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Open Access November 29, 2022

The Application of Machine Learning in the Corona Era, With an Emphasis on Economic Concepts and Sustainable Development Goals

Milad Shahvaroughi Farahani, Amirhossein Esfahani, Fardin Alipoor
International Journal of Mathematical, Engineering, Biological and Applied Computing 2022, 1(2), 95-149. DOI: 10.31586/ijmebac.2022.519
Abstract The aim of this article is to examine the impacts of Coronavirus Disease -19 (Covid-19) vaccines on economic condition and sustainable development goals. In other words, we are going to study the economic condition during Covid19. We have studied the economic costs of pandemic, benefits in terms of gross domestic product (GDP), public finances and employment, investment on vaccines around the [...] Read more.
The aim of this article is to examine the impacts of Coronavirus Disease -19 (Covid-19) vaccines on economic condition and sustainable development goals. In other words, we are going to study the economic condition during Covid19. We have studied the economic costs of pandemic, benefits in terms of gross domestic product (GDP), public finances and employment, investment on vaccines around the world, progress and totally the economic impacts of vaccines and the impacts of emerging markets (EM) on achieving sustainable development goals (SDGs), including no poverty, good health and well-being, zero hunger, reduced inequality etc. The importance of emerging economies in reducing the harmful effects of the Corona has also been noted. We have tried to do experimental results and forecast daily new death cases from Feb-2020 to Aug-2021 in Iran using Artificial Neural Network (ANN) and Beetle Antennae Search (BAS) algorithm as a case study with econometric models and regression analysis. The findings show that Covid19 has had devastating economic and health effects on the world, and the vaccine can be very helpful in eliminating these effects specially in long-term. We observed that there is inequality in the distribution of Corona vaccines in rich countries compared to poor which EM can decrease the gap between them. The results show that both models (i.e., Artificial intelligence (AI) and econometric models) almost have the same results but AI optimization models can robust the model and prediction. The main contribution of this article is that we have surveyed the impacts of vaccination from socio-economic viewpoint not just report some facts and truth. We have surveyed the impacts of vaccines on sustainable development goals and the role of EM in achieving SDGs. In addition to using the theoretical framework, we have also used quantitative and empirical results that have rarely been seen in other articles.
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Open Access June 28, 2025

Development of a Hemodialysis Data Collection and Clinical Information System and Establishment of an Intradialytic Blood Pressure/Pulse Rate Predictive Model

I-Hsuan Peng, Chen-Kang Tien, Pei-Chun Lee
Journal of Artificial Intelligence and Big Data 2025, 5(2), 1-23. DOI: 10.31586/jaibd.2025.6029
Abstract This research is a collaboration involving a university team, a partnering corporation, and a hemodialysis clinic, which is a cross-disciplinary research initiative in the field of Artificial Intelligence of Things (AIoT) within the medical informatics domain. The research has two objectives: (1) The development of an Internet of Things (IoT)-based Information System customized for the hemodialysis machines at the clinic, including transmission bridges, clinical personnel dedicated web/app, and a backend server. The system has been deployed at the clinic and is now officially operational; (2) The research also utilized de-identified, anonymous data (collected by the officially operational system) to train, evaluate, and compare Deep Learning-based Intradialytic Blood Pressure (BP)/Pulse Rate (PR) Predictive Models [...] Read more.
This research is a collaboration involving a university team, a partnering corporation, and a hemodialysis clinic, which is a cross-disciplinary research initiative in the field of Artificial Intelligence of Things (AIoT) within the medical informatics domain. The research has two objectives: (1) The development of an Internet of Things (IoT)-based Information System customized for the hemodialysis machines at the clinic, including transmission bridges, clinical personnel dedicated web/app, and a backend server. The system has been deployed at the clinic and is now officially operational; (2) The research also utilized de-identified, anonymous data (collected by the officially operational system) to train, evaluate, and compare Deep Learning-based Intradialytic Blood Pressure (BP)/Pulse Rate (PR) Predictive Models, with subsequent suggestions provided. Both objectives were executed under the supervision of the Institutional Review Board (IRB) at Mackay Memorial Hospital in Taiwan. The system completed for objective one has introduced three significant services to the clinic, including automated hemodialysis data collection, digitized data storage, and an information-rich human-machine interface as well as graphical data displays, which replaces traditional paper-based clinical administrative operations, thereby enhancing healthcare efficiency. The graphical data presented through web and app interfaces aids in real-time, intuitive comprehension of the patients’ conditions during hemodialysis. Moreover, the data stored in the backend database is available for physicians to conduct relevant analyses, unearth insights into medical practices, and provide precise medical care for individual patients. The training and evaluation of the predictive models for objective two, along with related comparisons, analyses, and recommendations, suggest that in situations with limited computational resources and data, an Artificial Neural Network (ANN) model with six hidden layers, SELU activation function, and a focus on artery-related features can be employed for hourly intradialytic BP/PR prediction tasks. It is believed that this contributes to the collaborating clinic and relevant research communities.
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Open Access November 04, 2022

An Artificial Intelligence Approach to Manage Crop Water Requirements in South Africa

Akinola Ikudayisi, Andre Calitz, Samuel Abejide
Online Journal of Engineering Sciences 2022, 2(1), 23-34. DOI: 10.31586/ojes.2022.377
Abstract Estimation of crop water requirements is of paramount importance towards the management of agricultural water resources, which is a major mitigating strategy against the effects of climate change on food security. South Africa water shortage poses a threat on agricultural efficiency. Since irrigation uses about 60% of the fresh water available, it therefore becomes important to optimise the use of [...] Read more.
Estimation of crop water requirements is of paramount importance towards the management of agricultural water resources, which is a major mitigating strategy against the effects of climate change on food security. South Africa water shortage poses a threat on agricultural efficiency. Since irrigation uses about 60% of the fresh water available, it therefore becomes important to optimise the use of irrigation water in order to maximize crop yield at the farm level in order to avoid wastage. In this study, combined application of an artificial neural network (ANN) and a crop – growth simulation model for the estimation of crop irrigation water requirements and the irrigation scheduling of potatoes at Winterton irrigation scheme, South Africa was investigated. The crop-water demand from planting to harvest date, when to irrigate, the optimum stage in the drying cycle when to apply water and the amount of irrigation water to be applied per time, were estimated in this study. Five feed –forward backward propagation artificial neural network predictive models were developed with varied number of neurons and hidden layers and evaluated. The optimal ANN model, which has 5 inputs, 5 neurons, 1 hidden layer and 1 output was used to predict monthly reference evapotranspiration (ETo) in the Winterton area. The optimal ANN model produced a root-mean-square error (RMSE) of 0.67, Pearson correlation coefficient (r) of 0.97 and coefficient of determination (R2) of 0.94. The validation of the model between the measured and predicted ETo shows a r value of 0.9048. The predicted ETo was one of the input variables into a crop growth simulation model, called CROPWAT. The results indicated that the total crop water requirement was 1259.2 mm/decade and net irrigation water requirement was 1276.9 mm/decade, spread over a 5-day irrigation time during the entire 140 days of cropping season for potatoes. A combination of the artificial neural networks and the crop growth simulation models have proved to be a robust technique for estimating crop irrigation water requirements in the face of limited or no daily meteorological datasets.
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Open Access April 22, 2022

Particle Swarm Network Design for UCAV Intelligence System Path Planning

Sheharyar Khan, Sohrab Khan
Universal Journal of Computer Sciences and Communications 2022, 1(1), 1-8. DOI: 10.31586/ujcsc.2022.267
Abstract In military battle, the unmanned combat aerial vehicle (UCAV) plays a critical role. The UCAV avoids the fatal military zone as well as radars. If there is just a narrow path between the defensive areas, it is dan-gerous. It chooses the quickest and safest path. The balance evolution technique is used to improve the path planning of UCAV in this study, which results in a novel artificial bee [...] Read more.
In military battle, the unmanned combat aerial vehicle (UCAV) plays a critical role. The UCAV avoids the fatal military zone as well as radars. If there is just a narrow path between the defensive areas, it is dan-gerous. It chooses the quickest and safest path. The balance evolution technique is used to improve the path planning of UCAV in this study, which results in a novel artificial bee colony. To regulate the position of a swarm of UCAVs, a particle swarm network is used to communicate between the UCAVs in the swarm. According to simulation data, the particle swarm network technique is more efficient than the ABC ap-proach. The intelligence system is taught via an artificial neural network.
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Open Access August 09, 2021

Optimization and Prediction of Biodiesel Yield from Moringa Seed Oil and Characterization

Owhor Sampson Chisa, Orafa Patience Nguseer, Samaila Yohanna Adidauki, Doyo Ethan
Journal of Biomedical and Life Sciences 2021, 1(1), 1-14. DOI: 10.31586/jbls.2021.010101
Abstract In this study, oil was extracted from Moringa seed using mechanical and solvent methods. To transesterify the oil into biodiesel, factorial design of experiment of 24 was used to obtain different combination factors at different level of reaction temperature, catalyst amount, reaction time and alcohol to oil ratio, giving rise to 48 experimental runs. The oil sample was transesterified [...] Read more.
In this study, oil was extracted from Moringa seed using mechanical and solvent methods. To transesterify the oil into biodiesel, factorial design of experiment of 24 was used to obtain different combination factors at different level of reaction temperature, catalyst amount, reaction time and alcohol to oil ratio, giving rise to 48 experimental runs. The oil sample was transesterified in 48 experimental runs, in each case the biodiesel yield was recorded in percentage. The biodiesel was then characterized according to ASTM test protocol. Factorial design model was developed using Design Expert 7.0, the model generated R of 0.987 and Mean Square Error (MSE) of 5.0453 and was used to predict and optimize biodiesel yield. Artificial Neural Network (ANN) model from MATLAB R2016a was developed using 4 input variables and 30 runs, the remaining 18 runs were tested with the ANN model to predict and compare the biodiesel yield with the experimental biodiesel yield, the model generated R value of 0.99687 and MSE of 3.50804. It was found that solvent method yielded more oil than mechanical method, the biodiesel has good thermo-physical property, optimum biodiesel yield of 91.45 % was obtained at 5:1 alcohol/ oil molar ratio, 18.89 wt% catalyst amounts, 45 minutes reaction time and at 45 reaction temperature. The experimental validation yielded 88.33 % biodiesel. The ANN model adequately predicted the remaining 18 runs with R2 value of 0.99649 and MSE of 4.914243. Both models proved adequate enough to predict biodiesel yield but ANN model proved more adequate.
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Keyword:  Artificial Neural network (ANN)

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