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The purpose of this paper is to appraise various service quality models and identify issues for future research based on the analysis of literature.

The paper examines 14 different service quality models reported in the literature applicable to the banking sector. The critical review of the different service quality models is intended to compile the various dimensions which emerged out of the studies, compare the commonality between them, study their relevance and importance in banking in the various cultural and cultural contexts and highlight the limitations of the studies.

The review of various service quality models in banking revealed that the meaning of service quality may have some universal aspects, as demonstrated by the similarities in the underlying dimensions as proposed in the different studies. This paper lends support to the contention that the dimensionality of SERVQUAL and importance of the dimensions vary with the cultural and country context even within the banking industry.

This paper comprises a compiled report on the different service quality models and the effect of application of SERVQUAL in banking in different countries.

Service quality has some common dimensions across the different models, however, the items involved and their operationalization in different cultural contexts within the same banking sector may vary. The paper indicates that a generic instrument for measurement of service quality or even one specifically developed for banking may not be applicable in its original form for all cultural contexts including Islamic society and banks. Development of the customized scale for measuring the service quality for a particular cultural or country context at that particular time is warranted. This paper brings a lot of information on service quality in banking under one roof and provides new directions to service quality researchers.

This paper offers practical help to researchers and practitioners in providing a direction for service quality improvement by indicating the common theme that emerges from the service quality models. Also, the differences in the relevance and importance of the dimensions, due to the change in the cultural and country contexts, have been brought to the forefront.

For achieving the profitable human activity recognition (HAR) method, this paper solves the HAR problem under wireless body area network (WBAN) using a developed ensemble learning approach. The purpose of this study is,to solve the HAR problem under WBAN using a developed ensemble learning approach for achieving the profitable HAR method. There are three data sets used for this HAR in WBAN, namely, human activity recognition using smartphones, wireless sensor data mining and Kaggle. The proposed model undergoes four phases, namely, “pre-processing, feature extraction, feature selection and classification.” Here, the data can be preprocessed by artifacts removal and median filtering techniques. Then, the features are extracted by techniques such as “t-Distributed Stochastic Neighbor Embedding”, “Short-time Fourier transform” and statistical approaches. The weighted optimal feature selection is considered as the next step for selecting the important features based on computing the data variance of each class. This new feature selection is achieved by the hybrid coyote Jaya optimization (HCJO). Finally, the meta-heuristic-based ensemble learning approach is used as a new recognition approach with three classifiers, namely, “support vector machine (SVM), deep neural network (DNN) and fuzzy classifiers.” Experimental analysis is performed.

The proposed HCJO algorithm was developed for optimizing the membership function of fuzzy, iteration limit of SVM and hidden neuron count of DNN for getting superior classified outcomes and to enhance the performance of ensemble classification.

The accuracy for enhanced HAR model was pretty high in comparison to conventional models, i.e. higher than 6.66% to fuzzy, 4.34% to DNN, 4.34% to SVM, 7.86% to ensemble and 6.66% to Improved Sealion optimization algorithm-Attention Pyramid-Convolutional Neural Network-AP-CNN, respectively.

The suggested HAR model with WBAN using HCJO algorithm is accurate and improves the effectiveness of the recognition.

Internet of Things (IoT) is an up-and-coming conception that intends to link multiple devices with each other. The aim of this study is to provide a significant analysis of Green IoT. The IoT devices sense, gather and send out significant data from their ambiance. This exchange of huge data among billions of devices demands enormous energy. Green IoT visualizes the concept of minimizing the energy consumption of IoT devices and keeping the environment safe.

This paper attempts to analyze diverse techniques associated with energy-efficient protocols in green IoT pertaining to machine-to-machine (M2M) communication. Here, it reviews 73 research papers and states a significant analysis. Initially, the analysis focuses on different contributions related to green energy constraints, especially energy efficiency, and different hierarchical routing protocols. Moreover, the contributions of different optimization algorithms in different state-of-the-art works are also observed and reviewed. Later the performance measures computed in entire contributions along with the energy constraints are also checked to validate the effectiveness of entire contributions. As the number of contributions to energy-efficient protocols in IoT is low, the research gap will focus on the development of intelligent energy-efficient protocols to build up green IoT.

The analysis was mainly focused on the green energy constraints and the different robust protocols and also gives information on a few powerful optimization algorithms. The parameters considered by the previous research works for improving the performance were also analyzed in this paper to get an idea for future works. Finally, the paper gives some brief description of the research gaps and challenges for future consideration that helps during the development of an energy-efficient green IoT pertaining to M2M communication.

To the best of the authors’ knowledge, this is the first work that reviews 65 research papers and states the significant analysis of green IoT.