Development of Phenol Red Colorimetric RT-LAMP Assay in High-Buffered SARS-CoV-2 Sample

Colorimetric RT-LAMP Assay is a diagnostic method that has attracted much attention because of its rapidity, simplicity, and accuracy compared to other disease diagnosis methods. Despite having many advantages, the RT-LAMP Colorimetric Assay has disadvantages, especially for kits that use phenol red as an indicator. The disadvantage derives from the input RNA/DNA samples containing high buffer levels, which causes no color change and false-negative results. This study aimed to develop and optimize the colorimetric RT-LAMP method on high-buffered SARS-CoV-2 RNA samples. We found that a temperature of 69°C for 50 minutes with the addition of post-treatment in the form of heating at 80°C for 10 minutes is an optimal condition for high-buffered SARS-CoV-2 samples. The condition proved effective in changing the result's color from red (negative) to yellow (positive). We also classified the analysis results based on the correlation between the Cycle threshold (Ct) value of SARS-CoV-2 viruses and the Optical Density (OD) value, which was quantified using a spectrophotometer at 415 nm (with a correlation value of-0.9084), where yellow color indicated Ct below 20, amber color indicated Ct between 20 and 30, orange color indicated Ct between 30 and 35, and red indicated Ct more than 35 (negative). In conclusion, this study successfully detects the SARS-CoV-2 virus in high-buffered samples using Phenol Red Colorimetric RT-LAMP Assay, with a sensitivity of 85% for Ct Cutoff 40. © 2023, Bogor Agricultural University. All rights reserved.

Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs. Chest X-ray (CXR) gained much interest after the COVID-19 outbreak thanks to its rapid imaging time, widespread availability, low cost, and portability. In radiological investigations, computer-aided diagnostic tools are implemented to reduce intra-and inter-observer variability. Using lately industrialized Artificial Intelligence (AI) algorithms and radiological techniques to diagnose and classify disease is advantageous. The current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm (GF-OSDL-ROA). This method is inclusive of preprocessing and classification based on optimization. The data is preprocessed using Gaussian filtering (GF) to remove any extraneous noise from the image's edges. Then, the OSDL model is applied to classify the CXRs under different severity levels based on CXR data. The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work. OSDL model, applied in this study, was validated using the COVID-19 dataset. The experiments were conducted upon the proposed OSDL model, which achieved a classification accuracy of 99.83%, while the current Convolutional Neural Network achieved less classification accuracy, i.e., 98.14%.

GIONet: Global information optimized network for multi-center COVID-19 diagnosis via COVID-GAN and domain adversarial strategy.

The outbreak of coronavirus disease (COVID-19) in 2019 has highlighted the need for automatic diagnosis of the disease, which can develop rapidly into a severe condition. Nevertheless, distinguishing between COVID-19 pneumonia and community-acquired pneumonia (CAP) through computed tomography scans can be challenging due to their similar characteristics. The existing methods often perform poorly in the 3-class classification task of healthy, CAP, and COVID-19 pneumonia, and they have poor ability to handle the heterogeneity of multi-centers data. To address these challenges, we design a COVID-19 classification model using global information optimized network (GIONet) and cross-centers domain adversarial learning strategy. Our approach includes proposing a 3D convolutional neural network with graph enhanced aggregation unit and multi-scale self-attention fusion unit to improve the global feature extraction capability. We also verified that domain adversarial training can effectively reduce feature distance between different centers to address the heterogeneity of multi-center data, and used specialized generative adversarial networks to balance data distribution and improve diagnostic performance. Our experiments demonstrate satisfying diagnosis results, with a mixed dataset accuracy of 99.17% and cross-centers task accuracies of 86.73% and 89.61%.

Optimized DEC: An effective cough detection framework using optimal weighted Features-aided deep Ensemble classifier for COVID-19.

Since the year 2019, the entire world has been facing the most hazardous and contagious disease as Corona Virus Disease 2019 (COVID-19). Based on the symptoms, the virus can be identified and diagnosed. Amongst, cough is the primary syndrome to detect COVID-19. Existing method requires a long processing time. Early screening and detection is a complex task. To surmount the research drawbacks, a novel ensemble-based deep learning model is designed on heuristic development. The prime intention of the designed work is to detect COVID-19 disease using cough audio signals. At the initial stage, the source signals are fetched and undergo for signal decomposition phase by Empirical Mean Curve Decomposition (EMCD). Consequently, the decomposed signal is called "Mel Frequency Cepstral Coefficients (MFCC), spectral features, and statistical features". Further, all three features are fused and provide the optimal weighted features with the optimal weight value with the help of "Modified Cat and Mouse Based Optimizer (MCMBO)". Lastly, the optimal weighted features are fed as input to the Optimized Deep Ensemble Classifier (ODEC) that is fused together with various classifiers such as "Radial Basis Function (RBF), Long-Short Term Memory (LSTM), and Deep Neural Network (DNN)". In order to attain the best detection results, the parameters in ODEC are optimized by the MCMBO algorithm. Throughout the validation, the designed method attains 96% and 92% concerning accuracy and precision. Thus, result analysis elucidates that the proposed work achieves the desired detective value that aids practitioners to early diagnose COVID-19 ailments.

A global optimization feedback model with PSO for large scale group decision making in hesitant fuzzy linguistic environments

In an increasingly complex and uncertain decision-making environment, large-scale group decision-making (LSGDM) can offer a more efficient method, allowing a large number of decision-makers (DMs) to truly participate in the decision-making process. The consensus-reaching process (CRP) is an effective method for resolving conflicting opinions among large-scale DMs. However, in the existing CRP of LSGDM, the new consensus state and the adjustment cost borne by inconsistent DMs after implementing feedback suggestions are not taken into consideration. To address this issue, this paper proposes a global optimization feedback model with particle swarm optimization (PSO) for LSGDM in hesitant fuzzy linguistic environments. An improved density-based spatial clustering of applications with noise (DBSCAN) on hesitant fuzzy linguistic term sets (HFLTSs) is introduced to classify large-scale DMs into several clusters, and a weight determination method that combines cluster size and intra-cluster tightness is also presented. The consensus degree of clusters is calculated at two levels: intra-consensus and inter-consensus. To improve the global consensus level with minimum cost, a global optimization feedback model is established to generate recommendation advice for inconsistent DMs, and the model is solved by PSO. A numerical example related to "COVID-19” and some comparisons are provided to verify the feasibility and advantages of the proposed method.

A Pilot Study to Evaluate Occupant Quality of Life in Optimised Retrofit Dwellings in Wales, UK

This paper discusses an investigation into quality of life (QoL) as a pilot study from a sample of occupants living in existing dwellings, that have been (2021) or will be retrofitted in 2022 and 2023, funded by the Welsh Government's (WG's) Optimised Retrofit (OR) project. The pan Wales OR project aims to retrofit close to 2000 existing social housing dwellings targeting nearly-zero/zero operational energy standards, to alleviate occupant fuel poverty and reduce energy costs and carbon emissions and increase occupant comfort and QoL. The methodology presented builds on two previous research projects undertaken and completed by two of the authors in 2010 and 2020, to adapt and create a hybrid Short Form-36 (HSF36) health survey, accompanied by the established RAND analysis system. The HSF36 questionnaire survey has been further refined for the OR project and has been used to collect occupant data through face-to-face interviews and online surveys. The occupants live in dwellings manged by one of Wales's largest registered social housing landlords (RSL's) with circa 8000 dwellings. The challenges and solutions for undertaking occupant engagement for surveys during Covid19 restrictions are illustrated. Once dwelling retrofits are completed in 2022 and 2023, the intention is to conduct a second and third phase of occupant engagement. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Effective Real Time Disaster Management Using Optimized Scheduling

In recent years we face many types of natural and man-created disasters such as tsunamis, earthquakes, hurricanes, Covid-19 pandemic, terrorist attacks, floods, etc. which cause diverse and worse effects on our daily lives and economy. In order to mitigate the impact of such disasters and reduce the causality, economic loss during disaster response cycle, the different disaster management resources such as rescue teams, transportation, healthcare and related services must be schedule and allocated efficiently. In this research, we proposed the Cluster-Based Real–Time Disaster Resource Management Framework which used edge and computing-based real-time scheduling of various resources and emergency services in disaster management. The edge computing resources are grouped into the cluster and a set of tasks is assigned to the cluster and scheduled on the edge computing cluster to increase resource utilization and acceptance rate which is the problem of existing partitioned scheduling and reduces response time, and overhead due to communication and migration which is the issue in exiting scheduling. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An efficient and low complex model for optimal RBM features with weighted score-based ensemble multi-disease prediction.

Multi-disease prediction is regarded as the capacity to simultaneously identify various diseases that are expected to be affected an individual at a certain period. These multiple diseases are seemed to be at various progression levels and need to be detected in the patient at the time of clinical visits. Diverse studies in the literature have included the predictive models for particular diseases yet, it is unable to notice humans with multiple diseases since humans are mostly suffered not only from a single disease but also from multiple diseases. Hence, this article aims to implement a novel multi-disease prediction model using an ensemble learning approach with deep features. The required data for the multi-disease prediction is collected from the standard datasets. Then, the collected data are given into the "Deep Belief Network (DBN)" approach, where the features are obtained from the RBM layers. These RBM features are tuned with the help of Deviation-based Hybrid Grasshopper Barnacles Mating Optimization (D-HGBMO) for improving the prediction performance. The optimized RBM features are considered in the ensemble learning model named Ensemble, in which the multi-disease prediction is performed with "Deep Neural Network (DNN), Extreme Learning Machine (ELM), and Long Short Term Memory." The predicted score from three classifiers is used in the optimized weighted score and thresholding-based final prediction using the same D-HGBMO for determining the accurate multi-disease prediction results. The experimental results show the effective performance of the proposed model by comparing it with the existing classifiers with the help of different quantitative measures.

Optimal gene prioritization and disease prediction using knowledge based ontology structure

Prioritizing candidate genes is essential for genome-based diagnostics of various hereditary disorders. Furthermore, it is a difficult task with particular and noisy information about genes, illnesses, and relationships. Although several computer methods for disease gene prioritization have been developed, their efficiency is limited by manually created traits, network architecture, or pre-established data fusion criteria. Hence, this research proposes a unique gene prioritization and disease prediction model. Initially, the gathered information is pre-processed by a data cleaning model. In the proposed gene prioritization phase, the pre-processed data are tokenized. Then a new knowledge-based ontology structure is constructed with the improved skewness-based semantic similarity function. The ensemble classifier is constructed along Recurrent Neural Network (RNN), optimized fuzzy logic, and also Deep Belief Network (DBN) to forecast the gene disorders in the prediction phase. The retrieved features from the feature extraction phase are used to train RNN;while the extracted knowledge bases are used to train the DBN, then the results are fed into the optimized fuzzy logic. The fuzzy logic is the primary indication;its fuzzification function is fine-tuned employing a methodology to improve illness prediction accuracy. A recommended new hybrid system, named as Cauchy's Mutated Corona Virus Optimization Algorithm (CMCOA), is the upgraded version of the CVOA, a typical coronavirus optimization technique. Finally, to evaluate the efficiency of the projected model, a comparison of the suggested and existent models is performed with respect to various measures. In particular, the proposed model has recorded the highest accuracy as 93 % at 60 % of training, which is 42.5 %, 36.1 %, 33.3 %, 41.1 %, 48.5 %, 48.5 %, 9 %, 8 %, 8 %, 8 %, 8 %, and 14.5 % improved over existing models like GCN, GCN [6], SVM, CNN, Bi-LSTM, LSTM, GRU, fuzzy, EC + GOA, EC + SSO, EC + CMBO, EC + SMA and EC + CCVOA, respectively. The precision of the suggested work with improved features &CMCOA is 15.5 %, and 14.42 % superior to the proposed work without existing features & CMCOA and proposed work with existing features & CMCOA approaches. © 2022 Elsevier Ltd

When COVID-19 Approaches Again: Early Detection of an Incoming New Wave of COVID-19 Positive Cases Inspired by Linear Breakpoint Models

From time to time, breakouts of COVID-19 income as waves of rapidly increasing numbers of positive cases alternated by time periods, varying in length, of low numbers of positive cases. Early detection of an incoming COVID-19 wave may enable pro-actively and quick activating all epidemiological measures that belong, together with vaccination, between a few effective weapons against unlimited COVID-19 transmission in a population. In this work, we applied an approach for the early detection of an incoming COVID-19 wave inspired by linear breakpoint models. Moreover, we refined the task of early detection as an optimization. Fitting a linear model on increasing numbers of positives from the present day to the past requires a sufficient number of daily records of positives from the past to get a significant positive regression slope. However, the more daily records of positives, usually stagnating in the past, we need to consider, the flatter the regression line becomes, which lowers the chance the regression slope is significant. Thus, finding a breakpoint, i. e. the beginning of the new wave of positives, is tricky. To address this issue, we propose an iterative algorithm searching for a breakpoint followed by a significantly positive regression slope. Finally, considering that a new wave is fitted by increasing the regression line, we also discuss an average number of days after the beginning of the incoming wave, when the early detection is, on average, firstly possible on a given statistical confidence level. © 2022 IEEE.

Optimized Deep-Neural Network for Content-based Medical Image Retrieval in a Brownfield IoMT Network

In this paper, a brownfield Internet of Medical Things network is introduced for imaging data that can be easily scaled out depending on the objectives, functional requirements, and the number of facilities and devices connected to it. This is further used to develop a novel Content-based Medical Image Retrieval framework. The developed framework uses DenseNet-201 architecture for generating the image descriptors. Then for classification, the optimized Deep Neural Network model has been configured through a population-based metaheuristic Differential Evolution. Differential Evolution iteratively performs the joint optimization of hyperparameters and architecture of Deep Neural Networks. The competence of the proposed model is validated on three publicly available datasets: Brain Tumor MRI dataset, Covid-19 Radiography database, and Breast Cancer MRI dataset, and by comparing it with selected models over different aspects of performance evaluation. Results show that the convergence rate of the proposed framework is very fast, and it achieves at least 97.28% accuracy across all the models. © 2022 Association for Computing Machinery.

A Live Attenuated COVID-19 Candidate Vaccine for Children: Protection against SARS-CoV-2 Challenge in Hamsters.

Children are at risk of infection from severe acute respiratory syndrome coronavirus-2 virus (SARS-CoV-2) resulting in coronavirus disease (COVID-19) and its more severe forms. New-born infants are expected to receive short-term protection from passively transferred maternal antibodies from their mothers who are immunized with first-generation COVID-19 vaccines. Passively transferred antibodies are expected to wane within first 6 months of infant's life, leaving them vulnerable to COVID-19. Live attenuated vaccines, unlike inactivated or viral-protein-based vaccines, offer broader immune engagement. Given effectiveness of live attenuated vaccines in controlling infectious diseases such as mumps, measles and rubella, we undertook development of a live attenuated COVID-19 vaccine with an aim to vaccinate children beyond 6 months of age. An attenuated vaccine candidate (dCoV), engineered to express sub-optimal codons and deleted polybasic furin cleavage sites in the spike protein of the SARS-CoV-2 WA/1 strain, was developed and tested in hamsters. Hamsters immunized with dCoV via intranasal or intramuscular routes induced high levels of neutralizing antibodies and exhibited complete protection against the SARS-CoV-2 wild-type isolates, i.e., the Wuhan-like (USA-WA1/2020) and Delta variants (B.1.617.2) in a challenge study. In addition, the dCoV formulated with the marketed measles-rubella (MR) vaccine, designated as MR-dCoV, administered to hamsters via intramuscular route, also protected against both SARS-CoV-2 challenges, and dCoV did not interfere with the MR vaccine-mediated immune response. The safety and efficacy of the dCoV and the MR-dCoV against both variants of SARS-CoV-2 opens the possibility of early immunization in children without an additional injection.

Buprenorphine exposure levels to optimize treatment outcomes in opioid use disorder.

The severity of the ongoing opioid crisis, recently exacerbated by the COVID-19 pandemic, emphasizes the importance for individuals suffering from opioid use disorder (OUD) to have access to and receive efficacious, evidence-based treatments. Optimal treatment of OUD should aim at blocking the effects of illicit opioids while controlling opioid craving and withdrawal to facilitate abstinence from opioid use and promote recovery. The present work analyses the relationship between buprenorphine plasma exposure and clinical efficacy in participants with moderate to severe OUD using data from two clinical studies (39 and 504 participants). Leveraging data from placebo-controlled measures assessing opioid blockade, craving, withdrawal and abstinence, we found that buprenorphine plasma concentrations sustained at 2-3 ng/ml (corresponding to ≥70% brain mu-opioid receptor occupancy) optimized treatment outcomes in the majority of participants, while some individuals (e.g., injecting opioid users) needed higher concentrations. Our work also included non-linear mixed effects modeling and survival analysis, which identified a number of demographic, genetic and social factors modulating treatment response and retention. Altogether, these findings provide key information on buprenorphine plasma levels that optimize clinical outcomes and increase the likelihood of individual treatment success. NLM identifiers: NCT02044094, NCT02357901.

The multistep Laplace optimized decomposition method for solving fractional-order coronavirus disease model (COVID-19) via the Caputo fractional approach

COVID-19, a novel coronavirus disease, is still causing concern all over the world. Recently, researchers have been concentrating their efforts on understanding the complex dynamics of this widespread illness. Mathematics plays a big role in understanding the mechanism of the spread of this disease by modeling it and trying to find approximate solutions. In this study, we implement a new technique for an approximation of the analytic series solution called the multistep Laplace optimized decomposition method for solving fractional nonlinear systems of ordinary differential equations. The proposed method is a combination of the multistep method, the Laplace transform, and the optimized decomposition method. To show the ability and effectiveness of this method, we chose the COVID-19 model to apply the proposed technique to it. To develop the model, the Caputo-type fractional-order derivative is employed. The suggested algorithm efficacy is assessed using the fourth-order Runge-Kutta method, and when compared to it, the results show that the proposed approach has a high level of accuracy. Several representative graphs are displayed and analyzed in two dimensions to show the growth and decay in the model concerning the fractional parameter α values. The central processing unit computational time cost in finding graphical results is utilized and tabulated. From a numerical viewpoint, the archived simulations and results justify that the proposed iterative algorithm is a straightforward and appropriate tool with computational efficiency for several coronavirus disease differential model solutions. © 2022 the author(s), published by De Gruyter.

Online Sequential Extreme learning machine-based hybrid model for forecasting of COVID-19 cases in India

COVID-19 has posed high stress on government and people with its disruptive effects on every sector of the nation. Accurate and reliable forecasting models are of great need to handle this unprecedented situation. A hybrid model, which is a combination of, cuckoo search optimization algorithm, variational mode decomposition and online sequential extreme learning machine has been proposed in this work for multistep forecasting of COVID-19 cases. The model showed reasonable accuracy of 1.363%, 1.596% and 1.933% for one, three and five days ahead forecasting. The model gave superior results when compared with partial autocorrelation function (PACF) for selection of number of input parameters. The robustness of the proposed model has been evident in comparison with other similar state of the art techniques discussed in the literature. © 2022 IEEE.

Optimal gene prioritization and disease prediction using knowledge based ontology structure

Prioritizing candidate genes is essential for genome-based diagnostics of various hereditary disorders. Furthermore, it is a difficult task with particular and noisy information about genes, illnesses, and relationships. Although several computer methods for disease gene prioritization have been developed, their efficiency is limited by manually created traits, network architecture, or pre-established data fusion criteria. Hence, this research proposes a unique gene prioritization and disease prediction model. Initially, the gathered information is pre-processed by a data cleaning model. In the proposed gene prioritization phase, the pre-processed data are tokenized. Then a new knowledge-based ontology structure is constructed with the improved skewness-based semantic similarity function. The ensemble classifier is constructed along Recurrent Neural Network (RNN), optimized fuzzy logic, and also Deep Belief Network (DBN) to forecast the gene disorders in the prediction phase. The retrieved features from the feature extraction phase are used to train RNN;while the extracted knowledge bases are used to train the DBN, then the results are fed into the optimized fuzzy logic. The fuzzy logic is the primary indication;its fuzzification function is fine-tuned employing a methodology to improve illness prediction accuracy. A recommended new hybrid system, named as Cauchy's Mutated Corona Virus Optimization Algorithm (CMCOA), is the upgraded version of the CVOA, a typical coronavirus optimization technique. Finally, to evaluate the efficiency of the projected model, a comparison of the suggested and existent models is performed with respect to various measures. In particular, the proposed model has recorded the highest accuracy as 93 % at 60 % of training, which is 42.5 %, 36.1 %, 33.3 %, 41.1 %, 48.5 %, 48.5 %, 9 %, 8 %, 8 %, 8 %, 8 %, and 14.5 % improved over existing models like GCN, GCN [6], SVM, CNN, Bi-LSTM, LSTM, GRU, fuzzy, EC + GOA, EC + SSO, EC + CMBO, EC + SMA and EC + CCVOA, respectively. The precision of the suggested work with improved features &CMCOA is 15.5 %, and 14.42 % superior to the proposed work without existing features & CMCOA and proposed work with existing features & CMCOA approaches.

Future assessment of the impact of the COVID-19 pandemic on the electricity market based on a stochastic socioeconomic model.

This paper proposes a time-series stochastic socioeconomic model for analyzing the impact of the pandemic on the regulated distribution electricity market. The proposed methodology combines the optimized tariff model (socioeconomic market model) and the random walk concept (risk assessment technique) to ensure robustness/accuracy. The model enables both a past and future analysis of the impact of the pandemic, which is essential to prepare regulatory agencies beforehand and allow enough time for the development of efficient public policies. By applying it to six Brazilian concession areas, results demonstrate that consumers have been/will be heavily affected in general, mainly due to the high electricity tariffs that took place with the pandemic, overcoming the natural trend of the market. In contrast, the model demonstrates that the pandemic did not/will not significantly harm power distribution companies in general, mainly due to the loan granted by the regulator agency, named COVID-account. Socioeconomic welfare losses averaging 500 (MR$/month) are estimated for the equivalent concession area, i.e., the sum of the six analyzed concession areas. Furthermore, this paper proposes a stochastic optimization problem to mitigate the impact of the pandemic on the electricity market over time, considering the interests of consumers, power distribution companies, and the government. Results demonstrate that it is successful as the tariffs provided by the algorithm compensate for the reduction in demand while increasing the socioeconomic welfare of the market.

Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19.

BACKGROUND AND OBJECTIVE: Efforts to alleviate the ongoing coronavirus disease 2019 (COVID-19) crisis showed that rapid, sensitive, and large-scale screening is critical for controlling the current infection and that of ongoing pandemics. METHODS: Here, we explored the potential of vibrational spectroscopy coupled with machine learning to screen COVID-19 patients in its initial stage. Herein presented is a hybrid classification model called grey wolf optimized support vector machine (GWO-SVM). The proposed model was tested and comprehensively compared with other machine learning models via vibrational spectroscopic fingerprinting including saliva FTIR spectra dataset and serum Raman scattering spectra dataset. RESULTS: For the unknown vibrational spectra, the presented GWO-SVM model provided an accuracy, specificity and F1_score value of 0.9825, 0.9714 and 0.9778 for saliva FTIR spectra dataset, respectively, while an overall accuracy, specificity and F1_score value of 0.9085, 0.9552 and 0.9036 for serum Raman scattering spectra dataset, respectively, which showed superiority than those of state-of-the-art models, thereby suggesting the suitability of the GWO-SVM model to be adopted in a clinical setting for initial screening of COVID-19 patients. CONCLUSIONS: Prospectively, the presented vibrational spectroscopy based GWO-SVM model can facilitate in screening of COVID-19 patients and alleviate the medical service burden. Therefore, herein proof-of-concept results showed the chance of vibrational spectroscopy coupled with GWO-SVM model to help COVID-19 diagnosis and have the potential be further used for early screening of other infectious diseases.

Suicide Ideation Detection of Covid Patients Using Machine Learning Algorithm

During Covid pandemic, many individuals are suffering from suicidal ideation in the world. Social distancing and quarantining, affects the patient emotionally. Affective computing is the study of recognizing human feelings and emotions. This technology can be used effectively during pandemic for facial expression recognition which automatically extracts the features from the human face. Monitoring system plays a very important role to detect the patient condition and to recognize the patterns of expression from the safest distance. In this paper, a new method is proposed for emotion recognition and suicide ideation detection in COVID patients. This helps to alert the nurse, when patient emotion is fear, cry or sad. The research presented in this paper has introduced Image Processing technology for emotional analysis of patients using Machine learning algorithm. The proposed Convolution Neural Networks (CNN) architecture with DnCNN preprocessing enhances the performance of recognition. The system can analyze the mood of patients either in real time or in the form of video files from CCTV cameras. The proposed method accuracy is more when compared to other methods. It detects the chances of suicide attempt based on stress level and emotional recognition. © 2023 CRL Publishing. All rights reserved.

An Optimized Modelling and Simulation on Task Scheduling for Multi-Processor System using Hybridized ACO-CVOA

Task allocation on the multi-processor system distributes the task according to capacity of each processor that optimally selects the best. The optimal selection of processor leads to increase performance and this also impact the makespan. In task scheduling, most of the research work focused on the objective of managing the power consumption and time complexity due to improper selection of processors for the given task items. This paper mainly focusses on the modelling of the optimal task allocation using a novel hybridization method of Ant Colony Optimization (ACO) with Corona Virus Optimization Algorithm (CVOA). There are several other methods that estimate the weight value of processors and find the best match to the task by using the traditional distance estimation method or by using standard rule-based validation. The proposed algorithm searches the best selection of machines for the corresponding parameters and weight value iteratively and finally recognizes the capacity of it. The performance of proposed method is evaluated on the parameters of elapsed time, throughput and compared with the state-of-art methods. © 2022 Institute of Information Science. All rights reserved.