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  • Open Access


    Early Warning of Commercial Housing Market Based on Bagging-GWO-SVM

    Yonghui Duan1, Keqing Zhao1,*, Yibin Guo2, Xiang Wang2

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2207-2222, 2023, DOI:10.32604/csse.2023.032297

    Abstract A number of risks exist in commercial housing, and it is critical for the government, the real estate industry, and consumers to establish an objective early warning indicator system for commercial housing risks and to conduct research regarding its measurement and early warning. In this paper, we examine the commodity housing market and construct a risk index for the commodity housing market at three levels: market level, the real estate industry and the national economy. Using the Bootstrap aggregating-grey wolf optimizer-support vector machine (Bagging-GWO-SVM) model after synthesizing the risk index by applying the CRITIC objective weighting method, the commercial housing… More >

  • Open Access


    Age and Gender Classification Using Backpropagation and Bagging Algorithms

    Ammar Almomani1,2,*, Mohammed Alweshah3, Waleed Alomoush4, Mohammad Alauthman5, Aseel Jabai2, Anwar Abbass2, Ghufran Hamad2, Meral Abdalla2, Brij B. Gupta1,6,7

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3045-3062, 2023, DOI:10.32604/cmc.2023.030567

    Abstract Voice classification is important in creating more intelligent systems that help with student exams, identifying criminals, and security systems. The main aim of the research is to develop a system able to predicate and classify gender, age, and accent. So, a new system called Classifying Voice Gender, Age, and Accent (CVGAA) is proposed. Backpropagation and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender categories. It has high precision compared to other algorithms used in this problem, as the adaptive… More >

  • Open Access


    Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on the Bagging and Sibling of Extra Trees Models

    Quang-Hieu Tran1,2,*, Hoang Nguyen1,2, Xuan-Nam Bui1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 2227-2246, 2023, DOI:10.32604/cmes.2022.021893

    Abstract This study considered and predicted blast-induced ground vibration (PPV) in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms. Accordingly, four machine learning algorithms, including support vector regression (SVR), extra trees (ExTree), K-nearest neighbors (KNN), and decision tree regression (DTR), were used as the base models for the purposes of combination and PPV initial prediction. The bagging regressor (BA) was then applied to combine these base models with the efforts of variance reduction, overfitting elimination, and generating more robust predictive models, abbreviated as BA-ExTree, BAKNN, BA-SVR, and BA-DTR. It is emphasized that the ExTree… More >

  • Open Access


    Metal Corrosion Rate Prediction of Small Samples Using an Ensemble Technique

    Yang Yang1,2,*, Pengfei Zheng3,4, Fanru Zeng5, Peng Xin6, Guoxi He1, Kexi Liao1

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 267-291, 2023, DOI:10.32604/cmes.2022.020220

    Abstract Accurate prediction of the internal corrosion rates of oil and gas pipelines could be an effective way to prevent pipeline leaks. In this study, a proposed framework for predicting corrosion rates under a small sample of metal corrosion data in the laboratory was developed to provide a new perspective on how to solve the problem of pipeline corrosion under the condition of insufficient real samples. This approach employed the bagging algorithm to construct a strong learner by integrating several KNN learners. A total of 99 data were collected and split into training and test set with a 9:1 ratio. The… More >

  • Open Access


    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

    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, respectively. The 8 classes of… More >

  • Open Access


    Text-Independent Algorithm for Source Printer Identification Based on Ensemble Learning

    Naglaa F. El Abady1,*, Mohamed Taha1, Hala H. Zayed1,2

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1417-1436, 2022, DOI:10.32604/cmc.2022.028044

    Abstract Because of the widespread availability of low-cost printers and scanners, document forgery has become extremely popular. Watermarks or signatures are used to protect important papers such as certificates, passports, and identification cards. Identifying the origins of printed documents is helpful for criminal investigations and also for authenticating digital versions of a document in today’s world. Source printer identification (SPI) has become increasingly popular for identifying frauds in printed documents. This paper provides a proposed algorithm for identifying the source printer and categorizing the questioned document into one of the printer classes. A dataset of 1200 papers from 20 distinct (13)… More >

  • Open Access


    Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

    Nguyen Thanh Hoan1, Nguyen Van Dung1, Ho Le Thu1, Hoa Thuy Quynh1, Nadhir Al-Ansari2,*, Tran Van Phong3, Phan Trong Trinh3, Dam Duc Nguyen4, Hiep Van Le4, Hanh Bich Thi Nguyen4, Mahdis Amiri5, Indra Prakash6, Binh Thai Pham4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1431-1449, 2022, DOI:10.32604/cmes.2022.018699

    Abstract Water level predictions in the river, lake and delta play an important role in flood management. Every year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high tides. Land subsidence may also aggravate flooding problems in this area. Therefore, accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and property. There are so many methods available to predict the water levels based on historical data but nowadays Machine Learning (ML) methods are considered the best… More >

  • Open Access


    BDLR: lncRNA identification using ensemble learning


    BIOCELL, Vol.46, No.4, pp. 951-960, 2022, DOI:10.32604/biocell.2022.016625

    Abstract Long non-coding RNAs (lncRNAs) play an important role in many life activities such as epigenetic material regulation, cell cycle regulation, dosage compensation and cell differentiation regulation, and are associated with many human diseases. There are many limitations in identifying and annotating lncRNAs using traditional biological experimental methods. With the development of high-throughput sequencing technology, it is of great practical significance to identify the lncRNAs from massive RNA sequence data using machine learning method. Based on the Bagging method and Decision Tree algorithm in ensemble learning, this paper proposes a method of lncRNAs gene sequence identification called BDLR. The identification results… More >

  • Open Access


    Realistic Smile Expression Recognition Approach Using Ensemble Classifier with Enhanced Bagging

    Oday A. Hassen1,*, Nur Azman Abu1, Zaheera Zainal Abidin1, Saad M. Darwish2

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2453-2469, 2022, DOI:10.32604/cmc.2022.019125

    Abstract A robust smile recognition system could be widely used for many real-world applications. Classification of a facial smile in an unconstrained setting is difficult due to the invertible and wide variety in face images. In this paper, an adaptive model for smile expression classification is suggested that integrates a fast features extraction algorithm and cascade classifiers. Our model takes advantage of the intrinsic association between face detection, smile, and other face features to alleviate the over-fitting issue on the limited training set and increase classification results. The features are extracted taking into account to exclude any unnecessary coefficients in the… More >

  • Open Access


    Novel Ensemble Modeling Method for Enhancing Subset Diversity Using Clustering Indicator Vector Based on Stacked Autoencoder

    Yanzhen Wang1, Xuefeng Yan1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.1, pp. 123-144, 2019, DOI:10.32604/cmes.2019.07052

    Abstract A single model cannot satisfy the high-precision prediction requirements given the high nonlinearity between variables. By contrast, ensemble models can effectively solve this problem. Three key factors for improving the accuracy of ensemble models are namely the high accuracy of a submodel, the diversity between subsample sets and the optimal ensemble method. This study presents an improved ensemble modeling method to improve the prediction precision and generalization capability of the model. Our proposed method first uses a bagging algorithm to generate multiple subsample sets. Second, an indicator vector is defined to describe these subsample sets. Third, subsample sets are selected… More >

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