Results 1 to 10 of about 39,898 (193)

Prediction of hematocrit through imbalanced dataset of blood spectra [PDF]

Healthcare Technology Letters, 2021
In spite of machine learning has been successfully used in a wide range of healthcare applications, there are several parameters that could influence the performance of a machine learning system.
Cristoforo Decaro, Giovanni Battista Montanari, Marco Bianconi, Gaetano Bellanca   +3 more
doaj   +4 more sources

An Imbalanced Generative Adversarial Network-Based Approach for Network Intrusion Detection in an Imbalanced Dataset [PDF]

Sensors, 2023
In modern networks, a Network Intrusion Detection System (NIDS) is a critical security device for detecting unauthorized activity. The categorization effectiveness for minority classes is limited by the imbalanced class issues connected with the dataset.
Yamarthi Narasimha Rao, Kunda Suresh Babu   +1 more
doaj   +2 more sources

Maturity Classification of “Hupingzao” Jujubes with an Imbalanced Dataset Based on Improved MobileNet V2 [PDF]

Agriculture, 2022
Fruits with various maturity levels coexist among the harvested jujubes, and have different tastes and uses. Manual grading has a low efficiency and a strong subjectivity. The number of “Hupingzao” jujubes between different maturity levels is unbalanced,
Haixia Sun, Shujuan Zhang, Rui Ren, Liyang Su   +3 more
doaj   +2 more sources

Handling imbalanced datasets through Optimum-Path Forest [PDF]

Knowledge-Based Systems, 2022
In the last decade, machine learning-based approaches became capable of performing a wide range of complex tasks sometimes better than humans, demanding a fraction of the time. Such an advance is partially due to the exponential growth in the amount of data available, which makes it possible to extract trustworthy real-world information from them ...
Leandro Aparecido Passos, Danilo S. Jodas, Luiz C.F. Ribeiro, Marco Akio, Andre Nunes de Souza, João Paulo Papa   +5 more
openaire   +3 more sources

Fault Diagnosis of Induction Motors with Imbalanced Data Using Deep Convolutional Generative Adversarial Network

Applied Sciences, 2022
A homemade defective model of an induction motor was created by the laboratory team to acquire the vibration acceleration signals of five operating states of an induction motor under different loads. Two major learning models, namely a deep convolutional
Hong-Chan Chang, Yi-Che Wang, Yu-Yang Shih, Cheng-Chien Kuo   +3 more
doaj   +1 more source

Deep ConvLSTM Network with Dataset Resampling for Upper Body Activity Recognition Using Minimal Number of IMU Sensors

Applied Sciences, 2021
Human activity recognition (HAR) is the study of the identification of specific human movement and action based on images, accelerometer data and inertia measurement unit (IMU) sensors.
Xiang Yang Lim, Kok Beng Gan, Noor Azah Abd Aziz   +2 more
doaj   +1 more source

Imbalanced Ensemble Classifier for Learning from Imbalanced Business School Dataset [PDF]

International Journal of Mathematical, Engineering and Management Sciences, 2019
Private business schools in India face a regular problem of picking quality students for their MBA programs to achieve the desired placement percentage. Generally, such datasets are biased towards one class, i.e., imbalanced in nature.
Tanujit Chakraborty
doaj   +1 more source

Class-Imbalanced Voice Pathology Detection and Classification Using Fuzzy Cluster Oversampling Method

Applied Sciences, 2021
The Massachusetts Eye and Ear Infirmary (MEEI) database is an international-standard training database for voice pathology detection (VPD) systems. However, there is a class-imbalanced distribution in normal and pathological voice samples and different ...
Ziqi Fan, Yuanbo Wu, Changwei Zhou, Xiaojun Zhang, Zhi Tao   +4 more
doaj   +1 more source

Dialog Speech Sentiment Classification for Imbalanced Datasets [PDF]

, 2021
Speech is the most common way humans express their feel- ings, and sentiment analysis is the use of tools such as natural language processing and computational algorithms to identify the polarity of these feelings. Even though this field has seen tremendous advancements in the last two decades, the task of effectively detecting under represented sen ...
Nicolaou, Sergis, Mavrides, Lambros, Tryfou, Georgina, Tolias, Kyriakos, Panousis, Konstantinos P., Chatzis, Sotirios P., Theodoridis, Sergios   +6 more
openaire   +4 more sources

Imbalanced SVM-Based Anomaly Detection Algorithm for Imbalanced Training Datasets [PDF]

ETRI Journal, 2017
Abnormal samples are usually difficult to obtain in production systems, resulting in imbalanced training sample sets. Namely, the number of positive samples is far less than the number of negative samples. Traditional Support Vector Machine (SVM)‐based anomaly detection algorithms perform poorly for highly imbalanced datasets: the learned ...
GuiPing Wang, JianXi Yang, Ren Li
openaire   +2 more sources