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In this research work, an Adaptive Multi-Scale Dual Attention Network with ZOA for Multi-Objective CHS with energy-aware routing in 6G wireless Communication (CHS-EAR-AM-SDAN-6G) is proposed to secure the data transmission by selecting optimum cluster heads in the 6G Wireless Communication network. Initially, the nodes are gathered together to form a cluster using an Adaptive Multi-Scale Dual Attention Network (AM-SDAN). The Zebra Optimization Algorithm (ZOA) strategically selects Cluster Heads (CHs) in wireless networks based on a multi-objective fitness function (MoFF) that minimizes energy consumption while considering factors, like distance, delay, and traffic density. The path and minimum value of fitness are recognized as the routing path and statistics are promoted to the sink node through the cluster head. The proposed scheme has been applied in Python and productivity of the proposed method is predictable with the help of several performances they are energy consumption, detection rate, computational time, packet delivery rate, number of alive nodes, and security. The performance of the proposed CHS-EAR-AMSDAN-6G method attains 25.93%, 24.81%, and 23.38% of alive nodes, 24.45%, 26.71% and 21.32% lower packet delivery rate, 27.56%, 26.43%, and 28.61% low computational period, related with three current methods, such as Generative Adversarial Learning for ITM in 6G Wireless Communication Networks (CHS-EAR-GAL-ITM-6G), ML Algorithms for the Future 6G WCN (CHS-EAR-ML-6G), Energy Efficient Distributed Federated Learning to the 6G Wireless Communication Networks (CHS-EAR-EEDFL-6G), respectively. [ABSTRACT FROM AUTHOR]

Analysis of Sentiment (SA) is a computational technique that seeks to extract subjective evaluations, attitudes, and emotional states from online platforms, specifically social media sites like Twitter. The subject has gained significant traction within the research community. The predominant emphasis of traditional sentiment analysis lies in the analysis of textual data. Twitter is widely recognized as a prominent online social networking platform that facilitates microblogging, wherein users share updates pertaining to various subjects through concise messages known as tweets. Twitter is a widely utilized platform that enables individuals to articulate their perspectives and emotions through the medium of tweets. Sentiment analysis refers to the computational methods of classification of given data in text format, categorizing it as either positive, negative, or neutral. The main goal of this study is to use deep learning methods for SA. The purpose is to guess sentiment and then evaluate the results based on accuracy, recall, and f-score. In this paper, a hybrid approach combining Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) techniques, specifically referred to as PSOGA, is proposed to optimize features for a modified neural network (MNN). The final step is to use the K-fold cross-validation method to assess the results. The dataset was obtained through the utilization of the Ruby Twitter API. The ultimate outcome is juxtaposed with the preceding Cuckoo Search (CS) algorithm that had been terminated. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) is made up of minimal power devices (or) units spread over geographically separated locations. Sensors are grouped in the form of clusters. Every cluster has a key node known as the Cluster Head (CH). CH gathers sensed information out of its sensor nodes and transmits into a Base Station (BS). Sensors are indeed installed using non-replaceable batteries. WSN is concerned about its energy usage to reduce (or) minimize the consumption of energy as well as increase network lifetime. An improved upgraded technique is presented, which is accomplished by improving appropriate energy balancing in clusters across every sensor node in order to reduce power dissipation while networking connections. The enhanced technique was built by employing a well-known technique named cluster head selection. Accordingly, the energy consumption of WSN is reduced to prolong the network life cycle other than the network models. Furthermore, an efficient routing CH is optimized by the Average Fitness-based Harris Hawks Optimization (AF-HHO). In the WSN network, this proposed algorithm is used to locate neighbouring nodes with higher energy efficiency measurements. As a result, when compared to other conventional approaches, the simulation results demonstrate superior performance. Through the sink node, an optimal routing path for transferring data packets to neighbouring sensor nodes was discovered. The suggested technique is evaluated using energy consumption, network lifespan, and residual energy performance estimations. [ABSTRACT FROM AUTHOR]

In present scenario of wireless communications, Long Term Evolution (LTE) based network technology is evolved and provides consistent data delivery with high speed and minimal delay through mobile devices. The traffic management and effective utilization of network resources are the key factors of LTE models. Moreover, there are some major issues in LTE that are to be considered are effective load scheduling and traffic management. Through LTE is a depraved technology, it is been suffering from these issues. On addressing that, this paper develops an Elite Opposition based Spider Monkey Optimization Framework for Efficient Load Balancing (SMO-ELB). In this model, load computation of each mobile node is done with Bounding Theory based Load derivations and optimal cell selection for seamless communication is processed with Spider Monkey Optimization Algorithm. The simulation results show that the proposed model provides better results than exiting works in terms of efficiency, packet delivery ratio, Call Dropping Ratio (CDR) and Call Blocking Ratio (CBR). [ABSTRACT FROM AUTHOR]

The wireless sensor network has many limitations like packet delay, packet loss, acknowledgement loss, complexity, scalability, robustness and energy efficiency which reduces the performance and the lifetime of the network. To improve that, the novel Consistent Routing protocol (CRP) is introduced along with the cluster formation method which is Long Lasting Energy Efficient Network (LLEEN) also called as L2E2N. The CRP is the dynamic routing protocol, which is developed to reduce the packet delay and packet loss during the transmission and also the efficiency of the transmission like throughput will be maintained even though the length of the packet is varied. The L2E2N is proposed to improve the lifetime of the network by consuming less energy during the transmission. The nodes should react during transmission like the rate of accepting packets which is equal to the rate of delivering packets, that can be possible in this L2E2N without consuming more energy. The performance measure is taken as throughput for the clusters with different sizes with respect to the energy consumption as the packet length is increased in terms of bytes. [ABSTRACT FROM AUTHOR]

Summary: Long‐Term Evolution (LTE) is progressively observed as a promising innovation for serving Internet of Things (IoT) because of high information rate, low dormancy, need services, and so forth. Orthogonal frequency division multiplexing (OFDM) meets the LTE needs to extend adaptability and enables cost‐profitable reactions for especially wide transporters. LTE contributes enormous information esteems with low inertness, improved services, and lower costs and takes into consideration range reexamining, on account of the recurrence and transfer speed adaptability. In this exploration work, 4G/5G received OFDM was proposed under binary‐phase shift keying (BPSK) guideline and multi‐equalizer. To diminish the bit error rate (BER), ideal wavelet coefficients are utilized in the transmitter end. Here, the discrete wavelet transform (DWT) is applied in input signals to choose the ideal wavelet coefficients Binary Firefly (BFF) calculation. At that point, ideal coefficients are accomplished; the balance BPSK is utilized for serial measure. Eventually, the multi‐equalizer such as maximum likelihood (MLE) and minimized mean square error (MMSE) are utilized to decrease BER in the OFDM model. The execution of the proposed strategy is dissected with boundaries BER, symbol error rate (SER), mean square error (MSE), and spectral efficiency. The proposed strategy gives accurate outcomes contrasted with existing methods. [ABSTRACT FROM AUTHOR]

This article presents the development of a conformal reconfigurable frequency circularly polarized jute textile antenna, working at 2.4, 3.5, 5.8, and 5.9 GHz in Wi‐Fi, Wi‐MAX, Industrial, Scientific and Medical radio bands and W‐LAN. The antenna topology is based on a semicircular structure as the main radiating component. The conductivity is realized by using copper paint, which was brush‐painted. The patch structure is rehashed in another side of the substrate against the ground with a rotation turn 180°. Two BAR64‐03 W PIN diodes switches are placed in between a semi‐elliptical structure and a rectangular structure on the bottom side of the ground plane. By governing the state of the switches, this antenna can operate at different operating frequencies. Furthermore, the antenna has an accomplished axial ratio (AR) of less than 3 dB for resonating frequencies at all four switching states. Other electromagnetic properties of the proposed jute textile antenna accomplished in simulations were confirmed through the measurement of the antenna in an anechoic chamber. The reconfigurable circularly‐polarized jute textile antenna shows a peak gain of 4.93, 5.67, 8.86, and10.07 dBi at 2.4, 3.5, 5.8, and 5.9 GHz (Wi‐Fi, Wi‐MAX, ISM, W‐LAN). [ABSTRACT FROM AUTHOR]

This paper critically scrutinises the positioning of niche tourism products in India. The paper will explore the different innovative ideas for improving tourism products. Thus, the paper is going to focus on different forms and positioning of tourism products in India. The benefits of this work to the academic community are the presentation of insights into niche tourism consumers, and an understanding of the challenges destinations face along the niche tourism life cycle. Finally, the practical benefits of this work to industry include a greater understanding of positioning and targeting their products. To illustrate this, our research is driven primarily on following significant niche tourism products: Cruise, Medical, and Film. [ABSTRACT FROM AUTHOR]