In machine learning system different types of approaches, machine learning strategies have applications are related sentiment analysis, classification approaches, data mining etc. Irregular Forest has huge capability of turning into a prevalent method for future classifiers in light of the fact that its execution has been observed to be practically identical with troupe strategies sacking and b...

The application of machine learning techniques in agriculture, particularly harvest forecasting, is gaining traction as a means addressing this issue. major project, "Optimizing Crop Yields through Machine Learning-Based Prediction," takes comprehensive approach to issue by considering variety parameters, including temperature, humidity, rainfall, and soil nutrient levels, Figure out which crop...

This work identifies effective computable features from the Breast Imaging Reporting and Data System (BI-RADS), to develop a computer-aided diagnosis (CAD) system for breast ultrasound. Computerized features corresponding to ultrasound BI-RADs categories were designed and tested using a database of 283 pathology-proven benign and malignant lesions. Features were selected based on classification...

Machine Learning tools are increasingly being applied to analyze data from microarray experiments. These include ensemble methods where weighted votes of constructed base classifiers are used to classify data. We compare the performance of AdaBoost, bagging and BagBoost on gene expression data from the yeast cell cycle. AdaBoost was found to be more effective for the data than bagging. BagBoost...

The goal of this paper is to investigate if the strong machine learning technique is able to retrieve information from past prices and predict price movements and future trends. The architecture of the system with the on-line adaptation ability to non-stationary two dimensional mixed Black-Scholes Markov time series model is presented. The methodology of investment strategies performance verifi...

In this paper, we employ machine learning and other statistical techniques to the problems of classifying and predicting crimes in San Francisco. Drawing upon existing research in the field to approach these two problems, we employ Random Forest and VAR(p) models, respectively. For the classification problem, our results across all 39 crime categories demonstrate the difficulty of the fully-spe...

The study reported was devoted to investigate to what extent bagging approach could lead to the improvement of the accuracy machine learning regression models. Four algorithms implemented in the KEEL tool, including two evolutionary fuzzy systems, decision trees for regression, and neural network, were used in the experiments. The results showed that some bagging ensembles ensured higher predic...

MicroRNAs (miRNAs) are key regulators of eukaryotic gene expression whose fundamental role has been already identified in many cell pathways. The correct identification of miRNAs targets is a major challenge in bioinformatics. So far, machine learning-based methods for miRNA-target prediction have shown the best results in terms of specificity and sensitivity. However, despite its well-known ef...

A common step in drug design is the formation of a quantitative structure-activity relationship (QSAR) to model an exploratory series of compounds. A QSAR generalizes how the structure of a compound relates to its biological activity. There is growing interest in the application of machine learning techniques in QSAR modeling research. However, no single technique can claim to be uniformly supe...