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Titlebook: Reliability Engineering for Industrial Processes; An Analytics Perspec P. K. Kapur,Hoang Pham,Vivek Kumar Book 2024 The Editor(s) (if appli

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樓主: 熱愛
21#
發(fā)表于 2025-3-25 07:15:16 | 只看該作者
22#
發(fā)表于 2025-3-25 11:19:51 | 只看該作者
23#
發(fā)表于 2025-3-25 15:40:49 | 只看該作者
24#
發(fā)表于 2025-3-25 19:23:30 | 只看該作者
Fault Removal Efficiency: A Key Driver in Software Reliability Growth Modeling,s management, FRE provides developers with invaluable insights into testing efficacy and aids in predicting additional efforts required. The chapter explores some SRGMs that incorporate FRE, providing readers with a comprehensive insight into how FRE shapes the dynamics of the SRGM.
25#
發(fā)表于 2025-3-25 20:04:29 | 只看該作者
,A Review on?Kidney Failure Prediction Using Machine Learning Models,on, decision trees, support vector machines, and deep learning. The review analyzes key studies and methodologies employed in predicting kidney failure, highlighting the strengths and limitations of different ML approaches. It emphasizes the importance of feature selection, data preprocessing, and m
26#
發(fā)表于 2025-3-26 03:45:13 | 只看該作者
,Machine Learning Based Remaining Useful Life Estimation—Concept and Case Study,ng useful life of machine components. We also demonstrate a case study using NASA’s CMAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset. The case study incorporates the successful implementation of ML algorithms and the subsequent use of Evolutionary Computing techniques like Parti
27#
發(fā)表于 2025-3-26 06:50:37 | 只看該作者
,Software Defect Prediction Using Abstract Syntax Trees Features and Object—Oriented Metrics,rom file-level ASTs of the source code of projects and compared with the models trained on OO metrics. The CNN model trained on file-level AST features produced MAE results similar to the LSTM model trained on OO metrics, but outperformed it in terms of MRE.
28#
發(fā)表于 2025-3-26 11:56:27 | 只看該作者
,A Review of Alzheimer’s Disease Identification by Machine Learning,p learning continue to evolve, the amalgamation of these techniques holds promise in revolutionizing our approach to Alzheimer’s disease, offering insights that may lead to more effective interventions and improved patient outcomes.
29#
發(fā)表于 2025-3-26 12:39:19 | 只看該作者
30#
發(fā)表于 2025-3-26 19:41:43 | 只看該作者
A Study on the Efficiency of Divergence Measure in Fuzzy TOPSIS Algorithm for Multi-attribute Decisthat the proposed model provides a accurate way to select the best university among the large number of choices available for the considered universities. The paper settles with a discussion of a case study and experimental findings.
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