scholarly journals Machine Learning and Radiogenomics: Lessons Learned and Future Directions

2018 ◽  
Vol 8 ◽  
Author(s):  
John Kang ◽  
Tiziana Rancati ◽  
Sangkyu Lee ◽  
Jung Hun Oh ◽  
Sarah L. Kerns ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lessons Learned ◽  
Future Directions

scholarly journals Communication and Networking Technologies for UAVs: A Survey

2020 ◽  
Author(s):  
chathuranga basnayaka
Keyword(s):  
Machine Learning ◽  
Power Management ◽  
Lessons Learned ◽  
Low Latency ◽  
Focal Points ◽  
Future Directions ◽  
Vehicle Communication ◽  
Work Related ◽  
Aerial Vehicle ◽  
Networking Technologies

<div>With the advancement in drone technology;</div><div>in just a few years; drones will be assisting humans</div><div>in every domain: But there are many challenges to</div><div>be tackled; communication being the chief one: This</div><div>paper aims at providing insights into the latest UAV</div><div>(Unmanned Aerial Vehicle ) communication technolo-</div><div>gies through investigation of suitable task modules;</div><div>antennas; resource handling platforms; and network</div><div>architectures: Additionally; we explore techniques such</div><div>as machine learning and path planning to enhance exist-</div><div>ing drone communication methods:Encryption and opti-</div><div>mization techniques for ensuring long􀀀lasting and se-</div><div>cure communications; as well as for power management;</div><div>are discussed:Moreover; applications of UAV networks</div><div>for di?erent contextual uses ranging from navigation to</div><div>surveillance; URLLC (Ultra-reliable and low􀀀latency</div><div>communications); edge computing and work related</div><div>to arti?cial intelligence are examined: In particular;</div><div>the intricate interplay between UAV; advanced cellu-</div><div>lar communication; and internet of things constitutes</div><div>one of the focal points of this paper: The survey en-</div><div>compasses lessons learned; insights; challenges; open</div><div>issues; and future directions in UAV communications:</div>

scholarly journals Communication and Networking Technologies for UAVs: A Survey

2020 ◽  
Author(s):  
chathuranga basnayaka
Keyword(s):  
Machine Learning ◽  
Power Management ◽  
Lessons Learned ◽  
Low Latency ◽  
Focal Points ◽  
Future Directions ◽  
Vehicle Communication ◽  
Work Related ◽  
Aerial Vehicle ◽  
Networking Technologies

<div>With the advancement in drone technology;</div><div>in just a few years; drones will be assisting humans</div><div>in every domain: But there are many challenges to</div><div>be tackled; communication being the chief one: This</div><div>paper aims at providing insights into the latest UAV</div><div>(Unmanned Aerial Vehicle ) communication technolo-</div><div>gies through investigation of suitable task modules;</div><div>antennas; resource handling platforms; and network</div><div>architectures: Additionally; we explore techniques such</div><div>as machine learning and path planning to enhance exist-</div><div>ing drone communication methods:Encryption and opti-</div><div>mization techniques for ensuring long􀀀lasting and se-</div><div>cure communications; as well as for power management;</div><div>are discussed:Moreover; applications of UAV networks</div><div>for di?erent contextual uses ranging from navigation to</div><div>surveillance; URLLC (Ultra-reliable and low􀀀latency</div><div>communications); edge computing and work related</div><div>to arti?cial intelligence are examined: In particular;</div><div>the intricate interplay between UAV; advanced cellu-</div><div>lar communication; and internet of things constitutes</div><div>one of the focal points of this paper: The survey en-</div><div>compasses lessons learned; insights; challenges; open</div><div>issues; and future directions in UAV communications:</div>

Paper2Wire – A Case Study of User-Centred Development of Machine Learning Tools for UX Designers

i-com ◽  
2021 ◽  
Vol 20 (1) ◽  
pp. 19-32
Author(s):  
Daniel Buschek ◽  
Charlotte Anlauff ◽  
Florian Lachner
Keyword(s):  
Machine Learning ◽  
Development Process ◽  
User Study ◽  
Concept Development ◽  
Lessons Learned ◽  
Design Tool ◽  
Learning Tools ◽  
Interface Elements ◽  
Industry Partner

Abstract This paper reflects on a case study of a user-centred concept development process for a Machine Learning (ML) based design tool, conducted at an industry partner. The resulting concept uses ML to match graphical user interface elements in sketches on paper to their digital counterparts to create consistent wireframes. A user study (N=20) with a working prototype shows that this concept is preferred by designers, compared to the previous manual procedure. Reflecting on our process and findings we discuss lessons learned for developing ML tools that respect practitioners’ needs and practices.

scholarly journals The graph neural networking challenge

2021 ◽  
Vol 51 (3) ◽  
pp. 9-16
Author(s):  
José Suárez-Varela ◽  
Miquel Ferriol-Galmés ◽  
Albert López ◽  
Paul Almasan ◽  
Guillermo Bernárdez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Networks ◽  
Real World ◽  
Large Scale ◽  
Lessons Learned ◽  
Educational Resources ◽  
Global Competition ◽  
International Telecommunication Union ◽  
International Telecommunication ◽  
Broad Audience

During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the "ITU AI/ML in 5G challenge", an open global competition that has introduced to a broad audience some of the current main challenges in ML for networks. This large-scale initiative has gathered 23 different challenges proposed by network operators, equipment manufacturers and academia, and has attracted a total of 1300+ participants from 60+ countries. This paper narrates our experience organizing one of the proposed challenges: the "Graph Neural Networking Challenge 2020". We describe the problem presented to participants, the tools and resources provided, some organization aspects and participation statistics, an outline of the top-3 awarded solutions, and a summary with some lessons learned during all this journey. As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic.

scholarly journals Artificial Intelligence and Behavioral Science Through the Looking Glass: Challenges for Real-World Application

2020 ◽  
Vol 54 (12) ◽  
pp. 942-947
Author(s):  
Pol Mac Aonghusa ◽  
Susan Michie
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Behavior Change ◽  
Behavioral Science ◽  
Lessons Learned ◽  
Learning Approaches ◽  
Intervention Evaluation ◽  
Research Activities ◽  
Behavior Change Interventions

Abstract Background Artificial Intelligence (AI) is transforming the process of scientific research. AI, coupled with availability of large datasets and increasing computational power, is accelerating progress in areas such as genetics, climate change and astronomy [NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning, Vancouver, Canada; Hausen R, Robertson BE. Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. Astrophys J Suppl Ser. 2020;248:20; Dias R, Torkamani A. AI in clinical and genomic diagnostics. Genome Med. 2019;11:70.]. The application of AI in behavioral science is still in its infancy and realizing the promise of AI requires adapting current practices. Purposes By using AI to synthesize and interpret behavior change intervention evaluation report findings at a scale beyond human capability, the HBCP seeks to improve the efficiency and effectiveness of research activities. We explore challenges facing AI adoption in behavioral science through the lens of lessons learned during the Human Behaviour-Change Project (HBCP). Methods The project used an iterative cycle of development and testing of AI algorithms. Using a corpus of published research reports of randomized controlled trials of behavioral interventions, behavioral science experts annotated occurrences of interventions and outcomes. AI algorithms were trained to recognize natural language patterns associated with interventions and outcomes from the expert human annotations. Once trained, the AI algorithms were used to predict outcomes for interventions that were checked by behavioral scientists. Results Intervention reports contain many items of information needing to be extracted and these are expressed in hugely variable and idiosyncratic language used in research reports to convey information makes developing algorithms to extract all the information with near perfect accuracy impractical. However, statistical matching algorithms combined with advanced machine learning approaches created reasonably accurate outcome predictions from incomplete data. Conclusions AI holds promise for achieving the goal of predicting outcomes of behavior change interventions, based on information that is automatically extracted from intervention evaluation reports. This information can be used to train knowledge systems using machine learning and reasoning algorithms.

scholarly journals Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

2021 ◽  
Vol 11 (4) ◽  
pp. 1627
Author(s):  
Yanbin Li ◽  
Gang Lei ◽  
Gerd Bramerdorfer ◽  
Sheng Peng ◽  
Xiaodong Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Design Optimization ◽  
Optimization Methods ◽  
Machine Learning Algorithms ◽  
Cloud Services ◽  
Robust Design Optimization ◽  
Support Vector ◽  
Future Directions ◽  
Electromagnetic Devices ◽  
Recent Developments

This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.

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