Machine Learning Enhanced CSI Acquisition and Training Strategy for FDD Massive MIMO

2021 ◽  
Author(s):  
Nuan Song ◽  
Tao Yang
Keyword(s):  
Machine Learning ◽  
Massive Mimo ◽  
Training Strategy ◽  
And Training

Current Situation and Training Strategy of Rehabilitation Nurses in China

2015 ◽  
Vol 25 (3) ◽  
pp. 58
Author(s):  
Zhuangmiao LI ◽  
Hongjia ZHAO ◽  
Fang LIU ◽  
Shuqin PANG ◽  
Liwei ZHENG ◽  
...  
Keyword(s):  
Current Situation ◽  
Training Strategy ◽  
And Training

scholarly journals How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

2021 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Oliwia Koteluk ◽  
Adrian Wartecki ◽  
Sylwia Mazurek ◽  
Iga Kołodziejczak ◽  
Andrzej Mackiewicz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Medical Data ◽  
General Process ◽  
New Era ◽  
Automated Evaluation ◽  
Machine Learning Applications ◽  
And Training ◽  
Current Standards

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

scholarly journals Testing the Suitability of Automated Machine Learning for Weeds Identification

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 34-47
Author(s):  
Borja Espejo-Garcia ◽  
Ioannis Malounas ◽  
Eleanna Vali ◽  
Spyros Fountas
Keyword(s):  
Machine Learning ◽  
Plant Protection ◽  
Crop Protection ◽  
Identification Problem ◽  
Learning System ◽  
Classifier Ensembles ◽  
Automated Machine Learning ◽  
A New Technique ◽  
Plant Seedlings ◽  
And Training

In the past years, several machine-learning-based techniques have arisen for providing effective crop protection. For instance, deep neural networks have been used to identify different types of weeds under different real-world conditions. However, these techniques usually require extensive involvement of experts working iteratively in the development of the most suitable machine learning system. To support this task and save resources, a new technique called Automated Machine Learning has started being studied. In this work, a complete open-source Automated Machine Learning system was evaluated with two different datasets, (i) The Early Crop Weeds dataset and (ii) the Plant Seedlings dataset, covering the weeds identification problem. Different configurations, such as the use of plant segmentation, the use of classifier ensembles instead of Softmax and training with noisy data, have been compared. The results showed promising performances of 93.8% and 90.74% F1 score depending on the dataset used. These performances were aligned with other related works in AutoML, but they are far from machine-learning-based systems manually fine-tuned by human experts. From these results, it can be concluded that finding a balance between manual expert work and Automated Machine Learning will be an interesting path to work in order to increase the efficiency in plant protection.

scholarly journals Computational biology: deep learning

2017 ◽  
Vol 1 (3) ◽  
pp. 257-274 ◽  
Author(s):  
William Jones ◽  
Kaur Alasoo ◽  
Dmytro Fishman ◽  
Leopold Parts
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Computational Biology ◽  
Medical Diagnostics ◽  
Network Architectures ◽  
Improve Performance ◽  
Prediction Problems ◽  
And Training ◽  
Abundant Data ◽  
Simple Network

Deep learning is the trendiest tool in a computational biologist's toolbox. This exciting class of methods, based on artificial neural networks, quickly became popular due to its competitive performance in prediction problems. In pioneering early work, applying simple network architectures to abundant data already provided gains over traditional counterparts in functional genomics, image analysis, and medical diagnostics. Now, ideas for constructing and training networks and even off-the-shelf models have been adapted from the rapidly developing machine learning subfield to improve performance in a range of computational biology tasks. Here, we review some of these advances in the last 2 years.

Machine Learning and Human Factors: Status, Applications, and Future Directions

2018 ◽  
Vol 62 (1) ◽  
pp. 135-138 ◽  
Author(s):  
Nathan Lau ◽  
Lex Fridman ◽  
Brett J. Borghetti ◽  
John D. Lee
Keyword(s):  
Machine Learning ◽  
Human Factors ◽  
Interaction Design ◽  
Intelligent Systems ◽  
Consumer Products ◽  
Learning Approaches ◽  
Future Directions ◽  
Industrial Systems ◽  
Human Factors Research ◽  
And Training

As machine learning approaches ubiquity in industrial systems and consumer products, human factors research must attend to machine learning, specifically on how intelligent systems built on machine learning are different from early generations of automated systems, and what these differences mean for human-system interaction, design, evaluation and training. This panel invites five researchers in different domains to discuss how human factors can contribute to machine learning research and applications, as well as how machine learning presents both challenges and contributions for human factors.

Sign in / Sign up

Export Citation Format

Share Document