Omar Sabri¹, Bassam Al-Shargabi^2,*, Abdelrahman Abuarqoub²

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2443-2486, 2025, DOI:10.32604/cmc.2025.066987 - 23 September 2025

Abstract This review comprehensively analyzes advancements in artificial intelligence, particularly machine learning and deep learning, in medical imaging, focusing on their transformative role in enhancing diagnostic accuracy. Our in-depth analysis of 138 selected studies reveals that artificial intelligence (AI) algorithms frequently achieve diagnostic performance comparable to, and often surpassing, that of human experts, excelling in complex pattern recognition. Key findings include earlier detection of conditions like skin cancer and diabetic retinopathy, alongside radiologist-level performance for pneumonia detection on chest X-rays. These technologies profoundly transform imaging by significantly improving processes in classification, segmentation, and sequential analysis across… More >

Jingfa Ma, Hu Liu^*, Lingxiao Chen

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 443-469, 2024, DOI:10.32604/cmc.2024.056209 - 15 October 2024

Abstract Demand-responsive transportation (DRT) is a flexible passenger service designed to enhance road efficiency, reduce peak-hour traffic, and boost passenger satisfaction. However, existing optimization methods for initial passenger requests fall short in addressing real-time passenger needs. Consequently, there is a need to develop real-time DRT route optimization methods that integrate both initial and real-time requests. This paper presents a two-stage, multi-objective optimization model for DRT vehicle scheduling. The first stage involves an initial scheduling model aimed at minimizing vehicle configuration, and operational, and CO₂ emission costs while ensuring passenger satisfaction. The second stage develops a real-time scheduling… More >

Fatma Harby¹, Mansor Alohali², Adel Thaljaoui^2,3,*, Amira Samy Talaat⁴

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2689-2719, 2024, DOI:10.32604/cmc.2024.046623 - 27 February 2024

Abstract Machine Learning (ML) algorithms play a pivotal role in Speech Emotion Recognition (SER), although they encounter a formidable obstacle in accurately discerning a speaker’s emotional state. The examination of the emotional states of speakers holds significant importance in a range of real-time applications, including but not limited to virtual reality, human-robot interaction, emergency centers, and human behavior assessment. Accurately identifying emotions in the SER process relies on extracting relevant information from audio inputs. Previous studies on SER have predominantly utilized short-time characteristics such as Mel Frequency Cepstral Coefficients (MFCCs) due to their ability to capture… More >

Mohammed Baz¹, Sabita Khatri², Abdullah Baz³, Hosam Alhakami⁴, Alka Agrawal², Raees Ahmad Khan^2,*

Computer Systems Science and Engineering, Vol.40, No.2, pp. 691-702, 2022, DOI:10.32604/csse.2022.019079 - 09 September 2021

Abstract The rapid emergence of novel virus named SARS-CoV2 and unchecked dissemination of this virus around the world ever since its outbreak in 2020, provide critical research criteria to assess the vulnerabilities of our current health system. The paper addresses our preparedness for the management of such acute health emergencies and the need to enhance awareness, about public health and healthcare mechanisms. In view of this unprecedented health crisis, distributed ledger and AI technology can be seen as one of the promising alternatives for fighting against such epidemics at the early stages, and with the higher… More >