The negative effect of financial crimes on financial institutions has grown dramatically over the years. To detect crimes such as credit card fraud, several single and hybrid machine learning approaches have been used. However, these approaches have significant limitations as no further investigation on different hybrid algorithms for a given dataset were studied. This research proposes and investigates seven hybrid machine learning models to detect fraudulent activities with a real word dataset. The developed hybrid models consisted of two phases, state-of-the-art machine learning algorithms were used first to detect credit card fraud, then, hybrid methods were constructed based on the best single algorithm from the first phase. Our findings indicated that the hybrid model Adaboost + LGBM is the champion model as it displayed the highest performance. Future studies should focus on studying different types of hybridization and algorithms in the credit card domain.

With the spread of m-commerce usage around the globe, mobile commerce can become the primary approach for many users in transactions. Such a method is considered an innovative way for the financial wallet but with some encumbrances. Trust in mobile commerce is one of the challenges yet to receive in-depth studies regarding linear and non-compensatory relationships. This study aims to evaluate the determinants of trust in mobile commerce using hybrid three-stage Fuzzy Delphi-Structural Equation Modeling (SEM)-Artificial Neural Network (ANN) approach. A hybrid approach was performed on 344 users mobile-commerce activities. The results of this study provide valuable insights for mobile commerce firms to come up with effective plans that increase customer trust in mobile commerce. Practically, the relative importance of specific determinants of trust in mobile commerce contributes to providing deep insight into the factors of customer trust in mobile commerce. The proposed model predicts 94.80% trust in mobile commerce with social presence and social support theories. A novel perspective was provided in this study to encourage e-marketing among consumers to persuade customers to trust mobile commerce.

There are continued efforts to build on the performance of deep learning (DL) models in various fields of application. Developing new DL models continues to open unprecedented opportunities in diverse application areas despite the enormous resources required. Generally, the learning mechanism of DL models depends on the term "cost function" (CF) or "loss function" (LF), and DL models require varied hyperparameter settings and, precisely, parameters that can help the model to continually minimize the cost function until faster convergence, with better generalization over the data in the loss landscape, is assumed. The learning rate (LR) update seeks to find the optimal solution for DL models through relative cost function minimization. Therefore, selecting the appropriate LR is essential to the performance of DL models. The existing cosine annealing LR, despite its demonstration for fast model convergence, lacks complete loss landscape exploration of the flat minima, hence limiting its ability to model better generalization. To address this, the paper proposes a period-shift Cosine Annealing Learning Rate with warm-up epochs (ps-CALR) to perturb the LR update. Six different publicly available datasets were used to benchmark the proposed LR method by experimenting with custom DL (multilayer perceptron and convolutional neural networks) and pre-trained DL models. The proposed ps-CARL enhances model generalization and convergence, pushing the solution to notably better performance compared with fixed LR and the existing cosine annealing method.

A smart grid is a new type of power system based on modern information technology, which utilises advanced communication, computing and control technologies and employs advanced sensors, measurement, communication and control devices that can monitor the status and operation of various devices in the power system in real-time and optimise the dispatch of the power system through intelligent algorithms to achieve efficient operation of the power system. However, due to its complexity and uncertainty, how to effectively perform real-time prediction is an important challenge. This paper proposes a smart grid real-time prediction model based on the attention mechanism of convolutional neural network (CNN) combined with bi-directional long and short-term memory BiLSTM.The model has stronger spatiotemporal feature extraction capability, more accurate prediction capability and better adaptability than ARMA and decision trees. The traditional prediction models ARMA and decision tree can often only use simple statistical methods for prediction, which cannot meet the requirements of high accuracy and efficiency of real-time load prediction, so the CNN-BiLSTM model based on Bayesian optimisation has the following advantages and is more suitable for smart grid real-time load prediction compared with ARMA and decision tree. CNN is a hierarchical neural network structure containing several layers such as a convolutional layer, pooling layer and fully connected layer. The convolutional layer is mainly used for extracting features from data such as images, the pooling layer is used for the dimensionality reduction of features, and the fully connected layer is used for classification and recognition. The core of CNN is the convolutional operation, a locally weighted summation operation on the input data that can effectively extract features from the data. In the convolution operation, different features can be extracted by setting different convolution kernels to achieve feature extraction and classification of data. BiLSTM can capture semantic dependencies in both directions. The BiLSTM structure consists of two LSTM layers that process the input sequence in the forward and backward directions to combine the information in both directions to obtain more comprehensive contextual information. BiLSTM can access both the front and back inputs at each time step to obtain more accurate prediction results. It effectively prevents gradient explosion and gradient disappearance while better capturing longer-distance dependencies. The CNN-BiLSTM extracts features of the data and then optimises them by Bayes. By collecting real-time data from the power system, including power, load, weather and other factors, our model uses the features of CNN-BiLSTM to deeply learn real-time load data from smart grids and extract key features to achieve future load prediction. Meanwhile, the Bayesian optimisation algorithm based on the model can optimise the model’s hyperparameters, thus improving the model’s prediction performance. The model can achieve accurate prediction of a real-time power system load, provide an important reference for the dispatch and operation of the power system, and help optimise the operation efficiency and energy utilisation efficiency of the power system.