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Jose Luis Vieira Sobrinho, Flavio Henrique Teles Vieira and Alisson Assis Cardoso
The high dimensionality of real-life datasets is one of the biggest challenges in the machine learning field. Due to the increased need for computational resources, the higher the dimension of the input data is, the more difficult the learning task will ...
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Somayeh Shahrabadi, Telmo Adão, Emanuel Peres, Raul Morais, Luís G. Magalhães and Victor Alves
The proliferation of classification-capable artificial intelligence (AI) across a wide range of domains (e.g., agriculture, construction, etc.) has been allowed to optimize and complement several tasks, typically operationalized by humans. The computatio...
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Falah Amer Abdulazeez, Ismail Taha Ahmed and Baraa Tareq Hammad
A significant quantity of malware is created on purpose every day. Users of smartphones and computer networks now mostly worry about malware. These days, malware detection is a major concern in the cybersecurity area. Several factors can impact malware d...
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Dingnan Song, Ran Liu, Zhiwei Zhang, Dingding Yang and Tianzhen Wang
Tidal stream turbines (TSTs) harness the kinetic energy of tides to generate electricity by rotating the rotor. Biofouling will lead to an imbalance between the blades, resulting in imbalanced torque and voltage across the windings, ultimately polluting ...
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Fengyun Xie, Gang Li, Hui Liu, Enguang Sun and Yang Wang
In the context of addressing the challenge posed by limited fault samples in agricultural machinery rolling bearings, especially when early fault characteristics are subtle, this study introduces a novel approach. The proposed multi-domain fault diagnosi...
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Yingze Song, Degang Yang, Weicheng Wu, Xin Zhang, Jie Zhou, Zhaoxu Tian, Chencan Wang and Yingxu Song
Landslide susceptibility assessment (LSA) based on machine learning methods has been widely used in landslide geological hazard management and research. However, the problem of sample imbalance in landslide susceptibility assessment, where landslide samp...
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Sikha Bagui, Dustin Mink, Subhash Bagui, Sakthivel Subramaniam and Daniel Wallace
This study, focusing on identifying rare attacks in imbalanced network intrusion datasets, explored the effect of using different ratios of oversampled to undersampled data for binary classification. Two designs were compared: random undersampling before...
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Giovanni Ceccarelli, Guido Cantelmo, Marialisa Nigro and Constantinos Antoniou
In bike-sharing systems, the inventory level is defined as the daily number of bicycles required to optimally meet the demand. Estimating these values is a major challenge for bike-sharing operators, as biased inventory levels lead to a reduced quality o...
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George Ioannou, Georgios Alexandridis and Andreas Stafylopatis
Importance sampling, a variant of online sampling, is often used in neural network training to improve the learning process, and, in particular, the convergence speed of the model. We study, here, the performance of a set of batch selection algorithms, n...
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Haoxiang Shi, Jun Ai, Jingyu Liu and Jiaxi Xu
Software defect prediction is a popular method for optimizing software testing and improving software quality and reliability. However, software defect datasets usually have quality problems, such as class imbalance and data noise. Oversampling by genera...
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