Yuqing Yang^{1, 3}, Peng Fu^{2, *}, Xiaojiang Yang^{1, 4}, Hong Hong⁵, Dequn Zhou¹

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2413-2423, 2020, DOI:10.32604/cmc.2020.011881

Abstract Massive Open Online Course (MOOC) has become a popular way of online learning used across the world by millions of people. Meanwhile, a vast amount of information has been collected from the MOOC learners and institutions. Based on the educational data, a lot of researches have been investigated for the prediction of the MOOC learner’s final grade. However, there are still two problems in this research field. The first problem is how to select the most proper features to improve the prediction accuracy, and the second problem is how to use or modify the data mining algorithms for a better… More >

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

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 violated traffic principle to know… More >

Abdullah Erdal Tümer^1,∗, Aytekin Akku¸s²

Computer Systems Science and Engineering, Vol.34, No.3, pp. 145-150, 2019, DOI:10.32604/csse.2019.34.145

Abstract In this work, a system based on Radial Basis Function Network was developed to estimate Gross Domestic Product per capita. The data set based on 180 academic parameters of 13 Organisation for Economic Co-operation and Development countries was used to verify the effectiveness and accuracy of the proposed method. Gross Domestic Product per capita was studied to be estimated for the first time with academic parameters in this study. The system has been optimized using feature selection method to eliminate unimportant features. Radial Basis Function network results and Radial Basis Function network with feature selection method results were compared. The… 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 of network, the consumption of… 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 time are used for comparison. 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

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 by plenty of data. In… 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

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 predictions of unknown data samples… More >

Juan C. Quiroz^a, Amit Banerjee^b, Sergiu M. Dascalu^c, Sian Lun Lau^a

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 785-793, 2018, DOI:10.1080/10798587.2017.1342400

Abstract We use the public Human Activity Recognition Using Smartphones (HARUS) data-set to investigate and identify the most informative features for determining the physical activity performed by a user based on smartphone accelerometer and gyroscope data. The HARUS data-set includes 561 time domain and frequency domain features extracted from sensor readings collected from a smartphone carried by 30 users while performing specific activities. We compare the performance of a decision tree, support vector machines, Naive Bayes, multilayer perceptron, and bagging. We report the various classification performances of these algorithms for subject independent cases. Our results show that bagging and the multilayer… More >

U. Kanimozhi, D. Manjula

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 701-709, 2018, DOI:10.1080/10798587.2017.1307626

Abstract We are witnessing the era of big data computing where computing the resources is becoming the main bottleneck to deal with those large datasets. In the case of high-dimensional data where each view of data is of high dimensionality, feature selection is necessary for further improving the clustering and classification results. In this paper, we propose a new feature selection method, Incremental Filtering Feature Selection (IF2S) algorithm, and a new clustering algorithm, Temporal Interval based Fuzzy Minimal Clustering (TIFMC) algorithm that employs the Fuzzy Rough Set for selecting optimal subset of features and for effective grouping of large volumes of… More >

Feng-Nong Chen^a,b#, Pu-Lan Chen^c#, Kai Fan^a, Fang Cheng^d

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 351-358, 2018, DOI:10.1080/10798587.2017.1293927

Abstract In recent years, foodstuff quality has triggered tremendous interest and attention in our society as a series of food safety problems. The hyperspectral imaging techniques have been widely applied for foodstuff quality. In this study, we were undertaken to explore the possibility of unsound kernel detecting (Triticum durum Desf), which were defined as black germ kernels, moldy kernels and broken kernels, by selecting the best band in hyperspectral imaging system. The system possessed a wavelength in the range of 400 to 1,000  nm with neighboring bands 2.73  nm apart, acquiring images of bulk wheat samples from different wheat varieties. A… More >