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A treasure chest for visual classification and recognition powered by PaddlePaddle - PaddlePaddle/PaddleClas
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Machine learning is applied widely in the field of intrusion detection at present, the existing intrusion detection algorithm is relatively mature, but how to solve the problem of unbalanced sample data still need further research. Aiming at the problem of detection accuracy and efficiency caused by sample data imbalance in the process of intrusion …
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Clinical decision support systems (CDSS) will play an in-creasing role in improving the quality of medical care for critically ill patients. However, due to limitations in current informatics infrastructure, CDSS do not always have com-plete information on state of supporting physiologic monitor-ing devices, which can limit the input data available to …
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Examples of this technique include Bayesian Filtering, SVM, kNN classifier, Neural Network, AdaBoost classifier, and others. Systems based on machine learning approach facilitates learning and adjustment to recent dangers posed to the security of spam filters. They also have the capacity to counter curative channels that spammers …
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In this system that we have proposed, we are making use of a machine learning technique that is based on a soft voting classifier. Other machine learning algorithms, such as Logistic Regression, KNN, SVM, Random Forest, Native Bayes, Decision Tree, Extremely Randomized Trees, AdaBoost, Gradient Tree Boosting, and …
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Expert Systems with Applications. v34 i1. 214-221. Google Scholar; Polat et al., 2008. A cascade learning system for classification of diabetes disease: generalized discriminant analysis and least square support vector machine. Expert Systems with Applications. v34. 482-487. Google Scholar; Polat et al., 2007.
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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 …
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Reliable gear fault diagnostic techniques and systems are critically needed to provide early warning of a possible defect so as to prevent machinery operation degradation and to reduce costs related to predictive maintenance. In this work, a new evolving neuro-fuzzy (eNF) classifier is proposed for real-time gear system fault diagnostics.
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Naive Bayes Classifier with Synthetic Dataset. In the first example, we will generate synthetic data using scikit-learn and train and evaluate the Gaussian Naive Bayes algorithm. Generating the Dataset. Scikit-learn provides us with a machine learning ecosystem so that you can generate the dataset and evaluate various machine learning …
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Abstract: The complexity of Machine Learning (ML) systems increases each year, with current implementations of large language models or text-to-image generators having billions of parameters and requiring billions of arithmetic operations. As these systems are widely utilized, ensuring their reliable operation is becoming a design …
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It is shown that the SVC can be applicable to the domain of analog fault classification and this novel classifier can be viewed as an alternative for the back-propagation (BP) neural network classifier. In order to make the analog fault classification more accurate, we present a method based on the Support V ector Machines Classifier (SVC) with …
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The Bayes Optimal Classifier is a probabilistic model that makes the most probable prediction for a new example. It is described using the Bayes Theorem that provides a principled way for calculating a conditional probability. It is also closely related to the Maximum a Posteriori: a probabilistic framework referred to as MAP that finds the …
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In both cases, real data is used to fit generative models. Then, synthetic data is produced from the generative models. Machine learning classifiers are trained with either real data, synthetic data, or a combination of real and synthetic data. These classifiers are tested with real labeled data. 3.5.1.
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Genetic algorithm Learning classifier system Figure 1: Field tree—foundations of the LCS community. with complex systems, seeking a single best-fit model is less desirable than evolving a population of rules which collectively model that system. LCSs represent the merger of different fields of research encapsulated within a single algorithm.
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Recommender systems use machine learning and data mining techniques to filter unseen information and predict whether a user would like a particular item. A major research challenge in this field is to make useful recommendation from available set of millions of items with sparse ratings. A large number of approaches have been proposed …
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Multi-Label Classification. Multi-label classification refers to those classification tasks that have two or more class labels, where one or more class labels may be predicted for each example.. Consider the example of photo classification, where a given photo may have multiple objects in the scene and a model may predict the presence of …
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A real-time online recognition system based on paddle-OCR for steel slab spray mark characters is designed to address the security problems of manual …
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Indoor human-carried object detection refers to the use of technologies and methods to detect objects that may be carried by individuals in indoor environments. This can include weapons, explosives, drugs, or other contraband that may endanger the safety and security of individuals or facilities. Detecting potential threats carried by individuals inside …
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In recent years, the remarkable progress of Machine Learning (ML) technologies within the domain of Artificial Intelligence (AI) systems has presented unprecedented opportunities for the aviation industry, paving the way for further advancements in automation, including the potential for single pilot or fully autonomous …
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Abstract: Machine learning is applied widely in the field of intrusion detection at present, the existing intrusion detection algorithm is relatively mature, but how to solve the problem of unbalanced sample data still need further research. Aiming at the problem of detection accuracy and efficiency caused by sample data imbalance in the process of intrusion …
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A Bayes classifier is a type of classifier that uses Bayes' theorem to compute the probability of a given class for a given data point. Naive Bayes is one of the most common types of Bayes classifiers. What is better than Naive Bayes? There are several classifiers that are better than Naive Bayes in some situations.
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As a widely adopted OCR framework, PaddleOCR contains rich text detection, text recognition, and end-to-end algorithms. It chooses Differentiable Binarization (DB) and Convolutional Recurrent Neural Network (CRNN) as the basic detection and recognition models, and proposes a series of models, named PP-OCR, for indust…
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The examples the system uses to learn are called the training set. In this case, the task (T) is to flag spam for new emails, the experience (E) is the training data, and the performance measure (P) …
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agent system approach using intelligent classifiers ISSN 1751-8660 Received on 20th July 2019 Revised 3rd October 2019 Accepted on 22nd October 2019 E-First on 16th January 2020 doi: 10.1049/iet-epa.2019.0619 ... networks, expert systems, k-NN method, support vector machine
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Autonomous (unmanned) combat systems will become an integral part of modern defense systems. However, limited operational capabilities, the need for coordination, and dynamic battlefield environments with the requirement of timeless in decision-making are peculiar difficulties to be solved in order to realize intelligent …
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Support Vector Machines are perhaps one of the most popular and talked about machine learning algorithms. They were extremely popular around the time they were developed in the 1990s …
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Abstract. This paper covers the development of a key component of an internal system to report invisible lost time (ILT) metrics across drilling operations. Specifically this paper covers the development of a generalizable rig state engine based on the application of supervised machine learning. The same steps used in the creation of …
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In recent years, local systems have become more open to the Internet, which increases the importance of secure networks. Interconnected systems are under the threat of network adversaries. An Intrusion Detection System (IDS) is one of the most powerful method that can handle computer network intrusions by monitoring unknown and suspicious …
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In comparison to Naive Bayes (NB) Classifier, innovative Linear Support Vector Machine (LSVM) is used to forecast enhanced drug review systems for boosting Accuracy. Materials and Methods: In this research, two groups are compared, novel Linear Support Vector Machine (N = 10),Naive Bayes (N = 10) was generated according to total sample size …
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The subject of FDD (fault detection and diagnosis) has gained widespread industrial interest in machine condition monitoring applications. This is mainly due to the potential advantage to be achieved from reduced maintenance costs, improved
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