Reduction of the ERP Measurement Time by a Weighted Averaging Using Deep Learning

Software Project Estimation is a challenging and important activity in developing software projects. Software Project Estimation includes Software Time Estimation, Software Resource Estimation, Software Cost Estimation, and Software Effort Estimation. Software Effort Estimation focuses on predicting the number of hours of work (effort in terms of person-hours or person-months) required to develop or maintain a software application. It is difficult to forecast effort during the initial stages of software development. Various machine learning and deep learning models have been developed to predict the effort estimation. In this paper, single model approaches and ensemble approaches were considered for estimation. Ensemble techniques are the combination of several single models. Ensemble techniques considered for estimation were averaging, weighted averaging, bagging, boosting, and stacking. Various stacking models considered and evaluated were stacking using a generalized linear model, stacking using decision tree, stacking using a support vector machine, and stacking using random forest. Datasets considered for estimation were Albrecht, China, Desharnais, Kemerer, Kitchenham, Maxwell, and Cocomo81. Evaluation measures used were mean absolute error, root mean squared error, and R-squared. The results proved that the proposed stacking using random forest provides the best results compared with single model approaches using the machine or deep learning algorithms and other ensemble techniques.

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Segmenting the Optic Disc Using a Deep Learning Ensemble Model Based on OWA Operators

10.3233/faia210149 ◽

2021 ◽

Author(s):

Mohammed Yousef Salem Ali ◽

Mohamed Abdel-Nasser ◽

Mohammed Jabreel ◽

Aida Valls ◽

Marc Baget

Keyword(s):

Deep Learning ◽

Optic Disc ◽

Vision Loss ◽

Semantic Segmentation ◽

Medical Image Segmentation ◽

Weighted Averaging ◽

Fundus Images ◽

Owa Operators ◽

Detection Systems ◽

Glaucoma Detection

The optic disc (OD) is the point where the retinal vessels begin. OD carries essential information linked to Diabetic Retinopathy and glaucoma that may cause vision loss. Therefore, accurate segmentation of the optic disc from eye fundus images is essential to develop efficient automated DR and glaucoma detection systems. This paper presents a deep learning-based system for OD segmentation based on an ensemble of efficient semantic segmentation models for medical image segmentation. The aggregation of the different DL models was performed with the ordered weighted averaging (OWA) operators. We proposed the use of a dynamically generated set of weights that can give a different contribution to the models according to their performance during the segmentation of OD in the eye fundus images. The effectiveness of the proposed system was assessed on a fundus image dataset collected from the Hospital Sant Joan de Reus. We obtained Jaccard, Dice, Precision, and Recall scores of 95.40, 95.10, 96.70, and 93.90%, respectively.

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Ordered Weighted Averaging (OWA), Decision Making Under Uncertainty, and Deep Learning: How Is This All Related?

10.20944/preprints202201.0028.v1 ◽

2022 ◽

Author(s):

Vladik Kreinovich

Keyword(s):

Decision Making ◽

Deep Learning ◽

Data Aggregation ◽

Deep Neural Networks ◽

Approximation Property ◽

Weighted Averaging ◽

Universal Approximation ◽

Decision Making Under Uncertainty ◽

Ordered Weighted Averaging ◽

Research Areas

Among many research areas to which Ron Yager contributed are decision making under uncertainty (in particular, under interval and fuzzy uncertainty) and aggregation – where he proposed, analyzed, and utilized the use of Ordered Weighted Averaging (OWA). The OWA algorithm itself provides only a specific type of data aggregation. However, it turns out that if we allows several OWA stages one after another, we get a scheme with a universal approximation property – moreover, a scheme which is perfectly equivalent to deep neural networks. In this sense, Ron Yager can be viewed as a (grand)father of deep learning. We also show that the existing schemes for decision making under uncertainty are also naturally interpretable in OWA terms.

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The Effect of Kalman-Weighted Averaging and Artifact Rejection on Residual Noise During Auditory Brainstem Response Testing

American Journal of Audiology ◽

10.1044/2018_aja-18-0123 ◽

2019 ◽

Vol 28 (1) ◽

pp. 114-124

Author(s):

Linda W. Norrix ◽

Julie Thein ◽

David Velenovsky

Keyword(s):

Repeated Measures ◽

Auditory Brainstem ◽

Weighted Averaging ◽

Auditory Brainstem Responses ◽

Residual Noise ◽

Electrophysiological Recordings ◽

Artifact Rejection ◽

Brainstem Response ◽

Active Motor ◽

Averaging Time

Purpose Low residual noise (RN) levels are critically important when obtaining electrophysiological recordings of threshold auditory brainstem responses. In this study, we examine the effectiveness and efficiency of Kalman-weighted averaging (KWA) implemented on the Vivosonic Integrity System and artifact rejection (AR) implemented on the Intelligent Hearing Systems SmartEP system for obtaining low RN levels. Method Sixteen adults participated. Electrophysiological measures were obtained using simultaneous recordings by the Vivosonic and Intelligent Hearing Systems for subjects in 2 relaxed conditions and 4 active motor conditions. Three averaging times were used for the relaxed states (1, 1.5, and 3 min) and for the active states (1.5, 3, and 6 min). Repeated-measures analyses of variance were used to examine RN levels as a function of noise reduction strategy (i.e., KWA, AR) and averaging time. Results Lower RN levels were obtained using KWA than AR in both the relaxed and active motor states. Thus, KWA was more effective than was AR under the conditions examined in this study. Using KWA, approximately 3 min of averaging was needed in the relaxed condition to obtain an average RN level of 0.025 μV. In contrast, in the active motor conditions, approximately 6 min of averaging was required using KWA. Mean RN levels of 0.025 μV were not attained using AR. Conclusions When patients are not physiologically quiet, low RN levels are more likely to be obtained and more efficiently obtained using KWA than AR. However, even when using KWA, in active motor states, 6 min of averaging or more may be required to obtain threshold responses. Averaging time needed and whether a low RN level can be attained will depend on the level of motor activity exhibited by the patient.

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Deep Learning

10.1017/cbo9780511780295 ◽

2009 ◽

Cited By ~ 94

Author(s):

Stellan Ohlsson

Keyword(s):

Deep Learning

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Intelligence artificielle : le futur de l’Orthodontie ?

Revue d Orthopédie Dento-Faciale ◽

10.1051/odf/2019026 ◽

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.

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