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  • Open Access

    ARTICLE

    Federation Boosting Tree for Originator Rights Protection

    Yinggang Sun1, Hongguo Zhang1, Chao Ma1,*, Hai Huang1, Dongyang Zhan2,3, Jiaxing Qu4

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4043-4058, 2023, DOI:10.32604/cmc.2023.031684 - 31 October 2022

    Abstract The problem of data island hinders the application of big data in artificial intelligence model training, so researchers propose a federated learning framework. It enables model training without having to centralize all data in a central storage point. In the current horizontal federated learning scheme, each participant gets the final jointly trained model. No solution is proposed for scenarios where participants only provide training data in exchange for benefits, but do not care about the final jointly trained model. Therefore, this paper proposes a new boosted tree algorithm, called RPBT (the originator Rights Protected federated… More >

  • Open Access

    ARTICLE

    A Data-Driven Oil Production Prediction Method Based on the Gradient Boosting Decision Tree Regression

    Hongfei Ma1,*, Wenqi Zhao2, Yurong Zhao1, Yu He1

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1773-1790, 2023, DOI:10.32604/cmes.2022.020498 - 20 September 2022

    Abstract Accurate prediction of monthly oil and gas production is essential for oil enterprises to make reasonable production plans, avoid blind investment and realize sustainable development. Traditional oil well production trend prediction methods are based on years of oil field production experience and expertise, and the application conditions are very demanding. With the rapid development of artificial intelligence technology, big data analysis methods are gradually applied in various sub-fields of the oil and gas reservoir development. Based on the data-driven artificial intelligence algorithm Gradient Boosting Decision Tree (GBDT), this paper predicts the initial single-layer production by More >

  • Open Access

    ARTICLE

    Pre Screening of Cervical Cancer Through Gradient Boosting Ensemble Learning Method

    S. Priya1,*, N. K. Karthikeyan1, D. Palanikkumar2

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2673-2685, 2023, DOI:10.32604/iasc.2023.028599 - 17 August 2022

    Abstract In recent years, cervical cancer is one of the most common diseases which occur in any woman regardless of any age. This is the deadliest disease since there were no symptoms shown till it is diagnosed to be the last stage. For women at a certain age, it is better to have a proper screening for cervical cancer. In most underdeveloped nations, it is very difficult to have frequent scanning for cervical cancer. Data Mining and machine learning methodologies help widely in finding the important causes for cervical cancer. The proposed work describes a multi-class More >

  • Open Access

    ARTICLE

    Prediction of Alzheimer’s Using Random Forest with Radiomic Features

    Anuj Singh*, Raman Kumar, Arvind Kumar Tiwari

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 513-530, 2023, DOI:10.32604/csse.2023.029608 - 16 August 2022

    Abstract Alzheimer’s disease is a non-reversible, non-curable, and progressive neurological disorder that induces the shrinkage and death of a specific neuronal population associated with memory formation and retention. It is a frequently occurring mental illness that occurs in about 60%–80% of cases of dementia. It is usually observed between people in the age group of 60 years and above. Depending upon the severity of symptoms the patients can be categorized in Cognitive Normal (CN), Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). Alzheimer’s disease is the last phase of the disease where the brain is severely… More >

  • Open Access

    ARTICLE

    Predictive-Analysis-based Machine Learning Model for Fraud Detection with Boosting Classifiers

    M. Valavan, S. Rita*

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 231-245, 2023, DOI:10.32604/csse.2023.026508 - 16 August 2022

    Abstract Fraud detection for credit/debit card, loan defaulters and similar types is achievable with the assistance of Machine Learning (ML) algorithms as they are well capable of learning from previous fraud trends or historical data and spot them in current or future transactions. Fraudulent cases are scant in the comparison of non-fraudulent observations, almost in all the datasets. In such cases detecting fraudulent transaction are quite difficult. The most effective way to prevent loan default is to identify non-performing loans as soon as possible. Machine learning algorithms are coming into sight as adept at handling such More >

  • Open Access

    ARTICLE

    An Intrusion Detection System for SDN Using Machine Learning

    G. Logeswari*, S. Bose, T. Anitha

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 867-880, 2023, DOI:10.32604/iasc.2023.026769 - 06 June 2022

    Abstract Software Defined Networking (SDN) has emerged as a promising and exciting option for the future growth of the internet. SDN has increased the flexibility and transparency of the managed, centralized, and controlled network. On the other hand, these advantages create a more vulnerable environment with substantial risks, culminating in network difficulties, system paralysis, online banking frauds, and robberies. These issues have a significant detrimental impact on organizations, enterprises, and even economies. Accuracy, high performance, and real-time systems are necessary to achieve this goal. Using a SDN to extend intelligent machine learning methodologies in an Intrusion… More >

  • Open Access

    ARTICLE

    Rock Strength Estimation Using Several Tree-Based ML Techniques

    Zida Liu1, Danial Jahed Armaghani2,*, Pouyan Fakharian3, Diyuan Li4, Dmitrii Vladimirovich Ulrikh5, Natalia Nikolaevna Orekhova6, Khaled Mohamed Khedher7,8

    CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.3, pp. 799-824, 2022, DOI:10.32604/cmes.2022.021165 - 03 August 2022

    Abstract The uniaxial compressive strength (UCS) of rock is an essential property of rock material in different relevant applications, such as rock slope, tunnel construction, and foundation. It takes enormous time and effort to obtain the UCS values directly in the laboratory. Accordingly, an indirect determination of UCS through conducting several rock index tests that are easy and fast to carry out is of interest and importance. This study presents powerful boosting trees evaluation framework, i.e., adaptive boosting machine, extreme gradient boosting machine (XGBoost), and category gradient boosting machine, for estimating the UCS of sandstone. Schmidt… More >

  • Open Access

    ARTICLE

    Ensemble Machine Learning to Enhance Q8 Protein Secondary Structure Prediction

    Moheb R. Girgis, Rofida M. Gamal, Enas Elgeldawi*

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3951-3967, 2022, DOI:10.32604/cmc.2022.030934 - 16 June 2022

    Abstract Protein structure prediction is one of the most essential objectives practiced by theoretical chemistry and bioinformatics as it is of a vital importance in medicine, biotechnology and more. Protein secondary structure prediction (PSSP) has a significant role in the prediction of protein tertiary structure, as it bridges the gap between the protein primary sequences and tertiary structure prediction. Protein secondary structures are classified into two categories: 3-state category and 8-state category. Predicting the 3 states and the 8 states of secondary structures from protein sequences are called the Q3 prediction and the Q8 prediction problems,… More >

  • Open Access

    ARTICLE

    Weather Forecasting Prediction Using Ensemble Machine Learning for Big Data Applications

    Hadil Shaiba1, Radwa Marzouk2, Mohamed K Nour3, Noha Negm4,5, Anwer Mustafa Hilal6,*, Abdullah Mohamed7, Abdelwahed Motwakel6, Ishfaq Yaseen6, Abu Sarwar Zamani6, Mohammed Rizwanullah6

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3367-3382, 2022, DOI:10.32604/cmc.2022.030067 - 16 June 2022

    Abstract The agricultural sector’s day-to-day operations, such as irrigation and sowing, are impacted by the weather. Therefore, weather constitutes a key role in all regular human activities. Weather forecasting must be accurate and precise to plan our activities and safeguard ourselves as well as our property from disasters. Rainfall, wind speed, humidity, wind direction, cloud, temperature, and other weather forecasting variables are used in this work for weather prediction. Many research works have been conducted on weather forecasting. The drawbacks of existing approaches are that they are less effective, inaccurate, and time-consuming. To overcome these issues,… More >

  • Open Access

    ARTICLE

    Diabetes Prediction Using Derived Features and Ensembling of Boosting Classifiers

    R. Rajkamal1,*, Anitha Karthi2, Xiao-Zhi Gao3

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2013-2033, 2022, DOI:10.32604/cmc.2022.027142 - 18 May 2022

    Abstract Diabetes is increasing commonly in people’s daily life and represents an extraordinary threat to human well-being. Machine Learning (ML) in the healthcare industry has recently made headlines. Several ML models are developed around different datasets for diabetic prediction. It is essential for ML models to predict diabetes accurately. Highly informative features of the dataset are vital to determine the capability factors of the model in the prediction of diabetes. Feature engineering (FE) is the way of taking forward in yielding highly informative features. Pima Indian Diabetes Dataset (PIDD) is used in this work, and the… More >

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