scholarly journals Image Classification using a Hybrid LSTM-CNN Deep Neural Network

2019 ◽  
Vol 8 (6) ◽  
pp. 1342-1348 ◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Short Term Memory ◽  
Wide Spectrum ◽  
New Paradigm ◽  
Long Duration ◽  
Proposed Model ◽  
Classification Tasks

This work elaborates on the integration of the rudimentary Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM), resulting in a new paradigm in the well-explored field of image classification. LSTM is one kind of Recurrent Neural Network (RNN) which has the potential to memorize long-term dependencies. It was observed that LSTMs are able to complement the feature extraction ability of CNN when used in a layered order. LSTMs have the capacity to selectively remember patterns for a long duration of time and CNNs are able to extract the important features out of it. This LSTM-CNN layered structure, when used for image classification, has an edge over conventional CNN classifier. The model which has been proposed is based on the sets of Artificial Neural Network like Recurrent and Convolutional neural network; hence this model is robust and suitable to a wide spectrum of classification tasks. To validate these results, we have tested our model on two standard datasets. The results have been compared with other classifiers to establish the significance of our proposed model.

scholarly journals Label Rectification Learning through Kernel Extreme Learning Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Qiang Cai ◽  
Fenghai Li ◽  
Yifan Chen ◽  
Haisheng Li ◽  
Jian Cao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Extreme Learning Machine ◽  
Classification Performance ◽  
Considerable Progress ◽  
Strong Representation ◽  
Kernel Extreme Learning Machine ◽  
Classification Tasks ◽  
Learning Machine

Along with the strong representation of the convolutional neural network (CNN), image classification tasks have achieved considerable progress. However, majority of works focus on designing complicated and redundant architectures for extracting informative features to improve classification performance. In this study, we concentrate on rectifying the incomplete outputs of CNN. To be concrete, we propose an innovative image classification method based on Label Rectification Learning (LRL) through kernel extreme learning machine (KELM). It mainly consists of two steps: (1) preclassification, extracting incomplete labels through a pretrained CNN, and (2) label rectification, rectifying the generated incomplete labels by the KELM to obtain the rectified labels. Experiments conducted on publicly available datasets demonstrate the effectiveness of our method. Notably, our method is extensible which can be easily integrated with off-the-shelf networks for improving performance.

scholarly journals Predicting Sentiment Polarity of Microblogs using an LSTM – CNN Deep Learning Model

2019 ◽  
Vol 8 (6) ◽  
pp. 4368-4373
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Learning Model ◽  
Supervised Machine Learning ◽  
Machine Learning Model ◽  
Proposed Model ◽  
Network Approaches

In this paper we propose a novel supervised machine learning model to predict the polarity of sentiments expressed in microblogs. The proposed model has a stacked neural network structure consisting of Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) layers. In order to capture the long-term dependencies of sentiments in the text ordering of a microblog, the proposed model employs an LSTM layer. The encodings produced by the LSTM layer are then fed to a CNN layer, which generates localized patterns of higher accuracy. These patterns are capable of capturing both local and global long-term dependences in the text of the microblogs. It was observed that the proposed model performs better and gives improved prediction accuracy when compared to semantic, machine learning and deep neural network approaches such as SVM, CNN, LSTM, CNN-LSTM, etc. This paper utilizes the benchmark Stanford Large Movie Review dataset to show the significance of the new approach. The prediction accuracy of the proposed approach is comparable to other state-of-art approaches.

scholarly journals Multi-Step Short-Term Wind Speed Prediction Using a Residual Dilated Causal Convolutional Network with Nonlinear Attention

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1772 ◽  
Author(s):  
Kumar Shivam ◽  
Jong-Chyuan Tzou ◽  
Shang-Chen Wu
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Convolutional Neural Network ◽  
Real World ◽  
Prediction Models ◽  
Short Term Memory ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Proposed Model ◽  
Speed Prediction

Wind energy is the most used renewable energy worldwide second only to hydropower. However, the stochastic nature of wind speed makes it harder for wind farms to manage the future power production and maintenance schedules efficiently. Many wind speed prediction models exist that focus on advance neural networks and/or preprocessing techniques to improve the accuracy. Since most of these models require a large amount of historic wind data and are validated using the data split method, the application to real-world scenarios cannot be determined. In this paper, we present a multi-step univariate prediction model for wind speed data inspired by the residual U-net architecture of the convolutional neural network (CNN). We propose a residual dilated causal convolutional neural network (Res-DCCNN) with nonlinear attention for multi-step-ahead wind speed forecasting. Our model can outperform long-term short-term memory networks (LSTM), gated recurrent units (GRU), and Res-DCCNN using sliding window validation techniques for 50-step-ahead wind speed prediction. We tested the performance of the proposed model on six real-world wind speed datasets with different probability distributions to confirm its effectiveness, and using several error metrics, we demonstrated that our proposed model was robust, precise, and applicable to real-world cases.

scholarly journals Deep convolutional neural network based medical image classification for disease diagnosis

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Samir S. Yadav ◽  
Shivajirao M. Jadhav
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Transfer Learning ◽  
Medical Image ◽  
Data Augmentation ◽  
Support Vector ◽  
Improve Performance ◽  
Classification Tasks ◽  
Medical Image Classification

AbstractMedical image classification plays an essential role in clinical treatment and teaching tasks. However, the traditional method has reached its ceiling on performance. Moreover, by using them, much time and effort need to be spent on extracting and selecting classification features. The deep neural network is an emerging machine learning method that has proven its potential for different classification tasks. Notably, the convolutional neural network dominates with the best results on varying image classification tasks. However, medical image datasets are hard to collect because it needs a lot of professional expertise to label them. Therefore, this paper researches how to apply the convolutional neural network (CNN) based algorithm on a chest X-ray dataset to classify pneumonia. Three techniques are evaluated through experiments. These are linear support vector machine classifier with local rotation and orientation free features, transfer learning on two convolutional neural network models: Visual Geometry Group i.e., VGG16 and InceptionV3, and a capsule network training from scratch. Data augmentation is a data preprocessing method applied to all three methods. The results of the experiments show that data augmentation generally is an effective way for all three algorithms to improve performance. Also, Transfer learning is a more useful classification method on a small dataset compared to a support vector machine with oriented fast and rotated binary (ORB) robust independent elementary features and capsule network. In transfer learning, retraining specific features on a new target dataset is essential to improve performance. And, the second important factor is a proper network complexity that matches the scale of the dataset.

scholarly journals Local Importance Representation Convolutional Neural Network for Fine-Grained Image Classification

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 479 ◽  
Author(s):  
Yadong Yang ◽  
Xiaofeng Wang ◽  
Hengzheng Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Real Life ◽  
Feature Learning ◽  
Local Area ◽  
Fine Grained ◽  
Benchmark Datasets ◽  
Classification Tasks ◽  
To Receive

Compared with ordinary image classification tasks, fine-grained image classification is closer to real-life scenes. Its key point is how to find the local areas with sufficient discrimination and perform effective feature learning. Based on a bilinear convolutional neural network (B-CNN), this paper designs a local importance representation convolutional neural network (LIR-CNN) model, which can be divided into three parts. Firstly, the super-pixel segmentation convolution method is used for the input layer of the model. It allows the model to receive images of different sizes and fully considers the complex geometric deformation of the images. Then, we replaced the standard convolution of B-CNN with the proposed local importance representation convolution. It can score each local area of the image using learning to distinguish their importance. Finally, channelwise convolution is proposed and it plays an important role in balancing lightweight network and classification accuracy. Experimental results on the benchmark datasets (e.g., CUB-200-2011, FGVC-Aircraft, and Stanford Cars) showed that the LIR-CNN model had good performance in fine-grained image classification tasks.

scholarly journals Image Classification using Supervised Convolutional Neural Network

2019 ◽  
Vol 8 (2) ◽  
pp. 4505-4507
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Large Scale ◽  
Computational Time ◽  
The Past ◽  
Input Layer ◽  
Classification Tasks ◽  
High Processing ◽  
Model Algorithm

Deep learning algorithms, in particular Convolutional Neural Networks have made notable accomplishments in many large-scale image classification tasks in the past decade. In this paper, image classification is performed using Supervised Convolutional Neural Network (SCNN). In supervised learning model, algorithm learns on a labeled dataset. SCNN architecture is built with 15 layers viz, input layer, 9 middle layers and 5 final layers. Two datasets of different sizes are tested on SCNN framework on single CPU. With CIFAR10 dataset of 60000 images the network yielded an accuracy of 73% taking high processing time, while for 3000 images taken from MIO-TCD dataset resulted 96% accuracy with less computational time

SACNN: Self-attentive Convolutional Neural Network Model for Natural Language Inference

2021 ◽  
Vol 20 (3) ◽  
pp. 1-16
Author(s):  
Waris Quamer ◽  
Praphula Kumar Jain ◽  
Arpit Rai ◽  
Vijayalakshmi Saravanan ◽  
Rajendra Pamula ◽  
...  
Keyword(s):  
Neural Network ◽  
Natural Language ◽  
Short Term Memory ◽  
Local Context ◽  
Short Term ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
Complex Relationships ◽  
Gated Recurrent Units

Inference has been central problem for understanding and reasoning in artificial intelligence. Especially, Natural Language Inference is an interesting problem that has attracted the attention of many researchers. Natural language inference intends to predict whether a hypothesis sentence can be inferred from the premise sentence. Most prior works rely on a simplistic association between the premise and hypothesis sentence pairs, which is not sufficient for learning complex relationships between them. The strategy also fails to exploit local context information fully. Long Short Term Memory (LSTM) or gated recurrent units networks (GRU) are not effective in modeling long-term dependencies, and their schemes are far more complex as compared to Convolutional Neural Networks (CNN). To address this problem of long-term dependency, and to involve context for modeling better representation of a sentence, in this article, a general Self-Attentive Convolution Neural Network (SACNN) is presented for natural language inference and sentence pair modeling tasks. The proposed model uses CNNs to integrate mutual interactions between sentences, and each sentence with their counterparts is taken into consideration for the formulation of their representation. Moreover, the self-attention mechanism helps fully exploit the context semantics and long-term dependencies within a sentence. Experimental results proved that SACNN was able to outperform strong baselines and achieved an accuracy of 89.7% on the stanford natural language inference (SNLI) dataset.

Sportswear retailing forecast model based on the combination of multi-layer perceptron and convolutional neural network

2021 ◽  
pp. 004051752110205
Author(s):  
Jun Xu ◽  
Yun Zhou ◽  
Liang Zhang ◽  
Jianming Wang ◽  
Damien Lefloch
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Inventory Management ◽  
Prediction Accuracy ◽  
Forecast Model ◽  
Forecasting Model ◽  
Multi Layer Perceptron ◽  
Proposed Model

Apparel sales forecasting plays an important role in production planning, distribution decision, and inventory management of enterprises. Especially, the sportswear market has been shown rapid growth characterized by long-term sales. This paper proposes a sales forecasting model for sportswear sales based on the multi-layer perceptron (MLP) and the convolutional neural network (CNN). A novel loss function is also proposed to improve the prediction accuracy. The proposed model is trained and validated on the time-series retailing data collected from three offline local sports stores in China. The influencing factors of retailing forecasting, such as time-series sales data, product features, distribution strategy, shop size, and other parameters, were also defined. Experimental results show that the proposed forecasting model outperforms the compared statistical methods by a large margin. Specifically, the proposed model provided 65% prediction accuracy, while the compared methods provided 16% prediction accuracy. The results show that the proposed model could be potentially used in sportswear sales forecasting, especially offline clothing and other long lifecycle clothing fields.

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