Purpose Due to its impact on business performance total quality management (TQM) has gained a lot of importance by businessmen, managers, practitioners, and research scholars over the last 20 years. Therefore, the purpose of this paper is to critically assess the literature on TQM and find out the areas where future research is required. Design/methodology/approach To achieve this purpose the articles published in the last 20 years were studied in a systematic way and a snapshot of the same was prepared in the tabular format with points such as year and journal of publication, application and country, statistical method used, and findings of the study such as practices and impact of TQM. After identifying the practices and impact of TQM a quality tool “Pareto Analysis” was applied on them for development of the model. Findings The findings provide the practices of TQM and its impact on the performance of a business. The gaps from the literature have been identified and areas for future research have been suggested. On the basis of the findings a generalized framework of TQM has been suggested which can be applicable irrespective of the sector. Practical implications The research will help academicians and future researchers to have a clear understanding of TQM in different rosters. Originality/value Ample literature is available on TQM but in the best knowledge of authors no study has taken place to integrate the reviews and findings of 102 research papers of the last two decades.

Alzheimer’s disease (AD) is one of the most important causes of mortality in elderly people, and it is often challenging to use traditional manual procedures when diagnosing a disease in the early stages. The successful implementation of machine learning (ML) techniques has also shown their effectiveness and its reliability as one of the better options for an early diagnosis of AD. But the heterogeneous dimensions and composition of the disease data have undoubtedly made diagnostics more difficult, needing a sufficient model choice to overcome the difficulty. Therefore, in this paper, four different 2D and 3D convolutional neural network (CNN) frameworks based on Bayesian search optimization are proposed to develop an optimized deep learning model to predict the early onset of AD binary and ternary classification on magnetic resonance imaging (MRI) scans. Moreover, certain hyperparameters such as learning rate, optimizers, and hidden units are to be set and adjusted for the performance boosting of the deep learning model. Bayesian optimization enables to leverage advantage throughout the experiments: A persistent hyperparameter space testing provides not only the output but also about the nearest conclusions. In this way, the series of experiments needed to explore space can be substantially reduced. Finally, alongside the use of Bayesian approaches, long short-term memory (LSTM) through the process of augmentation has resulted in finding the better settings of the model that too in less iterations with an relative improvement (RI) of 7.03%, 12.19%, 10.80%, and 11.99% over the four systems optimized with manual hyperparameters tuning such that hyperparameters that look more appealing from past data as well as the conventional techniques of manual selection.

Finance plays a key role in the growth of developed as well as developing nations. A financially well included society leads to stronger growth. Financial inclusion aims at providing easy and affordable access to financial products and services. The main concern for any developing nation from a growth point of view is advancement of low-income rural population just as much as the high-income population. Taking a note of this, identifying the key determinants that would lead to successful financial inclusion of low-income rural population is equally, if not more, important. The inclusion strategies have to be built around these determinants to promote inclusion and thus, a clear picture of these determinants is a must have for strategy and policy makers. Though the factors may be somewhat similar across the nation, but their significance and impact on financial inclusion varies greatly from one geographical area to other. In line with this, the purpose of this study is to identify the dimensions of successful financial inclusion in the low-income rural segments with special reference to Raipur, Chhattisgarh. The study uses factor analysis to identify the determinants and path analysis to analyse the significance of these factors in financial inclusion.

Alzheimer’s disease (AD) is the most generally known neurodegenerative disorder, leading to a steady deterioration in cognitive ability. Deep learning models have shown outstanding performance in the diagnosis of AD, and these models do not need any handcrafted feature extraction over conventional machine learning algorithms. Since the 2012 AlexNet accomplishment, the convolutional neural network (CNN) has been progressively utilized by the medical community to assist practitioners to early diagnose AD. This paper explores the current cutting edge applications of CNN on single and multimodality (combination of two or more modalities) neuroimaging data for the classification of AD. An exhaustive systematic search is conducted on four notable databases: Google Scholar, IEEE Xplore, ACM Digital Library, and PubMed in June 2021. The objective of this study is to examine the effectiveness of classification approaches on AD to analyze different kinds of datasets, neuroimaging modalities, preprocessing techniques, and data handling methods. However, CNN has achieved great success in the classification of AD; still, there are a lot of challenges particularly due to scarcity of medical imaging data and its possible scope in this field.

Machine learning (ML) techniques provide the learning capability to a system and encourage adaptation into the environment, based upon many logical and statistical operations. The prime goal of ML is to recognize the complex patterns and make decisions based on the results. There are various ML algorithms which are implemented to secure the mobile ad-hoc networks. The infrastructure-less environment of MANETs poses a great challenge in implementation of the security systems. The security approaches in MANETs mainly focus on intrusion detection, malicious attacks mitigation, elimination of outlier/misbehavior/selfish nodes and securing routing paths. The researchers have been using cutting edge technologies for providing efficient security solutions by taking into the consideration of dynamic environment of MANETs. These technologies include machine learning, Artificial Intelligence (AI), Genetic Algorithms based methods, biological-inspired algorithms and so on. This paper presents a comprehensive and systematic study of various modern approaches for intensifying security in MANETs.