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Search Results (102)
  • Open Access

    ARTICLE

    Variable Importance Measure System Based on Advanced Random Forest

    Shufang Song1,*, Ruyang He1, Zhaoyin Shi1, Weiya Zhang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.1, pp. 65-85, 2021, DOI:10.32604/cmes.2021.015378

    Abstract The variable importance measure (VIM) can be implemented to rank or select important variables, which can effectively reduce the variable dimension and shorten the computational time. Random forest (RF) is an ensemble learning method by constructing multiple decision trees. In order to improve the prediction accuracy of random forest, advanced random forest is presented by using Kriging models as the models of leaf nodes in all the decision trees. Referring to the Mean Decrease Accuracy (MDA) index based on Out-of-Bag (OOB) data, the single variable, group variables and correlated variables importance measures are proposed to establish a complete VIM system… More >

  • Open Access

    ARTICLE

    Improved Prediction and Understanding of Glass-Forming Ability Based on Random Forest Algorithm

    Chenjing Su1, Xiaoyu Li1,*, Mengru Li2, Qinsheng Zhu2, Hao Fu2, Shan Yang3

    Journal of Quantum Computing, Vol.3, No.2, pp. 79-87, 2021, DOI:10.32604/ jqc.2021.016651

    Abstract As an ideal material, bulk metallic glass (MG) has a wide range of applications because of its unique properties such as structural, functional and biomedical materials. However, it is difficult to predict the glass-forming ability (GFA) even given the criteria in theory and this problem greatly limits the application of bulk MG in industrial field. In this work, the proposed model uses the random forest classification method which is one of machine learning methods to solve the GFA prediction for binary metallic alloys. Compared with the previous SVM algorithm models of all features combinations, this new model is successfully constructed… More >

  • Open Access

    ARTICLE

    Robust Magnification Independent Colon Biopsy Grading System over Multiple Data Sources

    Tina Babu1, Deepa Gupta1, Tripty Singh1,*, Shahin Hameed2, Mohammed Zakariah3, Yousef Ajami Alotaibi4

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 99-128, 2021, DOI:10.32604/cmc.2021.016341

    Abstract Automated grading of colon biopsy images across all magnifications is challenging because of tailored segmentation and dependent features on each magnification. This work presents a novel approach of robust magnification-independent colon cancer grading framework to distinguish colon biopsy images into four classes: normal, well, moderate, and poor. The contribution of this research is to develop a magnification invariant hybrid feature set comprising cartoon feature, Gabor wavelet, wavelet moments, HSV histogram, color auto-correlogram, color moments, and morphological features that can be used to characterize different grades. Besides, the classifier is modeled as a multiclass structure with six binary class Bayesian optimized… More >

  • Open Access

    ARTICLE

    Suggestion Mining from Opinionated Text of Big Social Media Data

    Youseef Alotaibi1,*, Muhammad Noman Malik2, Huma Hayat Khan3, Anab Batool2, Saif ul Islam4, Abdulmajeed Alsufyani5, Saleh Alghamdi6

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3323-3338, 2021, DOI:10.32604/cmc.2021.016727

    Abstract Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and services. The increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making process. To overcome this challenge, extracting suggestions from opinionated text is a possible solution. In this study, the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers’ reviews. A classification using a word-embedding approach is used via the XGBoost classifier. The… More >

  • Open Access

    ARTICLE

    Machine Learning Based Framework for Classification of Children with ADHD and Healthy Controls

    Anshu Parashar*, Nidhi Kalra, Jaskirat Singh, Raman Kumar Goyal

    Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 669-682, 2021, DOI:10.32604/iasc.2021.017478

    Abstract Electrophysiological (EEG) signals provide good temporal resolution and can be effectively used to assess and diagnose children with Attention Deficit Hyperactivity Disorder (ADHD). This study aims to develop a machine learning model to classify children with ADHD and Healthy Controls. In this study, EEG signals captured under cognitive tasks were obtained from an open-access database of 60 children with ADHD and 60 Healthy Controls children of similar age. The regional contributions towards attaining higher accuracy are identified and further tested using three classifiers: AdaBoost, Random Forest and Support Vector Machine. The EEG data from 19 channels is taken as input… More >

  • Open Access

    ARTICLE

    An Anomaly Detection Method of Industrial Data Based on Stacking Integration

    Kunkun Wang1,2, Xianda Liu2,3,4,*

    Journal on Artificial Intelligence, Vol.3, No.1, pp. 9-19, 2021, DOI:10.32604/jai.2021.016706

    Abstract With the development of Internet technology, the computing power of data has increased, and the development of machine learning has become faster and faster. In the industrial production of industrial control systems, quality inspection and safety production of process products have always been our concern. Aiming at the low accuracy of anomaly detection in process data in industrial control system, this paper proposes an anomaly detection method based on stacking integration using the machine learning algorithm. Data are collected from the industrial site and processed by feature engineering. Principal component analysis (PCA) and integrated rule tree method are adopted to… More >

  • Open Access

    ARTICLE

    Residential Electricity Classification Method Based On Cloud Computing Platform and Random Forest

    Ming Li1, Zhong Fang2, Wanwan Cao1, Yong Ma1,*, Shang Wu1, Yang Guo1, Yu Xue3, Romany F. Mansour4

    Computer Systems Science and Engineering, Vol.38, No.1, pp. 39-46, 2021, DOI:10.32604/csse.2021.016189

    Abstract With the rapid development and popularization of new-generation technologies such as cloud computing, big data, and artificial intelligence, the construction of smart grids has become more diversified. Accurate quick reading and classification of the electricity consumption of residential users can provide a more in-depth perception of the actual power consumption of residents, which is essential to ensure the normal operation of the power system, energy management and planning. Based on the distributed architecture of cloud computing, this paper designs an improved random forest residential electricity classification method. It uses the unique out-of-bag error of random forest and combines the Drosophila… More >

  • Open Access

    ARTICLE

    Random Forests Algorithm Based Duplicate Detection in On-Site Programming Big Data Environment

    Qianqian Li1, Meng Li2, Lei Guo3,*, Zhen Zhang4

    Journal of Information Hiding and Privacy Protection, Vol.2, No.4, pp. 199-205, 2020, DOI:10.32604/jihpp.2020.016299

    Abstract On-site programming big data refers to the massive data generated in the process of software development with the characteristics of real-time, complexity and high-difficulty for processing. Therefore, data cleaning is essential for on-site programming big data. Duplicate data detection is an important step in data cleaning, which can save storage resources and enhance data consistency. Due to the insufficiency in traditional Sorted Neighborhood Method (SNM) and the difficulty of high-dimensional data detection, an optimized algorithm based on random forests with the dynamic and adaptive window size is proposed. The efficiency of the algorithm can be elevated by improving the method… More >

  • Open Access

    ARTICLE

    Prediction of Permeability Using Random Forest and Genetic Algorithm Model

    Junhui Wang1, Wanzi Yan1, Zhijun Wan1,*, Yi Wang2,*, Jiakun Lv1, Aiping Zhou3

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.3, pp. 1135-1157, 2020, DOI:10.32604/cmes.2020.014313

    Abstract Precise recovery of Coalbed Methane (CBM) based on transparent reconstruction of geological conditions is a branch of intelligent mining. The process of permeability reconstruction, ranging from data perception to real-time data visualization, is applicable to disaster risk warning and intelligent decision-making on gas drainage. In this study, a machine learning method integrating the Random Forest (RF) and the Genetic Algorithm (GA) was established for permeability prediction in the Xishan Coalfield based on Uniaxial Compressive Strength (UCS), effective stress, temperature and gas pressure. A total of 50 sets of data collected by a self-developed apparatus were used to generate datasets for… More >

  • Open Access

    ARTICLE

    Click through Rate Effectiveness Prediction on Mobile Ads Using Extreme Gradient Boosting

    AlAli Moneera, AlQahtani Maram, AlJuried Azizah, Taghareed AlOnizan, Dalia Alboqaytah, Nida Aslam*, Irfan Ullah Khan

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1681-1696, 2021, DOI:10.32604/cmc.2020.013466

    Abstract Online advertisements have a significant influence over the success or failure of your business. Therefore, it is important to somehow measure the impact of your advertisement before uploading it online, and this is can be done by calculating the Click Through Rate (CTR). Unfortunately, this method is not eco-friendly, since you have to gather the clicks from users then compute the CTR. This is where CTR prediction come in handy. Advertisement CTR prediction relies on the users’ log regarding click information data. Accurate prediction of CTR is a challenging and critical process for e-advertising platforms these days. CTR prediction uses… More >

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