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Found 16 papers, 1 papers with code
Lightweight Fish Classification Model for Sustainable Marine Management: Indonesian Case
The enormous demand for seafood products has led to exploitation of marine resources and near-extinction of some species.
e-Inu: Simulating A Quadruped Robot With Emotional Sentience
To this end, we use a combination of reinforcement learning and software engineering concepts to simulate a quadruped robot that can understand emotions, navigate through various terrains and detect sound sources, and respond to emotions using audio-visual feedback.
Synthetic Data for Feature Selection
Feature selection is an important and active field of research in machine learning and data science.
Partial Resampling of Imbalanced Data
Imbalanced data is a frequently encountered problem in machine learning.
Orthogonal variance-based feature selection for intrusion detection systems
In this paper, we apply a fusion machine learning method to construct an automatic intrusion detection system.
Kernel density estimation-based sampling for neural network classification
We conclude that KDE sampling is capable of significantly improving the performance of neural networks.
Feature selection for intrusion detection systems
In this paper, we analyze existing feature selection methods to identify the key elements of network traffic data that allow intrusion detection.
Stock price forecast with deep learning
In this paper, we compare various approaches to stock price prediction using neural networks.
Forecasting with Deep Learning: S&P 500 index
Stock price prediction has been the focus of a large amount of research but an acceptable solution has so far escaped academics.
Machine learning applications for COVID-19: A state-of-the-art review
The COVID-19 pandemic has galvanized the machine learning community to create new solutions that can help in the fight against the virus.
Gamma distribution-based sampling for imbalanced data
The proposed method is based on generating new minority instances in the neighborhood of the existing minority points via a gamma distribution.
Machine learning based forecasting of significant daily returns in foreign exchange markets
Numerical experiments show that outlier detection methods substantially outperform traditional machine learning and finance techniques.
Forecasting significant stock price changes using neural networks
Stock price prediction is a rich research topic that has attracted interest from various areas of science.
Orthogonal variance decomposition based feature selection
Existing feature selection methods fail to properly account for interactions between features when evaluating feature subsets.
Kernel density estimation based sampling for imbalanced class distribution
We believe that KDE offers a more natural way of generating new instances of minority class that is less prone to overfitting than other standard sampling techniques.
Outlier Detection in High Dimensional Data
In particular, outlier detection algorithms perform poorly on data set of small size with a large number of features.
