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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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Danilo Pau, Andrea Pisani and Antonio Candelieri
In the context of TinyML, many research efforts have been devoted to designing forward topologies to support On-Device Learning. Reaching this target would bring numerous advantages, including reductions in latency and computational complexity, stronger ...
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Xiaoqin Lian, Xue Huang, Chao Gao, Guochun Ma, Yelan Wu, Yonggang Gong, Wenyang Guan and Jin Li
In recent years, the advancement of deep learning technology has led to excellent performance in synthetic aperture radar (SAR) automatic target recognition (ATR) technology. However, due to the interference of speckle noise, the task of classifying SAR ...
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Florimond De Smedt, Prabin Kayastha and Megh Raj Dhital
Naïve Bayes classification is widely used for landslide susceptibility analysis, especially in the form of weights-of-evidence. However, when significant conditional dependence is present, the probabilities derived from weights-of-evidence are biased, re...
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Egor I. Chetkin, Sergei L. Shishkin and Bogdan L. Kozyrskiy
Bayesian neural networks (BNNs) are effective tools for a variety of tasks that allow for the estimation of the uncertainty of the model. As BNNs use prior constraints on parameters, they are better regularized and less prone to overfitting, which is a s...
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Mohammad H. Alshayeji and Jassim Al-Buloushi
Improved disease prediction accuracy and reliability are the main concerns in the development of models for the medical field. This study examined methods for increasing classification accuracy and proposed a precise and reliable framework for categorizi...
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Konstantinos Filippou, George Aifantis, George A. Papakostas and George E. Tsekouras
In this paper, we built an automated machine learning (AutoML) pipeline for structure-based learning and hyperparameter optimization purposes. The pipeline consists of three main automated stages. The first carries out the collection and preprocessing of...
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N. Aidossov, Vasilios Zarikas, Aigerim Mashekova, Yong Zhao, Eddie Yin Kwee Ng, Anna Midlenko and Olzhas Mukhmetov
Breast cancer comprises a serious public health concern. The three primary techniques for detecting breast cancer are ultrasound, mammography, and magnetic resonance imaging (MRI). However, the existing methods of diagnosis are not practical for regular ...
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Yong Zhu, Tao Zhou, Shengnan Tang and Shouqi Yuan
Hydraulic axial piston pumps are the power source of fluid power systems and have important applications in many fields. They have a compact structure, high efficiency, large transmission power, and excellent flow variable performance. However, the cruci...
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Bowen Xing, Liang Zhang, Zhenchong Liu, Hengjiang Sheng, Fujia Bi and Jingxiang Xu
The goal of this paper is to strengthen the supervision of fishing behavior in the East China Sea and effectively ensure the sustainable development of fishery resources. Based on AIS data, this paper analyzes three types of fishing boats (purse seine op...
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