Metaheuristic algorithms on feature selection: A survey of one decade of research (2009-2019)
Feature selection is a critical and prominent task in machine learning. To reduce the
dimension of the feature set while maintaining the accuracy of the performance is the main …
dimension of the feature set while maintaining the accuracy of the performance is the main …
A systematic review on recent advancements in deep and machine learning based detection and classification of acute lymphoblastic leukemia
Automatic Leukemia or blood cancer detection is a challenging job and is very much
required in healthcare centers. It has a significant role in early diagnosis and treatment …
required in healthcare centers. It has a significant role in early diagnosis and treatment …
[PDF][PDF] Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets
Selecting the most relevant subset of features from a dataset is a vital step in data mining
and machine learning. Each feature in a dataset has 2n possible subsets, making it …
and machine learning. Each feature in a dataset has 2n possible subsets, making it …
Randomly attracted rough firefly algorithm for histogram based fuzzy image clustering
Image segmentation process is one of the most interesting and challenging problems in
digital image processing tasks. The segmentation process involves finding similar regions …
digital image processing tasks. The segmentation process involves finding similar regions …
Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review
Multilevel Thresholding (MLT) is considered as a significant and imperative research field in
image segmentation that can efficiently resolve difficulties aroused while analyzing the …
image segmentation that can efficiently resolve difficulties aroused while analyzing the …
A survey on the utilization of Superpixel image for clustering based image segmentation
Superpixel become increasingly popular in image segmentation field as it greatly helps
image segmentation techniques to segment the region of interest accurately in noisy …
image segmentation techniques to segment the region of interest accurately in noisy …
DeepLeukNet—A CNN based microscopy adaptation model for acute lymphoblastic leukemia classification
Abstract Acute Lymphoblastic Leukemia is one of the fatal types of disease which causes a
high mortality rate among children and adults. Traditional diagnosing of this disease is …
high mortality rate among children and adults. Traditional diagnosing of this disease is …
Human-inspired optimization algorithms: Theoretical foundations, algorithms, open-research issues and application for multi-level thresholding
Humans take immense pride in their ability to be unpredictably intelligent and despite huge
advances in science over the past century; our understanding about human brain is still far …
advances in science over the past century; our understanding about human brain is still far …
A framework for interactive medical image segmentation using optimized swarm intelligence with convolutional neural networks
Recent improvements in current technology have had a significant impact on a wide range
of image processing applications, including medical imaging. Classification, detection, and …
of image processing applications, including medical imaging. Classification, detection, and …
Histogram-based fast and robust image clustering using stochastic fractal search and morphological reconstruction
Partitional clustering-based image segmentation is one of the most significant approaches.
K-means is the conventional clustering techniques even though very sensitive to noise and …
K-means is the conventional clustering techniques even though very sensitive to noise and …