AIMC Topic: Random Forest

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Evaluating the ecological vulnerability of Chongqing using deep learning.

Environmental science and pollution research international
This study used deep learning to evaluate the ecological vulnerability of Chongqing, China, discuss the deep learning evaluations of ecological vulnerability, and generate vulnerability maps that support local ecological environment protection and go...

HRFSVM: identification of fish disease using hybrid Random Forest and Support Vector Machine.

Environmental monitoring and assessment
Aquaculture fish diseases pose a serious threat to the security of food supplies. Fish species vary widely, and because they resemble one another so much, it is challenging to distinguish between them based solely on appearance. To stop the spread of...

Predicting dry weight change in Hemodialysis patients using machine learning.

BMC nephrology
BACKGROUND: Machine Learning has been increasingly used in the medical field, including managing patients undergoing hemodialysis. The random forest classifier is a Machine Learning method that can generate high accuracy and interpretability in the d...

Prediction of stock price movement using an improved NSGA-II-RF algorithm with a three-stage feature engineering process.

PloS one
Prediction of stock price has been a hot topic in artificial intelligence field. Computational intelligent methods such as machine learning or deep learning are explored in the prediction system in recent years. However, making accurate predictions o...

Artificial Intelligence Applied to a First Screening of Naevoid Melanoma: A New Use of Fast Random Forest Algorithm in Dermatopathology.

Current oncology (Toronto, Ont.)
Malignant melanoma (MM) is the "great mime" of dermatopathology, and it can present such rare variants that even the most experienced pathologist might miss or misdiagnose them. Naevoid melanoma (NM), which accounts for about 1% of all MM cases, is a...

Machine learning based canine posture estimation using inertial data.

PloS one
The aim of this study was to design a new canine posture estimation system specifically for working dogs. The system was composed of Inertial Measurement Units (IMUs) that are commercially available, and a supervised learning algorithm which was deve...

A Machine Learning Model Ensemble for Mixed Power Load Forecasting across Multiple Time Horizons.

Sensors (Basel, Switzerland)
The increasing penetration of renewable energy sources tends to redirect the power systems community's interest from the traditional power grid model towards the smart grid framework. During this transition, load forecasting for various time horizons...

Development of a 2D-QSAR Model for Tissue-to-Plasma Partition Coefficient Value with High Accuracy Using Machine Learning Method, Minimum Required Experimental Values, and Physicochemical Descriptors.

European journal of drug metabolism and pharmacokinetics
BACKGROUND: The demand for physiologically based pharmacokinetic (PBPK) model is increasing currently. New drug application (NDA) of many compounds is submitted with PBPK models for efficient drug development. Tissue-to-plasma partition coefficient (...

Forward propagation dropout in deep neural networks using Jensen-Shannon and random forest feature importance ranking.

Neural networks : the official journal of the International Neural Network Society
Dropout is a mechanism to prevent deep neural networks from overfitting and improving their generalization. Random dropout is the simplest method, where nodes are randomly terminated at each step of the training phase, which may lead to network accur...

Machine Learning and Electroencephalogram Signal based Diagnosis of Dipression.

Neuroscience letters
Depression is a psychological condition which hampers day to day activity (Thinking, Feeling or Action). The early detection of this illness will help to save many lives because it is now recognized as a global problem which could even lead to suicid...