Change arbitrary features to common sizes.Ex: Remove unnecessary fillets, add a relief hole to the apex of a sharp inside corner so that it is manufacturable.Remove or modify features that make manufacturing more difficult.UNITS: Machines at the Bechtel Center are Imperial, please use inches, not millimeters.Imperial Units, Remove Unnecessary Fillets, Add Internal Corner Relief.Online resources exist for you to learn on your own time.Design the part in a CAD program like Autodesk Fusion 360 (or import a design into Fusion 360).Tolerances and/or fits should be included for critical functions like alignments or dimensional accuracy.Consider design requirements and constraints, design to meet goals.Only the Front Desk can add you to the Fusion Team! So please get in touch with them.This item may be available elsewhere in EconPapers: Search for items with the same title.
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JEL-codes: C E F2 F3 G (search for similar items in EconPapers) Keywords: artificial intelligence neural networks training algorithm NVivo stock market forecast (search for similar items in EconPapers) We conclude by establishing a research agenda for potential financial market analysts, artificial intelligence, and soft computing scholarship. Our findings highlight that AI techniques can be used successfully to study and analyze stock market activity. We group the surveyed articles based on two major categories, namely, study characteristics and model characteristics, where ‘study characteristics’ are further categorized as the stock market covered, input data, and nature of the study and ‘model characteristics’ are classified as data pre-processing, artificial intelligence technique, training algorithm, and performance measure. This paper reviews 148 studies utilizing neural and hybrid-neuro techniques to predict stock markets, categorized based on 43 auto-coded themes obtained using NVivo 12 software. Artificial intelligence (AI) techniques can detect such non-linearity, resulting in much-improved forecast results. The stock market is characterized by extreme fluctuations, non-linearity, and shifts in internal and external environmental variables. Ritika Chopra: University School of Management Studies, Guru Gobind Singh Indraprastha University, Dwarka Sector 16-C, New Delhi 110078, India Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda
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