Support vector machine in structural reliability analysis: A review
Support vector machine (SVM) is a powerful machine learning technique relying on the
structural risk minimization principle. The applications of SVM in structural reliability analysis …
structural risk minimization principle. The applications of SVM in structural reliability analysis …
Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions
The research on intelligent fault diagnosis has yielded remarkable achievements based on
artificial intelligence-related technologies. In engineering scenarios, machines usually work …
artificial intelligence-related technologies. In engineering scenarios, machines usually work …
A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning
Class imbalance occurs when the class distribution is not equal. Namely, one class is under-
represented (minority class), and the other class has significantly more samples in the data …
represented (minority class), and the other class has significantly more samples in the data …
Fedhome: Cloud-edge based personalized federated learning for in-home health monitoring
In-home health monitoring has attracted great attention for the ageing population worldwide.
With the abundant user health data accessed by Internet of Things (IoT) devices and recent …
With the abundant user health data accessed by Internet of Things (IoT) devices and recent …
Perbandingan Evaluasi Kernel SVM untuk Klasifikasi Sentimen dalam Analisis Kenaikan Harga BBM: Comparative Evaluation of SVM Kernels for Sentiment …
S Rabbani, D Safitri, N Rahmadhani… - … : Indonesian Journal of …, 2023 - journal.irpi.or.id
Abstract Kebijakan perubahan harga Bahan Bakar Minyak (BBM) oleh pemerintah pada
September 2022 lalu menimbulkan kontroversi pengguna sosial media termasuk Twitter …
September 2022 lalu menimbulkan kontroversi pengguna sosial media termasuk Twitter …
Attack classification of imbalanced intrusion data for IoT network using ensemble-learning-based deep neural network
With the increase in popularity of Internet of Things (IoT) and the rise in interconnected
devices, the need to foster effective security mechanism to handle vulnerabilities and risks in …
devices, the need to foster effective security mechanism to handle vulnerabilities and risks in …
Tuning machine learning models using a group search firefly algorithm for credit card fraud detection
Recent advances in online payment technologies combined with the impact of the COVID-
19 global pandemic has led to a significant escalation in the number of online transactions …
19 global pandemic has led to a significant escalation in the number of online transactions …
CS-ResNet: Cost-sensitive residual convolutional neural network for PCB cosmetic defect detection
In the printed circuit board (PCB) industry, cosmetic defect detection is an essential process
to ensure product quality. However, existing PCB cosmetic defect detection approaches …
to ensure product quality. However, existing PCB cosmetic defect detection approaches …
AIPs-SnTCN: Predicting anti-inflammatory peptides using fastText and transformer encoder-based hybrid word embedding with self-normalized temporal …
Inflammation is a biologically resistant response to harmful stimuli, such as infection,
damaged cells, toxic chemicals, or tissue injuries. Its purpose is to eradicate pathogenic …
damaged cells, toxic chemicals, or tissue injuries. Its purpose is to eradicate pathogenic …
A novel oversampling technique for class-imbalanced learning based on SMOTE and natural neighbors
J Li, Q Zhu, Q Wu, Z Fan - Information Sciences, 2021 - Elsevier
Develo** techniques for the machine learning of a classifier from class-imbalanced data
presents an important challenge. Among the existing methods for addressing this problem …
presents an important challenge. Among the existing methods for addressing this problem …