scholarly journals A Position Paper on Predicting the Onset of Nocturnal Enuresis Using Advanced Machine Learning

2015 ◽  
pp. 689-700 ◽  
Author(s):  
Paul Fergus ◽  
Abir Hussain ◽  
Dhiya Al-Jumeily ◽  
Naeem Radi
Keyword(s):  
Machine Learning ◽  
Nocturnal Enuresis ◽  
Position Paper

Service Deployment Challenges in Cloud-to-Edge Continuum

2021 ◽  
Vol 22 (3) ◽  
pp. 313-320
Author(s):  
Dana Petcu
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Position Paper ◽  
Service Deployment ◽  
Future Challenges

This position paper aims to identify the current and future challenges in application, workload or service deployment mechanisms in Cloud-to-Edge environments. We argue that the adoption of the microservices and unikernels on large scale is adding new entries on the list of requirements of a deployment mechanism, but offers an opportunity to decentralize the associated processes and improve the scalability of the applications. Moreover, the deployment in Cloud-to-Edge environment needs the support of federated machine learning.

Visualizing Interrupts and Replication with Timer

2014 ◽  
Vol 721 ◽  
pp. 750-753
Author(s):  
Jian Sheng Pan ◽  
Shi Cheng
Keyword(s):  
Machine Learning ◽  
Simulated Annealing ◽  
Position Paper ◽  
Extreme Programming ◽  
Current Status

Statisticians agree that signed epistemologies are an interesting new topic in the field of machine learning, and cyberneticists concur. Given the current status of pseudorandom configurations, cryptographers famously desire the refinement of simulated annealing. Our focus in this position paper is not on whether superblocks and extreme programming can collaborate to answer this quagmire, but rather on introducing a methodology for modular information (Timer).

Towards Next-Generation Cybersecurity with Graph AI

2021 ◽  
Vol 55 (1) ◽  
pp. 61-67
Author(s):  
Benjamin Bowman ◽  
H. Howie Huang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Position Paper ◽  
Next Generation ◽  
Intelligent Algorithms

Cybersecurity professionals are inundated with large amounts of data, and require intelligent algorithms capable of distinguishing vulnerable from patched, normal from anomalous, and malicious from benign. Unfortunately, not all machine learning (ML) and artificial intelligence (AI) algorithms are created equal, and in this position paper we posit that a new breed of ML, specifically graph-based machine learning (Graph AI), is poised to make a significant impact in this domain. We will discuss the primary differentiators between traditional ML and graph ML, and provide reasons and justifications for why the latter is well-suited to many aspects of cybersecurity. We will present several example applications and result of graph ML in cybersecurity, followed by a discussion of the challenges that lie ahead.

scholarly journals Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT

2021 ◽  
Author(s):  
Riemer H. J. A. Slart ◽  
Michelle C. Williams ◽  
Luis Eduardo Juarez-Orozco ◽  
Christoph Rischpler ◽  
Marc R. Dweck ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computed Tomography ◽  
Clinical Practice ◽  
Nuclear Cardiology ◽  
European Association ◽  
Imaging Techniques ◽  
Cardiovascular Imaging ◽  
Position Paper ◽  
Daily Clinical Practice

AbstractIn daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

Bayesian, Distributed Modalities for Smalltalk

2018 ◽  
Author(s):  
Syukri ◽  
Sayed Achmady
Keyword(s):  
Machine Learning ◽  
Position Paper ◽  
Reliable Information ◽  
P System ◽  
Access Points ◽  
Linked Lists

System administrators agree that perfect methodologies are an interesting new topic in the field of machine learning, and cyber- informaticians concur. In this position paper, we show the evaluation of linked lists. We use reliable information to show that access points can be made virtual, autonomous, and probabilistic.

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