首页|Studies from Maharshi Dayanand University Yield New Information about Machine Le arning (Hybrid Machine Learning Approach for Accurate and Expeditious 3d Scannin g To Enhance Rapid Prototyping Reliability In Orthotics Using Rsm-rsmoga-mogann)
Studies from Maharshi Dayanand University Yield New Information about Machine Le arning (Hybrid Machine Learning Approach for Accurate and Expeditious 3d Scannin g To Enhance Rapid Prototyping Reliability In Orthotics Using Rsm-rsmoga-mogann)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Researchers detail new data in Machine Learning. According to news originating from Haryana, India, by NewsRx correspondents, res earch stated, "This study aims to develop a multidisciplinary artificial hybrid machine learning (AHML) approach to reduce the scanning time (ST) of the human w rist and improve the accuracy of 3D scanning for anthropometric data collection. A systematic AHML approach was deployed to scan the human wrist distal end opti mally using a portable SENSE 2.0 3D scanner." Financial support for this research came from Maharshi Dayanand University. Our news journalists obtained a quote from the research from Maharshi Dayanand U niversity, "A central composite design (CCD) matrix was developed for three inpu t variables; light intensity (LI = 12-20 W/m2), capture angle (CA = 10 degrees-5 0 degrees), and scanning distance (SD = 10-20 inches) for executing the experime ntal runs. For accuracy evaluation, the wrist perimeter on the distal end was ch ecked using CREO Parametric software for wrist perimeter error (WPE). Various AH ML tools were developed using: response surface methodology (RSM), multi-objecti ve genetic algorithm RSM, and multiobjective genetic algorithm neural networkin g (MOGANN). The optimal process parameters recommended by the hybrid tools were experimentally validated for their prediction accuracy. The MOGANN approach comb ined with the Bayesian regularization algorithm (trainabr) provided the best mut ual combination of optimal ST = 20.072 sec and WPE = 0.375 cm corresponding to L I = 12.001 W/m2, CA = 29.428 degrees, and SD = 18.214 inch, with a significant p ercentage reduction of 55.83% in WPE."
HaryanaIndiaAsiaAlgorithmsCyborg sEmerging TechnologiesGenetic AlgorithmsMachine LearningMaharshi Dayanan d University
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