H.3.7. Learning
Laleh Armi; Elham Abbasi
Abstract
In this paper, we propose an innovative classification method for tree bark classification and tree species identification. The proposed method consists of two steps. In the first step, we take the advantages of ILQP, a rotationally invariant, noise-resistant, and fully descriptive color texture feature ...
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In this paper, we propose an innovative classification method for tree bark classification and tree species identification. The proposed method consists of two steps. In the first step, we take the advantages of ILQP, a rotationally invariant, noise-resistant, and fully descriptive color texture feature extraction method. Then, in the second step, a new classification method called stacked mixture of ELM-based experts with a trainable gating network (stacked MEETG) is proposed. The proposed method is evaluated using the Trunk12, BarkTex, and AFF datasets. The performance of the proposed method on these three bark datasets shows that our approach provides better accuracy than other state-of-the-art methods.Our proposed method achieves an average classification accuracy of 92.79% (Trunk12), 92.54% (BarkTex), and 91.68% (AFF), respectively. Additionally, the results demonstrate that ILQP has better texture feature extraction capabilities than similar methods such as ILTP. Furthermore, stacked MEETG has shown a great influence on the classification accuracy.
Z. Nazari; H.R. Koohi; J. Mousavi
Abstract
Nowadays, with the expansion of the internet and its associated technologies, recommender systems have become increasingly common. In this work, the main purpose is to apply new deep learning-based clustering methods to overcome the data sparsity problem and increment the efficiency of recommender systems ...
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Nowadays, with the expansion of the internet and its associated technologies, recommender systems have become increasingly common. In this work, the main purpose is to apply new deep learning-based clustering methods to overcome the data sparsity problem and increment the efficiency of recommender systems based on precision, accuracy, F-measure, and recall. Within the suggested model of this research, the hidden biases and input weights values of the extreme learning machine algorithm are produced by the Restricted Boltzmann Machine and then clustering is performed. Also, this study employs the ELM for two approaches, clustering of training data and determine the clusters of test data. The results of the proposed method evaluated in two prediction methods by employing average and Pearson Correlation Coefficient in the MovieLens dataset. Considering the outcomes, it can be clearly said that the suggested method can overcome the problem of data sparsity and achieve higher performance in recommender systems. The results of evaluation of the proposed approach indicate a higher rate of all evaluation metrics while using the average method results in rates of precision, accuracy, recall, and F-Measure come to 80.49, 83.20, 67.84 and 73.62 respectively.
H.6.3.2. Feature evaluation and selection
A. Zangooei; V. Derhami; F. Jamshidi
Abstract
Phishing is one of the luring techniques used to exploit personal information. A phishing webpage detection system (PWDS) extracts features to determine whether it is a phishing webpage or not. Selecting appropriate features improves the performance of PWDS. Performance criteria are detection accuracy ...
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Phishing is one of the luring techniques used to exploit personal information. A phishing webpage detection system (PWDS) extracts features to determine whether it is a phishing webpage or not. Selecting appropriate features improves the performance of PWDS. Performance criteria are detection accuracy and system response time. The major time consumed by PWDS arises from feature extraction that is considered as feature cost in this paper. Here, two novel features are proposed. They use semantic similarity measure to determine the relationship between the content and the URL of a page. Since suggested features don't apply third-party services such as search engines result, the features extraction time decreases dramatically. Login form pre-filer is utilized to reduce unnecessary calculations and false positive rate. In this paper, a cost-based feature selection is presented as the most effective feature. The selected features are employed in the suggested PWDS. Extreme learning machine algorithm is used to classify webpages. The experimental results demonstrate that suggested PWDS achieves high accuracy of 97.6% and short average detection time of 120.07 milliseconds.
