Usman Khan¹, Muhammad Khalid Khan¹, Muhammad Ayub Latif¹, Muhammad Naveed^1,2,*, Muhammad Mansoor Alam^2,3,4, Salman A. Khan¹, Mazliham Mohd Su’ud^2,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2967-3000, 2024, DOI:10.32604/cmc.2024.045101

Abstract Recently, there has been a notable surge of interest in scientific research regarding spectral images. The potential of these images to revolutionize the digital photography industry, like aerial photography through Unmanned Aerial Vehicles (UAVs), has captured considerable attention. One encouraging aspect is their combination with machine learning and deep learning algorithms, which have demonstrated remarkable outcomes in image classification. As a result of this powerful amalgamation, the adoption of spectral images has experienced exponential growth across various domains, with agriculture being one of the prominent beneficiaries. This paper presents an extensive survey encompassing multispectral and hyperspectral images, focusing on their… More >

Rawan Sanyour^*, Manal Abdullah, Salha Abdullah

Journal on Internet of Things, Vol.4, No.4, pp. 263-282, 2022, DOI:10.32604/jiot.2022.040966

Abstract With the rapid growth of the Internet of Things paradigm, a tremendous number of applications and services that require minimal or no human involvement have been developed to enhance the quality of everyday life in various domains. In order to ensure that such services provide their functionalities with the expected quality, it is essential to measure and evaluate this quality, which can be in some cases a challenging task due to the lack of human intervention and feedback. Recently, the vast majority of the Quality of Experience QoE works mainly address the multimedia services. However, the introduction of Internet of… More >

Alia Alshehri, Duaa AlSaeed^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 939-970, 2023, DOI:10.32604/iasc.2023.037096

Abstract One of the most prevalent cancers in women is breast cancer. Early and accurate detection can decrease the mortality rate associated with breast cancer. Governments and health organizations emphasize the significance of early breast cancer screening since it is associated to a greater variety of available treatments and a higher chance of survival. Patients have the best chance of obtaining effective treatment when they are diagnosed early. The detection and diagnosis of breast cancer have involved using various image types and imaging modalities. Breast “infrared thermal” imaging is one of the imaging modalities., a screening instrument used to measure the… More >

Shroog Alshomrani^*, Muhammad Arif, Mohammed A. Al Ghamdi

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5717-5742, 2023, DOI:10.32604/cmc.2023.038059

Abstract Coronavirus has infected more than 753 million people, ranging in severity from one person to another, where more than six million infected people died worldwide. Computer-aided diagnostic (CAD) with artificial intelligence (AI) showed outstanding performance in effectively diagnosing this virus in real-time. Computed tomography is a complementary diagnostic tool to clarify the damage of COVID-19 in the lungs even before symptoms appear in patients. This paper conducts a systematic literature review of deep learning methods for classifying the segmentation of COVID-19 infection in the lungs. We used the methodology of systematic reviews and meta-analyses (PRISMA) flow method. This research aims… More >

Nur Laila Ab Ghani^1,2,*, Izzatdin Abdul Aziz^1,2, Said Jadid AbdulKadir^1,2

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4649-4668, 2023, DOI:10.32604/cmc.2023.035987

Abstract Clustering high dimensional data is challenging as data dimensionality increases the distance between data points, resulting in sparse regions that degrade clustering performance. Subspace clustering is a common approach for processing high-dimensional data by finding relevant features for each cluster in the data space. Subspace clustering methods extend traditional clustering to account for the constraints imposed by data streams. Data streams are not only high-dimensional, but also unbounded and evolving. This necessitates the development of subspace clustering algorithms that can handle high dimensionality and adapt to the unique characteristics of data streams. Although many articles have contributed to the literature… More >

Francisco José García-Peñalvo^*, Andrea Vázquez-Ingelmo, Alicia García-Holgado

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1023-1051, 2023, DOI:10.32604/cmes.2023.023897

Abstract The exponential use of artificial intelligence (AI) to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be addressed. While AI is a powerful means to discover interesting patterns and obtain predictive models, the use of these algorithms comes with a great responsibility, as an incomplete or unbalanced set of training data or an unproper interpretation of the models’ outcomes could result in misleading conclusions that ultimately could become very dangerous. For these reasons, it is important to rely on expert knowledge when applying these methods. However, not every user can count on this… More > Graphic Abstract

WAN AZANI MUSTAFA^1,2,*, AFIQAH HALIM², MOHD WAFI NASRUDIN², KHAIRUL SHAKIR AB RAHMAN³

BIOCELL, Vol.46, No.2, pp. 367-381, 2022, DOI:10.32604/biocell.2022.016814

Abstract Cervix cancer is one of Malaysia’s most significant cancers for women (around 12.9%, with an age-standardised incidence rate of 19.7 per 100,000). It was higher than other Asian, West, and even worldwide nations. The National Strategic Plan for Cancer Control Program 2016–2020 (Health Ministry) was presented to minimize cancer and mortality. The high incidence of cervical cancer in Malaysia is mainly due to women’s insufficient knowledge about its prevention and importance. Compared with traditional literature reviews, the systemic analysis provides many advantages. A clearer review process, a more prominent field of study, and essential priorities that can manage research bias… More >

Faseeha Matloob¹, Shabib Aftab^1,2, Munir Ahmad², Muhammad Adnan Khan^3,*, Areej Fatima⁴, Muhammad Iqbal², Wesam Mohsen Alruwaili⁵, Nouh Sabri Elmitwally^5,6

Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 403-421, 2021, DOI:10.32604/iasc.2021.017562

Abstract Software defect prediction (SDP) is the process of detecting defect-prone software modules before the testing stage. The testing stage in the software development life cycle is expensive and consumes the most resources of all the stages. SDP can minimize the cost of the testing stage, which can ultimately lead to the development of higher-quality software at a lower cost. With this approach, only those modules classified as defective are tested. Over the past two decades, many researchers have proposed methods and frameworks to improve the performance of the SDP process. The main research topics are association, estimation, clustering, classification, and… More >

Habib Ullah Khan^1,*, Mohammad Kamel Alomari¹, Sulaiman Khan², Shah Nazir², Asif Qumer Gill³, Alanoud Ali Al-Maadid⁴, Zaki Khalid Abu-Shawish¹, Mostafa Kamal Hassan¹

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1409-1428, 2021, DOI:10.32604/cmc.2021.016058

Abstract The revolution in Internet of Things (IoT)-based devices and applications has provided smart applications for humans. These applications range from healthcare to traffic-flow management, to communication devices, to smart security devices, and many others. In particular, government and private organizations are showing significant interest in IoT-enabled applications for smart homes. Despite the perceived benefits and interest, human safety is also a key concern. This research is aimed at systematically analyzing the available literature on smart homes and identifying areas of concern or risk with a view to supporting the design of safe and secure smart homes. For this systematic review… More >

Shahrul Nizam Ismail¹, Suraya Hamid^1,*, Muneer Ahmad¹, A. Alaboudi², Nz Jhanjhi³

Computer Systems Science and Engineering, Vol.37, No.1, pp. 73-87, 2021, DOI:10.32604/csse.2021.015261

Abstract Learning analytics is a rapidly evolving research discipline that uses the insights generated from data analysis to support learners as well as optimize both the learning process and environment. This paper studied students’ engagement level of the Learning Management System (LMS) via a learning analytics tool, student’s approach in managing their studies and possible learning analytic methods to analyze student data. Moreover, extensive systematic literature review (SLR) was employed for the selection, sorting and exclusion of articles from diverse renowned sources. The findings show that most of the engagement in LMS are driven by educators. Additionally, we have discussed the… More >