Best optimizer selection for predicting bushfire occurrences using deep learning

2020 ◽  
Vol 103 (1) ◽  
pp. 845-860
Author(s):  
Malka N. Halgamuge ◽  
Eshan Daminda ◽  
Ampalavanapillai Nirmalathas
Keyword(s):  
Deep Learning ◽  
Selection For

Data-driven method for training data selection for deep learning

2021 ◽  
Author(s):  
C. Lacombe ◽  
I. Hammoud ◽  
J. Messud ◽  
H. Peng ◽  
T. Lesieur ◽  
...  
Keyword(s):  
Deep Learning ◽  
Training Data ◽  
Data Selection ◽  
Data Driven ◽  
Selection For ◽  
Training Data Selection

scholarly journals Deep Learning-Based Decoding and AP Selection for Radio Stripe Network

2022 ◽  
Vol 32 (1) ◽  
pp. 131-148
Author(s):  
Aman Kumar Mishra ◽  
Vijayakumar Ponnusamy
Keyword(s):  
Deep Learning ◽  
Selection For

scholarly journals An Introduction to IoT Based Posture Detection and Underlying Sensor Technology

2020 ◽  
Author(s):  
Jahnvi Gupta ◽  
Nitin Gupta ◽  
Mukesh Kumar ◽  
Ritwik Duggal
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Patient Monitoring ◽  
Elderly Care ◽  
Medical Science ◽  
Sensor Technology ◽  
Human Posture ◽  
Posture Recognition ◽  
Selection For

Analysis of human posture has many applications in the field of sports and medical science including patient monitoring, lifestyle analysis, elderly care etc. Many of the works in this area have been based on computer vision techniques. These are limited in providing real-time solution. Thus, Internet of Things (IoT) based solution are being planned and used for the human posture recognition and detection. The data collected from sensors is then passed to machine learning or deep learning algorithms to find different patterns. In this chapter an introduction to IoT based posture detection is provided with an introduction to underlying sensor technology, which can help in selection for appropriate sensors for the posture detection.<br>

scholarly journals Same father, same face: Deep learning reveals selection for signaling kinship in a wild primate

2020 ◽  
Vol 6 (22) ◽  
pp. eaba3274 ◽  
Author(s):  
M. J. E. Charpentier ◽  
M. Harté ◽  
C. Poirotte ◽  
J. Meric de Bellefon ◽  
B. Laubi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Social Relationships ◽  
Genetic Relatedness ◽  
Kin Recognition ◽  
Phenotypic Evolution ◽  
Mandrillus Sphinx ◽  
Facial Resemblance ◽  
Selection For ◽  
Kin Recognition Mechanisms

Many animals rely on facial traits to recognize their kin; however, whether these traits have been selected specifically for this function remains unknown. Using deep learning for face recognition, we present the first evidence that interindividual facial resemblance has been selected to signal paternal kinship. Mandrills (Mandrillus sphinx) live in matrilineal societies, in which females spend their entire lives not only with maternal half-sisters (MHS) but also with paternal half-sisters (PHS). We show that PHS have more differentiated social relationships compared to nonkin, suggesting the existence of kin recognition mechanisms. We further demonstrate that facial resemblance increases with genetic relatedness. However, PHS resemble each other visually more than MHS do, despite both kin categories sharing similar degrees of genetic relatedness. This paternally derived facial resemblance among PHS indicates selection to facilitate kin recognition. This study also highlights the potential of artificial intelligence to study phenotypic evolution.

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