Hybrid bootstrap-based approach with binary artificial bee colony and particle swarm optimization in Taguchi's T-Method

Taguchi's T-Method is one of the Mahalanobis Taguchi System (MTS)-ruled prediction techniques that has been established specifically but not limited to small, multivariate sample data. When evaluating data using a system such as the Taguchi's T-Method, bias issues often appear due to inconsistencies induced by model complexity, variations between parameters that are not thoroughly configured, and generalization aspects. In Taguchi's T-Method, the unit space determination is too reliant on the characteristics of the dependent variables with no appropriate procedures designed. Similarly, the least square-proportional coefficient is well known not to be robust to the effect of the outliers, which indirectly affects the accuracy of the weightage of SNR that relies on the model-fit accuracy. The small effect of the outliers in the data analysis may influence the overall performance of the predictive model unless more development is incorporated into the current framework. In this research, the mechanism of improved unit space determination was explicitly designed by implementing the minimum-based error with the leave-one-out method, which was further enhanced by embedding strategies that aim to minimize the impact of variance within each parameter estimator using the leave-one-out bootstrap (LOOB) and 0.632 estimates approaches. The complexity aspect of the prediction model was further enhanced by removing features that did not provide valuable information on the overall prediction. In order to accomplish this, a matrix called Orthogonal Array (OA) was used within the existing Taguchi's T-Method. However, OA's fixed-scheme matrix, as well as its drawback in coping with the high-dimensionality factor, leads to a sub- optimal solution. On the other hand, the usage of SNR, decibel (dB) as its objective function proved to be a reliable measure. The architecture of a Hybrid Binary Artificial Bee Colony and Particle Swarm Optimization (Hybrid Binary ABC-PSO), including the Binary Bitwise ABC (BitABC) and Probability Binary PSO (PBPSO), has been developed as a novel search engine that helps to cater the limitation of OA. The SNR (dB) and mean absolute error (MAE) were the main part of the performance measure used in this research. The generalization aspect was a fundamental addition incorporated into this research to control the effect of overfitting in the analysis. The proposed enhanced parameter estimators with feature selection optimization in this analysis had been tested on 10 case studies and had improved predictive accuracy by an average of 46.21% depending on the cases. The average standard deviation of MAE, which describes the variability impact of the optimized method in all 10 case studies, displayed an improved trend relative to the Taguchi’s T-Method. The need for standardization and a robust approach to outliers is recommended for future research. This study proved that the developed architecture of Hybrid Binary ABC-PSO with Bootstrap and minimum-based error using leave-one-out as the proposed parameter estimators enhanced techniques in the methodology of Taguchi's T-Method by effectively improving its prediction accuracy

Novel strategies for process control based on hybrid semi-parametric mathematical systems

Tese de doutoramento. Engenharia Química. Universidade do Porto. Faculdade de Engenharia. 201

Principles and Applications of Data Science

Data science is an emerging multidisciplinary field which lies at the intersection of computer science, statistics, and mathematics, with different applications and related to data mining, deep learning, and big data. This Special Issue on “Principles and Applications of Data Science” focuses on the latest developments in the theories, techniques, and applications of data science. The topics include data cleansing, data mining, machine learning, deep learning, and the applications of medical and healthcare, as well as social media

Information resources management, 1984-1989: A bibliography with indexes

This bibliography contains 768 annotated references to reports and journal articles entered into the NASA scientific and technical information database 1984 to 1989

Dual-Use Space Technology Transfer Conference and Exhibition

This document contains papers presented at the Dual-Use Space Technology Transfer Conference and Exhibition held at the Johnson Space Center February 1-3, 1994. Possible technology transfers covered during the conference were in the areas of information access; innovative microwave and optical applications; materials and structures; marketing and barriers; intelligent systems; human factors and habitation; communications and data systems; business process and technology transfer; software engineering; biotechnology and advanced bioinstrumentation; communications signal processing and analysis; new ways of doing business; medical care; applications derived from control center data systems; human performance evaluation; technology transfer methods; mathematics, modeling, and simulation; propulsion; software analysis and decision tools systems/processes in human support technology; networks, control centers, and distributed systems; power; rapid development perception and vision technologies; integrated vehicle health management; automation technologies; advanced avionics; ans robotics technologies. More than 77 papers, 20 presentations, and 20 exhibits covering various disciplines were presented b experts from NASA, universities, and industry

Computational Methods for Medical and Cyber Security

Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

CORPORATE SOCIAL RESPONSIBILITY IN ROMANIA

The purpose of this paper is to identify the main opportunities and limitations of corporate social responsibility (CSR). The survey was defined with the aim to involve the highest possible number of relevant CSR topics and give the issue a more wholesome perspective. It provides a basis for further comprehension and deeper analyses of specific CSR areas. The conditions determining the success of CSR in Romania have been defined in the paper on the basis of the previously cumulative knowledge as well as the results of various researches. This paper provides knowledge which may be useful in the programs promoting CSR.Corporate social responsibility, Supportive policies, Romania

Investigation into stability and thermal-fluid behaviour of hybrid nanofluids as heat transfer fluids

Thesis (PhD (Mechanics))--University of Pretoria, 2021.The need to improve the poor thermal conductivity of conventional fluids to produce adequate heat transfer fluid cannot be over-emphasized, knowing fully well that heat transfer is key in any engineering process line. Hence, the birth of nanofluids, which is the formulation of a composite of suspended nanoparticles in a basefluid. Nanofluids have found wide applications ranging from heat exchangers, electronic cooling, automotive industry, medical, military, solar energy, manufacturing industry, to mention but a few. But the limitations of nanofluids led to the entrance of a new working fluid named binary nanofluid and ternary nanofluid. This study experimented with the trio influence of temperature (T), percent weight ratios (PWRs), nanoparticles size (NS) on the thermophysical behaviour of MgO–ZnO/Deionised water binary nanofluids (BNFs). 20 nm nano-size of ZnO nanoparticles were hybridised with MgO nanoparticles of nano-sizes 20 nm and 100 nm, and dispersed in deionised water to prepare 0.1 vol% binary nanofluids for percent weight ratios of MgO:ZnO (20:80, 40:60, 60:40 and 80:20). The viscosity (μ), electrical conductivity (σ), pH, and thermal conductivity (κ) of the binary nanofluids were experimentally evaluated for temperature 20 to 50 °C. Morphology was checked, and stability was monitored. The impact of temperature, PWRs, and nano-size on the pH, μ, σ, and κ of the binary nanofluid were ordered as PWR >NS >T, NS> PWR>T, T>NS >PWR, and T >NS >PWR, respectively. Using the obtained experimental dataset, correlations were proposed for the thermal property of each binary nanofluid as a function of temperature. Also, investigating the trio impact of PWR, temperature and � on the thermophysical characteristics of MgO-ZnO/DIW BNFs, to help close up the scarce literature gap. 20 nm nanoparticle sizes of MgO and ZnO were hybridized together and dissolved in deionized water to formulate 0.1 vol% and 0.05 vol.% binary nanofluids (NFs) for PWR of 20:80, 40:60, 60:40, 80:20 (MgO:ZnO). The κ for all BNFs was enhanced under the impact of rising temperature, with maximum κ enhancement of 5.60% and 22.07% relative to the deionised water (DIW) achieved for 0.05 vol% and 0.10 vol%, separately. The σ was enhanced slightly under the influence of increasing temperature, with maximum enhancement of 21.82% and 30.91% achieved for 0.050 vol% and 0.10 vol%, respectively. In addition, viscosity under temperature increase exhibited a decreasing pattern for all nanohybrids and basefluid. Furthermore, to better harness the benefit of the BNFs for thermal application, thermoelectrical conductivity (TEC) was evaluated with BNFs of 0.05 vol% observed to have higher TEC values than 0.10 vol% BNFs. The BNFs were found suitable as thermal fluids. A novel manner of furthering thermo-convection behaviour of thermal applications is the use of BNFs as heat transfer fluids. This study experimented the natural convection behaviour of MgO-ZnO NPs suspended in basefluid for � = 0.050 vol.% and 0.10 vol% at percent weight ratios of 20:80, 40:60, 60:40, 80:20 (MgO:ZnO) inside a square enclosure. Factors like Rayleigh number, Nusselt number (Nuav), coefficient of convective heat transfer (hav), and heat transfer rate (Qav) for various temperatures (20°C to 50°C) were examined. PWRs and temperature gradient of BNPs inside the binary nanofluids was observed to augment Nuav, hav, and Qav. Also, highest improvement of 72.60% (Nuav), 76.01% (hav), and 72.20% (Qav) was achieved. Employing BNFs in square enclosure yielded fine improvement for natural convection behaviour. Artificial intelligence (AI) methods, like artificial neural network (ANN) and surface fitting method were deployed to model the thermal conductivity of BNFs. For the ANN model, a learning algorithm was developed to determine the optimum neuron number. The ANN having 19 neurons in the inner layer got the optimized performance. A surface fitting method was also used on the experimental data, and the generated surface shows the behaviour of the BNFs. The outcome affirmed that the designed ANN model is best for predicting the thermal conductivity of MgO-ZnO/DIW binary nanofluids for different temperatures, nanoparticle sizes, PWRs and volume concentration over the surface fitting method.University of Pretoria Postgraduate Bursary for Doctoral Students.Olabisi Onabanjo University, Ago-Iwoye, Nigeria.Tertiary Education Trust Fund (TETFund), Abuja, Nigeria.Mechanical and Aeronautical EngineeringPhD (Mechanics)Unrestricte

Computer Aided Verification

This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book