Performance Accuracy of Classification Algorithms for Web Learning System
نویسنده
چکیده
This Research paper focused on Classification accuracy based on Users’ Preferences from the Web Learning System. This comparative study considers various classification algorithms like j48, Random Tree, Random Forest, CART and Naive Bayes in the Web Learning System. It also focuses on Artificial Neural Network (ANN) algorithms. The classification accuracy is identified by user’s requirements based on the cognitive input. In this research Neural Network approach like MLP, PMLP, GO PMLP and PSO PMLP algorithms are proposed and validated. These algorithms classify the user preferences of the Web Learning System. As the User Preferences have many potential applications, mining on the User Preferences of the Web Learning System users was contemplated. Based on the response of the current users, a decision tree induction algorithm is used to predict the requirements of future users.
منابع مشابه
Comparison of Machine Learning Algorithms for Broad Leaf Species Classification Using UAV-RGB Images
Abstract: Knowing the tree species combination of forests provides valuable information for studying the forest’s economic value, fire risk assessment, biodiversity monitoring, and wildlife habitat improvement. Fieldwork is often time-consuming and labor-required, free satellite data are available in coarse resolution and the use of manned aircraft is relatively costly. Recently, unmanned aeria...
متن کاملAnalyzing new features of infected web content in detection of malicious web pages
Recent improvements in web standards and technologies enable the attackers to hide and obfuscate infectious codes with new methods and thus escaping the security filters. In this paper, we study the application of machine learning techniques in detecting malicious web pages. In order to detect malicious web pages, we propose and analyze a novel set of features including HTML, JavaScript (jQuery...
متن کاملProposing a Novel Cost Sensitive Imbalanced Classification Method based on Hybrid of New Fuzzy Cost Assigning Approaches, Fuzzy Clustering and Evolutionary Algorithms
In this paper, a new hybrid methodology is introduced to design a cost-sensitive fuzzy rule-based classification system. A novel cost metric is proposed based on the combination of three different concepts: Entropy, Gini index and DKM criterion. In order to calculate the effective cost of patterns, a hybrid of fuzzy c-means clustering and particle swarm optimization algorithm is utilized. This ...
متن کاملBody Mass Index Classification based on Facial Features using Machine Learning Algorithms for utilizing in Telemedicine
Background and Objectives: Due to the impact of controlling BMI on life, BMI classification based on facial features can be used for developing Telemedicine systems and eliminating the limitations of measuring tools, especially for paralyzed people. So that physicians can help people online during the Covid-19 pandemic. Method: In this study, new features and some previous work features were e...
متن کاملA Random Forest Classifier based on Genetic Algorithm for Cardiovascular Diseases Diagnosis (RESEARCH NOTE)
Machine learning-based classification techniques provide support for the decision making process in the field of healthcare, especially in disease diagnosis, prognosis and screening. Healthcare datasets are voluminous in nature and their high dimensionality problem comprises in terms of slower learning rate and higher computational cost. Feature selection is expected to deal with the high dimen...
متن کاملPerformance Accuracy of Classification Algorithms for Web Learning System
This Research paper focused on Classification accuracy based on Users' Preferences from the Web Learning System. This comparative study considers various classification algorithms like j48, Random Tree, Random Forest, CART and Naive Bayes in the Web Learning System. It also focuses on Artificial Neural Network (ANN) algorithms. The classification accuracy is identified by user's requi...
متن کامل