Dengue outbreaks prediction in Bangladesh perspective using distinct multilayer perceptron NN and decision tree

Khan, Md. Ashikur Rahman; Akter, Jony; Ahammad, Ishtiaq; Ejaz, Sabbir; Khan, Tanvir Jaman

doi:10.1007/s13755-022-00202-x

Cited by 6 publications

(8 citation statements)

References 20 publications

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“…Previous cross-sectional studies in Bangladesh indicated HEV seroprevalence to range from 22.5% in rural areas to 60.1% in the capital of Dhaka [14]. Furthermore, a review of the current literature found five records of human HEV outbreaks in Bangladesh, which were all associated with contaminated drinking water: 2008-2009 in Dhaka [7], 2010 in Rajshahi City Corporation [11], 2013 in Noakhali [15], 2017 in Rajshahi City Corporation [15], and 2018 in Chattogram [15]. Evidence of seroprevalence of each HEV genotype in Bangladesh is limited [9,16]; however, the literature suggests outbreaks in Bangladesh have predominantly been due to genotypes 1 and 2 [3], with RNA testing during the 2010 outbreak in Rajshahi City detecting HEV genotype 1 [11].…”

Section: Introductionmentioning

confidence: 99%

Epidemiological Profile of a Human Hepatitis E Virus Outbreak in 2018, Chattogram, Bangladesh

et al. 2022

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Hepatitis E virus (HEV) is a waterborne zoonotic disease that can result in a high fatality rate in pregnant women and infants. In 2018, a large HEV outbreak emerged in Chattogram, Bangladesh, resulting in 2800 cases and a significant public health response to mitigate the transmission. While the source of the outbreak remained poorly understood, authorities suggested that possible risk factors for HEV infection included contamination of water supply, exacerbated by concurrent severe flooding events in the community. A cross-sectional study was conducted to investigate the distribution and risk factors for HEV seroprevalence between January and December 2018 in the Chattogram city area. A total of 505 blood samples were collected from symptomatic patients of 10 hospitals who met the case definition for an HEV infection. Standard ELISA tests were performed in all patients to identify anti-HEV antibodies. The size and location of HEV seroprevalence clusters within Chattogram were investigated using SaTScan. We investigated the association between risk of HEV infection and individual and environmentally lagged risk factors using Bernoulli generalised linear regression models. Our results indicate an overall HEV seroprevalence of 35% with significant variation according to sex, source of drinking water, and boiling of drinking water. A positive cross-correlation was found between HEV exposure and precipitation, modified normalised difference water index (MNDWI), and normalised difference vegetation index (NDVI). Our model indicated that risk of infection was associated with sex, age, source of drinking water, boiling of water, increased precipitation, and increased MNDWI. The results from this study indicate that source and boiling of drinking water and increased precipitation were critical drivers of the 2018 HEV outbreak. The communities at highest risk identified in our analyses should be targeted for investments in safe water infrastructure to reduce the likelihood of future HEV outbreaks in Chattogram.

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Section: Introductionmentioning

confidence: 99%

Epidemiological Profile of a Human Hepatitis E Virus Outbreak in 2018, Chattogram, Bangladesh

et al. 2022

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“…The mean value of 'total_strength' for each age group is presented in below Table 6. (20,25] 273.954545 (25,30] 281.392857 (30,55] 266.00000 For the 'bench press' column, we create a box plot showing the distribution of bench press values by age group. This is presented in Figure 5.…”

Section: Cricketers Fitness Data Analysismentioning

confidence: 99%

“…The primary goal of ML approaches is to replace time-consuming, and laborious human operations with an increased degree of mechanization in the knowledge development process and therefore, they are used in various sectors like education [21][22][23], finance [24], medicine and healthcare [25,26], and clustering [27,28]. These ML solutions must be developed with domain-specific expertise.…”

Section: Introductionmentioning

confidence: 99%

Cricket Players Selection for National Team and Franchise League using Machine Learning Algorithms

Md. Robel,

Md. Ashikur Rahman Khan,

Ishtiaq Ahammad

et al. 2023

Cloud Computing and Data Science

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Cricket player selection is a crucial task for both national teams and franchise leagues. Traditionally, selectors rely on their experience and knowledge to evaluate a player's physical fitness, batting, and bowling performance. However, with the advancements in machine learning algorithms, it is possible to automate and improve the selection process. In this research, a Machine Learning (ML)-based approach is proposed for cricket player selection. This approach uses a combination of physical fitness data, batting and bowling statistics, and other relevant metrics to create a comprehensive player profile. Then three ML algorithms-linear regression, support vector regression, and random forests-are employed to identify the most promising players. Data is collected on a large number of cricket players and their performance in national and franchise leagues from the two most prominent cricket websites, espncricinfo, and cricbuzz, to evaluate the proposed approach. Then the proposed approach trained and tested the ML models on this data and compared their accuracy and performance. Based on the performance scores obtained from these models, two squads are selected for the national team and one squad for each franchise league team. The results demonstrate that the proposed approach can significantly improve the selection process and identify players with high potential. Furthermore, results found that the support vector regression algorithm outperformed other ML models in terms of prediction and player selection.

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“…This paper analyzes employee data to determine whether or not they should be promoted, given an annual raise, or advanced in their careers. They utilized prior historical data of employees to generate an annual increase for an employee [12][13]. Another model is proposed by Pombo et al, where they cross-validate several Machine Learning methods and tune the key hyper-parameters for each algorithm.…”

Section: Introductionmentioning

confidence: 99%

Appropriate Job Selection Using Machine Learning Techniques

Khan

Paul

Rahman

et al. 2023

Preprint

Self Cite

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Job recruitment is the process of picking a qualified candidate for a vacant position in an organization. Suitable job selection is a challenging task, especially for freshers. Every year, millions of students complete their graduation and come up with many options for choosing their jobs. This job selection procedure depends on various factors. Considering these issues, this research aims to build a model to predict whether a job is suitable or not suitable for a candidate according to their skills, experiences, and desires for the job. In this situation, Machine Learning approaches can be useful. Data were collected from 120 people currently appointed to a job in various fields. Distinct machine learning techniques are used to predict whether they are satisfied with their current job or not. We also find the other performance matrices and compare them with other algorithms to evaluate the performance of the best model. The results show that the random forest technique is the most effective method for forecasting appropriate job selection, with 92% accuracy and 8% error. Based on the findings, this research will become an effective tool for selecting a suitable job based on people's desires.

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Dengue outbreaks prediction in Bangladesh perspective using distinct multilayer perceptron NN and decision tree

Cited by 6 publications

References 20 publications

Epidemiological Profile of a Human Hepatitis E Virus Outbreak in 2018, Chattogram, Bangladesh

Epidemiological Profile of a Human Hepatitis E Virus Outbreak in 2018, Chattogram, Bangladesh

Cricket Players Selection for National Team and Franchise League using Machine Learning Algorithms

Appropriate Job Selection Using Machine Learning Techniques

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