Gang Yu^1,4,5, Jian Tang^2,*, Jian Zhang³, Zhonghui Wang⁶

Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 273-290, 2021, DOI:10.32604/iasc.2021.016981

Abstract Kernel learning based on structure risk minimum can be employed to build a soft measuring model for analyzing small samples. However, it is difficult to select learning parameters, such as kernel parameter (KP) and regularization parameter (RP). In this paper, a soft measuring method is investigated to select learning parameters, which is based on adaptive multi-layer selective ensemble (AMLSEN) and least-square support vector machine (LSSVM). First, candidate kernels and RPs with K and R numbers are preset based on prior knowledge, and candidate sub-sub-models with K*R numbers are constructed through utilizing LSSVM. Second, the candidate sub-sub-models with same KPs and… More >

Abiola O. Ajayeoba^1,*, Adewoye A. Olanipekun², Wasiu A. Raheem³, Oluwaseun O. Ojo⁴, Ayowumi R. Soji–Adekunle⁴

Sound & Vibration, Vol.55, No.1, pp. 69-85, 2021, DOI:10.32604/sv.2021.011639

Abstract Economic wood processing employs the use of industrial machines for cutting, shaping, milling, and sawing timber, thereby leading to the generation of high levels of noise. Published data from empirical studies have categorized noise as an environmental hazard of global significance. Furthermore, noise exposure limits for different industries and all the industrial machines available has not been formally established as it presently exists in developed nations around the world. Therefore, this study assessed the daily exposure of sawmills workers to noise in Southwestern Nigeria. Reconnaissance surveys were first carried out in Osun, Oyo, Ondo, Ekiti, Lagos, and Ogun States to… More >

Giuseppe Corrente^*

Journal of Quantum Computing, Vol.2, No.3, pp. 137-145, 2020, DOI:10.32604/jqc.2020.014586

Abstract We want in this article to show the usefulness of Quantum Turing Machine (QTM) in a high-level didactic context as well as in theoretical studies. We use QTM to show its equivalence with quantum circuit model for Deutsch and Deutsch-Jozsa algorithms. Further we introduce a strategy of translation from Quantum Circuit to Quantum Turing models by these examples. Moreover we illustrate some features of Quantum Computing such as superposition from a QTM point of view and starting with few simple examples very known in Quantum Circuit form. More >

Yongqiang Bao^{1, *}, Qi Shao¹, Xuxu Zhang¹, Jiahui Jiang¹, Yue Xie¹, Tingting Liu¹, Weiye Xu²

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2557-2570, 2020, DOI:10.32604/cmc.2020.011241

Abstract The field of digital audio forensics aims to detect threats and fraud in audio signals. Contemporary audio forensic techniques use digital signal processing to detect the authenticity of recorded speech, recognize speakers, and recognize recording devices. User-generated audio recordings from mobile phones are very helpful in a number of forensic applications. This article proposed a novel method for recognizing recording devices based on recorded audio signals. First, a database of the features of various recording devices was constructed using 32 recording devices (20 mobile phones of different brands and 12 kinds of recording pens) in various environments. Second, the audio… More >

Marius Becherer^∗, Michael Zipperle^†, Achim Karduck^‡

Computer Systems Science and Engineering, Vol.35, No.2, pp. 81-89, 2020, DOI:10.32604/csse.2020.35.081

Abstract Machines are serviced too often or only when they fail. This can result in high costs for maintenance and machine failure. The trend of Industry 4.0 and the networking of machines opens up new possibilities for maintenance. Intelligent machines provide data that can be used to predict the ideal time of maintenance. There are different approaches to create a forecast. Depending on the method used, appropriate conditions must be created to improve the forecast. In this paper, results are compiled to give a state of the art of predictive maintenance. First, the different types of maintenance and economic relationships are… More >

Dunhong Yao^{1, 2, 3}, Shijun Li^{4, *}, Ang Li⁵, Yu Chen⁶

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1959-1975, 2020, DOI:10.32604/cmc.2020.010186

Abstract There is no reasonable scientific basis for selecting the excellent teachers of the school’s courses. To solve the practical problem, we firstly give a series of normalization models for defining the key attributes of teachers’ professional foundation, course difficulty coefficient, and comprehensive evaluation of teaching. Then, we define a partial weight function to calculate the key attributes, and obtain the partial recommendation values. Next, we construct a highly sparse Teaching Recommendation Factorization Machines (TRFMs) model, which takes the 5-tuples relation including teacher, course, teachers’ professional foundation, course difficulty, teaching evaluation as the feature vector, and take partial recommendation value as… More >

Caining Zhang¹, Huaguang Li², Xiaoya Guo³, David Molony⁴, Xiaopeng Guo², Habib Samady⁴, Don P. Giddens^4,5, Lambros Athanasiou⁶, Rencan Nie^2,*, Jinde Cao^3,*, Dalin Tang^1,*,7

Molecular & Cellular Biomechanics, Vol.16, Suppl.2, pp. 31-31, 2019, DOI:10.32604/mcb.2019.06983

Abstract Cardiovascular diseases are closely associated with deteriorating atherosclerotic plaques. Optical coherence tomography (OCT) is a recently developed intravascular imaging technique with high resolution approximately 10 microns and could provide accurate quantification of coronary plaque morphology. However, tissue segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and subjective. To overcome these limitations, two automatic segmentation methods for intracoronary OCT image based on support vector machine (SVM) and convolutional neural network (CNN) were performed to identify the plaque region and characterize plaque components. In vivo IVUS and OCT coronary plaque data from 5… More >

Syed Muhammad Saqlain Shah^1,*, Tahir Afzal Malik², Robina khatoon¹, Syed Saqlain Hassan³, Faiz Ali Shah⁴

CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 535-553, 2019, DOI:10.32604/cmc.2019.07948

Abstract Classification of human actions under video surveillance is gaining a lot of attention from computer vision researchers. In this paper, we have presented methodology to recognize human behavior in thin crowd which may be very helpful in surveillance. Research have mostly focused the problem of human detection in thin crowd, overall behavior of the crowd and actions of individuals in video sequences. Vision based Human behavior modeling is a complex task as it involves human detection, tracking, classifying normal and abnormal behavior. The proposed methodology takes input video and applies Gaussian based segmentation technique followed by post processing through presenting… More >

Caining Zhang¹, Huaguang Li², Xiaoya Guo³, David Molony⁴, Xiaopeng Guo², Habib Samady⁴, Don P. Giddens^4,5, Lambros Athanasiou⁶, Rencan Nie^2,*, Jinde Cao^3,*, Dalin Tang^1,*,7

Molecular & Cellular Biomechanics, Vol.16, No.2, pp. 153-161, 2019, DOI:10.32604/mcb.2019.06873

Abstract Cardiovascular diseases are closely associated with deteriorating atherosclerotic plaques. Optical coherence tomography (OCT) is a recently developed intravascular imaging technique with high resolution approximately 10 microns and could provide accurate quantification of coronary plaque morphology. However, tissue segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and subjective. To overcome these limitations, two automatic segmentation methods for intracoronary OCT image based on support vector machine (SVM) and convolutional neural network (CNN) were performed to identify the plaque region and characterize plaque components. In vivo IVUS and OCT coronary plaque data from 5… More >

Liming Yang^1,2, Junjian Bai¹, Qun Sun³

CMES-Computer Modeling in Engineering & Sciences, Vol.108, No.1, pp. 49-65, 2015, DOI:10.3970/cmes.2015.108.049

Abstract Extreme learning machine (ELM) has demonstrated great potential in machine learning and data mining fields owing to its simplicity, rapidity and good generalization performance. In this work, a general framework for ELM regression is first investigated based on least absolute deviation (LAD) estimation (called LADELM), and then we develop two regularized LADELM formulations with the l₂-norm and l₁-norm regularization, respectively. Moreover, the proposed models are posed as simple linear programming or quadratic programming problems. Furthermore, the proposed models are used directly to analyze the hard rate of licorice seeds using near-infrared spectroscopy data. Experimental results on eight different spectral regions… More >