Figure - available from: Neural Computing and Applications
This content is subject to copyright. Terms and conditions apply.
Source publication
The coronavirus pandemic has been globally impacting the health and prosperity of people. A persistent increase in the number of positive cases has boost the stress among governments across the globe. There is a need of approach which gives more accurate predictions of outbreak. This paper presents a novel approach called diffusion prediction model...
Similar publications
Several Coronaviruses (CoVs) are epidemic pathogens that cause severe respiratory syndrome and are associated with significant morbidity and mortality. In this paper, a machine learning method was developed for predicting the risk of human infection posed by CoVs as an early warning system. The proposed Spike-SVM (Support vector machine) model achi...
Purpose
This research was designed to investigate the application of artificial intelligence (AI) in the rapid and accurate diagnosis of coronavirus disease 2019 (COVID-19) using digital chest X-ray images, and to develop a robust computer-aided application for the automatic classification of COVID-19 pneumonia from other pneumonia and normal image...
The deadly coronavirus virus (COVID-19) was confirmed as a pandemic by the World Health Organization (WHO) in December 2019. It is important to identify suspected patients as early as possible in order to control the spread of the virus, improve the efficacy of medical treatment, and, as a result, lower the mortality rate. The adopted method of det...
Early diagnosis of COVID-19, the new coronavirus disease, is considered important for the treatment and control of this disease. The diagnosis of COVID-19 is based on two basic approaches of laboratory and chest radiography, and there has been a significant increase in studies performed in recent months by using chest computed tomography (CT) scans...
Automated disease prediction has now become a key concern in medical research due to exponential population growth. The automated disease identification framework aids physicians in diagnosing disease, which delivers accurate disease prediction that provides rapid outcomes and decreases the mortality rate. The spread of Coronavirus disease 2019 (CO...
Citations
... There may have been respondent bias through either exaggeration of answers or information retainment, especially for government-related questions. Finally, although logistic regression could accurately predict factors associated with good knowledge, positive attitude, good practice and good perception, other more robust machine learning techniques could have generated predictors of compliance to government preventive measures with more accuracy and precision as reported by some studies (40)(41)(42)(43). ...
Background
Several governments from African countries, including the Democratic Republic of the Congo (DRC), implemented stringent public health measures to curb COVID-19 transmission in the early phases of the pandemic. While these restrictive measures are believed to have contributed to lowering case incidence and related mortality in DRC, data on the population’s knowledge and adherence are limited. This study aimed to assess the knowledge, perception, attitudes, and practices of COVID-19 preventive measures and associated factors among adult residents of Matadi, thereby generating evidence for a strategy adjustment as the COVID-19 response is transitioning from emergency to control status.
Methods
We used data from a population-based cross-sectional study conducted in October 2021. Consenting participants were enrolled through a multi-stage cluster sampling approach and administered a pre-tested structured questionnaire using a mobile application (Epicollect 5). We analyzed adult participants’ data using STATA 15.1. Univariable and multivariable analyses were applied to identify factors associated with good knowledge, good perception, positive attitude and good practice.
Results
We included 1,269 adult respondents for the secondary analysis. One respondent in six was female. The median age was 36 years (IQR 24–50). Most respondents (76.5%) had good knowledge. Respondents aged 40–49 years and those with vocational education level were 1.7 time (AOR 1.75, 95% CI 1.07–2.87) and twice as likely (AOR 2.06, 95% CI 1.01–4.21) to have good knowledge. Preventive measures were perceived as efficient by 45% of respondents. Good perception was associated with education level, profession, average household monthly income and good knowledge. Only 40% of respondents had a positive attitude. A positive attitude was associated with age, education level, and good knowledge. Respondents having good practice represented 5.8%. Good practice was associated with good knowledge, attitude and perception.
Conclusion
Most respondents were knowledgeable, had a good perception of government-related COVID-19 preventive measures, a moderately positive attitude and an extremely low level of good practice. Current COVID-19 preventive strategies, including vaccination rollout, need adjustment into high-efficiency, context-based and risk group-specific interventions. Evidence generated by this study will improve preparedness and response to future outbreaks.
... As a result of the test, the proposed model is very successful in detecting the COVID-19 virus. Raheja et al. (2023) proposed a diffusion prediction model for prediction of number of COVID-19 cases in India, France, China and Nepal countries. They compared the proposed model with other stateof-the art models. ...
The development of vaccines and drugs is very important in combating the coronavirus disease 2019 (COVID-19) virus. The effectiveness of these developed vaccines and drugs has decreased as a result of the mutation of the COVID-19 virus. Therefore, it is very important to combat COVID-19 mutations. The majority of studies published in the literature are studies other than COVID-19 mutation prediction. We focused on this gap in this study. This study proposes a robust transformer encoder based model with Adam optimizer algorithm called TfrAdmCov for COVID-19 mutation prediction. Our main motivation is to predict the mutations occurring in the COVID-19 virus using the proposed TfrAdmCov model. The experimental results have shown that the proposed TfrAdmCov model outperforms both baseline models and several state-of-the-art models. The proposed TfrAdmCov model reached accuracy of 99.93%, precision of 100.00%, recall of 97.38%, f1-score of 98.67% and MCC of 98.65% on the COVID-19 testing dataset. Moreover, to evaluate the performance of the proposed TfrAdmCov model, we carried out mutation prediction on the influenza A/H3N2 HA dataset. The results obtained are promising for the development of vaccines and drugs.
... However, it requires extensive training and a large, well-annotated dataset to enhance the model's performance. Supervised DL algorithms play a crucial role in predictive analysis, demonstrating the substantial potential and providing solutions for various issues (Raheja et al., 2021). Conversely, Unsupervised DL algorithms have the potential to identify multiple hidden patterns in healthcare data (Raza and Singh 2021). ...
The severe progression of Diabetes Mellitus (DM) stands out as one of the most significant concerns for healthcare officials worldwide. Diabetic Retinopathy (DR) is a common complication associated with diabetes, particularly affecting individuals between the ages of 18 and 65. As per the findings of the International Diabetes Federation (IDF) report, 35–60% of individuals suffering from DR possess a diabetes history. DR emerges as a leading cause of worldwide visual impairment. Due to the absence of ophthalmologists worldwide, insufficient health resources, and healthcare services, patients cannot get timely eye screening services. Automated computer-aided detection of DR provides a wide range of potential benefits. In contrast to traditional observer-driven techniques, automatic detection allows for a more objective analysis of numerous images in a shorter time. Moreover, Unsupervised Learning (UL) holds a high potential for image classification in healthcare, particularly regarding explainability and interpretability. Many studies on the detection of DR with both supervised and unsupervised Deep Learning (DL) methodologies are available. Surprisingly, none of the reviews presented thus far have highlighted the potential benefits of both supervised and unsupervised DL methods in Medical Imaging for the detection of DR. After a rigorous selection process, 103 articles were retrieved from four diverse and well-known databases (Web of Science, Scopus, ScienceDirect, and IEEE). This review provides a comprehensive summary of both supervised and unsupervised DL methods applied in DR detection, explaining the significant benefits of both techniques and covering aspects such as datasets, pre-processing, segmentation techniques, and supervised and unsupervised DL methods for detection. The insights from this review will aid academics and researchers in medical imaging to make informed decisions and choose the best practices for DR detection.
... Linear regression algorithm only works when the relationship between the data is linear but in case of epidemic diseases, trends in the data are more sophisticated (Yang et al., 2023). In our case we cannot use a linear model as it will be unable to achieve the required amount of accuracy so here we have considered the Polynomial regression with degree between (2 to 10) according to our dataframe (Raheja et al., 2023). We must determine the link between a dependent variable and an independent variable in regression analysis (s). ...
There is an increasing risk of outbreaks escalating into epidemics, despite huge advances in medical science. Epidemics like COVID-19, Monkeypox, Influenza and HIV have been affecting people and public health infrastructure at an alarming rate around the world. COVID-19 alone has infected more than 500 million people out of which 6 million have died over 100 countries. HIV is also a major global public health issue and has claimed 85.6 million lives till 2023. Forecasting the trends of these epidemics is important in order to efficiently manage national and global health risks by improving early warning systems. Therefore an intelligent framework to forecast epidemic diseases is proposed and a detailed comparative analysis is conducted using different time-series models. This study contributes to (Sustainable Development Goal) SDG-3 by predicting epidemics disease trends precisely using ARIMA, Polynomial Regression, SARIMA, Holt’s, Fb-Prophet time-series models, which can decrease the burden on healthcare systems. Using the best-suited models, the Mean Absolute Percentage Error (MAPE) values for Monkeypox, HIV, COVID-19 and Influenza forecasting were 0.0129, 0.0035, 0.0011, and 0.024
... Medical image processing has been widely utilized for the past decade, particularly due to the success of deep neural networks (DNNs) in this field. Examples of DL usage in medical informatics include skin lesion detection [10], pneumonia detection [11], Covid-19 diagnosis [12,13], skin cancer identification [14], automatic diagnosis of malaria parasites detection [15], and stroke classification [16]. Detailed information regarding the DL techniques employed in this research is provided in the methodology section (see "Material and Method"). ...
Monkeypox (MPox) is an infectious disease caused by the monkeypox virus, presenting challenges in accurate identification due to its resemblance to other diseases. This study introduces a deep learning-based method to distinguish visually similar diseases, specifically MPox, chickenpox, and measles, addressing the 2022 global MPox outbreak. A two-stage optimization approach was presented in the study. By analyzing pre-trained deep neural networks including 71 models, this study optimizes accuracy through transfer learning, fine-tuning, and ensemble learning techniques. ConvNeXtBase, Large, and XLarge models were identified achieving 97.5% accuracy in the first stage. Afterwards, some selection criteria were followed for the models identified in the first stage for use in ensemble learning technique within the optimization approach. The top-performing ensemble model, EM3 (composed of RegNetX160, ResNetRS101, and ResNet101), attains an AUC of 0.9971 in the second stage. Evaluation on unseen data ensures model robustness and enhances the study’s overall validity and reliability. The design and implementation of the study have been optimized to address the limitations identified in the literature. This approach offers a rapid and highly accurate decision support system for timely MPox diagnosis, reducing human error, manual processes, and enhancing clinic efficiency. It aids in early MPox detection, addresses diverse disease challenges, and informs imaging device software development. The study’s broad implications support global health efforts and showcase artificial intelligence potential in medical informatics for disease identification and diagnosis.
... Optimizing energy consumption through time division multiple access (TDMA) for path estimation could significantly augment the WHMD computational model. Another model [55] is capable of estimating the anticipated number of health issues considering confirmed cases, recoveries, deaths, and active cases in weeks. Metrics such as accuracy and error rates, along with Support Vector Machine, Logistic Regression, and Convolutional Neural Network models, demonstrate efficiency in predicting disease diffusion. ...
The post-COVID-19 landscape has propelled the global telemedicine sector to a projected valuation of USD 91.2 billion by 2022, with a remarkable compounded annual growth rate (CAGR) of 18.6% from 2023 to 2030. This paper introduces an analytical wearable healthcare monitoring device (WHMD) designed for the timely detection and seamless transmission of crucial health vitals to telemedical cloud agents. The fractional order modeling approach is employed to delineate the efficacy of the WHMD in pregnancy-related contexts. The Caputo fractional calculus framework is harnessed to show the device potential in capturing and communicating vital health data to medical experts precisely at the cloud layer. Our formulation establishes the fractional order model's positivity, existence, and uniqueness, substantiating its mathematical validity. The investigation comprises two major equilibrium points: the disease-free equilibrium and the equilibrium accounting for disease presence, both interconnected with the WHMD. The paper explores the impact of integrating the WHMD during pregnancy cycles. Analytical findings show that the basic reproduction number remains below unity, showing the WHMD efficacy in mitigating health complications. Furthermore, the fractional multi-stage differential transform method (FMSDTM) facilitates optimal control scenarios involving WHMD utilisation among pregnant patients. The proposed approach exhibits robustness and conclusively elucidates the dynamic potential of WHMD in supporting maternal health and disease control throughout pregnancy. This paper significantly contributes to the evolving landscape of analytical wearable healthcare research, highlighting the critical role of WHMDs in safeguarding maternal well-being and mitigating disease risks in edge reconfigurable health architectures.
... Optimizing energy consumption through time division multiple access (TDMA) for path estimation could significantly augment the WHMD computational model. Another model [55] is capable of estimating the anticipated number of health issues considering confirmed cases, recoveries, deaths, and active cases in weeks. Metrics such as accuracy and error rates, along with Support Vector Machine, Logistic Regression, and Convolutional Neural Network models, demonstrate efficiency in predicting disease diffusion. ...
The post-COVID-19 landscape has propelled the global telemedicine sector to a projected valuation of USD 91.2 billion by 2022, with a remarkable compounded annual growth rate (CAGR) of 18.6% from 2023 to 2030. This paper introduces an analytical wearable healthcare monitoring device (WHMD) designed for the timely detection and seamless transmission of crucial health vitals to telemedical cloud agents. The fractional order modeling approach is employed to delineate the efficacy of the WHMD in pregnancy-related contexts. The Caputo fractional calculus framework is harnessed to show the device potential in capturing and communicating vital health data to medical experts precisely at the cloud layer. Our formulation establishes the fractional order model's positivity, existence, and uniqueness, substantiating its mathematical validity. The investigation comprises two major equilibrium points: the disease-free equilibrium and the equilibrium accounting for disease presence, both interconnected with the WHMD. The paper explores the impact of integrating the WHMD during pregnancy cycles. Analytical findings show that the basic reproduction number remains below unity, showing the WHMD efficacy in mitigating health complications. Furthermore, the fractional multi-stage differential transform method (FMSDTM) facilitates optimal control scenarios involving WHMD utilisation among pregnant patients. The proposed approach exhibits robustness and conclusively elucidates the dynamic potential of WHMD in supporting maternal health and disease control throughout pregnancy. This paper significantly contributes to the evolving landscape of analytical wearable healthcare research, highlighting the critical role of WHMDs in safeguarding maternal well-being and mitigating disease risks in edge reconfigurable health architectures.
... Optimizing energy consumption through time division multiple access (TDMA) for path estimation could significantly augment the WHMD computational model. Another model [55] is capable of estimating the anticipated number of health issues considering confirmed cases, recoveries, deaths, and active cases in weeks. Metrics such as accuracy and error rates, along with Support Vector Machine, Logistic Regression, and Convolutional Neural Network models, demonstrate efficiency in predicting disease diffusion. ...
The post-COVID-19 landscape has propelled the global telemedicine sector to a projected valuation of USD 91.2
billion by 2022, with a remarkable compounded annual growth rate (CAGR) of 18.6% from 2023 to 2030. This paper introduces an analytical wearable healthcare monitoring device (WHMD) designed for the timely detection and seamless transmission of crucial health vitals to telemedical cloud agents. The fractional order modeling approach is employed to delineate the efficacy of the WHMD in pregnancy-related contexts. The Caputo fractional calculus framework is harnessed to show the device potential in capturing and communicating vital health data to medical experts precisely at the cloud layer. Our formulation establishes the fractional order model’s positivity, existence, and uniqueness, substantiating its mathematical validity. The investigation comprises two major equilibrium points: the disease-free equilibrium and the equilibrium accounting for disease presence, both interconnected with the WHMD. The paper explores the impact of integrating the WHMD during pregnancy cycles. Analytical findings show that the basic reproduction number remains below unity, showing the WHMD efficacy in mitigating health complications. Furthermore, the fractional multi-stage differential transform method (FMSDTM) facilitates optimal control scenarios involving WHMD utilisation among pregnant patients.
The proposed approach exhibits robustness and conclusively elucidates the dynamic potential of WHMD in supporting maternal health and disease control throughout pregnancy. This paper significantly contributes to the evolving landscape of analytical wearable healthcare research, highlighting the critical role of WHMDs in safeguarding maternal well-being and mitigating disease risks in edge reconfigurable health architectures.
... Sentiment analysis models predict outbreaks by monitoring social media to obtain and analyze public sentiment related to health issues [22][23][24][25][26]. Regression models perform regression tasks to predict the outbreak related data in the future [27][28][29][30]. Spreading models predict the progress of outbreaks based on geography or human to human diffusion [31][32][33]. Besides ML models, some mathematical models also can perform outbreak risk prediction. ...
... Machine learning (ML) and deep learning (DL) algorithms have significantly contributed to advancements in the biomedical and healthcare fields thanks to their remarkable ability to extract valuable information from complex and noisy datasets. In [15] the authors proposed an ML-based diffusion model to forecast the progression of the coronavirus, and in [16] the authors used an ML feature selection algorithm to select significant biomarkers and predict the occurrence of breast cancer. Divya et al. [17] employed a deep learning approach to diagnose schizophrenia using EEG signals. ...
Brain-computer interface (BCI) is a new promising technology for control and communication, the BCI system aims to decode the measured brain activity into a command signal. This paper proposes a hybrid approach to improve the classification performance of motor imagery BCI (MI BCI). Our proposed method aims to take the advantage of two principal feature extraction approaches. The first approach named Multi-Band common spatial patterns (MBCSP) consists of decomposing the MI trial into multiple sub-bands, for each sub-band CSP is applied to extract the features. Then, the subject-specific frequency bands are selected. Simultaneously, the selected frequency bands are used as input to the second approach named boosted tangent space mapping (BTSM), which extracts the features from each sub-band. An automatic feature selection algorithm is introduced to select the subject-specific frequency bands and to reduce the high dimensionality space of extracted features. Finally, the LogitBoost (LB) classifiers are learned on the extracted features by each approach and the linear combination of these classifiers is used to identify the class of MI trial. The proposed model is evaluated on three public MI-BCI datasets, including multiclass motor imagery datasets (BCI competition IV dataset 2a and BCI competition III dataset 3a) and binary class motor imagery dataset (BCI competition III dataset 4a), The average accuracy attained on the three datasets are respectively 73.61%, 86.66%, and 86.68%. The conducted comparative study between the proposed approach and some state-of-the-the art methods showed a statistically significant improvement (p-value < 0.05) of the classification performance of MI BCI. The experiment results showed a significant improvement when using the proposed hybrid approach against the use of a single-feature extraction method.