With the things that go towards the efficiency of ICT usage in HRM, measuring and evaluating have become one of the key goals for researchers and practitioners. When it is related to crisis moments like the COVID-19 pandemic whereas considering the performance of organizations it becomes practically important to examine the level of performance efficiency in HRM. Therefore, in this study, some aspects are explored to know the efficiency of HRM performance with the application of Information and Communication Technology (ICT) in its functions maintaining social distance during the COVID-19 pandemic. While using the regression model, people management, regular HR activities, HR payroll and rewards, and performance evaluation and promotion are taken as independent variables while the HR performance efficiency is taken as a dependent variable. Correlation and regression analyses were conducted to examine the efficiency of HR functions during the pandemic following principal component analysis. The use of ICT assures Human resource management performance efficiency without taking face-to-face interaction during the pandemic. Further, the findings will help practitioners to adopt ICT in the HRM perspective of all organizations and to make the decision about how much modification needs to be done to maximize overall efficiency within the limited resources during any pandemic.

The core goal of the study is to examine the relationship between the service marketing mix and tourist satisfaction. The study also attempted to measures the impact of each element of service marketing on tourist satisfaction at Ahsan Manzil in Bangladesh. In order to attain the goal of the research, a good number of extant literature was reviewed and a structured questionnaire was developed to meet the research gap. Based on the studied variables non probabilistic convenience sampling method used to collect data from a sample of 250 respondents who visited the place and seven causal hypothesize was developed. Statistical measurement techniques employed for the study are descriptive, correlation, regression, ANOVA used and cronbach alpha measured the internal consistency of variables. Data analysis executed by using SPSS 20.0. The findings of the study revealed a positive linear relationship of all variables with tourist satisfaction except promotional activities. The novelty of the paper is that it exhibited the consequences of tourists’ satisfaction and dissatisfaction to guide decision-makers and to keep the specific focus on promotional activities.

The aim of this study is to capture the impact of different dimensions of services of mobile banking on customer satisfaction for the mobile banking users for rural areas of Bangladesh during the COVID-19 pandemic times. The study also finds out the affiliation between the customer satisfaction and loyalty of different types of mobile banking users during the pandemic times. The researchers designed a self-complete questionnaire that was used for data collection and received 180 questionnaires out of 250 questionnaires. This research conducted on the rural people in Bangladesh who are availing the service of mobile banking during the pandemic situation and for this reason, the results may not applicable to other times as well as other areas. The finding of the study indicated that reliability, responsiveness and efficiency dimensions of mobile banking service have significant influence on customer satisfaction during the COVID-19 lockdown times.

Diabetes is a disease that affects how your body processes blood sugar and is often referred to as diabetes mellitus. Insulin insufficiency and ineffective insulin use coincide when the pancreas cannot produce enough insulin or the human body cannot use the insulin that is produced. Insulin is a hormone produced by the pancreas that helps in the transport of glucose from food into cells for use as energy. The common effect of uncontrolled diabetes is hyper-glycemia, or high blood sugar, which plus other health concerns, raises serious health issues, majorly towards the nerves and blood vessels. According to 2014 statistics, people aged 18 or older had diabetes and, according to 2019 statistics, diabetes alone caused 1.5 million deaths. However, because of the rapid growth of machine learning(ML) and deep learning (DL) classification algorithms. indifferent sectors, like health science, it is now remarkably easy to detect diabetes in its early stages. In this experiment, we have conducted a comparative analysis of several ML and DL techniques for early diabetes disease prediction. Additionally, we used a diabetes dataset from the UCI repository that has 17 attributes, including class, and evaluated the performance of all proposed machine learning and deep learning classification algorithms using a variety of performance metrics. According to our experiments, the XGBoost classifier outperformed the rest of the algorithms by approximately 100.0%, while the rest of the algorithms were over 90.0% accurate.

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