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The objective of the proposed work is to identify the most commonly occurring non–small cell carcinoma types, such as adenocarcinoma and squamous cell carcinoma, within the human population. Another objective of the work is to reduce the false positive rate during the classification.

In this work, a hybrid method using convolutional neural networks (CNNs), extreme gradient boosting (XGBoost) and long-short-term memory networks (LSTMs) has been proposed to distinguish between lung adenocarcinoma and squamous cell carcinoma. To extract features from non–small cell lung carcinoma images, a three-layer convolution and three-layer max-pooling-based CNN is used. A few important features have been selected from the extracted features using the XGBoost algorithm as the optimal feature. Finally, LSTM has been used for the classification of carcinoma types. The accuracy of the proposed method is 99.57 per cent, and the false positive rate is 0.427 per cent.

The proposed CNN–XGBoost–LSTM hybrid method has significantly improved the results in distinguishing between adenocarcinoma and squamous cell carcinoma. The importance of the method can be outlined as follows: It has a very low false positive rate of 0.427 per cent. It has very high accuracy, i.e. 99.57 per cent. CNN-based features are providing accurate results in classifying lung carcinoma. It has the potential to serve as an assisting aid for doctors.

It can be used by doctors as a secondary tool for the analysis of non–small cell lung cancers.

It can help rural doctors by sending the patients to specialized doctors for more analysis of lung cancer.

In this work, a hybrid method using CNN, XGBoost and LSTM has been proposed to distinguish between lung adenocarcinoma and squamous cell carcinoma. A three-layer convolution and three-layer max-pooling-based CNN is used to extract features from the non–small cell lung carcinoma images. A few important features have been selected from the extracted features using the XGBoost algorithm as the optimal feature. Finally, LSTM has been used for the classification of carcinoma types.

The goal of this research is to analyse the obstacles to the implementation of mobile health (mHealth) in India and to gain an understanding of the contextual inter-relationships that exist amongst those obstacles.

Potential barriers and their interrelationships in their respective contexts have been uncovered. Using MICMAC analysis, the categorization of these barriers was done based on their degree of reliance and driving power (DP). Furthermore, an interpretive structural modeling (ISM) framework for the barriers to mHealth activities in India has been proposed.

The study explores a total of 15 factors that reduce the efficiency of mHealth adoption in India. The findings of the Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) investigation show that the economic situation of the government, concerns regarding the safety of intellectual technologies and privacy issues are the primary obstacles because of the significant driving power they have in mHealth applications.

Promoters of mHealth practices may be able to make better plans if they understand the social barriers and how they affect each other; this leads to easier adoption of these practices. The findings of this study might be helpful for governments of developing nations to produce standards relating to the deployment of mHealth; this will increase the efficiency with which it is adopted.

At this time, there is no comprehensive analysis of the factors that influence the adoption of mobile health care with social cognitive theory in developing nations like India. In addition, there is a lack of research in investigating how each of these elements affects the success of mHealth activities and how the others interact with them. Because developed nations learnt the value of mHealth practices during the recent pandemic, this study, by investigating the obstacles to the adoption of mHealth and their inter-relationships, makes an important addition to both theory and practice.

Artificial intelligence (AI) can enhance human resource resiliency (HRR) by providing the insights and resources needed to adapt to unexpected changes and disruptions. Therefore, the present research attempts to develop a framework for future researchers to gain insights into the actions of AI to enable HRR.

The present study used a systematic literature review, bibliometric analysis, and network analysis followed by content analysis. In doing so, we reviewed the literature to explore the present state of research in AI and HRR. A total of 98 articles were included, extracted from the Scopus database in the selected field of research.

The authors found that AI or AI-associated techniques help deliver various HRR-oriented outcomes, such as enhancing employee competency, performance management and risk management; enhancing leadership competencies and employee well-being measures; and developing effective compensation and reward management.

The present research has certain implications, such as increasing the HR team's proficiency, addressing the problem of job loss and how to fix it, improving working conditions and improving decision-making in HR.

The present research explores the role of AI in HRR following the COVID-19 pandemic, which has not been explored extensively.

The purpose of this paper is to investigate the scholarly communications in Journal of Financial Crime (JFC) during the last five years and to study the key dimensions of its publication trends.

For the analysis of the study, five volumes containing 20 issues of Journal of Financial Crime during the years 2006 to 2010 have been taken up for evaluation. The authors employ necessary bibliometric measures to analyze different publication parameters.

It is found that the contribution of articles to each volume of JFC is very consistent and the journal has published around 30 articles per year. Single authored papers are found to be the highest, followed by two‐authored and then three‐authored papers. The degree of collaboration in JFC is found to be 0.246. In regards to ranking of country productivity, the UK topped the list followed by the USA, Canada and Australia. Journal of Financial Crime, which is the source journal, leads the table followed by Journal of Business Ethics, Crime Law and Social Change and Journal of Money Laundering Control.

This paper focuses on the publication traits of Journal of Financial Crime over a five‐year period. Patterns of research output in 155 publications are analyzed. Further studies can include other journals in the field of economics.

Scholars can benefit from insights into the scholarly contributions of Journal of Financial Crime that has accommodated 220 authors from 41 different countries of the world.

The paper provides valuable insights into the nature of academic publishing of Journal of Financial Crime. It can help JFC readers to understand the most striking contributions, highly cited journals, the most prolific authors, country productivity, and assorted parameters.

This paper explores and interprets the linkage between total quality management (TQM) practices and organizational performance measures for improving the healthcare firms’ performance. Indian healthcare firms are aware of TQM practices and their benefits, but the awareness level varies among the firms and staff. The study looks into the effectiveness of quality awareness to meet quality performance in Indian hospitals.

A questionnaire based on previous research was circulated among the managers and medical staff. The model linking TQM and organizational performance is analyzed with structural equation modelling and confirmed the hypotheses stated. Interpretations to improve hospital performance are made.

The study identified ten relevant TQM factors and confirmed their importance towards the improved organizational performance of Indian hospitals. Top management initiative, continuous process improvement and team work are the most contributing TQM factors. Differences in the awareness levels by the management staff and medical staff are attributed. The managers and medical staff are aware of the benefits of TQM towards firm performance, but it is to be improved further.

Cross-validation and interpretation are affected due to the limited sample size. Longitudinal study is recommended to explore the individual hospital as specific cases. Larger sample size is suggested as an extended work to overcome the demographic and infrastructural limitations of the firms included.

The management is more interested in TQM, but there is lack of awareness among the staff. The quality awareness and customer focus by medical staff are the most weakly loaded factors, and the weaknesses can be remedied by the lead role by the hospital management in providing proper training and thereby improving the attitude of the medical staff.

Effectiveness of hospital operations is highly dependent on customer focus. Properly communicated, committed and trained staff with good-quality awareness can better implement TQM and thereby improve hospital performance. Lead role by the management is very important, and the paper lists ways to attain these outcomes.

Very little is reported from the Indian healthcare sector linking TQM and outcome performance. The quality awareness, customer focus, communication and learning by the medical staff are to be improved, and the paper suggests ways to link TQM more effectively to improve the performance in hospitals. These findings may be useful to the managers, medical staff and researchers in healthcare to bring better results.

Detecting cancer from the computed tomography (CT)images of lung nodules is very challenging for radiologists. Early detection of cancer helps to provide better treatment in advance and to enhance the recovery rate. Although a lot of research is being carried out to process clinical images, it still requires improvement to attain high reliability and accuracy. The main purpose of this paper is to achieve high accuracy in detecting and classifying the lung cancer and assisting the radiologists to detect cancer by using CT images. The CT images are collected from health-care centres and remote places through Internet of Things (IoT)-enabled platform and the image processing is carried out in the cloud servers.

IoT-based lung cancer detection is proposed to access the lung CT images from any remote place and to provide high accuracy in image processing. Here, the exact separation of lung nodule is performed by Otsu thresholding segmentation with the help of optimal characteristics and cuckoo search algorithm. The important features of the lung nodules are extracted by local binary pattern. From the extracted features, support vector machine (SVM) classifier is trained to recognize whether the lung nodule is malicious or non-malicious.

The proposed framework achieves 99.59% in accuracy, 99.31% in sensitivity and 71% in peak signal to noise ratio. The outcomes show that the proposed method has achieved high accuracy than other conventional methods in early detection of lung cancer.

The proposed algorithm is implemented and tested by using more than 500 images which are collected from public and private databases. The proposed research framework can be used to implement contextual diagnostic analysis.

The cancer nodules in CT images are precisely segmented by integrating the algorithms of cuckoo search and Otsu thresholding in order to classify malicious and non-malicious nodules.