Factors affecting Happiness of Expatriate Academicians and Expatriate Non-Academicians in Dubai.

The research measures the levels of happiness of expatriate academicians in selected Dubai universities and compares them with happiness levels of non-academic people. A face to face interview followed by a cross-sectional survey was used as a method to collect data from teaching staff from different universities in Dubai and also from the random public working in different business sectors in Dubai.? Happiness is significanlty related to the other factors. There is no significant relationship between knowledge sharing and happiness of academics and well as non-academics. The sample size of the academic group and the study targeted at the population of Dubai city only. The discoveries of the research give helpful recommendations to the administration of Universities to provide better knowledge sharing opportunities among their teaching faculty to improve their happiness levels. It will also provide recommendations for a developmental purpose to University of Dubai and UAE’s “Happiness and Positivity program”. There is no known research that studies the determinants of happiness for academics with non-academics in the UAE. Keywords: Happiness, Expatriate Academics, Job Satisfaction, Life Satisfactio

IMPROVING CORONARY HEART DISEASE PREDICTION BY OUTLIER ELIMINATION

Nowadays, heart disease is the major cause of deaths globally. According to a survey conducted by the World Health Organization, almost 18 million people die of heart diseases (or cardiovascular diseases) every day. So, there should be a system for early detection and prevention of heart disease. Detection of heart disease mostly depends on the huge pathological and clinical data that is quite complex. So, researchers and other medical professionals are showing keen interest in accurate prediction of heart disease.  Heart disease is a general term for a large number of medical conditions related to heart and one of them is the coronary heart disease (CHD). Coronary heart disease is caused by the amassing of plaque on the artery walls. In this paper, various machine learning base and ensemble classifiers have been applied on heart disease dataset for efficient prediction of coronary heart disease. Various machine learning classifiers that have been employed include k-nearest neighbor, multilayer perceptron, multinomial naïve bayes, logistic regression, decision tree, random forest and support vector machine classifiers. Ensemble classifiers that have been used include majority voting, weighted average, bagging and boosting classifiers. The dataset used in this study is obtained from the Framingham Heart Study which is a long-term, ongoing cardiovascular study of people from the Framingham city in Massachusetts, USA. To evaluate the performance of the classifiers, various evaluation metrics including accuracy, precision, recall and f1 score have been used. According to our results, the best accuracy was achieved by logistic regression, random forest, majority voting, weighted average and bagging classifiers but the highest accuracy among these was achieved using weighted average ensemble classifier.&nbsp

Factors affecting Happiness of Expatriate Academicians and Expatriate Non-Academicians in Dubai

Purpose: To determine the antecedents of happiness and compare academicians and non-academicians in selected Dubai Universities. Design/methodological/approach: Qualitative research using in-depth interviews followed by cross-sectional surveys of teaching staff and non-teaching staff from different universities in Dubai.?Findings: There is no significant relationship between knowledge sharing and happiness of academics and well as non-academics. Happiness is significantly related to the other factors.Research implications and limitations: The small sample size of the academic group and the study was targeted at the university staff in Dubai only. Practical implications: The findings of this research gives useful recommendations to Universities to improve happiness among their academic as well as non-academic staff. It will also provide recommendations for developmental purposes for the University of Dubai and the UAE’s “Happiness and Positivity program.”Originality/value: No known research studies the determinants of happiness for academics and non-academics in Dubai Universities.Paper type: Research pape

Use of AI applications for the drone industry

The unmanned aerial vehicle (UAV) industry, commonly referred to as the drone industry, has grown rapidly in recent years and changed many industries' operational procedures. Drones are adaptable AUs that have the ability to operate independently or remotely. The drone business has developed into a vibrant, diverse sector with applications in many other industries. Drone technology is set to grow and become more integrated into daily life and corporate operations as long as regulations keep up with technological advancements. Artificial intelligence (AI) technologies are increasingly used in various industries, notably drone companies. AI can improve drone technology's effectiveness, dependability, and efficiency, creating new opportunities for the drone industry to service multiple applications and sectors

HOW MACHINE LEARNING ALGORITHMS ARE USED IN METEOROLOGICAL DATA CLASSIFICATION: A COMPARATIVE APPROACH BETWEEN DT, LMT, M5-MT, GRADIENT BOOSTING AND GWLM-NARX MODELS

Rainfall prediction is one of the most challenging task faced by researchers over the years. Many machine learning and AI based algorithms have been implemented on different datasets for better prediction purposes, but there is not a single solution which perfectly predicts the rainfall. Accurate prediction still remains a question to researchers. We offer a machine learning-based comparison evaluation of rainfall models for Kashmir province. Both local geographic features and the time horizon has influence on weather forecasting. Decision trees, Logistic Model Trees (LMT), and M5 model trees are examples of predictive models based on algorithms. GWLM-NARX, Gradient Boosting, and other techniques were investigated. Weather predictors measured from three major meteorological stations in the Kashmir area of the UT of J&K, India, were utilized in the models. We compared the proposed models based on their accuracy, kappa, interpretability, and other statistics, as well as the significance of the predictors utilized. On the original dataset, the DT model delivers an accuracy of 80.12 percent, followed by the LMT and Gradient boosting models, which produce accuracy of 87.23 percent and 87.51 percent, respectively. Furthermore, when continuous data was used in the M5-MT and GWLM-NARX models, the NARX model performed better, with mean squared error (MSE) and regression value (R) predictions of 3.12 percent and 0.9899 percent in training, 0.144 percent and 0.9936 percent in validation, and 0.311 percent and 0.9988 percent in testing

RELATIONSHIP BETWEEN DIFFERENT ALLOGAMIC ASSOCIATED TRAIT CHARACTERISTICS OF THE FIVE NEWLY DEVELOPED CYTOPLASMIC MALE STERILE (CMS) LINES IN RICE

Five suitable maintainer varieties were identifi ed through testcrosses with IR58025A and the transfer of wild abortive cytoplasm was carried out by seven successive backcrosses. Five new CMS lines were developed by this approach in well adapted high yielding improved varietal background such as ‘Nemat’, ‘Neda’, ‘Dasht’, ‘Amol3’ and ‘Champa’. Agronomical characterization and allogamy-associated traits of the fi ve newly developed CMS lines were studied for their interrelationship. Anther length had a signifi cant positive correlation with the duration of glume opening (0.759) and high correlation of (0.698) with the angle between lemma and palea. The results indicated that ‘Nemat’ A, ‘Neda’ A, ‘Dasht’ A are more suitable as parents for hybrid seed production due to their favorable and superior fl oral characteristics in comparison to IR58025A

Up-regulation of microRNA-21 correlates with lower kidney cancer survival.

BackgroundMicroRNA-21 is up-regulated in a variety of cancers like, breast, colorectal, lung, head and neck etc. However, the regulation of miR-21 in renal cell carcinoma (RCC) has not yet been studied systematically.Methods and resultsWe measured miR-21 levels in 54 pairs of kidney cancers and their normal matched tissues by real-time PCR. The expression level of miR-21 was correlated with 5 year survival and the pathological stage. Functional studies were done after inhibiting miR-21 in RCC cell lines. We studied in vitro and in vivo effects of the chemo preventive agent genistein on miR-21 expression. In 48 cases (90%), miR-21 was increased. All patients with low miR-21 expression survived 5 years, while with high miR-21 expression, only 50% survived. Higher expression of miR-21 is associated with an increase in the stage of renal cancer. Functional studies after inhibiting miRNA-21 in RCC cell lines show cell cycle arrest, induction of apoptosis and reduced invasive and migratory capabilities. Western blot analysis showed an increase in the expression of p21 and p38 MAP kinase genes and a reduction in cyclin E2. Genistein inhibited the expression of miR-21 in A-498 cells and in the tumors formed after injecting genistein treated A-498 cells in nude mice besides inhibiting tumor formation.ConclusionsThe current study shows a clear correlation between miR-21 expression and clinical characteristics of renal cancer. Thus we believe that miR-21 can be used as a tumor marker and its inhibition may prove to be useful in controlling cancers with up-regulated miR-21

Optimizing Cardiovascular Disease Prediction: A Synergistic Approach of Grey Wolf Levenberg Model and Neural Networks

Background: One of the latest issues in predicting cardiovascular disease is the limited performance of current risk prediction models. Although several models have been developed, they often fail to identify a significant proportion of individuals who go on to develop the disease. This highlights the need for more accurate and personalized prediction models. Objective: This study aims to investigate the effectiveness of the Grey Wolf Levenberg Model and Neural Networks in predicting cardiovascular diseases. The objective is to identify a synergistic approach that can improve the accuracy of predictions. Through this research, the authors seek to contribute to the development of better tools for early detection and prevention of cardiovascular diseases. Methods: The study used a quantitative approach to develop and validate the GWLM_NARX model for predicting cardiovascular disease risk. The approach involved collecting and analyzing a large dataset of clinical and demographic variables. The performance of the model was then evaluated using various metrics such as accuracy, sensitivity, and specificity. Results: the study found that the GWLM_NARX model has shown promising results in predicting cardiovascular disease. The model was found to outperform other conventional methods, with an accuracy of over 90%. The synergistic approach of Grey Wolf Levenberg Model and Neural Networks has proved to be effective in predicting cardiovascular disease with high accuracy. Conclusion: The use of the Grey Wolf Levenberg-Marquardt Neural Network Autoregressive model (GWLM-NARX) in conjunction with traditional learning algorithms, as well as advanced machine learning tools, resulted in a more accurate and effective prediction model for cardiovascular disease. The study demonstrates the potential of machine learning techniques to improve diagnosis and treatment of heart disorders. However, further research is needed to improve the scalability and accuracy of these prediction systems, given the complexity of the data associated with cardiac illness. Keywords: Cardiovascular data, Clinical data., Decision tree, GWLM-NARX, Linear model function

Sleep deprivation and its associated factors among general ward patients at a tertiary care hospital in Pakistan

Objective: To estimate the occurrence rate of sleep deprivation and to identify the environmental, staff-related and patient-related factors associated with SD among general ward patients of a tertiary care hospital in Pakistan.Methods: In a cross-sectional study, a pre-tested questionnaire was administered to 108 patients admitted into the general medical and general surgical wards of Aga Khan University Hospital, Karachi.Results: In all, 50 (46.3%) respondents felt deprived of adequate sleep in the hospital. Worry about illness disturbed the night-time sleep of 47 (43.5%) patients; most of these had SD (70%) (p \u3c 0.001). Other patients\u27 noise disturbed 31.5% of study subjects and a significant majority (68%) of these had SD (p = 0.003). Over 17% of study subjects reported cell phone\u27s ringing as a disturbing factor; more by those with SD (68%) compared to those with no SD (32%); again the difference was significant (p = 0.003). Physical discomfort and presence of cannula were reported as disturbing factors by 41.7% and 28.7% of the study subjects respectively but these were not significantly associated with SD.CONCLUSION: Our study revealed that sleep deprivation occurs commonly among general ward patients in tertiary care setting. Factors found to be associated with SD were amenable to modification to a greater extent