Fahd N. Al-Wesabi^1,*, Hamad Almansour², Huda G. Iskandar^3,4, Ishfaq Yaseen⁵

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5765-5787, 2025, DOI:10.32604/cmc.2025.067571 - 23 October 2025

Abstract Earlier notification and fire detection methods provide safety information and fire prevention to blind and visually impaired (BVI) individuals in a limited timeframe in the event of emergencies, particularly in enclosed areas. Fire detection becomes crucial as it directly impacts human safety and the environment. While modern technology requires precise techniques for early detection to prevent damage and loss, few research has focused on artificial intelligence (AI)-based early fire alert systems for BVI individuals in indoor settings. To prevent such fire incidents, it is crucial to identify fires accurately and promptly, and alert BVI personnel… More >

Khlood M. Mehdar¹, Toufique A. Soomro^2,3,*, Ahmed Ali⁴, Faisal Bin Ubaid⁵, Muhammad Irfan^6,*, Sabah Elshafie Mohammed Elshafie¹, Aisha M. Mashraqi⁷, Abdullah A. Asiri⁸, Nagla Hussien Mohamed Khalid⁸, Hanan T. Halawani⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3091-3132, 2025, DOI:10.32604/cmes.2025.065471 - 30 June 2025

Abstract Breast cancer remains one of the most pressing global health concerns, and early detection plays a crucial role in improving survival rates. Integrating digital mammography with computational techniques and advanced image processing has significantly enhanced the ability to identify abnormalities. However, existing methodologies face persistent challenges, including low image contrast, noise interference, and inaccuracies in segmenting regions of interest. To address these limitations, this study introduces a novel computational framework for analyzing mammographic images, evaluated using the Mammographic Image Analysis Society (MIAS) dataset comprising 322 samples. The proposed methodology follows a structured three-stage approach. Initially,… More >

S. Deva Kumar, S. Venkatramaphanikumar^*, K. Venkata Krishna Kishore

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1013-1032, 2023, DOI:10.32604/iasc.2023.038165 - 29 April 2023

Abstract Type 2 diabetes patients often suffer from microvascular complications of diabetes. These complications, in turn, often lead to vision impairment. Diabetic Retinopathy (DR) detection in its early stage can rescue people from long-term complications that could lead to permanent blindness. In this study, we propose a complex deep convolutional neural network architecture with an inception module for automated diagnosis of DR. The proposed novel Hybrid Inception U-Net (HIUNET) comprises various inception modules connected in the U-Net fashion using activation maximization and filter map to produce the image mask. First, inception blocks were used to enlarge… More >

Shabana Habib¹, Saleh Alyahya², Aizaz Ahmed³, Muhammad Islam^2,*, Sheroz Khan², Ishrat Khan⁴, Muhammad Kamil⁵

Computer Systems Science and Engineering, Vol.43, No.2, pp. 671-682, 2022, DOI:10.32604/csse.2022.021812 - 20 April 2022

Abstract In early December 2019, the city of Wuhan, China, reported an outbreak of coronavirus disease (COVID-19), caused by a novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). On January 30, 2020, the World Health Organization (WHO) declared the outbreak a global pandemic crisis. In the face of the COVID-19 pandemic, the most important step has been the effective diagnosis and monitoring of infected patients. Identifying COVID-19 using Machine Learning (ML) technologies can help the health care unit through assistive diagnostic suggestions, which can reduce the health unit's burden to a certain extent. This paper investigates the… More >