scholarly journals High-Performance Deep Neural Network-Based Tomato Plant Diseases and Pests Diagnosis System With Refinement Filter Bank

2018 ◽  
Vol 9 ◽  
Author(s):  
Alvaro F. Fuentes ◽  
Sook Yoon ◽  
Jaesu Lee ◽  
Dong Sun Park
Keyword(s):  
Neural Network ◽  
Tomato Plant ◽  
High Performance ◽  
Filter Bank ◽  
Deep Neural Network ◽  
Plant Diseases ◽  
Diagnosis System

A novel PCA–whale optimization-based deep neural network model for classification of tomato plant diseases using GPU

2020 ◽  
Author(s):  
Thippa Reddy Gadekallu ◽  
Dharmendra Singh Rajput ◽  
M. Praveen Kumar Reddy ◽  
Kuruva Lakshmanna ◽  
Sweta Bhattacharya ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Tomato Plant ◽  
Deep Neural Network ◽  
Plant Diseases ◽  
Whale Optimization

scholarly journals The Effect of High Performance Computer on Deep Neural Network

2021 ◽  
Author(s):  
Xin Zhang ◽  
◽  
Ting Zhang ◽  
Jiang Lu ◽  
Xingang Fu ◽  
...  
Keyword(s):  
Neural Network ◽  
High Performance ◽  
Deep Neural Network ◽  
High Performance Computer

Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network

2020 ◽  
Vol 44 (6) ◽  
Author(s):  
S. K. Ghosh ◽  
R. K. Tripathy ◽  
Mario R. A. Paternina ◽  
Juan J. Arrieta ◽  
Alejandro Zamora-Mendez ◽  
...  
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Filter Bank ◽  
Deep Neural Network ◽  
Ecg Signal

scholarly journals Deep Learning for Computational Mode Decomposition in Optical Fibers

2020 ◽  
Vol 10 (4) ◽  
pp. 1367
Author(s):  
Stefan Rothe ◽  
Qian Zhang ◽  
Nektarios Koukourakis ◽  
Jürgen W. Czarske
Keyword(s):  
Neural Network ◽  
Optical Fibers ◽  
High Performance ◽  
Deep Neural Network ◽  
Light Propagation ◽  
Training Data ◽  
Steady Increase ◽  
Cyberphysical Systems ◽  
Multimode Fibers ◽  
Propagation Of Light

Multimode fibers are regarded as the key technology for the steady increase in data rates in optical communication. However, light propagation in multimode fibers is complex and can lead to distortions in the transmission of information. Therefore, strategies to control the propagation of light should be developed. These strategies include the measurement of the amplitude and phase of the light field after propagation through the fiber. This is usually done with holographic approaches. In this paper, we discuss the use of a deep neural network to determine the amplitude and phase information from simple intensity-only camera images. A new type of training was developed, which is much more robust and precise than conventional training data designs. We show that the performance of the deep neural network is comparable to digital holography, but requires significantly smaller efforts. The fast characterization of multimode fibers is particularly suitable for high-performance applications like cyberphysical systems in the internet of things.

scholarly journals Detection of Tomato Plant Diseases Using Deep Convolutional Neural Network

2020 ◽  
pp. 464-465
Author(s):  
Vijayaganth V ◽  
Naveenkumar M ◽  
Mohan M
Keyword(s):  
Neural Network ◽  
Early Diagnosis ◽  
Convolutional Neural Network ◽  
Tomato Plant ◽  
Learning Rate ◽  
Deep Convolutional Neural Network ◽  
Plant Diseases ◽  
Batch Size ◽  
Diagnosis Of Diseases

The disease in tomato leaves affects the quality and quantity of the crops. To overcome this problem an early diagnosis of diseases will benefit the farmers. This work uses PlantVillage dataset of 9 tomato leaves and fed to AlexNet and VGG16. It focuses on accuracy of the model by using hyperparameters like batch size, learning rate and optimizer.

scholarly journals Leaf Disease Identification using Convolution Neural Network (CNN)

2020 ◽  
Vol 9 (9) ◽  
pp. 477-481
Keyword(s):  
Neural Network ◽  
Late Blight ◽  
Tomato Plant ◽  
Extraction Methods ◽  
Plant Diseases ◽  
Viral Diseases ◽  
Leaf Disease ◽  
Proposed Model ◽  
Reliable Performance ◽  
First Time

The tomato plant is the most broadly cultivated produce in India. As the Convolutional Neural Network (CNN) which comes under the field of image classification is performing the progressive work, thus using an approach of deep learning which mainly centers on achieving high accuracy of leaf disease of the tomato plant. Therefore, the main objective of this paper is to acquire more reliable performance in the identification of diseases. Amidst various plant diseases that affect leaf comprise of Late blight, bacterial and viral diseases have been chosen to differentiate infected leaves from that of the healthy leaves includes Late blight, bacterial and viral diseases. As we know, none of the other method has been proposed earlier which helps in detecting plant leaf diseases for the first time. Hence the proposed model is designed in such a way that it effectively identifies specific diseases that affect leaves of tomato plants through the use of a dataset containing about 4000 leaf images. CNN achieves an overall accuracy of 96% without implementing any pre-processing and feature extraction methods.

scholarly journals AptaNet as a deep learning approach for aptamer–protein interaction prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Neda Emami ◽  
Reza Ferdousi
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Protein Interaction ◽  
High Performance ◽  
Deep Neural Network ◽  
High Specificity ◽  
Benchmark Dataset ◽  
Training Dataset

AbstractAptamers are short oligonucleotides (DNA/RNA) or peptide molecules that can selectively bind to their specific targets with high specificity and affinity. As a powerful new class of amino acid ligands, aptamers have high potentials in biosensing, therapeutic, and diagnostic fields. Here, we present AptaNet—a new deep neural network—to predict the aptamer–protein interaction pairs by integrating features derived from both aptamers and the target proteins. Aptamers were encoded by using two different strategies, including k-mer and reverse complement k-mer frequency. Amino acid composition (AAC) and pseudo amino acid composition (PseAAC) were applied to represent target information using 24 physicochemical and conformational properties of the proteins. To handle the imbalance problem in the data, we applied a neighborhood cleaning algorithm. The predictor was constructed based on a deep neural network, and optimal features were selected using the random forest algorithm. As a result, 99.79% accuracy was achieved for the training dataset, and 91.38% accuracy was obtained for the testing dataset. AptaNet achieved high performance on our constructed aptamer-protein benchmark dataset. The results indicate that AptaNet can help identify novel aptamer–protein interacting pairs and build more-efficient insights into the relationship between aptamers and proteins. Our benchmark dataset and the source codes for AptaNet are available in: https://github.com/nedaemami/AptaNet.

scholarly journals A Multichannel Deep Neural Network for Retina Vessel Segmentation via a Fusion Mechanism

2021 ◽  
Vol 9 ◽  
Author(s):  
Jiaqi Ding ◽  
Zehua Zhang ◽  
Jijun Tang ◽  
Fei Guo
Keyword(s):  
Neural Network ◽  
Blood Vessels ◽  
High Performance ◽  
Deep Neural Network ◽  
High Efficiency ◽  
Vessel Segmentation ◽  
Prediction Probability ◽  
Binary Segmentation ◽  
Public Datasets ◽  
Retina Vessel

Changes in fundus blood vessels reflect the occurrence of eye diseases, and from this, we can explore other physical diseases that cause fundus lesions, such as diabetes and hypertension complication. However, the existing computational methods lack high efficiency and precision segmentation for the vascular ends and thin retina vessels. It is important to construct a reliable and quantitative automatic diagnostic method for improving the diagnosis efficiency. In this study, we propose a multichannel deep neural network for retina vessel segmentation. First, we apply U-net on original and thin (or thick) vessels for multi-objective optimization for purposively training thick and thin vessels. Then, we design a specific fusion mechanism for combining three kinds of prediction probability maps into a final binary segmentation map. Experiments show that our method can effectively improve the segmentation performances of thin blood vessels and vascular ends. It outperforms many current excellent vessel segmentation methods on three public datasets. In particular, it is pretty impressive that we achieve the best F1-score of 0.8247 on the DRIVE dataset and 0.8239 on the STARE dataset. The findings of this study have the potential for the application in an automated retinal image analysis, and it may provide a new, general, and high-performance computing framework for image segmentation.

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