DEVELOPMENT OF NEURAL MECHANISMS FOR MACHINE LEARNING

2005 ◽  
Vol 15 (01n02) ◽  
pp. 41-54
Author(s):  
ARTUR M. ARSENIO
Keyword(s):  
Machine Learning ◽  
Experimental Evaluation ◽  
Infant Development ◽  
Humanoid Robot ◽  
Developmental Stages ◽  
Neural Mechanisms ◽  
Object Representations ◽  
Learning Aids ◽  
Educational Videos ◽  
Visual Properties

The goal of this work is to develop a humanoid robot's perceptual mechanisms through the use of learning aids. We describe methods to enable learning on a humanoid robot using learning aids such as books, drawing materials, boards, educational videos or other children toys. Visual properties of objects are learned and inserted into a recognition scheme, which is then applied to acquire new object representations — we propose learning through developmental stages. Inspired in infant development, we will also boost the robot's perceptual capabilities by having a human caregiver performing educational and play activities with the robot (such as drawing, painting or playing with a toy train on a railway). We describe original algorithms to extract meaningful percepts from such learning experiments. Experimental evaluation of the algorithms corroborates the theoretical framework.

Machine Learning Aids Stormwater Fee Assessment

2020 ◽  
Vol 90 (2) ◽  
pp. 38-40
Author(s):  
Catherine A. Cardno
Keyword(s):  
Machine Learning ◽  
Learning Aids

scholarly journals Solving the "right" problems for effective machine learning driven in vitro fertilization

2021 ◽  
Author(s):  
Itay Erlich ◽  
Assaf Ben-Meir ◽  
Iris Har-Vardi ◽  
James A Grifo ◽  
Assaf Zaritsky
Keyword(s):  
Machine Learning ◽  
In Vitro Fertilization ◽  
Clinical Factors ◽  
Vitro Fertilization ◽  
Data Intensive ◽  
Live Embryo ◽  
The Right ◽  
Visual Properties ◽  
Selection Of

Automated live embryo imaging has transformed in-vitro fertilization (IVF) into a data-intensive field. Unlike clinicians who rank embryos from the same IVF cycle cohort based on the embryos visual quality and determine how many embryos to transfer based on clinical factors, machine learning solutions usually combine these steps by optimizing for implantation prediction and using the same model for ranking the embryos within a cohort. Here we establish that this strategy can lead to sub-optimal selection of embryos. We reveal that despite enhancing implantation prediction, inclusion of clinical properties hampers ranking. Moreover, we find that ambiguous labels of failed implantations, due to either low quality embryos or poor clinical factors, confound both the optimal ranking and even implantation prediction. To overcome these limitations, we propose conceptual and practical steps to enhance machine-learning driven IVF solutions. These consist of separating the optimizing of implantation from ranking by focusing on visual properties for ranking, and reducing label ambiguity.

Machine Learning and Medication Adherence: Scoping Review (Preprint)

2021 ◽  
Author(s):  
Aaron Bohlmann ◽  
Javed Mostafa
Keyword(s):  
Machine Learning ◽  
Medication Adherence ◽  
Scoping Review ◽  
Developmental Stages ◽  
Medication Compliance ◽  
Medication Administration ◽  
Sensor Data ◽  
Medication Monitoring ◽  
User Acceptability ◽  
Traditional Approaches

BACKGROUND This is the first scoping review broadly focused on machine learning and medication adherence. OBJECTIVE To categorize and summarize literature focused on using machine learning for medication compliance activities. METHODS PubMed, Scopus, ACM Digital library, IEEE, and Web of Science were searched to find works that meet the inclusion criteria. Study information of interest was systematically charted before inclusion in the final draft. Studies were placed into natural categories for additional analysis dependent upon the combination of medication adherence activities carried out. The protocol for this scoping review was created using the PRISMA-ScR guidelines. RESULTS Publications focused on predicting medication adherence have uncovered strong predictors that were significant across multiple studies. Studies that used machine learning to monitor medication compliance are generally still in early developmental stages and used a variety of sensor data to detect medication administration. Systems that combined medication monitoring with intervention were mostly concerned with detecting medication administration and only a few compared their system against more traditional approaches. CONCLUSIONS In general, this topic currently has relatively few publications but has been generating more interest over the last few years. Although important features for predicting adherence have been identified more work needs to be done to understand the complex interplay between these features. Systems used to monitor medication compliance also require further testing in more realistic environments and user acceptability evaluations. When interventions are attempted the effectiveness of the system should be evaluated against current systems used to encourage medication compliance. CLINICALTRIAL NONE

scholarly journals Neural mechanisms for sigh generation during prenatal development

2018 ◽  
Vol 120 (3) ◽  
pp. 1162-1172 ◽  
Author(s):  
Muriel Thoby-Brisson
Keyword(s):  
Developmental Stages ◽  
Rhythmic Activity ◽  
Cellular Membrane ◽  
High Amplitude ◽  
Prenatal Development ◽  
Neural Mechanisms ◽  
Electrophysiological Recordings ◽  
Respiratory Network ◽  
Brainstem Slices ◽  
Low Amplitude

The respiratory network of the preBötzinger complex (preBötC), which controls inspiratory behavior, can in normal conditions simultaneously produce two types of inspiration-related rhythmic activities: the eupneic rhythm composed of monophasic, low-amplitude, and relatively high-frequency bursts, interspersed with sigh rhythmic activity, composed of biphasic, high-amplitude, and lower frequency bursts. By combining electrophysiological recordings from transverse brainstem slices with computational modeling, new advances in the mechanisms underlying sigh production have been obtained during prenatal development. The present review summarizes recent findings that establish when sigh rhythmogenesis starts to be produced during embryonic development as well as the cellular, membrane, and synaptic properties required for its expression. Together, the results demonstrate that although generated by the same network, the eupnea and sigh rhythms have different developmental onset times and rely on distinct network properties. Because sighs (also known as augmented breaths) are important in maintaining lung function (by reopening collapsed alveoli), gaining insight into their underlying neural mechanisms at early developmental stages is likely to help in the treatment of prematurely born babies often suffering from breathing deficiencies.

scholarly journals Multilevel Cognitive Machine-Learning-Based Concept for Artificial Awareness: Application to Humanoid Robot Awareness Using Visual Saliency

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Kurosh Madani ◽  
Dominik M. Ramik ◽  
Cristophe Sabourin
Keyword(s):  
Machine Learning ◽  
Humanoid Robot ◽  
Visual Saliency ◽  
The State ◽  
Complex Behavior ◽  
Unknown Environment ◽  
Surrounding Environment ◽  
Human Skills ◽  
Perceptual Abilities

As part of “intelligence,” the “awareness” is the state or ability to perceive, feel, or be mindful of events, objects, or sensory patterns: in other words, to be conscious of the surrounding environment and its interactions. Inspired by early-ages human skills developments and especially by early-ages awareness maturation, the present paper accosts the robots intelligence from a different slant directing the attention to combining both “cognitive” and “perceptual” abilities. Within such a slant, the machine (robot) shrewdness is constructed on the basis of a multilevel cognitive concept attempting to handle complex artificial behaviors. The intended complex behavior is the autonomous discovering of objects by robot exploring an unknown environment: in other words, proffering the robot autonomy and awareness in and about unknown backdrop.

A new 3D center of mass control approach for FES-assisted standing: First experimental evaluation with a humanoid robot

2016 ◽  
Vol 38 (11) ◽  
pp. 1270-1278 ◽  
Author(s):  
J. Jovic ◽  
V. Bonnet ◽  
C. Fattal ◽  
P. Fraisse ◽  
Ch. Azevedo Coste
Keyword(s):  
Experimental Evaluation ◽  
Humanoid Robot ◽  
Center Of Mass ◽  
Control Approach
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