Majdy M. Eltahir¹, Adil Yousif², Fadwa Alrowais³, Mohamed K. Nour⁴, Radwa Marzouk⁵, Hatim Dafaalla⁶, Asma Abbas Hassan Elnour⁶, Amira Sayed A. Aziz⁷, Manar Ahmed Hamza^8,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3239-3255, 2023, DOI:10.32604/cmc.2023.034037 - 31 March 2023

Abstract The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection. This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes. These sensors produce a huge volume of physical activity data that necessitates real-time recognition, especially during emergencies. Falling is one of the most important problems confronted by older people and people with movement disabilities. Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people. But, the costs incurred upon… More >

K. Kalyani¹, Sara A Althubiti², Mohammed Altaf Ahmed³, E. Laxmi Lydia⁴, Seifedine Kadry⁵, Neunggyu Han⁶, Yunyoung Nam^6,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 149-164, 2023, DOI:10.32604/cmc.2023.033005 - 06 February 2023

Abstract Melanoma is a skin disease with high mortality rate while early diagnoses of the disease can increase the survival chances of patients. It is challenging to automatically diagnose melanoma from dermoscopic skin samples. Computer-Aided Diagnostic (CAD) tool saves time and effort in diagnosing melanoma compared to existing medical approaches. In this background, there is a need exists to design an automated classification model for melanoma that can utilize deep and rich feature datasets of an image for disease classification. The current study develops an Intelligent Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification… More >

Mousa Alhajlah¹, Muhammad Nouman Noor², Muhammad Nazir², Awais Mahmood^1,*, Imran Ashraf³, Tehmina Karamat⁴

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2227-2245, 2023, DOI:10.32604/cmc.2023.031890 - 06 February 2023

Abstract Gastrointestinal diseases like ulcers, polyps’, and bleeding are increasing rapidly in the world over the last decade. On average 0.7 million cases are reported worldwide every year. The main cause of gastrointestinal diseases is a Helicobacter Pylori (H. Pylori) bacterium that presents in more than 50% of people around the globe. Many researchers have proposed different methods for gastrointestinal disease using computer vision techniques. Few of them focused on the detection process and the rest of them performed classification. The major challenges that they faced are the similarity of infected and healthy regions that misleads the… More >

Mehwish Shaikh¹, Isma Farah Siddiqui¹, Qasim Arain¹, Jahwan Koo^2,*, Mukhtiar Ali Unar³, Nawab Muhammad Faseeh Qureshi^4,*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 287-302, 2023, DOI:10.32604/csse.2023.035311 - 20 January 2023

Abstract Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful; thus, catching it early is crucial. Medical physicians’ time is limited in outdoor situations due to many patients; therefore, automated systems can be a rescue. The input images from the X-ray equipment are also highly unpredictable due to variances in radiologists’ experience. Therefore, radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest x-rays. In medical classifications, deep convolution neural networks are commonly used. This research aims to use deep pre-trained transfer learning models to… More >

Nabil Almalki^1,2, Mrim M. Alnfiai^1,3, Fahd N. Al-Wesabi⁴, Mesfer Alduhayyem⁵, Anwer Mustafa Hilal^6,*, Manar Ahmed Hamza⁶

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6719-6736, 2023, DOI:10.32604/cmc.2023.034417 - 28 December 2022

Abstract Mobile communication and the Internet of Things (IoT) technologies have recently been established to collect data from human beings and the environment. The data collected can be leveraged to provide intelligent services through different applications. It is an extreme challenge to monitor disabled people from remote locations. It is because day-to-day events like falls heavily result in accidents. For a person with disabilities, a fall event is an important cause of mortality and post-traumatic complications. Therefore, detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary… More >

Mohammed Maray¹, Badriyya B. Al-onazi², Jaber S. Alzahrani³, Saeed Masoud Alshahrani^4,*, Najm Alotaibi⁵, Sana Alazwari⁶, Mahmoud Othman⁷, Manar Ahmed Hamza⁸

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5467-5482, 2023, DOI:10.32604/cmc.2023.033534 - 28 December 2022

Abstract The recognition of the Arabic characters is a crucial task in computer vision and Natural Language Processing fields. Some major complications in recognizing handwritten texts include distortion and pattern variabilities. So, the feature extraction process is a significant task in NLP models. If the features are automatically selected, it might result in the unavailability of adequate data for accurately forecasting the character classes. But, many features usually create difficulties due to high dimensionality issues. Against this background, the current study develops a Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition (SFODTL-AHCR) model. The… More >

Mrim M. Alnfiai^*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6505-6521, 2023, DOI:10.32604/cmc.2023.033422 - 28 December 2022

Abstract There is a drastic increase experienced in the production of vehicles in recent years across the globe. In this scenario, vehicle classification system plays a vital part in designing Intelligent Transportation Systems (ITS) for automatic highway toll collection, autonomous driving, and traffic management. Recently, computer vision and pattern recognition models are useful in designing effective vehicle classification systems. But these models are trained using a small number of hand-engineered features derived from small datasets. So, such models cannot be applied for real-time road traffic conditions. Recent developments in Deep Learning (DL)-enabled vehicle classification models are… More >

Mahmoud Ragab^1,2,3,*, Mohammed W. Al-Rabia^4,5, Sami Saeed Binyamin⁶, Ahmed A. Aldarmahi^7,8

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2889-2903, 2023, DOI:10.32604/cmc.2023.032192 - 31 October 2022

Abstract With the increasing and rapid growth rate of COVID-19 cases, the healthcare scheme of several developed countries have reached the point of collapse. An important and critical steps in fighting against COVID-19 is powerful screening of diseased patients, in such a way that positive patient can be treated and isolated. A chest radiology image-based diagnosis scheme might have several benefits over traditional approach. The accomplishment of artificial intelligence (AI) based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition… More >

Jawad Rasheed^1,*, Shtwai Alsubai²

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4239-4259, 2023, DOI:10.32604/cmc.2023.031969 - 31 October 2022

Abstract Humankind is facing another deadliest pandemic of all times in history, caused by COVID-19. Apart from this challenging pandemic, World Health Organization (WHO) considers tuberculosis (TB) as a preeminent infectious disease due to its high infection rate. Generally, both TB and COVID-19 severely affect the lungs, thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation. Therefore, the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases. As one of the preliminary smart health systems that examine three clinical… More >

Mahmoud Ragab^1,2,3,*, Maged Mostafa Mahmoud^4,5,6, Amer H. Asseri^2,7, Hani Choudhry^2,7, Haitham A. Yacoub⁸

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3279-3295, 2023, DOI:10.32604/cmc.2023.031037 - 31 October 2022

Abstract Colorectal carcinoma (CRC) is one such dispersed cancer globally and also prominent one in causing cancer-based death. Conventionally, pathologists execute CRC diagnosis through visible scrutinizing under the microscope the resected tissue samples, stained and fixed through Haematoxylin and Eosin (H&E). The advancement of graphical processing systems has resulted in high potentiality for deep learning (DL) techniques in interpretating visual anatomy from high resolution medical images. This study develops a slime mould algorithm with deep transfer learning enabled colorectal cancer detection and classification (SMADTL-CCDC) algorithm. The presented SMADTL-CCDC technique intends to appropriately recognize the occurrence of… More >