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An efficient text classifier can automatically distinguish the data into categories efficiently with the use NLP algorithms. Text Classification is an example of supervised machine learning task since a labelled dataset containing text documents and their labels is used for train a classifier. Some common techniques for text …
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If complexity is your problem, learning classifier systems (LCSs) may offer a solution. These rule-based, multifaceted, machine learning algorithms originated and have evolved in the cradle of evolutionary biology and artificial intelligence. The LCS ...
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Explore powerful machine learning classification algorithms to classify data accurately. Learn about decision trees, logistic regression, support vector machines, and more. Master the art of predictive modelling and enhance your data analysis skills with these essential tools.
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Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale.
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Electrical tomography is a non-invasive method of monitoring the interior of objects, which is used in various industries. In particular, it is possible to monitor industrial processes inside reactors and tanks using tomography. Tomography enables real-time observation of crystals or gas bubbles growing in a liquid. However, obtaining high …
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The selective ensemble aims to search the optimal subset balanced accuracy and diversity from the original base classifier set to construct an ensemble classifier with strong generalization performance. A selective ensemble classifier named BRFS-APCSC is proposed in this paper, which realizes the generation and selection of …
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Lalwani et al. [18] presented a comparative study of customer churn prediction in the telecommunication industry using well-known machine learning techniques like Logistic Regression, Nave Bayes, Support Vector Machines, Decision Trees, Random Forest, XGBoost Classifier, CatBoost Classifier, AdaBoost Classifier, …
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This accessible introduction shows the reader how to understand, implement, adapt, and apply Learning Classifier Systems (LCSs) to interesting and difficult problems. The text builds an understanding from basic ideas and concepts. The authors first explore learning through environment interaction, and then walk through the …
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Federated transfer learning for auxiliary classifier generative adversarial networks: framework and industrial application. ... Kashyap, P. (2017). Industrial applications of machine learning. In Machine learning for decision makers: cognitive computing fundamentals for better decision making (pp. 189–233).
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In this paper, a multiple classifier machine learning (ML) methodology for predictive maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating the so-called "health factors," or …
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A review on machine learning and deep learning image-based plant disease classification for industrial farming systems. Author links open overlay panel P. Sajitha a, A. Diana Andrushia a, N. Anand b, M.Z. Naser c. Show more. Add to Mendeley ... Deep Learning classifiers exhibit high performance in terms of accuracy and efficiency in a …
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1 Introduction and Motivation Learning classifier models is an important problem in data mining. Observations from the real world are often recorded as a set of records, each characterized by multiple attributes. Associated with each record is a categorical attribute called class. Given a training set of records with known class labels, …
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Development of an automatic classifier for the prediction of hearing impairment from industrial noise exposure J Acoust Soc Am. ... kurtosis, was used to characterize the industrial noise. In addition to using all the data as one group, the data were also broken down into the following four subgroups based on the level of kurtosis: G/quasi-G ...
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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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Customer Churn Prediction In Telecommunication Industry Using Machine Learning Classifiers. Authors: Nurul Izzati Mohammad, Saiful Adli Ismail, Mohd Nazri Kama, Othman Mohd Yusop, Azri Azmi Authors Info & Claims. ICVISP 2019: Proceedings of the 3rd International Conference on Vision, Image and Signal Processing.
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This article proposes a robust end-to-end deep learning-induced fault recognition scheme by stacking multiple sparse-denoising autoencoders with a Softmax classifier, called stacked spare-denoising autoencoder (SSDAE)-Softmax, for the fault identification of complex industrial processes (CIPs). Specifically, sparse denoising autoencoder …
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To solve this problem, this study used a tri-training architecture-based semi-supervised ensemble learning method for industrial fault diagnosis under a small training set. Specifically, a heterogeneous classifier was utilised to increase the diversity of the base classifiers, and noise samples were removed through a sample pruning operation.
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A Low-Rank Learning-Based Multi-Label Security Solution for Industry 5.0 Consumers Using Machine Learning Classifiers Abstract: The need for networking in smart industries known as Industry 5.0 has grown critical, and it is especially important for the security and privacy of the applications.
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In this study, a SVM classifier with a supervised machine learning algorithm was developed to predict hearing impairment caused by a variety of industrial noise exposures. The ability to generate rules from data automatically and predict unknown data make machine learning a promising tool to predict hearing trauma from any industrial …
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