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  • Open Access


    A New Hybrid Feature Selection Sequence for Predicting Breast Cancer Survivability Using Clinical Datasets

    E. Jenifer Sweetlin*, S. Saudia

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 343-367, 2023, DOI:10.32604/iasc.2023.036742

    Abstract This paper proposes a hybrid feature selection sequence complemented with filter and wrapper concepts to improve the accuracy of Machine Learning (ML) based supervised classifiers for classifying the survivability of breast cancer patients into classes, living and deceased using METABRIC and Surveillance, Epidemiology and End Results (SEER) datasets. The ML-based classifiers used in the analysis are: Multiple Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine and Multilayer Perceptron. The workflow of the proposed ML algorithm sequence comprises the following stages: data cleaning, data balancing, feature selection via a filter and wrapper sequence, More >

  • Open Access


    Chaotic Krill Herd with Fuzzy Based Routing Protocol for Wireless Networks

    Ashit Kumar Dutta1,*, Yasser Albagory2, Farhan M. Obesat3, Anas Waleed Abulfaraj4

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1659-1674, 2022, DOI:10.32604/iasc.2022.026263

    Abstract Energy is considered a valuable source in wireless sensor networks (WSN) for effectively improving the survivability of the network. The non-uniform dispersion of load in the network causes unbalanced energy dissipation which can result in network interruption. The route selection process can be considered as an optimization problem and is solved by utilize of artificial intelligence (AI) techniques. This study introduces an energy efficient chaotic krill herd algorithm with adaptive neuro fuzzy inference system based routing (EECKHA-ANFIS) protocol for WSN. The goal of the EECKHA-ANFIS method is for deriving a better set of routes to… More >

  • Open Access


    Kernel Search-Framework for Dynamic Controller Placement in Software-Defined Network

    Ali Abdi Seyedkolaei1, Seyed Amin Hosseini Seno1,*, Rahmat Budiarto2

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3391-3410, 2021, DOI:10.32604/cmc.2021.017313

    Abstract In software-defined networking (SDN) networks, unlike traditional networks, the control plane is located separately in a device or program. One of the most critical problems in these networks is a controller placement problem, which has a significant impact on the network’s overall performance. This paper attempts to provide a solution to this problem aiming to reduce the operational cost of the network and improve their survivability and load balancing. The researchers have proposed a suitable framework called kernel search introducing integer programming formulations to address the controller placement problem. It demonstrates through careful computational studies More >

  • Open Access


    Machine Learning Techniques Applied to Electronic Healthcare Records to Predict Cancer Patient Survivability

    Ornela Bardhi1,2,*, Begonya Garcia Zapirain1

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1595-1613, 2021, DOI:10.32604/cmc.2021.015326

    Abstract Breast cancer (BCa) and prostate cancer (PCa) are the two most common types of cancer. Various factors play a role in these cancers, and discovering the most important ones might help patients live longer, better lives. This study aims to determine the variables that most affect patient survivability, and how the use of different machine learning algorithms can assist in such predictions. The AURIA database was used, which contains electronic healthcare records (EHRs) of 20,006 individual patients diagnosed with either breast or prostate cancer in a particular region in Finland. In total, there were 178… More >

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