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Pervasive health-care computing applications in medical field provide better diagnosis of various organs such as brain, spinal card, heart, lungs and so on. The purpose of this study is to find brain tumor diagnosis using Machine learning (ML) and Deep Learning(DL) techniques. The brain diagnosis process is an important task to medical research which is the most prominent step for providing the treatment to patient. Therefore, it is important to have high accuracy of diagnosis rate so that patients easily get treatment from medical consult. There are many earlier investigations on this research work to diagnose brain diseases. Moreover, it is necessary to improve the performance measures using deep and ML approaches.

In this paper, various brain disorders diagnosis applications are differentiated through following implemented techniques. These techniques are computed through segment and classify the brain magnetic resonance imaging or computerized tomography images clearly. The adaptive median, convolution neural network, gradient boosting machine learning (GBML) and improved support vector machine health-care applications are the advance methods used to extract the hidden features and providing the medical information for diagnosis. The proposed design is implemented on Python 3.7.8 software for simulation analysis.

This research is getting more help for investigators, diagnosis centers and doctors. In each and every model, performance measures are to be taken for estimating the application performance. The measures such as accuracy, sensitivity, recall, F1 score, peak-to-signal noise ratio and correlation coefficient have been estimated using proposed methodology. moreover these metrics are providing high improvement compared to earlier models.

The implemented deep and ML designs get outperformance the methodologies and proving good application successive score.

In cultivation, early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates, ensuring that the economy remains balanced. The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification. In this investigation, the accurate prior phase of crop imagery has been collected from different datasets like cropscience, yesmodes and nelsonwisc . In the current study, the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science, yes_modes, nelson_wisc dataset.

In this research work, random forest machine learning-based persuasive plants healthcare computing is provided. If proper ecological care is not applied to early harvesting, it can cause diseases in plants, decrease the cropping rate and less production. Until now different methods have been developed for crop analysis at an earlier stage, but it is necessary to implement methods to advanced techniques. So, the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation. This implemented design is verified on Python 3.7.8 software for simulation analysis.

In this work, different methods are developed for crops at an earlier stage, but more methods are needed to implement methods with prior stage crop harvesting. Because of this, a disease-finding system has been implemented. The methodologies like “Threshold segmentation” and RFO classifier lends 97.8% identification precision with 99.3% real optimistic rate, and 59.823 peak signal-to-noise (PSNR), 0.99894 structure similarity index (SSIM), 0.00812 machine squared error (MSE) values are attained.

The implemented machine learning design is outperformance methodology, and they are proving good application detection rate.

The research purpose is to explore the diffusion of mobile QR-code payment (MQP) in a coronavirus disease (COVID-19) context by formulating a behavioral response model based on an integration between protection motivation theory (PMT) and unified theory of acceptance and use of technology (UTAUT). This study also investigates the importance of physical distancing norm for behavioral intention toward MQP.

A web-based survey was designed and data were accumulated from 411 validated respondents who have used MQP or tend to utilize it in Vietnam. Statistical analysis was conducted using SPSS and AMOS to verify the hypotheses.

Results illustrated that behavioral intention is motivated by key antecedents of PMT (including perceived severity, perceived susceptibility and self-efficacy) and important factors of UTAUT (including performance expectancy, effort expectancy and social influence), and physical distancing norm. Moreover, perceived severity promotes performance expectancy, whereas self-efficacy boosts effort expectancy in MQP. Lastly, behavioral intention and recommendation were indicators of the diffusion of MQP under COVID-19.

MQP is just in its infant stage in Vietnam; thus, the findings provide managerial implications, which will aid service providers and firms to adopt marketing strategies that enhance consumers' acceptability and recommendation of MQP to the public.

Little is empirically considered the effects of perceived threat-related factors in PMT and physical distancing norm on behavioral intention toward MQP in a salient pandemic setting. Furthermore, the antecedents in UTAUT contribute greatly to behavioral intention. This study enlightens the diffusion of MQP based on behavioral intention and recommendation.

Several industries including banking are booming because of COVID-19. However, it is still unknown whether this growth is momentary or permanent in nature. Hence, this study aims to identify the role of health-related concerns and trust as stimuli on M-payment loyalty.

Data were collected through Google Forms from 431 participants. Subjects were M-payment users. The hypothesized model was tested using structural equational modeling.

Results of the study indicate that perceived severity and trust act as stimuli for M-payment loyalty. Further, trust not only influences loyalty directly but also through intimacy. Additionally, no linear relationship was found between perceived usefulness and M-payment loyalty.

This work is an early attempt to consider health-related concerns and trust as stimuli to predict M-payment loyalty. Further, this study focused on three new constructs, namely perceived severity, perceived susceptibility and intimacy, that are underexplored in digital banking literature.

This study aims to determine the impact of health-related views on mobile payment adoption in Sri Lanka from a broader viewpoint. The scale used to quantify each construct was based on earlier research, with modest alterations to fit the pandemic situation. First, an online survey was administered to undergraduates using convenience sampling to acquire appropriate replies. Eliminating incomplete and unusable questionnaires, 266 responses were gathered with an 88.7% response rate. Finally, after removing incomplete and ineffective questionnaires, 243 responses were selected for the analysis. Health consciousness, perceived ease of use, and usefulness have a significant positive relationship between attitude and behavioural intention to mobile payments. Moreover, the attitude has a significant positive relationship with mobile payment usage. As the health consciousness increases the usefulness and intention to use mobile payments, bank managers can focus on this new customer segment. Accordingly, they can use their promotional campaigns to highlight the importance of shifting towards m-payments during the pandemic times. This is the first study that investigates the role of health-related perceptions on the mobile payment adoption in Sri Lanka to the best of the authors’ knowledge.

The usage rate of mobile financial services (MFS) has shown an uptick since the emergence of the COVID-19 pandemic in Bangladesh. This study aims to reveal the underpinning reasons for such MFS surge and its continuance by integrating health belief model (HBM) and expectation confirmation model (ECM).

The study analyzes 529 MFS users' responses during the second wave of the COVID-19 outbreak in Bangladesh using the partial least square method.

Satisfaction is more predictive than perceived usefulness in explaining continuance usage intention. Expectation confirmation also indirectly affects continuance intention. Among the HBM constructs, the indirect effect of perceived severity on continuance intention via perceived usefulness and satisfaction is significant. Besides, the impact of self-efficacy on continuance intention is also significant. Moreover, perceived credibility significantly affects satisfaction and indirectly affected continuance usage intention via satisfaction.

The study projects boosting customers' satisfaction is critical for the successful retention of existing MFS customers. MFS service providers should emphasize the factors that amplify satisfaction. They must evaluate preadoption factors so that customers can have positive confirmation. Especially, the service providers, the policymakers and the regulators should take an active role in improving the users' self-efficacy and the system's credibility. Undertaking the MFS literacy program, installing hotline service to provide emergency help will boost users' confidence in using the system.

The study is a unique contribution in the context of Bangladesh. To the best of the authors’ knowledge, no previous MFS studies in Bangladesh explored MFS continuance usage intention during COVID-19 and beyond. Besides, the inclusion of “perceived credibility” in the framework will supplement the earlier studies conducted on this aspect.

The purpose of this study is to develop a conceptual framework which takes into account the perceived risk (PR) and the perceived benefits (PB) of using mobile payment systems (MPS) in the context of COVID-19 pandemic.

The study proposes a conceptual framework incorporating the uncertainties/risks associated with MPS like perceived technology uncertainty (PTU), perceived regulatory uncertainty (PRU), perceived service intangibility (PSI) and perceived information asymmetry (PIA), along with the benefits of using MPS such as trust, mobility, health consciousness (HC) and fear of Coronavirus (FOC). A survey comprising 1,253 participants was conducted in India. The proposed model was empirically examined through partial least square structural equation modelling.

The outcomes of the study revealed a significant positive influence of PTU, PRU, PIA and PSI on PR. On the other hand, HC and FOC were identified as the major antecedents having a significant positive influence on PB. Both PR and PB had a significant influence on the intention to adopt MPS, but the influence of PB was greater than the influence of PR.

The enablers and inhibitors play a crucial role in understanding the intention to adopt MPS. HC and fear of acquiring Coronavirus can be aggressively marketed by the government and service providers as a strategy to maintain social distancing. Government should address the regulatory concerns associated with the usage of MPS so as to alleviate any negative perception among the general public.

The current study is a novel attempt to understand the intention to adopt MPS in India as precautionary health behaviour to curb the transmission of Coronavirus pandemic. The study uses two constructs, HC and FOC, to better understand the behaviour of the people and explain the intention to adopt MPS during the COVID-19 pandemic.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.

Retailers are increasingly using self-service technologies to improve customer experience and reduce costs. The purpose of this study is to identify factors that could explain the level of continuance intention of mobile self-scanning applications in retail. Based on previous theoretical streams, the present study integrates technology readiness (TR) and service quality into the technology acceptance model.

Using data collected through an online survey of 217 users of a mobile self-scanning application of a large supermarket chain operating in Portugal, the study uses partial least squares structural equation modeling to test the proposed hypotheses.

The results indicate that the continuance usage of the self-scanning apps is directly driven by users' satisfaction and perceived usefulness. Findings also show that TR has a positive and significant impact on ease of use and perceived usefulness. Ease of use has a positive impact on users' satisfaction and perceived usefulness but has no direct effect on the continuance intention to use the application. Perceived quality has a positive direct effect on satisfaction and a positive indirect effect on continuance intention. Finally, need for interaction has a negative effect on TR.

This work contributes to a better understanding of the emerging market for mobile self-scanning applications in retail applications, particularly relevant in a digital transition context.

The purpose of this study is to empirically explore the predictors of e-marketing use continuance intention in the pharmaceutical business to business (B2B) supply chain during the COVID-19 pandemic.

This study adopted survey research strategy, and data were collected from managers dealing with marketing in 127 pharmaceutical firms in Harare Zimbabwe using a self-reported questionnaire. Partial least squares structural equation modeling (PLS-SEM) was employed to test the hypotheses.

Leadership support and perceived usefulness are significant predictors of e-marketing continuance intentions. The effect of perceived susceptibility and perceived severity on e-marketing use continuance intention was not significant. Perceived usefulness is a positive moderator in the relationship among leadership support, perceived susceptibility and e-marketing use continuance intention. However, the moderating effect of perceived usefulness created a significant but negative relationship between perceived severity and e-marketing use continuance intention.

This study provides empirical evidence of the moderating role of perceived usefulness in the relationships between e-marketing continuance intention and its predictors.