Nilesh Dhobale¹, Sharad Mulik², R. Jegadeeshwaran^3,*, Abhishek Patange⁴

Sound & Vibration, Vol.55, No.2, pp. 87-116, 2021, DOI:10.32604/sv.2021.014224 - 21 April 2021

Abstract Due to continuous cutting tool usage, tool supervision is essential for improving the metal cutting industry. In the metal removal process tool, supervision is carried out either by an operator or online tool supervision. Tool supervision helps to understand tool condition, dimensional accuracy, and surface superiority. For downtime in the metal cutting industry, the main reasons are tool breakage and excessive wear, so it is necessary to supervise tool which gives better tool life and enhance productivity. This paper presents different conventional and artificial intelligence techniques for tool supervision in the processing procedures that have 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 More >

Farhan Ullah^1,4,*, Abdullah Bajahzar², Hamza Aldabbas³, Muhammad Farhan⁴, Hamad Naeem¹, S. Sabahat H. Bukhari^4,5, Kaleem Razzaq Malik⁶

Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 169-180, 2020, DOI:10.31209/2019.100000138

Abstract This research aims to an electronic assessment (e-assessment) of students’ replies in response to the standard answer of teacher’s question to automate the assessment by WordNet semantic similarity. For this purpose, a new methodology for Semantic Similarity through WordNet Semantic Similarity Techniques (SS-WSST) has been proposed to calculate semantic similarity among teacher’ query and student’s reply. In the pilot study-1 42 words’ pairs extracted from 8 students’ replies, which marked by semantic similarity measures and compared with manually assigned teacher’s marks. The teacher is provided with 4 bins of the mark while our designed methodology More >

Peipeng Yu¹, Zhihua Xia^{1, *}, Jianwei Fei¹, Sunil Kumar Jha^{1, 2}

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 743-760, 2020, DOI:10.32604/cmc.2020.011391 - 23 July 2020

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 >

Amin Bemani¹, Alireza Baghban², Shahaboddin Shamshirband^{3, 4, *}, Amir Mosavi^{5, 6, 7}, Peter Csiba⁷, Annamaria R. Varkonyi-Koczy^{5, 7}

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1175-1204, 2020, DOI:10.32604/cmc.2020.07723 - 30 April 2020

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 >

Xiangao Jiang¹, Megan Coffee^{2, 3, *}, Anasse Bari^{4, *}, 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 - 30 March 2020

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 >

Youqin Huang¹, Jiayong Li¹, Jiyang Fu^1,*

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 >

T. Podoba¹, L. Tomsu¹, K. Vlcek¹, M. Heczko

The International Conference on Computational & Experimental Engineering and Sciences, Vol.15, No.4, pp. 111-122, 2010, DOI:10.3970/icces.2010.015.111

Abstract Surface reconstruction based on chaotic systems or exactly given point clouds is very difficult area. Current algorithms such as Marching Cube or Voronoi Filtering do not use methods based on artificial intelligence. In this paper, we investigate solution of polygonal surface construction based on AI. The main purpose is to generate complex polygonal mesh structures based on strange attractors with fractal structure. Attractors have to be created as 4D objects using quaternion algebra or using methods of AI. Polygonal mesh can have different numbers of polygons because of iterative application of this system. Our main More >

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 >