Yiming Xue¹, Nan Wang², Yan Niu¹, Ping Zhong^{2, ∗}, Shaozhang Niu³, Yuntao Song⁴

CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 741-756, 2019, DOI:10.32604/cmc.2019.05611

Abstract In practical application, many objects are described by multi-view features because multiple views can provide a more informative representation than the single view. When dealing with the multi-view data, the high dimensionality is often an obstacle as it can bring the expensive time consumption and an increased chance of over-fitting. So how to identify the relevant views and features is an important issue. The matrix-based multi-view feature selection that can integrate multiple views to select relevant feature subset has aroused widely concern in recent years. The existing supervised multi-view feature selection methods usually concatenate all… 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 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 More >

Shengqun Fang¹, Zhiping Cai^1,*, Wencheng Sun¹, Anfeng Liu², Fang Liu³, Zhiyao Liang⁴, Guoyan Wang⁵

CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 419-433, 2018, DOI:10.3970/cmc.2018.02289

Abstract By using efficient and timely medical diagnostic decision making, clinicians can positively impact the quality and cost of medical care. However, the high similarity of clinical manifestations between diseases and the limitation of clinicians’ knowledge both bring much difficulty to decision making in diagnosis. Therefore, building a decision support system that can assist medical staff in diagnosing and treating diseases has lately received growing attentions in the medical domain. In this paper, we employ a multi-label classification framework to classify the Chinese electronic medical records to establish corresponding relation between the medical records and disease… More >

Rahul Kumar Sharma^*1, V. Sugumaran¹, Hemantha Kumar², Amarnath M³

Structural Durability & Health Monitoring, Vol.11, No.1, pp. 1-16, 2017, DOI:10.3970/sdhm.2017.012.001

Abstract Most of the machineries in small or large scale industry have rotating element supported by bearings for rigid support and accurate movement. For proper functioning of machinery, condition monitoring of the bearing is very important. In present study sound signal is used to continuously monitor bearing health as sound signals of rotating machineries carry dynamic information of components. There are numerous studies in literature that are reporting superiority of vibration signal of bearing fault diagnosis. However, there are very few studies done using sound signal. The cost associated with condition monitoring using sound signal (Microphone)… More >

Alamelu Manghai T.M.¹, Jegadeeshwaran R², Sugumaran V.³

Structural Durability & Health Monitoring, Vol.11, No.1, pp. 43-67, 2017, DOI:10.3970/sdhm.2017.012.043

Abstract Diagnosis is the recognition of the nature and cause of a certain phenomenon. It is generally used to determine cause and effect of a problem. Machine fault diagnosis is a field of finding faults arising in machines. To identify the most probable faults leading to failure, many methods are used for data collection, including vibration monitoring, thermal imaging, oil particle analysis, etc. Then these data are processed using methods like spectral analysis, wavelet analysis, wavelet transform, short-term Fourier transform, high-resolution spectral analysis, waveform analysis, etc., The results of this analysis are used in a root More >