A5036666504- Artificial Intelligence in Healthcare
- Machine Fault Diagnosis Techniques
- Brain Tumor Detection and Classification
- Aluminum Alloys Composites Properties
- Material Properties and Applications
- Information Systems and Technology Applications
- Cutaneous Melanoma Detection and Management
- Impact of AI and Big Data on Business and Society
- Nanotechnology research and applications
- Advanced Clustering Algorithms Research
- Imbalanced Data Classification Techniques
- Digital Games and Media
- AI in cancer detection
- Machine Learning in Healthcare
- Gear and Bearing Dynamics Analysis
- Bayesian Methods and Mixture Models
- Reinforcement Learning in Robotics
- Advanced Neural Network Applications
- Engineering Diagnostics and Reliability
- Digital Imaging for Blood Diseases
- Artificial Intelligence in Games
- Data Management and Algorithms
- Aluminum Alloy Microstructure Properties
- Radiomics and Machine Learning in Medical Imaging
- Machine Learning and Data Classification
XIM University
2024
Sri Sri University
2018-2021
Indira Gandhi Institute of Technology
2017
Recommender systems are one of the important methodologies in machine learning technologies, which is using current business scenario. This article proposes a book recommender system deep technique and k-Nearest Neighbors (k-NN) classification. Deep most effective techniques field systems. intelligent Machine Learning that can make difference from other algorithms. considers application Technology we present an approach based system. We used classification algorithm to classify users analyze...
K-Means is known both for its usefulness in finding clusters of related data as well fragility with respect to initialization choices. This paper introduces a 95% more effective and 50% efficient methods, that could eliminate the need multiple executions find high quality clustering. To initialize centroids, it selects multiple, m, K real points, computes (mK)<sup>2</sup> distances keeps only maximum( minimum( distance ) points. A consequence this technique enables O(lnK) binary search...
This paper presents a number of novel approaches to help facilitate Deep Reinforcement Learning (DRL) for Neural Networks based agents in the domain General Video Game Playing (GVGP). Using common processing methods, NN can retain fixed predetermined input and output shape while having flexibility play variety games. Training also be made more efficient via reward normalization. Our results show that these modifications negligibly impact learning time performance model when testedupon games...