Development of Standalone Deep Learning Module for Right Mobile Feature Memory Logic Conjugated System (MLCS)

2018 ◽  
Author(s):  
Kanji Otsuka ◽  
Daisuke Ogawa ◽  
Kenji Ueda ◽  
Kazuyoshi Oshima ◽  
Yoichi Sato
Keyword(s):  
Deep Learning ◽  
Learning Module ◽  
Feature Memory

scholarly journals Deep Learning Method for Quantitative Analysis of Covid -19 using CT Chest Images

2021 ◽  
Author(s):  
Indrajeet Kumar ◽  
Jyoti Rawat
Keyword(s):  
Deep Learning ◽  
Diagnostic Tests ◽  
Ct Images ◽  
Rapid Diagnosis ◽  
Chest Ct ◽  
Visual Features ◽  
Learning Module ◽  
Volumetric Ct ◽  
The Cost

Abstract The manual diagnostic tests performed in laboratories for pandemic disease such as COVID19 is time-consuming, requires skills and expertise of the performer to yield accurate results. Moreover, it is very cost ineffective as the cost of test kits is high and also requires well-equipped labs to conduct them. Thus, other means of diagnosing the patients with presence of SARS-COV2 (the virus responsible for COVID19) must be explored. A radiography method like chest CT images is one such means that can be utilized for diagnosis of COVID19. The radio-graphical changes observed in CT images of COVID19 patient helps in developing a deep learning-based method for extraction of graphical features which are then used for automated diagnosis of the disease ahead of laboratory-based testing. The proposed work suggests an Artificial Intelligence (AI) based technique for rapid diagnosis of COVID19 from given volumetric CT images of patient’s chest by extracting its visual features and then using these features in the deep learning module. The proposed convolutional neural network is deployed for classifying the infectious and non-infectious SARS-COV2 subjects. The proposed network utilizes 746 chests scanned CT images of which 349 images belong to COVID19 positive cases while remaining 397 belong negative cases of COVID19. The extensive experiment has been completed with the accuracy of 98.4 %, sensitivity of 98.5 %, the specificity of 98.3 %, the precision of 97.1 %, F1score of 97.8 %. The obtained result shows the outstanding performance for classification of infectious and non-infectious for COVID19 cases.

Modular Neural Network for Learning Visual Features, Routes, and Operation Through Human Driving Data Toward Automatic Driving System

2020 ◽  
Vol 24 (3) ◽  
pp. 368-376
Author(s):  
Shun Otsubo ◽  
Yasutake Takahashi ◽  
Masaki Haruna ◽  
◽  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Visual Information ◽  
Machine Learning Techniques ◽  
Driving System ◽  
Modular Neural Networks ◽  
Automatic Driving ◽  
Learning Module ◽  
Learning Techniques ◽  
Almost All

This paper proposes an automatic driving system based on a combination of modular neural networks processing human driving data. Research on automatic driving vehicles has been actively conducted in recent years. Machine learning techniques are often utilized to realize an automatic driving system capable of imitating human driving operations. Almost all of them adopt a large monolithic learning module, as typified by deep learning. However, it is inefficient to use a monolithic deep learning module to learn human driving operations (accelerating, braking, and steering) using the visual information obtained from a human driving a vehicle. We propose combining a series of modular neural networks that independently learn visual feature quantities, routes, and driving maneuvers from human driving data, thereby imitating human driving operations and efficiently learning a plurality of routes. This paper demonstrates the effectiveness of the proposed method through experiments using a small vehicle.

scholarly journals Deep Relation Network for Hyperspectral Image Few-Shot Classification

2020 ◽  
Vol 12 (6) ◽  
pp. 923 ◽  
Author(s):  
Kuiliang Gao ◽  
Bing Liu ◽  
Xuchu Yu ◽  
Jinchun Qin ◽  
Pengqiang Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Classification Model ◽  
Support Vector ◽  
Data Sets ◽  
Learning Models ◽  
Shot Classification ◽  
Learning Module ◽  
Relation Learning

Deep learning has achieved great success in hyperspectral image classification. However, when processing new hyperspectral images, the existing deep learning models must be retrained from scratch with sufficient samples, which is inefficient and undesirable in practical tasks. This paper aims to explore how to accurately classify new hyperspectral images with only a few labeled samples, i.e., the hyperspectral images few-shot classification. Specifically, we design a new deep classification model based on relational network and train it with the idea of meta-learning. Firstly, the feature learning module and the relation learning module of the model can make full use of the spatial–spectral information in hyperspectral images and carry out relation learning by comparing the similarity between samples. Secondly, the task-based learning strategy can enable the model to continuously enhance its ability to learn how to learn with a large number of tasks randomly generated from different data sets. Benefitting from the above two points, the proposed method has excellent generalization ability and can obtain satisfactory classification results with only a few labeled samples. In order to verify the performance of the proposed method, experiments were carried out on three public data sets. The results indicate that the proposed method can achieve better classification results than the traditional semisupervised support vector machine and semisupervised deep learning models.

scholarly journals MASK DETECTION SYSTEM FOR COVID-19 SCENARIO USING COMPUTER VISION

2021 ◽  
Vol 5 (12) ◽  
Author(s):  
Dr. Prakash Prasad ◽  
Mukul Shende ◽  
Mayur Karemore ◽  
Lucky Khobragade ◽  
Amit Dravyakar ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Detection System ◽  
Face Mask ◽  
World Health ◽  
Public Places ◽  
Learning Module ◽  
Different Types ◽  
Health Organization ◽  
The Impact

The new pandemic of (Coronavirus Disease-2019) COVID-19 continues to spread worldwide. Every potential sector is experiencing a decline in growth. (World Health Organization) WHO suggests that Wearing Face Mask can reduce the impact of COVID-19. So, This Paper Proposed a system that controls the growth of COVID-19 by finding individuals who don't wear masks in populated areas like malls, markets where all public places are under surveillance with closed-circuit television cameras (CCTV). When a person without a mask is found, the corresponding authority is informed by the CCTV network. And it can calculate the number of people that do not wear the mask and emit an audible signal to inform the authority. A deep learning module is trained on a dataset composed of images of people wearing different types of masks and people without masks collected from various sources. It also contains some confusing images that help the model to achieve greater precision than other models. This model will use the dataset to build a COVID-19 face mask detector with computer vision using Computer Vision. This approach allowed extracting even the details from the pixels

scholarly journals Artifically intelligent physics solver: This AI understands Newtons law

2019 ◽  
Vol 7 (1) ◽  
pp. 22-28
Author(s):  
V. Nirmala ◽  
◽  
A. Rajagopal ◽  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Neural Network Model ◽  
Language Model ◽  
Source Code ◽  
Learning Module ◽  
Learning Technique ◽  
Third Law ◽  
The Given ◽  
Physics Problems

implemented a working prototype of a Deep Learning module that seem to understand Newton’s third law of motion. The networks. In this paper, a Google BERT neural network model was trained using transfer learning technique on a synthetic dataset of simple physics problems within the scope of solving Newton’s third law problems that requires understanding of concepts such as action and reaction, magnitude and direction forces, simple concepts of vectors in physics problems. The of Netwon’s third law assuming certain boundaries on the language model of the word problems. A working prototype of this AI can be accessed at the given website. This paper also contributes the source code for reproducible results. This novel idea can be extended to more science topics. Applications of this interdisciplinary area of AI and physics have impact not just in areas of robotics and computational physics, but also in how science uses AI in the future. In future, more areas of .

scholarly journals Efficient Post-Contour Correctness in Object Detection and Segmentation

2020 ◽  
Author(s):  
Than Le
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Transfer Learning ◽  
Shortest Path ◽  
Mobile Application ◽  
Web Applications ◽  
Industrial Applications ◽  
Simple Method ◽  
Learning Module

In this paper, we propose the simple method to optimize the datasets noise under the uncertainty applied to many applications in industry. Specifically, we use firstly the deep learning module at transfer learning based on using the mask-rcnn to detect the objects and segmentation effectively, then return the contours only. After that we address the shortest path for reduce the noise in order to increasing the highspeed in industrial applications. We illustrate adaptive many applications web applications such as mobile application where power computer is limited a source

scholarly journals Deep-cARe: Projection-Based Home Care Augmented Reality System with Deep Learning for Elderly

2019 ◽  
Vol 9 (18) ◽  
pp. 3897
Author(s):  
Yoon Jung Park ◽  
Hyocheol Ro ◽  
Nam Kyu Lee ◽  
Tack-Don Han
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Augmented Reality ◽  
Object Detection ◽  
Pose Estimation ◽  
Dimensional Space ◽  
The Elderly ◽  
Small Object ◽  
Learning Module ◽  
The Face

Developing innovative and pervasive smart technologies that provide medical support and improve the welfare of the elderly has become increasingly important as populations age. Elderly people frequently experience incidents of discomfort in their daily lives, including the deterioration of cognitive and memory abilities. To provide auxiliary functions and ensure the safety of the elderly in daily living situations, we propose a projection-based augmented reality (PAR) system equipped with a deep-learning module. In this study, we propose three-dimensional space reconstruction of a pervasive PAR space for the elderly. In addition, we propose the application of a deep-learning module to lay the foundation for contextual awareness. Performance experiments were conducted for grafting the deep-learning framework (pose estimation, face recognition, and object detection) onto the PAR technology through the proposed hardware for verification of execution possibility, real-time execution, and applicability. The precision of the face pose is particularly high by pose estimation; it is used to determine an abnormal user state. For face recognition results of whole class, the average detection rate (DR) was 74.84% and the precision was 78.72%. However, for face occlusions, the average DR was 46.83%. It was confirmed that the face recognition can be performed properly if the face occlusion situation is not frequent. By object detection experiment results, the DR increased as the distance from the system decreased for a small object. For a large object, the miss rate increased when the distance between the object and the system decreased. Scenarios for supporting the elderly, who experience degradation in movement and cognitive functions, were designed and realized, constructed using the proposed platform. In addition, several user interfaces (UI) were implemented according to the scenarios regardless of distance between users and the proposed system. In this study, we developed a bidirectional PAR system that provides the relevant information by understanding the user environment and action intentions instead of a unidirectional PAR system for simple information provision. We present a discussion of the possibility of care systems for the elderly through the fusion of PAR and deep-learning frameworks.

scholarly journals New Machine Learning Developments in ROOT/TMVA

2019 ◽  
Vol 214 ◽  
pp. 06014
Author(s):  
Kim Albertsson ◽  
Sergei Gleyzer ◽  
Marc Huwiler ◽  
Vladimir Ilievski ◽  
Lorenzo Moneta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Cross Validation ◽  
High Energy Physics ◽  
High Energy ◽  
Small Scale ◽  
Training Time ◽  
Learning Module ◽  
Boosted Decision Tree ◽  
High Level

The Toolkit for Multivariate Analysis, TMVA, the machine learning package integrated into the ROOT data analysis framework, has recently seen improvements to its deep learning module, parallelisation of multivariate methods and cross validation. Performance benchmarks on datasets from high-energy physics are presented with a particular focus on the new deep learning module which contains robust fully-connected, convolutional and recurrent deep neural networks implemented on CPU and GPU architectures. Both dense and convo-lutional layers are shown to be competitive on small-scale networks suitable for high-level physics analyses in both training and in single-event evaluation. Par-allelisation efforts show an asymptotical 3-fold reduction in boosted decision tree training time while the cross validation implementation shows significant speed up with parallel fold evaluation.

scholarly journals Efficient Post-Contour Correctness in Object Detection and Segmentation

2020 ◽  
Author(s):  
Than Le
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Transfer Learning ◽  
Shortest Path ◽  
Mobile Application ◽  
Web Applications ◽  
Industrial Applications ◽  
Simple Method ◽  
Learning Module

In this paper, we propose the simple method to optimize the datasets noise under the uncertainty applied to many applications in industry. Specifically, we use firstly the deep learning module at transfer learning based on using the mask-rcnn to detect the objects and segmentation effectively, then return the contours only. After that we address the shortest path for reduce the noise in order to increasing the highspeed in industrial applications. We illustrate adaptive many applications web applications such as mobile application where power computer is limited a source

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