Exploring the relation between production factors, ore grades, and life of mine for forecasting mining capital cost through a novel cascade forward neural network-based salp swarm optimization model

<span>This paper proposes the Particle Swarm Optimization model for enhancing the performance of an Artificial Neural Network. The learning process of Artificial Neural Network requires a long time to satisfy requirements because of processing complexity of the backpropagation algorithm that has been used in training Artificial Neural Network. It is a nonlinear complex model that can be used to configure and train an artificial neuron system. Both Artificial Neural Network and Particle Swarm Optimization model have been managed to solve and optimize several nonlinear models. Heuristic Optimization Weight of Artificial Neural Network (HNN) is a proactive metaheuristic model proposed to optimize the performance of Artificial Neural Network. The proposed system applies Particle Swarm Optimization to find the optimum weights of the Artificial Neural Network instead of using the Backpropagation algorithm. Experimentally, the proposed system has required less processing time (average of 76.91 Sec.) than Backpropagation (average of 93.32 Sec). Furthermore, It has provided better classification accuracy (start from 80% to 97.20%) comparing with Backpropagation (start from 75.32% to 94.32%).</span>

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Hardware-Based Principal Component Analysis for Hybrid Neural Network Trained by Particle Swarm Optimization on a Chip

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

10.1587/transfun.e102.a.1374 ◽

2019 ◽

Vol E102.A (10) ◽

pp. 1374-1382

Author(s):

Tuan Linh DANG ◽

Yukinobu HOSHINO

Keyword(s):

Neural Network ◽

Principal Component Analysis ◽

Particle Swarm Optimization ◽

Particle Swarm ◽

Principal Component ◽

Component Analysis ◽

Swarm Optimization ◽

Hybrid Neural Network

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Analysis of Feature Reduction Techniques for Online News Popularity Prediction

SMART MOVES JOURNAL IJOSCIENCE ◽

10.24113/ijo-science.v4i10.165 ◽

2018 ◽

Vol 4 (10) ◽

pp. 6

Author(s):

Shivangi Bhargava ◽

Dr. Shivnath Ghosh

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Particle Swarm Optimization ◽

Naive Bayes ◽

Particle Swarm ◽

Naïve Bayes ◽

Online News ◽

Feature Reduction ◽

Support Vector ◽

Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

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