Titlebook: High Performance Computing, Smart Devices and Networks; Select Proceedings o Ruchika Malhotra,L. Sumalatha,Naresh Babu Muppalan Conference

cartilage

Kuldeep Vayandade,Ritesh Pokarne,Mahalakshmi Phaldesai,Tanushri Bhuruk,Prachi Kumar,Tanmay Patil

Arb853

Lavanya Bagadi,B. Srinivas,D. Raja Ramesh,P. Suryaprasad

chronicle

spondylosis

Adrenal-Glands

代替

Pediatric Pneumonia Diagnosis Using Cost-Sensitive Attention Models,These values are concatenated as a vector and passed through a Tanh activation function. The sum of elements in this vector forms the weights. These weights when used in the weighted average classifier results in an accuracy of 96.79%, precision of 96.48%, recall of 98.46%, F1-score of 97.46%, and a

Monolithic

An Integrated Deep Learning Deepfakes Detection Method (IDL-DDM), Perceptron and Convolutional Neural Network (CNN). In addition, the Long Short-Term Memory (LSTM) approach is applied consecutively after CNN in order to grant sequential processing of data and overcome learning dependencies. Using this learning algorithm, several facial region characteristics such

牵连

Reinforcement Learning Based Spectrum Sensing and Resource Allocation in WSN-IoT Smart ApplicationsGI is involved. The role of the state–action–reward–state–action model is developed with an energy-efficient approach for optimizing the channel. Next, the Gittins index is designed to reduce the delay and enhance the accuracy of spectrum access. The simulation results are compared with two state-of

辫子带来帮助

Deep Learning-Based Automatic Speaker Recognition Using Self-Organized Feature Mapping,ms the feature database. Finally, a test voice sample is applied to the trained DLCNN model, which recognizes the speaker detail. The simulations carried out on Anaconda (TensorFlow) showed that the proposed ASR-Net system resulted in superior recognition performance as compared to conventional syst

FIN

,Machine Learning-Based Path Loss Estimation Model for a 2.4 GHz ZigBee Network, experimental setup was designed and tested in line-of-sight (LOS) and non-line-of-sight (NLOS) conditions to collect the influence parameters such as received signal strength indicator (RSSI), frequency, distance, and transmitter antenna gain. Besides that, environmental parameters such as temperat