A Deep Learning Model to Analyse Social-Cyber Psychological Problems in Youth

Facebook,Twitter,Instagram,and other social media have emerged as excellent platforms for interacting with friends and expressing thoughts,posts,comments,images,and videos that express moods,sentiments,and feelings.With this,it has become possible to examine user thoughts and feelings in social network data to better understand their perspectives and attitudes.However,the analysis of depression based on social media has gained widespread acceptance worldwide,other verticals still have yet to be discovered.The depression analysis uses Twitter data from a publicly available web source in this work.To assess the accuracy of depression detection,long-short-term memory(LSTM)and convolution neural network(CNN)techniques were used.This method is both efficient and scalable.The simulation results have shown an accuracy of 86.23%,which is reasonable compared to existing methods.

Energy-Efficient and Blockchain-Enabled Model for Internet of Things (IoT) in Smart Cities

Wireless sensor networks(WSNs)and Internet of Things(IoT)have gained more popularity in recent years as an underlying infrastructure for connected devices and sensors in smart cities.The data generated from these sensors are used by smart cities to strengthen their infrastructure,utilities,and public services.WSNs are suitable for long periods of data acquisition in smart cities.To make the networks of smart cities more reliable for sensitive information,the blockchain mechanism has been proposed.The key issues and challenges of WSNs in smart cities is efficiently scheduling the resources;leading to extending the network lifetime of sensors.In this paper,a linear network coding(LNC)for WSNs with blockchain-enabled IoT devices has been proposed.The consumption of energy is reduced for each node by applying LNC.The efficiency and the reliability of the proposed model are evaluated and compared to those of the existing models.Results from the simulation demonstrate that the proposed model increases the efficiency in terms of the number of live nodes,packet delivery ratio,throughput,and the optimized residual energy compared to other current techniques.

An Intelligent Cluster Verification Model Using WSN to Avoid Close Proximity and Control Outbreak of Pandemic in a Massive Crowd

Assemblage at public places for religious or sports events has become an integral part of our lives.These gatherings pose a challenge at places where fast crowd verification with social distancing(SD)is required,especially during a pandemic.Presently,verification of crowds is carried out in the form of a queue that increases waiting time resulting in congestion,stampede,and the spread of diseases.This article proposes a cluster verification model(CVM)using a wireless sensor network(WSN),single cluster approach(SCA),and split cluster approach(SpCA)to solve the aforementioned problem for pandemic cases.We show that SD,cluster approaches,and verification by WSN can overcome the management issues by optimizing the cluster size and verification time.Hence,our proposed method minimizes the chances of spreading diseases and stampedes in large events such as a pilgrimage.We consider the assembly points in the annual pilgrimage to Makkah Al-Mukarmah and Umrah for verification using Contiki/Cooja tool.We compute results such as verified cluster members(CMs)to define cluster size,success rate to determine the best success rate,and verification time to determine the optimal verification time for various scenarios.We validate ourmodel by comparing the results of each approach with the existing model.Our results showthat the SpCAwith SD is the best approach with a 96% success rate and optimization of verification time as compared to SCA with SD and the existing model.

A Netnographic-Based Semantic Analysis of Tweet Contents for Stress Management

Social media platforms provide new value for markets and research companies.This article explores the use of social media data to enhance customer value propositions.The case study involves a company that develops wearable Internet of Things(IoT)devices and services for stress management.Netnography and semantic annotation for recognizing and categorizing the context of tweets are conducted to gain a better understanding of users’stress management practices.The aim is to analyze the tweets about stress management practices and to identify the context from the tweets.Thereafter,we map the tweets on pleasure and arousal to elicit customer insights.We analyzed a case study of a marketing strategy on the Twitter platform.Participants in the marketing campaign shared photos and texts about their stress management practices.Machine learning techniques were used to evaluate and estimate the emotions and contexts of the tweets posted by the campaign participants.The computational semantic analysis of the tweets was compared to the text analysis of the tweets.The content analysis of only tweet images resulted in 96%accuracy in detecting tweet context,while that of the textual content of tweets yielded an accuracy of 91%.Semantic tagging by Ontotext was able to detect correct tweet context with an accuracy of 50%.

Intention to Use Mobile Augmented Reality in the Tourism Sector

This article examines the main variables that influence the intention to use Augmented Reality(AR)applications in the tourism sector in Jordan.The study model has been constructed based on the unified theory of acceptance and the use of technology2(UTAUT2),by incorporating a new construct(aesthetics)to explore the usage intention of Mobile Augmented Reality in Tourism(MART).A questionnaire was used and distributed to a sample of 450 participants.Data were analyzed using the Smart PLS version 3.0.for testing 12 hypotheses.29 measurement items were carefully reviewed based on previous studies that were selected to assess the research hypotheses.The findings revealed that the proposed model elucidates 35.7%of the variance in the users’intention to use MART.The results also showed that both performance expectancy and aesthetics were found to be the most significant factors at level(0.001).Four variables,respectively,were at level(0.01)which consisted of social influence,facilitating conditions,hedonic motivation,and price value.The weakest effect was effort expectancy at level(0.05).As the use of AR has become important for tourists,this study establishes a research base that can be built upon for future researchers.MART developers can benefit from the results of this research to design and deliver this service successfully and to ensure that its adoption by users is achieved.