A Comparative Study of Machine Learning Models for Spreading Factor Selection in LoRa Networks

2021 ◽  
Vol 10 (2) ◽  
pp. 100-121
Author(s):  
Christos John Bouras ◽  
Apostolos Gkamas ◽  
Spyridon Aniceto Katsampiris Salgado ◽  
Nikolaos Papachristos
Keyword(s):  
Machine Learning ◽  
Low Power ◽  
Network Performance ◽  
Delivery Ratio ◽  
Strongly Correlated ◽  
Learning Models ◽  
Spreading Factor ◽  
Efficient Communication ◽  
Machine Learning Models ◽  
Selection Of

Low power wide area networks (LPWAN) technologies offer reasonably priced connectivity to a large number of low-power devices spread over great geographical ranges. Long range (LoRa) is a LPWAN technology that empowers energy-efficient communication. In LoRaWAN networks, collisions are strongly correlated with spreading factor (SF) assignment of end-nodes which affects network performance. In this work, SF assignment using machine learning models in simulation environment is presented. This work examines three approaches for the selection of the SF during LoRa transmissions: 1) random SF assignment, 2) adaptive data rate (ADR), and 3) SF selection through machine learning (ML). The main target is to study and determine the most efficient approach as well as to investigate the benefits of using ML techniques in the context of LoRa networks. In this research, a library that enables the communication between ML libraries and OMNeT++ simulator was created. The performance of the approaches is evaluated for different scenarios using the delivery ratio and energy consumption metrics.

Machine learning approaches to understand and predict rate constants for organic processes in mixtures containing ionic liquids

2021 ◽  
Vol 23 (4) ◽  
pp. 2742-2752
Author(s):  
Tamar L. Greaves ◽  
Karin S. Schaffarczyk McHale ◽  
Raphael F. Burkart-Radke ◽  
Jason B. Harper ◽  
Tu C. Le
Keyword(s):  
Machine Learning ◽  
Ionic Liquids ◽  
Rate Constants ◽  
Learning Approaches ◽  
Learning Models ◽  
Organic Reaction ◽  
Machine Learning Models ◽  
Selection Of

Machine learning models were developed for an organic reaction in ionic liquids and validated on a selection of ionic liquids.

Biostatistiek versus machine learning: van traditionele predictiemodellen naar geautomatiseerde medische analyse

2021 ◽  
Author(s):  
M. VALKEMA ◽  
H. LINGSMA ◽  
P. LAMBIN ◽  
J. VAN LANSCHOT
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
External Validation ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Learning Models ◽  
Selection Of Variables ◽  
Medical Analysis ◽  
Machine Learning Models ◽  
Selection Of

Biostatistics versus machine learning: from traditional prediction models to automated medical analysis Machine learning is increasingly applied to medical data to develop clinical prediction models. This paper discusses the application of machine learning in comparison with traditional biostatistical methods. Biostatistics is well-suited for structured datasets. The selection of variables for a biostatistical prediction model is primarily knowledge-driven. A similar approach is possible with machine learning. But in addition, machine learning allows for analysis of unstructured datasets, which are e.g. derived from medical imaging and written texts in patient records. In contrast to biostatistics, the selection of variables with machine learning is mainly data-driven. Complex machine learning models are able to detect nonlinear patterns and interactions in data. However, this requires large datasets to prevent overfitting. For both machine learning and biostatistics, external validation of a developed model in a comparable setting is required to evaluate a model’s reproducibility. Machine learning models are not easily implemented in clinical practice, since they are recognized as black boxes (i.e. non-intuitive). For this purpose, research initiatives are ongoing within the field of explainable artificial intelligence. Finally, the application of machine learning for automated imaging analysis and development of clinical decision support systems is discussed.

scholarly journals MACHINE LEARNING AS A CORPORATION'S TOOL FOR SELECTION OF SUPPLIERS

2019 ◽  
pp. 48-53
Author(s):  
V. V. Baklushinskii ◽  
E. V. Pustynnikova
Keyword(s):  
Machine Learning ◽  
Consumer Behavior ◽  
Learning Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Securities Trading ◽  
Machine Learning Models ◽  
Selection Of

In the economics and finance, machine learning methods have spread when solving the problems of consumer behavior research and in currency and securities trading. However, they are poorly developed in dealing with issues related to interaction between enterprises. The article presents the results of the compilation and testing of machine learning models, created to assess the reliability of enterprises as suppliers. According to the analysis, carried out in the article, machine learning methods are applicable when conducting supplier evaluations. This article has been written on the theme of expanding the scope of machine learning in the field of analysis of the behavior of commercial enterprises.

Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models

2020 ◽  
Vol 185 ◽  
pp. 528-539 ◽  
Author(s):  
Yan Zhang ◽  
Cheng Wen ◽  
Changxin Wang ◽  
Stoichko Antonov ◽  
Dezhen Xue ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Entropy Alloys ◽  
Learning Models ◽  
Rational Selection ◽  
High Entropy ◽  
Phase Prediction ◽  
Machine Learning Models ◽  
Selection Of

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

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