Paddy Leaf Disease Analysis with Hybrid Algorithms Using Deep Learning Techniques

2020 ◽  
Vol 17 (8) ◽  
pp. 3491-3496
Author(s):  
M. S. Roobini ◽  
Talluri Pramod Sai ◽  
Thatavarthi Sri Krishna Kamal ◽  
Anitha Ponraj ◽  
A. Sivasangari ◽  
...  
Keyword(s):  
Deep Learning ◽  
General Public ◽  
Rice Yield ◽  
International Rice Research Institute ◽  
Cross Entropy ◽  
Learning Techniques ◽  
Minimization Procedure ◽  
Suitable Treatment ◽  
Number Of Individuals ◽  
Disease Analysis

According to the standards of India the financial, political and social security depend legitimately just as in a roundabout way on the yearly creation of rice. Wellspring of pay of hundred a large number of individuals relies just upon rice creation and nothing else included according to the report of International Rice Research Institute (IRRI), 37% of the rice yield misfortune is because of illnesses. In a general public the rancher can deal with crop on-time with suitable treatment. By a yield shrewd rice is the hugest humanoid sustenance on the planet, which can be taken care of legitimately than some other gather. Sequentially, the fundamental target of cultivating is to yield and feed nourishment to the country. In this way, these leaf ailments in any structures in rice crop will in general reason decrease in quality, yield and financial movement separately. This venture reports a novel methodology for recognition and distinguishing proof of rice leaf infections by utilizing different propelled calculations, for example, VGG16, Alex net, and so on to expand the precision to more prominent level. To apply misfortune minimization procedure, for example, straight out cross entropy to build the precision. It expands precision with utilization of mixture calculations by performing tweaking. Just as proposes the kind of substance compost to be utilized when any sort of malady gets distinguished. Just as proposes the sort of compound compost to be utilized when any kind of sickness gets recognized.

scholarly journals Co-Training for Visual Object Recognition Based on Self-Supervised Models Using a Cross-Entropy Regularization

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 423
Author(s):  
Gabriel Díaz ◽  
Billy Peralta ◽  
Luis Caro ◽  
Orietta Nicolis
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Object Recognition ◽  
Training Model ◽  
Cross Entropy ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Visual Objects ◽  
Learning Techniques ◽  
Proposed Model

Automatic recognition of visual objects using a deep learning approach has been successfully applied to multiple areas. However, deep learning techniques require a large amount of labeled data, which is usually expensive to obtain. An alternative is to use semi-supervised models, such as co-training, where multiple complementary views are combined using a small amount of labeled data. A simple way to associate views to visual objects is through the application of a degree of rotation or a type of filter. In this work, we propose a co-training model for visual object recognition using deep neural networks by adding layers of self-supervised neural networks as intermediate inputs to the views, where the views are diversified through the cross-entropy regularization of their outputs. Since the model merges the concepts of co-training and self-supervised learning by considering the differentiation of outputs, we called it Differential Self-Supervised Co-Training (DSSCo-Training). This paper presents some experiments using the DSSCo-Training model to well-known image datasets such as MNIST, CIFAR-100, and SVHN. The results indicate that the proposed model is competitive with the state-of-art models and shows an average relative improvement of 5% in accuracy for several datasets, despite its greater simplicity with respect to more recent approaches.

scholarly journals A Novel Evaluation Metric for Deep Learning-Based Side Channel Analysis and Its Extended Application to Imbalanced Data

2020 ◽  
pp. 73-96
Author(s):  
Jiajia Zhang ◽  
Mengce Zheng ◽  
Jiehui Nan ◽  
Honggang Hu ◽  
Nenghai Yu
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Imbalanced Data ◽  
Cross Entropy ◽  
Side Channel ◽  
Worst Case ◽  
Side Channel Analysis ◽  
Learning Techniques ◽  
Channel Analysis ◽  
Learning Metrics

Since Kocher (CRYPTO’96) proposed timing attack, side channel analysis (SCA) has shown great potential to break cryptosystems via physical leakage. Recently, deep learning techniques are widely used in SCA and show equivalent and even better performance compared to traditional methods. However, it remains unknown why and when deep learning techniques are effective and efficient for SCA. Masure et al. (IACR TCHES 2020(1):348–375) illustrated that deep learning paradigm is suitable for evaluating implementations against SCA from a worst-case scenario point of view, yet their work is limited to balanced data and a specific loss function. Besides, deep learning metrics are not consistent with side channel metrics. In most cases, they are deceptive in foreseeing the feasibility and complexity of mounting a successful attack, especially for imbalanced data. To mitigate the gap between deep learning metrics and side channel metrics, we propose a novel Cross Entropy Ratio (CER) metric to evaluate the performance of deep learning models for SCA. CER is closely related to traditional side channel metrics Guessing Entropy (GE) and Success Rate (SR) and fits to deep learning scenario. Besides, we show that it works stably while deep learning metrics such as accuracy becomes rather unreliable when the training data tends to be imbalanced. However, estimating CER can be done as easy as natural metrics in deep learning algorithms with low computational complexity. Furthermore, we adapt CER metric to a new kind of loss function, namely CER loss function, designed specifically for deep learning in side channel scenario. In this way, we link directly the SCA objective to deep learning optimization. Our experiments on several datasets show that, for SCA with imbalanced data, CER loss function outperforms Cross Entropy loss function in various conditions.

scholarly journals Object Detection Method Based on YOLOv3 using Deep Learning Networks

2019 ◽  
Vol 9 (1) ◽  
pp. 1414-1417
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Real World ◽  
Detection Method ◽  
Cross Entropy ◽  
Detection Methods ◽  
Learning Networks ◽  
Learning Techniques ◽  
Localization Errors

—Object Detection is being widely used in the industry right now. It is the method of detection and shaping real-world objects. Even though there exist many detection methods, the accuracy, rapidity, and efficiency of detection are not good enough. So, this paper demonstrates real-time detection using the YOLOv3 algorithm by deep learning techniques. It first makes expectations crosswise over 3 unique scales. The identification layer is utilized to make recognition at highlight maps of three distinct sizes, having strides 32, 16, 8 individually. This implies, with partner contribution of 416 x 416, we will in general form location on scales 13 x 13, 26 x 26 and 52x 52. Meanwhile, it also makes use of strategic relapse to anticipate the jumping box article score, the paired cross-entropy misfortune is utilized to foresee the classes that the bounding box may contain, the certainty is determined and afterward the forecast. It results in perform multi-label classification for objects detected in images, the average preciseness for tiny objects improved, it's higher than quicker RCNN. MAP increased significantly. As MAP increased localization errors decreased.

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

Deep Learning Techniques for Koala Activity Detection

2018 ◽  
Author(s):  
Ivan Himawan ◽  
Michael Towsey ◽  
Bradley Law ◽  
Paul Roe
Keyword(s):  
Deep Learning ◽  
Activity Detection ◽  
Learning Techniques
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