scholarly journals Advanced IT-Based Future Sustainable Computing (2017–2018)

2019 ◽  
Vol 11 (8) ◽  
pp. 2264
Author(s):  
Jong Hyuk Park
Keyword(s):  
Machine Learning ◽  
Information Processing ◽  
Cluster Computing ◽  
Smart Devices ◽  
Algorithmic Information ◽  
Prediction And Control ◽  
Computing Environments ◽  
Software And Hardware ◽  
Sustainability Problems ◽  
And Control

Future Sustainability Computing (FSC) is an emerging concept that holds various types of paradigms, rules, procedures, and policies to support breadth and length of the deployment of Information Technology (IT) for abundant life. However, advanced IT-based FCS is facing several sustainability problems in different information processing and computing environments. Solutions to these problems can call upon various computational and algorithmic frameworks that employ optimization, integration, generation, and utilization technique within cloud, mobile, and cluster computing, such as meta-heuristics, decision support systems, prediction and control, dynamical systems, machine learning, and so on. Therefore, this special issue deals with various software and hardware design, novel architectures and frameworks, specific mathematical models, and efficient modeling-simulation for advance IT-based FCS. We accepted eighteen articles in the six different IT dimensions: machine learning, blockchain, optimized resource provision, communication network, IT governance, and information security. All accepted articles contribute to the applications and research in the FCS, such as software and information processing, cloud storage organization, smart devices, efficient algorithmic information processing and distribution.

An insight into Case Management and Challenges in Veteran Suicide Prevention

2020 ◽  
pp. 14-25
Author(s):  
Tamara Green
Keyword(s):  
Case Management ◽  
Suicide Prevention ◽  
Suicide Risk ◽  
Digital Transformation ◽  
Prevention Science ◽  
Historical Records ◽  
Military Members ◽  
Prediction And Control ◽  
And Control ◽  
Insight Into

Much of the literature, policies, programs, and investment has been made on mental health, case management, and suicide prevention of veterans. The Australian “veteran community is facing a suicide epidemic for the reasons that are extremely complex and beyond the scope of those currently dealing with them.” (Menz, D: 2019). Only limited work has considered the digital transformation of loosely and manual-based historical records and no enablement of Artificial Intelligence (A.I) and machine learning to suicide risk prediction and control for serving military members and veterans to date. This paper presents issues and challenges in suicide prevention and management of veterans, from the standing of policymakers to stakeholders, campaigners of veteran suicide prevention, science and big data, and an opportunity for the digital transformation of case management.

Learning and control

2018 ◽  
Author(s):  
Ivan Herreros
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Brain Function ◽  
Control Strategies ◽  
Learning Problems ◽  
Animal Learning ◽  
Feed Forward Control ◽  
Machine Learning Applications ◽  
And Control

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

A case study of wear-type rail corrugation prediction and control using speed variation

2009 ◽  
Vol 325 (1-2) ◽  
pp. 85-105 ◽  
Author(s):  
P.A. Meehan ◽  
P.A. Bellette ◽  
R.D. Batten ◽  
W.J.T. Daniel ◽  
R.J. Horwood
Keyword(s):  
Rail Corrugation ◽  
Speed Variation ◽  
Prediction And Control ◽  
And Control
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