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Search Results (10)
  • Open Access

    ARTICLE

    Automatic Extraction of Medical Latent Variables from ECG Signals Utilizing a Mutual Information-Based Technique and Capsular Neural Networks for Arrhythmia Detection

    Abbas Ali Hassan, Fardin Abdali-Mohammadi*

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 971-983, 2024, DOI:10.32604/cmc.2024.053817 - 15 October 2024

    Abstract From a medical perspective, the 12 leads of the heart in an electrocardiogram (ECG) signal have functional dependencies with each other. Therefore, all these leads report different aspects of an arrhythmia. Their differences lie in the level of highlighting and displaying information about that arrhythmia. For example, although all leads show traces of atrial excitation, this function is more evident in lead II than in any other lead. In this article, a new model was proposed using ECG functional and structural dependencies between heart leads. In the prescreening stage, the ECG signals are segmented from… More >

  • Open Access

    ARTICLE

    Enhancing Unsupervised Domain Adaptation for Person Re-Identification with the Minimal Transfer Cost Framework

    Sheng Xu1, Shixiong Xiang2, Feiyu Meng1, Qiang Wu1,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4197-4218, 2024, DOI:10.32604/cmc.2024.055157 - 12 September 2024

    Abstract In Unsupervised Domain Adaptation (UDA) for person re-identification (re-ID), the primary challenge is reducing the distribution discrepancy between the source and target domains. This can be achieved by implicitly or explicitly constructing an appropriate intermediate domain to enhance recognition capability on the target domain. Implicit construction is difficult due to the absence of intermediate state supervision, making smooth knowledge transfer from the source to the target domain a challenge. To explicitly construct the most suitable intermediate domain for the model to gradually adapt to the feature distribution changes from the source to the target domain,… More >

  • Open Access

    ARTICLE

    Robust and Discriminative Feature Learning via Mutual Information Maximization for Object Detection in Aerial Images

    Xu Sun, Yinhui Yu*, Qing Cheng

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4149-4171, 2024, DOI:10.32604/cmc.2024.052725 - 12 September 2024

    Abstract Object detection in unmanned aerial vehicle (UAV) aerial images has become increasingly important in military and civil applications. General object detection models are not robust enough against interclass similarity and intraclass variability of small objects, and UAV-specific nuisances such as uncontrolled weather conditions. Unlike previous approaches focusing on high-level semantic information, we report the importance of underlying features to improve detection accuracy and robustness from the information-theoretic perspective. Specifically, we propose a robust and discriminative feature learning approach through mutual information maximization (RD-MIM), which can be integrated into numerous object detection methods for aerial images.… More >

  • Open Access

    ARTICLE

    Side-Channel Leakage Analysis of Inner Product Masking

    Yuyuan Li1,2, Lang Li1,2,*, Yu Ou1,2

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1245-1262, 2024, DOI:10.32604/cmc.2024.049882 - 25 April 2024

    Abstract The Inner Product Masking (IPM) scheme has been shown to provide higher theoretical security guarantees than the Boolean Masking (BM). This scheme aims to increase the algebraic complexity of the coding to achieve a higher level of security. Some previous work unfolds when certain (adversarial and implementation) conditions are met, and we seek to complement these investigations by understanding what happens when these conditions deviate from their expected behaviour. In this paper, we investigate the security characteristics of IPM under different conditions. In adversarial condition, the security properties of first-order IPMs obtained through parametric characterization More >

  • Open Access

    ARTICLE

    Prediction of Ground Vibration Induced by Rock Blasting Based on Optimized Support Vector Regression Models

    Yifan Huang1, Zikang Zhou1,2, Mingyu Li1, Xuedong Luo1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3147-3165, 2024, DOI:10.32604/cmes.2024.045947 - 11 March 2024

    Abstract Accurately estimating blasting vibration during rock blasting is the foundation of blasting vibration management. In this study, Tuna Swarm Optimization (TSO), Whale Optimization Algorithm (WOA), and Cuckoo Search (CS) were used to optimize two hyperparameters in support vector regression (SVR). Based on these methods, three hybrid models to predict peak particle velocity (PPV) for bench blasting were developed. Eighty-eight samples were collected to establish the PPV database, eight initial blasting parameters were chosen as input parameters for the prediction model, and the PPV was the output parameter. As predictive performance evaluation indicators, the coefficient of More >

  • Open Access

    ARTICLE

    Automatic Diagnosis of Polycystic Ovarian Syndrome Using Wrapper Methodology with Deep Learning Techniques

    Mohamed Abouhawwash1,2, S. Sridevi3, Suma Christal Mary Sundararajan4, Rohit Pachlor5, Faten Khalid Karim6, Doaa Sami Khafaga6,*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 239-253, 2023, DOI:10.32604/csse.2023.037812 - 26 May 2023

    Abstract One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome (PCOS). Consequently, timely screening of polycystic ovarian syndrome can help in the process of recovery. Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition. This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies. Additionally, feature selection methods that produce the most important subset of features can speed up calculation and enhance… More >

  • Open Access

    ARTICLE

    Research on Feature Extraction Method of Social Network Text

    Zheng Zhang*, Shu Zhou

    Journal of New Media, Vol.3, No.2, pp. 73-80, 2021, DOI:10.32604/jnm.2021.018923 - 23 April 2021

    Abstract The development of various applications based on social network text is in full swing. Studying text features and classifications is of great value to extract important information. This paper mainly introduces the common feature selection algorithms and feature representation methods, and introduces the basic principles, advantages and disadvantages of SVM and KNN, and the evaluation indexes of classification algorithms. In the aspect of mutual information feature selection function, it describes its processing flow, shortcomings and optimization improvements. In view of its weakness in not balancing the positive and negative correlation characteristics, a balance weight attribute More >

  • Open Access

    ARTICLE

    Feature Selection Based on Distance Measurement

    Mingming Yang*, Junchuan Yang

    Journal of New Media, Vol.3, No.1, pp. 19-27, 2021, DOI:10.32604/jnm.2021.018267 - 15 March 2021

    Abstract Every day we receive a large amount of information through different social media and software, and this data and information can be realized with the advent of data mining methods. In the process of data mining, to solve some high-dimensional problems, feature selection is carried out in limited training samples, and effective features are selected. This paper focuses on two Relief feature selection algorithms: Relief and ReliefF algorithm. The differences between them and their respective applicable scopes are analyzed. Based on Relief algorithm, the high weight feature subset is obtained, and the correlation between features More >

  • Open Access

    ARTICLE

    A Discovery Method for New Words From Mobile Product Comments

    Hai Yang Zhu1,∗,†, Xiaobo Yin1,∗,‡, Shunxiang Zhang1,∗,§, Zhongliang Wei1,¶, Guangli Zhu1,II, Meng-Yen Hsieh2,*,**

    Computer Systems Science and Engineering, Vol.35, No.6, pp. 399-410, 2020, DOI:10.32604/csse.2020.35.399

    Abstract A large number of new words in product reviews generated by mobile terminals are valuable indicators of the privacy preferences of customers. By clustering these privacy preferences, sufficient information can be collected to characterize users and provide a data basis for the research issues of privacy protection. The widespread use of mobile clients shortens the string length of the comment corpus generated by product reviews, resulting in a high repetition rate. Therefore, the effective and accurate recognition of new words is a problem that requires an urgent solution. Hence, in this paper, we propose a… More >

  • Open Access

    ARTICLE

    The Method for Extracting New Login Sentiment Words from Chinese Micro-Blog Basedf on Improved Mutual Information

    Guangli Zhu, Wenting Liu, Shunxiang Zhang*, Xiang Chen , Chang Yin

    Computer Systems Science and Engineering, Vol.35, No.3, pp. 223-232, 2020, DOI:10.32604/csse.2020.35.223

    Abstract The current method of extracting new login sentiment words not only ignores the diversity of patterns constituted by new multi-character words (the number of words is greater than two), but also disregards the influence of other new words co-occurring with a new word connoting sentiment. To solve this problem, this paper proposes a method for extracting new login sentiment words from Chinese micro-blog based on improved mutual information. First, micro-blog data are preprocessed, taking into consideration some nonsense signals such as web links and punctuation. Based on preprocessed data, the candidate strings are obtained by… More >

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