Table 4.
Proposals related to Smart manufacturing and Machine Learning.
| Bibliography | Keywords | Novelty of the Proposal |
|---|---|---|
| Lee, Jay, et al. (2018) | Artificial Inteligent, Smart manufacturing, Failt diagnosis | State of AI technologies and the eco-system required to harness the power of AI in industrial applications. |
| Henley, E. J., and Kumamoto, H. (1985) | Provides a quantitative treatment of the optimal design of safety systems focusing on information links (human and computer), sensors, and control systems. | |
| Li, Bo-hu, et al. (2017) | Based on research into the applications of artificial intelligence (AI) technology in the manufacturing industry in recent years. | |
| Xiaoli, X. et al. (2011) | A presentation of Intelligent internet of things for equipment maintenance (IITEM) which we can make intelligent processing of device information. | |
| Varian, Hal. (2018) | Summary of some of the forces at work and to describe some possible areas for future research. | |
| Wahab, L., and Jiang, H. (2019) | Machine Learning (ML), Decision Tree Classifier (DTC), Random Forest (RF), Multinomial logic model (MNLM), Support vector machine (SVMs), Receiver operating characteristic (ROS) | Traffic crash analysis using machine learning techniques. |
| Azar, A. T., et al. (2014) | A random forest classifier (RFC) approach is proposed to diagnose lymph diseases. | |
| Belgiu, M., and Drăguţ, L. (2016) | This review has revealed that RF classifier can successfully handle high data dimensionality and multicolinearity, being both fast and insensitive to overfitting. | |
| Khalilia, M., et al. | Method for predicting disease risk of individuals using random forest. | |
| Jedari, E., et al. (2015) | Machine learning approaches including k-nearest neighbor (k-NN), a rules-based classifier (JRip), and random forest have been investigated to estimate the indoor location of a user or an object using RSSI based fingerprinting method. | |
| Iranitalab, A., and Khattak, A. (2017) | This paper had three main objectives: comparison of the performance of four statistical and machine learning methods including Multinomial Logit (MNL), Nearest Neighbor Classification (NNC), Support Vector Machines (SVM) and Random Forests (RF), in predicting traffic crash severity. | |
| Pal, M. (2005) | To present the results obtained with the random forest classifier and to compare its performance with the support vector machines (SVMs) in terms of classification accuracy, training time and user defined parameters. | |
| Rodriguez-Galiano, V. F., et al. (2012) | The performance of the RF classifier for land cover classification of a complex area is explored. | |
| Yogameena, B., et al. (2019) | Complex software system, Mixture models, Convolutional neural networks | Intelligent video surveillance system for automatically detecting the motorcyclists with and without safety helmets. |
| Cockburn, D. (1996) | The benefit of taking a holistic perspective to developing complex software systems. |