ABSTRACT Cardiovascular diseases (CVDs) remain a leading global health challenge, emphasising the need for advanced diagnostic systems that enable accurate detection and classification. This study presents a novel deep learning framework based on a dense and inverted bottleneck residual mechanism, integrating two custom convolutional neural network architectures: an 8‐block Dense model and a 9‐block Inverted Bottleneck Layered model. These models are designed to extract multiscale features from cardiac MRI scans for precise classification across five CVD categories. We enhance image quality using Contrast‐Limited Adaptive Histogram Equalisation (CLAHE) in the preprocessing pipeline and apply data augmentation to address class imbalance and promote model generalisation. Atom Search optimisation (ASO) is employed to reduce feature dimensionality while retaining critical information, and a serial feature fusion strategy is used to integrate the optimised feature vectors. Classification is performed using several neural network variants (Narrow NN, Medium NN, Wide NN, and Bi‐Layered NN). The proposed method is evaluated on both the Automated Cardiac Diagnosis Challenge (ACDC) and Sunnybrook Cardiac Data (SCD) datasets, achieving superior accuracies of 97.3% and 96.8%, respectively, compared to state‐of‐the‐art techniques, demonstrating its effectiveness for cardiovascular disease diagnosis.

Logic locking is a widely adopted hardware obfuscation technique which can be further sub-categorised into static and dynamic approaches based on the nature of the employed key. Besides being susceptible to SAT and fault injection attacks, static logic locking is vulnerable to widespread compromise from a single key exposure or device breach. On the other hand, dynamic logic locking introduces complexities in resource utilisation, key management, design, and adaptability. In this work, we provide a comprehensive and up-to-date overview of existing IP Obfuscation techniques, highlighting their strengths, and potential vulnerabilities. We also propose, a hybrid logic locking technique that capitalises on the positive attributes of both static and dynamic logic locking methodologies while minimising their inherent limitations. An initial proof-of-concept implementation/simulation has been performed on the Xilinx SP605 FPGA development board. The suggested transient key logic locking scheme is applicable to all type of IPs.

Facial expressions are the most effective way to characterize people’s motives, emotions, and feelings. Several new methods are proposed each year; however, the accuracy of facial expression recognition still needs to be improved especially in uncontrolled conditions. In this paper, we propose a hybrid facial expression model that considers both texture and orientation features to classify expressions. Two types of descriptors namely Local binary pattern and Weber local descriptor are used to preserve the local intensity information and orientation of edges. In the next step, computing the Histograms of oriented gradients (HOG) features from the Local binary pattern and Weber local descriptor images to capture micro-expressions. Then, the AdaBoost feature selection algorithm is utilized to choose the best features from the combined HOG features. The results of the experiments demonstrate that the method proposed in this study performs better than existing methods.