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Search Results (9)
  • Open Access


    Identifying Cancer Disease Using Softmax-Feed Forward Recurrent Neural Classification

    P. Saranya*, P. Asha

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1137-1149, 2023, DOI:10.32604/iasc.2023.031470

    Abstract In today’s growing modern world environment, as human food activities are changing, it is affecting human health, thus leading to diseases like cancer. Cancer is a complex disease with many subtypes that affect human health without premature treatment and cause death. So the analysis of early diagnosis and prognosis of cancer studies can improve clinical management by analyzing various features of observation, which has become necessary to classify the type in cancer research. The research needs importance to organize the risk of the cancer patients based on data analysis to predict the result of premature treatment. This paper introduces a… More >

  • Open Access


    A Fault Risk Warning Method of Integrated Energy Systems Based on RelieF-Softmax Algorithm

    Qidai Lin1, Ying Gong2,*, Yizhi Shi1, Changsen Feng2, Youbing Zhang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 929-944, 2022, DOI:10.32604/cmes.2022.020752

    Abstract The integrated energy systems, usually including electric energy, natural gas and thermal energy, play a pivotal role in the energy Internet project, which could improve the accommodation of renewable energy through multi-energy complementary ways. Focusing on the regional integrated energy system composed of electrical microgrid and natural gas network, a fault risk warning method based on the improved RelieF-softmax method is proposed in this paper. The raw data-set was first clustered by the K-maxmin method to improve the preference of the random sampling process in the RelieF algorithm, and thereby achieved a hierarchical and non-repeated sampling. Then, the improved RelieF… More >

  • Open Access


    Optimal Sparse Autoencoder Based Sleep Stage Classification Using Biomedical Signals

    Ashit Kumar Dutta1,*, Yasser Albagory2, Manal Al Faraj1, Yasir A. M. Eltahir3, Abdul Rahaman Wahab Sait4

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1517-1529, 2023, DOI:10.32604/csse.2023.026482

    Abstract The recently developed machine learning (ML) models have the ability to obtain high detection rate using biomedical signals. Therefore, this article develops an Optimal Sparse Autoencoder based Sleep Stage Classification Model on Electroencephalography (EEG) Biomedical Signals, named OSAE-SSCEEG technique. The major intention of the OSAE-SSCEEG technique is to find the sleep stage disorders using the EEG biomedical signals. The OSAE-SSCEEG technique primarily undergoes preprocessing using min-max data normalization approach. Moreover, the classification of sleep stages takes place using the Sparse Autoencoder with Smoothed Regularization (SAE-SR) with softmax (SM) approach. Finally, the parameter optimization of the SAE-SR technique is carried out… More >

  • Open Access


    Fine-grained Ship Image Recognition Based on BCNN with Inception and AM-Softmax

    Zhilin Zhang1, Ting Zhang1, Zhaoying Liu1,*, Peijie Zhang1, Shanshan Tu1, Yujian Li2, Muhammad Waqas3

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1527-1539, 2022, DOI:10.32604/cmc.2022.029297

    Abstract The fine-grained ship image recognition task aims to identify various classes of ships. However, small inter-class, large intra-class differences between ships, and lacking of training samples are the reasons that make the task difficult. Therefore, to enhance the accuracy of the fine-grained ship image recognition, we design a fine-grained ship image recognition network based on bilinear convolutional neural network (BCNN) with Inception and additive margin Softmax (AM-Softmax). This network improves the BCNN in two aspects. Firstly, by introducing Inception branches to the BCNN network, it is helpful to enhance the ability of extracting comprehensive features from ships. Secondly, by adding… More >

  • Open Access


    Iterative Semi-Supervised Learning Using Softmax Probability

    Heewon Chung, Jinseok Lee*

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5607-5628, 2022, DOI:10.32604/cmc.2022.028154

    Abstract For the classification problem in practice, one of the challenging issues is to obtain enough labeled data for training. Moreover, even if such labeled data has been sufficiently accumulated, most datasets often exhibit long-tailed distribution with heavy class imbalance, which results in a biased model towards a majority class. To alleviate such class imbalance, semi-supervised learning methods using additional unlabeled data have been considered. However, as a matter of course, the accuracy is much lower than that from supervised learning. In this study, under the assumption that additional unlabeled data is available, we propose the iterative semi-supervised learning algorithms, which… More >

  • Open Access


    Fruits and Vegetable Diseases Recognition Using Convolutional Neural Networks

    Javaria Amin1, Muhammad Almas Anjum2, Muhammad Sharif3, Seifedine Kadry4, Yunyoung Nam5,*

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 619-635, 2022, DOI:10.32604/cmc.2022.018562

    Abstract As they have nutritional, therapeutic, so values, plants were regarded as important and they’re the main source of humankind’s energy supply. Plant pathogens will affect its leaves at a certain time during crop cultivation, leading to substantial harm to crop productivity & economic selling price. In the agriculture industry, the identification of fungal diseases plays a vital role. However, it requires immense labor, greater planning time, and extensive knowledge of plant pathogens. Computerized approaches are developed and tested by different researchers to classify plant disease identification, and that in many cases they have also had important results several times. Therefore,… More >

  • Open Access


    RP-NBSR: A Novel Network Attack Detection Model Based on Machine Learning

    Zihao Shen1,2, Hui Wang1,*, Kun Liu1, Peiqian Liu1, Menglong Ba1, MengYao Zhao3

    Computer Systems Science and Engineering, Vol.37, No.1, pp. 121-133, 2021, DOI:10.32604/csse.2021.014988

    Abstract The rapid progress of the Internet has exposed networks to an increased number of threats. Intrusion detection technology can effectively protect network security against malicious attacks. In this paper, we propose a ReliefF-P-Naive Bayes and softmax regression (RP-NBSR) model based on machine learning for network attack detection to improve the false detection rate and F1 score of unknown intrusion behavior. In the proposed model, the Pearson correlation coefficient is introduced to compensate for deficiencies in correlation analysis between features by the ReliefF feature selection algorithm, and a ReliefF-Pearson correlation coefficient (ReliefF-P) algorithm is proposed. Then, the Relief-P algorithm is used… More >

  • Open Access


    A Blockchain Based Framework for Stomach Abnormalities Recognition

    Muhammad Attique Khan1, Inzamam Mashood Nasir1, Muhammad Sharif2, Majed Alhaisoni3, Seifedine Kadry4, Syed Ahmad Chan Bukhari5, Yunyoung Nam6,*

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 141-158, 2021, DOI:10.32604/cmc.2021.013217

    Abstract Wireless Capsule Endoscopy (WCE) is an imaging technology, widely used in medical imaging for stomach infection recognition. However, a one patient procedure takes almost seven to eight minutes and approximately 57,000 frames are captured. The privacy of patients is very important and manual inspection is time consuming and costly. Therefore, an automated system for recognition of stomach infections from WCE frames is always needed. An existing block chain-based approach is employed in a convolutional neural network model to secure the network for accurate recognition of stomach infections such as ulcer and bleeding. Initially, images are normalized in fixed dimension and… More >

  • Open Access


    New Generation Model of Word Vector Representation Based on CBOW or Skip-Gram

    Zeyu Xiong1,*, Qiangqiang Shen1, Yueshan Xiong1, Yijie Wang1, Weizi Li2

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 259-273, 2019, DOI:10.32604/cmc.2019.05155

    Abstract Word vector representation is widely used in natural language processing tasks. Most word vectors are generated based on probability model, its bag-of-words features have two major weaknesses: they lose the ordering of the words and they also ignore semantics of the words. Recently, neural-network language models CBOW and Skip-Gram are developed as continuous-space language models for words representation in high dimensional real-valued vectors. These vector representations have recently demonstrated promising results in various NLP tasks because of their superiority in capturing syntactic and contextual regularities in language. In this paper, we propose a new strategy based on optimization in contiguous… More >

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