Aim/Purpose: This study aims at (1) extending an existing theoretical framework to gain a deeper understanding of the technology acceptance process, notably of the Facebook social network in an unexplored Middle East context, (2) investigating the influence of social support theory on Facebook adoption outside the work context, (3) validating the effectiveness of the proposed research model for enhancing Facebook adoption, and (4) determining the effect of individual differences (gender, age, experience, and educational level) amongst Facebook users on the associated path between the proposed model constructs. Background: Social networking sites (SNSs) are widely adopted to facilitate social interaction in the Web-based medium. As such, this present work contends that there is a gap in the existing literature, particularly in the Middle East context, as regards an empirical investigation of the relationship between the social, psychological, individual, and cognitive constructs potentially affecting users’ intention to accept SNSs. The present research, therefore, attempts to address this deficit. The relevance of this work is also considered in light of the scarcity of empirical evidence and lack of detailed research on the effect of social support theory with regard to SNS adoption in a non-work context. Methodology: A quantitative research approach was adopted for this study. The corresponding analysis was carried out based on structural equation modelling (SEM), more specifically, partial least squares (PLS), using SmartPLS software. Earlier research recommended the PLS approach for exploratory studies when extending an existing model or developing a new theory. PLS is also a superior method of complex causal modelling. Moreover, a multi-group analysis technique was adopted to investigate the moderating influence of individual differences. This method divides the dataset into two groups and then computes the cause and effect relationships between the research model variables for each set. The analysis of an in-person survey with a sample of Facebook users (N=369) subsequently suggested four significant predictors of continuous Facebook use. Contribution: This study contributes to the body of knowledge relating to SNSs by providing empirical evidence of constructs that influence Facebook acceptance in the case of a developing country. It raises awareness of antecedents of Facebook acceptance at a time when SNSs are widely used in Arab nations and worldwide. It also contributes to previous literature on the effectiveness of the unified theory of acceptance and use of technology (UTAUT) in different cultural contexts. Another significant contribution of this study is that it has reported on the relevance of social support theory to Facebook adoption, with this theory demonstrating a significant and direct ability to predict Facebook acceptance. Finally, the present research identified the significant moderating effect of individual differences on the associated path between the proposed model constructs. This means that regardless of technological development, individual gaps still appeared to exist among users. Findings: The findings suggested four significant predictors of continuous Facebook use, namely, (a) performance expectancy, (b) peer support, (c) family support, and (d) perceived playfulness. Furthermore, behavioral intention and facilitating conditions were found to be significant determinants of actual Facebook use, while individual differences were shown to moderate the path strength between several variables in the proposed research model. Recommendations for Practitioners: The results of the present study make practical contributions to SNS organizations. For example, this research revealed that users do not adopt Facebook because of its usefulness alone; instead, users’ acceptance is developed through a sequence of variables such as individual differences, psychological factors, and social and organizational beliefs. Accordingly, social media organizations should not consider only strategies that apply to just one context, but also to other contexts characterized by different beliefs, perceptions, and cultures. Moreover, the evidence provided here is that social support theory has a significant influence on SNSs acceptance. This suggests that social media organizations should provide services to support this concept. Furthermore, the significant positive effect of perceived playfulness on the intention to use SNSs implied that designers and organizations should pay further attention to the entertainment services provided by social networks. Recommendation for Researchers: To validate the proposed conceptual framework, researchers from different countries and cultures are invited to apply the model. Moreover, a longitudinal research design could be implemented to gather data over a longer period, in order to investigate whether users have changed their attitudes, beliefs, perceptions, and intention by the end of the study period. Other constructs, such as individual experience, compatibility, and quality of working life could be included to improve the power of the proposed model. Impact on Society: Middle Eastern Facebook users regard the network as an important tool for interacting with others. The increasing number of Facebook users renders it a tool of universal communication and enjoyment, as well as a marketing network. However, knowledge of the constructs affecting the application of SNSs is valuable for ensuring that such sites have the various functions required to suit different types of user. Future Research: It is hoped that our future research will build on the results of this work and attempt to provide further explanation of why users accept SNSs. In this future research, the proposed research model could be adopted to explore SNSs acceptance in other developing countries. Researchers might also include other factors of potential influence on SNSs acceptance. The constructs influencing acceptance of other social networks could then be compared to the present research findings and thus, the differences and similarities would be highlighted.

Aim/Purpose: In this article, we provide a better solution to Webpage access prediction. In particularly, our core proposed approach is to increase accuracy and efficiency by reducing the sequence space with integration of PageRank into CPT+. Background: The problem of predicting the next page on a web site has become significant because of the non-stop growth of Internet in terms of the volume of contents and the mass of users. The webpage prediction is complex because we should consider multiple kinds of information such as the webpage name, the contents of the webpage, the user profile, the time between webpage visits, differences among users, and the time spent on a page or on each part of the page. Therefore, webpage access prediction draws substantial effort of the web mining research community in order to obtain valuable information and improve user experience as well. Methodology: CPT+ is a complex prediction algorithm that dramatically offers more accurate predictions than other state-of-the-art models. The integration of the importance of every particular page on a website (i.e., the PageRank) regarding to its associations with other pages into CPT+ model can improve the performance of the existing model. Contribution: In this paper, we propose an approach to reduce prediction space while improving accuracy through combining CPT+ and PageRank algorithms. Experimental results on several real datasets indicate the space reduced by up to between 15% and 30%. As a result, the run-time is quicker. Furthermore, the prediction accuracy is improved. It is convenient that researchers go on using CPT+ to predict Webpage access. Findings: Our experimental results indicate that PageRank algorithm is a good solution to improve CPT+ prediction. An amount of though approximately 15 % to 30% of redundant data is removed from datasets while improving the accuracy. Recommendations for Practitioners: The result of the article could be used in developing relevant applications such as Webpage and product recommendation systems. Recommendation for Researchers: The paper provides a prediction model that integrates CPT+ and PageRank algorithms to tackle the problem of complexity and accuracy. The model has been experimented against several real datasets in order to show its performance. Impact on Society: Given an improving model to predict Webpage access using in several fields such as e-learning, product recommendation, link prediction, and user behavior prediction, the society can enjoy a better experience and more efficient environment while surfing the Web. Future Research: We intend to further improve the accuracy of webpage access prediction by using the combination of CPT+ and other algorithms.

Aim/Purpose: The aim of this study was to identify the critical predictors affecting project desertion in Blockchain projects. Background: Blockchain is one of the innovations that disrupt a broad range of industries and has attracted the interest of software developers. However, despite being an open-source software (OSS) project, the maintenance of the project ultimately relies on small core developers, and it is still uncertain whether the technology will continue to attract a sufficient number of developers. Methodology: The study utilized a systematic literature review (SLR) and an expert review method. The SLR identified 21 primary studies related to project desertion published in Scopus databases from the year 2010 to 2020. Then, Blockchain experts were asked to rank the importance of the identified predictors of project desertion in Blockchain. Contribution: A theoretical framework was constructed based on Social Cognitive Theory (SCT) constructs; personal, behavior, and environmental predictors and related theories. Findings: The findings indicate that the 12 predictors affecting Blockchain project desertion identified through SLR were important and significant. Recommendations for Practitioners: The framework proposed in this paper can be used by the Blockchain development community as a basis to identify developers who might have the tendency to abandon a Blockchain project. Recommendation for Researchers: The results show that some predictors, such as code testing tasks, contributed code decoupling, system integration and expert heterogeneity that are not covered in the existing developer turnover models can be integrated into future research efforts. Impact on Society: This study highlights how an individual’s design choices could determine the success or failure of IS projects. It could direct Blockchain crypto-currency investors and cyber-security managers to pay attention to the developer’s behavior while ensuring secure investments, especially for crypto-currencies projects. Future Research: Future research may employ additional methods, such as a meta-analysis, to provide a comprehensive picture of the main predictors that can predict project desertion in Blockchain.

Aim/Purpose: Systematic literature reviews seek to locate all studies that contain material of relevance to a research question and to synthesize the relevant outcomes of those studies. The primary aim of this paper was to synthesize both research and practice reports on micro-credentials (MCRs). Background: There has been an increase in reports and research on the plausibility of MCRs to support dynamic human skills development for an increasingly impatient and rapidly changing digital world. The integration of fast-paced emerging technologies and digitalization necessitate alternative learning paradigms. MCRs offer time, financial, and space flexibility and can be stacked into a larger qualification, thereby allowing for a broader range of transdisciplinary competencies within a qualification. However, MCRs often lack the academic rigor required for accreditation within existing disciplines. Methodology: The study followed the PRISMA framework (Preferred Reporting Items for Systematic Reviews and Meta Analyses), which offers a rigorous method to enhance reporting quality. The study used both academic research and practice reports. Contribution: The paper makes a theoretical contribution to the discourse about the need for innovation within existing educational paradigms for continued relevance in a changing world. It also contributes to the debate on the role of MCRs in bridging the gap between practice and academia despite the growing difference between their interests, and the role that MCRs play in the social-economic plans of countries. Findings: The key findings are that investments in MCRs are mainly in the Science, Technology, Engineering and Mathematics (STEM) and Education sectors, and have taken place mainly in high-income countries and regions – contexts that particularly value practice-accredited MCRs. Low-income countries, by contrast, remain traditional and insist on MCRs that are formally accredited by a recognized academic institution. This contributes to a widening skills gap between low- and high-income countries or regions, which results in greater global disparities. There is also a growing divide between academia and practice concerning their interest in MCRs (a reflection of the rigor versus relevance debate), which partially explains why many global and larger organizations have gone on to create their own learning institutions. Recommendations for Practitioners: We recommend that educational mechanisms consider the critical importance of MCRs as part of innovative efforts for life-wide (different sectors) and life-long (same sector) learning, especially in low-income countries. MCRs provide dynamic mechanisms to fill skills gaps in an increasing ruthless international battle for talent. Recommendation for Researchers: We recommend focused research into skills and career pathways using MCRs while at the same time remaining responsive to transdisciplinary efforts and sensitive to global and local changes within any sector. Impact on Society: Work and society have transformed over time, and more so in the new digital age, yet academia has been slow in adapting to the changes, forcing organizations to create their own learning institutions or to use MCRs to fill the skills gap. The purpose of education goes beyond preparing individuals for work, extending further to creating an environment where individuals and governments seek their own social and economic outcomes. MCRs provide a flexible means for co-creation between individuals, education, organizations, and government that could stem global rising unemployment, social exclusion, and redundancy. Future Research: Future research should focus on the co-creation of MCRs between practitioners and academia.

Aim/Purpose: The aim of this paper is to examine the influences of KM-centred strategies on innovation capability among Malaysian R&D firms. It also deepens understanding of the pathways and conditions to improve the innovation capability by assessing the mediating role of both KM practices, i.e., knowledge exploration practices, and knowledge exploitation practices. Background: Knowledge is the main organisational resource that is able to generate a competitive advantage through innovation. It is a critical success driver for both knowledge exploration and exploitation for firms to achieve sustainable competitive advantages. Methodology: A total of 320 questionnaires were disseminated to Malaysian R&D firms and the response rate was 47 percent. The paper utilised structural equation modelling and cross-sectional design to test hypotheses in the proposed research model. Contribution: This paper provides useful information and valuable initiatives in exploring the mediating role of knowledge exploration and knowledge exploitation in influencing innovation in Malaysian R&D firms. It helps R&D firms to frame their KM activities to drive the capability of creating and retaining a greater value onto their core business competencies. Findings: The findings indicate that all three KM-centred strategies (leadership, HR practices, and culture) have a direct effect on innovation. In addition, KM exploration practices mediate HR practices on innovation while KM exploitation mediates both leadership and HR practices on innovation. Recommendations for Practitioners: This paper serves as a guide for R&D managers to determine the gaps and appropriate actions to collectively achieve the desired R&D results and national innovation. It helps R&D firms frame their KM activities to enhance the capability of creating and retaining a greater value to their core business competencies. Recommendation for Researchers: This paper contributes significantly to knowledge management and innovation research by establishing new associations among KM-centred strategies, i.e., leadership, HR practices, and culture, both KM practices (knowledge exploration and knowledge exploitation), and innovation. Impact on Society: This paper highlights the important role of knowledge leaders and the practice of effective HR practices to help R&D firms to create a positive environment that facilitates both knowledge exploration and knowledge exploitation in enhancing innovation capabilities. Future Research: Further research could use a longitudinal sample to examine relationships of causality, offering a more comprehensive view of the effect of KM factors on innovation over the long term. Future research should also try to incorporate information from new external sources, such as customers or suppliers.

Aim/Purpose: Acquisitions play a pivotal role in the growth strategy of a firm. Extensive resources and time are dedicated by a firm toward the identification of prospective acquisition candidates. The Indian manufacturing sector is currently experiencing significant growth, organically and inorganically, through acquisitions. The principal aim of this study is to explore models that can predict acquisitions and compare their performance in the Indian manufacturing sector. Background: Mergers and Acquisitions (M&A) have been integral to a firm’s growth strategy. Over the years, academic research has investigated multiple models for predicting acquisitions. In the context of the Indian manufacturing industry, the research is limited to prediction models. This research paper explores three models, namely Logistic Regression, Decision Tree, and Multilayer Perceptron, to predict acquisitions. Methodology: The methodology includes defining the accounting variables to be used in the model which have been selected based on strong theoretical foundations. The Indian manufacturing industry was selected as the focus, specifically, data for firms listed in the Bombay Stock Exchange (BSE) between 2010 and 2022 from the Prowess database. There were multiple techniques, such as data transformation and data scrubbing, that were used to mitigate bias and enhance the data reliability. The dataset was split into 70% training and 30% test data. The performance of the three models was compared using standard metrics. Contribution: The research contributes to the existing body of knowledge in multiple dimensions. First, a prediction model customized to the Indian manufacturing sector has been developed. Second, there are accounting variables identified specific to the Indian manufacturing sector. Third, the paper contributes to prediction modeling in the Indian manufacturing sector where there is limited research. Findings: The study found significant supporting evidence for four of the proposed hypotheses indicating that accounting variables can be used to predict acquisitions. It has been ascertained that statistically significant variables influence acquisition likelihood: Quick Ratio, Equity Turnover, Pretax Margin, and Total Sales. These variables are intrinsically linked with the theories of liquidity, growth-resource mismatch, profitability, and firm size. Furthermore, comparing performance metrics reveals that the Decision Tree model exhibits the highest accuracy rate of 62.3%, specificity rate of 66.4%, and the lowest false positive ratio of 33.6%. In contrast, the Multilayer Perceptron model exhibits the highest precision rate of 61.4% and recall rate of 64.3%. Recommendations for Practitioners: The study findings can help practitioners build custom prediction models for their firms. The model can be developed as a live reference model, which is continually updated based on a firm’s results. In addition, there is an opportunity for industry practitioners to establish a benchmark score that provides a reference for acquisitions. Recommendation for Researchers: Researchers can expand the scope of research by including additional classification modeling techniques. The data quality can be enhanced by cross-validation with other databases. Textual commentary about the target firms, including management and analyst quotes, provides additional insight that can enhance the predictive power of the models. Impact on Society: The research provides insights into leveraging emerging technologies to predict acquisitions. The theoretical basis and modeling attributes provide a foundation that can be further expanded to suit specific industries and firms. Future Research: There are opportunities to expand the scope of research in various dimensions by comparing acquisition prediction models across industries and cross-border and domestic acquisitions. Additionally, it is plausible to explore further research by incorporating non-financial data, such as management commentary, to augment the acquisition prediction model.

Aim/Purpose: To predict the change-proneness of software from the continuous evolution using machine learning methods. To identify when software changes become statistically significant and how metrics change. Background: Software evolution is the most time-consuming activity after a software release. Understanding evolution patterns aids in understanding post-release software activities. Many methodologies have been proposed to comprehend software evolution and growth. As a result, change prediction is critical for future software maintenance. Methodology: I propose using machine learning methods to predict change-prone classes. Classes that are expected to change in future releases were defined as change-prone. The previous release was only considered by the researchers to define change-proneness. In this study, I use the evolution of software to redefine change-proneness. Many snapshots of software were studied to determine when changes became statistically significant, and snapshots were taken biweekly. The research was validated by looking at the evolution of five large open-source systems. Contribution: In this study, I use the evolution of software to redefine change-proneness. The research was validated by looking at the evolution of five large open-source systems. Findings: Software metrics can measure the significance of evolution in software. In addition, metric values change within different periods and the significance of change should be considered for each metric separately. For five classifiers, change-proneness prediction models were trained on one snapshot and tested on the next. In most snapshots, the prediction performance was excellent. For example, for Eclipse, the F-measure values were between 80 and 94. For other systems, the F-measure values were higher than 75 for most snapshots. Recommendations for Practitioners: Software change happens frequently in the evolution of software; however, the significance of change happens over a considerable length of time and this time should be considered when evaluating the quality of software. Recommendation for Researchers: Researchers should consider the significance of change when studying software evolution. Software changes should be taken from different perspectives besides the size or length of the code. Impact on Society: Software quality management is affected by the continuous evolution of projects. Knowing the appropriate time for software maintenance reduces the costs and impacts of software changes. Future Research: Studying the significance of software evolution for software refactoring helps improve the internal quality of software code.

Aim/Purpose: The aim of this study is to develop and evaluate a comprehensive model that predicts students’ engagement with and intent to continue using mobile-Learning Management Systems (m-LMS). Background: m-LMS are increasingly popular tools for delivering course content in higher education. Understanding the factors that affect student engagement and continuance intention can help educational institutions to develop more effective and user-friendly m-LMS platforms. Methodology: Participants with prior experience with m-LMS were employed to develop and evaluate the proposed model that draws on the Technology Acceptance Model (TAM), Task-Technology Fit (TTF), and other related models. Partial Least Squares-Structural Equation Modeling (PLS-SEM) was used to evaluate the model. Contribution: The study provides a comprehensive model that takes into account a variety of factors affecting engagement and continuance intention and has a strong predictive capability. Findings: The results of the study provide evidence for the strong predictive capability of the proposed model and supports previous research. The model identifies perceived usefulness, perceived ease of use, interactivity, compatibility, enjoyment, and social influence as factors that significantly influence student engagement and continuance intention. Recommendations for Practitioners: The findings of this study can help educational institutions to effectively meet the needs of students for interactive, effective, and user-friendly m-LMS platforms. Recommendation for Researchers: This study highlights the importance of understanding the antecedents of students’ engagement with m-LMS. Future research should be conducted to test the proposed model in different contexts and with different populations to further validate its applicability. Impact on Society: The engagement model can help educational institutions to understand how to improve student engagement and continuance intention with m-LMS, ultimately leading to more effective and efficient mobile learning. Future Research: Additional research should be conducted to test the proposed model in different contexts and with different populations to further validate its applicability.

Aim/Purpose: Previous research has generally concentrated on identifying the variables that most significantly influence customer churn or has used customer segmentation to identify a subset of potential consumers, excluding its effects on forecast accuracy. Consequently, there are two primary research goals in this work. The initial goal was to examine the impact of customer segmentation on the accuracy of customer churn prediction in the banking sector using machine learning models. The second objective is to experiment, contrast, and assess which machine learning approaches are most effective in predicting customer churn. Background: This paper reviews the theoretical basis of customer churn, and customer segmentation, and suggests using supervised machine-learning techniques for customer attrition prediction. Methodology: In this study, we use different machine learning models such as k-means clustering to segment customers, k-nearest neighbors, logistic regression, decision tree, random forest, and support vector machine to apply to the dataset to predict customer churn. Contribution: The results demonstrate that the dataset performs well with the random forest model, with an accuracy of about 97%, and that, following customer segmentation, the mean accuracy of each model performed well, with logistic regression having the lowest accuracy (87.27%) and random forest having the best (97.25%). Findings: Customer segmentation does not have much impact on the precision of predictions. It is dependent on the dataset and the models we choose. Recommendations for Practitioners: The practitioners can apply the proposed solutions to build a predictive system or apply them in other fields such as education, tourism, marketing, and human resources. Recommendation for Researchers: The research paradigm is also applicable in other areas such as artificial intelligence, machine learning, and churn prediction. Impact on Society: Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. Future Research: Build a real-time or near real-time application to provide close information to make good decisions. Furthermore, handle the imbalanced data using new techniques.

Aim/Purpose: The last decade has witnessed a tremendous progression in mobile penetration across the world and, most importantly, in developing countries like India. This research aims to investigate and analyze the factors influencing the adoption of mobile payments (M-payments) in the Indian rural population. This, in turn, would bring about positive changes in the lives of people in these countries. Background: A conceptual framework was worked upon using UTAUT as a foundation, which included constructs, namely, facilitating conditions, social influences, performance expectancy, and effort expectancy. The model was further extended by incorporating the awareness construct of m-payments to make it more comprehensive and to understand behavioral intentions and usage behavior for m-payments in rural India. Methodology: A questionnaire-based study was conducted to collect primary data from 410 respondents residing in rural areas in the state of Punjab. Convenience sampling was conducted to collect the data. Structural equation modeling was used to conduct statistical analysis, including exploratory and confirmatory factor analyses. Contribution: A new conceptual model for M-payments adoption in rural India was developed based on the study’s findings. Using the findings of the study, marketers, policymakers, and academicians can gain insight into the factors that motivate the rural population to use M-payments. Findings: The study has found that M-payment Awareness (AW) is the strongest factor within the proposed model for deeper diffusion of M-payments in rural areas in the state of Punjab. Performance expectancy (PE), effort expectancy (EE), social influences (SI), and facilitating conditions (FC) are also positively and significantly related to behavioral intentions for using M-payments among the Indian rural population in the state of Punjab. Recommendations for Practitioners: M-payments are emerging as a new mode of transactions among the Indian masses. The government needs to play a pivotal role in advocating the benefits linked with the usage of M-payments by planning financial literacy and awareness campaigns, promoting transparency and accountability of the intermediaries, and reducing transaction costs of using M-payments. Mobile manufacturing companies should come up with devices that are easy to use and incorporate multilanguage mobile applications, especially for rural areas, as India is a multi-lingual country. A robust regulatory framework will not only shape consumer trust but also prevent privacy breaches. Recommendation for Researchers: It is recommended that a comparative study among different M-payment platforms be conducted by exploring constructs such as usefulness and ease of use. However, the vulnerability of data leakage may result in insecurity and skepticism about its adoption. Impact on Society: India’s rural areas have immense potential for adoption of M-payments. Appropriate policies, awareness drives, and necessary infrastructure will boost faster and smoother adoption of M-payments in rural India to thrive in the digital economy. Future Research: The adapted model can be further tested with moderating factors like age, gender, occupation, and education to understand better the complexities of M-payments, especially in rural areas of India. Additionally, cross-sectional studies could be conducted to evaluate the behavioral intentions of different sections of society.

Aim/Purpose: While existing literature has extensively explored factors influencing the success of big data projects and proposed big data maturity models, no study has harnessed machine learning to predict project success and identify the critical features contributing significantly to that success. The purpose of this paper is to offer fresh insights into the realm of big data projects by leveraging machine-learning algorithms. Background: Previously, we introduced the Global Big Data Maturity Model (GBDMM), which encompassed various domains inspired by the success factors of big data projects. In this paper, we transformed these maturity domains into a survey and collected feedback from 90 big data experts across the Middle East, Gulf, Africa, and Turkey regions regarding their own projects. This approach aims to gather firsthand insights from practitioners and experts in the field. Methodology: To analyze the feedback obtained from the survey, we applied several algorithms suitable for small datasets and categorical features. Our approach included cross-validation and feature selection techniques to mitigate overfitting and enhance model performance. Notably, the best-performing algorithms in our study were the Decision Tree (achieving an F1 score of 67%) and the Cat Boost classifier (also achieving an F1 score of 67%). Contribution: This research makes a significant contribution to the field of big data projects. By utilizing machine-learning techniques, we predict the success or failure of such projects and identify the key features that significantly contribute to their success. This provides companies with a valuable model for predicting their own big data project outcomes. Findings: Our analysis revealed that the domains of strategy and data have the most influential impact on the success of big data projects. Therefore, companies should prioritize these domains when undertaking such projects. Furthermore, we now have an initial model capable of predicting project success or failure, which can be invaluable for companies. Recommendations for Practitioners: Based on our findings, we recommend that practitioners concentrate on developing robust strategies and prioritize data management to enhance the outcomes of their big data projects. Additionally, practitioners can leverage machine-learning techniques to predict the success rate of these projects. Recommendation for Researchers: For further research in this field, we suggest exploring additional algorithms and techniques and refining existing models to enhance the accuracy and reliability of predicting the success of big data projects. Researchers may also investigate further into the interplay between strategy, data, and the success of such projects. Impact on Society: By improving the success rate of big data projects, our findings enable organizations to create more efficient and impactful data-driven solutions across various sectors. This, in turn, facilitates informed decision-making, effective resource allocation, improved operational efficiency, and overall performance enhancement. Future Research: In the future, gathering additional feedback from a broader range of big data experts will be valuable and help refine the prediction algorithm. Conducting longitudinal studies to analyze the long-term success and outcomes of Big Data projects would be beneficial. Furthermore, exploring the applicability of our model across different regions and industries will provide further insights into the field.

Aim/Purpose: Accurate detection of vacant parking spaces is crucial for autonomous parking. Deep learning, particularly Graph Neural Networks (GNNs), holds promise for addressing the challenges of diverse parking lot appearances and complex visual environments. Our GNN-based approach leverages the spatial layout of detected marking points in around-view images to learn robust feature representations that are resilient to occlusions and lighting variations. We demonstrate significant accuracy improvements on benchmark datasets compared to existing methods, showcasing the effectiveness of our GNN-based solution. Further research is needed to explore the scalability and generalizability of this approach in real-world scenarios and to consider the potential ethical implications of autonomous parking technologies. Background: GNNs offer a number of advantages over traditional parking spot detection methods. Unlike methods that treat objects as discrete entities, GNNs may leverage the inherent connections among parking markers (lines, dots) inside an image. This ability to exploit spatial connections leads to more accurate parking space detection, even in challenging scenarios with shifting illumination. Real-time applications are another area where GNNs exhibit promise, which is critical for autonomous vehicles. Their ability to intuitively understand linkages across marking sites may further simplify the process compared to traditional deep-learning approaches that need complex feature development. Furthermore, the proposed GNN model streamlines parking space recognition by potentially combining slot inference and marking point recognition in a single step. All things considered, GNNs present a viable method for obtaining stronger and more precise parking slot recognition, opening the door for autonomous car self-parking technology developments. Methodology: The proposed research introduces a novel, end-to-end trainable method for parking slot detection using bird’s-eye images and GNNs. The approach involves a two-stage process. First, a marking-point detector network is employed to identify potential parking markers, extracting features such as confidence scores and positions. After refining these detections, a marking-point encoder network extracts and embeds location and appearance information. The enhanced data is then loaded into a fully linked network, with each node representing a marker. An attentional GNN is then utilized to leverage the spatial relationships between neighbors, allowing for selective information aggregation and capturing intricate interactions. Finally, a dedicated entrance line discriminator network, trained on GNN outputs, classifies pairs of markers as potential entry lines based on learned node attributes. This multi-stage approach, evaluated on benchmark datasets, aims to achieve robust and accurate parking slot detection even in diverse and challenging environments. Contribution: The present study makes a significant contribution to the parking slot detection domain by introducing an attentional GNN-based approach that capitalizes on the spatial relationships between marking points for enhanced robustness. Additionally, the paper offers a fully trainable end-to-end model that eliminates the need for manual post-processing, thereby streamlining the process. Furthermore, the study reduces training costs by dispensing with the need for detailed annotations of marking point properties, thereby making it more accessible and cost-effective. Findings: The goal of this research is to present a unique approach to parking space recognition using GNNs and bird’s-eye photos. The study’s findings demonstrated significant improvements over earlier algorithms, with accuracy on par with the state-of-the-art DMPR-PS method. Moreover, the suggested method provides a fully trainable solution with less reliance on manually specified rules and more economical training needs. One crucial component of this approach is the GNN’s performance. By making use of the spatial correlations between marking locations, the GNN delivers greater accuracy and recall than a completely linked baseline. The GNN successfully learns discriminative features by separating paired marking points (creating parking spots) from unpaired ones, according to further analysis using cosine similarity. There are restrictions, though, especially where there are unclear markings. Successful parking slot identification in various circumstances proves the recommended method’s usefulness, with occasional failures in poor visibility conditions. Future work addresses these limitations and explores adapting the model to different image formats (e.g., side-view) and scenarios without relying on prior entry line information. An ablation study is conducted to investigate the impact of different backbone architectures on image feature extraction. The results reveal that VGG16 is optimal for balancing accuracy and real-time processing requirements. Recommendations for Practitioners: Developers of parking systems are encouraged to incorporate GNN-based techniques into their autonomous parking systems, as these methods exhibit enhanced accuracy and robustness when handling a wide range of parking scenarios. Furthermore, attention mechanisms within deep learning models can provide significant advantages for tasks that involve spatial relationships and contextual information in other vision-based applications. Recommendation for Researchers: Further research is necessary to assess the effectiveness of GNN-based methods in real-world situations. To obtain accurate results, it is important to employ large-scale datasets that include diverse lighting conditions, parking layouts, and vehicle types. Incorporating semantic information such as parking signs and lane markings into GNN models can enhance their ability to interpret and understand context. Moreover, it is crucial to address ethical concerns, including privacy, potential biases, and responsible deployment, in the development of autonomous parking technologies. Impact on Society: Optimized utilization of parking spaces can help cities manage parking resources efficiently, thereby reducing traffic congestion and fuel consumption. Automating parking processes can also enhance accessibility and provide safer and more convenient parking experiences, especially for individuals with disabilities. The development of dependable parking capabilities for autonomous vehicles can also contribute to smoother traffic flow, potentially reducing accidents and positively impacting society. Future Research: Developing and optimizing graph neural network-based models for real-time deployment in autonomous vehicles with limited resources is a critical objective. Investigating the integration of GNNs with other deep learning techniques for multi-modal parking slot detection, radar, and other sensors is essential for enhancing the understanding of the environment. Lastly, it is crucial to develop explainable AI methods to elucidate the decision-making processes of GNN models in parking slot detection, ensuring fairness, transparency, and responsible utilization of this technology.

DSSP stands as a foundational tool in the domain of protein secondary structure prediction, yet it encounters notable challenges in accurately annotating irregular structures, such as β-turns and γ-turns, which constitute approximately 25%-30% and 10%-15% of protein turns, respectively. This limitation arises from DSSP's reliance on hydrogen-bond analysis, resulting in annotation gaps and reduced consensus on irregular structures. Alternatively, PROMOTIF excels at identifying these irregular structure annotations using phi-psi information. Despite their complementary strengths, previous methodologies utilised DSSP and PROMOTIF separately, leading to disparate prediction methods for protein secondary structures, hampering comprehensive structure analysis crucial for drug development. In this work, we bridge this gap using an annotation fusion approach, combining DSSP structures with beta, and gamma turns. We introduce IRNN-SS, a model employing deep inception and bidirectional gated recurrent neural networks, achieving 77.4% prediction accuracy on benchmark datasets, outpacing current models.

Epidemiologists recommended contact-tracing as an effective control measure for the global infection like COVID-19 pandemic. Despite its effectiveness in infection containment, it has many limitations such as labour-intensive process, prone to human errors and most importantly, user privacy concerns. To address these shortcomings, we proposed location-aware blockchain-based hierarchical contact-tracing framework for anonymised data collection and processing. This infectious disease control framework serves both the infected users with localised alerts as well as stakeholders such as city officials and health workers with health statistics. Our proposed solution uses hierarchical network design that offloads individual infection block data to create hospital and city-level 'chains' for generating macro-level infection statistics. Results demonstrate that our system can represent the dynamic complexities of contract tracing in highly infection situations. Overall, our design emphasises on data processing and verification mechanism for large volume of infection data over a significant period of time for active risk assessment.

Prediction of route based on historical trip observation of users is widely employed in location-based services. This work concentrates on building a route prediction system using Lempel-Ziv technique applied to a historical corpus of user travel data. Huge continuous logs of historical GPS traces representing the user's location in past are decomposed into smaller logical units known as trips. User trips are converted into sequences of road network edges using a process known as map matching. Lempel-Ziv is applied on road network edges to build the prediction model that captures the user's travel pattern in the past. A two-phased model is proposed using a map reduce framework without losing accuracy and efficiency. Model is then used to predict the user's end-to-end route given a partial route travelled by the user at any point in time. The objective of the proposed work is to build a Route Prediction system in which model building and prediction both are horizontally scalable.

Aim/Purpose: Our research focuses on a unique group a students, who study CS: ultra-orthodox Jewish men. Their previous education is based mostly on studying Talmud and hence they lacked a conventional high-school education. Our research goal was to examine whether their prior education is merely a barrier to their CS studies or whether it can be recruited to leverage academic learning. Background: This work is in line with the growing interest in extending the diversity of students studying computer science (CS). Methodology: We employed a mixed-methods approach. We compared the scores in CS courses of two groups of students who started their studies in the same college in 2015: 58 ultraorthodox men and 139 men with a conventional background of Israeli K-12 schooling. We also traced the solution processes of ultraorthodox men in tasks involving Logic, in which their group scored significantly better than the other group. Contribution: The main contribution of this work lies in challenging the idea that the knowledge of unique cultures is merely a barrier and in illustrating the importance of further mapping such knowledge. Findings: The ultraorthodox group’s grades in the courses never fell below the grades of the other group for the duration of the five semesters. Due to their intensive Talmud studies (which embeds Logic), we hypothesized they would have leverage in subjects relating to Logic; however this hypothesis was refuted. Nevertheless, we found that the ultraorthodox students tended to recruit conceptual knowledge rather than merely recalling a procedure to solve the task, as novices often do. Recommendations for Practitioners: We concluded that these students’ unique knowledge should not be viewed merely as a barrier. Rather, it can and should be considered in terms of what and how it can anchor and leverage learning; this could facilitate the education of this unique population. Impact on Society: This conclusion has an important implication, given the growing interest in diversifying higher education and CS in particular, to include representatives of groups in society that come from different, unique cultures. Future Research: Students’ unique previous knowledge can and should be mapped, not only to foresee weaknesses that are an outcome of “fragile knowledge” , but also in terms of possible strengths, knowledge, values, and practices that can be used to anchor and expand the new knowledge.

Aim/Purpose: This study examines differences in credit and noncredit users’ learning and usage of the Plant Sciences E-Library (PASSEL, http://passel.unl.edu), a large international, open-source multidisciplinary learning object repository. Background: Advances in online education are helping educators to meet the needs of formal academic credit students, as well as informal noncredit learners. Since online learning attracts learners with a wide variety of backgrounds and intentions, it is important understand learner behavior so that instructional resources can be designed to meet the diversity of learner motivations and needs. Methodology: This research uses both descriptive statistics and cluster analysis. The descriptive statistics address the research question of how credit learners differ from noncredit learners in using an international e-library of learning objects. Cluster analysis identifies high and low credit/noncredit students based on their quiz scores and follow-up descriptive statistics to (a) differentiate their usage patterns and (b) help describe possible learning approaches (deep, surface, and strategic). Contribution: This research is unique in its use of objective, web-tracking data and its novel use of clustering and descriptive analytic approaches to compare credit and noncredit learners’ online behavior of the same educational materials. It is also one of the first to begin to identify learning approaches of the noncredit learner. Findings: Results showed that credit users scored higher on quizzes and spent more time on the online quizzes and lessons than did noncredit learners, suggesting their academic orientation. Similarly, high credit scorers spent more time on individual lessons and quizzes than did the low scorers. The most striking difference among noncredit learners was in session times, with the low scorers spending more time in a session, suggesting more browsing behavior. Results were used to develop learner profiles for the four groups (high/low quiz scorers x credit/noncredit). Recommendations for Practitioners: These results provide preliminary insight for instructors or instructional designers. For example, low scoring credit students are spending a reasonable amount of time on a lesson but still score low on the quiz. Results suggest that they may need more online scaffolding or auto-generated guidance, such as the availability of relevant animations or the need to review certain parts of a lesson based on questions missed. Recommendation for Researchers: The study showed the value of objective, web-tracking data and novel use of clustering and descriptive analytic approaches to compare different types of learners. One conclusion of the study was that this web-tracking data be combined with student self-report data to provide more validation of results. Another conclusion was that demographic data from noncredit learners could be instrumental in further refining learning approaches for noncredit learners. Impact on Society: Learning object repositories, online courses, blended courses, and MOOCs often provide learners the option of moving freely among educational content, choosing not only topics of interest but also formats of material they feel will advance their learning. Since online learning is becoming more prolific and attracts learners with a wide variety of backgrounds and intentions, these results show the importance of understanding learner behavior so that e-learning instructional resources can be designed to meet the diversity of learner motivations and needs. Future Research: Future research should combine web-tracking data with student self-report to provide more validation of results. In addition, collection of demographic data and disaggregation of noncredit student usage motivations would help further refining learning approaches for this growing population of online users.

Aim/Purpose: To examine how positive/negative message framing – based on peripheral cues (regarding popularity, source, visuals, and hyperlink) – affects perceptions of credibility of scientific information posted on social networking sites (in this case, Facebook), while exploring the mechanisms of viewing the different components. Background: Credibility assessment of information is a key skill in today's information society. However, it is a demanding cognitive task, which is impossible to perform for every piece of online information. Additionally, message framing — that is, the context and approach used to construct information— may impact perceptions of credibility. In practice, people rely on various cues and cognitive heuristics to determine whether they think a piece of content is true or not. In social networking sites, content is usually enriched by additional information (e.g., popularity), which may impact the users' perceived credibility of the content. Methodology: A quantitative controlled experiment was designed (N=19 undergraduate students), collecting fine grained data with an eye tracking camera, while analyzing it using transition graphs. Contribution: The findings on the mechanisms of that process, enabled by the use of eye tracking data, point to the different roles of specific peripheral cues, when the message is overall peripherally positive or negative. It also contributes to the theoretical literature on framing effects in science communication, as it highlights the peripheral cues that make a strong frame. Findings: The positively framed status was perceived, as expected from the Elaboration Likelihood Model, more credible than the negatively framed status, demonstrating the effects of the visual framing. Differences in participants' mechanisms of assessing credibility between the two scenarios were evident in the specific ways the participants examined the various status components. Recommendations for Practitioners: As part of digital literacy education, major focus should be given to the role of peripheral cues on credibility assessment in social networking sites. Educators should emphasize the mechanisms by which these cues interact with message framing, so Internet users would be encouraged to reflect upon their own credibility assessment skills, and eventually improve them. Recommendation for Researchers: The use of eye tracking data may help in collecting and analyzing fine grained data on credibility assessment processes, and on Internet behavior at large. The data shown here may shed new light on previously studied phenomena, enabling a more nuanced understanding of them. Impact on Society: In an era when Internet users are flooded with information that can be created by virtually anyone, credibility assessment skills have become ever more important, hence the prominence of this skill. Improving citizens' assessment of information credibility — to which we believe this study contributes — results on a greater impact on society. Future Research: The role of peripheral cues and of message framing should be studied in other contexts (not just scientific news) and in other platforms. Additional peripheral cues not tested here should be also taken into consideration (e.g., connections between the information consumer and the information sharer, or the type of the leading image).

Based on the diffusion of innovation (DOI) theory (Rogers, 2003), the uses and gratifications (U&G) theory, and trust theory, this study investigated the factors that influence the use of Twitter among the Kuwaiti community. The study surveyed Twitter users in Kuwait. A structured online questionnaire was used to collect data, and 463 respondents who provided complete answers participated. Multiple regression analysis was used to examine the effect of three theoretical perspectives on Twitter usage. The result of the analysis showed that Twitter usage is better explained by DOI constructs than by U&G constructs. The findings indicated that the perceived relative advantage from DOI, and the need for information, need to pass time, and need for interpersonal utility from the U&G approach, have a direct positive significant effect on the use of Twitter. None of the trust theory constructs was found to be significant in predicting the general use of Twitter. The study results help Twitter providers and users in individual or organizational contexts to understand what factors generally affect the usage of the Twitter service.

Aim/Purpose: The aim of this paper is to identify the possibilities for reducing the second and third levels of the digital divide (or inequality) through conscious application of digital technologies, especially through the promotion of digital means for information, enlightenment, and entertainment. Background: This article reviews studies carried out between 2000 and 2017, which investigate the social benefits of digital technology use for disadvantaged user groups and, especially, of their outcomes in terms of increasing digital skills and motivation to use information and communication technologies. Methodology: The literature review of the selected texts was carried out using thematic content analysis. The coding scheme was open but based on the theory of three levels of digital divide by van Dijk. Contribution: The results of the analysis show the difficulties related to the attempts of reducing the digital divide on the second and third level using only digital interventions, but also reveal the potential of these interventions. Findings: The literature review confirms the connection of different levels of digital divide with other relational and structural inequalities. It provides insights into the strengths and weaknesses of digital interventions aimed at the reduction of digital inequalities. Their success depends on the consideration of the context and participants needs as well as on carefully planned strategies. The paper summarizes and demonstrates the shortcomings and limitations of poorly designed interventions in reducing the digital divide but emphasizes the possibilities of raising the motivation and benefits for the participants of strategically planned and implemented projects. Recommendations for Practitioners: While planning a digital intervention with the aim of reducing digital inequalities, it is necessary to assess carefully the context and the needs of participants. Educational interventions should be based on suitable didactic and learning strategies. Recommendation for Researchers: More research is needed into the factors that increase the effectiveness of digital interventions aimed at reducing the digital divide. Future Research: We will apply the findings of this literature review in an intervention in the context of Lithuanian towns of different sizes.

Aim/Purpose: Provides a theoretical model as to where we should source our information as the environment becomes more complex. Background: Develops a theoretical model built on extrinsic complexity and offers a conceptual scheme relating to the relative value of different sources. Methodology: The paper is purely conceptual in nature. Contribution: Develops a model that could be tested relating to where clients should search for information. Findings: Arguments can be made that different environments warrant different priorities for informing sources. Recommendations for Practitioners: Assess how your sources of information match your perceived environment. Recommendation for Researchers: Consider developing research designs to test the proposed model. Impact on Society: Offers a new way of thinking about informing sources. Future Research: Develop propositions from the model that could be empirically tested in future research.

Aim/Purpose: Labeling a journal as “predatory” can do great damage to the journal and the individuals that have contributed to it. This paper considers whether the predatory classification has outlived its usefulness and what might replace it. Background: With the advent of open access publishing, the term “predatory” has increasingly been used to identify academic journals, conferences, and publishers whose practices are driven by profit or self-interest rather than the advancement of science. Absent clear standards for determining what is predatory and what is not, concerns have been raised about the misuse of the label. Methodology: Mixed methods: A brief review of the literature, some illustrative case studies, and conceptual analysis. Contribution: The paper provides recommendations for reducing the impact of illegitimate journals. Findings: Current predatory classifications are being assigned with little or no systematic research and virtually no accountability. The predatory/not predatory distinction does not accommodate alternative journal missions. Recommendations for Researchers: The distinction between legitimate and illegitimate journals requires consideration of each journal’s mission. To serve as a useful guide, a process akin to that used for accrediting institutions needs to be put in place. Impact on Society: Avoiding unnecessary damage to the careers of researchers starting out. Future Research: Refining the initial classification scheme proposed in the paper.

Aim/Purpose: To explore the incidence of addiction potential within the Iranian public working population, describing how many Iranian public employees fall within the diagnostic categories of low, moderate, and high addiction potential. Also, to investigate the predicting role of occupational variables such as perceived stress, job security, and job satisfaction on addiction potential and belonging to low, moderate, and high addiction potential diagnostic categories. Background: Substance addiction among employees can lead to several negative consequences at the individual and organizational levels. Also, it is the fourth cause of death in Iran. However, few studies have been conducted on the topic among employees, and non among Iranian employees. Methodology: The study participants were 430 employees working in governmental offices of the North Khorasan province, Iran. Descriptive statistical analysis and multiple linear regression analysis were conducted to explore the incidence of addiction potential within the analyzed population and to investigate whether occupational variables such as perceived stress, job security, and job satisfaction predicted low, moderate, or high addiction potential. Contribution: This paper suggests that perceived stress might act as a risk factor for developing addiction, whereas job security and job satisfaction might be protective factors against the likelihood of addiction development. Findings: More than half of the sample showed moderate to high addiction potential. Perceived stress was positively related to addiction potential. Job security and job satisfaction were negatively related to addiction potential. Recommendation for Researchers: When addressing the topic of substance addiction, researchers should focus on the preventative side of investigating it; that is, addiction risk rather than already unfolded addiction. Also, researchers should be mindful of the cultural context in which studies are conducted. Future Research: Future research might investigate other relevant occupational predictors in relation to employee addiction potential, such as leadership style, work-life balance, and worktime schedule, or expand on the relevant causal chain by including personality traits such as neuroticism.

Aim/Purpose: This study aims to explore students’ digital entrepreneurial intention (DEI) in Kuwait. Specifically, the aim is twofold: (i) to identify and examine the factors influencing and predicting students’ DEI, and (ii) to validate a model of DEI. Background: The advent of modern digital technologies has provided entrepreneurs with many opportunities to establish and expand their firms through online platforms. Although the existing literature on DEI has explored various factors, certain factors that could be linked to DEI have been neglected, and others have not been given sufficient attention. Nonetheless, there has been little research on students’ DEI, particularly in Kuwait. Methodology: To fulfill the research’s aims, a study was conducted using a quantitative method (a survey of 305 students at a non-profit university in Kuwait). Contribution: This study aimed to fill the research gap on the limited DEI research among Kuwait’s students. Several recommendations were suggested to improve the DEI among students in Kuwait. Findings: The study identified five factors that could influence an individual’s intention to engage in digital entrepreneurship. These factors include self-perceived creativity, social media use, risk-taking and opportunity recognition, digital entrepreneurship knowledge, and entrepreneurial self-perceived confidence. Significant solid correlations were between all five identified factors and DEI. However, only self-perceived creativity and entrepreneurial self-perceived confidence were identified as significant positive predictors of DEI among undergraduates in Kuwait. Nevertheless, the main contributor to this intention was the students’ self-perceived confidence as entrepreneurs. Recommendation for Researchers: Researchers should conduct further longitudinal studies to understand better the dynamic nature of DEI and execution. Future Research: Additional research is required to utilize probability sampling approaches and increase the sample size for more generalizable findings.

Aim/Purpose: This review paper aims to unveil some underlying machine-learning classification algorithms used for political election predictions and how stack ensembles have been explored. Additionally, it examines the types of datasets available to researchers and presents the results they have achieved. Background: Predicting the outcomes of presidential elections has always been a significant aspect of political systems in numerous countries. Analysts and researchers examining political elections rely on existing datasets from various sources, including tweets, Facebook posts, and so forth to forecast future elections. However, these data sources often struggle to establish a direct correlation between voters and their voting patterns, primarily due to the manual nature of the voting process. Numerous factors influence election outcomes, including ethnicity, voter incentives, and campaign messages. The voting patterns of successors in regions of countries remain uncertain, and the reasons behind such patterns remain ambiguous. Methodology: The study examined a collection of articles obtained from Google Scholar, through search, focusing on the use of ensemble classifiers and machine learning classifiers and their application in predicting political elections through machine learning algorithms. Some specific keywords for the search include “ensemble classifier,” “political election prediction,” and “machine learning”, “stack ensemble”. Contribution: The study provides a broad and deep review of political election predictions through the use of machine learning algorithms and summarizes the major source of the dataset in the said analysis. Findings: Single classifiers have featured greatly in political election predictions, though ensemble classifiers have been used and have proven potent use in the said field is rather low. Recommendation for Researchers: The efficacy of stack classification algorithms can play a significant role in machine learning classification when modelled tactfully and is efficient in handling labelled datasets. however, runtime becomes a hindrance when the dataset grows larger with the increased number of base classifiers forming the stack. Future Research: There is the need to ensure a more comprehensive analysis, alternative data sources rather than depending largely on tweets, and explore ensemble machine learning classifiers in predicting political elections. Also, ensemble classification algorithms have indeed demonstrated superior performance when carefully chosen and combined.

Labor pain and its relieving methods is one of the anxieties of mothers having a great impact on the quality of care during delivery as well as the patients' satisfaction. The propensity of using non-medicinal pain relief methods is increasing. The present study aimed to compare the effect of Acupressure at two GB-21 and SP06 points on the severity of labor pain. In this quasi-experimental single blind study started on December 2010 and ended on June 2011 in which 150 primiparous women were divided into three groups of Acupressure at GB-21 point, Acupressure at SP-6 point and control group. The intervention was carried out for 20 min at 3-4 and 20 min at 7-8 cm dilatation of Cervix. The pain severity was measured by Visual Analog Scale before and immediately, 30 and 60 min after the intervention. Then, the data were statistically analyzed. No significant difference was found among the 3 groups regarding the pain severity before the intervention. However, the pain severity it was reduced at 3-4 and 7-8 cm dilatation immediately, 30 and 60 min after the intervention in the two intervention groups compared to the control group (p<0.001). Nonetheless, no statistically significant difference was observed between the two intervention groups (p = 0.93). The results of the study showed that application of Acupressure at two GB-21 and SP-6 points was effective in the reduction of the severity of labor pain. Therefore, further studies are recommended to be performed on the application of Acupressure together with non-medicinal methods.

Mental illnesses are common among college students as well as their non-student peers, and the number and severity of these problems are increasing. It can be difficult to identify people suffering from mental illness and get the help they need early. So in this paper, the SqueezeR_MobileNet method is proposed. It performs feature fusion and early mental health prediction. Initially, outliers in the input data are detected and removed. After that, using missing data imputation and Z-score normalisation the pre-processing phase is executed. Next to this, for feature fusion, a combination of the Soergel metric and deep Kronecker network (DKN) is used. By utilising bootstrapping data augmentation is performed. Finally, early mental health prediction is done using SqueezeR_MobileNet, which is the incorporation of residual SqueezeNet and MobileNet. The devised approach has reached the highest specificity of 0.937, accuracy of 0.911 and sensitivity of 0.907.

This paper presents a novel deep learning technique called quantum dilated convolutional neural network-DenseNet (Q-DenseNet) for prediction of heart disease in spark framework. At first, the input data taken from the database is allowed for data partitioning using fast fuzzy C-means clustering (FFCM). The partitioned data is fed into spark framework, where pre-processed by missing data imputation and quantile normalisation. The pre-processed data is further allowed for selection of suitable features. Then, the selected features from the slave nodes are merged and fed into master node. The Q-DenseNet is used in master node for the prediction of heart disease. The performance improvement of the designed Q-DenseNet model is validated by comparing with traditional prediction models. Here, the Q-DenseNet method achieved superior performance with maximum of 92.65% specificity, 91.74% sensitivity, and 90.15% accuracy.

One of the main tasks in tax administration is debt management. The main goal of this function is tax due collection. Statements are processed in order to select strategies to use in the debt management process to optimise the debt collection process. This work proposes to carry out a data mining process to predict debts of taxpayers with high probability of non-payment. The data mining process identifies high-risk debts using a survival analysis on a dataset from a tax administration. Three groups of tax debtors with similar payment behaviour were identified and a success rate of up to 90% was reached in estimating the payment time of taxpayers. The concordance index (C-index) was used to determine the performance of the constructed model. The highest prediction rate reached was 90.37% corresponding to the third group.

A semantically rich framework to enable real-time knowledge extraction from short length semi-structured documents Lavana Elluri 10:30-11:30 Monday, 4 November 2019, ITE346 Knowledge is currently maintained as a large volume of unstructured text data in books, laws, regulations and policies, news and social media, academic and scientific reports, conversation and correspondence, etc. Most of these […]

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In my knowledge graph class yesterday we talked about the SPARQL query language and I illustrated it with DBpedia queries, including an example getting data about the movie Double Indemnity. I had brought a google assistant device and used it to compare its answers to those from DBpedia. When I asked the Google assistant “Who […]

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Psychological empowerment has been studied extensively over the past few decades in a variety of contexts and appears to be especially salient within dynamic and complex environments such as healthcare. However, a recent meta-analysis found that psychological empowerment relationships vary significantly across studies, and there is still a rather limited understanding of how empowerment operates across levels. Accordingly, we advance and test a multi-level model of empowerment which seeks to better understand the unique and synergistic effects between unit and individual empowerment in hospital units. Analysis of data involving 544 individuals in 78 units, collected from multiple sources over three different time periods, revealed that unit empowerment evidenced a synergistic interaction with individual-level psychological empowerment as related to individuals' job performance, as well as an indirect effect on performance via individual empowerment, while controlling for previous performance levels. Notably, these effects were significant at relatively high, but not at relatively low levels of unit empowerment. Furthermore, we found that unit voice climate increased unit empowerment and thereby enhanced individual psychological empowerment. These findings suggest that, in complex and dynamic environments, empowering work units is an important means by which leaders can enhance individuals' performance.

As research begins to accumulate on employee volunteering, it appears that this behavior is largely beneficial to employee performance and commitment. It is less clear, however, how employee volunteering is perceived by others in the workplace. Do colleagues award volunteering "credit"- for example, associating it with being concerned about others - or do they "stigmatize" it - for example, associating it with being distracted from work? Moreover, do those evaluations go on to predict how colleagues actually treat employees who volunteer more often? Adopting a reputation perspective, we draw from theories of person perception and attribution to explore these research questions. The results of a field study revealed that colleagues gave credit to employee volunteering when they attributed it to intrinsic reasons and stigmatized employee volunteering when they attributed it to impression management reasons. Ultimately, through the awarded credits, volunteering was rewarded by supervisors (with the allocation of more resources) and coworkers (with the provision of more helping behavior) when it was attributed to intrinsic motives - a relationship that was amplified when stigmas were low and mitigated when stigmas were high. The results of a laboratory experiment further confirmed that volunteering was both credited and stigmatized, distinguishing it from citizenship behavior, which was credited but not stigmatized.

Rory McIlroy vows to retain DP World Tour events as a key part of his schedule next year while skipping some tournaments in America after an intense 2024.