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  • Open Access

    EDITORIAL

    Special Issue on Recent Advances in Artificial Intelligence for Smart Manufacturing – Part II

    Zheng Xu1, Qingyuan Zhou2, Zhiguo Yan3

    Intelligent Automation & Soft Computing, Vol.25, No.4, pp. 1-3, 2019, DOI:10.31209/2019.100000082

    Abstract This article has no abstract. More >

  • Open Access

    EDITORIAL

    Special Section on Recent Advances in Artificial Intelligence for Smart Manufacturing – Part I

    Zheng Xu1, Qingyuan Zhou2, Zhiguo Yan3

    Intelligent Automation & Soft Computing, Vol.25, No.4, pp. 693-694, 2019, DOI:10.31209/2019.100000072

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    An Application Review of Artificial Intelligence in Prevention and Cure of COVID-19 Pandemic

    Peipeng Yu1, Zhihua Xia1, *, Jianwei Fei1, Sunil Kumar Jha1, 2

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 743-760, 2020, DOI:10.32604/cmc.2020.011391

    Abstract Coronaviruses are a well-known family of viruses that can infect humans or animals. Recently, the new coronavirus (COVID-19) has spread worldwide. All countries in the world are working hard to control the coronavirus disease. However, many countries are faced with a lack of medical equipment and an insufficient number of medical personnel because of the limitations of the medical system, which leads to the mass spread of diseases. As a powerful tool, artificial intelligence (AI) has been successfully applied to solve various complex problems ranging from big data analysis to computer vision. In the process More >

  • Open Access

    ARTICLE

    Applying ANN, ANFIS and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO2

    Amin Bemani1, Alireza Baghban2, Shahaboddin Shamshirband3, 4, *, Amir Mosavi5, 6, 7, Peter Csiba7, Annamaria R. Varkonyi-Koczy5, 7

    CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1175-1204, 2020, DOI:10.32604/cmc.2020.07723

    Abstract In the present work, a novel machine learning computational investigation is carried out to accurately predict the solubility of different acids in supercritical carbon dioxide. Four different machine learning algorithms of radial basis function, multi-layer perceptron (MLP), artificial neural networks (ANN), least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are used to model the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and the dissociation constant of acid. To evaluate the proposed models, different graphical and statistical analyses, along with novel More >

  • Open Access

    ARTICLE

    Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity

    Xiangao Jiang1, Megan Coffee2, 3, *, Anasse Bari4, *, Junzhang Wang4, Xinyue Jiang5, Jianping Huang1, Jichan Shi1, Jianyi Dai1, Jing Cai1, Tianxiao Zhang6, Zhengxing Wu1, Guiqing He1, Yitong Huang7

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 537-551, 2020, DOI:10.32604/cmc.2020.010691

    Abstract The virus SARS-CoV2, which causes coronavirus disease (COVID-19) has become a pandemic and has spread to every inhabited continent. Given the increasing caseload, there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness. We present a first step towards building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support. COVID-19 has presented a pressing need as a) clinicians are still developing clinical acumen to this novel… More >

  • Open Access

    ARTICLE

    Cold Start Problem of Vehicle Model Recognition under Cross-Scenario Based on Transfer Learning

    Hongbo Wang1, *, Qian Xue1, Tong Cui1, Yangyang Li2, Huacheng Zeng3

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 337-351, 2020, DOI:10.32604/cmc.2020.07290

    Abstract As a major function of smart transportation in smart cities, vehicle model recognition plays an important role in intelligent transportation. Due to the difference among different vehicle models recognition datasets, the accuracy of network model training in one scene will be greatly reduced in another one. However, if you don’t have a lot of vehicle model datasets for the current scene, you cannot properly train a model. To address this problem, we study the problem of cold start of vehicle model recognition under cross-scenario. Under the condition of small amount of datasets, combined with the More >

  • Open Access

    ARTICLE

    Synthesized AI LMI-based Criterion for Mechanical Systems

    Jcy Chen1,*, Wc Chen1, Tim Chen1, Alex Wilson2, N. Fadilah Jamaludin3, Nertrand Kapron1, Tim Chen4,5, John Burno5

    Sound & Vibration, Vol.53, No.6, pp. 245-250, 2019, DOI:10.32604/sv.2019.04233

    Abstract This paper proposes a novel artificial intelligence sythethized controller in the mechanical system which has high speed computation because of the LMI type criterion. The proposed membership functions are adopted and stabilization criterion of the closed-loop T-S fuzzy systems are obtained through a new parametrized LMI (linear matrix) inequality which is rearranged by machine learning membership functions. More >

  • Open Access

    REVIEW

    Review on Application of Artificial Intelligence in Civil Engineering

    Youqin Huang1, Jiayong Li1, Jiyang Fu1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.3, pp. 845-875, 2019, DOI:10.32604/cmes.2019.07653

    Abstract In last few years, big data and deep learning technologies have been successfully applied in various fields of civil engineering with the great progress of machine learning techniques. However, until now, there has been no comprehensive review on its applications in civil engineering. To fill this gap, this paper reviews the application and development of artificial intelligence in civil engineering in recent years, including intelligent algorithms, big data and deep learning. Through the work of this paper, the research direction and difficulties of artificial intelligence in civil engineering for the past few years can be More >

  • Open Access

    ARTICLE

    Identification of axillary buds of potato seedlings based on a vision system with fuzzy logic

    Martínez Corral L1, E Martínez-Rubin2, F F lores-García3, M Vázquez-Rueda3, J Frías-Ramírez2, MA Segura-Castruita2

    Phyton-International Journal of Experimental Botany, Vol.80, pp. 79-84, 2011, DOI:10.32604/phyton.2011.80.079

    Abstract Potato (Solanum tuberosum L.) is a crop whose production yield at national level is very low compared with that in the most productive countries. This is because it is a partially automated crop with deficient and inadequate agronomic practices, low technification levels and great quantity of work wages required per hectare of cultivation. The necessity to generate technical and modern procedures that increase crop production, quality and yield has fostered development of projects leading to obtain seedlings free of pathogens with material of high genetic, physiological and sanitary quality. Utilization of a vision system for the… More >

  • Open Access

    ARTICLE

    Database development for alfalfa (Medicago sativa L.) characterization in an artificial vision system

    Martínez-Corral1 L, E Martínez-Rubín2, F Flores-García1, GC Castellanos2, AR Juárez2, MJD López3

    Phyton-International Journal of Experimental Botany, Vol.78, pp. 43-47, 2009, DOI:10.32604/phyton.2009.78.043

    Abstract The increasing demand of alfalfa crop production in the Lagunera Region has caused the search of new alternatives to the conventional methods of nutritional and hydric evaluation of alfalfa, where costs and time are optimized. The use of a machine vision system for computerized visual recognition of the crop hydric and/or nutritional stress implies the analysis and processing of certain characteristics, such as color, shape and object dimensions from a digital image. Due to the fact that identification parameters are closely related, it is necessary to compile information from specialists, foliar analysis, mathematical morphology and More >

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