scholarly journals Comparing Convolution Neural Network Models for Leaf Recognition

2018 ◽  
Vol 7 (3.15) ◽  
pp. 141 ◽  
Author(s):  
Nurbaity Sabri ◽  
Zalilah Abdul Aziz ◽  
Zaidah Ibrahim ◽  
Muhammad Akmal Rasydan Bin Mohd Rosni ◽  
Abdul Hafiz bin Abd Ghapul
Keyword(s):  
Neural Network ◽  
Processing Time ◽  
Recognition Performance ◽  
Network Models ◽  
Convolution Neural Network ◽  
The Other ◽  
Great Success ◽  
Classification Models ◽  
Neural Network Models ◽  
Number Of Layers

This research compares the recognition performance between pre-trained models, GoogLeNet and AlexNet, with basic Convolution Neural Network (CNN) for leaf recognition. Lately, CNN has gained a lot of interest in image processing applications. Numerous pre-trained models have been introduced and the most popular pre-trained models are GoogLeNet and AlexNet. Each model has its own layers of convolution and computational complexity. A great success has been achieved using these classification models in computer vision and this research investigates their performances for leaf recognition using MalayaKew (MK), an open access leaf dataset. GoogLeNet achieves a perfect 100% accuracy, outperforms both AlexNet and basic CNN. On the other hand, the processing time for GoogLeNet is longer compared to the other models due to the high number of layers in its architecture.  

scholarly journals Classification of Germination Images of Pear Pollen Using Random Forest and Convolution Neural Network Models

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 45993-45999
Author(s):  
Ung Yang ◽  
Seungwon Oh ◽  
Seung Gon Wi ◽  
Bok-Rye Lee ◽  
Sang-Hyun Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Network Models ◽  
Convolution Neural Network ◽  
Neural Network Models

scholarly journals Using self-organizing maps for determination of soil fertility (case study: Shiraz plain)

2018 ◽  
Vol 13 (No. 1) ◽  
pp. 11-17 ◽  
Author(s):  
M. Mokarram ◽  
M. Najafi-Ghiri ◽  
A.R. Zarei
Keyword(s):  
Neural Network ◽  
Soil Fertility ◽  
Network Models ◽  
The Other ◽  
Neural Network Models ◽  
Self Organizing Maps ◽  
Soil Features ◽  
Close Relationship ◽  
Artificial Neural Network Ann ◽  
Self Organizing

Soil fertility refers to the ability of a soil to supply plant nutrients. Naturally, micro and macro elements are made available to plants by breakdown of the mineral and organic materials in the soil. Artificial neural network (ANN) provides deeper understanding of human cognitive capabilities. Among various methods of ANN and learning an algorithm, self-organizing maps (SOM) are one of the most popular neural network models. The aim of this study was to classify the factors influencing soil fertility in Shiraz plain, southern Iran. The relationships among soil features were studied using the SOM in which, according to qualitative data, the clustering tendency of soil fertility was investigated using seven parameters (N, P, K, Fe, Zn, Mn, and Cu). The results showed that for soil fertility there is a close relationship between P and N, and also between P and Zn. The other parameters, such as K, Fe, Mn, and Cu, are not mutually related. The results showed that there are six clusters for soil fertility and also that group 1 soils are more fertile than the other.

scholarly journals Deep Learning-based Integrated Stacked Model for the Stock Market Prediction

2019 ◽  
Vol 9 (1) ◽  
pp. 5167-5174 ◽  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Stock Market ◽  
Network Models ◽  
Time Series Forecasting ◽  
Convolution Neural Network ◽  
Neural Network Models ◽  
Stock Market Prediction ◽  
Market Data

Recently, the stock market prediction has become one of the essential application areas of time-series forecasting research. The successful prediction of the stock market can be better guided to the investors to maximize their profit and to minimize the risk of investment. The stock market data are very much complex, non-linear and dynamic. Due to this reason, still, it is a challenging task. In recent time, deep learning method has become one of the most popular machine learning methods for time-series forecasting due to their temporal feature extraction capabilities. In this paper, we have proposed a novel Deep Learning-based Integrated Stacked Model (DISM) that integrates both the 1D Convolution neural network and LSTM recurrent neural network to find the spatial and temporal features from the stock market data. Our proposed DISM is applied to forecast the stock market. Here, we have also compared our proposed DISM with the single structured stacked LSTM, and 1D Convolution neural network models, and some other statistical models. We have observed that our proposed DISM produces better results in terms of accuracy and stability.

scholarly journals Object Recognition in Image Sequence using Heuristic Convolution Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 520-525
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Heuristic Method ◽  
Recognition Task ◽  
Network Models ◽  
Poor Performance ◽  
Image Sequence ◽  
Convolution Neural Network ◽  
Neural Network Models ◽  
Self Learning

The human visual system can make a distinction of tiger from cat very easily without taking any efforts. But in case of a computer system, it is a very complicated job. Identifying and differentiating task has to deal with many challenges but the human brain makes it effortless. Self learning or heuristic techniques are most relevant in this area. The recognition task is to search for the particular object of same shape, color and texture and so on, of the trained objects and match with input. The geometrical distinction such as zoom in, zoom out, rotation etc result in poor performance. This paper uses convolution neural network models Alexnet and VGGNet on object recognition problems which are added with novel heuristic method. We have used CIFAR-10 dataset. The performance and computation speeds are found efficient.

scholarly journals Context-Aware POI Sequence Recommendation with Attention-Based Neural Network

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Jianfeng Huang ◽  
Yuefeng Liu ◽  
Yue Chen ◽  
Chen Jia
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Learning Ability ◽  
Great Success ◽  
Context Aware ◽  
Neural Network Models ◽  
Powerful Learning ◽  
The Neural Network ◽  
Poi Recommendation ◽  
Location Based Social Networks

<p><strong>Abstract.</strong> Location-based social networks (LBSNs) is playing an increasingly important role in our daily life, through which users can share their locations and location-related contents at any time. The Location information implicitly expresses user's behaviour preference. Therefore, LBSNs is being widely explored for Point-of-Interest (POI) recommendation in recent years. Most of existing POI recommenders only recommend a single POI, while sometimes successive POI sequence recommendation is more practical. For example, when we travel to a strange city, what we expect is not a single POI recommendation, but a POI sequence recommendation which contains a set of POIs and the order of visiting them. To solve this problem, this paper proposes a novel model called Context-Aware POI Sequence Recommendation (CPSR), which is developed based on an attention-based neural network. Neural network has made a great success in various of field because of its powerful learning ability. Recently, dozens of works has demonstrated that attention mechanism can make the neural network models more reasonable.</p>

scholarly journals Hybrid machine learning hydrological model for flood forecast purpose

2020 ◽  
Vol 12 (1) ◽  
pp. 813-820
Author(s):  
Guangyuan Kan ◽  
Ke Liang ◽  
Haijun Yu ◽  
Bowen Sun ◽  
Liuqian Ding ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Nearest Neighbor ◽  
Hydrological Model ◽  
Network Models ◽  
Great Success ◽  
Flood Forecast ◽  
K Nearest Neighbor ◽  
Neural Network Models ◽  
Hybrid Machine

AbstractMachine learning-based data-driven models have achieved great success since their invention. Nowadays, the artificial neural network (ANN)-based machine learning methods have made great progress than ever before, such as the deep learning and reinforcement learning, etc. In this study, we coupled the ANN with the K-nearest neighbor method to propose a novel hybrid machine learning (HML) hydrological model for flood forecast purpose. The advantage of the proposed model over traditional neural network models is that it can predict discharge continuously without accuracy loss owed to its specially designed model structure. In order to overcome the local minimum issue of the traditional neural network training, a genetic algorithm and Levenberg–Marquardt-based multi-objective training method was also proposed. Real-world applications of the HML hydrological model indicated its satisfactory performance and reliable stability, which enlightened the possibility of further applications of the HML hydrological model in flood forecast problems.

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