Human Activity Recognition (HAR) plays a significant role in several fields by automatically identifying and monitoring human activities using advanced techniques. It enhances safety, improves healthcare services, optimizes fitness routines, and enables context-aware applications in various fields. HAR contributes to a more efficient and intelligent interaction between humans and technology. It has emerged as an essential research domain with applications in healthcare, smart environments, and human-computer interaction. This study aims to provide a comprehensive survey of the evolving landscape of HAR, including key methodologies, techniques, and trends in existing research. The study discusses various applications of HAR and their significance in modern smart environments. The survey also highlights different types of HAR and data collection techniques. Additionally, it explores various methods for analyzing the collected data and provides a comprehensive analysis of existing human activity classification datasets. It offers valuable insights into understanding the strengths and limitations of various HAR techniques. The study also discusses various challenges and future directions for HAR.
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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.
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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.