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  • Open Access


    Breast Cancer Diagnosis Using Artificial Intelligence Approaches: A Systematic Literature Review

    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 >

  • Open Access


    A Systematic Literature Review of Deep Learning Algorithms for Segmentation of the COVID-19 Infection

    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 >

  • Open Access


    Subspace Clustering in High-Dimensional Data Streams: A Systematic Literature Review

    Nur Laila Ab Ghani1,2,*, Izzatdin Abdul Aziz1,2, Said Jadid AbdulKadir1,2

    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 >

  • Open Access


    Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches—A Systematic Literature Review and Mapping Study

    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

    Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches—A Systematic Literature Review and Mapping Study

  • Open Access


    Cervical cancer situation in Malaysia: A systematic literature review


    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 >

  • Open Access


    Software Defect Prediction Using Supervised Machine Learning Techniques: A Systematic Literature Review

    Faseeha Matloob1, Shabib Aftab1,2, Munir Ahmad2, Muhammad Adnan Khan3,*, Areej Fatima4, Muhammad Iqbal2, Wesam Mohsen Alruwaili5, Nouh Sabri Elmitwally5,6

    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 >

  • Open Access


    Systematic Analysis of Safety and Security Risks in Smart Homes

    Habib Ullah Khan1,*, 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 >

  • Open Access


    Exploring Students Engagement Towards the Learning Management System (LMS) Using Learning Analytics

    Shahrul Nizam Ismail1, Suraya Hamid1,*, 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 >

  • Open Access


    Automated Test Case Generation from Requirements: A Systematic Literature Review

    Ahmad Mustafa1, Wan M. N. Wan-Kadir1, Noraini Ibrahim1, Muhammad Arif Shah3,*, Muhammad Younas2, Atif Khan4, Mahdi Zareei5, Faisal Alanazi6

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1819-1833, 2021, DOI:10.32604/cmc.2021.014391

    Abstract Software testing is an important and cost intensive activity in software development. The major contribution in cost is due to test case generations. Requirement-based testing is an approach in which test cases are derivative from requirements without considering the implementation’s internal structure. Requirement-based testing includes functional and nonfunctional requirements. The objective of this study is to explore the approaches that generate test cases from requirements. A systematic literature review based on two research questions and extensive quality assessment criteria includes studies. The study identifies 30 primary studies from 410 studies spanned from 2000 to 2018. The review’s finding shows that… More >

  • Open Access


    Economical Requirements Elicitation Techniques During COVID-19: A Systematic Literature Review

    Tauqeer ul Amin1,*, Basit Shahzad1, Fazal-e-Amin2, Muhammad Shoaib2

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2665-2680, 2021, DOI:10.32604/cmc.2021.013263

    Abstract Requirements elicitation is a fundamental phase of software development in which an analyst discovers the needs of different stakeholders and transforms them into requirements. This phase is cost- and time-intensive, and a project may fail if there are excessive costs and schedule overruns. COVID-19 has affected the software industry by reducing interactions between developers and customers. Such a lack of interaction is a key reason for the failure of software projects. Projects can also fail when customers do not know precisely what they want. Furthermore, selecting the unsuitable elicitation technique can also cause project failure. The present study, therefore, aimed… More >

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