Baseline Evaluation of COVID-19 Impact on Developing Countries Workforce by Machine Learning

2022 ◽  
pp. 615-630
Author(s):  
Forhad An Naim
Keyword(s):  
Machine Learning ◽  
Developing Countries ◽  
Baseline Evaluation

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

Critical Review of Data Analytics techniques used in the Expanded Program on Immunization (EPI)

2020 ◽  
Vol 16 ◽  
Author(s):  
Sadaf Qazi ◽  
Muhammad Usman
Keyword(s):  
Machine Learning ◽  
Developing Countries ◽  
Data Analytics ◽  
Public Health Intervention ◽  
Cold Chain ◽  
Data Repository ◽  
Quality Of Data ◽  
Mortality And Morbidity ◽  
Healthcare Facilities ◽  
Expanded Program On Immunization

Background: Immunization is a significant public health intervention to reduce child mortality and morbidity. However, its coverage, in spite of free accessibility, is still very low in developing countries. One of the primary reasons for this low coverage is the lack of analysis and proper utilization of immunization data at various healthcare facilities. Purpose: In this paper, the existing machine learning based data analytics techniques have been reviewed critically to highlight the gaps where this high potential data could be exploited in a meaningful manner. Results: It has been revealed from our review, that the existing approaches use data analytics techniques without considering the complete complexity of Expanded Program on Immunization which includes the maintenance of cold chain systems, proper distribution of vaccine and quality of data captured at various healthcare facilities. Moreover, in developing countries, there is no centralized data repository where all data related to immunization is being gathered to perform analytics at various levels of granularities. Conclusion: We believe that the existing non-centralized immunization data with the right set of machine learning and Artificial Intelligence based techniques will not only improve the vaccination coverage but will also help in predicting the future trends and patterns of its coverage at different geographical locations.

scholarly journals Machine learning Clustering Algorithms Based on the DEA Optimization Approach for Banking System in Developing Countries

2020 ◽  
Vol 5 (6) ◽  
pp. 651-658 ◽  
Author(s):  
Mirpouya Mirmozaffari ◽  
Azam Boskabadi ◽  
Gohar Azeem ◽  
Reza Massah ◽  
Elahe Boskabadi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Developing Countries ◽  
Data Envelopment Analysis ◽  
Clustering Algorithms ◽  
Banking System ◽  
Additive Models ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Optimization Approach ◽  
Data Envelopment

Machine learning grows quickly, which has made numerous academic discoveries and is extensively evaluated in several areas. Optimization, as a vital part of machine learning, has fascinated much consideration of practitioners. The primary purpose of this paper is to combine optimization and machine learning to extract hidden rules, remove unrelated data, introduce the most productive Decision-Making Units (DMUs) in the optimization part, and to introduce the algorithm with the highest accuracy in Machine learning part. In the optimization part, we evaluate the productivity of 30 banks from eight developing countries over the period 2015-2019 by utilizing Data Envelopment Analysis (DEA). An additive Data Envelopment Analysis (DEA) model for measuring the efficiency of decision processes is used. The additive models are often named Slack Based Measure (SBM). This group of models measures efficiency via slack variables. After applying the proposed model, the Malmquist Productivity Index (MPI) is computed to evaluate the productivity of companies. In the machine learning part, we use a specific two-layer data mining filtering pre-processes for clustering algorithms to increase the efficiency and to find the superior algorithm. This study tackles data and methodology-related issues in measuring the productivity of the banks in developing countries and highlights the significance of DMUs productivity and algorithms accuracy in the banking industry by comparing suggested models.

scholarly journals AI in global healthcare: Need for robust governance frameworks

2020 ◽  
Author(s):  
Sandeep Reddy ◽  
Sonia Allan ◽  
Simon Coghlan ◽  
Paul Cooper
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Developing Countries ◽  
Clinical Care ◽  
Ethical Guidelines ◽  
Popular Discourse ◽  
The World ◽  
Healthcare Need

The re-emergence of artificial intelligence (AI) in popular discourse and its application in medicine, especially via machine learning (ML) algorithms, has excited interest from policymakers and clinicians alike. The use of AI in clinical care in both developed and developing countries is no longer a question of ‘if?’ but ‘when?’. This creates a pressing need not only for sound ethical guidelines but also for robust governance frameworks to regulate AI in medicine around the world. In this article, we discuss what components need to be considered in developing these governance frameworks and who should lead this worldwide effort?

scholarly journals Integration of Machine Learning and Open Access Geospatial Data for Land Cover Mapping

2019 ◽  
Vol 11 (16) ◽  
pp. 1907 ◽  
Author(s):  
Mohammad Mardani ◽  
Hossein Mardani ◽  
Lorenzo De Simone ◽  
Samuel Varas ◽  
Naoki Kita ◽  
...  
Keyword(s):  
Machine Learning ◽  
Developing Countries ◽  
Open Access ◽  
Land Cover ◽  
Satellite Images ◽  
National Level ◽  
Google Earth ◽  
Support Vector ◽  
Land Cover Mapping ◽  
Sustainable Food

In-time and accurate monitoring of land cover and land use are essential tools for countries to achieve sustainable food production. However, many developing countries are struggling to efficiently monitor land resources due to the lack of financial support and limited access to adequate technology. This study aims at offering a solution to fill in such a gap in developing countries, by developing a land cover solution that is free of costs. A fully automated framework for land cover mapping was developed using 10-m resolution open access satellite images and machine learning (ML) techniques for the African country of Lesotho. Sentinel-2 satellite images were accessed through Google Earth Engine (GEE) for initial processing and feature extraction at a national level. Also, Food and Agriculture Organization’s land cover of Lesotho (FAO LCL) data were used to train a support vector machine (SVM) and bagged trees (BT) classifiers. SVM successfully classified urban and agricultural lands with 62 and 67% accuracy, respectively. Also, BT could classify the two categories with 81 and 65% accuracy, correspondingly. The trained models could provide precise LC maps in minutes or hours. they can also be utilized as a viable solution for developing countries as an alternative to traditional geographic information system (GIS) methods, which are often labor intensive, require acquisition of very high-resolution commercial satellite imagery, time consuming and call for high budgets.

scholarly journals Seroprevalence and Molecular Identification of Brucella spp. in Bovines in Pakistan—Investigating Association With Risk Factors Using Machine Learning

2020 ◽  
Vol 7 ◽  
Author(s):  
Aman Ullah Khan ◽  
Falk Melzer ◽  
Ashraf Hendam ◽  
Ashraf E. Sayour ◽  
Iahtasham Khan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Developing Countries ◽  
Real Time ◽  
Molecular Identification ◽  
Real Time Pcr ◽  
Ring Test ◽  
Test Results ◽  
Bovine Brucellosis ◽  
Milk Samples

Bovine brucellosis is a global zoonosis of public health importance. It is an endemic disease in many developing countries including Pakistan. This study aimed to estimate the seroprevalence and molecular detection of bovine brucellosis and to assess the association of potential risk factors with test results. A total of 176 milk and 402 serum samples were collected from cattle and buffaloes in three districts of upper Punjab, Pakistan. Milk samples were investigated using milk ring test (MRT), while sera were tested by Rose–Bengal plate agglutination test (RBPT) and indirect enzyme-linked immunosorbent assay (i-ELISA). Real-time PCR was used for detection of Brucella DNA in investigated samples. Anti-Brucella antibodies were detected in 37 (21.02%) bovine milk samples using MRT and in 66 (16.4%) and 71 (17.7%) bovine sera using RBPT and i-ELISA, respectively. Real-time PCR detected Brucella DNA in 31 (7.71%) from a total of 402 bovine sera and identified as Brucella abortus. Seroprevalence and molecular identification of bovine brucellosis varied in some regions in Pakistan. With the use of machine learning, the association of test results with risk factors including age, animal species/type, herd size, history of abortion, pregnancy status, lactation status, and geographical location was analyzed. Machine learning confirmed a real observation that lactation status was found to be the highest significant factor, while abortion, age, and pregnancy came second in terms of significance. To the authors' best knowledge, this is the first time to use machine learning to assess brucellosis in Pakistan; this is a model that can be applied for other developing countries in the future. The development of control strategies for bovine brucellosis through the implementation of uninterrupted surveillance and interactive extension programs in Pakistan is highly recommended.

Data Avalanche

2020 ◽  
pp. 28-49
Author(s):  
Emmanuel Awuni Kolog ◽  
Acheampong Owusu ◽  
Samuel Nii Odoi Devine ◽  
Edward Entee
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Developing Countries ◽  
Fraud Detection ◽  
Mobile Payment ◽  
Mobile Money ◽  
Governmental Agencies ◽  
Learning Framework ◽  
Formal And Informal Sectors ◽  
Existing Data

Globalizing businesses from developing countries require a thoughtful strategy and adoption of state-of-the-art technologies to meet up with the rapidly changing society. Mobile money payment service is a growing service that provides opportunities for both the formal and informal sectors in Ghana. Despite its importance, fraudsters have capitalized on the vulnerabilities of users to defraud them. In this chapter, the authors have reviewed existing data mining techniques for exploring the detection of mobile payment fraud. With this technique, a hybrid-based machine learning framework for mobile money fraud detection is proposed. With the use of the machine learning technique, an avalanche of fraud-related cases is leveraged, as a corpus, for fraud detection. The implementation of the framework hinges on the formulation of policies and regulations that will guide the adoption and enforcement by Telcos and governmental agencies with oversight responsibilities in the telecommunication space. The authors, therefore, envision the implementation of the proposed framework by practitioners.

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