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This paper has used the well-known machine learning (ML) computational algorithm with Internet of Things (IoT) devices to predict the COVID-19 disease and to analyze the peak rate of the disease in the world. ML is the best tool to analyze and predict the object in reasonable time with great level of accuracy. The Purpose of this paper is to develop a model to predict the coronavirus by considering majorly related symptoms, attributes and also to predict and analyze the peak rate of the disease.

COVID-19 or coronavirus disease threatens the human lives in various ways, which leads to deaths in most of the cases. It affects the respiratory organs slowly and this penetration leads to multiple organ failure, which causes death in some cases having poor immunity system. In recent times, it has drawn the international attention because of the pandemic threat that is harder to control the spreading of infection around the world.

This proposed model is implemented by support vector machine classifier and Bayesian network algorithm, which yields high accuracy. The K-means algorithm has been applied for clustering the data set models. For data collection, IoT devices and related sensors were used in the identified hotspots. The data sets were collected from the selected hotspots, which are placed on the regions selected by the government agencies. The proposed COVID-19 prediction models improve the accuracy of the prediction and peak accuracy ratio. This model is also tested with best, worst and average cases of data set to achieve the better prediction rate.

From that hotspots, the IoT devices were fixed and accessed through wireless sensors (802.11) to transfer the data to the authors’ database, which is dedicated in data collection server. The data set and the proposed model yield good results and perform well with expected accuracy rate in the analysis and monitoring of the recovery rate of COVID-19.

The current and on-going coronavirus (COVID-19) has disrupted many human lives all over the world and seems very difficult to confront this global crisis as the infection is transmitted by physical contact. As no vaccine or medical treatment made available till date, the only solution is to detect the COVID-19 cases, block the transmission, isolate the infected and protect the susceptible population. In this scenario, the pervasive computing becomes essential, as it is environment-centric and data acquisition via smart devices provides better way for analysing diseases with various parameters.

For data collection, Infrared Thermometer, Hikvision’s Thermographic Camera and Acoustic device are deployed. Data-imputation is carried out by principal component analysis. A mathematical model susceptible, infected and recovered (SIR) is implemented for classifying COVID-19 cases. The recurrent neural network (RNN) with long-term short memory is enacted to predict the COVID-19 disease.

Machine learning models are very efficient in predicting diseases. In the proposed research work, besides contribution of smart devices, Artificial Intelligence detector is deployed to reduce false alarms. A mathematical model SIR is integrated with machine learning techniques for better classification. Implementation of RNN with Long Short Term Memory (LSTM) model furnishes better prediction holding the previous history.

The proposed research collected COVID −19 data using three types of sensors for temperature sensing and detecting the respiratory rate. After pre-processing, 300 instances are taken for experimental results considering the demographic features: Sex, Patient Age, Temperature, Finding and Clinical Trials. Classification is performed using SIR mode and finally predicted 188 confirmed cases using RNN with LSTM model.

The purpose of this paper is to provide a model for prediction of respiratory symptoms in the progression of COVID-19, social distancing, frequent hand washes, wearing of face mask in public are some of the potential measures of preventing the disease from further spreading. In spite of the effects and efforts taken by governments, the pandemic is still uncontrolled in major cities of the world. The proposed technique in this paper introduces a non-intrusive and major screening of vital symptoms and changes in the respiratory organs.

The novel coronavirus or Covid-19 has become a serious threat to social and economic growth of many nations worldwide. The pace of progression was significantly higher in the past two months. Identified by severe respiratory illness, fever and coughs, the disease has been threatening the lives of human society. Early detection and prognosis is absolutely necessary to isolate the potential spreaders of the disease and to control the rate of progression.

Recent studies have highlighted the changes observed in breathing characteristics of infected patients. Respiratory pattern of Covid-19 patients can be differentiated from the respiratory pattern of normal cold/flu affected patients. Tachypnoea is one among the vital signs identified to be distinguishing feature of Covid-19. The proposed respiratory data capture will commence with facial recognition, use of infrared sensors and machine-learning approaches to classify the respiratory patterns, which finally narrows down as a symptom of Covid-19.

Proposed system produced outcome of 94% accuracy, precision, recall and a F1-measure as an average in the conducted experiments. This method also proves to be a fruitful solution for large-scale monitoring and categorisation of people based on the symptoms.

The purpose of this study is to evaluate the efficiency of the relative performances of Mother’s canteen across the regions of Tamil Nadu and find out the determinants of inefficiencies in the scheme.

An untargeted food security scheme called Amma (Mother's) canteen was started in Tamil Nadu, India, with an aim to provide the urban poor with hygienic and healthy food at an affordable price. Along with secondary data, interviews were conducted to understand the operational details of Mother's canteen. Data envelopment analysis (DEA) was used to find the relative efficiency of the scheme operated by nine corporations.

Based on the daily expenditure, number of meals served and revenue, seven of nine corporations were found to be inefficient. Further, sensitivity analyses found that among six procurement variables, procurement (quantity and price) of black gram and cooking oil were determinants of inefficiency.

As an untargeted scheme, the cost of delivering service-based evaluation was used for performance evaluation. Policymakers could use centralized procurement instead of open market procurement at the corporation level and standardized ingredients' usage (quantity) to further reduce the cost of the food security scheme.

The proposed DEA model may be used by policymakers to empirically evaluate the food security scheme's delivery effectiveness across various corporations in a region. Inefficient branches are identified here with empirical support for further performance improvement changes.

There are limited number of studies evaluating untargeted schemes. This paper presents the challenges of evaluating an untargeted scheme which allows self-selection of beneficiaries. The outcome of this study will help in identifying inefficient corporations, and further, improve the performance and cost of delivering untargeted food security scheme.

As the Latinx population continues to increase in the United States, so has the number of families who experience disability. Latinx families of children and youth with disabilities face unique challenges as they navigate services and systems to advocate for the rights of their children. These challenges impact their health and wellbeing. Grounded in the Social Ecological Model (SEM), in this chapter, the authors discuss the challenges, support systems, and resources available to Latinx families of children and youth with disabilities across levels of influence, including the individual/family, interpersonal, community, and societal/systems levels. The authors highlight empowerment-focused interventions designed to promote advocacy efforts and the health and wellbeing of Latinx families of children and youth with disabilities, and the authors close with recommendations for future research, practice, and policy.

Prior research treated entrepreneurs’ actions as purely opportunistic and voluntary, excluding social and economic systems’ influence on entrepreneurial actions. However, the applications of communication strategies, project management and social network are anchored in socioeconomic systems in which the entrepreneurs are rooted. To address the gap, this study aims to articulate – through the prism of institutional theory – how restaurant crowdfunding (CF) success is affected by socioeconomic prosperity according to entrepreneurs’ race and geographic area.

The current study analyzed 2,008 restaurant CF projects launched in the USA through the Kickstarter platform from 2010 to 2020. By conducting one-way analysis of variance and multilevel mixed-effect logistic regression models, this study examined the relative socioeconomic prosperity and CF success according to the race of the restaurant entrepreneurs. The study also examined how socioeconomic prosperity affected CF success and how that relationship was moderated by the entrepreneurs’ level of restaurant experience.

This study finds that relative socioeconomic prosperity and CF success does differ according to race. Also in the CF context, lower socioeconomic prosperity does impede fundraising success. While the level of restaurant experience significantly increased an entrepreneur’s CF success, the impact was not so significant as to overcome the impact of socioeconomic prosperity.

Drawing on institutional theory, this study examines the impact of socioeconomic prosperity on CF project outcomes. By uncovering the significant impact of socioeconomic systems on CF success, this study fills the research gap. Previous studies have generally treated minority entrepreneurs as an aggregated form. The authors’ results extend the literature by including major ethnic groups – whites, African Americans and Asians.

The findings of the current study show restaurant entrepreneurs can raise the likelihood of CF success by doing two things: first, accumulate experience in the restaurant industry; second, use their CF websites to highlight testimonials about the value of that experience. Federal, state and local governments can institute policies to help improve racial minorities’ socioeconomic conditions and thereby promote startups’ fundraising success.

To the best of the authors’ knowledge, this study is one of the first to examine unexplored institutional effect on CF outcomes. It examines how and why socioeconomic factors affect minority entrepreneurs’ funding success. It compares the prosperity and CF success of white, African American and Asian entrepreneurs.

The purpose of this study was to assess equity investors satisfaction with stockbroker services. Four components emerged from a factor analysis of 14 variables of retail equity investors’ satisfaction with stockbroker services. According to the findings, these elements have a substantial impact on investors’ trust and confidence in stockbrokers.

By physically visiting stockbrokers’ offices in Punjab, including Amritsar, Jalandhar, Ludhiana and Mohali, 1,000 questionnaires were distributed to retail equities investors. Stockbrokers were chosen using a simple random selection process due to their large number. Questionnaires were filled out by personally visiting stockbrokers’ offices and handing over surveys, instructing them to fill them out with information from their clients and personally visiting stockbrokers’ offices and instructing their clients to complete the questionnaires. The respondents completed 373 surveys. A total of 45 surveys were determined to be incomplete and were removed from the study. The remaining 328 surveys were used to conduct the analysis. The study uses ordinal regression to assess investors’ trust and confidence in stockbrokers’ services.

The findings of the study highlighted the importance of variables evaluated by respondents when seeking stockbroker services. These criteria included the accuracy of stockbrokers’ information, the speed with which accounts are settled and the brokers’ willingness to give valuable service to investors. These 14 variables, which measure investor satisfaction with stockbroker services, were subjected to factor analysis. With the use of component analysis, four factors were identified: satisfaction with stockbroker services, stockbroker regulations, stockbroker transactional services and stockbrokers’ image in the eyes of investors, which explained 72.55% of the variation in the data. With the use of ordinal regression analysis, it was discovered that these four criteria have a considerable impact on investors’ trust and confidence in stockbrokers.

The current study, which is being conducted at the state level, might be expanded to include the entire country. It might be possible to look into the impact of retail capital market investment on rural investors. The research might be expanded to include a look at how reforms affect the functioning of stock markets. A study on the awareness of retail investment trends among women investors could be conducted. It is possible to investigate the ramifications of internet stock trading in India. It is possible to investigate the impact of technical innovation on capital markets. In this study, a survey has been conducted, in the future, the behavior of the investors can be observed to analyze whether they are satisfied with the services of stockbrokers or not.

This research would be extremely beneficial to investors who make investment decisions and employ stockbrokers to help them make those selections. Because with the aid of the factors revealed investors can match the service quality of their own intermediary and only if they will be satisfied they will trust their intermediary.

This research will aid stockbrokers in providing investors with efficient and effective services. As they will have knowledge about the needs and aspirations of their clients, they will try to render their services as per their expectations. This will ultimately lead to the satisfaction of the retail equity investors, and they will have trust and confidence in the services provided by the stockbrokers. The present study helps the stockbrokers in understanding the fact that the qualitative aspects of their services are crucial for building investors’ trust and confidence otherwise investors will not be satisfied with their services. This study is extremely important for government as well. They can also take cues from witnessed the positive impact of their regulations on the quality of the stockbrokers’ services. This improvement in the quality of stockbroker services has further enhanced the trust and confidence of investors. Regulations are essential for improving the quality of stockbrokers’ services.

This paper reveals that a variety of factors, i.e. satisfaction with stockbroker services, stockbroker regulations, stockbroker transactional services and stockbrokers’ image in the eyes of investors influence retail equities investors’ trust and faith in brokerage services.

The globalisation of supply chains has contributed to modern slavery by degrading labour standards and work practices. The inherent difficulties involved in monitoring extremely fragmented production processes also render workers in and from developing countries vulnerable to labour exploitation. This research adopts a benchmark methodology that will help examine the inherent modern slavery challenges.

This study examines how the benchmark model, including governance, risk assessment, purchasing practice, recruitment and remedy of victims, addresses supply chain modern slavery challenges. The proposed hypotheses are tested based on the reoccurring issues of modern slavery in global supply chains.

Estimations suggest that modern slavery is a growing and increasingly prominent international problem, indicating that it is the second largest and fastest growing criminal enterprise worldwide except for narcotics trafficking. These social issues in global supply chains have drawn attention to the importance of verifying, monitoring and mapping supply chains, especially in lengthy and complex supply chains. However, the advent of digital technologies and benchmarking methodologies has become one of the existing key performance indicators (KPIs) for measuring the effectiveness of modern slavery initiatives in supply chains.

This review provides an understanding of the current situation of global supply chains concerning the growing social issue of modern slavery. However, this includes various individual specialities relating to global supply chains, modern slavery, socially sustainable supply chain management (SCM), logistic social responsibility, corporate social responsibility and digitalisation. Furthermore, the review provided important implications for researchers examining the activities on benchmarking the effectiveness of the existing initiatives to prevent modern slavery in the supply chains.

This investigation delves into the rationale behind the preferential applicability of the non-Newtonian nanofluid model over alternative frameworks, particularly those incorporating porous medium considerations. The study focuses on analyzing the mass and heat transfer characteristics inherent in the Williamson nanofluid’s non-Newtonian flow over a stretched sheet, accounting for influences such as chemical reactions, viscous dissipation, magnetic field and slip velocity. Emphasis is placed on scenarios where the properties of the Williamson nanofluid, including thermal conductivity and viscosity, exhibit temperature-dependent variations.

Following the use of the OHAM approach, an analytical resolution to the proposed issue is provided. The findings are elucidated through the construction of graphical representations, illustrating the impact of diverse physical parameters on temperature, velocity and concentration profiles.

Remarkably, it is discerned that the magnetic field, viscous dissipation phenomena and slip velocity assumption significantly influence the heat and mass transmission processes. Numerical and theoretical outcomes exhibit a noteworthy level of qualitative concurrence, underscoring the robustness and reliability of the non-Newtonian nanofluid model in capturing the intricacies of the studied phenomena.

Available studies show that no work on the Williamson model is conducted by considering viscous dissipation and the MHD effect past over an exponentially stretched porous sheet. This contribution fills this gap.

This study aims to present the consequences of activation energy and the chemical reactions on the unsteady MHD squeezing flow of an incompressible ternary hybrid nanofluid (THN) comprising magnetite (FE₃O₄), multiwalled carbon nano-tubes (MWCNT) and copper (Cu) along with water (H2O) as the base fluid. This investigation is performed within the framework of two moving parallel plates under the influence of magnetic field and viscous dissipation.

Due to the complementary benefits of nanoparticles, THN is used to augment the heat transmit fluid’s efficacy. The flow situation is expressed as a system of dimensionless, nonlinear partial differential equations, which are reduced to a set of nonlinear ordinary differential equations (ODEs) by suitable similarity substitutions. These transformed ODEs are then solved through a semianalytical technique called differential transform method (DTM). The effects of several changing physical parameters on the flow, temperature, concentration and the substantial measures of interest have been deliberated through graphs. This study verifies the reliability of the results by performing a comparison analysis with prior researches.

The enhanced activation energy results in improved concentration distribution and declined Sherwood number. Enhancement in chemical reaction parameter causes disparities in concentration of the ternary nanofluid. When the Hartmann number is zero, value of skin friction is high, but Nusselt and Sherwood numbers values are small. Rising nanoparticles concentrations correspond to a boost in overall thermal conductivity, causing reduced temperature profile.

Due to its firm and simple nature, its implications are in various fields like chemical industry and medical industry for designing practical problems into mathematical models and experimental analysis.

Deployment of the squeezed flow of ternary nanofluid with activation energy has significant consideration in nuclear reactors, vehicles, manufacturing facilities and engineering environments.

This study would be contributing significantly in the field of medical technology for treating cancer through hyperthermia treatment, and in industrial processes like water desalination and purification.

In this problem, a semianalytical approach called DTM is adopted to explore the consequences of activation energy and chemical reactions on the squeezing flow of ternary nanofluid.