The COVID-19 pandemic is a significant milestone in the modern history of civilization with a catastrophic effect on global wellbeing and monetary. The situation is very complex as the COVID-19 test kits are limited, therefore, more diagnostic methods must be developed urgently. A significant initial step towards the successful diagnosis of the COVID-19 is the chest X-ray or Computed Tomography (CT), where any chest anomalies (e.g., lung inflammation) can be easily identified. Most hospitals possess X-ray or CT imaging equipments that can be used for early detection of COVID-19. Motivated by this, various artificial intelligence (AI) techniques have been developed to identify COVID-19 positive patients using the chest X-ray or CT images. However, the advance of these AI-based systems and their highly tailored results are strongly bonded to high-end GPUs, which is not widely available in several countries. This paper introduces a technique for early COVID-19 diagnosis based on medical experience and light-weight Convolutional Neural Networks (CNNs), which does not require a custom hardware to run compared to currently available CNN models. The proposed deep learning model is built carefully and fine-tuned by removing all unnecessary parameters and layers to achieve the light-weight attribute that could run smoothly on a normal CPU (0.54% of AlexNet parameters). This model is highly beneficial for countries where high-end GPUs are luxuries. Experimental outcomes on some new benchmark datasets shows the robustness of the proposed technique robustness in recognizing COVID-19 with 96% accuracy.

Automated glasses detection is a cardinal component in facial/ocular analysis that powers forensic, surveillance and biometric authentication systems. Throughout literature, glasses detection was always experimented by either utilizing hand‐crafted or deep learning features. Nevertheless, in both cases, highly standard face/ocular images were needed to derive the suggested technique. Both working methods performed reasonably well, but the results were bonded to the quality of the facial image and extracted features, where a slight shift and/or rotation in the input face image negatively affects the results. In addition, the obtained performance is even worse on real‐world (non‐standard) images, especially when compared to recent achievements in other computer vision research areas. In this paper, we present a robust deep learning approach for glasses detection from selfie photos full/partial frontal body non‐standard images captured in real‐life uncontrolled environments that do not utilize any facial landmarks. To the best of our knowledge this paper is the first to experiment detecting glasses from selfie photos, using a robust deep learning approach. Experimental results on various benchmark facial analysis datasets demonstrated the superior performance of the proposed technique with 96% accuracy.

This article presents a framework for speedy video matching and retrieval through detection and measurement of visual similarity. The framework’s efficiency stems from its power to encode a given shot content into a compact fixed-length signature that helps in robust real-time matching. Separate scene and motion signatures are developed and fused together to fully represent and match respective video shots. Scene information is captured through the Statistical Dominant Color Profile (SDCP), while motion information is captured through a graph-based signature called the Dominant Color Graph Profile (DCGP). The SDCP is a fixed-length compact signature that statistically encodes the colors’ spatiotemporal patterns across video frames. The DCGP is a fixed-length signature that records and tracks the gray levels across subsampled video frames, where the graph structural properties are used to extract the signature values. Finally, the overall video signature is generated by fusing the individual scene and motion signatures. The signature-based aspect of the proposed framework is the key to its high matching speed (> 2000 fps) compared to current techniques that rely on exhaustive processing. To maximize the benefit of the framework, compressed-domain videos are utilized as a case study following their wide availability. However, the framework avoids full video decompression and operates on tiny frames rather than full-size decompressed frames. Experiments on various standard and challenging dataset groups show the framework’s robust performance in terms of both retrieval and computational performance.

Alzheimer’s Disease (AD) is a significant neurological condition characterized by progressive cognitive deterioration, with prevalence rising exponentially with age. Currently, there is no effective cure, and the disease progression impacts patients’ quality of life, often leading to severe symptoms before death. The gradual development of AD symptoms, often mistaken for typical aging, frequently leads to delayed diagnosis. This underscores the critical need for precise, early diagnostic methodologies, as timely intervention plays a crucial role in managing progression. Accordingly, this paper introduces DeepALZNET, a dual-pathway computational framework designed to enhance AD prediction by offering two independent processing pathways: one for structured clinical data and another for unstructured brain MRI scans. The first pathway combines a 1D Convolutional Neural Network (CNN) with a Random Forest classifier to analyze clinical data, while the second employs a transfer-learning-based VGG19 architecture to detect subtle structural changes in MRI scans. Empirical validation on two publicly available datasets (2k clinical cases and 40k MRI images) demonstrated that both pathways achieved competitive accuracy ( $$>90\%$$ ), with further evaluation on ADNI and OASIS benchmarks confirming robustness. Unlike recent transformer-based or attention-driven methods, which often demand large multimodal datasets and high computational resources, DeepALZNET prioritizes practical applicability, interpretability, and adaptability, operating effectively with either clinical or imaging data alone. This design bridges the gap between benchmark-driven research and deployable real-world solutions, with potential for multimodal fusion or attention integration in future work.