Title: Deep Learning with a Classifier System: Initial Results …
This article presents the first results from using a learning classifier system capable of performing adaptive computation with deep neural networks. Individual classifiers within the population are composed of two neural networks. The first acts as a gating or guarding component, which enables the conditional computation of an …
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The Development of an Industrial Learning Classifier System …
Many bespoke and commercial data-mining tools exist, but the novel Artificial Intelligence (AI) technique of Learning Classifier Systems (LCS) has unique properties that could …
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The Potentiality of Integrating Model-Based Residuals and …
In the recent development of induction motors fault diagnosis, machine-learning algorithms have been implemented to replace the need for experts in fault diagnostic decisions. In industrial practice, faults exhibit symptoms but not in the early stage. This condition limits the availability of fault datasets for machine-learning classifier training. Therefore, the …
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The Development of an Industrial Learning Classifier
An Industrial Learning Classifier System (LCS) was developed a decade ago for the mining of information in process industries, specifically for a Steel Hot Strip Mill. Despite encouraging results ...
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Ensemble Learning for Multi-Label Classification …
The Extreme Gradient Boosting (XGBoost) classifier is a widely recognized ensemble learning technique with advantages in predictive accuracy and speed, robustly able to handle missing data …
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Interpretable machine learning classifiers for the reliable …
Classical computing methods are costly and require advanced skills, limiting their clinical use. A data-driven framework offers an effective alternative for disease diagnosis and prediction. This study aims to apply and evaluate machine learning (ML) classifiers to predict hip fracture risk using a binary classification based on the fracture …
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An Industrial-Grade Brain Imaging-Based Deep …
and sharing an industrial-g rade brain imaging-based deep learning classifier, we invite research ers (especi ally comput er scientis ts) to joi n the e ffort to deciphe r the brain by open ly
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Industrial ML Datasets
A curated list of datasets, publically available for machine learning research in the area of manufacturing - nicolasj92/industrial-ml-datasets
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Types of Solids Separation Classifier in Mechanical Operations
Additionally, gravity classifiers can be used to separate products that are of different densities. One common use for a gravity classifier is to remove fine coal from a product stream. By removing the fine coal, power plants can reduce their emissions. Gravity classifiers are also used in the food industry.
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Federated transfer learning for auxiliary classifier generative
Machine learning with considering data privacy-preservation and personalized models has received attentions, especially in the manufacturing field. The data often exist in the form of isolated islands and cannot be shared because of data privacy in real industrial scenarios. It is difficult to gathe …
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An industrial Learning Classifier System: the importance of …
DOI: 10.1016/S0952-1976(99)00034-2 Corpus ID: 62132246; An industrial Learning Classifier System: the importance of pre-processing real data and choice of alphabet @article{Browne2000AnIL, title={An industrial Learning Classifier System: the importance of pre-processing real data and choice of alphabet}, author={Will N. Browne …
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Evaluation of the machine learning classifier in wafer defects
3. Result and discussion. Fig. 3 shows the comparison of the average accuracy performance of four different machine learning classifier models in terms of wafer defect classification. Out of the four machine learning classifiers evaluated, Logistic Regression classifier gives the best classification accuracy with 86.0% during training …
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Adversarial attacks on machine learning cybersecurity defences in
Industrial Control Systems (ICS) play a key role in Critical National Infrastructure (CNI) concepts such as manufacturing, power/smart grids, water treatment plants, gas and oil refineries, and health-care. ... Subsequently, in this paper, the robustness of supervised machine learning classifiers against AML is further evaluated …
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Federated transfer learning for auxiliary classifier generative
Keywords: Federated transfer learning, Auxiliary classifier generative adversarial networks, Data privacy, Personalized model. ... How to quickly and accurately train a personalized model is the core target of industrial machine learning. Federated Transfer Learning (FTL) can be applied to surmount the aforementioned bottlenecks …
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Prediction of Particulate Matter (PM2.5) for Industrial Area …
This research focuses on a comparative analysis of two machine learning algorithms to predict PM 2.5 concentration monitored in an industrial area. The random forest and Naïve Bayes classifiers have been compared to predict the class of PM 2.5 concentration monitored in the industrial area of Haridwar City (SIDCUL).
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FECS: An efficiency based learning classifier system applied …
One of the first GBML implementations is the Learning Classifier System (LCS) defined by Goldberg [4]. Learning is done by the so called Bucket Brigade Algorithm (BBA) which assigns a strength (payoff value) to each classifier based upon its interaction with the environment.
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Machine Learning–Assisted Risk Assessment of Pitting …
A novel microreactor is used to generate data for alcoholate corrosion of AA1050 in ethanol-containing fuels. Based on these data, a supervised machine learning classifier is trained, which correlate...
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Forecasting faults of industrial equipment using machine learning …
DOI: 10.1109/INISTA.2018.8466309 Corpus ID: 52304501; Forecasting faults of industrial equipment using machine learning classifiers @article{Kolokas2018ForecastingFO, title={Forecasting faults of industrial equipment using machine learning classifiers}, author={Nikolaos Kolokas and Thanasis Vafeiadis and Dimosthenis Ioannidis and …
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Condition Monitoring of Industrial Motors using …
and 20% of data is used to test the supervised learning classifier. Since, the k-means and ELM suits smaller dimensions, numerical and continuous data, both the ML algorithms are utilized as a classifier in the proposed work. Fig. 1 – Block diagram for developed decision support hardware. 2.2. Machine Learning Classifiers
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How To Build a Machine Learning Industry Classifier
Founded upon the premises of big data and deep learning, machine learning enables us to go beyond explicitly programing computers to perform certain actions. It empowers us to teach them how to ...
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An industrial Learning Classifier System: the importance of …
This paper describes the development of an Industrial Learning Classifier System for application in the steel industry. The real domain problem was the prediction …
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Introduction to Learning Classifier Systems (SpringerBriefs in
"Introduction to Learning Classifier Systems is an excellent textbook and introduction to Learning Classifier Systems. … The book is completed with Python code available through a link included in the book. … Urbanowicz and Browne recommend their book for undergraduate and postgraduate students, data analysts, and machine …
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Multi-Rate Vibration Signal Analysis for Bearing Fault …
Industrial organizations often seek cost-effective and qualitative measurements, while reducing sensor resolution to optimize their resource allocation. This paper compares the performance of supervised learning classifiers for the fault detection of bearing faults in induction machines using vibration signals sampled at various frequencies.
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Self-supervised learning-based dual-classifier domain …
However, output consistency does not mean output determinacy, and the same output of classifiers may turn wrong. Therefore, a novel dual-classifier classification determinacy metric is designed, and a self-supervised learning-based dual-classifier domain adaptation for cross-domain rolling bearings fault diagnosis method is proposed.
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Lightweight Industrial Image Classifier Based on Federated …
Image classification using convolutional neural networks (CNNs) is critical for broader industrial applications like defect detection. To protect sensitive data during the industrial process, increasing institutions are highly interested in training CNN classifiers collaboratively with federated learning (FL). However, the existing FL solutions cannot …
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The Development of an Industrial Learning Classifier …
industrial environment was examined to form the basis of an industrialised LCS technique. This unique starting point lead to insight into the operation of the LCS …
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An Industrial-Grade Brain Imaging-Based Deep …
individual differences using deep learning/transfer learning on big data. We pooled 34 datasets to constitute the largest brain magnetic resonance image sample to date (85,721 samples from 50,876 participants), and then applied a state-of-the-art deep convolutional neural network, Inception-ResNet-V2, to build an industrial-grade …
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Fault Prognostics in Industrial Domains using Unsupervised …
Keywords— Data-driven approach, Industry 4.0, Machine learning, Predictive Maintenance, RUL prognosis, Smart sensors. ... etc. Unsupervised machine learning classifiers were used by Kolokas et ...
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The Development of an Industrial Learning Classifier …
The Development of an Industrial Learning Classifier System for Data-Mining in a Steel Hop Strip Mill William N L Browne1 1 Department of Cybernetics, University of Reading, Whiteknights, Reading, Berkshire, RG66AY, UK [email protected] 1. Introduction Industrial domains seek to maximise profits from existing plant due to the large
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A Novel Traffic Classifier With Attention Mechanism for Industrial …
With the development of the Industrial Internet of Things (IIoT), the complex traffic generated by large-scale IIoT devices presents challenges for traffic analysis. Most of existing deep learning-based traffic analysis methods use a single flow for classification, resulting in being misled by the irrelevant flow. Thus, it is necessary to use flow …
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Federated transfer learning for auxiliary classifier generative
Alzubi JA Alzubi OA Singh A Ramachandran M Cloud-IIoT based electronic health record privacy-preserving by CNN and blockchain-enabled federated learning IEEE Transactions on Industrial Informatics 2022 19 1 1080 1087 10.1109/TII.2022.3189170 Google Scholar Cross Ref; Bai Y Xie J Wang D Zhang W Li C A manufacturing quality prediction model …
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A Spectral-Based Blade Fault Detection in Shot Blast …
The optimal functionality and dependability of mechanical systems are important for the sustained productivity and operational reliability of industrial machinery, and have a direct impact on its longevity and profitability. Therefore, the failure of a mechanical system or any of its components would be detrimental to production …
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The Development of an Industrial Learning Classifier System …
The Development of an Industrial Learning Classifier System for Application to a Steel Hot Strip Mill, Doctoral Thesis, University of Wales, Cardiff (1999) Google Scholar Goldberg D. E.: The Theory of Virtual Alphabets, Parallel Problem Solving from Nature 1, Eds. Schwefel H. P. and Maenner R., Springer-Verlag, Berlin, (1990) 13–22.
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