Citrus Pests and Diseases Recognition Model Using Weakly Dense Connected Convolution Network

Pests and diseases can cause severe damage to citrus fruits. Farmers used to rely on experienced experts to recognize them, which is a time consuming and costly process. With the popularity of image sensors and the development of computer vision technology, using convolutional neural network (CNN) models to identify pests and diseases has become a recent trend in the field of agriculture. However, many researchers refer to pre-trained models of ImageNet to execute different recognition tasks without considering their own dataset scale, resulting in a waste of computational resources. In this paper, a simple but effective CNN model was developed based on our image dataset. The proposed network was designed from the aspect of parameter efficiency. To achieve this goal, the complexity of cross-channel operation was increased and the frequency of feature reuse was adapted to network depth. Experiment results showed that Weakly DenseNet-16 got the highest classification accuracy with fewer parameters. Because this network is lightweight, it can be used in mobile devices.

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Fabric Defect Detection based on Improved Faster RCNN

International Journal of Artificial Intelligence & Applications ◽

10.5121/ijaia.2021.12402 ◽

2021 ◽

Vol 12 (04) ◽

pp. 23-32

Author(s):

Yuan He ◽

Han-Dong Zhang ◽

Xin-Yue Huang ◽

Francis Eng Hock Tay

Keyword(s):

Neural Network ◽

Computer Vision ◽

Production Process ◽

Product Quality ◽

Defect Detection ◽

Classification Accuracy ◽

Detection Method ◽

Fabric Defect Detection ◽

Fabric Defect ◽

Computer Vision Technology

In the production process of fabric, defect detection plays an important role in the control of product quality. Consider that traditional manual fabric defect detection method are time-consuming and inaccuracy, utilizing computer vision technology to automatically detect fabric defects can better fulfill the manufacture requirement. In this project, we improved Faster RCNN with convolutional block attention module (CBAM) to detect fabric defects. Attention module is introduced from graph neural network, it can infer the attention map from the intermediate feature map and multiply the attention map to adaptively refine the feature. This method improve the performance of classification and detection without increase the computation-consuming. The experiment results show that Faster RCNN with attention module can efficient improve the classification accuracy.

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Optimizing Convolutional Neural Network Hyperparameters by Enhanced Swarm Intelligence Metaheuristics

Algorithms ◽

10.3390/a13030067 ◽

2020 ◽

Vol 13 (3) ◽

pp. 67 ◽

Cited By ~ 10

Author(s):

Nebojsa Bacanin ◽

Timea Bezdan ◽

Eva Tuba ◽

Ivana Strumberger ◽

Milan Tuba

Keyword(s):

Neural Network ◽

Computer Vision ◽

Classification Accuracy ◽

Structure Design ◽

The Other ◽

Automatic Method ◽

The Right ◽

Computational Resources ◽

Improved Methods ◽

Real World Problems

Computer vision is one of the most frontier technologies in computer science. It is used to build artificial systems to extract valuable information from images and has a broad range of applications in various areas such as agriculture, business, and healthcare. Convolutional neural networks represent the key algorithms in computer vision, and in recent years, they have attained notable advances in many real-world problems. The accuracy of the network for a particular task profoundly relies on the hyperparameters’ configuration. Obtaining the right set of hyperparameters is a time-consuming process and requires expertise. To approach this concern, we propose an automatic method for hyperparameters’ optimization and structure design by implementing enhanced metaheuristic algorithms. The aim of this paper is twofold. First, we propose enhanced versions of the tree growth and firefly algorithms that improve the original implementations. Second, we adopt the proposed enhanced algorithms for hyperparameters’ optimization. First, the modified metaheuristics are evaluated on standard unconstrained benchmark functions and compared to the original algorithms. Afterward, the improved algorithms are employed for the network design. The experiments are carried out on the famous image classification benchmark dataset, the MNIST dataset, and comparative analysis with other outstanding approaches that were tested on the same problem is conducted. The experimental results show that both proposed improved methods establish higher performance than the other existing techniques in terms of classification accuracy and the use of computational resources.

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Human Gender Classification Using Transfer Learning via Pareto Frontier CNN Networks

Inventions ◽

10.3390/inventions5020016 ◽

2020 ◽

Vol 5 (2) ◽

pp. 16

Author(s):

Md. Mahbubul Islam ◽

Nusrat Tasnim ◽

Joong-Hwan Baek

Keyword(s):

Neural Network ◽

Transfer Learning ◽

Classification Accuracy ◽

Pareto Frontier ◽

Gender Classification ◽

Learning Networks ◽

Machine Learning Methods ◽

Image Dataset ◽

Internet Images ◽

Learning Concept

Human gender is deemed as a prime demographic trait due to its various usage in the practical domain. Human gender classification in an unconstrained environment is a sophisticated task due to large variations in the image scenarios. Due to the multifariousness of internet images, the classification accuracy suffers from traditional machine learning methods. The aim of this research is to streamline the gender classification process using the transfer learning concept. This research proposes a framework that performs automatic gender classification in unconstrained internet images deploying Pareto frontier deep learning networks; GoogleNet, SqueezeNet, and ResNet50. We analyze the experiment with three different Pareto frontier Convolutional Neural Network (CNN) models pre-trained on ImageNet. The massive experiments demonstrate that the performance of the Pareto frontier CNN networks is remarkable in the unconstrained internet image dataset as well as in the frontal images that pave the way to developing an automatic gender classification system.

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Improving Sentiment Analysis using Hybrid Deep Learning Model

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190328200012 ◽

2020 ◽

Vol 13 (4) ◽

pp. 627-640 ◽

Cited By ~ 1

Author(s):

Avinash Chandra Pandey ◽

Dharmveer Singh Rajpoot

Keyword(s):

Neural Network ◽

Deep Learning ◽

Sentiment Analysis ◽

Classification Accuracy ◽

Short Term Memory ◽

Computational Cost ◽

Extraction Process ◽

Learning Model ◽

Sentiment Classification ◽

Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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Using artificial neural network condensation to facilitate adaption of machine learning in medical settings by reducing computational burden (Preprint)

10.2196/preprints.20767 ◽

2020 ◽

Author(s):

Dianbo Liu

Keyword(s):

Neural Network ◽

Machine Learning ◽

Third World ◽

Mortality Prediction ◽

Neural Net ◽

Medical Settings ◽

Hidden Layer ◽

Applications Of Machine Learning ◽

Computational Resources ◽

Developed Nations

BACKGROUND Applications of machine learning (ML) on health care can have a great impact on people’s lives. At the same time, medical data is usually big, requiring a significant amount of computational resources. Although it might not be a problem for wide-adoption of ML tools in developed nations, availability of computational resource can very well be limited in third-world nations and on mobile devices. This can prevent many people from benefiting of the advancement in ML applications for healthcare. OBJECTIVE In this paper we explored three methods to increase computational efficiency of either recurrent neural net-work(RNN) or feedforward (deep) neural network (DNN) while not compromising its accuracy. We used in-patient mortality prediction as our case analysis upon intensive care dataset. METHODS We reduced the size of RNN and DNN by applying pruning of “unused” neurons. Additionally, we modified the RNN structure by adding a hidden-layer to the RNN cell but reduce the total number of recurrent layers to accomplish a reduction of total parameters in the network. Finally, we implemented quantization on DNN—forcing the weights to be 8-bits instead of 32-bits. RESULTS We found that all methods increased implementation efficiency–including training speed, memory size and inference speed–without reducing the accuracy of mortality prediction. CONCLUSIONS This improvements allow the implementation of sophisticated NN algorithms on devices with lower computational resources.

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Estimation of the influence of spiking neural network parameters on classification accuracy using a genetic algorithm

Procedia Computer Science ◽

10.1016/j.procs.2018.11.111 ◽

2018 ◽

Vol 145 ◽

pp. 488-494 ◽

Cited By ~ 3

Author(s):

Aleksandr Sboev ◽

Alexey Serenko ◽

Roman Rybka ◽

Danila Vlasov ◽

Andrey Filchenkov

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Classification Accuracy ◽

Spiking Neural Network ◽

Network Parameters

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A visual terrain classification method for mobile robots’ navigation based on convolutional neural network and support vector machine

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220987917 ◽

2021 ◽

pp. 014233122098791

Author(s):

Wanli Wang ◽

Botao Zhang ◽

Kaiqi Wu ◽

Sergey A Chepinskiy ◽

Anton A Zhilenkov ◽

...

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Mobile Robots ◽

Convolutional Neural Network ◽

Hybrid Method ◽

Classification Accuracy ◽

Support Vector ◽

High Classification Accuracy ◽

Enhance Efficiency ◽

Multi Class Classification

In this paper, a hybrid method based on deep learning is proposed to visually classify terrains encountered by mobile robots. Considering the limited computing resource on mobile robots and the requirement for high classification accuracy, the proposed hybrid method combines a convolutional neural network with a support vector machine to keep a high classification accuracy while improve work efficiency. The key idea is that the convolutional neural network is used to finish a multi-class classification and simultaneously the support vector machine is used to make a two-class classification. The two-class classification performed by the support vector machine is aimed at one kind of terrain that users are mostly concerned with. Results of the two classifications will be consolidated to get the final classification result. The convolutional neural network used in this method is modified for the on-board usage of mobile robots. In order to enhance efficiency, the convolutional neural network has a simple architecture. The convolutional neural network and the support vector machine are trained and tested by using RGB images of six kinds of common terrains. Experimental results demonstrate that this method can help robots classify terrains accurately and efficiently. Therefore, the proposed method has a significant potential for being applied to the on-board usage of mobile robots.

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