Deep Residual Adaptive Neural Network Based Feature Extraction for Cognitive Computing with Multimodal Sentiment Sensing and Emotion Recognition Process

For the healthcare framework, automatic recognition of patients’ emotions is considered to be a good facilitator. Feedback about the status of patients and satisfaction levels can be provided automatically to the stakeholders of the healthcare industry. Multimodal sentiment analysis of human is considered as the attractive and hot topic of research in artificial intelligence (AI) and is the much finer classification issue which differs from other classification issues. In cognitive science, as emotional processing procedure has inspired more, the abilities of both binary and multi-classification tasks are enhanced by splitting complex issues to simpler ones which can be handled more easily. This article proposes an automated audio-visual emotional recognition model for a healthcare industry. The model uses Deep Residual Adaptive Neural Network (DeepResANNet) for feature extraction where the scores are computed based on the differences between feature and class values of adjacent instances. Based on the output of feature extraction, positive and negative sub-nets are trained separately by the fusion module thereby improving accuracy. The proposed method is extensively evaluated using eNTERFACE’05, BAUM-2 and MOSI databases by comparing with three standard methods in terms of various parameters. As a result, DeepResANNet method achieves 97.9% of accuracy, 51.5% of RMSE, 42.5% of RAE and 44.9%of MAE in 78.9sec for eNTERFACE’05 dataset.  For BAUM-2 dataset, this model achieves 94.5% of accuracy, 46.9% of RMSE, 42.9%of RAE and 30.2% MAE in 78.9 sec. By utilizing MOSI dataset, this model achieves 82.9% of accuracy, 51.2% of RMSE, 40.1% of RAE and 37.6% of MAE in 69.2sec. By analysing all these three databases, eNTERFACE’05 is best in terms of accuracy achieving 97.9%. BAUM-2 is best in terms of error rate as it achieved 30.2 % of MAE and 46.9% of RMSE. Finally MOSI is best in terms of RAE and minimal response time by achieving 40.1% of RAE in 69.2 sec

Energy Efficiency Based Load Balancing Optimization Routing Protocol In 5G Wireless Communication Networks

A significant study area in cloud computing that still requires attention is how to distribute the workload among virtual machines and resources. Main goal of this research is to develop an efficient cloud load balancing approach, improve response time, decrease readiness time, maximise source utilisation, and decrease activity rejection time. This research propose novel technique in load balancing based network optimization using routing protocol for 5G wireless communication networks. the network load balancing has been carried out using cloud based software defined multi-objective optimization routing protocol. then the network security has been enhanced by data classification utilizing deep belief Boltzmann NN. Experimental analysis has been carried out based on load balancing and security data classification in terms of throughput, packet delivery ratio, energy efficiency, latency, accuracy, precision, recall

HYBRID AC/MT-HVDC TRANSMISSION LINES AND ITS EFFECTIVE OPERATION USING ENHANCED-FISH SWARM OPTIMIZER

Multi-Terminal-High Voltage Direct Current (MT-HVDC) transmission lines are used to transmit electricity over long distances with lower losses compared to traditional Alternating Current (AC) transmission lines. However, HVDC transmission lines have limitations in terms of their ability to handle AC system faults and the need for expensive AC/DC converters at each end of the line. To address these limitations, a hybrid AC/MT-HVDC transmission line can be used. This type of transmission line combines the benefits of both AC and DC transmission, allowing for more efficient and reliable operation. In this article, an Enhanced-Fish Swarm Optimizer (E-FSO) is presented for the efficient maintenance of hybrid AC/MT-HVDC transmission lines. An external repository is included in the proposed E-FSO to preserve that is not dominant. Additionally, fuzzy decision-making is used to choose the hybrid AC/HVDC transmission lines' optimal compromise operating point. In these systems, in addition to the complete control of AC line through parameters for the dedicated generators and transformers connections, and Volt-Ampere Reactive (VAR) compensations, the Voltage Source Converters (VSCs) action and response controllable voltage is engaged. The outcomes of the experiment show the efficacy and dominance of the suggested algorithm, which has high stability indices compared to several competitive algorithms. Nevertheless, the suggested E-FSO is effective in producing a compromise operating point that satisfies the operator's needs while also obtaining well-diversified solutions