Automated Die Inking through On-line Machine Learning

2019 ◽  
Author(s):  
Constantinos Xanthopoulos ◽  
Arnold Neckermann ◽  
Paulus List ◽  
Klaus-Peter Tschernay ◽  
Peter Sarson ◽  
...  
Keyword(s):  
Machine Learning ◽  
On Line

scholarly journals Who Donates on Line? Segmentation Analysis and Marketing Strategies Based on Machine Learning for Online Charitable Donations in Taiwan

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 52728-52740
Author(s):  
Chiung-Wen Hsu ◽  
Yu-Lin Chang ◽  
Tzer-Shyong Chen ◽  
Te-Yi Chang ◽  
Yu-Da Lin
Keyword(s):  
Machine Learning ◽  
Marketing Strategies ◽  
Charitable Donations ◽  
Segmentation Analysis ◽  
On Line ◽  
Line Segmentation

Reinforcement and Non-Reinforcement Machine Learning Classifiers for User Movement Prediction

2013 ◽  
pp. 218-237 ◽  
Author(s):  
Theodoros Anagnostopoulos
Keyword(s):  
Machine Learning ◽  
Classification Problem ◽  
Location Prediction ◽  
Context Aware ◽  
Resonance Theory ◽  
Self Organizing Maps ◽  
Adaptive Resonance ◽  
Proposed Model ◽  
On Line ◽  
Mobile Context

Mobile context-aware applications are required to sense and react to changing environment conditions. Such applications, usually, need to recognize, classify, and predict context in order to act efficiently, beforehand, for the benefit of the user. In this chapter, the authors propose a mobility prediction model, which deals with context representation and location prediction of moving users. Machine Learning (ML) techniques are used for trajectory classification. Spatial and temporal on-line clustering is adopted. They rely on Adaptive Resonance Theory (ART) for location prediction. Location prediction is treated as a context classification problem. The authors introduce a novel classifier that applies a Hausdorff-like distance over the extracted trajectories handling location prediction. Two learning methods (non-reinforcement and reinforcement learning) are presented and evaluated. They compare ART with Self-Organizing Maps (SOM), Offline kMeans, and Online kMeans algorithms. Their findings are very promising for the use of the proposed model in mobile context aware applications.

scholarly journals Adaptive neurofuzzy control of a robotic gripper with on-line machine learning

2004 ◽  
Vol 48 (2-3) ◽  
pp. 93-110 ◽  
Author(s):  
J.A. Domínguez-López ◽  
R.I. Damper ◽  
R.M. Crowder ◽  
C.J. Harris
Keyword(s):  
Machine Learning ◽  
Neurofuzzy Control ◽  
On Line

scholarly journals An Efficient Image Classification of Malaria Parasite Using Convolutional Neural Network and ADAM Optimizer

2021 ◽  
Vol 12 (2) ◽  
pp. 3376-3384
Author(s):  
K. Kranthi Kumar, Et. al.
Keyword(s):  
Machine Learning ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Information Science ◽  
Data Intensive ◽  
Learning Techniques ◽  
Protozoan Infection ◽  
Life Threatening ◽  
On Line ◽  
Infected Cells

Machine learning can be a technique of nursing lysis that automatically develops an analytical model. It is a branch of synthetic intelligence that believes that systems are going to learn information, determine patterns of information and decide with degraded human intervention. Machine learning addresses the question of how computers can be constructed that improve mechanically through knowledge. It lies at the intersection of technology and statistics and at the center of artificial data and information science, one in all the quickest increasing technical fields of nowadays. Recent advances in machine learning were driven by the event of latest learning and theories also as by the constant explosion. The event of latest learning algorithms and also theory and the in-progress growth within the accessibility of on-line information also as low-priced computation crystal rectifier to recent progress within the field of machine learning. Additional evidence-based decision-making could be carried out in science, technology and trade, including healthcare, production, education and monetary modelling, enforcement and promotion, with adoption of mechanical learning techniques based on data-intensive methods. The results are also available. The infection can be a life-threatening disease. The bite of a nursing partner is often transmitted in dipterous Anopheles. In infected mosquitoes, plasmodium parasite is a gift. The parasite is discharged into your blood after you bite this dipterous insect once it bites you. Once your body is composed of the parasites, they mature into the liver. The mature parasites enter the blood for several days when red blood cells start to infect. In red blood cells, parasites increase over 48-72 hours, causing infected cells to divide. The parasites still infect red blood cells, which last 2 to 3 days in cycles. This paper is used for observation of protozoan infection with a deep learning idea.

scholarly journals On-Line Spot Welding Quality Prediction with Analysis of Welding Parameters

2020 ◽  
Vol 3 (1) ◽  
pp. 127-140
Author(s):  
Emin Cantez ◽  
İsmail Atalay ◽  
Oğuz Alper İsen ◽  
Serkan Aydın
Keyword(s):  
Machine Learning ◽  
Automotive Industry ◽  
Spot Welding ◽  
Welding Parameters ◽  
Quality Prediction ◽  
Welding Quality ◽  
Important Place ◽  
Machine Learning Methods ◽  
On Line

Spot welding is one of the metal joining technologies and has an important place especially in the automotive industry. A passenger car has average 5000 spots. Destructive inspection is carried out at certain periods to check these spots. However, not all parts can be checked. In this work, welding parameters were collected and analyzed. By applying different machine learning methods, the quality of the spot welding was tried to be estimated and the results were compared.

scholarly journals Gaze Tracking in Semi-Autonomous Grasping

2008 ◽  
Vol 2 (4) ◽  
Author(s):  
Claudio Castellini
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Human Subjects ◽  
Learning System ◽  
Support Vector ◽  
Hand Position ◽  
Human Being ◽  
Gaze Tracking ◽  
Vector Machines ◽  
On Line

In critical human/robotic interactions such as, e.g., teleoperation by a disabled master or with insufficient bandwidth, it is highly desirable to have semi-autonomous robotic artifacts interact with a human being. Semi-autonomous grasping, for instance, consists of having a smart slave able to guess the master’s intentions and initiating a grasping sequence whenever the master wants to grasp an object in the slave’s workspace. In this paper we investigate the possibility of building such an intelligent robotic artifact by training a machine learning system on data gathered from several human subjects while trying to grasp objects in a teleoperation setup. In particular, we investigate the usefulness of gaze tracking in such a scenario. The resulting system must be light enough to be usable on-line and flexible enough to adapt to different masters, e.g., elderly and/or slow. The outcome of the experiment is that such a system, based upon Support Vector Machines, meets all the requirements, being (a) highly accurate, (b) compact and fast, and (c) largely unaffected by the subjects’ diversity. It is also clearly shown that gaze tracking significantly improves both the accuracy and compactness of the obtained models, if compared with the use of the hand position alone. The system can be trained with something like 3.5 minutes of human data in the worst case.task is neutral.

Sign in / Sign up

Export Citation Format

Share Document