Optical Character Recognition (OCR) is the process of identifying text in an image and convert it into a digital form. Several approaches have been attempted to accurately recognize characters in printed Arabic language. This survey focuses on OCR in handwritten Arabic language. We will describe the general difficulties in Arabic language text, the main process of a typical OCR system and some enhancements to Arabic OCR systems. We will also describe a novel approach for identifying handwritten isolated Arabic characters using encoded Freeman chain code. Several handwritten Arabic characters were trained and tested, and the preliminary experimental results are promising.

Recognizing normality and abnormality from heart sound recordings (phonocardiograms or PCG) has promote a scientific research in cardiology. However, only a small number of PCG recordings is publicly available. Also, current recognition approaches have not reached a satisfiable accuracy. In this paper, we apply transfer learning to automate the recognition for heart rates normalities and abnormalities. Mel Frequency Cepstrum Coefficients (MFCC) signal representation is adopted to transform the output feature into a PCG image which is used as an input to pre-trained Convolutional Neural Network (CNN) models. Then, several deep pre-trained CNN models are fine-tuned. The proposed approach is effective. Inception-ResNet-v2 pre-trained model achieves better performance than other models and other recent methods with an 89.5% of average classification accuracy using Pascal Heart Sound Challenge dataset.

High-quality image reconstruction is essential for many electrical capacitance tomography (CT) applications. Raw capacitance measurements are used in the literature to generate low-resolution images. However, such low-resolution images are not sufficient for proper functionality of most systems. In this paper, we propose a novel adversarial resolution enhancement (ARE-ECT) model to reconstruct high-resolution images of inner distributions based on low-quality initial images, which are generated from the capacitance measurements. The proposed model uses a UNet as the generator of a conditional generative adversarial network (CGAN). The generator’s input is set to the low-resolution image rather than the typical random input signal. Additionally, the CGAN is conditioned by the input low-resolution image itself. For evaluation purposes, a massive ECT dataset of 320 K synthetic image–measurement pairs was created. This dataset is used for training, validating, and testing the proposed model. New flow patterns, which are not exposed to the model during the training phase, are used to evaluate the feasibility and generalization ability of the ARE-ECT model. The superiority of ARE-ECT, in the efficient generation of more accurate ECT images than traditional and other deep learning-based image reconstruction algorithms, is proved by the evaluation results. The ARE-ECT model achieved an average image correlation coefficient of more than $98.8\%$ and an average relative image error about $0.1\%$.

Managing large-scale gatherings, such as global festivals, sporting events, and religious congregations, presents substantial challenges in ensuring crowd safety and control. Innovative frameworks are essential to address these complexities effectively. The Integrated Intelligent Crowd Control and Management (IICCM) framework combines cutting-edge technologies, including Computer Vision (CV), Artificial Intelligence (AI), and the Internet of Things (IoT), to enhance participant safety and optimize crowd management. CV enables precise real time identification and tracking, AI analyzes crowd behavior to anticipate risks, and IoT gathers environmental data to improve crowd flow, alleviate congestion, and provide timely assistance. Additionally, the framework facilitates emergency evacuation planning by modeling crowd dynamics and identifying safe, efficient escape routes. Although suitable for diverse events, the Hajj pilgrimage—a uniquely large and dynamic annual gathering—provides a rigorous test case for the IICCM framework. Managing millions of participants from varied cultural and linguistic backgrounds highlights the system’s adaptability and robustness. By effectively addressing Hajj specific challenges, the IICCM framework demonstrates its scalability and applicability to other large-scale events. This research offers valuable insights for decision-makers seeking to implement advanced crowd management technologies.

Pneumonia ranks among the most prevalent lung diseases and poses a significant concern since it is one of the diseases that may lead to death around the world. Diagnosing pneumonia necessitates a chest X-ray and substantial expertise to ensure accurate assessments. Despite the critical role of lateral X-rays in providing additional diagnostic information alongside frontal X-rays, they have not been widely used. Obtaining X-rays from multiple perspectives is crucial, significantly improving the precision of disease diagnosis. In this paper, we propose a multi-view multi-feature fusion model (MV-MFF) that integrates latent representations from a variational autoencoder and a $\beta$-variational autoencoder. Our model aims to classify pneumonia presence using multi-view X-rays. Experimental results demonstrate that the MV-MFF model achieves an accuracy of 80.4% and an area under the curve of 0.775, outperforming current state-of-the-art methods. These findings underscore the efficacy of our approach in improving pneumonia diagnosis through multi-view X-ray analysis.