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(Student)溫柏叡 : Note that I did not find the time on the subtitles, the teacher cast 2.3 to write, and then speak 3.3 start, so pay attention to start from 2.3.

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• Update Date: 2017-01-23
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• Update Date: 2017-04-15
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Parallelization of a Self-adaptive Harmony Search Algorithm on Graphics Processing Units

Abstract In recent years, in order to reduce the execution time, some evolutionary algorithms that run on GPUs using Compute Unified Device Architecture (i.e., CUDA) have been proposed. In these evolutionary algorithms, they compared the execution time and precision of GPU versions with those of CPU versions. In this study, we parallelize a self-adaptive harmony search algorithm and compare with the existing evolutionary algorithms on the same GPU platform. The proposed algorithm is divided into four steps: initialization, improvising, sorting, and updating. In the experiments, we use eight well-known optimization problems to evaluate the proposed algorithm and the other existing algorithms. As a result, our algorithm achieves the best performances among all the algorithms on the single-objective optimization problems with more dimensions or populations. Keywords: evolutionary algorithms, CUDA, self-adaptive harmony search algorithms, parallel algorithms, optimization problems

• Update Date: 2019-01-14
• Author: Sun-Ho, Chou
• Category: Algorithm

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Optimizing Convolutional Neural Network Architecture Using a Self-adaptive Harmony Search Algorithm

Abstract In recent years, the advance of GPUs led to the development in neural networks and deep learning. However, it is difficult to find a good CNN architecture people desire. In the past, people had to manually find the CNN architecture, and this is quite time-consuming and labor-intensive. In this paper, we use a self-adaptive harmony search algorithm to find the optimized convolutional neural network architecture for image recognition. The system architecture is divided into two phases. In the first phase, we search for the most suitable layer length of a CNN. In the second phase, we fine-tune the architecture found in the first phase or a pre-trained architecture. In the experiments, three popular and well-known datasets are used to evaluate the proposed methods and the state-of-the-art CNN search methods. The experimental results show that our methods achieve competitive performances compared with the other methods on MNIST and CIFAR-10, and have the best performance among all the methods on Caltech-101. Keywords: convolutional neural networks, deep learning, hyper-parameter optimization, self-adaptive harmony search algorithms, transfer learning

• Update Date: 2019-01-14
• Author: Jung-Sheng, Liu
• Category: Algorithm

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Text Analysis and Detection on Fake News

Abstract

In general, the features of fake news are almost the same as those of real news, so it is not easy to identify them. In this paper, we propose a fake news detection system using a deep learning model. First, news articles are preprocessed and analyzed based on different training models. Then, an ensemble learning model combining four different models called embedding LSTM, depth LSTM, LIWC CNN, and N-gram CNN is proposed for fake news detection. Besides, to achieve high accuracy of detecting fake news, the optimized weights of the ensemble learning model are determined using the Self-Adaptive Harmony Search (SAHS) algorithm. In the experiments, we verify that the proposed model is superior to the state-of-the-art methods, with the highest accuracy 99.4%. Furthermore, we also investigate the cross-domain intangibility issue and achieve the highest accuracy 72.3%. Finally, we believe there is still room for improving the ensemble learning model in addressing the cross-domain intangibility issue.

Keywords: deep learning, fake news, natural language processing, text mining

• Update Date: 2018-07-26
• Author: Bo-Hong, Chen
• Category: Deep learning

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Image Retrieval Based on AND/OR-construction Models

Abstract

With the rapid development of the Internet, the number of images on the Internet is increasing by millions. Finding desired images from numerous images has become an important research topic. In this paper, we propose an image retrieval system facilitating retrieval time and accuracy. Since the performance of image retrieval is deeply influenced by image features and retrieval methods. Five different types of features and five different methods are used to find the best combination for an image retrieval system. First, we segment out the main object in an image and then extract its features. Next, relevant features are selected from the original feature set for facilitating image retrieval, using the SAHS algorithm. Then, five methods based on AND/ORconstruction are proposed to build the image retrieval model, using the relevant features. Finally, the experimental results not only show that our methods are more effective than the other state-of-the-art methods, but also present some observations never explored by the previous research.

Keywords: data mining, image retrieval, locality sensitive hashing, Kmeans, deep learning

• Update Date: 2018-07-26
• Author: Yun-Xin, Xie
• Category: Data mining

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Solving Multi-Objective Optimization Problem Using a Self-Adaptive Harmony Search Algorithm

Abstract

In recent years, there have been many Multi-Objective Evolutionary Algorithms (MOEAs) proposed to solve multi-objective optimization problems. These evolutionary algorithms generate many solutions for iterations and move to the true Pareto optimal region gradually. As expected, since the Harmony Search (HS) algorithm can also iterate over a large number of solutions (in HM memory) and moves to the true Pareto optimal region, we use it to solve multi-objective optimization problems. In this paper, the proposed system architecture can be divided into two phases. In the first phase, we aim to search feasible solution regions as widely as possible in the entire process. In the second phase, we focus on searching optimized solutions stepwise in the feasible solution regions. Since the proposed algorithm uses many parameters, we adjust some of them in a self-adaptive way and call the algorithm self-adaptive. In the experiments, we use the eleven well-known multi-objective problems to examine the proposed algorithm and other existing algorithms, based on five performance indicators. As a result, our algorithm achieves better performances than the others in IGD, HV, and Spread indicators.

Keywords：harmony search algorithm, optimization method, Pareto optimization, multi-objective optimization, self-adaptive

• Update Date: 2018-02-09
• Author: Si-Hao, Chen
• Category: Algorithm

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Picture Recommendation System Built on Instagram

DEMO

You can try it here!

• Update Date: 2017-01-19
• Author: Pei-Lun, Wang
• Category: Recommendation System

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Design of generating solar power

Recently in exploiting green energy, solar power generation is a must-be trend and approach, especially for the countries with nature resource shortage. However, how to build solar power plants with the best power generation efficiency in limited spaces is always a crucial issue. In this paper, the approach of finding the optimum models of generating solar power is proposed to build solar power plants for different environments in Taiwan. First, we collect all the data from existing solar power farms, including 1) design methods of power generation, 2) actual power generation, and 3) surrounding environments. Then, after a series of preprocessing steps and system analysis on them, the optimal models of generating solar power could be mined out. Finally, in the experiments, we evaluate the system from five aspects regarding to input and output parameters. As a result, we observe that using the majority voting strategy improves the system accuracy and helps engineers build solar power plants with the maximum power generation.

• Update Date: 2017-01-19
• Author: Shun-Hao, Chang
• Category: Data Mining

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Implementation of a Medical-care Management System

• Update Date: 2016-12-31
• Author: Yun-Xin, Xie
• Category: Industry-university cooperation

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