C. Vairavel^1,*, N. S. Nithya²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2159-2175, 2023, DOI:10.32604/csse.2023.036598

Abstract The immune system goes through a profound transformation during pregnancy, and certain unexpected maternal complications have been correlated to this transition. The ability to correctly examine, diagnoses, and predict pregnancy-hastened diseases via the available big data is a delicate problem since the range of information continuously increases and is scalable. Many approaches for disease diagnosis/classification have been established with the use of data mining concepts. However, such methods do not provide an appropriate classification/diagnosis model. Furthermore, single learning approaches are used to create the bulk of these systems. Classification issues may be made more accurate by combining predictions from many… More >

Milan Tair¹, Nebojsa Bacanin¹, Miodrag Zivkovic¹, K. Venkatachalam^2,*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 959-982, 2022, DOI:10.32604/cmc.2022.024989

Abstract There is a growing interest in the study development of artificial intelligence and machine learning, especially regarding the support vector machine pattern classification method. This study proposes an enhanced implementation of the well-known whale optimisation algorithm, which combines chaotic and opposition-based learning strategies, which is adopted for hyper-parameter optimisation and feature selection machine learning challenges. The whale optimisation algorithm is a relatively recent addition to the group of swarm intelligence algorithms commonly used for optimisation. The Proposed improved whale optimisation algorithm was first tested for standard unconstrained CEC2017 benchmark suite and it was later adapted for simultaneous feature selection and… More >

Meenakshi Malik¹, Rainu Nandal¹, Surjeet Dalal², Vivek Jalglan³, Dac-Nhuong Le^4,5,*

Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 887-906, 2021, DOI:10.32604/iasc.2021.016272

Abstract The last several decades have witnessed an exponential growth in the means of transport globally, shrinking geographical distances and connecting the world. The automotive industry has grown by leaps and bounds, with millions of new vehicles being sold annually, be it for personal commuting or for public or commodity transport. However, millions of motor vehicles on the roads also mean an equal number of drivers with varying levels of skill and adherence to safety regulations. Very little has been done in the way of exploring and profiling driving patterns and vehicular usage using real world data. This paper focuses on… More >

Xian Ma¹, Tingyan Lv^2,*, Yingqiang Jin², Rongmin Chen², Dengxian Dong², Yingtao Jia²

Energy Engineering, Vol.118, No.3, pp. 691-705, 2021, DOI:10.32604/EE.2021.012701

Abstract The large number of gas turbines in large power companies is difficult to manage. A large amount of the data from the generating units is not mined and utilized for fault analysis. This study focuses on F-class (9F.05) gas turbine generators and uses unsupervised learning and cloud computing technologies to analyse the faults for the gas turbines. Remote monitoring of the operational status are conducted. The study proposes a cloud computing service architecture for large gas turbine objects, which uses unsupervised learning models to monitor the operational state of the gas turbine. Faults such as chamber seal failure, load abnormality… More >

Hai-Kun Wang^1,2, Robert Haynes³, Hong-Zhong Huang¹, Leiting Dong^2,4, Satya N. Atluri²

CMES-Computer Modeling in Engineering & Sciences, Vol.105, No.1, pp. 1-24, 2015, DOI:10.3970/cmes.2015.105.001

Abstract In this paper, we propose an approach for diagnostics and prognostics of damaged aircraft structures, by combing high-performance fatigue mechanics with filtering theories. Fast & accurate deterministic analyses of fatigue crack propagations are carried out, by using the Finite Element Alternating Method (FEAM) for computing SIFs, and by using the newly developed Moving Least Squares (MLS) law for computing fatigue crack growth rates. Such algorithms for simulating fatigue crack propagations are embedded in the computer program Safe- Flaw, which is called upon as a subroutine within the probabilistic framework of filter theories. Both the extended Kalman as well as particle… More >

V. Pletser¹, R. Bosch², L. Potthast², R. Kassel³

FDMP-Fluid Dynamics & Materials Processing, Vol.2, No.1, pp. 65-76, 2006, DOI:10.3970/fdmp.2006.002.065

Abstract Orbital weightless conditions have been shown to yield better and larger crystals. The Solution Crystallization Diagnostics Facility (SCDF) is a third generation instrument developed by ESA and dedicated to the observation and study with advanced diagnostics nucleation and crystallisation processes of molecules from solutions on board the International Space Station. The SCDF is intended to be used for studies of proteins and large biomolecules, and more generally of any kind of molecules growing from solutions, using the powerful set of diagnostics means available in the SCDF platform. Several protein crystallisation reactors have been developed to study protein and macro-biomolecules assembling… More >