scholarly journals Disease Detection in Tomato Plants Using Deep Learning

2020 ◽  
Author(s):  
Marimuthu S ◽  
Bhuvana J ◽  
Mirnalinee T T
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Soil Quality ◽  
Tomato Plant ◽  
Network Architecture ◽  
Disease Detection ◽  
Neural Network Architecture ◽  
Disease Category ◽  
Tomato Plants

Agriculture is the backbone of the economy of any country. Productivity of the crops depends on soil quality, proper irrigation and fertilizer, appropriate pesticide. Mostly pesticides were applied without having knowledge about the type of diseases or pests. Type and the quantity of pesticide for any depends on the disease category. If we could identify the appropriate disease, then applying appropriate pesticide will increase the yield of the crop. To address this issue, we propose a method to detect the diseases in tomato plants. We have designed a Convolutional Neural Network architecture that efficiently detects the disease of tomato plant. The system is evaluated with Plant Village benchmark dataset. Results show that our network is detecting the diseases with 90.86% accuracy. We have identified a suitable variant of Convolutional Neural Network that efficiently detects the disease of tomato plant.

scholarly journals AMBIENT: Accelerated Convolutional Neural Network Architecture Search for Regulatory Genomics

2021 ◽  
Author(s):  
Zijun Zhang ◽  
Evan M. Cofer ◽  
Olga G. Troyanskaya
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Environmental Issue ◽  
Biological Sequences ◽  
Neural Network Architecture ◽  
Computing Power ◽  
Neural Architecture

Convolutional neural networks (CNN) have become a standard approach for modeling genomic sequences. CNNs can be effectively built by Neural Architecture Search (NAS) by trading computing power for accurate neural architectures. Yet, the consumption of immense computing power is a major practical, financial, and environmental issue for deep learning. Here, we present a novel NAS framework, AMBIENT, that generates highly accurate CNN architectures for biological sequences of diverse functions, while substantially reducing the computing cost of conventional NAS.

Malicious Code Invariance Based On Deep Learning

2021 ◽  
Vol 10 (3) ◽  
pp. 1-7
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Malicious Code ◽  
Neural Network Architecture ◽  
Security Issues ◽  
The Neural Network ◽  
Code Identification ◽  
Malicious Code Detection

The malicious code detection is critical task for in the field of security. The malicious code detection can be possibly by using convolutional neural network (CNN).Themalicious code can be categorized in to different families. The malicious code identification helps to identify the affected malware on the system. Malicious code theft data from our system and it yields high security issues in real time. The neural network architecture classifies the malicious code based on the collected dataset. The dataset contains different families of malicious code. The malicious code detection can be done with the help of model created from CNN architecture

scholarly journals Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2852
Author(s):  
Parvathaneni Naga Srinivasu ◽  
Jalluri Gnana SivaSai ◽  
Muhammad Fazal Ijaz ◽  
Akash Kumar Bhoi ◽  
Wonjoon Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Skin Disease ◽  
Network Architecture ◽  
Large Scale ◽  
Short Term Memory ◽  
Convolutional Networks ◽  
Occurrence Matrix

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

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