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Search Results (16)
  • Open Access

    ARTICLE

    An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate

    Yingui Qiu1, Shuai Huang1, Danial Jahed Armaghani2, Biswajeet Pradhan3, Annan Zhou4, Jian Zhou1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2873-2897, 2024, DOI:10.32604/cmes.2023.029938

    Abstract As massive underground projects have become popular in dense urban cities, a problem has arisen: which model predicts the best for Tunnel Boring Machine (TBM) performance in these tunneling projects? However, performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers. On the other hand, a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule. The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications. The previously-proposed intelligent techniques in this field are mostly based on a… More >

  • Open Access

    ARTICLE

    An Improved Lung Cancer Segmentation Based on Nature-Inspired Optimization Approaches

    Shazia Shamas1, Surya Narayan Panda1,*, Ishu Sharma1,*, Kalpna Guleria1, Aman Singh2,3,4, Ahmad Ali AlZubi5, Mallak Ahmad AlZubi6

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1051-1075, 2024, DOI:10.32604/cmes.2023.030712

    Abstract The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis and planning intervention. This research work addresses the major issues pertaining to the field of medical image processing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposes an improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. The better resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In this process, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarm intelligent techniques. The techniques… More >

  • Open Access

    ARTICLE

    An Improved Fully Automated Breast Cancer Detection and Classification System

    Tawfeeq Shawly1, Ahmed A. Alsheikhy2,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 731-751, 2023, DOI:10.32604/cmc.2023.039433

    Abstract More than 500,000 patients are diagnosed with breast cancer annually. Authorities worldwide reported a death rate of 11.6% in 2018. Breast tumors are considered a fatal disease and primarily affect middle-aged women. Various approaches to identify and classify the disease using different technologies, such as deep learning and image segmentation, have been developed. Some of these methods reach 99% accuracy. However, boosting accuracy remains highly important as patients’ lives depend on early diagnosis and specified treatment plans. This paper presents a fully computerized method to detect and categorize tumor masses in the breast using two deep-learning models and a classifier… More >

  • Open Access

    ARTICLE

    Fusing Satellite Images Using ABC Optimizing Algorithm

    Nguyen Hai Minh1, Nguyen Tu Trung2,*, Tran Thi Ngan2, Tran Manh Tuan2

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3901-3909, 2023, DOI:10.32604/csse.2023.032311

    Abstract Fusing satellite (remote sensing) images is an interesting topic in processing satellite images. The result image is achieved through fusing information from spectral and panchromatic images for sharpening. In this paper, a new algorithm based on based the Artificial bee colony (ABC) algorithm with peak signal-to-noise ratio (PSNR) index optimization is proposed to fusing remote sensing images in this paper. Firstly, Wavelet transform is used to split the input images into components over the high and low frequency domains. Then, two fusing rules are used for obtaining the fused images. The first rule is “the high frequency components are fused… More >

  • Open Access

    ARTICLE

    Cardiac CT Image Segmentation for Deep Learning–Based Coronary Calcium Detection Using K-Means Clustering and Grabcut Algorithm

    Sungjin Lee1, Ahyoung Lee2, Min Hong3,*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2543-2554, 2023, DOI:10.32604/csse.2023.037055

    Abstract Specific medical data has limitations in that there are not many numbers and it is not standardized. to solve these limitations, it is necessary to study how to efficiently process these limited amounts of data. In this paper, deep learning methods for automatically determining cardiovascular diseases are described, and an effective preprocessing method for CT images that can be applied to improve the performance of deep learning was conducted. The cardiac CT images include several parts of the body such as the heart, lungs, spine, and ribs. The preprocessing step proposed in this paper divided CT image data into regions… More >

  • Open Access

    ARTICLE

    A Detailed Mathematical Analysis of the Vaccination Model for COVID-19

    Abeer S. Alnahdi1,*, Mdi B. Jeelani1, Hanan A. Wahash2, Mansour A. Abdulwasaa3,4

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1315-1343, 2023, DOI:10.32604/cmes.2022.023694

    Abstract This study aims to structure and evaluate a new COVID-19 model which predicts vaccination effect in the Kingdom of Saudi Arabia (KSA) under Atangana-Baleanu-Caputo (ABC) fractional derivatives. On the statistical aspect, we analyze the collected statistical data of fully vaccinated people from June 01, 2021, to February 15, 2022. Then we apply the Eviews program to find the best model for predicting the vaccination against this pandemic, based on daily series data from February 16, 2022, to April 15, 2022. The results of data analysis show that the appropriate model is autoregressive integrated moving average ARIMA (1, 1, 2), and… More >

  • Open Access

    ARTICLE

    B Class Floral Homeotic Genes are Involved in the Petal Identity and Flower Meristem Determinations in Chrysanthemum morifolium

    Jiayou Liu, Lian Ding, Xue Zhang, Song Li, Yunxiao Guan, Diwen Jia, Aiping Song, Jiafu Jiang, Fadi Chen*

    Phyton-International Journal of Experimental Botany, Vol.92, No.2, pp. 311-331, 2023, DOI:10.32604/phyton.2022.023896

    Abstract Chrysanthemum morifolium, an ornamental crop with diverse forms of inflorescence, is a good model for studying flower development in Asteraceae. However, the genetic background is complex and the mechanisms of regulating flower development are still unclear. Here, we identified two natural mutant lines of chrysanthemum and named them M1 and M2 according to the severity of the phenotype. Both lines showed defects in petal identity, and the petals of the M1 line had a mild phenotype: partially loss of petal identity and conversion of petals into green, leaf-like organs. The M2 line had severe phenotypes: in addition to severe petal… More >

  • Open Access

    ARTICLE

    ABCC8 is correlated with immune cell infiltration and overall survival in lower grade glioma

    LIPING GONG1, MING JIA2,*

    BIOCELL, Vol.47, No.1, pp. 109-123, 2023, DOI:10.32604/biocell.2023.024620

    Abstract ATP binding cassette subfamily C member 8 (ABCC8) encodes a protein regulating the ATP-sensitive potassium channel. Whether the level of ABCC8 mRNA in lower grade glioma (LGG) correlates with immune cell infiltration and patient outcomes has not been evaluated until now. Comparisons of ABCC8 expression between different tumors and normal tissues were evaluated by exploring publicly available datasets. The association between ABCC8 and tumor immune cell infiltration, diverse gene mutation characteristics, tumor mutation burden (TMB), and survival in LGG was also investigated in several independent datasets. Pathway enrichment analysis was conducted to search for ABCC8-associated signaling pathways. Through an online… More >

  • Open Access

    ARTICLE

    Hybrid GrabCut Hidden Markov Model for Segmentation

    Soobia Saeed1,*, Afnizanfaizal Abdullah1, N. Z. Jhanjhi2, Mehmood Naqvi3, Mehedi Masud4, Mohammed A. AlZain5

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 851-869, 2022, DOI:10.32604/cmc.2022.024085

    Abstract Diagnosing data or object detection in medical images is one of the important parts of image segmentation especially those data which is less effective to identify in MRI such as low-grade tumors or cerebral spinal fluid (CSF) leaks in the brain. The aim of the study is to address the problems associated with detecting the low-grade tumor and CSF in brain is difficult in magnetic resonance imaging (MRI) images and another problem also relates to efficiency and less execution time for segmentation of medical images. For tumor and CSF segmentation using trained light field database (LFD) datasets of MRI images.… More >

  • Open Access

    ARTICLE

    Transmitter-Receiver Path Selection for Cell Range Extension Using Multi-Hop D2D

    Farah Akif1, Kiran Sultan2,*, Aqdas N. Malik1, Ijaz M. Qureshi3, Saba Mahmood4

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2075-2093, 2021, DOI:10.32604/cmc.2021.016721

    Abstract Conventional approach of dealing with more users per coverage area in cellular networks implies densifying the amount of (Access Point) AP which will eventually result in a larger carbon footprint. In this paper, we propose a base station off-loading and cell range extension (CRE) scheme based on multi-hop device-to-device (MHD2D) path selection between transmitter and receiver node. The paper also provides derivations of upper and lower bounds for energy efficiency, capacity, and transmit power. The proposed path selection scheme is inspired by the foraging behavior of honey bees. We present the algorithm as a modified variant of the artificial bee… More >

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