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This paper describes a software architecture which supports the design of hierarchical controllers that provide facilities for adaptation, supervision and task planning. It details how this form of functional hierarchy differs from the structural hierarchy also inherent within a complex control system. Then, both forms of hierarchy are combined in a single design notation and development methodology. The system utilises intelligent control techniques (neuro‐fuzzy and genetic optimisation) for controlling a cryogenic plant used for superconductor testing by cooling the test samples to temperatures below 1008K. The system supports the design of a hierarchical controller that provides facilities for adaptation, supervision and task planning. Simulation results are presented.

The objective of this study is to provide a systematic review of the literature on artificial intelligence (AI) in customer-facing financial services, providing an overview of explored contexts and research foci, identifying gaps in the literature and setting a comprehensive agenda for future research.

Combining database (i.e. Scopus, Web of Science, EBSCO, ScienceDirect) and manual journal search, the authors identify 90 articles published in Australian Business Deans Council (ABDC) journals for investigation, using the TCCM (Theory, Context, Characteristics and Methodology) framework.

The results indicate a split between data-driven and theory-driven research, with most studies either adopting an experimental research design focused on testing the accuracy and performance of AI algorithms to assist with credit scoring or investigating AI consumer adoption behaviors in a banking context. The authors call for more research building overarching theories or extending existing theoretical perspectives, such as actor networks. More empirical research is required, especially focusing on consumers' financial behaviors as well as the role of regulation, ethics and policy concerned with AI in financial service contexts, such as insurance or pensions.

The review focuses on AI in customer-facing financial services. Future work may want to investigate back-office and operations contexts.

The authors are the first to systematically synthesize the literature on the use of AI in customer-facing financial services, offering a valuable agenda for future research.

A crucial decision in financial services is how to classify credit or loan applicants into good and bad applicants. The purpose of this paper is to propose a four-stage hybrid data mining approach to support the decision-making process.

The approach is inspired by the bagging ensemble learning method and proposes a new voting method, namely two-level majority voting in the last stage. First some training subsets are generated. Then some different base classifiers are tuned and afterward some ensemble methods are applied to strengthen tuned classifiers. Finally, two-level majority voting schemes help the approach to achieve more accuracy.

A comparison of results shows the proposed model outperforms powerful single classifiers such as multilayer perceptron (MLP), support vector machine, logistic regression (LR). In addition, it is more accurate than ensemble learning methods such as bagging-LR or rotation forest (RF)-MLP. The model outperforms single classifiers in terms of type I and II errors; it is close to some ensemble approaches such as bagging-LR and RF-MLP but fails to outperform them in terms of type I and II errors. Moreover, majority voting in the final stage provides more reliable results.

The study concludes the approach would be beneficial for banks, credit card companies and other credit provider organisations.

A novel four stages hybrid approach inspired by bagging ensemble method proposed. Moreover the two-level majority voting in two different schemes in the last stage provides more accuracy. An integrated evaluation criterion for classification errors provides an enhanced insight for error comparisons.

The purpose of this paper is to supplement and update previously published articles about artificial intelligence (AI) instruments and operations in banking sectors with the following objectives in mind: to understand the role of AI in banking sectors; to explore the themes and context in this area based on keywords, co-citations and co-words; and to identify future research direction by evaluating the trend and direction of previous research.

This study adopts a semi-inductive approach with the convolution of bibliometrics and literature review. This study used bibliometrics for the identification of literature across multiple databases and systematic literature review on identified articles to explore heterogeneous sectors within AI in banking and finance.

This study contributes a literature-based model that accounts for both the broadly in AI application in banking and finance: predictive modeling in risk assessment and detection; financial decision-making; client service delivery; and emerging FinTech applications of AI and machine learning.

This study is among the few to address the literature of tools and application of AI in banking through mixed-methods approach and produce a synthesized model for the same.

Internet marketing underground industry users refer to people who use technology means to simulate a large number of real consumer behaviors to obtain marketing activities rewards illegally, which leads to increased cost of enterprises and reduced effect of marketing. Therefore, this paper aims to construct a user risk assessment model to identify potential underground industry users to protect the interests of real consumers and reduce the marketing costs of enterprises.

Method feature extraction is based on two aspects. The first aspect is based on traditional statistical characteristics, using density-based spatial clustering of applications with noise clustering method to obtain user-dense regions. According to the total number of users in the region, the corresponding risk level of the receiving address is assigned. So that high-quality address information can be extracted. The second aspect is based on the time period during which users participate in activities, using frequent item set mining to find multiple users with similar operations within the same time period. Extract the behavior flow chart according to the user participation, so that the model can mine the deep relationship between the participating behavior and the underground industry users.

Based on the real underground industry user data set, the features of the data set are extracted by the proposed method. The features are experimentally verified by different models such as random forest, fully-connected layer network, SVM and XGBOST, and the proposed method is comprehensively evaluated. Experimental results show that in the best case, our method can improve the F1-score of traditional models by 55.37%.

This paper investigates the relative importance of static information and dynamic behavior characteristics of users in predicting underground industry users, and whether the absence of features of these categories affects the prediction results. This investigation can go a long way in aiding further research on this subject and found the features which improved the accuracy of predicting underground industry users.

Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.

The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.

The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.

Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.

The main purpose of this controller is to carryout irrigation by the farmers with renewable energy resources.

The proposed design includes the Deep learning based intelligent stand-alone energy management system used for irrigation purpose. The deep algorithm applied here is Radial basis function neural network which tracks the maximum power, maintains the battery as well as load system.

The Radial Basis Function Neural Network algorithm is used for carrying out the training process. In comparison with other conventional algorithms, this algorithm outperforms by higher efficiency and lower tracking time without oscillation.

It is little complex to implement the hardware setup of neural network in terms of training process but the work is under progress.

The practical hardware implementation is under progress.

If controller are implemented in a real-time environment, definitely it helps the human-less farming and irrigation process.

If this system is implemented in real-time environment, every farmer gets benefitted.

The purpose of this study is to examine the moderating effect of mandatory corporate social responsibility (CSR) reporting on CSR expenditure and financial performance of listed firms in India. It uses institutional theory to explain the relationship.

The study used the Indian stock market as the testing grounds and applied descriptive statistics, hierarchical regression and panel regression with fixed effect assumptions for 800 firm-year observations for the period 2010 to 2019.

The study shows a positive and statistically significant association between CSR expenditure and financial performance [return on assets (ROA) and Tobin’s q]. Also, the study shows a positive association between financial performance (ROA and Tobin’s q) and CSR expenditure. Furthermore, the study shows that mandatory CSR reporting leads to an increase in CSR expenditure. Finally, the study shows that mandatory CSR reporting moderates the association between CSR expenditure and financial performance stock price returns). The study control for any form of heteroscedasticity, serial correlation and endogeneity effects.

The study used one country data to represent the emerging economies. The use of one country data can limit the generalisation of the study.

Different studies have examined mandatory CSR reporting association with CSR disclosure or financial performance. However, this study takes the discussion further and contribute a novelty to sustainability development studies with the examined moderating effect of mandatory CSR reporting in the association between CSR expenditure and financial performance.

To provide a selective bibliography for researchers working with bulk material forming (specifically the forging, rolling, extrusion and drawing processes) with sources which can help them to be up‐to‐date.

A range of published (1996‐2005) works, which aims to provide theoretical as well as practical information on the material processing namely bulk material forming. Bulk deformation processes used in practice change the shape of the workpiece by plastic deformations under forces applied by tools and dies.

Provides information about each source, indicating what can be found there. Listed references contain journal papers, conference proceedings and theses/dissertations on the subject.

It is an exhaustive list of papers (1,693 references are listed) but some papers may be omitted. The emphasis is to present papers written in English language. Sheet material forming processes are not included.

A very useful source of information for theoretical and practical researchers in computational material forming as well as in academia or for those who have recently obtained a position in this field.

There are not many bibliographies published in this field of engineering. This paper offers help to experts and individuals interested in computational analyses and simulations of material forming processes.

The current approach to valuing customers is based on the notion of discounted profit generated by the customers over the lifetime of the relationship, also known as customer lifetime value (CLV). However, in the financial services industry, the customers who contribute the most to the profitability of a firm are also the riskiest customers. If the riskiness of a customer is not considered, firms will overestimate the true value of that customer. This paper proposes a methodology to adjust CLV for different types of risk factors and creates a comprehensive measure of risk-adjusted lifetime value (RALTV).

Using data from a major credit card company, we develop a measure of risk adjusted lifetime value (RALTV) that accounts for diverse types of customer risks. The model is estimated using Stochastic Frontier Analysis (SFA).

Major findings indicate that rewards cardholders and affinity cardholders tend to score higher within the RALTV framework than non-rewards cardholders and non-affinity cardholders, respectively. Among the four different modes of acquisition, the Internet generates the highest RALTV, followed by direct mail.

This paper not only controls for different types of consumer risks in the financial industry and creates a comprehensive risk-adjusted lifetime value (RALTV) model but also shows empirically the value of using RALTV over CLV for predicting future performance of a set of customers. Further, we investigate the impact of a firm’s acquisition and retention strategies on RALTV. The measure of risk-adjusted lifetime value is invaluable for managers in financial services.