In recent years, the underwater wireless sensor network (UWSN) has received a significant interest among research communities for several applications, such as disaster management, water quality prediction, environmental observance, underwater navigation, etc. The UWSN comprises a massive number of sensors placed in rivers and oceans for observing the underwater environment. However, the underwater sensors are restricted to energy and it is tedious to recharge/replace batteries, resulting in energy efficiency being a major challenge. Clustering and multi-hop routing protocols are considered energy-efficient solutions for UWSN. However, the cluster-based routing protocols for traditional wireless networks could not be feasible for UWSN owing to the underwater current, low bandwidth, high water pressure, propagation delay, and error probability. To resolve these issues and achieve energy efficiency in UWSN, this study focuses on designing the metaheuristics-based clustering with a routing protocol for UWSN, named MCR-UWSN. The goal of the MCR-UWSN technique is to elect an efficient set of cluster heads (CHs) and route to destination. The MCR-UWSN technique involves the designing of cultural emperor penguin optimizer-based clustering (CEPOC) techniques to construct clusters. Besides, the multi-hop routing technique, alongside the grasshopper optimization (MHR-GOA) technique, is derived using multiple input parameters. The performance of the MCR-UWSN technique was validated, and the results are inspected in terms of different measures. The experimental results highlighted an enhanced performance of the MCR-UWSN technique over the recent state-of-art techniques.

Underwater wireless sensor networks (UWSNs) comprise numerous underwater wireless sensor nodes dispersed in the marine environment, which find applicability in several areas like data collection, navigation, resource investigation, surveillance, and disaster prediction. Because of the usage of restricted battery capacity and the difficulty in replacing or charging the inbuilt batteries, energy efficiency becomes a challenging issue in the design of UWSN. Earlier studies reported that clustering and routing are considered effective ways of attaining energy efficacy in the UWSN. Clustering and routing processes can be treated as nondeterministic polynomial-time (NP) hard optimization problems, and they can be addressed by the use of metaheuristics. This study introduces an improved metaheuristics-based clustering with multihop routing protocol for underwater wireless sensor networks, named the IMCMR-UWSN technique. The major aim of the IMCMR-UWSN technique is to choose cluster heads (CHs) and optimal routes to a destination. The IMCMR-UWSN technique incorporates two major processes, namely the chaotic krill head algorithm (CKHA)-based clustering and self-adaptive glow worm swarm optimization algorithm (SA-GSO)-based multihop routing. The CKHA technique selects CHs and organizes clusters based on different parameters such as residual energy, intra-cluster distance, and inter-cluster distance. Similarly, the SA-GSO algorithm derives a fitness function involving four parameters, namely residual energy, delay, distance, and trust. Utilization of the IMCMR-UWSN technique helps to significantly boost the energy efficiency and lifetime of the UWSN. To ensure the improved performance of the IMCMR-UWSN technique, a series of simulations were carried out, and the comparative results reported the supremacy of the IMCMR-UWSN technique in terms of different measures.

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The modern society runs over the social media for their most time of every day. The web users spend their most time in social media and they share many details with their friends. Such information obtained from their chat has been used in several applications. The sentiment analysis is the one which has been applied with Twitter data set toward identifying the emotion of any user and based on those different problems can be solved. Primarily, the data as of the Twitter database is preprocessed. In this step, tokenization, stemming, stop word removal, and number removal are done. The proposed automated learning with CA-SVM based sentiment analysis model reads the Twitter data set. After that they have been processed to extract the features which yield set of terms. Using the terms, the tweets are clustered using TGS-K means clustering which measures Euclidean distance according to different features like semantic sentiment score (SSS), gazetteer and symbolic sentiment support (GSSS), and topical sentiment score (TSS). Further, the method classifies the tweets according to support vector machine (CA-SVM) which classifies the tweet according to the support value which is measured based on the above two measures. The attained results are validated utilizing k-fold cross-validation methodology. Then, the classification is performed by utilizing the Balanced CA-SVM (Deep Learning Modified Neural Network). The results are evaluated and compared with the existing works. The Proposed model achieved 92.48 % accuracy and 92.05% sentiment score contrasted with the existing works.

Cloud interoperability provides cloud services such as Software as a Service (SaaS) or customer system to communicate between the cloud providers. However, one of the most important barriers for existing researches was to adopt the application’s or data’s in cloud computing environments so as to obtain efficient cloud interoperability. This paper focuses on reliable cloud interoperability with a heterogeneous cloud computing resource environment with the objective of providing unilateral provision computing capabilities of a cloud server without the help of human interaction and allowing proper utilization of applications and services across various domains by using an effective cloud environment available at runtime. Moreover, the framework uses hybrid squirrel search genetic algorithm (HSSGA) to select the relevant features from a set of extracted features in order to eliminate irrelevant data which provides advantages of low computational time and less memory usage. Thereafter, for a proper selection of cloud server with respect to the selected features, the system has developed the improved adaptive neuro-fuzzy inference system (I-ANFIS) which provides accurate server selection and helps against uncertainties caused by servers or applications. Hence, the experimental result of the proposed framework gives an accuracy of 94.24% and remains more efficient compared to existing frameworks.

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