Towards paddy rice smart farming: a review on big data, machine learning, and rice production tasks
Big Data (BD), Machine Learning (ML) and Internet of Things (IoT) are expected to have a
large impact on Smart Farming and involve the whole supply chain, particularly for rice …
large impact on Smart Farming and involve the whole supply chain, particularly for rice …
A comparative study of various feature selection techniques in high-dimensional data set to improve classification accuracy
KP Shroff, HH Maheta - 2015 international conference on …, 2015 - ieeexplore.ieee.org
The performance of machine learning algorithm depends on features considered from the
dataset. High dimensional dataset degrades the performance of learning algorithm as …
dataset. High dimensional dataset degrades the performance of learning algorithm as …
Towards an unsupervised feature selection method for effective dynamic features
Dynamic features applications present new obstacles for the selection of streaming features.
The dynamic features applications have various characteristics: a) features are processed …
The dynamic features applications have various characteristics: a) features are processed …
[HTML][HTML] Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm
Cancer investigations in microarray data play a major role in cancer analysis and the
treatment. Cancer microarray data consists of complex gene expressed patterns of cancer …
treatment. Cancer microarray data consists of complex gene expressed patterns of cancer …
Dimensionality reduction for intrusion detection systems in multi-data streams—A review and proposal of unsupervised feature selection scheme
Abstract An Intrusion Detection System (IDS) is a security mechanism that is intended to
dynamically inspect traffic in order to detect any suspicious behaviour or launched attacks …
dynamically inspect traffic in order to detect any suspicious behaviour or launched attacks …
A multistart tabu search-based method for feature selection in medical applications
In the design of classification models, irrelevant or noisy features are often generated. In
some cases, there may even be negative interactions among features. These weaknesses …
some cases, there may even be negative interactions among features. These weaknesses …
Peer to peer lending risk analysis based on embedded technique and stacking ensemble learning
Peer to peer lending is famous for easy and fast loans from complicated traditional lending
institutions. Therefore, big data and machine learning are needed for credit risk analysis …
institutions. Therefore, big data and machine learning are needed for credit risk analysis …
Optimization of convolutional neural network in paddy disease detection
In Sabah, agriculture is an important economic sector. The situation has recently worsened
due to paddy cultivation and rice production management issues. A traditional form, such as …
due to paddy cultivation and rice production management issues. A traditional form, such as …
[PDF][PDF] Filter-Based Feature Selection and Machine-Learning Classification of Cancer Data.
M Farsi - Intelligent Automation & Soft Computing, 2021 - academia.edu
Microarray cancer data poses many challenges for machine-learning (ML) classification
including noisy data, small sample size, high dimensionality, and imbalanced class labels …
including noisy data, small sample size, high dimensionality, and imbalanced class labels …
Optimizing the Classification Performance by Fine-Tuning the Machine Learning Hyperparameters and Utilizing PCA and RFE Feature Selection Methods
AA Adam, R Alfred - … Conference on Advances in Computational Science …, 2023 - Springer
Breast cancer is among the most common and potentially fatal cancers, especially among
women. Breast cancer usually has no obvious early signs, and doctors sometimes have …
women. Breast cancer usually has no obvious early signs, and doctors sometimes have …