Machine Learning

This data set is a data set used for aircraft theoretical velocity ranking. Four sensors are randomly arranged in a 1*1 square map, and three aircraft will fly over the map coverage area at the same time. The velocity of the aircraft is simulated by a random process. The theoretical velocities of the three aircraft are similar, and the velocity of the aircraft will be disturbed during actual flight, causing large fluctuations, so that it is difficult to distinguish the theoretical velocity order of the aircraft flying into the map.

Nowadays, the high cost of customer acquisition makes telecom operators encounter the “ceiling”, and even fall into the dilemma of customer acquisition. As market saturation increases, telecom operators need to solve the problem of increasing subscriber stickiness and prolonging subscriber life cycle. Therefore, it is crucial to analyse and predict the churn of telecom users. The dataset is ”Telecom Operator Customer Dataset”. The dataset obtained from the official Kaggle competition website in this study, which comprised 21 fields.

Manual palpation of organs played a vital role in detecting abnormalities in open surgeries. However, surgeons
have lost this ability with the development of minimally invasive surgeries. This challenge led to the development of artificial sensors for palpating the patient's organs and tissue. The majority of research done is related to improving the measurement of tissue compliance by the development of versatile force sensors for surgical

In situations when the precise position of a machine is unknown, localization becomes crucial. It is crucial to identify and ascertain the machine's position. This research focuses on improving the position prediction accuracy over long-range networks using a unique machine learning-based technique. In order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting approach using LoRa technology, this study suggested an ML-based algorithm.