Results 41 to 50 of about 2,573 (145)

Stacking of DT, RF, and Gradient Boosting Algorithms for Classification of Building Damage Due to Earthquakes

Journal of Applied Informatics and Computing
Classification of building damage levels due to earthquakes is an important aspect in disaster mitigation and post-disaster risk assessment. This study aims to improve classification accuracy on imbalanced data using an ensemble stacking method.
Nur Aqliah Ilmi, Nurul Anisa Sri Winarsih   +1 more
doaj   +1 more source

Improving imbalanced class intrusion detection in IoT with ensemble learning and ADASYN-MLP approach [PDF]

The exponential growth of the internet of things (IoT) has revolutionized daily activities, but it also brings forth significant vulnerabilities. intrusion detection systems (IDS) are pivotal in efficiently detecting and identifying suspicious activities
Al Amien, Januar, Mohd Daud, Kauthar, Soni, Soni, Remli, Muhammad Akmal   +3 more
core   +1 more source

The Impact of Extreme Data Imbalance on Evaluation Metrics of Deep Learning Models for Loan Default Prediction

Emitor: Jurnal Teknik Elektro
The growth of financial technology has made online loans more accessible, but it has also increased the risk of borrowers failing to repay. Developing a reliable system to predict loan defaults is therefore very important.
Irfan Budiyanto, Arief Hermawan, Donny Avianto, Muhammad Kusban   +3 more
doaj   +1 more source

Integration of Adasyn Method with Decision Tree Algorithm in Handling Imbalance Class for Loan Status Prediction

Jurnal Riset Informatika
Determining the provision of credit is generally carried out based on measuring credibility using credit analysis principles (5C principles). However, this method requires quite a long processing time and is very susceptible to subjective judgments which
Ami Rahmawati, Ita Yulianti, Tati Mardiana, Denny Pribadi   +3 more
doaj   +1 more source

Revisiting Resampling Strategies under Extreme Class Imbalance: Evidence from Large-Scale Online Payment Fraud Detection

Edumatic
Extreme class imbalance in online payment fraud detection creates an accuracy paradox and an operational risk in which improving fraud capture can generate costly false alarms.
Mursyid Ardiansyah, Ali Asgar Zainal Abidin   +1 more
doaj   +1 more source

A Novel Wireless Network Intrusion Detection Method Based on Adaptive Synthetic Sampling and an Improved Convolutional Neural Network

IEEE Access, 2020
The diversity of network attacks poses severe challenges to intrusion detection systems (IDSs). Traditional attack recognition methods usually adopt mining data associations to identify anomalies, which has the disadvantages of a high false alarm rate ...
Zhiquan Hu, Liejun Wang, Lei Qi, Yongming Li, Wenzhong Yang   +4 more
doaj   +1 more source

Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model

Scientific Reports, 2022
Blood cancer has been a growing concern during the last decade and requires early diagnosis to start proper treatment. The diagnosis process is costly and time-consuming involving medical experts and several tests. Thus, an automatic diagnosis system for
Vaibhav Rupapara, Furqan Rustam, Wajdi Aljedaani, Hina Fatima Shahzad, Ernesto Lee, Imran Ashraf   +5 more
doaj   +1 more source

Evaluating the Performance of Generative Adversarial Network, Synthetic Minority Oversampling Technique, and Adaptive Synthetic Sampling in Marine Diesel Engine Fault Diagnosis using Vibration Data

Engineered Science
Machine learning-driven fault diagnosis of marine diesel engines is a major research focus, challenged by scarce, insufficient fault data. This study explores vibration data under five conditions: normal operation, single-cylinder ignition failure ...
Zhijun Chen, Ziyu Xiao, Zhongjun Wang, Pengcheng Wang, Jie Wang, Yijie Qu, Xiong Bao, Xiaofeng Guo   +7 more
doaj   +1 more source

Impact of Data Balancing and Feature Engineering on Accident Severity Models

Promet (Zagreb)
This study investigates the impacts of feature engineering techniques, including Clustering, Target Encoding and Anomaly Detection, in conjunction with data balancing methods, on the efficacy of machine learning models for predicting road accident ...
Fayez ALANAZI, Aminu SULEIMAN
doaj   +1 more source

Results of the learning models using ADASYN upsampled.

Results of the learning models using ADASYN upsampled.
Raafat M. Munshi (17768876)
core   +1 more source