scholarly journals Deep Learning does not Replace Bayesian Modeling: Comparing research use via citation counting

2022 ◽  
Author(s):  
Breck Baldwin
Keyword(s):  
Deep Learning ◽  
Bayesian Modeling ◽  
Research Use ◽  
Citation Counting

scholarly journals Deep Learning does not Replace Bayesian Modeling : Comparing research use via citation counting

2021 ◽  
Author(s):  
Breck Baldwin
Keyword(s):  
Artificial Intelligence ◽  
Popular Culture ◽  
Deep Learning ◽  
Computer Science ◽  
Bayesian Modeling ◽  
Citation Count ◽  
Funding Agency ◽  
Research Use ◽  
Learning Approaches ◽  
The Impact

One could be excused for assuming that deep learning had or will soon usurp all credible work in reasoning, artificial intelligence and statistics, but like most ‘meme’ class broad generalizations the concept does not hold up to scrutiny. Memes don’t generally matter since the experts will always know better but in the case of Bayesian software like Stan and PyMC3 even its developers and advocates bemoan the apparent dominance of deep learning as manifested in popular culture, breathtaking performance and most problematically from funding agency peer review that impacts our ability to further advance the field. The facts however do not support the assumed dominance of deep learning in science upon closer examination. This letter simply makes the argument by the crudest of possible metrics, citation count, that once Computer Science is subtracted, Bayesian software accounts for nearly a third of research citations. Stan and PyMC3 dominate some fields, PyTorch, Keras and TensorFlow dominate others with lots of variation in between. Bayesian and deep learning approaches are related but very different technologies in goals, implementation and applicability with little actual overlap so this is not a surprise. While deep learning is backed by industry behemoths (Google, Facebook) the Bayesian efforts are not and it would behoove funders to recognize the impact of Bayesian software given its centrality to science.

scholarly journals Pashtu Numerals Recognition through Convolutional Neural Networks

2019 ◽  
pp. 91-96
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Accuracy ◽  
Research Use ◽  
Optical Character ◽  
Classification Tasks ◽  
Scanned Images

In the proposed paper we introduce a new Pashtu numerals dataset having handwritten scanned images. We make the dataset publically available for scientific and research use. Pashtu language is used by more than fifty million people both for oral and written communication, but still no efforts are devoted to the Optical Character Recognition (OCR) system for Pashtu language. We introduce a new method for handwritten numerals recognition of Pashtu language through the deep learning based models. We use convolutional neural networks (CNNs) both for features extraction and classification tasks. We assess the performance of the proposed CNNs based model and obtained recognition accuracy of 91.45%.

scholarly journals Feature Extraction aligned Email Classification based on Imperative Sentence Selection through Deep Learning

2021 ◽  
Vol 3 (1) ◽  
pp. 93-114
Author(s):  
Nashit Ali ◽  
◽  
Anum Fatima ◽  
Hureeza Shahzadi ◽  
Aman Ullah ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Experimental Result ◽  
Training Dataset ◽  
Research Use ◽  
Huge Amount ◽  
Testing Dataset ◽  
Result Show ◽  
Email Classification

Most commonly used channel for communication among peoples is emails. In this era where everyone is so busy in their routine and work, it is very difficult to check all email when one receives huge amount of emails. Previous research has done work on email categorization in which they have mostly done spam filtration. The problem with spam filtration is that sometimes person mistakenly mark an important email received from high authority as spam and according to previous research, this email will be filtered as spam that can cause a great threat for job of an employee. In this research, we are introducing a methodology which classifies email text into three categories i.e. order, request and general on basis of imperative sentences. This research use Word2Wec for words conversion into vector and use two approaches of deep learning i.e. Convolutional neural network and Recurrent neural network for email classification. We conduct experiment on Dataset collected from Personal Gmail account and Enron which consists of 1000 emails. The experiment result show that RNN gives better accuracy than CNN. We also compare our methods with previously used method Fuzzy ANN results and Our proposed methods CNN and RNN gives better results than Fuzzy ANN. This research has also included different experimental result in which CNN and RNN applied on different ratios of training and testing dataset. These experiment show that increasing in the ratio of training dataset results in increasing accuracy of algorithm.

scholarly journals Penerapan Deep Learning dalam Pendeteksian Autism Toddler

2019 ◽  
Vol 4 (1) ◽  
pp. 138-141
Author(s):  
Diah Ayu Ambarsari ◽  
Ridan Nurfalah ◽  
Sandra Jamu Kuryanti
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Growth Period ◽  
Research Use ◽  
Data Sets ◽  
Autism Symptoms ◽  
Data Set ◽  
New Methods ◽  
Autism Treatment ◽  
Improve Accuracy

Health is a very important thing. Everyone can overcome health problems. Children's health is the dream of every parent. During the growth period the child will switch several times which can stop their development. Parents must be more sensitive and have extensive knowledge in health. The problem that often occurs is that parents do not know the initial autism symptoms that occur in the baby, so more parents assume if it is okay, this situation accelerates the diagnosis process, whereas autism disorders can be detected early by looking at growing habits child development every time an autism transfer is a developmental development in children, autism must facilitate quickly, because with autism treatment quickly and quickly will help autistic patients grow back to normal. To help understand the children mengamalim autism, the authors conducted research with new methods. In a previous study, Fades Tahbatan conducted research to ascertain whether the child was autistic or not using a tool. But it only produces data sets., It turns out to have attributes that are not yet precise, which increases the level of accuracy. In this research, use the method of deep learning and improve accuracy, the application used is fast miners. The variables are then processed so as to produce a prediction model from the data set obtained. Accuracy values that can be processed are sufficient while accuracy = 98.96% precision = 96.74%, recall = 98.49% with AUC of = 0.90 Keywords: Autism, deep learning, toddlers  

Instrumentation for Measurement of Lingual Strength

1968 ◽  
Vol 11 (1) ◽  
pp. 189-193 ◽  
Author(s):  
Lois Joan Sanders
Keyword(s):  
Single Channel ◽  
Displacement Transducer ◽  
Tongue Pressure ◽  
Maximum Pressure ◽  
Research Use ◽  
Direct Writing ◽  
Significant Difference ◽  
Pressure Unit ◽  
Force Displacement ◽  
Consistent Response

A tongue pressure unit for measurement of lingual strength and patterns of tongue pressure is described. It consists of a force displacement transducer, a single channel, direct writing recording system, and a specially designed tongue pressure disk, head stabilizer, and pressure unit holder. Calibration with known weights indicated an essentially linear and consistent response. An evaluation of subject reliability in which 17 young adults were tested on two occasions revealed no significant difference in maximum pressure exerted during the two test trials. Suggestions for clinical and research use of the instrumentation are noted.

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.

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