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Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

Computer Systems Science & Engineering 2023年第5期45卷 1393-1407页

作者： Hanan Abdullah Mengash Jaber S.Alzahrani Majdy M.Eltahir Fahd N.Al-Wesabi Abdullah Mohamed Manar Ahmed Hamza Radwa Marzouk Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Presently,smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping,e-learning,ehealthcare,etc.Despite the benefits of advanced technologies,issues are also existed from the transformation of the physical word into digital word,particularly in online social networks(OSN).Cyberbullying(CB)is a major problem in OSN which needs to be addressed by the use of automated natural language processing(NLP)and machine learning(ML)approaches.This article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks,named SRO-MLCOSN model.The presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking sites.The SRO-MLCOSN model initially employs Glove technique for word embedding process.Besides,a multiclass-weighted kernel extreme learning machine(M-WKELM)model is utilized for effectual identification and categorization of CB.Finally,Search and Rescue Optimization(SRO)algorithm is exploited to fine tune the parameters involved in the M-WKELM model.The experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision,recall,and F1-score of 96.24%,98.71%,and 97.46%respectively.

关键词： Cybersecurity cyberbullying social networking machine learning search and rescue optimization

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Web Intelligence with Enhanced Sunflower Optimization Algorithm for Sentiment Analysis

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Computer Systems Science & Engineering 2023年第10期47卷 1233-1247页

作者： Abeer D.Algarni Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Exponential increase in the quantity of user generated content in websites and social networks have resulted in the emergence of web intelligence approaches.Several natural language processing(NLP)tools are commonly used to examine the large quantity of data generated online.Particularly,sentiment analysis(SA)is an effective way of classifying the data into different classes of user opinions or sentiments.The latest advances in machine learning(ML)and deep learning(DL)approaches offer an intelligent way of analyzing sentiments.In this view,this study introduces a web intelligence with enhanced sunflower optimization based deep learning model for sentiment analysis(WIESFO-DLSA)technique.The major intention of the WIESFO-DLSA technique is to identify the expressions or sentiments that exist in the social networking data.The WIESFO-DLSA technique initially performs pre-processing and word2vec feature extraction processes to generate a meaningful set of features.At the same time,bidirectional long short term memory(BiLSTM)model is applied for classification of sentiments into different class labels.Moreover,an enhanced sunflower optimization(ESFO)algorithm is exploited to optimally adjust the hyperparameters of the BiLSTM model.A wide range of simulation analyses is performed to report the better outcomes of the WISFO-DLSA technique and the experimental outcomes ensured its promising performance under several measures.

关键词： Sentiment analysis web intelligence deep learning social networking natural language processing

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Tobacco control challenges in China: Big data analysis of online tobacco marketing information

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International Journal of Nursing Sciences 2020年第S1期7卷 52-60页

作者： Xiuli Wang Ying Xiong Wenwen Zhao School of New Media Peking UniversityDirector of Center for Social Media ResearchBeijingChina

Objective:To develop tobacco control strategies by analyzing online tobacco marketing information in China.Methods:Using web-crawler software,this study acquired 106,485 pieces of online tobacco marketing information published on 11 different Internet platforms including Weibo,WeChat,Baidu,etc.,from January-June 2018.The data were used to investigate the characteristics and social networks of online tobacco marketing via content and social network analysis.Results:The total volume of online tobacco marketing during the study period was high,showing a positive trend.Of all the marketing subjects,those involving"flavor capsule","Marlboro",and"Esse"were the most popular.The Weibo platform had the highest volume of online tobacco marketing information as well as the largest proportion of explicit marketing information.This was followed by other social media platforms such as Baidu Search,Baidu Tieba,and Xiaohongshu,where implicit marketing information predominated.The overall network structure of tobacco websites exhibited a significant centralization feature,where traditional and novel tobacco websites formed two clusters with almost no intersections.The China Tobacco Science and Education Website(http://***/)and E-Cigarette Home(http://***/)were the two nodes of the highest degree centrality within the respective"circle",while the China Tobacco Monopoly Bureau Website(http://***/)was the node with the highest closeness centrality.By contrast,Baidu Tieba's overall network structure was more decentralized,and the degree of correlation between different nodes was relatively low.Conclusion:Online tobacco marketing demonstrated high volumes and wide coverage,and an intertwined network,thereby creating major obstacles for tobacco control.To address this issue,the government should strengthen network supervision of tobacco marketing and revise its current regulations.Meanwhile,Internet platforms should improve self-regulation by comprehensively removing and blocking tobacco-related information.Lastly,the media and public

关键词： Big data China Internet Marketing social media social networking Tobacco Tobacco products

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Topology-Based Recommendation of Users in Micro-Blogging Communities

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Journal of Computer Science & Technology 2012年第3期27卷 624-634页

作者： Marcelo G.Armentano Daniela Godoy Analía Amandi High Institute of Software Engineering Tandil National University of the Center of Buenos Aires ProvinceTandil Buenos AiresArgentina National Council of Scientific and Technological Research Av.Rivadavia 1917CABAArgentina

Nowadays, more and more users share real-time news and information in micro-blogging communities such as Twitter, Tumblr or Plurk. In these sites, information is shared via a followers/followees social network structure in which a follower will receive all the micro-blogs from the users he/she follows, named followees. With the increasing number of registered users in this kind of sites, finding relevant and reliable sources of information becomes essential. The reduced number of characters present in micro-posts along with the informal language commonly used in these sites make it difficult to apply standard content-based approaches to the problem of user recommendation. To address this problem, we propose an algorithm for recommending relevant users that explores the topology of the network considering different factors that allow us to identify users that can be considered good information sources. Experimental evaluation conducted with a group of users is reported, demonstrating the potential of the approach.

关键词： social networking social technology recommender system

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Dialogue with Worldwide Literacy Classroom:From the Perspectives of Situated Learning Method in China

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Journal of Literature and Art Studies 2022年第12期12卷 1265-1274页

作者： KAN Wei Beijing Normal University BeijingChina

Situated learning method in western educational culture holds that effective education requires learning that is embedded in authentic contexts of practice,wherein students engage in increasingly more complex tasks within social communities.One of the Chinese situated learing methods,hich had been proposed and practiced by Li has high similarities with counterpartners on knowledge acquisition.In this essay,the researcher compared the situated learning method in west and its development in China based on Li’s exploration,contrasting participatory and acquisitive learning models as they apply in classroom settings.The dialogue indicates that no matter in west and in Chinese classroom,situated learning is one of effective methods.This study emphysizes the fact that teacher acts as a provider,facilitator,and mediator of knowledge;students are able to learn from that knowledge and practice it in group activities.In turn,students arrive at a new level of knowledge and understanding based on their experience as a real practitioner in the group or community.The different norms and values attached to participation and acquisition complement one another,and either one alone would be insufficient.The researcher then assess Chinese literacy education in Li’s situated learning methods.It suggests that a blend of participatory and acquisitive learning models may be the most effective approach to classroom instruction.

关键词： situated learning classroom instruction cognition social networking

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Study on Emergency Anomaly Detection for Tourism Activities

Study on Emergency Anomaly Detection for Tourism Activities

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2015年中国智能自动化学术会议

作者： Xiaoyu Han Junping Du Yipeng Zhou Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications School of Computer and Information Engineering Beijing Technology and Business University

With the development of society and economy, tourism activities become an increasingly important part of people’s life, at the same time the emergencies are more frequent than ever before. In this paper, we have studied the past system of emergency anomaly detection for tourism activities, eliminated the limitation of the system, and created many modules about emergency anomaly detection. This paper adopts the method with group of mental perception including the Internet, social networking microblogging, and video data using social force model, implements the functions and examines the effect of each module. Finally,we verify this paper by experimental results. This research provides reliable basis for emergency anomaly detection in the tourism activities, and provides security and convenience for tourists traveling activity.

关键词： Anomaly detection Emergency social force model Group of mental perception social networking

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