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  • Open Access

    ARTICLE

    Stroke Risk Assessment Decision-Making Using a Machine Learning Model: Logistic-AdaBoost

    Congjun Rao1, Mengxi Li1, Tingting Huang2,*, Feiyu Li1

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 699-724, 2024, DOI:10.32604/cmes.2023.044898

    Abstract Stroke is a chronic cerebrovascular disease that carries a high risk. Stroke risk assessment is of great significance in preventing, reversing and reducing the spread and the health hazards caused by stroke. Aiming to objectively predict and identify strokes, this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost (Logistic-AB) based on machine learning. First, the categorical boosting (CatBoost) method is used to perform feature selection for all features of stroke, and 8 main features are selected to form a new index evaluation system to predict the risk of stroke. Second, the borderline synthetic minority oversampling technique (SMOTE)… More >

  • Open Access

    ARTICLE

    Electroencephalography (EEG) Based Neonatal Sleep Staging and Detection Using Various Classification Algorithms

    Hafza Ayesha Siddiqa1, Muhammad Irfan1, Saadullah Farooq Abbasi2,*, Wei Chen1

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1759-1778, 2023, DOI:10.32604/cmc.2023.041970

    Abstract Automatic sleep staging of neonates is essential for monitoring their brain development and maturity of the nervous system. EEG based neonatal sleep staging provides valuable information about an infant’s growth and health, but is challenging due to the unique characteristics of EEG and lack of standardized protocols. This study aims to develop and compare 18 machine learning models using Automated Machine Learning (autoML) technique for accurate and reliable multi-channel EEG-based neonatal sleep-wake classification. The study investigates autoML feasibility without extensive manual selection of features or hyperparameter tuning. The data is obtained from neonates at post-menstrual age 37 ± 05 weeks.… More >

  • Open Access

    ARTICLE

    Detecting Ethereum Ponzi Schemes Through Opcode Context Analysis and Oversampling-Based AdaBoost Algorithm

    Mengxiao Wang1,2, Jing Huang1,2,*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1023-1042, 2023, DOI:10.32604/csse.2023.039569

    Abstract Due to the anonymity of blockchain, frequent security incidents and attacks occur through it, among which the Ponzi scheme smart contract is a classic type of fraud resulting in huge economic losses. Machine learning-based methods are believed to be promising for detecting ethereum Ponzi schemes. However, there are still some flaws in current research, e.g., insufficient feature extraction of Ponzi scheme smart contracts, without considering class imbalance. In addition, there is room for improvement in detection precision. Aiming at the above problems, this paper proposes an ethereum Ponzi scheme detection scheme through opcode context analysis and adaptive boosting (AdaBoost) algorithm.… More >

  • Open Access

    ARTICLE

    Ensemble Learning for Fetal Health Classification

    Mesfer Al Duhayyim1,*, Sidra Abbas2, Abdullah Al Hejaili3, Natalia Kryvinska4,*, Ahmad Almadhor5, Huma Mughal6

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 823-842, 2023, DOI:10.32604/csse.2023.037488

    Abstract : Cardiotocography (CTG) represents the fetus’s health inside the womb during labor. However, assessment of its readings can be a highly subjective process depending on the expertise of the obstetrician. Digital signals from fetal monitors acquire parameters (i.e., fetal heart rate, contractions, acceleration). Objective:: This paper aims to classify the CTG readings containing imbalanced healthy, suspected, and pathological fetus readings. Method:: We perform two sets of experiments. Firstly, we employ five classifiers: Random Forest (RF), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM) without over-sampling to classify CTG readings into three categories:… More >

  • Open Access

    ARTICLE

    An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms

    Shahid Mohammad Ganie1, Pijush Kanti Dutta Pramanik2, Majid Bashir Malik3, Anand Nayyar4, Kyung Sup Kwak5,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3993-4006, 2023, DOI:10.32604/csse.2023.035244

    Abstract Cardiovascular disease is among the top five fatal diseases that affect lives worldwide. Therefore, its early prediction and detection are crucial, allowing one to take proper and necessary measures at earlier stages. Machine learning (ML) techniques are used to assist healthcare providers in better diagnosing heart disease. This study employed three boosting algorithms, namely, gradient boost, XGBoost, and AdaBoost, to predict heart disease. The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository. Exploratory data analysis is performed to find the characteristics of data samples about descriptive and inferential statistics. Specifically, it was… More >

  • Open Access

    ARTICLE

    Prediction of NFT Sale Price Fluctuations on OpenSea Using Machine Learning Approaches

    Zixiong Wang, Qiuying Chen, Sang-Joon Lee*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2443-2459, 2023, DOI:10.32604/cmc.2023.037553

    Abstract The rapid expansion of the non-fungible token (NFT) market has attracted many investors. However, studies on the NFT price fluctuations have been relatively limited. To date, the machine learning approach has not been used to demonstrate a specific error in NFT sale price fluctuation prediction. The aim of this study was to develop a prediction model for NFT price fluctuations using the NFT trading information obtained from OpenSea, the world’s largest NFT marketplace. We used Python programs to collect data and summarized them as: NFT information, collection information, and related account information. AdaBoost and Random Forest (RF) algorithms were employed… More >

  • Open Access

    ARTICLE

    Human Verification over Activity Analysis via Deep Data Mining

    Kumar Abhishek1,*, Sheikh Badar ud din Tahir2

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1391-1409, 2023, DOI:10.32604/cmc.2023.035894

    Abstract Human verification and activity analysis (HVAA) are primarily employed to observe, track, and monitor human motion patterns using red-green-blue (RGB) images and videos. Interpreting human interaction using RGB images is one of the most complex machine learning tasks in recent times. Numerous models rely on various parameters, such as the detection rate, position, and direction of human body components in RGB images. This paper presents robust human activity analysis for event recognition via the extraction of contextual intelligence-based features. To use human interaction image sequences as input data, we first perform a few denoising steps. Then, human-to-human analyses are employed… More >

  • Open Access

    ARTICLE

    Moth Flame Optimization Based FCNN for Prediction of Bugs in Software

    C. Anjali*, Julia Punitha Malar Dhas, J. Amar Pratap Singh

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1241-1256, 2023, DOI:10.32604/iasc.2023.029678

    Abstract The software engineering technique makes it possible to create high-quality software. One of the most significant qualities of good software is that it is devoid of bugs. One of the most time-consuming and costly software procedures is finding and fixing bugs. Although it is impossible to eradicate all bugs, it is feasible to reduce the number of bugs and their negative effects. To broaden the scope of bug prediction techniques and increase software quality, numerous causes of software problems must be identified, and successful bug prediction models must be implemented. This study employs a hybrid of Faster Convolution Neural Network… More >

  • Open Access

    ARTICLE

    A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost

    Wang Ning1,*, Siliang Chen2,*, Fu Qiang2, Haitao Tang2, Shen Jie2

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5951-5965, 2023, DOI:10.32604/cmc.2023.035558

    Abstract With the popularity of online payment, how to perform credit card fraud detection more accurately has also become a hot issue. And with the emergence of the adaptive boosting algorithm (Adaboost), credit card fraud detection has started to use this method in large numbers, but the traditional Adaboost is prone to overfitting in the presence of noisy samples. Therefore, in order to alleviate this phenomenon, this paper proposes a new idea: using the number of consecutive sample misclassifications to determine the noisy samples, while constructing a penalty factor to reconstruct the sample weight assignment. Firstly, the theoretical analysis shows that… More >

  • Open Access

    ARTICLE

    An Efficient Differential Evolution for Truss Sizing Optimization Using AdaBoost Classifier

    Tran-Hieu Nguyen*, Anh-Tuan Vu

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 429-458, 2023, DOI:10.32604/cmes.2022.020819

    Abstract Design constraints verification is the most computationally expensive task in evolutionary structural optimization due to a large number of structural analyses that must be conducted. Building a surrogate model to approximate the behavior of structures instead of the exact structural analyses is a possible solution to tackle this problem. However, most existing surrogate models have been designed based on regression techniques. This paper proposes a novel method, called CaDE, which adopts a machine learning classification technique for enhancing the performance of the Differential Evolution (DE) optimization. The proposed method is separated into two stages. During the first optimization stage, the… More >

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