An efficient algorithm for recognition of emotions from speaker and language independent speech using deep learning

2021 ◽  
Author(s):  
Youddha Beer Singh ◽  
Shivani Goel
Keyword(s):  
Deep Learning ◽  
Efficient Algorithm ◽  
Recognition Of Emotions

scholarly journals Credit Card Fraud Detection Using Autoencoder Model in Unbalanced Datasets

2019 ◽  
pp. 1-16 ◽  
Author(s):  
M. A. Al-Shabi
Keyword(s):  
Logistic Regression ◽  
Deep Learning ◽  
Efficient Algorithm ◽  
Credit Card ◽  
Learning Algorithm ◽  
Credit Cards ◽  
Reconstruction Error ◽  
Insurance Companies ◽  
Deep Learning Algorithm ◽  
Unbalanced Dataset

Fraudulent credit card transaction is still one of problems that face the companies and banks sectors; it causes them to lose billions of dollars every year. The design of efficient algorithm is one of the most important challenges in this area. This paper aims to propose an efficient approach that automatic detects fraud credit card related to insurance companies using deep learning algorithm called Autoencoders. The effectiveness of the proposed method has been proved in identifying fraud in actual data from transactions made by credit cards in September 2013 by European cardholders. In addition, a solution for data unbalancing is provided in this paper, which affects most current algorithms. The suggested solution relies on training for the autoencoder for the reconstruction normal data. Anomalies are detected by defining a reconstruction error threshold and considering the cases with a superior threshold as anomalies. The algorithm's performance was able to detected fraudulent transactions between 64% at the threshold = 5, 79% at the threshold = 3 and 91% at threshold= 0.7, it is better in performance compare with logistic regression 57% in unbalanced dataset.

Recognition of emotions using multimodal physiological signals and an ensemble deep learning model

2017 ◽  
Vol 140 ◽  
pp. 93-110 ◽  
Author(s):  
Zhong Yin ◽  
Mengyuan Zhao ◽  
Yongxiong Wang ◽  
Jingdong Yang ◽  
Jianhua Zhang
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Physiological Signals ◽  
Recognition Of Emotions ◽  
Deep Learning Model

Computational Micrograph Registration with Sieve Processes

1996 ◽  
Vol 54 ◽  
pp. 440-441
Author(s):  
P.J. Phillips ◽  
J. Huang ◽  
S. M. Dunn
Keyword(s):  
Planar Graph ◽  
Efficient Algorithm ◽  
Spatial Organization ◽  
Spatial Arrangement ◽  
Stereo Image ◽  
Image Model ◽  
Geometric Invariants ◽  
Point Set

In this paper we present an efficient algorithm for automatically finding the correspondence between pairs of stereo micrographs, the key step in forming a stereo image. The computation burden in this problem is solving for the optimal mapping and transformation between the two micrographs. In this paper, we present a sieve algorithm for efficiently estimating the transformation and correspondence.In a sieve algorithm, a sequence of stages gradually reduce the number of transformations and correspondences that need to be examined, i.e., the analogy of sieving through the set of mappings with gradually finer meshes until the answer is found. The set of sieves is derived from an image model, here a planar graph that encodes the spatial organization of the features. In the sieve algorithm, the graph represents the spatial arrangement of objects in the image. The algorithm for finding the correspondence restricts its attention to the graph, with the correspondence being found by a combination of graph matchings, point set matching and geometric invariants.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

Vollautomatische Quantifizierung der Volumina und Funktion des linken Ventrikels in der kardialen MRT: Evaluation eines Deep-Learning basierten Algorithmus

2020 ◽  
Author(s):  
B Böttcher ◽  
E Beller ◽  
A Busse ◽  
F Streckenbach ◽  
M Weber ◽  
...  
Keyword(s):  
Deep Learning
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