An effective and accurate identification system of Mycobacterium tuberculosis using convolution neural networks

2019 ◽  
Vol 82 (6) ◽  
pp. 709-719 ◽  
Author(s):  
Chan‐Pang Kuok ◽  
Ming‐Huwi Horng ◽  
Yu‐Ming Liao ◽  
Nan‐Haw Chow ◽  
Yung‐Nien Sun
Keyword(s):  
Neural Networks ◽  
Mycobacterium Tuberculosis ◽  
Identification System ◽  
Accurate Identification ◽  
Convolution Neural Networks

scholarly journals Predicting the Crop Type and Region of Abundance using Machine Learning

2021 ◽  
Vol 9 (9) ◽  
pp. 1411-1416
Author(s):  
Mohd Khalid Shaikh
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Rural Area ◽  
Identification System ◽  
Plant Identification ◽  
Convolution Neural Networks ◽  
Technology Students ◽  
Modern Age ◽  
Crop Type ◽  
As If

Abstract: In this modern age of science too technology, students and people in big cities ignorance of many things, such as how we get food, how things are processed, and much more. We are just it focuses on the results we get, because of this morality our knowledge diminishes, as if we did not know the crops or the goods ourselves using. As we visit the rural area when we arrive beyond some kind of plant, we can't know that, so we have identified this place to resolve the problem of students, researchers and many more people by creating a plant identification system which will predict the type of crop and the location of abundance where the harvest is planted. Keywords: Crop Identification System, Convolution Neural networks, MobilenetV2.

scholarly journals Applying Artificial Intelligence for the Diagnosis and Classification of Rosacea (Preprint)

2020 ◽  
Author(s):  
Zhixiang Zhao ◽  
CheMing Wu ◽  
Shuping Zhang ◽  
Fanping He ◽  
Fangfen Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Skin Diseases ◽  
Seborrheic Dermatitis ◽  
Characteristic Curve ◽  
Clinical Manifestations ◽  
Accurate Identification ◽  
Clinical Images ◽  
Convolution Neural Networks ◽  
Accuracy And Precision

BACKGROUND Rosacea is a chronic inflammatory disease with variable clinical presentations including transient flushing, fixed erythema, papules, pustules and phymatous changes on the central face. Owing to the diversity of clinical manifestations, the lack of objective biochemical examinations and non-specificity of histopathology, accurate identification of rosacea is a big challenge. Artificial intelligence has emerged as a potential tool in the identification and evaluation of some skin diseases such as melanoma, basal cell carcinoma and psoriasis. OBJECTIVE In this work, we utilized convolution neural networks (CNN) to identify the clinical photos (from three different angles) of patients with rosacea and other diseases that would be easily confused with rosacea (such as acne, seborrheic dermatitis and eczema). METHODS In this work, we utilized convolution neural networks (CNN) to identify the clinical photos (from three different angles) of patients with rosacea and other diseases that would be easily confused with rosacea (such as acne, seborrheic dermatitis and eczema). RESULTS The CNN in our study achieved an overall accuracy and precision of 0.914 and 0.898, with an area under the receiver operating characteristic curve (AUROC) of 0.972 for the detection of rosacea. The accuracy of classifying the three subtypes of rosacea, ETR, PPR, PhR was 83.9%, 74.3% and 80.0%, respectively. Moreover, the accuracy and precision of our CNN to distinguish rosacea from acne reached 0.931 and 0.893, respectively. For the identificaiton between rosacea, seborrheic dermatitis and eczema, the overall accuracy was 0.757 and the precision was 0.667. Finally, by comparing the CNN with different levels of dermatologists, we showed that our CNN system is capable of identifying rosacea with a performance superior to resident doctors or attending physicians and comparable to experienced specialists. CONCLUSIONS In conclusion, by assessing clinical images, the CNN system in our study performed at dermatologist-level in the identification of rosacea. CLINICALTRIAL None

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

A Novel Approach for Image Super Resolution Problems with Convolution Neural Networks

2017 ◽  
Vol 6 (4) ◽  
pp. 15
Author(s):  
JANARDHAN CHIDADALA ◽  
RAMANAIAH K.V. ◽  
BABULU K ◽  
◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Super Resolution ◽  
Convolution Neural Networks ◽  
Novel Approach ◽  
Image Super Resolution

Convolution Neural Networks for Traffic Sign Classification

2019 ◽  
Author(s):  
Rajashekar A ◽  
Shruti Hegdekar ◽  
Dikpal Shrestha ◽  
Prabin Nepal ◽  
Sujanb Neupane
Keyword(s):  
Neural Networks ◽  
Traffic Sign ◽  
Convolution Neural Networks ◽  
Traffic Sign Classification
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