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In the customer-oriented enterprise environment, data mining business intelligence (BI) performs key functions that enable business organizations to determine investment risks and estimate returns on investment. It aids in evaluating the gathered information and presenting them in an understandable and practical design that promote data mining analytics. In the current study, banking and retail services were viewed among the rapidly developing technological industry that maintains significant amounts of electronic data. On the account that fraudsters constantly come up with new schemes and refine their existing schemes so they may avoid being easily identified, it is challenging to identify fraud and prevent it from happening. In order to monitor activities relating to fraud prevention, fraud detection, customer retention, business risk management, investment performance, and improving customer satisfaction, the chapter analyzed an artificial neural network machine learning algorithm in data mining techniques and its applications in the retail sector towards service reliability.

The era of artificial neural network (ANN) began with a simplified application in many fields and remarkable success in pattern recognition (PR) even in manufacturing industries. Although significant progress achieved and surveyed in addressing ANN application to PR challenges, nevertheless, some problems are yet to be resolved like whimsical orientation (the unknown path that cannot be accurately calculated due to its directional position). Other problem includes; object classification, location, scaling, neurons behavior analysis in hidden layers, rule, and template matching. Also, the lack of extant literature on the issues associated with ANN application to PR seems to slow down research focus and progress in the field. Hence, there is a need for state-of-the-art in neural networks application to PR to urgently address the above-highlights problems for more successes. The study furnishes readers with a clearer understanding of the current, and new trend in ANN models that effectively addresses PR challenges to enable research focus and topics. Similarly, the comprehensive review reveals the diverse areas of the success of ANN models and their application to PR. In evaluating the performance of ANN models, some statistical indicators for measuring the performance of the ANN model in many studies were adopted. Such as the use of mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and variance of absolute percentage error (VAPE). The result shows that the current ANN models such as GAN, SAE, DBN, RBM, RNN, RBFN, PNN, CNN, SLP, MLP, MLNN, Reservoir computing, and Transformer models are performing excellently in their application to PR tasks. Therefore, the study recommends the research focus on current models and the development of new models concurrently for more successes in the field.

Cloud computing is a technology that tends to address our present and future challenges in the IT world, such as the cost of hardware and its power consumption, storage sizes are becoming smaller in comparison with the large volume of data in recent usage (Big data), the cost and management of software and hardware etc. with the use of visualization technique. Cloud computing still faces lots of challenges, topmost is security and privacy of its data. Among of the data security challenges the cloud faces are data (breach, loss, retention, segregation and remanence etc.). But remanence which is the residue of data that still remains in storage after deletion, is a crucial challenge most especially in the cloud and has received minimal attention from the users and CSP (cloud service providers). Data breach and disclosure by unauthorized persons can occur in different forms among of which is improper disposal of data when it is no longer needed. We will be reviewing various work related to data remanence, its challenges, and its prospects. This will help and guide researchers, cloud users, and cloud service providers, etc., in knowing and understanding better this challenge that poses a threat to confidentiality, integrity, and authorization of data in the cloud environment.

The widespread adoption of automatic sliding doors in both commercial and non-commercial environments globally has necessitated the need to improve their efficiency, safety, and mode of operation. The automatic door gives access to go into or outside a building by sensing the approaching individual using sensors. However, it does not have the intuition to understand when a person is not authorized to go outside based on their age limit, for example, children. To address this problem, researchers have proposed solutions ranging from the use of fuzzy logic to rule-based approaches to make automatic doors better than the previous ones. In this study, an AI-based automatic door system is proposed, which uses a supervised machine learning approach to train classifiers using human body measurement. Our evaluation of different classifiers indicates that SVM is capable of classifying the instances correctly while achieving about 88.9% F-score. Thus, the proposed approach is expected to improve the safety of automatic doors, thereby making them smarter and more intelligent.