Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (31)
  • Open Access


    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… More >

  • Open Access


    A Productivity Prediction Method Based on Artificial Neural Networks and Particle Swarm Optimization for Shale-Gas Horizontal Wells

    Bin Li*

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.10, pp. 2729-2748, 2023, DOI:10.32604/fdmp.2023.029649

    Abstract In order to overcome the deficiencies of current methods for the prediction of the productivity of shale gas horizontal wells after fracturing, a new sophisticated approach is proposed in this study. This new model stems from the combination several techniques, namely, artificial neural network (ANN), particle swarm optimization (PSO), Imperialist Competitive Algorithms (ICA), and Ant Clony Optimization (ACO). These are properly implemented by using the geological and engineering parameters collected from 317 wells. The results show that the optimum PSO-ANN model has a high accuracy, obtaining a R2 of 0.847 on the testing. The partial dependence More >

  • Open Access


    Meta-Heuristic Optimized Hybrid Wavelet Features for Arrhythmia Classification

    S. R. Deepa1, M. Subramoniam2,*, R. Swarnalatha3, S. Poornapushpakala2, S. Barani2

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 745-761, 2023, DOI:10.32604/iasc.2023.034211

    Abstract The non-invasive evaluation of the heart through EectroCardioGraphy (ECG) has played a key role in detecting heart disease. The analysis of ECG signals requires years of learning and experience to interpret and extract useful information from them. Thus, a computerized system is needed to classify ECG signals with more accurate results effectively. Abnormal heart rhythms are called arrhythmias and cause sudden cardiac deaths. In this work, a Computerized Abnormal Heart Rhythms Detection (CAHRD) system is developed using ECG signals. It consists of four stages; preprocessing, feature extraction, feature optimization and classifier. At first, Pan and… More >

  • Open Access


    On Layout Optimization of Wireless Sensor Network Using Meta-Heuristic Approach

    Abeeda Akram1, Kashif Zafar1, Adnan Noor Mian2, Abdul Rauf Baig3, Riyad Almakki3, Lulwah AlSuwaidan3, Shakir Khan3,4,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3685-3701, 2023, DOI:10.32604/csse.2023.032024

    Abstract One of the important research issues in wireless sensor networks (WSNs) is the optimal layout designing for the deployment of sensor nodes. It directly affects the quality of monitoring, cost, and detection capability of WSNs. Layout optimization is an NP-hard combinatorial problem, which requires optimization of multiple competing objectives like cost, coverage, connectivity, lifetime, load balancing, and energy consumption of sensor nodes. In the last decade, several meta-heuristic optimization techniques have been proposed to solve this problem, such as genetic algorithms (GA) and particle swarm optimization (PSO). However, these approaches either provided computationally expensive solutions… More >

  • Open Access


    A Double Adaptive Random Spare Reinforced Sine Cosine Algorithm

    Abdelazim G. Hussien1,2, Guoxi Liang3, Huiling Chen4,*, Haiping Lin5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2267-2289, 2023, DOI:10.32604/cmes.2023.024247

    Abstract Many complex optimization problems in the real world can easily fall into local optimality and fail to find the optimal solution, so more new techniques and methods are needed to solve such challenges. Metaheuristic algorithms have received a lot of attention in recent years because of their efficient performance and simple structure. Sine Cosine Algorithm (SCA) is a recent Metaheuristic algorithm that is based on two trigonometric functions Sine & Cosine. However, like all other metaheuristic algorithms, SCA has a slow convergence and may fail in sub-optimal regions. In this study, an enhanced version of More >

  • Open Access


    A Novel Meta-Heuristic Optimization Algorithm in White Blood Cells Classification

    Khaled A. Fathy, Humam K. Yaseen*, Mohammad T. Abou-Kreisha, Kamal A. ElDahshan

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1527-1545, 2023, DOI:10.32604/cmc.2023.036322

    Abstract Some human diseases are recognized through of each type of White Blood Cell (WBC) count, so detecting and classifying each type is important for human healthcare. The main aim of this paper is to propose a computer-aided WBCs utility analysis tool designed, developed, and evaluated to classify WBCs into five types namely neutrophils, eosinophils, lymphocytes, monocytes, and basophils. Using a computer-artificial model reduces resource and time consumption. Various pre-trained deep learning models have been used to extract features, including AlexNet, Visual Geometry Group (VGG), Residual Network (ResNet), which belong to different taxonomy types of deep… More >

  • Open Access


    Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data

    Ghada Atteia1, El-Sayed M. El-kenawy2,3, Nagwan Abdel Samee1,*, Mona M. Jamjoom4, Abdelhameed Ibrahim5, Abdelaziz A. Abdelhamid6,7, Ahmad Taher Azar8,9, Nima Khodadadi10,11, Reham A. Ghanem12, Mahmoud Y. Shams13

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1883-1900, 2023, DOI:10.32604/cmc.2023.031723

    Abstract The rapid population growth results in a crucial problem in the early detection of diseases in medical research. Among all the cancers unveiled, breast cancer is considered the second most severe cancer. Consequently, an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical diagnoses. Utilizing recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective, reliable, and rapid responses, which could help in decreasing the death risk. In this paper, we propose… More >

  • Open Access


    Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

    El-Sayed M. El-kenawy1, Abdelaziz A. Abdelhamid2,3, Abdelhameed Ibrahim4, Mostafa Abotaleb5, Tatiana Makarovskikh5, Amal H. Alharbi6,*, Doaa Sami Khafaga6

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 883-896, 2023, DOI:10.32604/csse.2023.034697

    Abstract Several instances of pneumonia with no clear etiology were recorded in Wuhan, China, on December 31, 2019. The world health organization (WHO) called it COVID-19 that stands for “Coronavirus Disease 2019,” which is the second version of the previously known severe acute respiratory syndrome (SARS) Coronavirus and identified in short as (SARSCoV-2). There have been regular restrictions to avoid the infection spread in all countries, including Saudi Arabia. The prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus spread. Methodology: Analysis and forecasting of epidemic patterns… More >

  • Open Access


    Optimization Techniques in University Timetabling Problem: Constraints, Methodologies, Benchmarks, and Open Issues

    Abeer Bashab1, Ashraf Osman Ibrahim2,*, Ibrahim Abakar Tarigo Hashem3, Karan Aggarwal4, Fadhil Mukhlif5, Fuad A. Ghaleb5, Abdelzahir Abdelmaboud6

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6461-6484, 2023, DOI:10.32604/cmc.2023.034051

    Abstract University timetabling problems are a yearly challenging task and are faced repeatedly each semester. The problems are considered non-polynomial time (NP) and combinatorial optimization problems (COP), which means that they can be solved through optimization algorithms to produce the aspired optimal timetable. Several techniques have been used to solve university timetabling problems, and most of them use optimization techniques. This paper provides a comprehensive review of the most recent studies dealing with concepts, methodologies, optimization, benchmarks, and open issues of university timetabling problems. The comprehensive review starts by presenting the essence of university timetabling as… More >

  • Open Access


    Fault Coverage-Based Test Case Prioritization and Selection Using African Buffalo Optimization

    Shweta Singhal1, Nishtha Jatana2, Ahmad F Subahi3, Charu Gupta4,*, Osamah Ibrahim Khalaf5, Youseef Alotaibi6

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6755-6774, 2023, DOI:10.32604/cmc.2023.032308

    Abstract Software needs modifications and requires revisions regularly. Owing to these revisions, retesting software becomes essential to ensure that the enhancements made, have not affected its bug-free functioning. The time and cost incurred in this process, need to be reduced by the method of test case selection and prioritization. It is observed that many nature-inspired techniques are applied in this area. African Buffalo Optimization is one such approach, applied to regression test selection and prioritization. In this paper, the proposed work explains and proves the applicability of the African Buffalo Optimization approach to test case selection… More >

Displaying 1-10 on page 1 of 31. Per Page