Predictive Analysis of Dengue Outbreak Based on an Improved Salp Swarm Algorithm

Mustaffa, Zuriani; Sulaiman, Mohd Herwan; Rosli, Khairunnisa Amalina Mohd; Mohsin, Mohamad Farhan Mohamad; Yusof, Yuhanis

doi:10.2478/cait-2020-0053

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…We employed the mini-max indexing of Mustaffa and Yusof approach for normalizing the values between 0 to 1 [ 50 ]. This approach makes all indicators in similar forms of units and derives the index values of each indicator.…”

Section: Methodsmentioning

confidence: 99%

Measuring multi-dimensional disparity index: A case of Nepal

Bhattarai,

Shrestha,

Paudel

2023

PLoS ONE

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This study introduces Multi-dimensional Disparity Index (MDI) to measure multi-form of disparity in different level of governments referencing Nepal. The measurement scale of MDI was developed by adopting Santos and Alkire’s (2011) approaches. A wide range of thematic experts was consulted, employing the Semi-Delphi approach to determine its dimensions and indicators. The MDI in this study includes six dimensions and 34 indicators composited with dimension-wise indices like Economy Disparity Index [ECODI], Educational Disparity Index [EDUDI], Health Disparity Index [HDI], Geography and Climatic Vulnerability Index [GCVI], Living Standard Disparity Index [LSDI], and Demography Disparity Index [DDI]. Overall, the study revealed the extent of multi-dimensional disparity across three tiers of government in Nepal. More specifically, Nepal scored 0.388 MDI value. Karnali and Bagmati provinces are accounted as the highest and least deprived. This study contributes essential knowledge, particularly in exploring the dimensions and their indicators and develops an approach to measure multi-dimensional disparities. Most existing approaches for assessing disparities are mono-dimensional and measure the disparities in a single aspect. In this context, MDI provides a broader approach to consider multiple dimensions and measures multiple aspects in a country like Nepal, where disparity manifests at multiple levels.

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Section: Methodsmentioning

confidence: 99%

Measuring multi-dimensional disparity index: A case of Nepal

Bhattarai,

Shrestha,

Paudel

2023

PLoS ONE

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Improved Barnacle Mating Optimizer-Based Least Square Support Vector Machine to Predict COVID-19 Confirmed Cases with Total Vaccination

Ahmed

Sulaiman

Mohamad

2023

Cybernetics and Information Technologies

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Every country must have an accurate and efficient forecasting model to avoid and manage the epidemic. This paper suggests an upgrade to one of the evolutionary algorithms inspired by nature, the Barnacle Mating Optimizer (BMO). First, the exploration phase of the original BMO is enhanced by enforcing and replacing the sperm cast equation through Levy flight. Then, the Least Square Support Vector Machine (LSSVM) is partnered with the improved BMO (IBMO). This hybrid approach, IBMO-LSSVM, has been deployed effectively for time-series forecasting to enhance the RBF kernel-based LSSVM model since vaccination started against COVID-19 in Malaysia. In comparison to other well-known algorithms, our outcomes are superior. In addition, the IBMO is assessed on 19 conventional benchmarks and the IEEE Congress of Evolutionary Computation Benchmark Test Functions (CECC06, 2019 Competition). In most cases, IBMO outputs are better than comparison algorithms. However, in other circumstances, the outcomes are comparable.

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Predictive Analysis of Dengue Outbreak Based on an Improved Salp Swarm Algorithm

Cited by 2 publications

References 32 publications

Measuring multi-dimensional disparity index: A case of Nepal

Measuring multi-dimensional disparity index: A case of Nepal

Improved Barnacle Mating Optimizer-Based Least Square Support Vector Machine to Predict COVID-19 Confirmed Cases with Total Vaccination

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