Handling data imbalance in machine learning based landslide susceptibility mapping: a case study of Mandakini River Basin, North-Western Himalayas
Abstract Machine learning methods require a vast amount of data to train a model. The data
necessary for landslide susceptibility mapping is a collection of landslide causative factors …
necessary for landslide susceptibility mapping is a collection of landslide causative factors …
Predicting children with ADHD using behavioral activity: a machine learning analysis
Attention deficit hyperactivity disorder (ADHD) is one of childhood's most frequent
neurobehavioral disorders. The purpose of this study is to:(i) extract the most prominent risk …
neurobehavioral disorders. The purpose of this study is to:(i) extract the most prominent risk …
Underwater SONAR Image Classification and Analysis using LIME-based Explainable Artificial Intelligence
P Natarajan, A Nambiar - arXiv preprint arXiv:2408.12837, 2024 - arxiv.org
Deep learning techniques have revolutionized image classification by mimicking human
cognition and automating complex decision-making processes. However, the deployment of …
cognition and automating complex decision-making processes. However, the deployment of …
Machine learning models for prediction of double and triple burdens of non-communicable diseases in Bangladesh
Increasing prevalence of non-communicable diseases (NCDs) has become the leading
cause of death and disability in Bangladesh. Therefore, this study aimed to measure the …
cause of death and disability in Bangladesh. Therefore, this study aimed to measure the …
Noise-free sampling with majority framework for an imbalanced classification problem
Class imbalance has been widely accepted as a significant factor that negatively impacts a
machine learning classifier's performance. One of the techniques to avoid this problem is to …
machine learning classifier's performance. One of the techniques to avoid this problem is to …
Tropical cyclone dataset for a high-resolution global nonhydrostatic atmospheric simulation
This dataset is a time series of tropical cyclones simulated using the high-resolution
Nonhydrostatic Icosahedral Atmospheric Model (NICAM). By tracking tropical cyclones from …
Nonhydrostatic Icosahedral Atmospheric Model (NICAM). By tracking tropical cyclones from …
Cumulonimbus cloud classification using transfer learning with AlexNet
S Chattopadhyay, A Chowdhury, SP Dutta… - AIP Conference …, 2024 - pubs.aip.org
Since weather is chaotic in nature, cloud classification is a challenging task due to its high
variability. In this study, we apply a pre-trained Convolutional Neural Network (CNN) such as …
variability. In this study, we apply a pre-trained Convolutional Neural Network (CNN) such as …
Neural style transfer between observed and simulated cloud images to improve the detection performance of tropical cyclone precursors
D Matsuoka, S Easterbrook - Environmental Data Science, 2023 - cambridge.org
A common observation in the field of pattern recognition for atmospheric phenomena using
supervised machine learning is that recognition performance decreases for events with few …
supervised machine learning is that recognition performance decreases for events with few …
[PDF][PDF] IMBALANCED CLASS LEARNING IN VISION BASED CLASSIFICATION OF VECTOR MOSQUITO SPECIES
Vector-borne diseases, primarily transmitted by mosquitoes, remain a significant global
public health concern. Accurate and timely identification of mosquito species is crucial for …
public health concern. Accurate and timely identification of mosquito species is crucial for …
Noise-Free Sampling with Majority for Imbalanced Classification Problem
Class imbalance has been widely accepted as a significant factor that negatively impacts a
machine learning classifier's performance. One of the techniques to avoid this problem is to …
machine learning classifier's performance. One of the techniques to avoid this problem is to …