scholarly journals Multimodal Emotion Recognition using Deep Learning

2021 ◽  
Vol 2 (02) ◽  
pp. 52-58
Author(s):  
Sharmeen M.Saleem Abdullah Abdullah ◽  
Siddeeq Y. Ameen Ameen ◽  
Mohammed Mohammed sadeeq ◽  
Subhi Zeebaree
Keyword(s):  
Deep Learning ◽  
Affective Computing ◽  
Emotional Awareness ◽  
Computing Systems ◽  
Multimodal Signals ◽  
Current State ◽  
Multimodal Emotion Recognition ◽  
New Research ◽  
State Of The Science ◽  
Facial Images

New research into human-computer interaction seeks to consider the consumer's emotional status to provide a seamless human-computer interface. This would make it possible for people to survive and be used in widespread fields, including education and medicine. Multiple techniques can be defined through human feelings, including expressions, facial images, physiological signs, and neuroimaging strategies. This paper presents a review of emotional recognition of multimodal signals using deep learning and comparing their applications based on current studies. Multimodal affective computing systems are studied alongside unimodal solutions as they offer higher accuracy of classification. Accuracy varies according to the number of emotions observed, features extracted, classification system and database consistency. Numerous theories on the methodology of emotional detection and recent emotional science address the following topics. This would encourage studies to understand better physiological signals of the current state of the science and its emotional awareness problems.

scholarly journals Disentangling the Fairness & Discrimination and Synergy Perspectives on Diversity Climate

2016 ◽  
Vol 42 (5) ◽  
pp. 1136-1168 ◽  
Author(s):  
David J. G. Dwertmann ◽  
Lisa H. Nishii ◽  
Daan van Knippenberg
Keyword(s):  
Organizational Climate ◽  
Research Agenda ◽  
Psychological Climate ◽  
Diversity Climate ◽  
Current State ◽  
Key Issues ◽  
Antecedents And Consequences ◽  
New Research ◽  
State Of The Science ◽  
Core Issues

We provide a theory-driven review of empirical research in diversity climate to identify a number of problems with the current state of the science as well as a research agenda to move the field forward. The core issues we identify include (a) the fact that diversity climate is typically treated as unidimensional, whereas diversity research would suggest that there are two major perspectives that could be reflected in diversity climate—efforts to ensure equal employment opportunity and the absence of discrimination versus efforts to create synergy from diversity; (b) a tendency to let the level of analysis (individual psychological climate or shared team or organizational climate) be dictated by convenience rather than by careful theoretical consideration, thus sidestepping key issues for research concerning the causes and consequences of the sharedness, or lack thereof, of diversity climate perceptions; and (c) the tendency to include diversity attitudes and other nonclimate elements in climate measures even though they are different from climate both conceptually and in their antecedents and consequences. The research agenda we advance suggests a need both for different operationalizations and for new research questions in diversity climate, diversity, and relational demography research.

scholarly journals Face Verification Across Age Progression using Enhanced Convolution Neural Network

2020 ◽  
Vol 11 (5) ◽  
pp. 61-73
Author(s):  
Areeg Mohammed Osman ◽  
Serestina Viriri
Keyword(s):  
Deep Learning ◽  
Euclidean Distance ◽  
Local Binary Pattern ◽  
Learning Method ◽  
Facial Aging ◽  
Age Progression ◽  
Orientation Gradient ◽  
Current State ◽  
Age Gap ◽  
Facial Images

This paper proposes a deep learning method for facial verification of aging subjects. Facial aging is a texture and shape variations that affect the human face as time progresses. Accordingly, there is a demand to develop robust methods to verify facial images when they age. In this paper, a deep learning method based on GoogLeNet pre-trained convolution network fused with Histogram Orientation Gradient (HOG) and Local Binary Pattern (LBP) feature descriptors have been applied for feature extraction and classification. The experiments are based on the facial images collected from MORPH and FG-Net benchmarked datasets. Euclidean distance has been used to measure the similarity between pairs of feature vectors with the age gap. Experiments results show an improvement in the validation accuracy conducted on the FG-NET database, which it reached 100%, while with MORPH database the validation accuracy is 99.8%. The proposed method has better performance and higher accuracy than current state-of-the-art methods.

scholarly journals Emotion Mining: from Unimodal to Multimodal Approaches

2021 ◽  
pp. 143-158
Author(s):  
Chiara Zucco ◽  
Barbara Calabrese ◽  
Mario Cannataro
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Emotion Recognition ◽  
Affective Computing ◽  
Large Datasets ◽  
Social Media Platforms ◽  
Basic Concepts ◽  
Multimodal Emotion Recognition ◽  
Learning Architectures ◽  
Emotion Mining

AbstractIn the last decade, Sentiment Analysis and Affective Computing have found applications in different domains. In particular, the interest of extracting emotions in healthcare is demonstrated by the various applications which encompass patient monitoring and adverse events prediction. Thanks to the availability of large datasets, most of which are extracted from social media platforms, several techniques for extracting emotion and opinion from different modalities have been proposed, using both unimodal and multimodal approaches. After introducing the basic concepts related to emotion theories, mainly borrowed from social sciences, the present work reviews three basic modalities used in emotion recognition, i.e. textual, audio and video, presenting for each of these i) some basic methodologies, ii) some among the widely used datasets for the training of supervised algorithms and iii) briefly discussing some deep Learning architectures. Furthermore, the paper outlines the challenges and existing resources to perform a multimodal emotion recognition which may improve performances by combining at least two unimodal approaches. architecture to perform multimodal emotion recognition.

Analysis of Aluminium Beam-to-Column Joints by the Component Method: Existing Studies and Research Needs

2016 ◽  
Vol 710 ◽  
pp. 409-414 ◽  
Author(s):  
Gianfranco De Matteis ◽  
Giuseppe Brando
Keyword(s):  
Current Knowledge ◽  
Component Method ◽  
The Past ◽  
Research Fields ◽  
Numerical Studies ◽  
Current State ◽  
Moment Resisting ◽  
New Research ◽  
Steel Joints

This paper aims at providing an overview on the current state of the art and on possible future developments concerning the component method implementation for the classification of beam-to-column joints belonging to aluminum moment resisting frames.After a brief discussion on the component method theoretical bases, developed in the past to give a feasible calculation procedure for steel joints, recent experimental and numerical studies, carried out for investigating some aluminum components, are presented and discussed. In particular strengths and weaknesses of the current knowledge are put into evidence, also in light of the peculiarities that make aluminum alloys different from steel. The launch of new research fields, aimed at pursuing an update of the current codes dealing with aluminum structures, is therefore proposed.

Effects of Deep Learning Technologies on Employment in the Field of Digital Communication Systems

2021 ◽  
Vol 12 (4) ◽  
pp. 35-42
Author(s):  
Thomas Alan Woolman ◽  
Philip Lee
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Communication Systems ◽  
Digital Communication ◽  
The United States ◽  
Learning Technologies ◽  
Technological Unemployment ◽  
Current State ◽  
Challenges And Opportunities ◽  
Digital Communication Systems

There are significant challenges and opportunities facing the economies of the United States in the coming decades of the 21st century that are being driven by elements of technological unemployment. Deep learning systems, an advanced form of machine learning that is often referred to as artificial intelligence, is presently reshaping many aspects of traditional digital communication technology employment, primarily network system administration and network security system design and maintenance. This paper provides an overview of the current state-of-the-art developments associated with deep learning and artificial intelligence and the ongoing revolutions that this technology is having not only on the field of digital communication systems but also related technology fields. This paper will also explore issues and concerns related to past technological unemployment challenges, as well as opportunities that may be present as a result of these ongoing technological upheavals.

What’s So Special about the Developing Immune System?

2008 ◽  
Vol 27 (2) ◽  
pp. 223-254 ◽  
Author(s):  
Leigh Ann Burns-Naas ◽  
Kenneth L. Hastings ◽  
Gregory S. Ladics ◽  
Susan L. Makris ◽  
George A. Parker ◽  
...  
Keyword(s):  
Immune System ◽  
Toxicity Testing ◽  
Life Stages ◽  
Significant Progress ◽  
Immune Development ◽  
Immune Parameters ◽  
Current State ◽  
State Of The Science ◽  
Developmental Immunotoxicity ◽  
Nonhuman Species

The evolution of the subdiscipline of developmental immunotoxicology (DIT) as it exists today has been shaped by significant regulatory pressures as well as key scientific advances. This review considers the role played by legislation to protect children’s health, and on the emergence of immunotoxcity and developmental immunotoxicity guidelines, as well as providing some context to the need for special attention on DIT by considering the evidence that the developing immune system may have unique susceptibilities when compared to the adult immune system. Understanding the full extent of this potential has been complicated by a paucity of data detailing the development of the immune system during critical life stages as well as by the complexities of comparisons across species. Notably, there are differences between humans and nonhuman species used in toxicity testing that include specific differences relative to the timing of the development of the immune system as well as more general anatomic differences, and these differences must be factored into the interpretation of DIT studies. Likewise, understanding how the timing of the immune development impacts on various immune parameters is critical to the design of DIT studies, parameters most extensively characterized to date in young adult animals. Other factors important to DIT, which are considered in this review, are the recognition that effects other than suppression (e.g., allergy and autoimmunity) are important; the need to improve our understanding of how to assess the potential for DIT in humans; and the role that pathology has played in DIT studies in test animals. The latter point receives special emphasis in this review because pathology evaluations have been a major component of standard nonclinical toxicology studies, and could serve an important role in studies to evaluate DIT. This possibility is very consistent with recommendations to incorporate a DIT evaluation into standard developmental and reproductive toxicology (DART) protocols. The overall objective of this review is to provide a ‘snapshot’ of the current state-of-the-science of DIT. Despite significant progress, DIT is still evolving and it is our hope that this review will advance the science.

Effective Emotion Recognition from Partially Occluded Facial Images Using Deep Learning

2020 ◽  
pp. 213-221
Author(s):  
Smitha Engoor ◽  
Sendhilkumar Selvaraju ◽  
Hepsibah Sharon Christopher ◽  
Mahalakshmi Guruvayur Suryanarayanan ◽  
Bhuvaneshwari Ranganathan
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Partially Occluded ◽  
Facial Images
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