Applications of machine learning and artificial intelligence in different cancer cells prediction and prophecy

Stock Price Prediction using Machine Learning and Swarm Intelligence

Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem. Methods: In this...

individual qualities and integrative motivation and their prediction of non-linguistic outcomes of learning english in intermediate iranian students: a psychological perspective

abstract this study investigated the predictability of variables from a motivational framework as well as individuals qualities to predict three non-linguistic outcomes of language learning. gardners socio-educational model with its measures has been used in the current study. individual qualities presented in this study include (1) age, (2) gender, and (3) language learning experience. the...

Next Generation Artificial Intelligence and Machine Learning

simulation and experimental studies for prediction mineral scale formation in oil field during mixing of injection and formation water

abstract: mineral scaling in oil and gas production equipment is one of the most important problem that occurs while water injection and it has been recognized to be a major operational problem. the incompatibility between injected and formation waters may result in inorganic scale precipitation in the equipment and reservoir and then reduction of oil production rate and water injection rate. ...

Applications of Machine Learning in Cancer Prediction and Prognosis

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to "learn" from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomi...