653 results for machine learning
Showing 1–20 · 2.53 seconds · RSS
-
Journal Article
Diagnosing Breast Cancer by Machine Learning
…to diagnose cancer, Decision Tree, Random Forest, and Support Vector Machine methods were used as machine learning methods. Results: In the first step, by content analysis method, 29 effective factors in Breast cancer were identified. Then, taking into account the indigenous and climatic conditions…
-
Journal Article Full text
Machine Learning Applications in Organic Solar Cells
…and optimization of solar cells. One of the mos invaluable approaches is machine learning methods. Machine learning is considered as a new branch of academic science that provides valuable new information by processing exising data. Areas of application of machine learning in the design and…
-
Journal Article Full text
Predicting Bank Customer Churn Using Machine Learning
…during the training process. To predict customer churn, widely used machine learning algorithms—including Naive Bayes, k-Nearest Neighbors, Support Vector Machine, Logistic Regression, and Decision Tree—were applied, along with ensemble learning methods such as Random Forest, Adaptive Boosting, and…
From your institution’s collections
- Machine learning dan computational intelligenceWidodo Budiharto · 2016 · UNILAK
- Machine learning (konsep dan implementasi)Yaya Heryadi · 2020 · UNILAK
-
Journal Article Full text
Modeling Groundwater Potential Using Machine Learning Models
…is to determine the potential of groundwater using support vector machine learning (SVM) models as well as metaheuristic algorithms (hybrid support vector machine model and the bee metaheuristic optimization algorithm (SVM-BA) and hybrid model of the support vector machine and particle swarm…
-
Journal Article Full text
A novel machine learning approach for portfolio optimization
…are serious challenges in investing in the stock market. The use of machine learning capacities in the process of optimal capital allocation among portfolio assets has received less attention and usually, the same weight is assigned to portfolio stocks or traditional risk assessment methods are…
-
Journal Article Full text
Predicting Kidney Transplantation Outcomes Using Machine Learning Techniques
…treatment method for patients with end-stage renal disease (ESRD). Machine learning, as one of the most valuable branches of artificial intelligence in the field of predicting patient outcomes or predicting various conditions in patients, has significant applications. The purpose of this research…
-
Journal Article Full text
Intelligent Diagnosis of Larynx Cancer Using Machine Learning Methods
Introduction: Larynx cancer can be benign or malignant based on various factors. This research aimed to provide a machine learning-based model to improve the diagnosis of individuals with larynx cancer. Method: In the first step, the voices of the people who visited the medical centers (including…
-
Journal Article
A survey on intrinsic motivation models in machine learning
…in artificial intelligence. Additionally, we discuss the reinforcement learning method as a successful approach for incorporating intrinsic motivation. Finally, we explore the practical applications, limitations, and future intrinsic motivation research.
-
Journal Article Full text
Pairs trading with machine learning in Tehran Stock Exchange
…gap between two components of the pair (taking a trade). Methodology: Machine learning can help define a search space by clustering correlated securities and then selecting the most promising pairs for trading opportunities. However, after finding a price gap in pairs, there may be divergence even…
-
Journal Article Full text
Analyzing and Predicting Hiring Decisions Using Machine Learning and Deep Learning
Objective The aim of this article is to explore the use of machine learning and deep learning algorithms to predict the outcomes of hiring decisions. Selecting the right human resources is one of the most fundamental elements in any organization or institution, as it directly influences key…
-
Journal Article
De-identification of Electronic Health Records Using Machine Learning Algorithms
…published studies on the usage of de-identification methods based on Machine Learning (ML) approaches for removing all identifiable information from electronic health records. Methods: A systematic review was performed in electronic databases like PubMed and ScienceDirect between 2006 and 2016…
-
Journal Article Full text
Next-Gen Machine Learning Models: Pushing the Boundaries of AI
ABSTRACTBackground: Machine learning (ML) has developed significantly over the years, changing several industries through the use of automation and Big Data. By building better next-generation machine learning models, AI’s future has the potential of improving on existing problematic methods such…
-
Journal Article Full text
Machine Learning for Detection of Pests in Tomato: A Review
Influence of a single atmospheric component or meteorological variable on the host, pathogen, or their interaction in controlled environments has accounted for the majority of climate change’s impact on plant pests and diseases. Climate change can lead to alterations in the stages and rates of…
-
Journal Article Full text
Early Prediction of Alzheimer’s Disease Using Interpretable Machine Learning Algorithms
…was used. Spark framework was used for data management and three machine learning algorithms including Naïve Bayes, Decision Tree, and Artificial Neural Networks with the best hyperparameters were implemented and compared. To prevent overfitting and measure the efficiency of the models, a 5-fold…
-
Journal Article Full text
Predicting Scientific Research Impacts in Biotechnology by Machine Learning Algorithms
…influencing scientific outputs and to identify the most effective machine learning algorithms for predicting their scientific, social, and economic impacts.Methodology: The current research is applied in purpose and descriptive in method, utilizing a scientometric approach. This study aims to…
-
Journal Article Full text
A New Machine Learning Model for Predicting Suspended Sediment Load
Predicting suspended sediment load is important for water resource management, water quality protection, erosion control, infrastructure planning, flood management, ecological conservation, pollution control, and environmental impact assessments. As a novel aspect of this study, the ANFIS-M5T model…
-
Journal Article Full text
Flood risk zoning in Nowshahr city using machine learning models
…to the characteristics of Nowshahr port city in Mazandaran province using machine learning models. The criteria and indicators affecting flood hazard, including climate criteria (6-hour rainfall indicator), hydrological criteria (surface runoff height indicators, drainage density, and groundwater…
-
Journal Article Full text
Modeling the Behavior of Density Current with Machine Learning Algorithms
…and discharge, the values of the density current head were determined. Machine learning algorithms such as adaptive neural fuzzy inference system and artificial neural network were used to model the results.Findings: Based on the results, the density salt flow head was modeled using machine…
-
Journal Article Full text
Demand forecasting in a Supply Chain using Machine Learning Algorithms
…step, the modeling step, an artificial neural network and support vector machine is presented. The structure of artificial neural network is selected based on previous researchers' results. For measuring errors we use Mean squared error and we use another index for time which is used running the…
-
Journal Article
Improving detection of phishing websites using machine learning and deep learning models
Phishing attacks represent a significant cybersecurity threat, targeting internet users to steal confidential information. This research presents a hybrid deep learning model that employs data preprocessing, data balancing via SMOTE, and dimensionality reduction via PCA. The primary innovation of…