scholarly journals Reinforcement based Multi-Model Deep Learning Algorithm for Classification

2020 ◽  
Vol 9 (2) ◽  
pp. 1704-1710
Keyword(s):  
Deep Learning ◽  
Maximum Entropy ◽  
Learning Algorithm ◽  
Dimensional Space ◽  
Image Data ◽  
Space Policy ◽  
Model Classification ◽  
Deep Learning Algorithm ◽  
Robust Model ◽  
Neural Network Algorithm

Nowadays researchers are focused on processing the multi-media data for classifying the queries of end users by using search engines. The hybrid combination of a powerful classifier and deep feature extractor are used to develop a robust model, which is performed in a high dimensional space. In this research, a three different types of algorithms are combined to attain a stochastic belief space policy, where these algorithms include generative adversary modelling, maximum entropy Reinforcement Learning (RL) and belief space planning which leads to develop a multi-model classification algorithm. In the simulation framework, different adversarial behaviours are used to minimize the agent's action predictability, which has resulted the proposed method to attain robustness, while comparing with unmodelled adversarial strategies. The proposed reinforcement based Deep Learning (DL) algorithm can be used as multi-model classification purpose. The single neural network algorithm can perform the classification on text data and image data. The RL learns the appropriate belief space policy from the feature extracted information of the text and image data, the belief space policy is generated based on the maximum entropy computation

scholarly journals GI-SleepNet: A Highly Versatile Image-based Sleep Classification using a Deep Learning Algorithm

2021 ◽  
Author(s):  
Tian Xiang Gao ◽  
Jia Yi Li ◽  
Yuji Watanabe ◽  
Chi Jung Hung ◽  
Akihiro Yamanaka ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Sleep Stage ◽  
Image Data ◽  
Sleep Research ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Deep Learning Algorithm ◽  
Technical Requirements ◽  
Sleep Stage Classification

Abstract Sleep-stage classification is essential for sleep research. Various automatic judgment programs including deep learning algorithms using artificial intelligence (AI) have been developed, but with limitations in data format compatibility, human interpretability, cost, and technical requirements. We developed a novel program called GI-SleepNet, generative adversarial network (GAN)-assisted image-based sleep staging for mice that is accurate, versatile, compact, and easy to use. In this program, electroencephalogram and electromyography data are first visualized as images and then classified into three stages (wake, NREM, and REM) by a supervised image learning algorithm. To increase the accuracy, we adopted GAN and artificially generated fake REM sleep data to equalize the number of stages. This resulted in improved accuracy, and as few as one mouse data yielded significant accuracy. Because of its image-based nature, it is easy to apply to data of different formats, of different species of animals, and even outside of sleep research. Image data can be easily understood by humans, thus especially confirmation by experts is easy when there are some anomalies of prediction. Because deep learning of images is one of the leading fields in AI, numerous algorithms are also available.

Malaria Detection System Using Convolutional Neural Network Algorithm

2020 ◽  
pp. 219-230
Author(s):  
Kanika Gautam ◽  
Sunil Kumar Jangir ◽  
Manish Kumar ◽  
Jay Sharma
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Detection System ◽  
Learning Algorithms ◽  
Anopheles Mosquito ◽  
Deep Learning Algorithm ◽  
Blood Smears ◽  
Neural Network Algorithm ◽  
Modern Technologies

Malaria is a disease caused when a female Anopheles mosquito bites. There are over 200 million cases recorded per year with more than 400,000 deaths. Current methods of diagnosis are effective; however, they work on technologies that do not produce higher accuracy results. Henceforth, to improve the prediction rate of the disease, modern technologies need to be performed for obtain accurate results. Deep learning algorithms are developed to detect, learn, and determine the containing parasites from the red blood smears. This chapter shows the implementation of a deep learning algorithm to identify the malaria parasites with higher accuracy.

scholarly journals Recognition of Masked Face Using Convolutional Neural Network Algorithm

2021 ◽  
Vol 9 (VII) ◽  
pp. 3233-3237
Author(s):  
Sai Kiruthika K. M
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Skin Color ◽  
Learning Algorithm ◽  
Huge Number ◽  
Skin Texture ◽  
Deep Learning Algorithm ◽  
Face Region ◽  
Security Problem ◽  
Neural Network Algorithm

The covid -19 is an unparalleled crisis resulting in huge number of casualties security problem. So has to scale back the spread of corona virus, people often wear a mask to guard themselves. Indeed, during this challenging context, the matter of face recognition is usually like periocular recognition involving iris, pupil, sclera, upper and lower eyelids, eye folds, eye corners, skin texture, fine wrinkles, complexion, skin color, skin pores etc. In this paper, we propose a reliable method supported discard masked region and deep learning based features so as to deal with the matter of masked face recognition process. The primary step to discard the masked face region. Next, we apply deep learning algorithm to extract the simplest features from obtained regions (mostly eyes and forehead regions). This leads to good accuracy than the previous work for detecting the masked face.

scholarly journals GI-SleepNet: A Highly Versatile Image-Based Sleep Classification Using a Deep Learning Algorithm

2021 ◽  
Vol 3 (4) ◽  
pp. 581-597
Author(s):  
Tianxiang Gao ◽  
Jiayi Li ◽  
Yuji Watanabe ◽  
Chijung Hung ◽  
Akihiro Yamanaka ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Sleep Stage ◽  
Image Data ◽  
Sleep Research ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Deep Learning Algorithm ◽  
Technical Requirements ◽  
Sleep Stage Classification

Sleep-stage classification is essential for sleep research. Various automatic judgment programs, including deep learning algorithms using artificial intelligence (AI), have been developed, but have limitations with regard to data format compatibility, human interpretability, cost, and technical requirements. We developed a novel program called GI-SleepNet, generative adversarial network (GAN)-assisted image-based sleep staging for mice that is accurate, versatile, compact, and easy to use. In this program, electroencephalogram and electromyography data are first visualized as images, and then classified into three stages (wake, NREM, and REM) by a supervised image learning algorithm. To increase its accuracy, we adopted GAN and artificially generated fake REM sleep data to equalize the number of stages. This resulted in improved accuracy, and as little as one mouse’s data yielded significant accuracy. Due to its image-based nature, the program is easy to apply to data of different formats, different species of animals, and even outside sleep research. Image data can be easily understood; thus, confirmation by experts is easily obtained, even when there are prediction anomalies. As deep learning in image processing is one of the leading fields in AI, numerous algorithms are also available.

scholarly journals A Multilevel Isolation Forrest and Convolutional Neural Network Algorithm for Impact Characterization on Composite Structures

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5896 ◽  
Author(s):  
Amin Ebrahim Salehzadeh Nobari ◽  
M.H.Ferri Aliabadi
Keyword(s):  
Deep Learning ◽  
Composite Structures ◽  
Learning Algorithm ◽  
Energy Levels ◽  
Composite Panel ◽  
Deep Learning Algorithm ◽  
Feature Extraction And Selection ◽  
Energy Location ◽  
Neural Network Algorithm ◽  
The Right

In this paper, a Deep Learning approach is proposed to classify impact data based on the type of impact (Hard or Soft Impacts), via obtaining voltage signals from Piezo-Electric sensors, mounted on a composite panel. The data is processed further to be classified based on their energy, location and material. Minimalistic and Automated feature extraction and selection is achieved via a deep learning algorithm. Convolutional Neural Networks (CNN) are employed to extract and select important features from the voltage data. Once features are selected the impacts, are classified based on either, Hard Impacts (simulated from steel impactors in a lab setting), Soft Impacts (simulated from silicon impactors in a lab setting) and their corresponding location and energy levels. Furthermore, in order to use the right data for training they are obtained from the signals as anomalies via Isolation Forests (IF) to speed up the process. Using this approach Hard and Soft Impacts, their corresponding locations and respective energies are identified with high accuracy.

scholarly journals Labelling of data on fundus color pictures used to train a deep learning model enhances its macular pathology recognition capabilities

2021 ◽  
Author(s):  
KhP Takhchidi ◽  
PV Gliznitsa ◽  
SN Svetozarskiy ◽  
AI Bursov ◽  
KA Shusterzon
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Image Data ◽  
Diagnostic Methods ◽  
Age Related Macular Degeneration ◽  
Screening Programs ◽  
Deep Learning Algorithm ◽  
Age Related ◽  
Fundus Photographs

Retinal diseases remain one of the leading causes of visual impairments in the world. The development of automated diagnostic methods can improve the efficiency and availability of the macular pathology mass screening programs. The objective of this work was to develop and validate deep learning algorithms detecting macular pathology (age-related macular degeneration, AMD) based on the analysis of color fundus photographs with and without data labeling. We used 1200 color fundus photographs from local databases, including 575 retinal images of AMD patients and 625 pictures of the retina of healthy people. The deep learning algorithm was deployed in the Faster RCNN neural network with ResNet50 for convolution. The process employed the transfer learning method. As a result, in the absence of labeling, the accuracy of the model was unsatisfactory (79%) because the neural network selected the areas of attention incorrectly. Data labeling improved the efficacy of the developed method: with the test dataset, the model determined the areas with informative features adequately, and the classification accuracy reached 96.6%. Thus, image data labeling significantly improves the accuracy of retinal color images recognition by a neural network and enables development and training of effective models with limited datasets.

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