scholarly journals Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 99 ◽  
Author(s):  
Kleopatra Pirpinia ◽  
Peter A. N. Bosman ◽  
Jan-Jakob Sonke ◽  
Marcel van Herk ◽  
Tanja Alderliesten
Keyword(s):  
Machine Learning ◽  
Image Registration ◽  
State Of The Art ◽  
Deformable Image Registration ◽  
Optimization Approach ◽  
High Quality ◽  
Trade Off ◽  
Multi Objective ◽  
Current State ◽  
Image Artefacts

Current state-of-the-art medical deformable image registration (DIR) methods optimize a weighted sum of key objectives of interest. Having a pre-determined weight combination that leads to high-quality results for any instance of a specific DIR problem (i.e., a class solution) would facilitate clinical application of DIR. However, such a combination can vary widely for each instance and is currently often manually determined. A multi-objective optimization approach for DIR removes the need for manual tuning, providing a set of high-quality trade-off solutions. Here, we investigate machine learning for a multi-objective class solution, i.e., not a single weight combination, but a set thereof, that, when used on any instance of a specific DIR problem, approximates such a set of trade-off solutions. To this end, we employed a multi-objective evolutionary algorithm to learn sets of weight combinations for three breast DIR problems of increasing difficulty: 10 prone-prone cases, 4 prone-supine cases with limited deformations and 6 prone-supine cases with larger deformations and image artefacts. Clinically-acceptable results were obtained for the first two problems. Therefore, for DIR problems with limited deformations, a multi-objective class solution can be machine learned and used to compute straightforwardly multiple high-quality DIR outcomes, potentially leading to more efficient use of DIR in clinical practice.

UBAR

2021 ◽  
Vol 20 (3) ◽  
pp. 1-25
Author(s):  
Elham Shamsa ◽  
Alma Pröbstl ◽  
Nima TaheriNejad ◽  
Anil Kanduri ◽  
Samarjit Chakraborty ◽  
...  
Keyword(s):  
Resource Management ◽  
Quality Of Experience ◽  
State Of The Art ◽  
State Of Charge ◽  
User Preference ◽  
Management Approach ◽  
High Quality ◽  
Trade Off ◽  
Run Time

Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.

Occupant pre-crash kinematics in rotated seat arrangements

2021 ◽  
pp. 095440702110045
Author(s):  
Alexander Diederich ◽  
Christophe Bastien ◽  
Karthikeyan Ekambaram ◽  
Alexis Wilson
Keyword(s):  
State Of The Art ◽  
The Other ◽  
Human Model ◽  
Multi Objective ◽  
Emergency Braking ◽  
Seating Arrangement ◽  
Current State ◽  
Occupant Comfort ◽  
Occupant Kinematics ◽  
Belt System

The introduction of automated L5 driving technologies will revolutionise the design of vehicle interiors and seating configurations, improving occupant comfort and experience. It is foreseen that pre-crash emergency braking and swerving manoeuvres will affect occupant posture, which could lead to an interaction with a deploying airbag. This research addresses the urgent safety need of defining the occupant’s kinematics envelope during that pre-crash phase, considering rotated seat arrangements and different seatbelt configurations. The research used two different sets of volunteer tests experiencing L5 vehicle manoeuvres, based in the first instance on 22 50th percentile fit males wearing a lap-belt (OM4IS), while the other dataset is based on 87 volunteers with a BMI range of 19 to 67 kg/m2 wearing a 3-point belt (UMTRI). Unique biomechanics kinematics corridors were then defined, as a function of belt configuration and vehicle manoeuvre, to calibrate an Active Human Model (AHM) using a multi-objective optimisation coupled with a Correlation and Analysis (CORA) rating. The research improved the AHM omnidirectional kinematics response over current state of the art in a generic lap-belted environment. The AHM was then tested in a rotated seating arrangement under extreme braking, highlighting that maximum lateral and frontal motions are comparable, independent of the belt system, while the asymmetry of the 3-point belt increased the occupant’s motion towards the seatbelt buckle. It was observed that the frontal occupant kinematics decrease by 200 mm compared to a lap-belted configuration. This improved omnidirectional AHM is the first step towards designing safer future L5 vehicle interiors.

scholarly journals MODC: A Pareto-Optimal Optimization Approach for Network Traffic Classification Based on the Divide and Conquer Strategy

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 233 ◽  
Author(s):  
Zuleika Nascimento ◽  
Djamel Sadok
Keyword(s):  
Machine Learning ◽  
Network Traffic ◽  
Machine Learning Algorithms ◽  
Divide And Conquer ◽  
Pareto Optimal ◽  
Optimization Approach ◽  
Traffic Classification ◽  
Multi Objective ◽  
Network Traffic Classification ◽  
Changes Over Time

Network traffic classification aims to identify categories of traffic or applications of network packets or flows. It is an area that continues to gain attention by researchers due to the necessity of understanding the composition of network traffics, which changes over time, to ensure the network Quality of Service (QoS). Among the different methods of network traffic classification, the payload-based one (DPI) is the most accurate, but presents some drawbacks, such as the inability of classifying encrypted data, the concerns regarding the users’ privacy, the high computational costs, and ambiguity when multiple signatures might match. For that reason, machine learning methods have been proposed to overcome these issues. This work proposes a Multi-Objective Divide and Conquer (MODC) model for network traffic classification, by combining, into a hybrid model, supervised and unsupervised machine learning algorithms, based on the divide and conquer strategy. Additionally, it is a flexible model since it allows network administrators to choose between a set of parameters (pareto-optimal solutions), led by a multi-objective optimization process, by prioritizing flow or byte accuracies. Our method achieved 94.14% of average flow accuracy for the analyzed dataset, outperforming the six DPI-based tools investigated, including two commercial ones, and other machine learning-based methods.

scholarly journals Detecting Emotions in English and Arabic Tweets

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 98 ◽  
Author(s):  
Tariq Ahmad ◽  
Allan Ramsay ◽  
Hanady Ahmed
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Learning Algorithms ◽  
General Purpose ◽  
Machine Learning Algorithms ◽  
Current State ◽  
Optimal Thresholds ◽  
Alternative Approach

Assigning sentiment labels to documents is, at first sight, a standard multi-label classification task. Many approaches have been used for this task, but the current state-of-the-art solutions use deep neural networks (DNNs). As such, it seems likely that standard machine learning algorithms, such as these, will provide an effective approach. We describe an alternative approach, involving the use of probabilities to construct a weighted lexicon of sentiment terms, then modifying the lexicon and calculating optimal thresholds for each class. We show that this approach outperforms the use of DNNs and other standard algorithms. We believe that DNNs are not a universal panacea and that paying attention to the nature of the data that you are trying to learn from can be more important than trying out ever more powerful general purpose machine learning algorithms.

Thermoelectric Properties of C-Axis-Oriented Ca3Co4O9+δ Films Grown on MgO-Buffered Si (100) by PLD Technique

2010 ◽  
Vol 29-32 ◽  
pp. 1913-1918
Author(s):  
Xia Zhang ◽  
Hong Chen ◽  
Qiu Hui Liao ◽  
Xia Li
Keyword(s):  
Thin Films ◽  
State Of The Art ◽  
Pulsed Laser ◽  
Buffer Layers ◽  
Laser Deposition ◽  
X Ray Diffraction ◽  
High Quality ◽  
X Ray ◽  
Current State ◽  
Good Crystallinity

High-quality c-axis-oriented Ca3Co4O9+δ thin films have been grown directly on Si (100) wafers with inserting MgO buffer layers by pulsed-laser deposition (PLD). X-ray diffraction and scan electron microscopy show good crystallinity of the Ca3Co4O9+δ films. The resistivity and Seebeck coefficient of the Ca3Co4O9+δ thin films on Si (100) substrates are 9.8 mΩcm and 189 μV/K at the temperature of 500K, respectively, comparable to the single-crystal samples. This advance demonstrates the possibility of integrating the cobaltate-based high thermoelectric materials with the current state-of-the-art silicon technology for thermoelectricity-on-a-chip applications.

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