The Experts below are selected from a list of 188049 Experts worldwide ranked by ideXlab platform
Mehmet Cunkas - One of the best experts on this subject based on the ideXlab platform.
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Modeling and prediction of surface roughness in turning operations using artificial neural network and Multiple Regression method
Expert Systems With Applications, 2011Co-Authors: Ilhan Asilturk, Mehmet CunkasAbstract:Research highlights? The surface roughness is measured during turning at different cutting parameters such as speed, feed, and depth of cut by full factorial experimental design. ? Artificial neural networks (ANN) and Multiple Regression approaches are used to Model the surface roughness of AISI 1040 steel. ? The ANN Model estimates the surface roughness with high accuracy compared to the Multiple Regression Model. Machine parts during their useful life are significantly influenced by surface roughness quality. The machining process is more complex, and therefore, it is very hard to develop a comprehensive Model involving all cutting parameters. In this study, the surface roughness is measured during turning at different cutting parameters such as speed, feed, and depth of cut. Full factorial experimental design is implemented to increase the confidence limit and reliability of the experimental data. Artificial neural networks (ANN) and Multiple Regression approaches are used to Model the surface roughness of AISI 1040 steel. Multiple Regression and neural network-based Models are compared using statistical methods. It is clearly seen that the proposed Models are capable of prediction of the surface roughness. The ANN Model estimates the surface roughness with high accuracy compared to the Multiple Regression Model.
Fariborz Haghighat - One of the best experts on this subject based on the ideXlab platform.
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a new Multiple Regression Model for predictions of urban water use
Sustainable Cities and Society, 2016Co-Authors: Alireza S Eslamian, Fariborz HaghighatAbstract:Abstract Shortages of freshwater have become a serious issue in many regions around the world, partly due to rapid urbanisation and climate change. Sustainable city development should consider minimising water use by people living in cities and urban areas. The purpose of this paper is to improve our understanding of water-use behaviour and to reliably predict water use. We collected appropriate data of daily water use, meteorological parameters, and socioeconomic factors for the City of Brossard in Quebec, Canada, and analysed these data using Multiple Regression techniques. The techniques represent a new approach to predictions of daily water use; its base use component is predicted using a function of socioeconomic factors, as opposed to a function of time as in existing approaches. The quality of the new approach is quantitatively demonstrated. Time series of predicted daily water-use captures observed characteristics very well, and improves the results of the weighted coefficient of determination, the relative index of agreement and the root mean square error from the existing approaches. Water use in the city exhibits a downward trend possibly due to an increasing annual charge for water use. Water use increases due to weekend effect. It decreases in the occurrence of rainfall; the decrease is more sensitive to previous-day than current-day rainfall. The analysis procedures reported in this paper can be applied to analyse water use in any other cities. The new approach would be a useful tool for decision makers to manage water use by adjusting water consumption policies and price.
Somasundaram Kumanan - One of the best experts on this subject based on the ideXlab platform.
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neuro hybrid Model to predict weld bead width in submer ged arc welding process
Journal of Scientific & Industrial Research, 2010Co-Authors: Edwin Raja J Dhas, Somasundaram KumananAbstract:This paper presents development of neuro hybrid Model (NHM) to predict weld bead width in submerged arc welding. Experiments were designed using Taguchi's principles and results were used to develop a Multiple Regression Model. Data set generated from Multiple Regression Analysis (MRA) was utilized in ANN Model, which was trained with backpropagation algorithm in MATLAB platform and used to develop NHM to predict quality of weld. NHM is flexible and accurate than existing Models for a better online monitoring system.
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anfis for prediction of weld bead width in a submerged arc welding process
Journal of Scientific & Industrial Research, 2007Co-Authors: Edwin Raja J Dhas, Somasundaram KumananAbstract:This paper proposes an intelligent technique, Adaptive Neuro-Fuzzy Inference System (ANFIS), to predict the weld bead width in the submerged arc welding (SAW) process for a given set of welding parameters. Experiments are designed according to Taguchi’s principles and its results are used to develop a Multiple Regression Model . Multiple sets of data from Multiple Regression analysis are utilized to train the intelligent network. The trained network is used to predict the quality of weld. The proposed ANFIS, developed using MATLAB functions, is flexible, accurate than existing Models and it scopes for a better online monitoring system.
P. Amiotte Suchet - One of the best experts on this subject based on the ideXlab platform.
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Fluvial suspended sediment transport and mechanical erosionin the Maghreb (North Africa)
Hydrological Sciences Journal, 1992Co-Authors: Jean-luc Probst, P. Amiotte SuchetAbstract:Abstract Available data on suspended sediment transported by rivers in the Maghreb are reviewed for 130 drainage basins. These data allow a new estimate to be proposed for the delivery of river sediment to both the Atlantic Ocean and the Mediterranean Sea from the Maghreb region. The influences of several environmental factors (precipitation, runoff, drainage area size and lithology) on mechanical erosion and fluvial sediment transport are analysed. Finally, a Multiple Regression Model is proposed to estimate the river sediment yields in the Maghreb.
James M. Lebreton - One of the best experts on this subject based on the ideXlab platform.
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RWA Web: A Free, Comprehensive, Web-Based, and User-Friendly Tool for Relative Weight Analyses
Journal of Business and Psychology, 2015Co-Authors: Scott Tonidandel, James M. LebretonAbstract:Over the last 15 years, a number of methodological developments have enabled researchers to draw more accurate inferences concerning the relative contribution (i.e., relative importance) among Multiple (often correlated) predictor variables in a Regression analysis. One such development has been relative weight analysis (RWA). Researchers can use a RWA to decompose the total variance predicted in a Regression Model ( R ^2) into weights that accurately reflect the proportional contribution of the various predictor variables. Prior to RWA, researchers were forced to rely on traditional statistics (e.g., correlations; standardized Regression weights), which are known to yield faulty or misleading information concerning variable importance (especially when predictor variables are correlated with one another, which is often the case in organizational research). Although there has been a surge of interest in RWA over the last 10 years, integration of this statistical tool into organizational research has been hampered by the lack of a user-friendly statistical package for implementing RWA. Indeed, most popular statistical packages (e.g., SPSS, SAS) have yet to include RWA protocols into their Regression modules. The purpose of this paper is to present a new, free, comprehensive, web-based, user-friendly resource, RWA-Web, which may be used by anyone having simple access to the internet. Our paper is structured as a tutorial on using RWA-Web to examine relative importance in the classic Multiple Regression Model, the multivariate Multiple Regression Model, and the logistic Regression Model. We also illustrate how RWA-Web may be used to conduct null hypothesis significance tests using advanced bootstrapping procedures.
