A5055260770- Machine Learning and Data Classification
- Data Mining Algorithms and Applications
- Rough Sets and Fuzzy Logic
- Neural Networks and Applications
- Imbalanced Data Classification Techniques
- Artificial Intelligence in Healthcare
- Engineering Education and Technology
- Time Series Analysis and Forecasting
- Information Systems and Technology Applications
- Enterprise Management and Information Systems
- Explainable Artificial Intelligence (XAI)
- Bayesian Modeling and Causal Inference
- Machine Learning and Algorithms
- Advanced Statistical Methods and Models
- Anomaly Detection Techniques and Applications
- Economic and Technological Systems Analysis
- Face and Expression Recognition
- AI in cancer detection
- Natural Language Processing Techniques
- Machine Learning in Healthcare
- Construction Project Management and Performance
- Fault Detection and Control Systems
- BIM and Construction Integration
- Image Retrieval and Classification Techniques
- Sports Analytics and Performance
National Technical University "Kharkiv Polytechnic Institute"
2015-2024
Azerbaijan University of Architecture and Construction
2024
University of Ljubljana
2010-2023
West University of Timişoara
2022-2023
Ljubljana Passenger Transport
2022
University of National and World Economy
2021
All-Russian Scientific Research Institute for Operation of Nuclear Power Plants
2018
University of Split
2015
Kranj School Centre
2002
Institute for Nuclear Research
1991
Abstract Although successful in medical diagnostic problems, inductive learning systems were not widely accepted practice. In this paper two different approaches to machine applications are compared: the system for of decision trees Assistant, and naive Bayesian classifier. Both methodologies tested four problems: localization primary tumor, prognostics recurrence breast cancer, diagnosis thyroid diseases, rheumatology. The accuracy automatically acquired knowledge from stored data records...
We present a method for explaining predictions individual instances. The presented approach is general and can be used with all classification models that output probabilities. It based on decomposition of model's contributions each attribute. Our works so called black box such as support vector machines, neural networks, nearest neighbor algorithms well ensemble methods, boosting random forests. demonstrate the generated explanations closely follow learned visualization technique which...
Introduction Learning and intelligence Machine learning basics Knowledge representation as search Attribute quality measures Data pre-processing Constructive induction Symbolic Statistical Artificial neural networks Cluster analysis theory Computational Definitions References index.
In Machine Learning, estimation of the predictive accuracy for a given model is most commonly approached by analyzing average model. general, models do not provide estimates their individual predictions. The reliability pred ictions require analysis various and instance properties. paper we make an overview approaches prediction reliability. We start summarizing three research fields, that provided ideas motivation our work: (a) to perturbing learning data, (b) usage unlabeled data in...