Robust Model for Rural Education Using Deep Learning and Robotics

2022 ◽  
pp. 175-182
Author(s):  
Sailesh Iyer
Keyword(s):  
Rural Education ◽  
Deep Learning ◽  
Robust Model ◽  
And Robotics

Combining Deep Learning and Robotics for Automated Concrete Delamination Assessment

2019 ◽  
Author(s):  
Evan McLaughlin ◽  
Nicholas Charron ◽  
Sriram Narasimhan
Keyword(s):  
Deep Learning ◽  
And Robotics

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 SeDAR: Reading Floorplans Like a Human—Using Deep Learning to Enable Human-Inspired Localisation

2019 ◽  
Vol 128 (5) ◽  
pp. 1286-1310 ◽  
Author(s):  
Oscar Mendez ◽  
Simon Hadfield ◽  
Nicolas Pugeault ◽  
Richard Bowden
Keyword(s):  
Deep Learning ◽  
Semantic Information ◽  
State Of The Art ◽  
Depth Information ◽  
Semantic Maps ◽  
Novel Method ◽  
Rgb Images ◽  
High Level ◽  
Robotic Tasks ◽  
And Robotics

Abstract The use of human-level semantic information to aid robotic tasks has recently become an important area for both Computer Vision and Robotics. This has been enabled by advances in Deep Learning that allow consistent and robust semantic understanding. Leveraging this semantic vision of the world has allowed human-level understanding to naturally emerge from many different approaches. Particularly, the use of semantic information to aid in localisation and reconstruction has been at the forefront of both fields. Like robots, humans also require the ability to localise within a structure. To aid this, humans have designed high-level semantic maps of our structures called floorplans. We are extremely good at localising in them, even with limited access to the depth information used by robots. This is because we focus on the distribution of semantic elements, rather than geometric ones. Evidence of this is that humans are normally able to localise in a floorplan that has not been scaled properly. In order to grant this ability to robots, it is necessary to use localisation approaches that leverage the same semantic information humans use. In this paper, we present a novel method for semantically enabled global localisation. Our approach relies on the semantic labels present in the floorplan. Deep Learning is leveraged to extract semantic labels from RGB images, which are compared to the floorplan for localisation. While our approach is able to use range measurements if available, we demonstrate that they are unnecessary as we can achieve results comparable to state-of-the-art without them.

A Study on Role of A.I. and Robotics in Health Care Sector: Its Impact and Contribution

2021 ◽  
Vol 07 (3&4) ◽  
pp. 7-14
Author(s):  
Devnath Jayaswal ◽  
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Health Care Sector ◽  
Healthcare Sector ◽  
Current Scenario ◽  
Health Complications ◽  
And Robotics ◽  
Major Domain

Health Care is one of the major domain sectors of our country. As this domain has many different aspect of implementation, as per the current scenario of Diseases and health complications. This paper will discuss about how, the Artificial Intelligence (A.I.) and robotics can be beneficial and plays a major role on, health care domain with respect to the Efficiently Diagnose, Developing New Medicines, Earlier Detection of Diseases, Advance Treatment Care, A.I-Deep learning For the Critical Decision’s. As this Information will help to give more clarity on what, A.I. & Robotics contributes for the major Diseases Treatment by the advancement of Technology. This can be beneficial for not only Doctors, Patients, or Firm but can also be helpful for citizen people as well. The objective of this paper is to study the role of AI and Robotics in Healthcare Sector and its impact.

scholarly journals Autonomous development and learning in artificial intelligence and robotics: Scaling up deep learning to human-like learning

2017 ◽  
Vol 40 ◽  
Author(s):  
Pierre-Yves Oudeyer
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Social Learning ◽  
Intrinsic Motivation ◽  
Scaling Up ◽  
Learning Systems ◽  
Natural Interaction ◽  
Autonomous Choice ◽  
Autonomous Development ◽  
And Robotics

AbstractAutonomous lifelong development and learning are fundamental capabilities of humans, differentiating them from current deep learning systems. However, other branches of artificial intelligence have designed crucial ingredients towards autonomous learning: curiosity and intrinsic motivation, social learning and natural interaction with peers, and embodiment. These mechanisms guide exploration and autonomous choice of goals, and integrating them with deep learning opens stimulating perspectives.

scholarly journals Machine Learning Vs Deep Learning: Which Is Better For Human Activity Recognition

2020 ◽  
Vol 9 (4) ◽  
pp. 1344-1349
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Confusion Matrix ◽  
Human Activity Recognition ◽  
Future Research ◽  
Sport Training ◽  
Upward Moving ◽  
And Robotics

Human activity recognition(HAR) is used to describe basic activities that humans are performing using the sensors that we have in smartphones. The data for this activity recognition is captured by various sensors of mobile phones or wristbands such as accelerometer, gyroscope and gravity sensors.HAR has grabbed the attention of various researchers due to its vast demand in the fields of sport training, security, entertainment health monitoring,computer vision and robotics. In this project we compare different machine learning and deep learning algorithms to find a better approach for HAR. The dataset comprises six activities i.e. walking, sleeping, sitting,moving upward, moving downwards and standing.In this demonstration we also showed confusion matrix,accuracy and multi log loss of various algorithms. With the help of accuracy, confusion matrix of algorithms we compare and determine the best approach for HAR. This will help in future research to map the activities of humans using one of the best approaches used

scholarly journals Sequential transfer learning based on hierarchical clustering for improved performance in deep learning based food segmentation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mia S. N. Siemon ◽  
A. S. M. Shihavuddin ◽  
Gitte Ravn-Haren
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Hierarchical Clustering ◽  
Automated Identification ◽  
Network Training ◽  
Optical Images ◽  
Robust Model ◽  
Novel Approach ◽  
Improved Performance ◽  
Danish School

AbstractAccurately segmenting foods from optical images is a challenging task, yet becoming possible with the help of recent advances in Deep Learning based solutions. Automated identification of food items opens up possibilities of useful applications like nutrition intake monitoring. Given large variations in food choices, Deep Learning based solutions still struggle to generate human level accuracy. In this work, we propose a novel Sequential Transfer Learning method using Hierarchical Clustering. This novel approach simulates a step by step problem solving framework based on clustering of similar types of foods. The proposed approach provides up to 6% gain in accuracy compared to traditional network training and generated a robust model performing better in challenging unseen cases. This approach is also tested for segmenting foods in Danish school children meals for dietary intake monitoring as an application.

scholarly journals Deep Learning Methods for Modeling Bitcoin Price

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1245 ◽  
Author(s):  
Prosper Lamothe-Fernández ◽  
David Alaminos ◽  
Prosper Lamothe-López ◽  
Manuel A. Fernández-Gámez
Keyword(s):  
Deep Learning ◽  
Financial Markets ◽  
Poor Performance ◽  
Asset Valuation ◽  
Prediction Horizon ◽  
Explanatory Factors ◽  
Robust Model ◽  
Future Prediction ◽  
Precise Prediction ◽  
Potential Impact

A precise prediction of Bitcoin price is an important aspect of digital financial markets because it improves the valuation of an asset belonging to a decentralized control market. Numerous studies have studied the accuracy of models from a set of factors. Hence, previous literature shows how models for the prediction of Bitcoin suffer from poor performance capacity and, therefore, more progress is needed on predictive models, and they do not select the most significant variables. This paper presents a comparison of deep learning methodologies for forecasting Bitcoin price and, therefore, a new prediction model with the ability to estimate accurately. A sample of 29 initial factors was used, which has made possible the application of explanatory factors of different aspects related to the formation of the price of Bitcoin. To the sample under study, different methods have been applied to achieve a robust model, namely, deep recurrent convolutional neural networks, which have shown the importance of transaction costs and difficulty in Bitcoin price, among others. Our results have a great potential impact on the adequacy of asset pricing against the uncertainties derived from digital currencies, providing tools that help to achieve stability in cryptocurrency markets. Our models offer high and stable success results for a future prediction horizon, something useful for asset valuation of cryptocurrencies like Bitcoin.

scholarly journals Attempting to Estimate the Unseen—Correction for Occluded Fruit in Tree Fruit Load Estimation by Machine Vision with Deep Learning

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 347
Author(s):  
Anand Koirala ◽  
Kerry B. Walsh ◽  
Zhenglin Wang
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Machine Vision ◽  
Reference Method ◽  
Ground Truth ◽  
Image Features ◽  
Ground Vehicles ◽  
Method Error ◽  
Robust Model ◽  
Fruit Load

Machine vision from ground vehicles is being used for estimation of fruit load on trees, but a correction is required for occlusion by foliage or other fruits. This requires a manually estimated factor (the reference method). It was hypothesised that canopy images could hold information related to the number of occluded fruits. Several image features, such as the proportion of fruit that were partly occluded, were used in training Random forest and multi-layered perceptron (MLP) models for estimation of a correction factor per tree. In another approach, deep learning convolutional neural networks (CNNs) were directly trained against harvest count of fruit per tree. A R2 of 0.98 (n = 98 trees) was achieved for the correlation of fruit count predicted by a Random forest model and the ground truth fruit count, compared to a R2 of 0.68 for the reference method. Error on prediction of whole orchard (880 trees) fruit load compared to packhouse count was 1.6% for the MLP model and 13.6% for the reference method. However, the performance of these models on data of another season was at best equivalent and generally poorer than the reference method. This result indicates that training on one season of data was insufficient for the development of a robust model.

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