Rui Li¹, Guang Sun^1,*, Jingyi He¹, Ying Jiang¹, Rui Sun¹, Haixia Li¹, Peng Guo^1,2, Jianjun Zhang³

Journal on Internet of Things, Vol.2, No.2, pp. 65-73, 2020, DOI:10.32604/jiot.2020.09868 - 14 September 2020

Abstract User portrait has been a booming concept in big data industry in recent years which is a direct way to restore users’ information. When it talks about user portrait, it will be connected with precise marketing and operating. However, there are more ways which can reflect the good use of user portrait. Commercial use is the most acceptable use but it also can be used in different industries widely. The goal of this paper is forecasting gender by user portrait and making it useful in transportation safety. It can extract the information from people who More >

Vijayalakshmi. K^1,*, Anandan. P²

Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 303-311, 2020, DOI:10.31209/2020.100000165

Abstract The advent of sensors that are light in weight, small-sized, low power and are enabled by wireless network has led to growth of Wireless Sensor Networks (WSNs) in multiple areas of applications. The key problems faced in WSNs are decreased network lifetime and time delay in transmission of data. Several key issues in the WSN design can be addressed using the Multi-Objective Optimization (MOO) Algorithms. The selection of the Cluster Head is a NP Hard optimization problem in nature. The CH selection is also challenging as the sensor nodes are organized in clusters. Through partitioning… More >

S.Ilangovan^1,*, A. Vincent Antony Kumar²

Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 261-268, 2020, DOI:10.31209/2019.100000154

Abstract In this work, a Novel Feature selection framework called SU embedded PSO Feature Selector has been proposed (SU-PSO) towards the selection of optimal feature subset for the improvement of detection performance of classifiers. The feature space ranking is done through the Symmetrical Uncertainty method. Further, memetic operators of PSO include features and remove features are used to choose relevant features and the best of best features are selected using PSO. The proposed feature selector efficiently removes not only irrelevant but also redundant features. Performance metric such as classification accuracy, subset of features selected and running More >

Wanxin Zhang^1,*, Jihong Zhu²

CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 1119-1130, 2020, DOI:10.32604/cmes.2020.010986 - 21 August 2020

Abstract With the increasing demand for the automation of operations and processes in mechatronic systems, fault detection and diagnosis has become a major topic to guarantee the process performance. There exist numerous studies on the topic of applying artificial intelligence methods for fault detection and diagnosis. However, much of the focus has been given on the detection of faults. In terms of the diagnosis of faults, on one hand, assumptions are required, which restricts the diagnosis range. On the other hand, different faults with similar symptoms cannot be distinguished, especially when the model is not trained… More >

Wenbin Bi¹, Fang Yu², Ning Cao³, Wei Huo³, Guangsheng Cao^{4, *}, Xiuli Han⁵, Lili Sun⁶, Russell Higgs⁷

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1837-1854, 2020, DOI:10.32604/cmc.2020.010420 - 20 August 2020

Abstract Software defect feature selection has problems of feature space dimensionality reduction and large search space. This research proposes a defect prediction feature selection framework based on improved shuffled frog leaping algorithm (ISFLA).Using the two-level structure of the framework and the improved hybrid leapfrog algorithm's own advantages, the feature values are sorted, and some features with high correlation are selected to avoid other heuristic algorithms in the defect prediction that are easy to produce local The case where the convergence rate of the optimal or parameter optimization process is relatively slow. The framework improves generalization of… More >

Chuqing Zhang¹, Jiangyuan Yao^{2, *}, Guangwu Hu³, Thomas Schøtt⁴

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1741-1753, 2020, DOI:10.32604/cmc.2020.010306 - 30 June 2020

Abstract Due to its outstanding ability in processing large quantity and high-dimensional data, machine learning models have been used in many cases, such as pattern recognition, classification, spam filtering, data mining and forecasting. As an outstanding machine learning algorithm, K-Nearest Neighbor (KNN) has been widely used in different situations, yet in selecting qualified applicants for winning a funding is almost new. The major problem lies in how to accurately determine the importance of attributes. In this paper, we propose a Feature-weighted Gradient Decent K-Nearest Neighbor (FGDKNN) method to classify funding applicants in to two types: approved More >

Jianwei Zhang¹, Ze Qin¹, Shunfeng Wang^{1, *}

Journal of Cyber Security, Vol.2, No.1, pp. 25-36, 2020, DOI:10.32604/jcs.2020.06429

Abstract Digital images have been applied to various areas such as evidence in courts. However, it always suffers from noise by criminals. This type of computer network security has become a hot issue that can’t be ignored. In this paper, we focus on noise removal so as to provide guarantees for computer network security. Firstly, we introduce a well-known denoising method called Expected Patch Log Likelihood (EPLL) with Gaussian Mixture Model as its prior. This method achieves exciting results in noise removal. However, there remain problems to be solved such as preserving the edge and meaningful… More >

Bolun Chen^{1, 2}, Guochang Zhu^{1, *}, Min Ji¹, Yongtao Yu¹, Jianyang Zhao¹, Wei Liu³

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1039-1049, 2020, DOI:10.32604/cmc.2020.010583 - 10 June 2020

Abstract Air quality prediction is an important part of environmental governance. The accuracy of the air quality prediction also affects the planning of people’s outdoor activities. How to mine effective information from historical data of air pollution and reduce unimportant factors to predict the law of pollution change is of great significance for pollution prevention, pollution control and pollution early warning. In this paper, we take into account that there are different trends in air pollutants and that different climatic factors have different effects on air pollutants. Firstly, the data of air pollutants in different cities… More >

N. S. B. Yusof^1,2, S. M. Sapuan^1,3,*, M. T. H. Sultan^1,4, M. Jawaid¹

Journal of Renewable Materials, Vol.8, No.6, pp. 647-660, 2020, DOI:10.32604/jrm.2020.08309 - 12 May 2020

Abstract In this study, the best manufacturing process will be selected to build an automotive crash box using green oil palm natural fibre-reinforced polyurethane composite materials. This paper introduces an approach consist of technical aspects (T), the economic point of view (E) and availability (A), and it’s also called as TEA requirement. This approach was developed with the goal of assisting the design engineer in the selection of the best manufacturing process during the design phase at the criteria selection stage. In this study, the TEA requirement will integrate with the analytical hierarchy process (AHP) to… More >

Anman Zhang¹, Bohan Li^{1, 2, 3, *}, Wenhuan Wang¹, Shuo Wan¹, Weitong Chen⁴

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1499-1514, 2020, DOI:10.32604/cmc.2020.09962 - 30 April 2020

Abstract Active learning has been widely utilized to reduce the labeling cost of supervised learning. By selecting specific instances to train the model, the performance of the model was improved within limited steps. However, rare work paid attention to the effectiveness of active learning on it. In this paper, we proposed a deep active learning model with bidirectional encoder representations from transformers (BERT) for text classification. BERT takes advantage of the self-attention mechanism to integrate contextual information, which is beneficial to accelerate the convergence of training. As for the process of active learning, we design an More >