Medical image fusion technology and its collective diagnosis are becoming crucial day by day.This task confers the latest algorithm for image fusion of medical images to many diagnostic complications. Firstly, transform is employed on input source images. The result of the application of transform is the decomposition of source images into various subbands. Eminent features are extracted from these subbands by using resnet50. These features are fused by phase congruency and guided filtering fusion rules. Finally, inverse transform gives the original image. The experiment results of this algorithm are compared with different methods by taking some pairs of medical images. Subjective and objective outcomes prove that the proposed algorithm exceeds the current methods by giving optimal performance measures in the area of medical diagnosis. Thus, it is revealed that the suggested multimodal image fusion model provides elevated performance over existing models via diverse diseases using MRI-SPECT and MRI-PET.
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Unmanned Aerial Vehicles (UAVs) have grown into a more powerful type of data transmission due to this rapid progress of
evolution of wireless communication technology. In addition, UAVs have been proven to be effective in a variety of applications, including
intelligent transport, disaster risk management, surveillance, and environmental monitoring. When UAVs are deployed randomly, however,
they can effectively accomplish challenging tasks because of the UAVs’ has low battery capacity, quick mobility, and dynamic in nature
orientation. Due to this reason, a new technique must be designed for an optimal energy efficient UAV clustering as well as data routing
protocols. In this work proposes a new hybrid model of Emperor penguin-based Generalized Approximate Reasoning Based Intelligent
Control (EP-GARIC) cluster-based network topology. Furthermore, the optimal routing function is achieved by the proposed Artificial
Jellyfish Optimization (AJO). The implementation of this research is carried out using Network Simulator (NS2). The simulation results
displays the effective performance of the suggested approach in terms of reduced energy consumption, improved packet delivery ratio,
reduced loss, and so on over compared to the conventional approaches.
Testimonials : 05
Unmanned Aerial Vehicles (UAVs) have evolved into a potent form of data transmission, benefiting from the rapid advancements
in wireless communication technology. Furthermore, UAVs have demonstrated their effectiveness across diverse applications, such as
intelligent transportation, disaster risk management, surveillance, and environmental monitoring. When UAVs are deployed randomly,
however, they can effectively accomplish challenging tasks because of the UAVs’ has low battery capacity, quick mobility, and dynamic in
nature orientation. Due to this reason, a new technique must be designed for an optimal energy efficient UAV clustering as well as data routing
protocols. In this work proposes a new hybrid model of Emperor penguin-based Generalized Approximate Reasoning Based Intelligent Control
(EP-GARIC) cluster-based network topology. Moreover, the proposed model achieves the most efficient routing function through the utilization
of the novel Artificial Jellyfish Optimization (AJO) technique. The execution of this study is conducted within the Network Simulator (NS2)
environment. The outcomes of the simulations distinctly demonstrate the notable effectiveness of the suggested methodology. This is evidenced
by a marked decrease in energy consumption, a substantial improvement in packet delivery ratio, a noteworthy reduction in losses, and other
comparable metrics when contrasted with established conventional methods.
Keywords—Clustering, Neural Network, Fuzzy method, Energy Efficiency, Parameter Tuning.
Testimonials : 06
Tropical cyclones (TC) are among the worst natural disasters, that cause massive damage to property and lives. The meteorologists track these natural phenomena using Satellite imagery. The spiral rain bands appear in a cyclic pattern with an eye as a center in the satellite image. Automatic identification of the cyclic pattern is a challenging task due to the clouds present around the structure. Conventional approaches use only image data to detect the cyclic structure using deep learning algorithms. The training and testing data consist of positive and negative samples of TC. But the cyclic structure's texture pattern makes it difficult for the deep learning algorithms to extract useful features. This paper presents an automatic TC detection algorithm using optical flow estimation and deep learning algorithms to overcome this draw-back. The optical flow vectors are estimated using the Horn-Schunck estimator, the Liu-Shen estimator, and the Lagrange multiplier. The deep learning algorithms take the optical flow vectors as input during the training stage and extract the features to identify the cyclone's circular pattern. The software used for experimental analysis is MATLAB 2021a. The proposed method increases the accuracy of detecting the cyclone pattern through optical flow vectors compared to using the pixel intensity values. By using proposed method 98% of accuracy will be achieved when compared with the existing methods.