Zihao Song, Yan Zhou^*, Wei Cheng, Futai Liang, Chenhao Zhang

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3349-3376, 2024, DOI:10.32604/cmc.2024.047034

Abstract The frequent missing values in radar-derived time-series tracks of aerial targets (RTT-AT) lead to significant challenges in subsequent data-driven tasks. However, the majority of imputation research focuses on random missing (RM) that differs significantly from common missing patterns of RTT-AT. The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation. Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss. In this paper, a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed. Our model consists of two… 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 increases. To triumph over the… More >

Weijun Wang^1,*, Min Chen¹, Hui Yin¹, Yuan Li²

Energy Engineering, Vol.120, No.10, pp. 2433-2448, 2023, DOI:10.32604/ee.2023.028620

Abstract To identify the parameters of the extended Debye model of XLPE cables, and therefore evaluate the insulation performance of the samples, the sparsity-promoting dynamic mode decomposition (SPDMD) method was introduced, as well the basics and processes of its application were explained. The amplitude vector based on polarization current was first calculated. Based on the non-zero elements of the vector, the number of branches and parameters including the coefficients and time constants of each branch of the extended Debye model were derived. Further research on parameter identification of XLPE cables at different aging stages based on the SPDMD method was carried… More >

Wangpeng He^1,*, Yue Zhou¹, Xiaoya Guo², Deshun Hu¹, Junjie Ye³

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2495-2511, 2023, DOI:10.32604/cmes.2023.027896

Abstract In today’s world, smart electric vehicles are deeply integrated with smart energy, smart transportation and smart cities. In electric vehicles (EVs), owing to the harsh working conditions, mechanical parts are prone to fatigue damages, which endanger the driving safety of EVs. The practice has proved that the identification of periodic impact characteristics (PICs) can effectively indicate mechanical faults. This paper proposes a novel model-based approach for intelligent fault diagnosis of mechanical transmission train in EVs. The essential idea of this approach lies in the fusion of statistical information and model information from a dynamic process. In the algorithm, a novel… More >

Soban Arshad¹, Khalid Iqbal^1,*, Sheneela Naz², Sadaf Yasmin¹, Zobia Rehman²

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4283-4301, 2022, DOI:10.32604/cmc.2022.025442

Abstract Telecom industry relies on churn prediction models to retain their customers. These prediction models help in precise and right time recognition of future switching by a group of customers to other service providers. Retention not only contributes to the profit of an organization, but it is also important for upholding a position in the competitive market. In the past, numerous churn prediction models have been proposed, but the current models have a number of flaws that prevent them from being used in real-world large-scale telecom datasets. These schemes, fail to incorporate frequently changing requirements. Data sparsity, noisy data, and the… More >

Ishani Mishra^1,*, Sanjay Jain²

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 415-428, 2022, DOI:10.32604/iasc.2022.022860

Abstract In wireless body sensor network (WBSN), the set of electrocardiograms (ECG) data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient. However, due to the size of the ECG data, the performance of the signal compression and reconstruction is degraded. For efficient wireless transmission of ECG data, compressive sensing (CS) frame work plays significant role recently in WBSN. So, this work focuses to present CS for ECG signal compression and reconstruction. Although CS minimizes mean square error (MSE), compression rate and reconstruction probability of the CS is… More >

Adel Binbusayyis^*

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 501-513, 2022, DOI:10.32604/iasc.2022.022239

Abstract The increasing user of Internet has witnessed a continued exploration in applications and services that can bring more convenience in people's life than ever before. At the same time, with the exploration of online services, the people face unprecedented difficulty in selecting the most relevant service on the fly. In this context, the need for recommendation system is of paramount importance especially in helping the users to improve their experience with best value-added service. But, most of the traditional techniques including collaborative filtering (CF) which is one of the most successful recommendation technique suffer from two inherent issues namely, rating… More >

Thavavel Vaiyapuri^*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 487-503, 2021, DOI:10.32604/cmc.2021.015998

Abstract The era of the Internet of things (IoT) has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before. However, the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate services. Thus, there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of service. Most of the existing techniques—including collaborative filtering (CF), which is most widely adopted when building recommendation systems—suffer from rating sparsity and cold-start problems, preventing… More >

Adel Saad Assiri^*

Computer Systems Science and Engineering, Vol.36, No.3, pp. 435-450, 2021, DOI:10.32604/csse.2021.014894

Abstract Artificial neural networks (ANNs) are one of the hottest topics in computer science and artificial intelligence due to their potential and advantages in analyzing real-world problems in various disciplines, including but not limited to physics, biology, chemistry, and engineering. However, ANNs lack several key characteristics of biological neural networks, such as sparsity, scale-freeness, and small-worldness. The concept of sparse and scale-free neural networks has been introduced to fill this gap. Network sparsity is implemented by removing weak weights between neurons during the learning process and replacing them with random weights. When the network is initialized, the neural network is fully… More >

Caifeng Cheng^{1, 2}, Xiang’e Sun^{1, *}, Deshu Lin³, Yiliu Tu⁴

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 651-664, 2020, DOI:10.32604/cmc.2020.09874

Abstract In actual exploration, the demand for 3D seismic data collection is increasing, and the requirements for data are becoming higher and higher. Accordingly, the collection cost and data volume also increase. Aiming at this problem, we make use of the nature of data sparse expression, based on the theory of compressed sensing, to carry out the research on the efficient collection method of seismic data. It combines the collection of seismic data and the compression in data processing in practical work, breaking through the limitation of the traditional sampling frequency, and the sparse characteristics of the seismic signal are utilized… More >