.

The candlestick charts, which were developed in the 18th century and were initially used in the Japanese rice market, are widely used in trading strategies in all financial markets after 1991. Candlestick charts can interpret opening, high, low and closing values of an asset in a single visual. In addition to these advantages, the large number of candlestick chart patterns makes their practical use difficult. In the study developed for this purpose, a software framework that uses candlestick charts and predicts the trend direction was created. The study consists of four stages. In the first step, a system that recognizes candle patterns is created. In the second stage, the performance of the model is measured by running training and testing processes on data sets in which candlestick chart types and trend direction are labeled. In the machine learning phase, community methods such as xgboost were used. In the last stage of the study, it was seen that with the strategy based on only recognizing the candlestick pattern and taking position in the direction of the trend based on proposed approach, higher profit was obtained in 11 world indices compared to Buy&Hold strategy.

Moving averages and indicators derived from these averages are used to predict the future direction the stocks will move. In manual and algorithmic trading, moving averages play a decisive role in decision making. In this study, a new hybrid approach has been developed that can be used as an alternative to moving averages such as SMA, WMA and EMA used in the literature. In BIST30 stocks in Turkey, the proposed method performs better than widely used indicators such as MACD, Stochastic and RSI, commonly used in the literature.