Co-authorship network and the correlation with academic performance

This paper aims to study the internal structure of the co-authorship network and the relationship between the network and the authors’ academic performance in the network. In order to conduct this research, bibliographic data of 166 authors from three top higher education institutions of Shanghai was collected and the method of social network analysis (SNA) was performed to analyze the data. In the link analysis, the centrality, egocentric network efficiency, authorities, and hubs were analyzed. In the graph cluster analysis, this paper employs clustering algorithms based on betweenness. Lastly, the Spearman correlation test was performed to analyze the relationship between academic performance and SNA metrics. This paper found that and betweenness centrality, eigenvector centrality, authority and hub position, and efficiency were significant to g-index. The research provided a glimpse of the co-authorship network's internal structure in China. Additionally, the SNA method of identifying productive scholars can also be applied to other areas, such as the network of equipment in the Industry 5.0 to help companies identify the strong and weak links in the producing process

An emoji feature-incorporated multi-view deep learning for explainable sentiment classification of social media reviews

Sentiment analysis has demonstrated its value in a range of high-stakes domains. From financial markets to supply chain management, logistics, and technology legitimacy assessment, sentiment analysis offers insights into public sentiment, actionable data, and improved decision forecasting. This study contributes to this growing body of research by offering a novel multi-view deep learning approach to sentiment analysis that incorporates non-textual features like emojis. The proposed approach considers both textual and emoji views as distinct views of emotional information for the sentiment classification model, and the results acknowledge their individual and combined contributions to sentiment analysis. Comparative analysis with baseline classifiers reveals that incorporating emoji features significantly enriches sentiment analysis, enhancing the accuracy, F1-score, and execution time of the proposed model. Additionally, this study employs LIME for explainable sentiment analysis to provide insights into the model's decision-making process, enabling high-stakes businesses to understand the factors driving customer sentiment. The present study contributes to the literature on multi-view text classification in the context of social media and provides an innovative analytics method for businesses to extract valuable emotional information from electronic word of mouth (eWOM), which can help them stay ahead of the competition in a rapidly evolving digital landscape. In addition, the findings of this paper have important implications for policy development in digital communication and social media monitoring. Recognizing the importance of emojis in sentiment expression can inform policies by helping them better understand public sentiment and tailor policy solutions that better address the concerns of the public

The Ethical Issues of Location-Based Services on Big Data and IoT

Both Internet of Things (IoT) and big data are hot topics in recent years. They indeed have brought about the change of business, promoted the progress of science and technology, and facilitated the lives of human beings. IoT creates the opportunity to connect every item to the Internet, and countless science and technology have supported the achievement of this goal. LBS is one of the indispensable technologies. It brings significant benefits to the business community, the individual, the society, and the national defense. However, at the same time, an individual’s personal information is disclosed and even attacked by ‘information thieves’. An inevitable reality is that the prerequisite of getting a location service is to expose your position first. Therefore, the privacy-related ethics issues are generated, and the danger is imminent, although there are corresponding protective measures

Smart Medical and Its Ethical Problems

With the rapid development of smart devices, big data and the Internet of Things, the concept of “smart city” has been raised and developed. Over the past decade, the concept of smart medical has received extensive attention. Currently, some simple smart medical technologies have begun to be applied, such as online appointment registration, electronic medical records, etc. This report aims to introduce the vision of “smart medical.” Based on the analytics of this study, some potential ethical problems are discovered, including privacy problems, data ownership problems, security and liability problems, and unemployment problems

Job satisfaction and turnover decision of employees in the Internet sector in the US

This paper proposes that high value on the work-life balance, compensation, career opportunity and fitness of culture and management style would improve job satisfaction. A turnover risk prediction model based on the random forest is constructed to understand the turnover risk feature and identify risk. Using a sample of 17,724 online reviews of employees from Glassdoor, the positive effect of antecedents, the job satisfaction variable as a mediator, and the unemployment rate variable as a moderator is verified. Finally, job satisfaction is identified as the most important feature for predicting turnover based on the random forest algorithm

Towards an effective negotiation modeling:Investigating transboundary disputes with cases of lower possibilities

Existing literature in group-decision making proposed various rules of aggregating individuals’ opinions to group outcomes.With anonymity maintained, this paper can model round-robin assessments by a group with individuals updating their assessments every round in a Bayesian manner as per Bordley (1983, 1986, 2009). Utilizing the properties of the finite Markov Chain process, the analysis shows (a) the conditions for a group consensus to converge, (b) the maximum number of rounds before such convergence occurs, and (c) the consensus assessment. The resulting dynamic model is tested to show that it also captures the results of several empirical studies. We apply them to the negotiationfor the transboundary dispute and our simulations demonstrate the convergence of three different cases of lower possibilities, which support transboundary cases and resolutions. We also develop algorithms based on Fuzzy Delphi (Murray et al. 1985, Ishikawa et al. 1993) and Grey Delphi Methods (Ma et al., 2011) to predict the probability and likely outcomes of the transboundary dispute between China and India, which is one of the cases with low probability. Upon 1,000 simulations under volatile international relations, the development of theconvergence demonstrates the integrated Delphi Method is more suitable for predicting volatile situations

An Exploration into Human–Computer Interaction::Hand Gesture Recognition Management in a Challenging Environment

Scientists are developing hand gesture recognition systems to improve authentic, efficient, and effortless human–computer interactions without additional gadgets, particularly for the speech-impaired community, which relies on hand gestures as their only mode of communication. Unfortunately, the speech-impaired community has been underrepresented in the majority of human–computer interaction research, such as natural language processing and other automation fields, which makes it more difficult for them to interact with systems and people through these advanced systems. This system’s algorithm is in two phases. The first step is the Region of Interest Segmentation, based on the color space segmentation technique, with a pre-set color range that will remove pixels (hand) of the region of interest from the background (pixels not in the desired area of interest). The system’s second phase is inputting the segmented images into a Convolutional Neural Network (CNN) model for image categorization. For image training, we utilized the Python Keras package. The system proved the need for image segmentation in hand gesture recognition. The performance of the optimal model is 58 percent which is about 10 percent higher than the accuracy obtained without image segmentation

Comprehensive analysis of UK AADF traffic dataset set within four geographical regions of England

Traffic flow detection plays a significant part in freeway traffic surveillance systems. Currently, effective autonomous traffic analysis is a challenging task due to the complexity of traffic delays, despite the significant investment spent by authorities in monitoring and analysing traffic congestion. This study builds an intelligent analytic method based on machine‐learning algorithms to investigate and predict road traffic flows in four locations in the United Kingdom (London, Yorkshire and the Humber, North East, and North West) with a range of relevant factors. While aiming to conduct the study, the dataset ‘estimated annual average daily flows (AADFs) Data—major and minor roads’ from the UK government was used. Machine‐learning algorithms are used for this research and classification applied consists of Logistic Regression, Decision Trees, Random Forests, K‐Nearest Neighbors, and Gradient Boosting. Each of these algorithms achieves an accuracy of over 93% and the F1 score of over 95%, with Random Forest outperforming the other algorithms. This analytical approach helps to focus attention on critical areas to reduce traffic flows on major and minor roads in the area. In summary, the findings on traffic analysis have been discussed in detail to demonstrate the practical insights of this study

Adoption of cloud computing as innovation in the organization

Over the years, there has been a heavy reliance on cloud computing as IT has innovated through time. In recent times cloud computing has grown monumentally. Many organizations rely on this technology to perform their business as usual and use it as a backbone of their companies' IT infrastructure. This paper investigates the organizational adaptation for cloud computing technology - reviewing case studies from various institutions and companies worldwide to provide a detailed analysis of innovative techniques with cloud computing. We investigate the features and delivery approaches cloud computing offers and the potential challenges and constraints we face when adopting cloud computing into the business setting. We also explore the cybersecurity elements associated with cloud computing, focusing on intrusion detection and prevention and understanding how that can be applied in the cloud. Finally, we investigate the future research directions for cloud computing and expand this paper into further articles with experiments and results