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Rubén E. Nogales and Marco E. Benalcázar
Gesture recognition is widely used to express emotions or to communicate with other people or machines. Hand gesture recognition is a problem of great interest to researchers because it is a high-dimensional pattern recognition problem. The high dimensio...
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Jun Li, Hongchao Wang, Simin Li, Liang Chen and Qiqian Dang
To extract the weak fault features hidden in strong background interference in the event of the early failure of rolling bearings, a two-stage based method is proposed. The broadband noise elimination ability of an adaptive morphological filter (AMF) and...
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Jean-Sébastien Dessureault and Daniel Massicotte
This paper examines the critical decision process of reducing the dimensionality of a dataset before applying a clustering algorithm. It is always a challenge to choose between extracting or selecting features. It is not obvious to evaluate the importanc...
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Jogeswar Tripathy, Rasmita Dash, Binod Kumar Pattanayak, Sambit Kumar Mishra, Tapas Kumar Mishra and Deepak Puthal
In high-dimensional data analysis, Feature Selection (FS) is one of the most fundamental issues in machine learning and requires the attention of researchers. These datasets are characterized by huge space due to a high number of features, out of which o...
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Yousif A. Alhaj, Abdelghani Dahou, Mohammed A. A. Al-qaness, Laith Abualigah, Aaqif Afzaal Abbasi, Nasser Ahmed Obad Almaweri, Mohamed Abd Elaziz and Robertas Dama?evicius
We propose a novel text classification model, which aims to improve the performance of Arabic text classification using machine learning techniques. One of the effective solutions in Arabic text classification is to find the suitable feature selection me...
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Nasser Tamim, Mohamed Elshrkawey and Hamed Nassar
Diabetic retinopathy (DR) and glaucoma can both be incurable if they are not detected early enough. Therefore, ophthalmologists worldwide are striving to detect them by personally screening retinal fundus images. However, this procedure is not only tedio...
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Xuanyuan Su, Hongmei Liu and Laifa Tao
In practical engineering, the vibration-based fault diagnosis with few failure samples is gaining more and more attention from researchers, since it is generally hard to collect sufficient failure records of centrifugal pumps. In such circumstances, effe...
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Jidong Wang, Zhilin Xu and Yanbo Che
In order to effectively identify complex power quality disturbances, a power quality disturbance classification method based on empirical wavelet transform and a multi-layer perceptron extreme learning machine (ELM) is proposed. The model uses the discre...
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Chinyang Henry Tseng, Woei-Jiunn Tsaur and Yueh-Mao Shen
In detecting large-scale attacks, deep neural networks (DNNs) are an effective approach based on high-quality training data samples. Feature selection and feature extraction are the primary approaches for data quality enhancement for high-accuracy intrus...
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Lei Fu, Tiantian Zhu, Guobing Pan, Sihan Chen, Qi Zhong and Yanding Wei
Power quality disturbances (PQDs) have a large negative impact on electric power systems with the increasing use of sensitive electrical loads. This paper presents a novel hybrid algorithm for PQD detection and classification. The proposed method is cons...
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Ons Aouedi, Kandaraj Piamrat and Benoît Parrein
The recent development of smart devices has lead to an explosion in data generation and heterogeneity. Hence, current networks should evolve to become more intelligent, efficient, and most importantly, scalable in order to deal with the evolution of netw...
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Wenwei Zhao, Suprith Reddy Gurudu, Shayan Taheri, Shajib Ghosh, Mukhil Azhagan Mallaiyan Sathiaseelan and Navid Asadizanjani
Printed circuit board (PCB) assurance in the optical domain is a crucial field of study. Though there are many existing PCB assurance methods using image processing, computer vision (CV), and machine learning (ML), the PCB field is complex and increasing...
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Mike Nkongolo, Jacobus Philippus van Deventer and Sydney Mambwe Kasongo
This research attempts to introduce the production methodology of an anomaly detection dataset using ten desirable requirements. Subsequently, the article presents the produced dataset named UGRansome, created with up-to-date and modern network traffic (...
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Réne-Vinicio Sánchez, Pablo Lucero, Jean-Carlo Macancela, Higinio Rubio Alonso, Mariela Cerrada, Diego Cabrera and Cristina Castejón
Railway safety is a matter of importance as a single failure can involve risks associated with economic and human losses. The early fault detection in railway axles and other railway parts represents a broad field of research that is currently under stud...
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Jingliang Hu, Pedram Ghamisi and Xiao Xiang Zhu
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Shilpa Gite, Shruti Patil, Deepak Dharrao, Madhuri Yadav, Sneha Basak, Arundarasi Rajendran and Ketan Kotecha
Feature selection and feature extraction have always been of utmost importance owing to their capability to remove redundant and irrelevant features, reduce the vector space size, control the computational time, and improve performance for more accurate ...
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Chengjiang Zhou, Ling Xing, Yunhua Jia, Shuyi Wan and Zixuan Zhou
Aiming at the problem that fault feature extraction is susceptible to background noises and burrs, we proposed a new feature extraction method based on a new decomposition method and an effective intrinsic mode function (IMF) selection method. Firstly, p...
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Fatma Yaprakdal and Merve Varol Arisoy
In the smart grid paradigm, precise electrical load forecasting (ELF) offers significant advantages for enhancing grid reliability and informing energy planning decisions. Specifically, mid-term ELF is a key priority for power system planning and operati...
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Junartho Halomoan, Kalamullah Ramli, Dodi Sudiana, Teddy Surya Gunawan and Muhammad Salman
One of the WHO?s strategies to reduce road traffic injuries and fatalities is to enhance vehicle safety. Driving fatigue detection can be used to increase vehicle safety. Our previous study developed an ECG-based driving fatigue detection framework with ...
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Siti Nurmaini, Radiyati Umi Partan, Wahyu Caesarendra, Tresna Dewi, Muhammad Naufal Rahmatullah, Annisa Darmawahyuni, Vicko Bhayyu and Firdaus Firdaus
An automated classification system based on a Deep Learning (DL) technique for Cardiac Disease (CD) monitoring and detection is proposed in this paper. The proposed DL architecture is divided into Deep Auto-Encoders (DAEs) as an unsupervised form of feat...
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