Human migration is based on pull factors that individuals evaluate when it comes to moving to a different territory. Likewise, employee attrition is a phenomenon that represents the tendency to a reduction in employees within an organization. This research paper aims to develop and evaluate machine learning algorithms, namely Decision Tree, Random Forest, and Binary Logistic Regression, to predict employee attrition using the IBM dataset available on Kaggle. The objective is to provide organizations with a proactive approach to employee retention and human resource management by creating accurate predictive models. Employee attrition has significant implications for an organization's reputation, profitability, and overall structure. By accurately predicting employee attrition, organizations can identify the factors contributing to it and implement data-driven human resources management practices. This study contributes to improving decision-making processes, including hiring and firing decisions, and ultimately enhances an organization's capital. The IBM dataset used in this study consists of anonymized employee records and their employment outcomes. It provides a comprehensive HR data representation for analysis and prediction. Three machine learning algorithms, Decision Tree, Random Forest, and Binary Logistic Regression, were utilized in this research. These algorithms were selected for their potential to improve accuracy in predicting employee attrition. The Logistic Regression model yielded the highest accuracy of 87.44% among the tested algorithms. By leveraging this study's findings, organizations can develop predictive models to identify factors contributing to employee attrition. These insights can inform strategic decisions and optimize human resource management practices.

Diversity, equity, and inclusion (DEI) are crucial for commercial success, fostering innovation, fresh ideas, and mutual respect. Prioritizing DEI is vital for businesses to connect with a broader customer base. A study examined how effective diversity management influences a brand's diverse customer base and DEI policies. It explored the impact of DEI culture, workforce, evaluation practices, product issue resolution, and diversity partnerships on customers. Multinational entities surveyed, with 176 online HR and marketing experts participating. Likert scales, gauged responses, and structured equation modeling analyzed data. The study found that embedding DEI into company culture, maintaining diverse personnel, monitoring DEI performance, addressing DEI challenges with products, and engaging diverse partners positively affect a varied customer base. The research emphasizes that multicultural team leaders must prioritize diversity management for successful implementation, providing a theoretical and statistical framework for effective DEI policies across diverse clienteles.

Social media usage has increased tremendously in recent years. However, when users cannot control their social media usage, it might have some negative impacts on personal and social life, which lead to the cyberloafing phenomenon. This study aims to examine the influence of cyberloafing activities (sharing, shopping, gaming, accessing online content, real-time updating) and social media addiction among netizens. This study utilized Uses and Gratification Theory (U&G) as a theoretical basis to explain the framework. The quantitative method was implemented in this study. An online survey questionnaire was used to collect data and 318 valid respondents were generated. Partial Least Square Structural Equation Modelling via Smart-PLS was used to analyze the data. The study showed that two cyberloafing activities, namely real-time updating and sharing significantly impact social media addiction. However, the other cyberloafing activities (accessing online content, gaming, shopping) do not contribute to social media addiction. This study may help students and employees to be cognizant of the symptoms of cyberloafing and social media addiction. In addition, it also helps government agencies such as Malaysian Communication and Multimedia Commission (MCMC) to produce strategies that can address addiction among netizens and youths. Conclusion, implications, and future research directions were discussed.

Diabetes Mellitus (DM) is a frequent condition in which the body's sugar levels are abnormally high for an extended length of time. It is a major cause of death with high mortality rates and the second leading cause of total years lived with disability worldwide. Its seriousness comes from its long-term complications, including nephropathy, retinopathy, and neuropathy leading to kidney failure, poor vision and blindness, and peripheral sensory loss, respectively. Such conditions are life-threatening and affect patients’ quality of life. Therefore, this paper aims to identify the most relevant features in the diagnosis of DM and identify the best classifier that can efficiently diagnose DM based on a set of relevant features. To achieve this, four different feature selection methods have been utilized. Moreover, twelve different classifiers that belong to six learning strategies have been evaluated using two datasets and several evaluation metrics such as Accuracy, Precision, Recall, F1-measure, and ROC area. The obtained results revealed that the correlation attribute evaluation method would be the best choice to handle the task of feature selection and ranking for the considered datasets, especially when considering the Accuracy metric. Furthermore, MultiClassClassifier would be the best classifier to handle Diabetes datasets, especially when considering True Positive, precision, and Recall metrics.

This work investigated and predicted the fatigue life durability of Austenitic Stainless Steel 316L due to its importance in plant industries worldwide. Modelling and simulations were performed to clarify the fracture as well as stress distribution using integrated mechanism. Experimental fatigue validations were also carried out to demonstrate the effect of various fatigue life parameters. Various loading conditions with variable load amplitudes were validated utilizing a frequency of 5 Hz and a stress ratio of 0.1. The accuracy of the simulation results were also verified based on the experimental data. High consistencies between the predicted fatigue life and the experimental results were achieved which increases the validity of the built model.

One of the primary concerns in project development is to detect all sorts of risks associated with a particular project. The main objective of this article is to identify the risks in the construction project and to grade them based on their importance on the project. The designed indicator in this paper is the combinational model of the Analytical Hierarchal Process (AHP) method and the group decision – making applied for risks measurement and ranking. This indicator is called "R" which includes three main steps: creating the risks broken structure (RBS), obtaining each risk weight and efficacy, and finally performing the model to rank the risks. A questionnaire is used for gathering data. Based on the results of this survey, there are important risks associated with construction projects. There we need to use some guidelines to reduce the inherent risks including recognition of the common risks beside the political risks; suggestion of a simple, understandable, and practical model; and using plenty of the experts and specialists & apos; opinions through applying step. After analyzing data, the final result from applying R index showed that the risk “economic changes / currency rate and inflation change" has the most importance for the analysis. In the other words, if these risks occur, the project may face with the more threats and it is suggested that an organization should centralize its equipment, personnel, cost, and time on the risk more than ever. The most obvious issue in this paper is a tremendous difference between an importance of the financial risks and the other risks.

Portfolio optimization problem follows the calculation of investment income per share, based on return and risk criteria. Since stock risk is achieved by calculating its return, which is itself computed based on stock price, it is essential to forecast the stock price, efficiently. In this paper, in order to predict the stock price, grey fuzzy technique with high efficiency is employed. The proposed study of this paper calculates the return and risk of each asset and portfolio optimization model is developed based on cardinality constraint and investment income per share. To solve the resulted model, Invasive Weed Optimization (IWO) algorithm is applied. In an example this algorithm is compared with other metaheuristic algorithms such as Imperialist Competitive Algorithm (ICA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The results show that the applied algorithm performs significantly better than other algorithms.

Liver disease indicates inflammatory condition of the liver, liver cirrhosis, cancer, or an overload of toxic substances. A liver transplant may reinstate and extend life if a patient has severe liver disease. In the last few years, machine learning (ML) based diagnosis systems have played a vital role in assessing liver patients which eventually leads to proper treatment and saves human life. In this study, we try to predict liver patients by adopting a hybrid feature extraction method to enhance the performance of the ML algorithm. Medical data frequently exhibits non-linear patterns and class imbalances. This is undesirable for the majority of ML algorithms and degrades performance. Here, we present a hybrid feature space that combines t-SNE, Isomap nonlinear features, and kernel principal components that can explain 90% of the variation in the data as a solution to this issue. Before feeding the ML model, data preprocessing techniques including class balancing, identifying outliers, and impute missing values are used. A simulation study and ensemble learning also conducted to justify the proposed prediction performances. Our suggested hybrid non-linear feature exhibits a 2-20 % improvement over existing studies and the ensemble classifier achieved an ideal and outstanding accuracy of 91.33 %.