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| Paper Type | : | Research Paper |
| Title | : | An Effective Investigation On Deep Learning Based CSI-Driven Adaptive Handover Among OFDM, FBMC And Noma For QOS Enhancement In Next-Generation Wireless Networks |
| Country | : | India |
| Authors | : | Rinkita Shah || Dr. Reeta Pawar || Dr. Varkuti Kumara Swamy |
 |
: | 10.9790/4200-16023036  |
ABSTRACT: Machine Learning and Deep Learning are transforming modern wireless communications. With the increasing number of users in cellular networks and need for increased bandwidth requirement owing to multimedia applications, the choice and utilization of effective multiplexing techniques for 6G onwards has become mandatory. Orthogonal Frequency Division Multiplexing (OFDM), Filter Bank Multicarrier (FBMC) and Non....
Keywords - Wireless Communications, Machine Learning, Software Defined Networks, Channel State Information (CSI)
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| Paper Type | : | Research Paper |
| Title | : | Urban Area Classification From Satilite Images Using Convolution Neural Network |
| Country | : | India |
| Authors | : | Atkuri Vinay || Challa Srikanth || Burla Devi Vara Prasad || Dodda Hemanth Reddy || Dr. P. Siddaiah |
|
: | 10.9790/4200-16023742 |
ABSTRACT: This project presents the design and implementation Accurate urban and environmental Land Use/Land Cover (LULC) classification from satellite imagery is essential for sustainable urban planning, environmental monitoring, and resource management. This study presents a high-precision land cover classification framework based on deep image segmentation using a U-Net convolutional neural network architecture. The proposed system....
Keywords - Land Use/Land Cover (LULC) Classification, Satellite Image Segmentation, Deep Learning, U-Net Architecture, Urban and Environmental Mapping
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