Yifan Zhang¹, Qiancheng Yu^1,2,*, Lisi Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 661-677, 2024, DOI:10.32604/cmes.2023.044255

Abstract Widely used deep neural networks currently face limitations in achieving optimal performance for purchase intention prediction due to constraints on data volume and hyperparameter selection. To address this issue, based on the deep forest algorithm and further integrating evolutionary ensemble learning methods, this paper proposes a novel Deep Adaptive Evolutionary Ensemble (DAEE) model. This model introduces model diversity into the cascade layer, allowing it to adaptively adjust its structure to accommodate complex and evolving purchasing behavior patterns. Moreover, this paper optimizes the methods of obtaining feature vectors, enhancement vectors, and prediction results within the deep forest algorithm to enhance the… More >

Mohamed Esmail Karar^1,2,*, Omar Reyad^1,3, Hazem Ibrahim Shehata^1,4

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2377-2389, 2023, DOI:10.32604/csse.2023.032931

Abstract This article introduces a new medical internet of things (IoT) framework for intelligent fall detection system of senior people based on our proposed deep forest model. The cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural networks. Moreover, the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated layer. The suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset, which is acquired from three-axis accelerometer in a smartwatch. It… More >

Xin Tong¹, Changlin Zhang^2,*, Jingya Wang¹, Zhiyan Zhao¹, Zhuoxian Liu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 561-581, 2023, DOI:10.32604/cmes.2022.022495

Abstract The dark web is a shadow area hidden in the depths of the Internet, which is difficult to access through common search engines. Because of its anonymity, the dark web has gradually become a hotbed for a variety of cyber-crimes. Although some research based on machine learning or deep learning has been shown to be effective in the task of analyzing dark web traffic in recent years, there are still pain points such as low accuracy, insufficient real-time performance, and limited application scenarios. Aiming at the difficulties faced by the existing automated dark web traffic analysis methods, a novel method… More >

Letao Gao^1,*, Xiaoming Wang², Tao Wang³, Mengyu Chang⁴

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4741-4754, 2022, DOI:10.32604/cmc.2022.027204

Abstract In the research field of bearing fault diagnosis, classical deep learning models have the problems of too many parameters and high computing cost. In addition, the classical deep learning models are not effective in the scenario of small data. In recent years, deep forest is proposed, which has less hyper parameters and adaptive depth of deep model. In addition, weighted deep forest (WDF) is proposed to further improve deep forest by assigning weights for decisions trees based on the accuracy of each decision tree. In this paper, weighted deep forest model-based bearing fault diagnosis method (WDBM) is proposed. The WDBM… More >