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Analysis of monitoring on the Well-being and Security of Employees https://doi.org/10.56294/dm2024422
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The high accident rate in the construction industry has a major impact on how well projects turn out. Despite substantial investments in safety planning and supervision, there has been a marked increase in the construction industry's accident rate compared to other sectors. Serious games based on VR have recently been used in the study, suggesting...
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... this proposed method the cost benefit analysis ratio is increased by 98,33 %. Shlash Mohammad AA, et al Improving safety outcomes for construction workers may be achieved in figure 8 and equation (15) via the use of DT and AI technology to monitor their emotional, mental, and physical health in real-time. Because it continuously measures pulse, blood pressure and levels of stress and fatigue, the device may identify potential health issues before they lead to accidents. ...
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... All constructs had AVE values above 0.50, with the highest for digital manufacturing (0.667) and the lowest for organizational capabilities (0.539), demonstrating strong convergence. Discriminant validity results ensured that constructs capture distinct concepts using the comparison approach (Shlash et al., 2024). AVE values need to be greater than MSV values for the identical construct. ...
Amidst a period characterized by the restructuring of global supply chains via digitalization, its implications on sustainability have become a necessity, especially in developing economies. This research investigates the contribution of the digital supply chain to sustainable performance, with big data analytics capacity as a moderating factor, in the Jordanian industrial sector. Based on a comprehensive dataset collected from 308 managers spanning various industrial sectors, the research offers empirical evidence on how digitalization reshapes industrial sustainability. The results indicate that the overall level of digital supply chain adoption in Jordanian industries is moderate. Similarly, big data analytics capability revealed a positive significant effect on sustainable performance, with human capability being the most essential. Nevertheless, the moderating effect of big data analytics capability was selective; it effectively improved the connection between digital logistics and sustainable performance, but did not have any notable effect on digital suppliers or digital manufacturing. This study gives a holistic model for industrial companies to deal with the dynamics of contemporary supply chains by incorporating digital transformation, big data, and sustainability, thereby resulting in environmental, social, and economic sustainability in the long run.
... For instance, these individuals invest more attention and mental energy in uncertain conditions and the matters being concerned about [30]. Since they are never certain that their solutions are correct, they are constantly worried, and worry is an ongoing process that is both intrusive and repetitive [31,32]. ...
... For instance, if managers want to prevent creating similar portfolios, they must identify investors' preferences for risk, according to Diener, et al. [45]. Shlash Mohammad, et al. [31] argued that the best portfolios can be developed by dividing investors into categories depending on their risk profile, with high-risk-taking investors being placed in high-risk portfolios and low-risk takers being placed in low-risk portfolios. ...
This study investigates the moderating effect of individual differences, as measured by the intolerance of uncertainty scale (IUS), on investors' portfolio allocation decisions among assets with varying risk levels. The securities varied from safer government securities to riskier assets. We manipulate the investment risk in each security by varying the market risk, also known as beta, the standard deviation of the return, and the expected return. The findings show that less risk-tolerant individuals invested less capital in riskier stocks and more money in safer government securities. However, those with more risk tolerance allocated more money to the less hazardous investments. As anticipated, those with less risk tolerance allocated more capital to the more secure stocks.
... Blockchain technology, with its decentralized and immutable ledger systems, has proven highly effective in enhancing transparency and trust within supply chains. Studies such as Kshetri (13) and Mohammad et al. (14) emphasize its ability to address the challenges of fragmented metadata management and improve data accuracy. https://doi.org/10.56294/dm2025683 ...
Sustainability in food supply chains is a critical global challenge, particularly in resource-constrained regions like Jordan, where operational inefficiencies and environmental concerns are prevalent. This study explores the integration of blockchain and artificial intelligence (AI) technologies to enhance metadata management, forecast sustainability metrics, and support decision-making in Jordan’s food supply chains. Blockchain's ability to improve metadata accuracy, standardization, and traceability, combined with AI’s predictive capabilities, offers a powerful solution for addressing sustainability challenges.Methods
The research employed a mixed-methods approach, combining real-time data from blockchain transaction logs, AI-generated forecasts, and stakeholder surveys. Blockchain data from platforms like Hyperledger Fabric and Ethereum provided insights into metadata accuracy and traceability. AI models were developed using machine learning techniques, such as linear regression, to forecast food waste reduction, carbon footprint reduction, and energy efficiency. Multi-Criteria Decision Analysis (MCDA), using AHP and TOPSIS, was applied to evaluate trade-offs among sustainability goals.ResultsThe results revealed significant improvements in metadata accuracy (from 83% to 96.66%) and reductions in traceability time (from 4.0 to 2.35 hours) following blockchain implementation. AI models demonstrated high predictive accuracy, explaining 88%, 81%, and 76% of the variance in food waste reduction, carbon footprint reduction, and energy efficiency, respectively. Conclusion
This study underscores the transformative potential of blockchain and AI technologies in achieving sustainability goals. By fostering transparency, predictive insights, and data-driven decision-making, these innovations can address key challenges in Jordan’s food supply chains, offering actionable strategies for stakeholders.
... Big data analytics has fundamentally changed the way organizations process consumer interaction data, which originates from various touchpoints such as social media platforms, e-commerce websites, and clickstream logs. (1,8,9) This interaction data is often complex, consisting of structured, semi-structured, and unstructured formats. The ability to process and analyse such data provides businesses with insights into consumer preferences, behaviours, and trends. ...
IntroductionBig data analytics and machine learning have transformed digital marketing by enabling data-driven insights for personalization. This study investigates the role of engagement metrics, sentiment analysis, and consumer segmentation in enhancing marketing effectiveness. Specifically, it examines how these technologies process consumer interaction data to uncover actionable insights, segment audiences, and drive purchase conversions.Method
The study employed a mixed-methods approach, integrating big data analytics and machine learning techniques. Descriptive statistics highlighted engagement patterns, while k-means clustering segmented consumers based on behavioural and emotional data. Sentiment analysis, conducted using Natural Language Processing (NLP), captured consumer emotions as positive, neutral, or negative. Regression analysis evaluated the influence of social media activity, click-through rates, session duration, and sentiment scores on purchase conversion rates.ResultsDescriptive analysis revealed significant variability in consumer engagement and sentiment, with 37.5% of consumers expressing positive sentiment. Clustering identified three distinct consumer segments, reflecting differences in engagement and sentiment. Regression analysis showed that sentiment had a positive but statistically insignificant relationship with purchase conversions, while other metrics, such as click-through rates and session duration, exhibited minimal impact. The overall explanatory power of the regression model was low (R-squared = 0.001), indicating the need for additional factors to understand purchase behaviour.Conclusion
The findings emphasize the potential of big data analytics and machine learning in consumer segmentation and sentiment analysis. However, their direct impact on purchase conversion is limited without integrating broader variables. A holistic, adaptive framework combining behavioural, emotional, and contextual insights is essential for maximizing marketing personalization and driving outcomes in dynamic digital environments.
... Moreover, machine learning based tactics provide proactive threat detection, automated incident handling, and flexible security protocols in dynamic cloud environments, reducing the mean time to detect security breaches and enhancing overall security effectiveness. (52,53,54,55,56,57,58) Compared to other research on cloud security attack detection as mentioned in table 10, our study adopts a more detailed strategy by segmenting the dataset into three groups to do a thorough examination of the most significant characteristics. Weconducted another experiment using all features without any categorization for features and it shows great accuracy ratestypically 99,9 %. ...
Introduction: Cloud computing is considered a remarkable paradigm shift in Information Technology (IT), offering scalable and virtualized resources to end users at a low cost in terms of infrastructure and maintenance. These resources offer an exceptional degree of flexibility and adhere to established standards, formats, and networking protocols while being managed by several management entities. However, the existence of flaws and vulnerabilities in underlying technology and outdated protocols opens the door for malicious network attacks.Methods: This study addresses these vulnerabilities by introducing a method for classifying attacks in Infrastructure as a Service (IaaS) cloud environments, utilizing machine learning methodologies within a digital forensics framework. Various machine learning algorithms are employed to automatically identify and categorize cyber-attacks based on metrics related to process performance. The dataset is divided into three distinct categories—CPU usage, memory usage, and disk usage—to assess each category’s impact on the detection of attacks within cloud computing systems.Results: Decision Tree and Neural Network models are recommended for analyzing disk-related features due to their superior performance in detecting attacks with an accuracy of 90% and 87.9%, respectively. Neural Network is deemed more suitable for identifying CPU behavior, achieving an accuracy of 86.2%. For memory-related features, K-Nearest Neighbor (KNN) demonstrates the best False Negative Rate (FNR) value of 1.8%.Discussion: Our study highlights the significance of customizing the selection of classifiers based on the specific system feature and the intended focus of detection. By tailoring machine learning models to particular system features, the detection of malicious activities in IaaS cloud environments can be enhanced, offering practical insights into effective attack classification.
Introduction: Employee attrition poses significant challenges for organizations, impacting productivity and profitability. This study explores attrition patterns using machine learning models, integrating predictive analytics with established human resource theories to identify key drivers of workforce turnover. Methods: The research analysed a dataset comprising demographic, job-related, and engagement factors. Logistic Regression was employed as the baseline model to interpret linear relationships, while Random Forest and Decision Trees captured non-linear interactions. Performance metrics such as accuracy, precision, recall, F1-score, and AUC-ROC were used to evaluate model effectiveness, alongside feature importance analysis for actionable insights. Results: Results revealed that job satisfaction, tenure, departmental dynamics, and engagement levels are critical predictors of attrition. Random Forest emerged as the most effective model, achieving an accuracy of 92% and an AUC-ROC of 94%, highlighting its capability to capture complex patterns. Decision Trees provided interpretable decision rules, offering practical thresholds for HR interventions. Logistic Regression complemented these models by offering insights into direct, linear relationships between predictors and attrition. Conclusion: The study finds that machine learning improves attrition analysis by identifying complex patterns and enabling proactive retention strategies. Predictive analytics strengthens traditional theories, providing a structured approach to reducing employee turnover. Organizations can use these insights to enhance workforce stability and performance. Future research could incorporate qualitative methods and longitudinal studies to refine strategies and assess long-term impacts.
