Learning Management System Arrangement on Virtualization Server at UCS, Kalay

presents how to consolidate and implement Learning Management System (LMS) serve

Prevalence of Depression and its Associated Factors Among Adults during Third Wave of COVID-19 Pandemic in Malaysia, 2021

Malaysia recently entered third-wave of COVID-19 pandemic starting from October 2020 to end of January 2021. Therefore, objective of our study was to identify the prevalence of depression and its associated factors among adults during third wave of COVID-19 pandemic in Malaysia. A total of 1468 Malaysian adults participated in this cross-sectional web-based survey. A standardized questionnaire was generated using the Google Form, and the link was shared through social media such as Facebook, Twitter, Instagram and WhatsApp. Patient Health Questionnaires (PHQ-9) was used to assess the levels of depression. Among 1468 participants, 320 (22 %) and 358 (24.6 %) indicated to have moderate to severe depression during third-wave of COVID-19 in Malaysia. Multiple predictors were identified that contributed to depression. These included female gender, family’s source income affected by the pandemic, do not perform exercise, victim of abuse and those with family and/or friends infected with COVID-19 virus. COVID-19 pandemic had caused the implementation of lockdown and physical distancing in Malaysia and nations across the globe. The pandemic had brought serious negative impacts on mental health of the adults especially depression especially during third wave of pandemic. The findings of our study suggested that new interventions or strategies are needed to be developed to address the severity of depression among Malaysian adults

Assessment of Post-Traumatic Stress Disorders and its Associations with Suicidal Behaviour among Adults Following Movement Control Order During COVID-19 Pandemic in Malaysia

A rapid review of previous outbreaks shows that a quarantine policy had greater effects on one’s psychological state including post-traumatic stress disorder (PTSD), confusion and anger caused by various stressors. This study aimed to assess the levels of Post-Traumatic Stress Disorder (PTSD) and its associations with suicidal behavior among Malaysian adults following Movement Control Order (MCO) during the COVID-19 pandemic in Malaysia. This cross-sectional study was distributed using an online standardized questionnaire composed of three parts, the socio-demographic characteristics, PTSD assessment using PTSD Checklist for DSM-5 and the suicidal behavior assessment using Suicidal Behaviors Questionnaire-Revised (SBQ-R). Almost half of the respondents had high PTSD symptoms (41.7 %) and low PTSD was 58.3 % among Malaysian adults. Furthermore, 69.6 % of participants had no suicidal behavior but, 30.4 % from the participants has suicidal behavior. This study found single status with highest PTSD (83.3 %) and marital status had significant correlation with PTSD which p-value was < 0.05. Malay was high percentage in high PTSD (74.6 %) and significant correlation between race and PTSD (p < 0.05). Employment status also had significant correlation with PTSD with p-value was 0.002 and students was counted highest PTSD (65.7 %). This study identified some socio-demographic factors and suicidal behavior associated with PTSD among Malaysian adults, which may lay ground for further interventions

Factors associated with psychological distress among Myanmar residents during COVID-19 pandemic crises

Background: COVID-19 pandemic reached a public health emergency status of international concern. The impacts and events associated with this were associated with adverse psychological impacts among the general public globally. This study aimed to determine the prevalence of psychological distress and to identify predictors associated with psychological distress due to the COVID-19 pandemic among the population in Myanmar. Design and Methods: A cross-sectional survey was conducted from March to April 2020 among adults, 18 years old and above, who reside in Myanmar through a structured questionnaire distributed in social media platforms. Univariate and Bivariate analyses were used to estimate the prevalence of COVID-19 Peritraumatic Distress Index (CPDI) symptoms and to test the associations between CPDI and the exposure variables. Logistic Regression Analysis was done to identify significant predictors of distress. Results: There were 530 participants in this study.37.4% of them did not have psychological distress,55.6% experienced mild to moderate psychological distress, and 7% experienced severe psychological distress due to COVID-19 pandemic. Simple and Multiple Logistic Regression Analyses were performed to determine the factors associated with psychological distress due to COVID-19. Conclusions: It was shown that the self-employed group and age group older than 45 years old had more psychological distress than others. However, Buddhists and people from the capital city had less distress than other religions and districts. This study recommends the government to develop better strategies for self-employed groups, elders, and the poor for a support, relief, and resettlement of their ruined status

Mining Multilevel Association Rulesbased on Boolean Matrix for Food Sales shop

The rapid growth in data and the number of database,there is a need for discovering valuable knowledge in large database which havebusiness data. Today, many companies which are to gain profit from their previousbusiness data are becoming interested to analysis their data for discovering usefulinformation. Because this information can support business decision making andbenefit of organizations. Data mining is approach to fill these requirements and isa machine learning technique on large data items. Association rule mining is oneof the data mining techniques. This paper discuss the multilevel assocaition rulemining from business transcation data and the Boolean Matrix based approachwhich has employed to discover frequent itemsets at different levels. The systemscans the transcation database once and then uses Boolean logical operation togenerate the multilevel association rules. The system implements the applicationwith real-life food products of Food sales Shop

Recommending Generalized Products in Collaborative Filtering

Recommender systems are particularly useful forcomputer users, here decisions must be normallytaken in a short time and the effort required forinteracting with the system must be limited as muchas possible. The Recommendation systems can helpthe user to take a decision suggesting those productswhich best suit his needs and preferences.Recommendation systems have been an importantapplication area and the focus of considerablerecent academic and commercial interest. In theclassical collaborative filtering recommendationapproach, the voting prediction method is based onthe computation of the similarity of the active user,to whom a recommendation has to be made, with theother users .Collaborative filtering (CF) describes avariety of processes that automate the interactionsof human advisors; a collaborative filterrecommends items based upon the opinions ofhuman advisors. In this paper, we implement therecommendation system are the use of Voting byCategory method in memory-based collaborativefiltering method

Spam Filtering System using Case -Based Reasoning Approach

As the vast increases of the electronic mail (email) usages continue, spam (unsolicited bulk mail) has continued to grow because of it is a very inexpensive method of advertising. These unwanted emails can cause serious problem by filling up the email box and thereby leaving no space for ham (legitimate email) to pass through. Case- based filter can adapt to filter new spam by adding new spam case to the case base. Thus, case-based spam filters are suitable for spam filtering because of the dynamic nature of spam. In this paper, a spam filtering system is implemented by using case-based reasoning approach. K-nearest neighbor algorithm is used as case retrieval and case adaption. Edited nearest neighbor rule is used as case maintenance

Web based Decision Support System for Job Selection Using Analytic Hierarchy Process

Decision Support System (DSS) provides information, models, and data manipulation tools to help make decisions in semi-structured and unstructured situations where no one knows exactly how the decision should be made [3]. DSS on the web and the Internet are being developed to support decision making, providing to various database and information pool along with software for data analysis. Computer based DSS are widely deployed in real projects. Analytic hierarchy process (AHP) is a technique to solve multicriteria decision problems. In this paper, the decision support system for job selection is implemented by using AHP approach. AHP makes comparison between criteria and comparison between alternatives and then calculate the overall ranking of the alternatives by mathematically combining the priority matrices of criteria. The system assists jobseekers to find appropriate job easily within a short time

Quantitative Association Rules Mining for Business Transactional Data

The explosive growth in data and database has generated a need for techniques and tools that can transform the processed data into useful information and knowledge that improves marketing strategy. Association rules mining is finding frequent patterns, associations, correlations, or causal structures among item sets in transaction databases, relational databases, and other information repositories. The relational tables that stored the transactions have richer attribute types such as quantitative and categorical attribute. Thus the development of tools that can extract useful information from this large database is greatly demand. This paper discusses the quantitative association rules mining from business transactional database that store the textile store. We introduce the quantitative association rules mining using with the direct application using on a real-life dataset