Weiming Huang^1,2, Baisong Liu^1,*, Zhaoliang Wang¹

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4449-4469, 2024, DOI:10.32604/cmc.2024.050389

Abstract In the tag recommendation task on academic platforms, existing methods disregard users’ customized preferences in favor of extracting tags based just on the content of the articles. Besides, it uses co-occurrence techniques and tries to combine nodes’ textual content for modelling. They still do not, however, directly simulate many interactions in network learning. In order to address these issues, we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations. Specifically, we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles… More >

Feng Zhao, Guangdi Liu^*, Xiaoqiang Chen, Ying Wang

Energy Engineering, Vol.121, No.7, pp. 1865-1882, 2024, DOI:10.32604/ee.2024.048209

Abstract In light of the prevailing issue that the existing convolutional neural network (CNN) power quality disturbance identification method can only extract single-scale features, which leads to a lack of feature information and weak anti-noise performance, a new approach for identifying power quality disturbances based on an adaptive Kalman filter (KF) and multi-scale channel attention (MS-CAM) fused convolutional neural network is suggested. Single and composite-disruption signals are generated through simulation. The adaptive maximum likelihood Kalman filter is employed for noise reduction in the initial disturbance signal, and subsequent integration of multi-scale features into the conventional CNN… More >

Tao Bao^*, Xiyuan Ma, Zhuohuan Li, Duotong Yang, Pengyu Wang, Changcheng Zhou

Energy Engineering, Vol.121, No.6, pp. 1713-1737, 2024, DOI:10.32604/ee.2023.046150

Abstract The stability problem of power grids has become increasingly serious in recent years as the size of novel power systems increases. In order to improve and ensure the stable operation of the novel power system, this study proposes an artificial emotional lazy Q-learning method, which combines artificial emotion, lazy learning, and reinforcement learning for static security and stability analysis of power systems. Moreover, this study compares the analysis results of the proposed method with those of the small disturbance method for a stand-alone power system and verifies that the proposed lazy Q-learning method is able More >

S. Abinaya^*, K. Uttej Kumar

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2269-2286, 2024, DOI:10.32604/cmc.2024.047167

Abstract A Recommender System (RS) is a crucial part of several firms, particularly those involved in e-commerce. In conventional RS, a user may only offer a single rating for an item-that is insufficient to perceive consumer preferences. Nowadays, businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’ preferences. On the other hand, the collaborative filtering (CF) algorithm utilizing AutoEncoder (AE) is seen to be effective in identifying user-interested items. However, the cost of these computations increases nonlinearly as the number of items and users… More >

Othman S. Al-Heety^1,*, Zahriladha Zakaria^1,*, Ahmed Abu-Khadrah², Mahamod Ismail³, Sarmad Nozad Mahmood⁴, Mohammed Mudhafar Shakir⁵, Sameer Alani⁶, Hussein Alsariera¹

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2103-2127, 2024, DOI:10.32604/cmes.2023.029509

Abstract Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision. In this article, these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data. The framework integrates Kalman filtering and Q-learning. Unlike smoothing Kalman filtering, our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error. Unlike traditional Q-learning, our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from… More >

Xiuhui Diao¹, Pengfei Wang^1,2,*, Weidong Li², Xianwu Chu², Yunming Wang²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1795-1812, 2024, DOI:10.32604/cmes.2023.030008

Abstract To solve the problem of data fusion for prior information such as track information and train status in train positioning, an adaptive H_∞ filtering algorithm with combination constraint is proposed, which fuses prior information with other sensor information in the form of constraints. Firstly, the train precise track constraint method of the train is proposed, and the plane position constraint and train motion state constraints are analysed. A model for combining prior information with constraints is established. Then an adaptive H_∞ filter with combination constraints is derived based on the adaptive adjustment method of the robustness More >

Seungmin Lee, Hyunghoon Kim, Haehyun Cho, Hyo Jin Jo^*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2941-2954, 2023, DOI:10.32604/iasc.2023.039992

Abstract Modern vehicles are equipped with multiple Electronic Control Units (ECUs) that support various convenient driving functions, such as the Advanced Driver Assistance System (ADAS). To enable communication between these ECUs, the Controller Area Network (CAN) protocol is widely used. However, since CAN lacks any security technologies, it is vulnerable to cyber attacks. To address this, researchers have conducted studies on machine learning-based intrusion detection systems (IDSs) for CAN. However, most existing IDSs still have non-negligible detection errors. In this paper, we propose a new filtering-based intrusion detection system (FIDS) to minimize the detection errors of… More >

Siti Julia Rosli^1,2, Hasliza A Rahim^1,2,*, Khairul Najmy Abdul Rani^1,2, Ruzelita Ngadiran^2,3, Wan Azani Mustafa^3,4, Muzammil Jusoh^1,2, Mohd Najib Mohd Yasin^1,2, Thennarasan Sabapathy^1,2, Mohamedfareq Abdulmalek⁵, Wan Suryani Firuz Wan Ariffin², Ahmed Alkhayyat⁶

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2571-2571, 2023, DOI:10.32604/cmc.2023.045533

Abstract This article has no abstract. More >

Siyu Lu¹, Ligao Cai¹, Zhixin Liu², Shan Liu¹, Bo Yang¹, Lirong Yin³, Mingzhe Liu⁴, Wenfeng Zheng^1,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1881-1899, 2023, DOI:10.32604/csse.2023.034853

Abstract With the development of Internet technology, the explosive growth of Internet information presentation has led to difficulty in filtering effective information. Finding a model with high accuracy for text classification has become a critical problem to be solved by text filtering, especially for Chinese texts. This paper selected the manually calibrated Douban movie website comment data for research. First, a text filtering model based on the BP neural network has been built; Second, based on the Term Frequency-Inverse Document Frequency (TF-IDF) vector space model and the doc2vec method, the text word frequency vector and the More >

Yongbin Zhang^*

Frontiers in Heat and Mass Transfer, Vol.9, pp. 1-6, 2017, DOI:10.5098/hmt.9.26

Abstract The influence of pore wall surface property on the flux of a novel cylindrical-shaped nanoporous filtering membrane is analytically studied by using the flow factor approach model for a nanoscale flow. Across the thickness of the membrane are manufactured two concentric cylindrical pores with different radii. The smaller nanoscale pore is for filtration, while the other larger pore is for reducing the flow resistance. It was found that when the larger pore wall surface is hydrophobic, the interaction between the filtered liquid and the smaller pore wall surface has a very significant effect on the More >