Assessing the sustainability of the homestay industry for the East Coast of Malaysia using the Delphi approach.

Homestay ecotourism in Malaysia has been extensively examined in terms of its concepts, approaches, activities, and community engagement. However, a comprehensive assessment of the sustainability factors pertaining to host families remains a critical area awaiting exploration. This is paramount for ensuring the long-term viability of homestays and fostering economic benefits within rural communities.

Enhancing COVID-19 Classification Accuracy with a Hybrid SVM-LR Model.

Support ector achine (SVM) is a newer machine learning algorithm for classification, while logistic regression (LR) is an older statistical classification method. Despite the numerous studies contrasting SVM and LR, new improvements such as bagging and ensemble have been applied to them since these comparisons were made. This study proposes a new hybrid model based on SVM and LR for predicting small events per variable (EPV).

Developing forecasting model for future pandemic applications based on COVID-19 data 2020-2022.

Improving forecasting particularly time series forecasting accuracy, efficiency and precisely become crucial for the authorities to forecast, monitor, and prevent the COVID-19 cases so that its spread can be controlled more effectively. However, the results obtained from prediction models are inaccurate, imprecise as well as inefficient due to linear and non-linear patterns exist in the data set, respectively. Therefore, to produce more accurate and efficient COVID-19 prediction value that is closer to the true COVID-19 value, a hybrid approach has been implemented.

Improvement of Time Forecasting Models Using Machine Learning for Future Pandemic Applications Based on COVID-19 Data 2020-2022.

Improving forecasts, particularly the accuracy, efficiency, and precision of time-series forecasts, is becoming critical for authorities to predict, monitor, and prevent the spread of the Coronavirus disease. However, the results obtained from the predictive models are imprecise and inefficient because the dataset contains linear and non-linear patterns, respectively. Linear models such as autoregressive integrated moving average cannot be used effectively to predict complex time series, so nonlinear approaches are better suited for such a purpose.

Related Atrophic Gastritis Detection Using Enhanced Convolution Neural Network (CNN) Learner.

Atrophic gastritis (AG) is commonly caused by the infection of the () bacteria. If untreated, AG may develop into a chronic condition leading to gastric cancer, which is deemed to be the third primary cause of cancer-related deaths worldwide. Precursory detection of AG is crucial to avoid such cases.

Data on ectoparasites infestation on small mammals from different habitats in east-coast Peninsular Malaysia.

This data article presents on the ectoparasites infestation on small mammals in Peninsular Malaysia. The dataset on ectoparasites infestation is important because it raises a major medical concern regarding the spread of potentially zoonotic disease from wildlife to human. Tick and chigger are the primary ectoparasites as reservoirs of vector-borne diseases found on small mammals in Malaysia.

Conspectus of flora, fauna and micro-climate data in Tasik Kenyir from Mac 2015-February 2016.

This data article presents the diversity of flora and selected fauna in Tasik Kenyir, Malaysia. This man-made lake once suffered huge loss of biodiversity for allowing an earth-dam construction during 1980s. Series of publications on different types of target taxa have been published separately after the post-dam construction.

Data on diet and growth by giant panda in zoo Negara, Malaysia.

In this data article we present the determinations of the diet preference and growth of a pair of the giant panda, (David, 1869) from Zoo Negara Malaysia. Once considered as endangered, the captive giant pandas were given with nine species of local bamboo in separate indoor enclosures. We recorded data between May 25, 2014 and December 31, 2016 and analysed it based on food preference, the pattern toward food consumption and body weights using SPSS v25.