scholarly journals A Research Paper on Traffic Sign Recognition with Machine Learning and IOT

2021 ◽  
Vol 9 (VI) ◽  
pp. 1774-1778
Author(s):  
Mahesh Singh
Keyword(s):  
Machine Learning ◽  
Object Detection ◽  
Real World ◽  
Research Paper ◽  
Raspberry Pi ◽  
Detection Accuracy ◽  
Great Success ◽  
The Real ◽  
Road Traffic Safety ◽  
Execution Speed

This paper will help to bring out some amazing findings about autonomous prediction and performing action by establishing a connection between the real world with machine learning and Internet Of thing. The purpose of this research paper is to perform our machine to analyze different signs in the real world and act accordingly. We have explored and found detection of several features in our model which helped us to establish a better interaction of our model with the surroundings. Our algorithms give very optimized predictions performing the right action .Nowadays, autonomous vehicles are a great area of research where we can make it more optimized and more multi - performing .This paper contributes to a huge survey of varied object detection and feature extraction techniques. At the moment, there are loads of object classification and recognition techniques and algorithms found and developed around the world. TSD research is of great significance for improving road traffic safety. In recent years, CNN (Convolutional Neural Networks) have achieved great success in object detection tasks. It shows better accuracy or faster execution speed than traditional methods. However, the execution speed and the detection accuracy of the existing CNN methods cannot be obtained at the same time. What's more, the hardware requirements are also higher than before, resulting in a larger detection cost. In order to solve these problems, this paper proposes an improved algorithm based on convolutional model A classic robot which uses this algorithm which is installed through raspberry pi and performs dedicated action.

scholarly journals How to train your robot with deep reinforcement learning: lessons we have learned

2021 ◽  
pp. 027836492098785
Author(s):  
Julian Ibarz ◽  
Jie Tan ◽  
Chelsea Finn ◽  
Mrinal Kalakrishnan ◽  
Peter Pastor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Case Studies ◽  
Real World ◽  
Review Article ◽  
The Real ◽  
Complex Skills ◽  
Real World Learning ◽  
Level Sensor ◽  
Embodied Agent

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low-level sensor observations. Although a large portion of deep RL research has focused on applications in video games and simulated control, which does not connect with the constraints of learning in real environments, deep RL has also demonstrated promise in enabling physical robots to learn complex skills in the real world. At the same time, real-world robotics provides an appealing domain for evaluating such algorithms, as it connects directly to how humans learn: as an embodied agent in the real world. Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains. In this review article, we present a number of case studies involving robotic deep RL. Building off of these case studies, we discuss commonly perceived challenges in deep RL and how they have been addressed in these works. We also provide an overview of other outstanding challenges, many of which are unique to the real-world robotics setting and are not often the focus of mainstream RL research. Our goal is to provide a resource both for roboticists and machine learning researchers who are interested in furthering the progress of deep RL in the real world.

scholarly journals Augmented Reality and Machine Learning Incorporation Using YOLOv3 and ARKit

2021 ◽  
Vol 11 (13) ◽  
pp. 6006
Author(s):  
Huy Le ◽  
Minh Nguyen ◽  
Wei Qi Yan ◽  
Hoa Nguyen
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Object Detection ◽  
Feature Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Data Annotation ◽  
Machine Learning Model ◽  
Potential Benefits ◽  
Feature Detection And Tracking

Augmented reality is one of the fastest growing fields, receiving increased funding for the last few years as people realise the potential benefits of rendering virtual information in the real world. Most of today’s augmented reality marker-based applications use local feature detection and tracking techniques. The disadvantage of applying these techniques is that the markers must be modified to match the unique classified algorithms or they suffer from low detection accuracy. Machine learning is an ideal solution to overcome the current drawbacks of image processing in augmented reality applications. However, traditional data annotation requires extensive time and labour, as it is usually done manually. This study incorporates machine learning to detect and track augmented reality marker targets in an application using deep neural networks. We firstly implement the auto-generated dataset tool, which is used for the machine learning dataset preparation. The final iOS prototype application incorporates object detection, object tracking and augmented reality. The machine learning model is trained to recognise the differences between targets using one of YOLO’s most well-known object detection methods. The final product makes use of a valuable toolkit for developing augmented reality applications called ARKit.

scholarly journals DISAIN SISTEM OTOMASI SUHU RUANGAN PERTEMUAN DENGAN PENERAPAN TEKNIK MACHINE LEARNING

2019 ◽  
pp. 117-120
Author(s):  
Stephanie Imelda Pella ◽  
Hendro FJ L
Keyword(s):  
Machine Learning ◽  
Object Detection ◽  
Room Temperature ◽  
Cooling System ◽  
Raspberry Pi ◽  
Air Cooling ◽  
Minimum Value ◽  
Air Cooling System ◽  
Four Levels ◽  
And Control

This research presents an automation process of controlling room temperature based on the number of people detected in a room. The system consists of a single board raspberry pi computer, esp8266 micro controller, pi camera, and an infrared module. This research is divided into two parts, namely object detection using Raspbery Pi and Tensorflow and Open CV libraries and controlling air cooling system (ACS) using esp8266 and infrared modules by transmitting hexadecimal AC control codes. The ACS temperature is divided into four levels with a minimum value at 18o C and a maximum at 24o C. System testings were carried out in an empty room and a room with a capacity of 50 people that is fully occupied. The results show that the system is able to detect the number of people in the room and control the ACS, but under certain conditions some objects are not detected because the position and camera tilt is not optimal.

scholarly journals End-to-end machine learning for experimental physics: using simulated data to train a neural network for object detection in video microscopy

Soft Matter ◽  
2020 ◽  
Vol 16 (7) ◽  
pp. 1751-1759 ◽  
Author(s):  
Eric N. Minor ◽  
Stian D. Howard ◽  
Adam A. S. Green ◽  
Matthew A. Glaser ◽  
Cheol S. Park ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Experimental Data ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real World ◽  
Simulated Data ◽  
Video Microscopy ◽  
Experimental Physics ◽  
Simulated Images

We demonstrate a method for training a convolutional neural network with simulated images for usage on real-world experimental data.

[Research Paper] Combining Obfuscation and Optimizations in the Real World

2018 ◽  
Author(s):  
Serge Guelton ◽  
Adrien Guinet ◽  
Pierrick Brunet ◽  
Juan Manuel Martinez ◽  
Fabien Dagnat ◽  
...  
Keyword(s):  
Real World ◽  
Research Paper ◽  
The Real

Object Detection-Based License Plate Localization and Recognition in Complex Environments

2020 ◽  
Vol 2674 (12) ◽  
pp. 212-223
Author(s):  
Ting Tao ◽  
Decun Dong ◽  
Shize Huang ◽  
Wei Chen ◽  
Lingyu Yang
Keyword(s):  
Object Detection ◽  
Real World ◽  
License Plate ◽  
License Plate Recognition ◽  
Complex Environments ◽  
Running Speed ◽  
The Real ◽  
Detection Algorithms ◽  
Great Progress ◽  
Bad Weather

Automatic license plate recognition (ALPR) has made great progress, yet is still challenged by various factors in the real world, such as blurred or occluded plates, skewed camera angles, bad weather, and so on. Therefore, we propose a method that uses a cascade of object detection algorithms to accurately and speedily recognize plates’ contents. In our method, YOLOv3-Tiny, an end-to-end object detection network, is used to locate license plate areas, and YOLOv3 to recognize license plate characters. According to the type and position of the recognized characters, a logical judgment is made to obtain the license plate number. We applied our method to a truck weighing system and constructed a dataset called SM-ALPR, encapsulating pictures captured by this system. It is demonstrated by experiment and by comparison with two other methods applied to this dataset that our method can locate 99.51% of license plate areas in the images and recognize 99.02% of the characters on the plates while maintaining a higher running speed. Specifically, our method exhibits a better performance on challenging images that contain blurred plates, skewed angles, or accidental occlusion, or have been captured in bad weather or poor light, which implies its potential in more diversified practice scenarios.

On the Applicability of Machine Learning Fairness Notions

2021 ◽  
Vol 23 (1) ◽  
pp. 14-23
Author(s):  
Karima Makhlouf ◽  
Sami Zhioua ◽  
Catuscia Palamidessi
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Real World ◽  
Child Custody ◽  
College Admission ◽  
Policy Makers ◽  
The Real ◽  
Important Requirement

Machine Learning (ML) based predictive systems are increasingly used to support decisions with a critical impact on individuals' lives such as college admission, job hiring, child custody, criminal risk assessment, etc. As a result, fairness emerged as an important requirement to guarantee that ML predictive systems do not discriminate against specific individuals or entire sub-populations, in particular, minorities. Given the inherent subjectivity of viewing the concept of fairness, several notions of fairness have been introduced in the literature. This paper is a survey of fairness notions that, unlike other surveys in the literature, addresses the question of "which notion of fairness is most suited to a given real-world scenario and why?". Our attempt to answer this question consists in (1) identifying the set of fairness-related characteristics of the real-world scenario at hand, (2) analyzing the behavior of each fairness notion, and then (3) fitting these two elements to recommend the most suitable fairness notion in every specific setup. The results are summarized in a decision diagram that can be used by practitioners and policy makers to navigate the relatively large catalogue of ML fairness notions.

scholarly journals Virtual to Real-World Transfer Learning: A Systematic Review

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1491
Author(s):  
Mahesh Ranaweera ◽  
Qusay H. Mahmoud
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Transfer Learning ◽  
Real World ◽  
High Performance ◽  
Research Area ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Current Status ◽  
The Real

Machine learning has become an important research area in many domains and real-world applications. The prevailing assumption in traditional machine learning techniques, that training and testing data should be of the same domain, is a challenge. In the real world, gathering enough training data to create high-performance learning models is not easy. Sometimes data are not available, very expensive, or dangerous to collect. In this scenario, the concept of machine learning does not hold up to its potential. Transfer learning has recently gained much acclaim in the field of research as it has the capability to create high performance learners through virtual environments or by using data gathered from other domains. This systematic review defines (a) transfer learning; (b) discusses the recent research conducted; (c) the current status of transfer learning and finally, (d) discusses how transfer learning can bridge the gap between the virtual and the real.

scholarly journals Framework for developing algorithmic fairness

2020 ◽  
Vol 9 (4) ◽  
pp. 1550-1557
Author(s):  
Dedy Prasetya Kristiadi ◽  
Po Abas Sunarya ◽  
Melvin Ismanto ◽  
Joshua Dylan ◽  
Ignasius Raffael Santoso ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Science ◽  
Real World ◽  
Development Process ◽  
Multiple Points ◽  
The Real ◽  
Domain Expertise ◽  
Points Of View ◽  
And Gender ◽  
Future Work

In a world where the algorithm can control the lives of society, it is not surprising that specific complications in determining the fairness in the algorithmic decision will arise at some point. Machine learning has been the de facto tool to forecast a problem that humans cannot reliably predict without injecting some amount of subjectivity in it (i.e., eliminating the “irrational” nature of humans). In this paper, we proposed a framework for defining a fair algorithm metric by compiling information and propositions from various papers into a single summarized list of fairness requirements (guideline alike). The researcher can then adopt it as a foundation or reference to aid them in developing their interpretation of algorithmic fairness. Therefore, future work for this domain would have a more straightforward development process. We also found while structuring this framework that to develop a concept of fairness that everyone can accept, it would require collaboration with other domain expertise (e.g., social science, law, etc.) to avoid any misinformation or naivety that might occur from that particular subject. That is because this field of algorithmic fairness is far broader than one would think initially; various problems from the multiple points of view could come by unnoticed to the novice’s eye. In the real world, using active discriminator attributes such as religion, race, nation, tribe, religion, and gender become the problems, but in the algorithm, it becomes the fairness reason.

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