The UK financial sector increasingly employs machine learning techniques to enhance revenue and understand customer behaviour. In this study, we develop a machine learning workflow for high classification accuracy and improved prediction confidence using a binary classification approach on a publicly available dataset from a Portuguese financial institution as a proof of concept. Our methodology includes data analysis, transformation, training, and testing machine learning classifiers such as Naïve Bayes, Decision Trees, Random Forests, Support Vector Machines, Logistic Regression, Artificial Neural Networks, AdaBoost, and Gradient Descent. We use stratified k-folding (k=5) cross-validation and assemble the top-performing classifiers into a decision-making committee, resulting in over 92% accuracy with two-thirds voting confidence. The workflow is simple, adaptable, and suitable for UK banks, demonstrating the potential for practical implementation and data privacy. Future work will extend our approach to UK banks, reformulate the problem as a multi-class classification, and introduce pre-training automated steps for data analysis and transformation.

This study presents the development of an advanced machine learning model based on a two-dimensional (2D) Radio Frequency (RF) sensing framework for refined monitoring of femoral bone fractures. Utilising MATLAB simulations, we created a comprehensive dataset enhanced with variations in bone diameter, muscle thickness, fat thickness, and hematoma size, augmented with multiple sensor configurations (two, four, six, and eight sensors). The model aims to provide a frequent, non-invasive assessment of the fracture healing process compared to conventional imaging methods. Our approach leverages data from six RF sensors, achieving a high overall accuracy of 99.2% in classifying different fracture stages, including "no fracture" and varying degrees of hematoma sizes. The findings indicate that increasing the number of sensors up to six significantly enhances detection accuracy and sensitivity across all fracture stages. However, the marginal improvement from six to eight sensors was not statistically significant, suggesting that a six-sensor configuration offers an optimal balance between performance and system complexity. The results demonstrate significant potential for this technology to revolutionise orthopaedic treatment and recovery management by offering continuous, real-time monitoring without radiation exposure. The proposed system enhances personalised patient care by integrating RF sensing with artificial intelligence, enabling timely interventions and more informed, data-driven treatment strategies. This research lays a robust foundation for future advancements, including three-dimensional modelling and clinical validations, toward the practical implementation of non-invasive fracture monitoring systems.

Recent studies and research have centred on new solutions in different elements and stages to the increasing energy and data rate demands for the fifth generation and beyond (B5G). Based on a new-efficient digital beamforming approach for 5G wireless communication networks, this work offers a compact-size circular patch antenna operating at 60 GHz and covering a 4 GHz spectrum bandwidth. Massive Multiple Input Multiple Output (M–MIMO) and beamforming technology build and simulate an active multiple beams antenna system. Thirty-two linear and sixty-four planar antenna array configurations are modelled and constructed to work as base stations for 5G mobile communication networks. Furthermore, a new beamforming approach called Projection Noise Correlation Matrix (PNCM) is presented to compute and optimise the fed weights of the array elements. The key idea of the PNCM method is to sample a portion of the measured noise correlation matrix uniformly in order to provide the best representation of the entire measured matrix. The sampled data will then be utilised to build a projected matrix using the pseudoinverse approach in order to determine the best fit solution for a system and prevent any potential singularities caused by the matrix inversion process. The PNCM is a low-complexity method since it avoids eigenvalue decomposition and computing the entire matrix inversion procedure and does not require including signal and interference correlation matrices in the weight optimisation process. The suggested approach is compared to three standard beamforming methods based on an intensive Monte Carlo simulation to demonstrate its advantage. The experiment results reveal that the proposed method delivers the best Signal to Interference Ratio (SIR) augmentation among the compared beamformers.

Accurate angle-of-arrival (AoA) estimation is critical for precise localisation in wearable devices, particularly in challenging wireless environments such as Rician fading with low signal-to-noise ratios (SNRs). This paper proposes a pilot-assisted AoA estimation technique that integrates pseudo-random permutations and Walsh sequences within an OFDM-based transmission framework. The method preserves phase coherence and enhances spatial resolution by optimising pilot allocation and leveraging advanced signal processing. Comprehensive Matlab simulations show sub-degree AoA errors, with robustness validated in ultra-dense multipath scenarios where the Rician K-factor is less than 3 dB. Furthermore, a lightweight, one-dimensional (1D) convolutional neural network (CNN) reduces residual carrier-frequency offsets by over 30%, highlighting a promising synergy between classical signal processing and data-driven learning. Comparative analysis against state-of-the-art techniques and a discussion of computational complexity are provided, underscoring the suitability of the proposed method for next-generation wearable and IoT direction-finding applications.

Recent studies have centered on finding the most effective methods for increasing the gain, directivity, and bandwidth of a microstrip patch antenna for usage in a variety of wireless applications. Microstrip patch antennas (MPAs) have been presented as a space-efficient and low-profile method of achieving a wide frequency range by employing slots of varying shapes. To demonstrate the concept behind the proposed antenna, parametric simulations were carried out. To increase bandwidth, many slots have been added. The slots are set up in the form of a word RAED on the patch. The Rogers RT Duroid 03003 substrate is used in both the design and simulation processes for the proposed antenna. This substrate has a standard thickness of 1.52 mm, a relative dielectric constant of 2.2, and a tangent loss of 0.0009 at the operating frequency. Simulated findings reveal a return loss of-62.247 dB, bandwidth of 11.031 GHz, and gain of 6.48 dBi at a central frequency of 28.427 GHz. The proposed antenna has a nearly 10-fold improvement in bandwidth over the MPA without the slots. This demonstrates how antenna slots greatly enhance gain and bandwidth. Therefore, the suggested antenna's compact design makes it well-suited for 5G mobile communication networks.