Shapley-Additive-Explanations-Based Factor Analysis for Dengue Severity Prediction using Machine Learning.

Dengue is a viral disease that primarily affects tropical and subtropical regions and is especially prevalent in South-East Asia. This mosquito-borne disease sometimes triggers nationwide epidemics, which results in a large number of fatalities. The development of Dengue Haemorrhagic Fever (DHF) is where most cases occur, and a large portion of them are detected among children under the age of ten, with severe conditions often progressing to a critical state known as Dengue Shock Syndrome (DSS). In this study, we analysed two separate datasets from two different countries- Vietnam and Bangladesh, which we referred as VDengu and BDengue, respectively. For the VDengu dataset, as it was structured, supervised learning models were effective for predictive analysis, among which, the decision tree classifier XGBoost in particular produced the best outcome. Furthermore, Shapley Additive Explanation (SHAP) was used over the XGBoost model to assess the significance of individual attributes of the dataset. Among the significant attributes, we applied the SHAP dependence plot to identify the range for each attribute against the number of DHF or DSS cases. In parallel, the dataset from Bangladesh was unstructured; therefore, we applied an unsupervised learning technique, i.e., hierarchical clustering, to find clusters of vital blood components of the patients according to their complete blood count reports. The clusters were further analysed to find the attributes in the dataset that led to DSS or DHF.

Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh.

The rapid expansion of a country's economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Additional staff are also required to follow the delivery vehicle while it transports documents or records to another destination. This study proposes Delicar, a self-driving product delivery vehicle that can drive the vehicle on the road and report the current geographical location to the authority in real-time through a map. The equipped camera module captures the road image and transfers it to the computer via socket server programming. The raspberry pi sends the camera image and waits for the steering angle value. The image is fed to the pre-trained deep learning model that predicts the steering angle regarding that situation. Then the steering angle value is passed to the raspberry pi that directs the L298 motor driver which direction the wheel should follow. Based upon this direction, L298 decides either forward or left or right or backwards movement. The 3-cell 12V LiPo battery handles the power supply to the raspberry pi and L298 motor driver. A buck converter regulates a 5V 3A power supply to the raspberry pi to be working. Nvidia CNN architecture has been followed, containing nine layers including five convolution layers and three dense layers to develop the steering angle predictive model. Geoip2 (a python library) retrieves the longitude and latitude from the equipped system's IP address to report the live geographical position to the authorities. After that, Folium is used to depict the geographical location. Moreover, the system's infrastructure is far too low-cost and easy to install.

Critical Factors Influencing Decision to Adopt Human Resource Information System (HRIS) in Hospitals.

The aim of this research is to explore factors influencing the management decisions to adopt human resource information system (HRIS) in the hospital industry of Bangladesh-an emerging developing country. To understand this issue, this paper integrates two prominent adoption theories-Human-Organization-Technology fit (HOT-fit) model and Technology-Organization-Environment (TOE) framework. Thirteen factors under four dimensions were investigated to explore their influence on HRIS adoption decisions in hospitals. Employing non-probability sampling method, a total of 550 copies of structured questionnaires were distributed among HR executives of 92 private hospitals in Bangladesh. Among the respondents, usable questionnaires were 383 that suggesting a valid response rate of 69.63%. We classify the sample into 3 core groups based on the HRIS initial implementation, namely adopters, prospectors, and laggards. The obtained results specify 5 most critical factors i.e. IT infrastructure, top management support, IT capabilities of staff, perceived cost, and competitive pressure. Moreover, the most significant dimension is technological dimension followed by organisational, human, and environmental among the proposed 4 dimensions. Lastly, the study found existence of significant differences in all factors across different adopting groups. The study results also expose constructive proposals to researchers, hospitals, and the government to enhance the likelihood of adopting HRIS. The present study has important implications in understanding HRIS implementation in developing countries.

Job satisfaction and intention to quit: an empirical analysis of nurses in Turkey.

The aim of this study was to identify the facets influencing job satisfaction and intention to quit of nurses employed in Turkey. Using a non-probability sampling technique, 417 nurses from six large private hospitals were surveyed from March 2014 to June 2014. The nurses' demographic data, their job-related satisfaction and turnover intentions were recorded through a self-administered questionnaire. In this study, descriptive and bivariate analyses were used to explore data, and multivariate analysis was performed using logistic regression. Nurses' job satisfaction was found at a moderate level with 61% of the nurses intended to quit. Nevertheless, nurses reported a high satisfaction level with work environment, supervisor support, and co-workers among the selected nine facets of job satisfaction. They also reported a low satisfaction level with contingent reward, fringe benefits, and pay. The impact of demographic characteristics on job satisfaction and intention to quit was also examined. The study revealed a negative relationship between job satisfaction and intention to quit the existing employment. Moreover, satisfaction with supervisor support was the only facet that significantly explained turnover intent when controlling for gender, age, marital status, education, and experience. The implications for nurse management were also described for increasing nurses' job satisfaction and retention. This study is beneficial for hospital management to ensure proper nursing care that would lead to a better quality healthcare service.

Domestic Banks in Bangladesh Could Ensure Efficiency by Improving Human Resource Management Practices.

The paper aims to examine the influence of human resource management (HRM) practices on bank efficiency using Malmquist index of total factor productivity. The model comprises HRM index that represents the quality of HRM practices. The results are decomposed into three efficiency scores, namely, technical efficiency, pure efficiency, and scale efficiency. In this study, panel data for 44 banks in Bangladesh are used for the period 2008-2013. This paper reveals that foreign banks are ahead in converting the influence of HRM practices into efficiency scores (0.946>0.833). On the other hand, domestic banks performed better than foreign banks in terms of pure efficiency and scale efficiency. But, in terms of technical efficiency, the domestic banks are regressed by 6.7% annually whereas foreign banks are progressed with a yearly value of 5.8%. The results are robust, because the Mann-Whitney test and Kruskall-Wallis test (non-parametric tests) also confirm the same results. This study emphasizes HRM practices in the banking industry to ensure efficiency in the long-term scenario. Domestic banks are suggested to ensure continuous development in HRM practices in order to compete with foreign banks.

Determinants of academics' job satisfaction: empirical evidence from private universities in Bangladesh.

The job satisfaction of academics is related to a number of variables of complex function such as demographic characters, the work itself, pay, work responsibilities, variety of tasks, promotional opportunities, relationship with co-workers and others. Academics may be simultaneously satisfied with some facets of the job and dissatisfied with others. This paper aims at determining the influential factors that contribute to the enhancement or reduction of academics' job satisfaction among private universities in Bangladesh with special reference to Dhaka, the capital city of Bangladesh. A total of 346 respondents are considered from ten private universities using non-probability sampling. A pre-tested and closed-ended questionnaire using a seven-point Likert scale is used for data collection. In this study, descriptive statistics, Pearson product moment correlation, multiple regression, and factor analysis are exercised as statistical tools. A conceptual model of job satisfaction is developed and applied for academics' job satisfaction. The results reveal that compensation package, supervisory support, job security, training and development opportunities, team cohesion, career growth, working conditions, and organizational culture and policies are positively associated with the academics' job satisfaction. Amongst them, three factors stood out as significant contributors for job satisfaction of academics i.e. compensation package, job security, and working conditions. Therefore, the management of private universities should focus their effort on these areas of human resource management for maintaining academics' job satisfaction and employee retention. The study will be useful for university management in improving overall job satisfaction as it suggests some strategies for employee satisfaction practices.