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Eleni Vlachou, Aristeidis Karras, Christos Karras, Leonidas Theodorakopoulos, Constantinos Halkiopoulos and Spyros Sioutas
In this work, we present a Distributed Bayesian Inference Classifier for Large-Scale Systems, where we assess its performance and scalability on distributed environments such as PySpark. The presented classifier consistently showcases efficient inference...
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Wiem Chebil, Mohammad Wedyan, Moutaz Alazab, Ryan Alturki and Omar Elshaweesh
This research proposes a new approach to improve information retrieval systems based on a multinomial naive Bayes classifier (MNBC), Bayesian networks (BNs), and a multi-terminology which includes MeSH thesaurus (Medical Subject Headings) and SNOMED CT (...
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Elie Azeraf, Emmanuel Monfrini and Wojciech Pieczynski
Practitioners have used hidden Markov models (HMMs) in different problems for about sixty years. Moreover, conditional random fields (CRFs) are an alternative to HMMs and appear in the literature as different and somewhat concurrent models. We propose tw...
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Duo Sun, Lei Zhang, Kai Jin, Jiasheng Ling and Xiaoyuan Zheng
Aiming at the imbalance of industrial control system data and the poor detection effect of industrial control intrusion detection systems on network attack traffic problems, we propose an ETM-TBD model based on hybrid machine learning and neural network ...
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Wenyi Zhou, Hongguang Fan, Jihong Zhu, Hui Wen and Ying Xie
This paper first studies the generalization ability of the convolutional layer as a feature mapper (CFM) for extracting image features and the classification ability of the multilayer perception (MLP) in a CNN. Then, a novel generalized hybrid probabilit...
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Olga V. Okhlupina,Dmitry S. Murashko
Pág. 17 - 20
Among the common methods of combating spam, a special place is occupied by a probabilistic machine learning algorithm, which is based on the well-known Bayes theorem. The so-called "naive" Bayesian classifier establishes the class of the document by dete...
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Enas Elgeldawi, Awny Sayed, Ahmed R. Galal and Alaa M. Zaki
Machine learning models are used today to solve problems within a broad span of disciplines. If the proper hyperparameter tuning of a machine learning classifier is performed, significantly higher accuracy can be obtained. In this paper, a comprehensive ...
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Girma Neshir, Andreas Rauber and Solomon Atnafu
Topic Modeling is a statistical process, which derives the latent themes from extensive collections of text. Three approaches to topic modeling exist, namely, unsupervised, semi-supervised and supervised. In this work, we develop a supervised topic model...
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Lorenzo Ricciardi Celsi, Andrea Caliciotti, Matteo D'Onorio, Eugenio Scocchi, Nour Alhuda Sulieman and Massimo Villari
The paper proposes a data-driven strategy for predicting technical ticket reopening in the context of customer service for telecommunications companies providing 5G fiber optic networks. Namely, the main aim is to ensure that, between end user and servic...
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Atallah AL-Shatnawi,Faisal Al-Saqqar,Safa?a Alhusban
Pág. pp. 20 - 34
In this paper, we introduce a multi-stage offline holistic handwritten Arabic text recognition model using the Local Binary Pattern (LBP) technique and two machine-learning approaches; Support Vector Machines (SVM) and Artificial Neural Network (ANN). In...
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