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The purpose of this paper is to analyze the evolution of total quality management (TQM) models, frameworks, and tools and techniques in higher education (HE) over the last thirty years from 1991 till 2020, based on a literature review

30 articles from 52 journals were used to perform this detailed literature review. For the detailed analysis, the focus was only on articles related to TQM in higher education and specifically related to models, frameworks and tools and techniques. The study has investigated the growth of research articles, research streams, research methodologies, models and frameworks in the higher education sector and tools and techniques related to those.

This review addresses the progress and gaps in the application of TQM in HE, including the shift in global research in this area from the USA and Europe to Asia in recent years. The articles have been classified into four research methodologies and two research streams which have been reviewed in detail. The findings include reasons for multiple models/frameworks in HE proposed by researchers over the years and the importance of tools and techniques used in TQM implementation.

This study, which tries to bring a perspective to the main trends in TQM application to higher education wrt models, frameworks, tools and techniques over the last thirty years, is expected to add to the body of knowledge in this area and help future researchers to focus on the relevant areas identified in this paper.

In parallel sampling method, the size of the sampling capacitor is reduced to improve the bandwidth of the ADC.

Various low-power techniques for 10-bit 200MS/s pipelined analog-to-digital converter (ADC) are presented. This work comprises two techniques including parallel sampling and switched op-amp sharing technique.

This paper aims to study the effect of parallel sampling and switched op-amp sharing techniques on power consumption in pipelined ADC. In switched op-amp sharing technique, the numbers of op-amps used in the stages are reduced. Because of the reduction in the size of capacitors in parallel sampling technique and op-amps in the switched op-amp sharing technique, the power consumption of the proposed pipelined ADC is reduced to a greater extent.

Simulated the 10-bit 200MS/s pipelined ADC with complementary metal oxide semiconductor process and the simulation results shows a maximum differential non-linearity of +0.31/−0.31 LSB and the maximum integral non-linearity (of +0.74/−0.74 LSB with 62.9 dB SFDR, 55.90 dB SNDR and ENOB of 8.99 bits, respectively, for 18mW power consumption with the supply voltage of 1.8 V.

Fifth-generation (5G) networks play a significant role in handover methods. 5G wireless network is open, flexible and highly heterogeneous along with the overlay coverage and small cell deployments. Handover management is one of the main problems in the heterogeneous network. Also, handover satisfies the needs of ultra-reliable communications along with very high reliability and availability in 5G networks. Handover management deals with every active connection of a user’s device, which moves the connection between the user’s device and the counterparty from one network point to another. Thus, the handover decision determines the best access network and also decides whether the handover is performed or not.

The main intention of this survey is to review several existing handover technologies in 5G. Using the categories of analysis, the existing techniques are divided into different techniques such as authentication-based techniques, blockchain-based techniques, software-defined-based techniques and radio access-based techniques. The survey is made by considering the methods such as used software, categorization of methods and used in the research works. Furthermore, the handover rate is considered for performance evaluation for the handover techniques in 5G. The drawbacks present in the existing review papers are elaborated in research gaps and issues division.

Through the detailed analysis and discussion, it can be summarized that the widely concerned evaluation metric for the performance evaluation is the handover rate. It is exploited that the handover rate within the range of 91%–99% is achieved by three research papers.

A survey on the various handover mechanisms in 5G networks is expected in this study. The research papers used in this survey are gathered from different sources such as Google Scholar and IEEE. Also, this survey suggests a further extension for the handover mechanism in 5G networks by considering various research gaps and issues.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

Additive manufacturing (AM) or solid freeform fabrication (SFF) technique is extensively used to produce intrinsic 3D structures with high accuracy. Its significant contributions in the field of tissue engineering (TE) have significantly increased in the recent years. TE is used to regenerate or repair impaired tissues which are caused by trauma, disease and injury in human body. There are a number of novel materials such as polymers, ceramics and composites, which possess immense potential for production of scaffolds. However, the major challenge is in developing those bioactive and patient-specific scaffolds, which have a required controlled design like pore architecture with good interconnectivity, optimized porosity and microstructure. Such design not only supports cell proliferation but also promotes good adhesion and differentiation. However, the traditional techniques fail to fulfill all the required specific properties in tissue scaffold. The purpose of this study is to report the review on AM techniques for the fabrication of TE scaffolds.

The present review paper provides a detailed analysis of the widely used AM techniques to construct tissue scaffolds using stereolithography (SLA), selective laser sintering (SLS), fused deposition modeling (FDM), binder jetting (BJ) and advanced or hybrid additive manufacturing methods.

Subsequently, this study also focuses on understanding the concepts of TE scaffolds and their characteristics, working principle of scaffolds fabrication process. Besides this, mechanical properties, characteristics of microstructure, in vitro and in vivo analysis of the fabricated scaffolds have also been discussed in detail.

The review paper highlights the way forward in the area of additive manufacturing applications in TE field by following a systematic review methodology.

The purpose of this paper is to apply various data mining techniques for predicting the financial performance of Islamic banking in Indonesia through the main exogenous determinants of profitability by choosing the best data mining technique based on the criteria of the highest accuracy score of testing and training.

This paper used data mining techniques to predict the financial performance of Islamic banking by applying all of LASSO regression, random forest (RF), artificial neural networks and k-nearest neighbor (KNN) over monthly data sets of all the full-fledged Islamic banks working in Indonesia from January 2011 until March 2020. This study used return on assets as a real measurement of financial performance, whereas the capital adequacy ratio, asset quality and liquidity management were used as exogenous determinants of financial performance.

The experimental results showed that the optimal task for predicting the financial performance of Islamic banking in Indonesia is the KNN technique, which affords the best-predicting accuracy, and gives the optimal knowledge from the financial performance of Islamic banking determinants in Indonesia. As well, the RF provides closer values to the optimal accuracy of the KNN, which makes it another robust technique in predicting the financial performance of Islamic banking.

This paper restricted modeling the financial performance of Islamic banking to profitability through the main determinants of return of assets in Indonesia. Future research could consider enlarging the modeling of financial performance using other models such as CAMELS and Z-Score to predict the financial performance of Islamic banking under data mining techniques.

Owing to the lack of using data mining techniques in the Islamic banking sector, this paper would fill the literature gap by providing new effective techniques for predicting financial performance in the Islamic banking sector using data mining approaches, which can be efficient tools in business and management modeling for financial researchers and decision-makers in the Islamic banking sector.

According to the author’s knowledge, this paper is the first that provides data mining techniques for predicting the financial performance of the Islamic banking sector in Indonesia.

During recent years a number of techniques have been developed to aid in the forecasting of corporate sales, individual product demand, economic indicators, and other related series. These techniques have included classical time series analysis, multiple regression and adaptive forecasting procedures. As a result of these developments, the individual company and decision maker is faced with the task of selecting the forecasting technique that is most appropriate for his situation. This article reports research conducted at INSEAD on how simulation can be used to compare and evaluate alternative forecasting techniques for a specific application.

This paper presents the results of the second part of a study into the “barriers to acceptance of production management techniques in UK manufacturing industry”. The results of a telephone survey and detailed in‐company work carried out with seven manufacturing companies are presented. The important factors contributing to the successful usage of the techniques and concepts are discussed in detail and a proposed methodology for widening the application of techniques is outlined.

Four active‐like (A‐like) and six passive‐like (P‐like) business teaching/learning techniques are described. It is proposed that students enrolled and faculty teaching in the international business (INTB), marketing (MKT), and management (MAN) business concentrations would rate the A‐like techniques higher than students enrolled and faculty teaching in the management information systems (MIS), finance (FIN), and accounting (ACC) business concentrations. And that students enrolled and faculty teaching in the MIS, FIN, and ACC concentrations would rate the P‐like techniques higher than the students and faculty in the INTB, MKT, and MAN concentrations. Using a survey questionnaire, upper undergraduate and MBA university business students and faculty were asked to indicate the importance level for each technique. Students' ratings do not support the proposition in nine techniques and the faculty ratings do not support it in eight. The conclusion is that the study at least provides a framework that can aid instructors in understanding that different students prefer and different situations require different instructional techniques.

The first article in this series explained why store location decisions are becoming more difficult. This article, and the next two, look at the pros and cons of various techniques that are now available to help retailers make such decisions. The three articles are presented in the sequence a retailer might follow as part of an ‘ideal’ store location strategy. This article looks at techniques aimed at searching out areas of the country that might have potential for new stores; these are referred to as search techniques. The next article discusses techniques that will forecast the likely turnover of a store on a particular site selected within the area of identified potential; these are referred to as viability techniques. The fourth and final article in the series will, as part of a consideration of evaluation of existing stores, look at techniques concerned with the effect of localised design, siting and perception issues on store performance; we can call these micro assessment techniques. The first part of this article will act as useful background for discussion of all three levels of technique application.