The present pandemic has tremendously raised the health systems’ burden around the globe. It is important to understand the transmission dynamics of the infection and impose localized strategies across different geographies to curtail the spread of the infection. The present study was designed to assess the transmission dynamics and the health systems’ burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using an agent-based modeling (ABM) approach. The study used a synthetic population with 31,738,240 agents representing 90.67 percent of the overall population of Telangana, India. The effects of imposing and lifting lockdowns, nonpharmaceutical interventions, and the role of immunity were analyzed. The distribution of people in different health states was measured separately for each district of Telangana. The spread dramatically increased and reached a peak soon after the lockdowns were relaxed. It was evident that is the protection offered is higher when a higher proportion of the population is exposed to the interventions. ABMs help to analyze grassroots details compared to compartmental models. Risk estimates provide insights on the proportion of the population protected by the adoption of one or more of the control measures, which is of practical significance for policymaking.
Machine learning approaches in the diagnosis of infectious diseases-a review.pdfSmriti Mishra
Infectious diseases are a group of medical conditions caused by infectious agents such as parasites, bacteria, viruses, or fungus. Patients who are undiagnosed may unwittingly spread the disease to others. Because of the transmission of these agents, epidemics, if not pandemics, are possible. Early detection can help to prevent the spread of an outbreak or put an end to it. Infectious disease prevention, early identification, and management can be aided by machine learning (ML) methods. The implementation of ML algorithms such as logistic regression, support vector machine, Naive Bayes, decision tree, random forest, K-nearest neighbor, artificial neural network, convolutional neural network, and ensemble techniques to automate the process of infectious disease diagnosis is investigated in this study. We examined a number of ML models for tuberculosis (TB), influenza, human immunodeficiency virus (HIV), dengue fever, COVID-19, cystitis, and nonspecific urethritis. Existing models have constraints in data handling concerns such data types, amount, quality, temporality, and availability. Based on the research, ensemble approaches, rather than a typical ML classifier, can be used to improve the overall performance of diagnosis. We highlight the need of having enough diverse data in the database to create a model or representation that closely mimics reality.
A comprehensive study on disease risk predictions in machine learning IJECEIAES
Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. A Comprehensive study on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavors have been shifted.
Coronavirus disease situation analysis and prediction using machine learning...IJECEIAES
During a pandemic, early prognostication of patient infected rates can reduce the death by ensuring treatment facility and proper resource allocation. In recent months, the number of death and infected rates has increased more distinguished than before in Bangladesh. The country is struggling to provide moderate medical treatment to many patients. This study distinguishes machine learning models and creates a prediction system to anticipate the infected and death rate for the coming days. Equipping a dataset with data from March 1, 2020, to August 10, 2021, a multi-layer perceptron (MLP) model was trained. The data was managed from a trusted government website and concocted manually for training purposes. Several test cases determine the model's accuracy and prediction capability. The comparison between specific models assumes that the MLP model has more reliable prediction capability than the support vector regression (SVR) and linear regression model. The model presents a report about the risky situation and impending coronavirus disease (COVID-19) attack. According to the prediction produced by the model, Bangladesh may suffer another COVID-19 attack, where the number of infected cases can be between 929 to 2443 and death cases between 19 to 57.
Root cause analysis of COVID-19 cases by enhanced text mining processIJECEIAES
The main focus of this research is to find the reasons behind the fresh cases of COVID-19 from the public’s perception for data specific to India. The analysis is done using machine learning approaches and validating the inferences with medical professionals. The data processing and analysis is accomplished in three steps. First, the dimensionality of the vector space model (VSM) is reduced with improvised feature engineering (FE) process by using a weighted term frequency-inverse document frequency (TF-IDF) and forward scan trigrams (FST) followed by removal of weak features using feature hashing technique. In the second step, an enhanced K-means clustering algorithm is used for grouping, based on the public posts from Twitter®. In the last step, latent dirichlet allocation (LDA) is applied for discovering the trigram topics relevant to the reasons behind the increase of fresh COVID-19 cases. The enhanced K-means clustering improved Dunn index value by 18.11% when compared with the traditional K-means method. By incorporating improvised two-step FE process, LDA model improved by 14% in terms of coherence score and by 19% and 15% when compared with latent semantic analysis (LSA) and hierarchical dirichlet process (HDP) respectively thereby resulting in 14 root causes for spike in the disease.
A novel predictive model for capturing threats for facilitating effective soc...IJECEIAES
Social distancing is one of the simple and effective shields for every individual to control spreading of virus in present scenario of pandemic coronavirus disease (COVID-19). However, existing application of social distancing is a basic model and it is also characterized by various pitfalls in case of dynamic monitoring of infected individual accurately. Review of existing literature shows that there has been various dedicated research attempt towards social distancing using available technologies, however, there are further scope of improvement too. This paper has introduced a novel framework which is capable of computing the level of threat with much higher degree of accuracy using distance and duration of stay as elementary parameters. Finally, the model can successfully classify the level of threats using deep learning. The study outcome shows that proposed system offers better predictive performance in contrast to other approaches.
The susceptible-infected-recovered-dead model for long-term identification o...IJECEIAES
The coronavirus (COVID-19) epidemic has spread massively to almost all countries including Indonesia, in just a few months. An important step to overcoming the spread of the COVID-19 is understanding its epidemiology through mathematical modeling intervention. Knowledge of epidemic dynamics patterns is an important part of making timely decisions and preparing hospitals for the outbreak peak. In this study, we developed the susceptible-infected-recovered-dead (SIRD) model, which incorporates the key epidemiological parameters to model and estimate the long-term spread of the COVID-19. The proposed model formulation is data-based analysis using public COVID-19 data from March 2, 2020 to May 15, 2021. Based on numerical analysis, the spread of the pandemic will begin to fade out after November 5, 2021. As a consequence of this virus attack, the cumulative number of infected, recovered, and dead people were estimated at ≈ 3,200,000, ≈ 3,437,000 and ≈ 63,000 people, respectively. Besides, the key epidemiological parameter indicates that the average reproduction number value of COVID-19 in Indonesia is 7.32. The long-term prediction of COVID-19 in Indonesia and its epidemiology can be well described using the SIRD model. The model can be applied in specific regions or cities in understanding the epidemic pattern of COVID-19.
Clinical Simulation as an Evaluation Method in Health Inf.docxbartholomeocoombs
Clinical Simulation as an Evaluation Method
in Health Informatics
Sanne JENSEN
a,1
a
The Capital Region of Denmark, Copenhagen, Denmark
Abstract. Safe work processes and information systems are vital in health care.
Methods for design of health IT focusing on patient safety are one of many
initiatives trying to prevent adverse events. Possible patient safety hazards need to
be investigated before health IT is integrated with local clinical work practice
including other technology and organizational structure. Clinical simulation is
ideal for proactive evaluation of new technology for clinical work practice.
Clinical simulations involve real end-users as they simulate the use of technology
in realistic environments performing realistic tasks. Clinical simulation study
assesses effects on clinical workflow and enables identification and evaluation of
patient safety hazards before implementation at a hospital. Clinical simulation also
offers an opportunity to create a space in which healthcare professionals working
in different locations or sectors can meet and exchange knowledge about work
practices and requirement needs. This contribution will discuss benefits and
challenges of using clinical simulation, and will describe how clinical simulation
fits into classical usability studies, how patient safety may benefit by use of
clinical simulation, and it will describe the different steps of how to conduct
clinical simulation. Furthermore a case study is presented.
Keywords. Ergonomics, eHealth, qualitative evaluation, clinical simulation, risk,
safety.
1. Introduction
Implementation of health IT in relation to improvement of patient safety and
optimization of work flow is a paradox [1]. Even though health IT is intended and
anticipated to have a positive impact on quality and efficiency of health care [2], the
application of new technology in healthcare may also increase patient safety hazards [3,
4]. Studies show that adverse events are indeed often related to the use of technology
[5-7].
Design of health IT focusing on protecting patient safety is one of many initiatives
trying to prevent adverse events [8, 9].
2
Patient safety does not entirely rely on
technology but is highly influenced by the interaction between users and technology in
a specific context [10], and sociotechnical issues and human factors are related to many
unintended consequences and patient safety hazards [7, 8, 11]. Possible patient safety
hazards such as design of the IT system itself; embedding of IT system into local work
1
Corresponding author: Sanne Jensen, The Capital Region of Denmark, Borgervanget 7, 2100
Copenhagen O, Denmark, [email protected]
2
See also: F. Magrabi et al., Health IT for patient safety and improving the safety of health IT, in: E.
Ammenwerth, M. Rigby (eds.), Evidence-Based Health Informatics, Stud Health .
Genetic algorithm to optimization mobility-based dengue mathematical modelIJECEIAES
Implementation of vaccines, mosquito repellents and several Wolbachia schemes have been proposed recently as strategies against dengue. Research showed that the use of vaccine and repellent is highly effective when implemented to individuals who are in area with high transmission rates, while the use of Wolbachia bacteria is strongly effective when implemented in area with low transmission rates. This research is to show a three-strategy combination to cope with the dengue using mathematical model. In dengue mathematical model construction, several parameters are not yet known, therefore a genetic algorithm method was used to estimate dengue model parameters. Numerical simulation results showed that the combination of three strategies were able to reduce the number of infected humans. The dynamic of the human population with the combination of three strategies on average was able to reduce the infected human population by 45.2% in immobility aspect. Furthermore, the mobility aspect in dengue model was presented by reviewing two areas; Yogyakarta and Semarang in Indonesia. The numerical solutions showed that the trend graph was almost similar between the two areas. With the maximum effort given, the combination control values decreased slowly until the 100th day.
Machine learning approaches in the diagnosis of infectious diseases-a review.pdfSmriti Mishra
Infectious diseases are a group of medical conditions caused by infectious agents such as parasites, bacteria, viruses, or fungus. Patients who are undiagnosed may unwittingly spread the disease to others. Because of the transmission of these agents, epidemics, if not pandemics, are possible. Early detection can help to prevent the spread of an outbreak or put an end to it. Infectious disease prevention, early identification, and management can be aided by machine learning (ML) methods. The implementation of ML algorithms such as logistic regression, support vector machine, Naive Bayes, decision tree, random forest, K-nearest neighbor, artificial neural network, convolutional neural network, and ensemble techniques to automate the process of infectious disease diagnosis is investigated in this study. We examined a number of ML models for tuberculosis (TB), influenza, human immunodeficiency virus (HIV), dengue fever, COVID-19, cystitis, and nonspecific urethritis. Existing models have constraints in data handling concerns such data types, amount, quality, temporality, and availability. Based on the research, ensemble approaches, rather than a typical ML classifier, can be used to improve the overall performance of diagnosis. We highlight the need of having enough diverse data in the database to create a model or representation that closely mimics reality.
A comprehensive study on disease risk predictions in machine learning IJECEIAES
Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. A Comprehensive study on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavors have been shifted.
Coronavirus disease situation analysis and prediction using machine learning...IJECEIAES
During a pandemic, early prognostication of patient infected rates can reduce the death by ensuring treatment facility and proper resource allocation. In recent months, the number of death and infected rates has increased more distinguished than before in Bangladesh. The country is struggling to provide moderate medical treatment to many patients. This study distinguishes machine learning models and creates a prediction system to anticipate the infected and death rate for the coming days. Equipping a dataset with data from March 1, 2020, to August 10, 2021, a multi-layer perceptron (MLP) model was trained. The data was managed from a trusted government website and concocted manually for training purposes. Several test cases determine the model's accuracy and prediction capability. The comparison between specific models assumes that the MLP model has more reliable prediction capability than the support vector regression (SVR) and linear regression model. The model presents a report about the risky situation and impending coronavirus disease (COVID-19) attack. According to the prediction produced by the model, Bangladesh may suffer another COVID-19 attack, where the number of infected cases can be between 929 to 2443 and death cases between 19 to 57.
Root cause analysis of COVID-19 cases by enhanced text mining processIJECEIAES
The main focus of this research is to find the reasons behind the fresh cases of COVID-19 from the public’s perception for data specific to India. The analysis is done using machine learning approaches and validating the inferences with medical professionals. The data processing and analysis is accomplished in three steps. First, the dimensionality of the vector space model (VSM) is reduced with improvised feature engineering (FE) process by using a weighted term frequency-inverse document frequency (TF-IDF) and forward scan trigrams (FST) followed by removal of weak features using feature hashing technique. In the second step, an enhanced K-means clustering algorithm is used for grouping, based on the public posts from Twitter®. In the last step, latent dirichlet allocation (LDA) is applied for discovering the trigram topics relevant to the reasons behind the increase of fresh COVID-19 cases. The enhanced K-means clustering improved Dunn index value by 18.11% when compared with the traditional K-means method. By incorporating improvised two-step FE process, LDA model improved by 14% in terms of coherence score and by 19% and 15% when compared with latent semantic analysis (LSA) and hierarchical dirichlet process (HDP) respectively thereby resulting in 14 root causes for spike in the disease.
A novel predictive model for capturing threats for facilitating effective soc...IJECEIAES
Social distancing is one of the simple and effective shields for every individual to control spreading of virus in present scenario of pandemic coronavirus disease (COVID-19). However, existing application of social distancing is a basic model and it is also characterized by various pitfalls in case of dynamic monitoring of infected individual accurately. Review of existing literature shows that there has been various dedicated research attempt towards social distancing using available technologies, however, there are further scope of improvement too. This paper has introduced a novel framework which is capable of computing the level of threat with much higher degree of accuracy using distance and duration of stay as elementary parameters. Finally, the model can successfully classify the level of threats using deep learning. The study outcome shows that proposed system offers better predictive performance in contrast to other approaches.
The susceptible-infected-recovered-dead model for long-term identification o...IJECEIAES
The coronavirus (COVID-19) epidemic has spread massively to almost all countries including Indonesia, in just a few months. An important step to overcoming the spread of the COVID-19 is understanding its epidemiology through mathematical modeling intervention. Knowledge of epidemic dynamics patterns is an important part of making timely decisions and preparing hospitals for the outbreak peak. In this study, we developed the susceptible-infected-recovered-dead (SIRD) model, which incorporates the key epidemiological parameters to model and estimate the long-term spread of the COVID-19. The proposed model formulation is data-based analysis using public COVID-19 data from March 2, 2020 to May 15, 2021. Based on numerical analysis, the spread of the pandemic will begin to fade out after November 5, 2021. As a consequence of this virus attack, the cumulative number of infected, recovered, and dead people were estimated at ≈ 3,200,000, ≈ 3,437,000 and ≈ 63,000 people, respectively. Besides, the key epidemiological parameter indicates that the average reproduction number value of COVID-19 in Indonesia is 7.32. The long-term prediction of COVID-19 in Indonesia and its epidemiology can be well described using the SIRD model. The model can be applied in specific regions or cities in understanding the epidemic pattern of COVID-19.
Clinical Simulation as an Evaluation Method in Health Inf.docxbartholomeocoombs
Clinical Simulation as an Evaluation Method
in Health Informatics
Sanne JENSEN
a,1
a
The Capital Region of Denmark, Copenhagen, Denmark
Abstract. Safe work processes and information systems are vital in health care.
Methods for design of health IT focusing on patient safety are one of many
initiatives trying to prevent adverse events. Possible patient safety hazards need to
be investigated before health IT is integrated with local clinical work practice
including other technology and organizational structure. Clinical simulation is
ideal for proactive evaluation of new technology for clinical work practice.
Clinical simulations involve real end-users as they simulate the use of technology
in realistic environments performing realistic tasks. Clinical simulation study
assesses effects on clinical workflow and enables identification and evaluation of
patient safety hazards before implementation at a hospital. Clinical simulation also
offers an opportunity to create a space in which healthcare professionals working
in different locations or sectors can meet and exchange knowledge about work
practices and requirement needs. This contribution will discuss benefits and
challenges of using clinical simulation, and will describe how clinical simulation
fits into classical usability studies, how patient safety may benefit by use of
clinical simulation, and it will describe the different steps of how to conduct
clinical simulation. Furthermore a case study is presented.
Keywords. Ergonomics, eHealth, qualitative evaluation, clinical simulation, risk,
safety.
1. Introduction
Implementation of health IT in relation to improvement of patient safety and
optimization of work flow is a paradox [1]. Even though health IT is intended and
anticipated to have a positive impact on quality and efficiency of health care [2], the
application of new technology in healthcare may also increase patient safety hazards [3,
4]. Studies show that adverse events are indeed often related to the use of technology
[5-7].
Design of health IT focusing on protecting patient safety is one of many initiatives
trying to prevent adverse events [8, 9].
2
Patient safety does not entirely rely on
technology but is highly influenced by the interaction between users and technology in
a specific context [10], and sociotechnical issues and human factors are related to many
unintended consequences and patient safety hazards [7, 8, 11]. Possible patient safety
hazards such as design of the IT system itself; embedding of IT system into local work
1
Corresponding author: Sanne Jensen, The Capital Region of Denmark, Borgervanget 7, 2100
Copenhagen O, Denmark, [email protected]
2
See also: F. Magrabi et al., Health IT for patient safety and improving the safety of health IT, in: E.
Ammenwerth, M. Rigby (eds.), Evidence-Based Health Informatics, Stud Health .
Genetic algorithm to optimization mobility-based dengue mathematical modelIJECEIAES
Implementation of vaccines, mosquito repellents and several Wolbachia schemes have been proposed recently as strategies against dengue. Research showed that the use of vaccine and repellent is highly effective when implemented to individuals who are in area with high transmission rates, while the use of Wolbachia bacteria is strongly effective when implemented in area with low transmission rates. This research is to show a three-strategy combination to cope with the dengue using mathematical model. In dengue mathematical model construction, several parameters are not yet known, therefore a genetic algorithm method was used to estimate dengue model parameters. Numerical simulation results showed that the combination of three strategies were able to reduce the number of infected humans. The dynamic of the human population with the combination of three strategies on average was able to reduce the infected human population by 45.2% in immobility aspect. Furthermore, the mobility aspect in dengue model was presented by reviewing two areas; Yogyakarta and Semarang in Indonesia. The numerical solutions showed that the trend graph was almost similar between the two areas. With the maximum effort given, the combination control values decreased slowly until the 100th day.
Covid 19 Prediction in India using Machine LearningYogeshIJTSRD
Various computational models are used around the world to predict the number of infected individuals and the death rate of the COVID 19 outbreak 3 . Machine learning based models are important to take proper actions. Due to the ample of uncertainty and crucial data, the aerodynamic models have been challenged regarding higher accuracy for long term prediction of this disease 1 . By researching the COVID19 problem, it is observed that lockdown and isolation are important techniques for preventing the spread of COVID 19 2 . In India, public health and the economical condition are impacted by COVID 19, our goal is to visualize the spread of this disease 5 . Machine Learning Algorithms are used in various applications for detecting adverse risk factors. Three ML algorithms we are using that is Logistic Regression LR , Support Vector Machine SVM , and Random Forest Classifier RFC . These machine learning models are predicting the total number of recovered patients as per the date of each state in India 8 . Sarfraj Alam | Vipul Kumar | Sweta Singh | Sweta Joshi | Madhu Kirola "Covid-19 Prediction in India using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advances in Engineering, Science and Technology - 2021 , May 2021, URL: https://www.ijtsrd.com/papers/ijtsrd42458.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/42458/covid19-prediction-in-india-using-machine-learning/sarfraj-alam
Susceptible exposed infectious recovered-machine learning for COVID-19 predi...IJECEIAES
Susceptible exposed infectious recovered (SEIR) is among the epidemiological models used in forecasting the spread of disease in large populations. SEIR is a fitting model for coronavirus disease (COVID-19) spread prediction. Somehow, in its original form, SEIR could not measure the impact of lockdowns. So, in the SEIR equations system utilized in this study, a variable was included to evaluate the impact of varying levels of social distance on the transmission of COVID-19. Additionally, we applied artificial intelligence utilizing the deep neural network machine learning (ML) technique. On the initial spread data for Saudi Arabia that were available up to June 25th, 2021, this improved SEIR model was used. The study shows possible infection to around 3.1 million persons without lockdown in Saudi Arabia at the peak of spread, which lasts for about 3 months beginning from the lockdown date (March 21st). On the other hand, the Kingdom's current partial lockdown policy was estimated to cut the estimated number of infections to 0.5 million over nine months. The data shows that stricter lockdowns may successfully flatten the COVID-19 graph curve in Saudi Arabia. We successfully predicted the COVID-19 epidemic's peaks and sizes using our modified deep neural network (DNN) and SEIR model.
Assessment of the main features of the model of dissemination of information ...IJECEIAES
Social networks provide a fairly wide range of data that allows one way or another to evaluate the effect of the dissemination of information. This article presents the results of a study that describes methods for determining the key parameters of the model needed to analyze and predict the dissemination of information in social networks. An approach based on the analysis of statistical data on user behavior in social networks is proposed. The process of evaluating the main features of the model is described, including the mathematical methods used for data analysis and information dissemination modeling. The study aims to understand the processes of information dissemination in social networks and develop recommendations for the effective use of social networks as a communication and brand promotion tool, as well as to consider the analytical properties of the classical susceptible-infected-removed (SIR) model and evaluate its applicability to the problem of information dissemination. The results of the study can be used to create algorithms and techniques that will effectively manage the process of information dissemination in social networks.
The prediction of coronavirus disease 2019 outbreak on Bangladesh perspectiv...IJECEIAES
Coronavirus disease 2019 (COVID-19) has made a huge pandemic situation in many countries of the world including Bangladesh. If the increase rate of this threat can be forecasted, immediate measures can be taken. This study is an effort to forecast the threat of present pandemic situation using machine learning (ML) forecasting models. Forecasting was done in three categories in the next 30 days range. In our study, multiple linear regression performed best among the other algorithms in all categories with R2 score of 99% for first two categories and 94% for the third category. Ridge regression performed great for the first two categories with R2 scores of 99% each but performed poorly for the third category with R2 score of 43%. Lasso regression performed reasonably well with R2 scores of 97%, 99% and 75% for the three categories. We also used Facebook Prophet to predict 30 days beyond our train data which gave us healthy R2 scores of 92% and 83% for the first two categories but performed poorly for the third category with R2 score of 34%. Also, all the models’ performances were evaluated with a 40- day prediction interval in which multiple linear regression outperformed other algorithms.
RECOMMENDER SYSTEM FOR DETECTION OF DENGUE USING FUZZY LOGICIAEME Publication
The recommender System involved in health care is important since user can detect whether he has problem or not. A user will get whole information on the go. Today user doesn’t have much time and information about the dengue and it will be disclosed to the user at later stages. The dengue is deadly disease so its information should be disclosed at earlier stage. The proposed system works toward this aspect. The set of parameters including fever, TLC, blood pressure, severe headache etc. are analysed in proposed system. The filtering mechanism is also utilised in the proposed system which is integral part of recommender system. The content based filtering will be utilised in proposed system.
Prediction analysis on the pre and post COVID outbreak assessment using machi...IJICTJOURNAL
In this time of a global urgency where people are losing lives each day in a large number, people are trying to develop ways/technology to solve the challenges of COVID-19. Machine learning (ML) and artificial intelligence (AI) tools have been employed previously as well to the times of pandemic where they have proven their worth by providing reliable results in varied fields this is why ML tools are being used extensively to fight this pandemic as well. This review describes the applications of ML in the post and pre COVID-19 conditions for contact tracing, vaccine development, prediction and diagnosis, risk management, and outbreak predictions to help the healthcare system to work efficiently. This review discusses the ongoing research on the pandemic virus where various ML models have been employed to a certain data set to produce outputs that can be used for risk or outbreak prediction of virus in the population, vaccine development, and contact tracing. Thus, the significance and the contribution of ML against COVID-19 are self-explanatory but what should not be compromised is the quality and accuracy based on which solutions/methods/policies adopted or produced from this analysis which will be implied in the real world to real people.
A data mining analysis of COVID-19 cases in states of United States of AmericaIJECEIAES
Epidemic diseases can be extremely dangerous with its hazarding influences. They may have negative effects on economies, businesses, environment, humans, and workforce. In this paper, some of the factors that are interrelated with COVID-19 pandemic have been examined using data mining methodologies and approaches. As a result of the analysis some rules and insights have been discovered and performances of the data mining algorithms have been evaluated. According to the analysis results, JRip algorithmic technique had the most correct classification rate and the lowest root mean squared error (RMSE). Considering classification rate and RMSE measure, JRip can be considered as an effective method in understanding factors that are related with corona virus caused deaths.
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
Advances in information and communication technologies have led to the emergence of Internet of Things
(IoT). In the modern health care environment, the usage of IoT technologies brings convenience to physicians and
patients since they are applied to various medical areas (such as real-time monitoring, patient information and healthcare
management). The body sensor network (BSN) technology is one of the core technologies of IoT developments in
healthcare system, where a patient can be monitored using a collection of tiny-powered and lightweight wireless sensor
nodes
IMPACT OF ARTIFICIAL INTELLIGENCE ON THE AUTOMATION OF DIGITAL HEALTH SYSTEMijseajournal
Automating digital systems in healthcare plays a significant role in transforming the quality-of-care
services delivered to patients across the board. This role is anticipated to be accomplished by the
development and implementation of artificial intelligence in healthcare which has the potential to impact
the provision of healthcare services. This paper sought to investigate the impact of adopting and
implementing artificial intelligence on the automation of digital health systems within the different levels of
healthcare. The general objective of the research study was to investigate the impact of artificial
intelligence in the automation of digital health systems. The specific goals were to understand the concept
of artificial intelligence and how it automates digital strategies, to determine the AI systems that have been
developed and implemented in the healthcare systems, to establish the factors that influence the adoption
of AI in healthcare, and to find out the outcomes of implementing AI in digital health systems. The research
employed the descriptive research design. The study population included healthcare workers,
policymakers, IT specialists, and management teams in the healthcare sector in the State of Kentucky. The
sampling technique for the study was the purposive sampling technique. The study collected data using
semi-structured interviews administered through Google Teams and Zoom. Data analysis was analyzed
using the computer-assisted software for analyzing qualitative data, NVivo. The findings were that AI as a
technological concept has the potential to impact the automation of digital health systems and is key to
automating health services such as the diagnosis and treatment of illnesses and management of claims and
payments. The study recommended that policy supports the application of artificial intelligence in
healthcare, thus enabling the automation of several healthcare services and thus improving the delivery of
care.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
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Covid 19 Prediction in India using Machine LearningYogeshIJTSRD
Various computational models are used around the world to predict the number of infected individuals and the death rate of the COVID 19 outbreak 3 . Machine learning based models are important to take proper actions. Due to the ample of uncertainty and crucial data, the aerodynamic models have been challenged regarding higher accuracy for long term prediction of this disease 1 . By researching the COVID19 problem, it is observed that lockdown and isolation are important techniques for preventing the spread of COVID 19 2 . In India, public health and the economical condition are impacted by COVID 19, our goal is to visualize the spread of this disease 5 . Machine Learning Algorithms are used in various applications for detecting adverse risk factors. Three ML algorithms we are using that is Logistic Regression LR , Support Vector Machine SVM , and Random Forest Classifier RFC . These machine learning models are predicting the total number of recovered patients as per the date of each state in India 8 . Sarfraj Alam | Vipul Kumar | Sweta Singh | Sweta Joshi | Madhu Kirola "Covid-19 Prediction in India using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advances in Engineering, Science and Technology - 2021 , May 2021, URL: https://www.ijtsrd.com/papers/ijtsrd42458.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/42458/covid19-prediction-in-india-using-machine-learning/sarfraj-alam
Susceptible exposed infectious recovered-machine learning for COVID-19 predi...IJECEIAES
Susceptible exposed infectious recovered (SEIR) is among the epidemiological models used in forecasting the spread of disease in large populations. SEIR is a fitting model for coronavirus disease (COVID-19) spread prediction. Somehow, in its original form, SEIR could not measure the impact of lockdowns. So, in the SEIR equations system utilized in this study, a variable was included to evaluate the impact of varying levels of social distance on the transmission of COVID-19. Additionally, we applied artificial intelligence utilizing the deep neural network machine learning (ML) technique. On the initial spread data for Saudi Arabia that were available up to June 25th, 2021, this improved SEIR model was used. The study shows possible infection to around 3.1 million persons without lockdown in Saudi Arabia at the peak of spread, which lasts for about 3 months beginning from the lockdown date (March 21st). On the other hand, the Kingdom's current partial lockdown policy was estimated to cut the estimated number of infections to 0.5 million over nine months. The data shows that stricter lockdowns may successfully flatten the COVID-19 graph curve in Saudi Arabia. We successfully predicted the COVID-19 epidemic's peaks and sizes using our modified deep neural network (DNN) and SEIR model.
Assessment of the main features of the model of dissemination of information ...IJECEIAES
Social networks provide a fairly wide range of data that allows one way or another to evaluate the effect of the dissemination of information. This article presents the results of a study that describes methods for determining the key parameters of the model needed to analyze and predict the dissemination of information in social networks. An approach based on the analysis of statistical data on user behavior in social networks is proposed. The process of evaluating the main features of the model is described, including the mathematical methods used for data analysis and information dissemination modeling. The study aims to understand the processes of information dissemination in social networks and develop recommendations for the effective use of social networks as a communication and brand promotion tool, as well as to consider the analytical properties of the classical susceptible-infected-removed (SIR) model and evaluate its applicability to the problem of information dissemination. The results of the study can be used to create algorithms and techniques that will effectively manage the process of information dissemination in social networks.
The prediction of coronavirus disease 2019 outbreak on Bangladesh perspectiv...IJECEIAES
Coronavirus disease 2019 (COVID-19) has made a huge pandemic situation in many countries of the world including Bangladesh. If the increase rate of this threat can be forecasted, immediate measures can be taken. This study is an effort to forecast the threat of present pandemic situation using machine learning (ML) forecasting models. Forecasting was done in three categories in the next 30 days range. In our study, multiple linear regression performed best among the other algorithms in all categories with R2 score of 99% for first two categories and 94% for the third category. Ridge regression performed great for the first two categories with R2 scores of 99% each but performed poorly for the third category with R2 score of 43%. Lasso regression performed reasonably well with R2 scores of 97%, 99% and 75% for the three categories. We also used Facebook Prophet to predict 30 days beyond our train data which gave us healthy R2 scores of 92% and 83% for the first two categories but performed poorly for the third category with R2 score of 34%. Also, all the models’ performances were evaluated with a 40- day prediction interval in which multiple linear regression outperformed other algorithms.
RECOMMENDER SYSTEM FOR DETECTION OF DENGUE USING FUZZY LOGICIAEME Publication
The recommender System involved in health care is important since user can detect whether he has problem or not. A user will get whole information on the go. Today user doesn’t have much time and information about the dengue and it will be disclosed to the user at later stages. The dengue is deadly disease so its information should be disclosed at earlier stage. The proposed system works toward this aspect. The set of parameters including fever, TLC, blood pressure, severe headache etc. are analysed in proposed system. The filtering mechanism is also utilised in the proposed system which is integral part of recommender system. The content based filtering will be utilised in proposed system.
Prediction analysis on the pre and post COVID outbreak assessment using machi...IJICTJOURNAL
In this time of a global urgency where people are losing lives each day in a large number, people are trying to develop ways/technology to solve the challenges of COVID-19. Machine learning (ML) and artificial intelligence (AI) tools have been employed previously as well to the times of pandemic where they have proven their worth by providing reliable results in varied fields this is why ML tools are being used extensively to fight this pandemic as well. This review describes the applications of ML in the post and pre COVID-19 conditions for contact tracing, vaccine development, prediction and diagnosis, risk management, and outbreak predictions to help the healthcare system to work efficiently. This review discusses the ongoing research on the pandemic virus where various ML models have been employed to a certain data set to produce outputs that can be used for risk or outbreak prediction of virus in the population, vaccine development, and contact tracing. Thus, the significance and the contribution of ML against COVID-19 are self-explanatory but what should not be compromised is the quality and accuracy based on which solutions/methods/policies adopted or produced from this analysis which will be implied in the real world to real people.
A data mining analysis of COVID-19 cases in states of United States of AmericaIJECEIAES
Epidemic diseases can be extremely dangerous with its hazarding influences. They may have negative effects on economies, businesses, environment, humans, and workforce. In this paper, some of the factors that are interrelated with COVID-19 pandemic have been examined using data mining methodologies and approaches. As a result of the analysis some rules and insights have been discovered and performances of the data mining algorithms have been evaluated. According to the analysis results, JRip algorithmic technique had the most correct classification rate and the lowest root mean squared error (RMSE). Considering classification rate and RMSE measure, JRip can be considered as an effective method in understanding factors that are related with corona virus caused deaths.
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
Advances in information and communication technologies have led to the emergence of Internet of Things
(IoT). In the modern health care environment, the usage of IoT technologies brings convenience to physicians and
patients since they are applied to various medical areas (such as real-time monitoring, patient information and healthcare
management). The body sensor network (BSN) technology is one of the core technologies of IoT developments in
healthcare system, where a patient can be monitored using a collection of tiny-powered and lightweight wireless sensor
nodes
IMPACT OF ARTIFICIAL INTELLIGENCE ON THE AUTOMATION OF DIGITAL HEALTH SYSTEMijseajournal
Automating digital systems in healthcare plays a significant role in transforming the quality-of-care
services delivered to patients across the board. This role is anticipated to be accomplished by the
development and implementation of artificial intelligence in healthcare which has the potential to impact
the provision of healthcare services. This paper sought to investigate the impact of adopting and
implementing artificial intelligence on the automation of digital health systems within the different levels of
healthcare. The general objective of the research study was to investigate the impact of artificial
intelligence in the automation of digital health systems. The specific goals were to understand the concept
of artificial intelligence and how it automates digital strategies, to determine the AI systems that have been
developed and implemented in the healthcare systems, to establish the factors that influence the adoption
of AI in healthcare, and to find out the outcomes of implementing AI in digital health systems. The research
employed the descriptive research design. The study population included healthcare workers,
policymakers, IT specialists, and management teams in the healthcare sector in the State of Kentucky. The
sampling technique for the study was the purposive sampling technique. The study collected data using
semi-structured interviews administered through Google Teams and Zoom. Data analysis was analyzed
using the computer-assisted software for analyzing qualitative data, NVivo. The findings were that AI as a
technological concept has the potential to impact the automation of digital health systems and is key to
automating health services such as the diagnosis and treatment of illnesses and management of claims and
payments. The study recommended that policy supports the application of artificial intelligence in
healthcare, thus enabling the automation of several healthcare services and thus improving the delivery of
care.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
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Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
An agent-based model to assess coronavirus disease 19 spread and health systems burden
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 12, No. 4, August 2022, pp. 4118~4128
ISSN: 2088-8708, DOI: 10.11591/ijece.v12i4.pp4118-4128 4118
Journal homepage: http://ijece.iaescore.com
An agent-based model to assess coronavirus disease 19 spread
and health systems burden
Madhavarao Seshadri Narassima1
, Singanallur Palanisamy Anbuudayasankar1
, Guru Rajesh Jammy2
,
AnanthaPadmanabhan Sankarshana3
, Rashmi Pant2
, Lincoln Choudhury4
, Vijay Yeldandi2
,
Shubham Singh5
, Denny John6
1
Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India
2
Society for Health, Allied Research and Education, Hyderabad, India
3
Department of Computer Science Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India.
4
Krashapana Consultancy Private limited, New Delhi, India
5
Tata Consultancy Services Limited, Mumbai, India
6
Department of Public Health, Amrita Institute of Medical Sciences and Research Centre, Amrita Vishwa Vidyapeetham, Kochi, India
Article Info ABSTRACT
Article history:
Received Jun 14, 2021
Revised Mar 24, 2022
Accepted Apr 8, 2022
The present pandemic has tremendously raised the health systems’ burden
around the globe. It is important to understand the transmission dynamics of
the infection and impose localized strategies across different geographies to
curtail the spread of the infection. The present study was designed to assess
the transmission dynamics and the health systems’ burden of severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2) using an agent-based
modeling (ABM) approach. The study used a synthetic population with
31,738,240 agents representing 90.67 percent of the overall population of
Telangana, India. The effects of imposing and lifting lockdowns, non-
pharmaceutical interventions, and the role of immunity were analyzed. The
distribution of people in different health states was measured separately for
each district of Telangana. The spread dramatically increased and reached a
peak soon after the lockdowns were relaxed. It was evident that is the
protection offered is higher when a higher proportion of the population is
exposed to the interventions. ABMs help to analyze grassroots details
compared to compartmental models. Risk estimates provide insights on the
proportion of the population protected by the adoption of one or more of the
control measures, which is of practical significance for policymaking.
Keywords:
Agent-based model
Coronavirus disease
SARS-CoV-2
Non-pharmaceutical
interventions
India
This is an open access article under the CC BY-SA license.
Corresponding Author:
Singanallur Palanisamy Anbuudayasankar
Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham
Coimbatore, India
Email: sp_anbu@cb.amrita.edu
1. INTRODUCTION
The first case of coronavirus disease (COVID-19) in India was reported on January 30, 2020, post
which “public health emergency of international concern” was declared by the World Health Organization
(WHO) considering the impact it could create globally [1]. The epidemic has spread across 221 countries
with 228,946,779 reported cases and 4,700,214 mortalities globally as of September 19, 2021 [2]. In India,
total infections reported are 1,73,06,420 with 28,07,388 active cases, 1,42,96,703 recoveries and
1,95,118 deaths, till April 25, 2021 [3]. The influx of infections has bothered the countries with a denser
population [1]. Several factors such as gender, pollution level, viral load, comorbidities, and others also
govern the intensity and duration of infection [4]. A larger proportion of the infected people remaining
asymptomatic has further raised serious concerns as they are indistinguishable and act as the potential
2. Int J Elec & Comp Eng ISSN: 2088-8708
An agent-based model to assess corona virus disease 19 spread and … (Madhavarao Seshadri Narassima)
4119
sources of transmitting the infection [4], [5]. The healthcare fraternity, researchers, and policymakers from
several domains have been trying hard to curtail the transmission of the infection completely [6]. Simulation
studies in the past have been successful in addressing issues like preparing evacuation plans for airborne
infections [7], devising vaccination strategies for influenza [8], smallpox [9], containing measles [10], and
tuberculosis [11]. Agent-based models (ABM) have supported various application areas such as dynamics of
transmission [12], tracking contacts [13], scheduling time and geography dependent contacts [12], [14],
planning non-pharmaceutical interventions (NPI) [14], [15] such as enforcing lockdowns [12], [13], using
face masks, and adopting social distancing [14], and shielding the susceptible population [14], [16].
In India, most researches have employed a compartmental approach based on the susceptible (S),
infective (I), and recovered (R) model with modifications such as susceptible (S), exposed (E), infective (I),
and recovered (R) (SEIR) [17], susceptible (S), hospitalized or quarantined (H), symptomatic (I), purely
asymptomatic (P), exposed (E), recovered (R) and deceased (D) (SIPHERD) [18], and analytical models
[19]. Simulations are used to study the real-life systems’ behavior in its existing state and upon implementing
modifications without associated risks and investment of time and cost [20], [21]. However, the accuracy is
subject to authenticity of data, constraints, and assumptions [21]. Complex and dynamic problems could be
effectively addressed using simulations [22]. ABM, discrete event simulation (DES), and system dynamics
(SD) are three broad classifications of simulations. ABMs entitles the users to define agent-level details [22],
[23] and are capable of reporting details of individual agents while DES and SD provide only collective
measures [24]. Each agent in the population can be simulated based on different conditions and can be made
to perform different actions. A bottom-up approach is employed by ABM wherein the behavior of each agent
contributes to the behavior of the system. Each agent holds a specified state at any instant of the simulation
[23]. Technological advancements have improved the capability of systems to handle complex models [22].
2. RESEARCH METHOD
From the literature, it was evident that most of the studies to assess the transmission dynamics of
infectious diseases employ compartmental models, which fail to incorporate agent-level details. Hence, the
present study aims to provide an ABM-based simulation to estimate the spread of COVID-19 by developing
a disease model and simulating it using Python. Such simulation studies based on synthetic populations could
be helpful for the policymakers and healthcare systems to equip themselves based on the estimates. The
present study simulates agents of the synthetic population that represent 90.67% (n=35,003,674) of the
overall population of a state. The parameters such as age and geographic information system (GIS)
coordinates have been mapped to each individual in the population to ensure exact representation of the state.
The incorporation of such agent-level details would help in effectively devising policies locally. Analyzing
the NPIs and risk estimates have practical significance in terms of policymaking and governance. In
accordance with the Swiss cheese model, the combined effect of multiple interventions on curtailing the
spread of infection has been analyzed. Hospitals have been benefited from this approach of setting up
multiple defense strategies [25], [26].
2.1. Research design
An ABM approach is employed to assess the outbreak of COVID-19 and its burden on health
systems using Telangana state’s synthetic population as shown in Table 1. The code for simulation was
developed in python, an object-oriented programming (OOP) language using PyCharm, an integrated
development environment. The model was simulated for 365 days for various lockdown strictness as per the
Indian scenario [27], [28]. The main functionalities of the code involve creating agents, defining contact
networks, developing a disease model, devising interventions, and simulating. Transparency of code,
assumptions, variables, and scope of the study are retained throughout in adherence with the ethical good
practices in modeling and the International Society for Pharmacoeconomics and Outcomes Research
(ISPOR-SMDM) modeling good research practices [29]–[31].
2.2. Agent creation
The main idea to employ an ABM was to represent the population of a state by defining the actual
attributes to each of them. Data of 31,738,270 people were taken from the 2011 census of India to generate
the synthetic population of Telangana. Unique identifiers for person and household, district codes, and
geocoordinates were mapped to the agents. During the data cleansing process, 30 invalid entries were
eliminated to obtain 35,003,674 valid records that represent 90.67% of the state’s population as shown in
Table 1 [32], [33].
3. ISSN: 2088-8708
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4120
Table 1. Model parameters
Parameters <5 5-59 >59 References
Number of contacts per day Supplementary [28] [34], [35]
Probability of getting infected through contact (%) i) closer circle: (3 to 10); ii) other contacts: (1 to 5) [34]
Proportion of people remaining asymptomatic 0.8 [36], [37]
Average incubation period (in days) 5 [23], [38]
Average treatment duration (in days) 14 [38]
Proportion of hospitalized cases in ICU 0.11 [39], [40]
Treatment duration in ICU (in days) Triangular (7, 8, 9) [38], [40]
Proportion of people moving from ICU to critical illness (Ventilator) 0.88 [40]
Treatment duration in ventilator state (in days) Triangular (5, 7, 12) [38]
Time between symptom arrival and admission (in days) 3 [41]
Proportion of people who die As per Indian statistics [3]
Risk difference for use of control measures (percentage) i) Mask: 10.2; ii) Distancing: 14.3 [42]
2.3. Contact network
The spread of infection is majorly governed by transmission rates and contact networks. The rate of
transmission was varied from 3 to 10% and 1 to 5% for external contacts and household contacts respectively
[34]. Contact rates for the present study were assumed to be density-dependent to be varied rationally across
districts [15]. The probability with which any two agents meet was assumed to be inversely proportional to
the distance between them. Kumar et al. [34] conducted a study in Ballabgarh, India to define contact rates
for close-contact infections. This was used in integration with the density-dependent contact rate assumption
to determine the contact distributions for all the districts. A multiplication factor (ratio of population density
of the district under consideration to that of Ballabgarh) was used to find the proportionate corresponding
contact rates for each district [15], [34]. Input analyzer tool of arena that helps to fit datasets into various
distributions with corresponding errors was utilized to derive the distributions for contact rates of each
district [43].
2.4. Disease model
Disease models depict the progression of any disease through various states that govern the behavior
of agents. Each agent exists in one of the states at any point in time which changes based on the conditions
presented in the statechart as shown in Figure 1. State chart indicates the state of existence of agents in the
simulation.
Figure 1. Statechart
4. Int J Elec & Comp Eng ISSN: 2088-8708
An agent-based model to assess corona virus disease 19 spread and … (Madhavarao Seshadri Narassima)
4121
At any instant, each agent can exist in one of the states mentioned in the statechart. These are
governed by the actions that are defined for each agent during the simulation. The interaction of agents drives
the transmission of infection from an infected to a healthy agent. Post transmission of infection, agents turn
to be either asymptomatic or symptomatic. The latter undergo treatment after the incubation period while the
former are untraceable and are not admitted for treatment. However, they spread the infection till recovery.
Symptomatic agents further traverse along three states admitted, intensive care unit (ICU) and ventilator
during which they either recover or decease. The conditions that govern the progression across states with the
period of existence in each state are shown in Table 1.
2.5. Model initialization
Various variables that were used to develop the model were obtained from secondary sources
including those from the models of infectious disease agent study (MIDAS) [44]. Spatial resolution cannot be
attributed as the paths of agents are not considered rather the geo-coordinates are only taken into account. A
time step of one day was considered for the simulation.
2.6. Model simulation
The Python code was simulated for the six scenarios defined in Table 2 to compare the effects of
various NPIs [45]. The variations in strictness of lockdowns were taken care of by altering the contacts of
agents at different locations such as home, schools, and work, depending on the place and age. The contacts
outside the home were reduced in accordance with the stringency of lockdowns.
Table 2. Risk estimations
Duration
(days)
Scenario
Number of people
infected
Relative Risk (95%
CI)
Attributable Risk (AR)
(95% CI)
PAR
PAR
%
Unexposed Exposed
0 to 104 MD100I90 2554 69371 NA NA
MD75I90 21310 31228 0.48 (0.471, 0.506) -0.001 (-0.0014, -0.00137) -0.001 -
60.41
MD50I90 20348 14235 0.70 (0.678, 0.721) -0.0004(-0.00042,
-0.00038)
-0.0002 -
18.36
MD100I180 2100 33437 NA NA
MD75I180 32016 55428 0.58 (0.563, 0.591) -0.0017(-0.00174,
-0.00166)
-0.0013 -
47.18
MD50I180 19685 12586 0.64 (0.617, 0.662) -0.0004(-0.00042,
-0.00038)
-0.0002 -
19.67
105 to 204 MD100I90 0 6547839 NA NA
MD75I90 1938398 4330703 0.74 (0.743, 0.746) -0.0624(-0.0627, -0.0621) -0.0468 -
23.69
MD50I90 3266488 2541571 0.78 (0.777, 0.78) -0.0457 (-0.046, -0.0454) -0.0228 -
12.46
MD100I180 0 5668435 NA NA
MD75I180 1693952 3907223 0.77 (0.767, 0.77) -0.0493 (-0.0496, -0.049) -0.037 -
20.96
MD50I180 3382489 2782060 0.82 (0.821, 0.824) -0.0378 (-0.0385, -0.0375) -0.0189 -9.74
205 to 304 MD100I90 0 5999937 NA NA
MD75I90 1841228 4716186 0.85 (0.852, 0.855) -0.034 (-0.0342, -0.0336) -0.0254 -
12.29
MD50I90 3332683 2445789 0.73 (0.732, 0.735) -0.056 (-0.0562, -0.0556) -0.0279 -
15.32
MD100I180 0 1171268 NA NA
MD75I180 427366 1008006 0.79 (0.783, 0.79) -0.011 (-0.0117, -0.0113) -0.0086 -
19.01
MD50I180 544102 297366 0.54 (0.542, 0.551) -0.015 (-0.0156, -0.0154) -0.0078 -
29.42
3. RESULTS AND DISCUSSION
The effects of various NPIs such as lockdowns with varied stringency, adoption of social distancing,
and use of face masks along with the impact of immunity on the spread of infection was observed over 365
days by simulation of six scenarios. The six scenarios would be referred to as MD100I90, MD75I90,
MD50I90, MD100I180, MD75I180, and MD50I180 in subsequent sections. The numbers after ‘MD’ and ‘I’
indicate the proportion of the population exposed to control measures and immunity period (days)
post-recovery respectively. Time-series graphs representing the number of people in asymptomatic and
symptomatic states from the overall population are presented in Figure 2. The supplementary file contains
time series data of individual districts.
5. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 12, No. 4, August 2022: 4118-4128
4122
Figure 2 reveals that the spread of infection has risen sharply after the lifting of lockdowns. The
reason for subsequent spikes in Figure 2(a) as compared to Figure 2(b) and also in Figure 2(c) as compared to
Figure 2(d) is due to the loss of immunity in Figures 2(a) and 2(c) after 90 days of recovery. Lockdowns
prove to be the most effective control measure as the variation in terms of infections reduced relatively lesser
even though a higher proportion of the population adopt social distancing and mask. However, there is a
decline in the peaks of graphs in scenarios with a larger population exposed to interventions.
(a) (b)
(c) (d)
Figure 2. Time trend of infections (a) number of asymptomatic people (90 days immunity), (b) number of
asymptomatic people (180 days immunity), (c) number of symptomatic people (90 days immunity),
and (d) number of symptomatic people (180 days immunity)
The possibilities of subsequent peaks are higher in the absence of vaccination, as observed in
Figure 3. These graphs could improve the preparedness of the healthcare systems and shed light on the
capacity required to treat the admitted infections as shown in Figures 3(a) and 3(b), make arrangements for
ICU as shown in Figures 3(c) and 3(d), and ventilators as shown in Figures 3(e) and 3(f). The secondary
infections are observed as people start to lose their immunity over time. There is a spike in the number of
deceased people post-lockdown with subsequent peaks in accordance with the trend of infections. Prolonged
immunity provides an additional time window for planning the capacity and vaccination policies. Figure 4
shows a similar pattern with some temporal offset governed by the duration of existence in the preceding
states. The second spike in cumulative infections is earlier in case of Figure 4(a) as compared to that of
Figure 4(b) due to shorter immunity. Proportionate spikes are seen in Figures 4(c) and (d) indicating recovery
post infections. Despite the high recovery rate, untraceable asymptomatic people pose a major challenge for
curtailing the spread as they are highly untraceable to be isolated.
The risk estimates as shown in Table 2 reveal the level of protection offered through various
interventions. The interpretation of these estimates are: i) relative risk (RR) is the probability of an event
occurring to exposed vs unexposed groups; ii) attributable risk (AR) indicates the excess risk due to a risk
factor. A negative value indicates protection offered; iii) population attributable risk (PAR) indicates the
percentage of cases in the total population that can be attributed to the risk factor; and iv) PAR% is the
proportion of the incidence of disease in the population due to exposure. The first period of 104 days
indicates the period after which the first recovered person would lose immunity. Successively, these
parameters are calculated for further time intervals to analyze how they vary for different lockdown and
intervention scenarios. The RR being lesser than 1 denotes that the exposure offers protection rather than risk
6. Int J Elec & Comp Eng ISSN: 2088-8708
An agent-based model to assess corona virus disease 19 spread and … (Madhavarao Seshadri Narassima)
4123
[46]. Lifting of lockdowns is the reason for the increase in RR over time indicating the reduction in the
protection offered. Owing to the higher protection imparted by the exposure, MD75I90 has a lower RR and
PAR values than MD50I90 in the first two timeframes as a higher proportion of the population are exposed in
the former scenario. The fact that higher protection is offered during stricter lockdowns is evident from and
PAR%. The lifting of lockdowns on the 143rd
day as shown in Table 3 has accelerated the transmission
which has caused the values to peak drastically. The scenarios corresponding to 100 percent exposure i.e., the
entire population follows control measures, have the least peak values.
The research by the center for disease dynamics, economics and policy (CDDEP) and Princeton
University complements the present study as it provides information on the estimated state-wise surge in
India to help the healthcare fraternity to equip themselves [47]. Considering some other parameters such as
clustering in contact networks, especially in the context of the spread of infections would provide improved
results. The inclusion of movement patterns along with GIS would enhance the accuracy of estimates. Using
wearable devices would offer real-time tracking of COVID-19 patients [48]. The structure of communication
networks could be studied deeper to establish contact networks [49], [50]. Detailed analyses on dynamics of
population and contact patterns have a strong scope to understand the spread of infections better.
(a) (b)
(c) (d)
(e) (f)
Figure 3. Time trend of hospitalized cases (a) number of admitted people (90 days immunity), (b) number of
admitted people (180 days immunity), (c) number of people in ICU (90 days immunity), (d) number of
people in ICU (180 days immunity), (e) number of people on ventilators (90 days immunity), (f) number of
people on ventilators (180 days immunity)
7. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 12, No. 4, August 2022: 4118-4128
4124
(a) (b)
(c) (d)
Figure 4. Total infections and recoveries (a) number of cumulative infections (90 days immunity), (b)
number of cumulative infections (180 days immunity), (c) number of recoveries (90 days immunity),
and (d) number of recoveries (180 days immunity)
Table 3. Peak values
Peak values/
Scenarios
MD100I90 MD75I90 MD50I90 MD100I180 MD75I180 MD50I180
Value Day Value Day Value Day Value Day Value Day Value Day
Asymptomatic 2491332 160 2950047 157 2842052 158 2416859 161 2440704 154 3341037 161
Symptomatic 416618 152 498176 152 513427 152 377067 155 405197 152 532776 154
Admitted 538683 166 639556 166 634877 166 511264 168 516807 164 715501 167
ICU 37256 176 44739 175 46224 176 34393 179 37176 175 47353 178
Ventilator 29641 184 35580 183 36589 184 27389 187 29574 183 37891 186
Immune 6443023 228 6508198 333 6620998 358 6724187 323 6853529 302 6895030 325
4. CONCLUSION
Localized research just as the present one provides tailored and accurate insights that are more
suitable to be materialized by policymakers for specific geographies. The use of the ABM approach promotes
the level of detail offered to individuals in the population. Important factors such as protective factors could
provide insights on the proportion of the population that would be shielded by imposing control measures. A
total of 31738240 agents that represent 90.67% of Telangana’s population were generated to be used for
simulation. The simulation coded in python was run to compare the six different NPI scenarios for 365 days.
Time series corresponding to each health state were obtained for each district to get localized measures that
could help policymaking. The study also measures the effect of the use of control measures and the role of
immunity in the spread of infection. Understanding the variation in the spread of infection with respect to the
interventions provide better insights to the policymakers on how to strategize the policies to curtail the spread
in different areas. Defining interactions of agents based on GIS coordinates and considering contacts at
workspace and closer circle allow us to show variations in the spread of disease during different lockdown
setups. This has more practical implications to deliver healthcare services with capacity requirements to more
vulnerable people. The ethical good practices in modeling and ISPOR-SMDM modeling good research
practices have been adopted throughout the study. As evident from the results, the interventions help to
curtail the transmission of the infection which provides more time window for the policymakers to devise apt
strategies locally and to research on developing vaccination programs. Lockdown was found to be the most
efficient intervention to curtail the spread as its lifting drastically increased the infections in a much shorter
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time. The risk estimates further support these as the RR, AR, and PAR% values revealed higher protection
during the lockdown period and in scenarios where a higher proportion of the population followed control
measures. These values indicated the level of protection offered in each scenario and the proportion of the
population that could be shielded by the control measures (exposure). The effect of immunity provides
information about possible secondary infections after the loss of immunity. These estimates could be of
practical significance to plan the interventions based on the population to be shielded. Limitations to the
study include the exclusion of comorbidities, transportation modes, and indirect transmission through
suspended particles, which could be considered to improve the accuracy.
DATA AVAILABILITY
The python code, supplementary file, and detailed district-wise estimates files shall be shared by the
authors upon request.
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BIOGRAPHIES OF AUTHORS
Madhavarao Seshadri Narassima is a Research Scholar in the Department of
Mechanical Engineering at Amrita Vishwa Vidyapeetham, India. He completed his M.Tech
in Manufacturing Engineering and B.Tech Mechanical Engineering from Amrita Vishwa
Vidyapeetham. He has industrial experience of 3 years in the area of data analytics, system
modelling, and supply chain. Presently, he has published several papers in international
journals and has attended International Conferences. He serves as a reviewer in multiple
International journals. His areas of interest include Supply Chain Management, Simulation,
and Modelling, Statistical analysis, Production management, and algorithms. He can be
contacted at email: msnarassima@gmail.com.
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Singanallur Palanisamy Anbuudayasankar works as a Professor in the
Mechanical Engineering Department at the School of Engineering, Amrita Vishwa
Vidyapeetham, Coimbatore, India. He holds a Bachelor’s degree in Mechanical Engineering,
a Master’s degree in Industrial Engineering, a Management degree in Production. He holds a
Doctorate in Supply chain and Logistics. His main research interests include optimization,
modelling and simulation, and supply chain. He has published several papers and book
chapters in national and international journals and conferences. He also serves on the
editorial board of peer-reviewed International Journals. He can be contacted at email:
sp_anbu@cb.amrita.edu.
Guru Rajesh Jammy is a medical epidemiologist and a public health
professional with more than a decade of enriching experience and expertise, backed by
education (MBA, MD and Ph.D.). He has technically led public health implementation
programs (HIV, TB, and Surveillance) in India funded through the Centers for Disease
Control and Prevention (CDC), Atlanta, USA. He served as a key researcher for several
research projects in the areas of infectious diseases, disease modelling, maternal-child health,
geriatrics, and harmonization of data from various cohort studies globally. He has secured
administrative supplement research grants from Fogarty International Center of NIH, USA
on HIV and disease modelling. He has also secured funding from the National Biopharma
Mission, Department of Biotechnology, Government of India for conducting an
Epidemiological study on COVID-19, Dengue, and Chikungunya. He has served as a
short-term consultant at the World Bank in the areas of quality of care, NCDs, and
COVID-19. He is interested to work on evidence-based planning, management, and
decision-making towards strengthening health systems. He can be contacted at email:
jammyrajesh1@gmail.com.
AnanthaPadmanabhan Sankarshana is an undergraduate student in the
Department of Computer Science Engineering, Amrita School of Engineering, Coimbatore,
Amrita Vishwa Vidyapeetham, India. He has built a full-stack project to maintain and
display attendance records of employees in a private company using MySQL database, Java
backend, HTML, CSS, Javascript frontend. He developed a detection software to detect if
people are wearing masks. He has worked on various real-time projects involving coding. He
can be contacted at email: sankarshanaprofessional@gmail.com.
Rashmi Pant is Director of Data Sciences At Share India And a senior public
health research professional with over 14 years of experience in evidence-based research,
dissemination and teaching. Her core competencies in monitoring and evaluation, research
design, quantitative and qualitative analysis, and scientific writing. She is the Principal
Investigator for the BigData for Epidemiology project at Share India and is involved in
several other projects related to infectious diseases and maternal and child health. He can be
contacted at email: rashmi.pant@sharefoundations.org.
Lincoln Choudhury has worked for twenty years in public health including
multilateral and bilateral (WHO UNAIDS, UNDP, World Bank) and NGOs to provide
technical, operational, and management support. He has extensive experience in the design
and implementation of results base financing, institutional arrangement for decentralized
planning and implementation of public health programs, and demonstrated task shifting for
improving service delivery. He has implemented public health projects following a multi-
sectoral approach, finding linkages with water sanitation, rural development, and the
education sector. He has hands-on experience in the implementation of large-scale public
health programs on the ground in coordination with various state governments and donor
partners. He has authored several publications, research papers, and policy briefs in public
health including the COVID-19 epidemic, Nutrition, HIV epidemic, Technical and
Allocative efficiency analysis. Lincoln is pursuing a Ph.D. in public health and has
completed a postgraduate in public health, MBBS additional Credential includes a diploma
in public procurement and a Certified national level external evaluator for NQAS. He can be
contacted at email: lincolnchoudhury@gmail.com.
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Vijay Yeldandi M.D., FACP, FCCP, FIDSA is a specialist in infectious
diseases with a special interest in disorders of the immune system primarily recipients of
transplants. An educator for his entire career. He is board-certified in Infectious Diseases
(ABIM, USA) and is currently the head of Infectious Diseases and Public Health at Share
India www.shareindia.org and a Clinical Professor of Medicine and Surgery at the University
of Illinois at Chicago www.uihealth.care. Faculty at Public Health Foundation of India
www.phfi.org His work over the last three decades includes i) laboratory research in
antimicrobial resistance and molecular genetics ii) Translational research in infectious
disease diagnostics iii) Clinical research in transplant infectious diseases; HIV epidemiology
iv) Antimicrobial development and clinical trials v) Public Health in India vi) Infection
Prevention and Patient Safety vii) Six Sigma (Black Belt) in health care. His work in
providing technical support to the National AIDS Control Organization of the Ministry of
health and family welfare, Government of India has been funded by the United States
Centers for Disease Control and Prevention: (2005-2025). He is a Fullbright Specialist from
December 2019-December 2022. His work has been recognized by several awards for
excellence in teaching and leadership. He is a member of the Community of Practice for
“Implementing Public Policy” Harvard Kennedy School of Government. He can be
contacted at email: miidridirector@hotmail.com.
Shubham Singh currently works in Tata Consultancy Services Limited,
Mumbai, India. He completed his B.Tech in Computer Science and Engineering from BBD
NITM (Uttar Pradesh Technical University), Lucknow, India. He has industrial experience of
4+ years in the area of data analytics, system modelling, and application development. He
works in application development and performance enhancement of software applications.
His areas of interest include Problem Solving, and Algorithms. He can be contacted at email:
tantric.singh73@gmail.com.
Denny John is Adjunct Assistant Professor, Department of Public Health,
Amrita Institute of Medical Sciences and Research Centre, Amrita University, Kochi, India.
He is Chair, Campbell and Cochrane Economic Methods Group (CCEMG), and Co-Chair,
Early Career Network, Health Technology Assessment International (HTAi). He is a
Member of the Management Board, IIHMR University, Jaipur. He has also been associated
as Adjunct Scientist, at National Institute of Medical Statistics, Indian Council of Medical
Research (ICMR), where he engaged in research/training on evidence synthesis, health
economics, and health technology assessment in India. He has served as an Expert Member
in the Advisory Boards of the World Health Organization (WHO) and ICMR. Denny has
published over 85 academic peer-reviewed articles and book chapters in a wide array of
academic journals and presses. He is Associate Editor for Systematic Reviews, BMC Public
Health, Cost-Effectiveness and Resource Allocation, and Cochrane EPOC journals. Denny
has been awarded several international and national grants from government and funding
bodies. He participates extensively in academic activities, conducts innovative research
training activities, and provides teaching programs nationally and overseas. He supervises a
number of master and doctoral students nationally and abroad. Highlights include; over 15
years of engagement in healthcare management, public health, health economics, evidence
synthesis, and health technology assessment in various countries (e.g. India, Bangladesh,
Nigeria, Sri Lanka, and the USA) through research, fieldwork, and consulting experiences.
He was a Commonwealth Scholarship recipient for his MPH studies at the University of
Auckland, New Zealand. He can be contacted at email: djohn1976@gmail.com.