An efficient hybrid multilayer perceptron neural network with grasshopper optimization
This paper proposes a new hybrid stochastic training algorithm using the recently proposed
grasshopper optimization algorithm (GOA) for multilayer perceptrons (MLPs) neural …
grasshopper optimization algorithm (GOA) for multilayer perceptrons (MLPs) neural …
Efficient k-nearest neighbor search based on clustering and adaptive k values
Abstract The k-Nearest Neighbor (k NN) algorithm is widely used in the supervised learning
field and, particularly, in search and classification tasks, owing to its simplicity, competitive …
field and, particularly, in search and classification tasks, owing to its simplicity, competitive …
Clustering-based k-nearest neighbor classification for large-scale data with neural codes representation
While standing as one of the most widely considered and successful supervised
classification algorithms, the k-nearest Neighbor (kNN) classifier generally depicts a poor …
classification algorithms, the k-nearest Neighbor (kNN) classifier generally depicts a poor …
Feature selection by using chaotic cuckoo optimization algorithm with levy flight, opposition-based learning and disruption operator
M Kelidari, J Hamidzadeh - Soft Computing, 2021 - Springer
Feature selection, which plays an important role in high-dimensional data analysis, is
drawing increasing attention recently. Finding the most relevant and important features for …
drawing increasing attention recently. Finding the most relevant and important features for …
New Hermite orthogonal polynomial kernel and combined kernels in support vector machine classifier
VH Moghaddam, J Hamidzadeh - Pattern Recognition, 2016 - Elsevier
Abstract Support Vector Machine is a desired method for classification of different types of
data, but the main obstacle to using this method is the considerable reduction of …
data, but the main obstacle to using this method is the considerable reduction of …
Multiple-boundary clustering and prioritization to promote neural network retraining
W Shen, Y Li, L Chen, Y Han, Y Zhou… - Proceedings of the 35th …, 2020 - dl.acm.org
With the increasing application of deep learning (DL) models in many safety-critical
scenarios, effective and efficient DL testing techniques are much in demand to improve the …
scenarios, effective and efficient DL testing techniques are much in demand to improve the …
A fuzzy c-means algorithm guided by attribute correlations and its application in the big data analysis of tunnel boring machine
Tunnel boring machine (TBM) is a complex engineering system used for tunnel construction,
and its design is mainly based on knowledge from previous projects. With the development …
and its design is mainly based on knowledge from previous projects. With the development …
Automatic support vector data description
R Sadeghi, J Hamidzadeh - Soft Computing, 2018 - Springer
Event handlers have wide range of applications such as medical assistant systems and fire
suppression systems. These systems try to provide accurate responses based on the least …
suppression systems. These systems try to provide accurate responses based on the least …
Detection of Web site visitors based on fuzzy rough sets
Despite emerging of Web 2.0 applications and increasing requirements to well-behaved
Web robots, malicious ones can reveal irreparable risks for Web sites. Regardless of …
Web robots, malicious ones can reveal irreparable risks for Web sites. Regardless of …
Adaptive edited natural neighbor algorithm
L Yang, Q Zhu, J Huang, D Cheng - Neurocomputing, 2017 - Elsevier
Reduction techniques can reduce prohibitive computational costs and the storage
requirements for classifying patterns while maintaining classification accuracy. The edited …
requirements for classifying patterns while maintaining classification accuracy. The edited …