A5094056734- Tropical and Extratropical Cyclones Research
- Video Surveillance and Tracking Methods
- Meteorological Phenomena and Simulations
- Consumer Market Behavior and Pricing
- Customer churn and segmentation
- Hydrological Forecasting Using AI
- Advanced Neural Network Applications
- Consumer Retail Behavior Studies
- Traffic Prediction and Management Techniques
- IoT-based Smart Home Systems
Koneru Lakshmaiah Education Foundation
2024
Tashkent State University of Economics
2024
Predicting cyclone intensity is an important aspect of weather forecasting since it influences disaster preparation and response. This framework addresses the pressing need for precise prediction by presenting a unique predictive model based on hybrid CNN Bi-LSTM architecture optimized using Genetic Algorithm (GA) enhanced Fruit Fly Optimizer (FFO). Existing methods have primarily relied traditional machine learning models meteorological data, demonstrating limitations in capturing complex...
Federated Learning (FL), a crucial advancement in smart city technology, combines real-time traffic predictions with the potential to enhance urban mobility. This paper suggests novel approach prediction cities: hybrid Convolutional Neural Network-Recurrent Network (CNN-RNN) architecture. The investigation started systematic collection and preprocessing of low-resolution dataset (1.6 GB) derived from Closed Circuit Television (CCTV) camera images at significant intersections Guntur...