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Deep Learning for Tomato Diseases: Classification and Symptoms Visualization
TLDR
We have used a large dataset compared to the state-of-the art to train a deep classifier to protect tomato from diseases by processing leaf images. Expand
Deep Learning for Plant Diseases: Detection and Saliency Map Visualisation
TLDR
In this chapter, we have tested multiple state-of-the-art Convolutional Neural Network (CNN) architectures using three learning strategies on a public dataset for plant diseases classification. Expand
Penguins Search Optimization Algorithm (PeSOA)
TLDR
In this paper we propose a new meta-heuristic algorithm called penguins Search Optimization Algorithm (PeSOA), based on collaborative hunting strategy of penguins. Expand
A cooperative swarm intelligence algorithm for multi-objective discrete optimization with application to the knapsack problem
TLDR
We propose a novel cooperative swarm intelligence algorithm to solve multi-objective discrete optimization problems (MODP) using firefly and particle swarm optimization. Expand
Penguins Search Optimisation Algorithm for Association Rules Mining
TLDR
We propose a new ARM approach based on penguins search optimisation algorithm (Pe-ARM for short) for association rules mining. Expand
A review on methods to estimate a CT from MRI data in the context of MRI-alone RT
TLDR
In this paper, we review methods for creating pseudo-CT images from MRI data for MRI-alone RT. Expand
Penguin Search Optimisation Algorithm for Finding Optimal Spaced Seeds
TLDR
This paper develops PeSeeD, a new metaheuristic algorithm for finding optimal spaced seeds subset which improves sensitivity without increasing the computation time. Expand
Information retrieval: A new multilingual stemmer based on a statistical approach
TLDR
In this paper, we propose a new multilingual stemmer based on the extraction of word root and in which we use the technique of n-grams. Expand
Node similarity and modularity for finding communities in networks
Abstract Community detection in networks has become a very important axis of research for understanding the structure of networks. Several methods have been proposed to detect the most optimalExpand
EMeD-Part: An Efficient Methodology for Horizontal Partitioning in Data Warehouses
TLDR
We present a new methodology based on Jaccard index, data mining and particle swarm optimization for solving the horizontal partitioning problem in data warehouses using relatively large query workload. Expand
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