The fourth industrial revolution is forthcoming, bringing with it revolutionary alterations in connectivity, work dynamics, and everyday operations. The fundamental foundation of this transformation, artificial intelligence (AI) is at its core. The purpose of this research is to identify new and distinctive features of how artificial intelligence may improve and enhance business knowledge management systems. For this purpose, systematic literature review (SLR) was used as the research method and preferred reporting items for systematic reviews and meta-analyses (PRISMA) as an approach. Articles based on specific search queries from 2013 to 2023 were selected from major scientific databases such as Scopus, Web of Science, and ScienceDirect, with a focus on AI, knowledge management systems, and corporate organizations for the PRISMA. It covers four main research questions by utilizing 14 selected papers. The main findings of this research paper call attention to AI’s vital role in shaping the future of knowledge management which helps organizations make informed decisions and remain competitive and relevant in this ever-changing world of commerce. This study also explores the importance of data security in AI systems which need ethical considerations and responsible practices to address the vulnerabilities.

Background: After the World Health Organization declared the COVID-19 pandemic, the role of Vitamin D has become even more critical for people worldwide. The most accurate way to define vitamin D level is 25-hydroxy vitamin D(25-OH-D) blood test. However, this blood test is not always feasible. Most data sets used in health science research usually contain highly correlated features, which is referred to as multicollinearity problem. This problem can lead to misleading results and overfitting problems in the ML training process. Therefore, the proposed study aims to determine a clinically acceptable ML model for the detection of the vitamin D status of the North Cyprus adult participants accurately, without the need to determine 25-OH-D level, taking into account the multicollinearity problem. Method: The study was conducted with 481 observations who applied voluntarily to Internal Medicine Department at NEU Hospital. The classification performance of four conventional supervised ML models, namely, Ordinal logistic regression(OLR), Elastic-net ordinal regression(ENOR), Support Vector Machine(SVM), and Random Forest (RF) was compared. The comparative analysis is performed regarding the model's sensitivity to the participant’s metabolic syndrome(MtS)'positive status, hyper-parameter tuning, sensitivities to the size of training data, and the classification performance of the models. Results: Due to the presence of multicollinearity, the findings showed that the performance of the SVM(RBF) is obviously negatively affected when the test is examined. Moreover, it can be obviously detected that RF is more robust than other models when the variations in the size of training data are examined. This experiment's result showed that the selected RF and ENOR showed better performances than the other two models when the size of training samples was reduced. Since the multicollinearity is more severe in the small samples, it can be concluded that RF and ENOR are not affected by the presence of the multicollinearity problem. The comparative analysis revealed that the RF classifier performed better and was more robust than the other proposed models in terms of accuracy (0.94), specificity (0.96), sensitivity or recall (0.94), precision (0.95), F1-score (0.95), and Cohen's kappa (0.90). Conclusion: It is evident that the RF achieved better than the SVM(RBF), ENOR, and OLR. These comparison findings will be applied to develop a Vitamin D level intelligent detection system for being used in routine clinical, biochemical tests, and lifestyle characteristics of individuals to decrease the cost and time of vitamin D level detection.

In contemporary research, high-dimensional data has become more popular in many scientific fields with the rapid advancement of technology in collecting and storing large datasets. As in any modeling process with high-dimensional data, it is very important to accurately identify a subset of the features and reduce the dimensionality in the Cox modeling process in the case of high-dimensionality. Numerous penalized techniques for the Cox model with high-dimensional data have been developed to handle the multicollinearity problem and decrease variability. Adaptive Elastic-net is one of the penalized methods used for feature selection that both handles the grouping effect and has the oracle property. However, providing these advantageous properties of Adaptive Elastic-net for variable selection in the Cox model depends on the optimal selection of hyperparameters, α, and λ values. For this reason, the appropriate selection of these parameters is quite important. Hyperparameters are generally selected by maximizing k-fold cross-validated log partial likelihood based on grid search over (α, λ) for the model. However, this method does not guarantee optimal α and λ values. In grid search, hyperparameters are typically allowed to take values specified in a limited sequence in a grid. The purpose of this study is to propose a novel method to determine the optimum hyperparameters (α, λ) pair of Adaptive Elastic-net for variable selection in the Cox model with high dimensional data based on modified particle swarm optimization (MPSO). The introduced metaheuristic-based method has been evaluated by extensive simulation studies by comparing it with different traditional penalized methods using various evaluation criteria under different scenarios. According to the comprehensive simulation study, the proposed method outperforms other penalized methods in terms of both variable selection and prediction and estimation accuracy performance for the Cox model in investigating the high-dimensional data.

Today, the interest and the needs of all sectors for digital signatures are increasing day by day. The next approvals in businesses cannot take place without the previous approvals, which follow one after the other, such as a chain of transactions. For this reason, the approval (that is, the signature of a relevant unit) should be carried out as soon as a document is received and immediately forwarded to the next unit. Digital signatures are needed to carry out business transactions quickly and reliably. The use of digital platforms and systems, which are indispensable parts of digital transformation, can be achieved primarily with awareness and understanding of their importance. However, a digital signature awareness scale was not found in the literature. Therefore, in this study, a new scale was developed which can be used to determine the digital signature awareness of people and it is hoped to eliminate this deficiency in the current literature. The developed scale consists of two dimensions: awareness and benefits. The study was designed as descriptive research and 567 participants voluntarily participated in the study. Factor analysis and descriptive analysis methods were used to analyze the collected data. In line with the statistical analysis results, it has been observed that the developed scale provides validity and reliability features and is qualified to be used in scientific research to determine the awareness of people about digital signatures. With the digital signature scale, which is thought to fill the gap in the literature, it is hoped that the digital signatures awareness of people in developing countries, and in particular in Cyprus will be determined, and it is also hoped that people will be encouraged to use digital signatures after necessary training.

This study aims to investigate the correlation between exercise participation motivation and social physique anxiety and their differences across the exercise stages of change in individuals. A convenience sampling technique was used to gather data from 374 sedentary individuals aged 18 and over, using a questionnaire that included the Behavioral Regulations in Exercise Questionnaire-2 (BREQ-2), Social Physique Anxiety (SPA) scale, and Exercise Stages of Change Questionnaire (ESOCQ). Welch’s ANOVA test was utilized to examine if there were significant differences between the average levels of behavioral regulations in exercise and SPA within exercise stages of change, respectively. Based on Welch’s ANOVA results, it has been found that there are significant differences in the total score of SPAS according to exercise stages of change (F = 15.801, p < 0.001). Furthermore, the sub-factors intrinsic regulation, introjected regulation, and external motivation statistically significantly differ according to exercise stages of change ($\mathrm{F}=6.681$, p < 0.001 for IR; $\mathrm{F}=30.186,$p < 0.001 for introjected regulation; $\mathrm{F}=10.104$, p < 0.001 for external motivation, $\mathrm{F}=0.481,$p = 0.750 for AM). Pearson’s r correlation coefficients were also computed to determine the relationship between behavioral regulations in exercise and SPA. A significant negative moderate correlation was found between intrinsic regulation and SPA (r = −0.645, p < 0.001). Furthermore, a significant medium positive correlation was found between introjected regulation and SPA (r = 0.534, p < 0.001), external motivation and SPA (r = 0.588, p < 0.001), and amotivation and SPA (r = 0.564, p < 0.001). The findings suggest that being self-motivated for exercise might decrease SPA. On the other hand, exercise motivated by external pressures could lead to increased SPA. The findings also suggest that those who started exercising with intrinsic motivation reached more advanced stages of exercise than those who started exercising with extrinsic factors. Finally, extrinsic motives may help in the first stages of the exercise, but intrinsic motives are required to continue the exercise. Consequently, these findings may guide physical activity specialists, trainers, etc., to develop more effective strategies to motivate exercise participation by considering social physique anxiety among individuals.

Greece has been affected by the Covid-19 pandemic since early 2020. The country has implemented various measures such as lockdowns and restrictions on travel and gatherings to try to slow the spread of the virus. Predicting the number of Covid-19 cases in Greece will assist in the development of mitigation strategies, economic planning, public awareness, planning and allocation of resources needed for the vaccination campaign, and also understanding the effectiveness of the campaign in reducing the spread of the virus. Historical data on Covid-19 cases in Greece, as well as relevant factors as the independent variables, can be used to train machine learning models for the prediction of Covid-19 cases. This study aims to deploy machine learning models, such as multiple linear regression (MLR) and artificial neural networks (ANN), to predict the number of Covid-19 cases in Greece. We have used the determination coefficient (R2), the mean square error (MSE), the root mean square error (RMSE), and the correlation coefficient (R) to measure the model’s performance. The determination coefficient (R2) shows that ANN is far superior to MLR when it comes to the effective prediction of Covid-19 cases in Greece. The ANN predictive model is both efficient and accurate, with an accuracy of (99%).