scholarly journals Evaluation and Construction of College Students’ Growth and Development Index System Based on Data Association Mining and Deep Learning Model

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yanjie Li ◽  
He Mao
Keyword(s):  
College Students ◽  
Big Data ◽  
Deep Learning ◽  
Growth And Development ◽  
Language Model ◽  
Association Mining ◽  
Student Management ◽  
Passive Response ◽  
Point Data

The rise of big data in the field of education provides an opportunity to solve college students’ growth and development. The establishment of a personalized student management mode based on big data in universities will promote the change of personalized student management from the empirical mode to the scientific mode, from passive response to active warning, from reliance on point data to holistic data, and thus improve the efficiency and quality of personalized student management. In this paper, using the latest ideas and techniques in deep learning such as self-supervised learning and multitask learning, we propose an open-source educational big data pretrained language model F-BERT based on the BERT model architecture. Based on the BERT architecture, F-BERT can effectively and automatically extract knowledge from educational big data and memorize it in the model without modifying the model structure specific to educational big data tasks so that it can be directly applied to various educational big data domain tasks downstream. The experiment demonstrates that Vanilla F-BERT outperformed the two Vanilla BERT-based models, Vanilla BERT and BERT tasks, by 0.0.6 and 0.03 percent, respectively, in terms of accuracy.

Deep Learning in IoT

2021 ◽  
pp. 1-54
Author(s):  
Devika G. ◽  
Asha Gowda Karegowda
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Big Data Analytics ◽  
Future Research ◽  
Great Success ◽  
Sensory Data ◽  
Digital Era ◽  
New Information ◽  
Wide Range

The internet of things (IoT), big data analytics, and deep learning (DL) applications in the mechanical internet are expanding. The current digital era has various sensory devices for a wide range of fields and applications, which all generate various sensory data. DL is being applied for handling big data and has achieved great success in the IoT and other fields. The applications for data streams to discover new information, predict future insights, and make control decisions are crucial processes that make the IoT a worthy paradigm for businesses and a quality-of-life improving technology. This chapter provides a detailed account of the IoT domain, machine learning, and DL techniques and applications. The IoT that consists of DL with intelligence backgrounds is also discussed. Recent research on DL in the IoT within the big data domain is also discussed. Current challenges and potential areas for future research are discussed.

A Survey on Algorithms in Deep Learning

2020 ◽  
pp. 339-350
Author(s):  
Sindhu P. Menon
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Unsupervised Learning ◽  
Learning Algorithms ◽  
Activation Functions ◽  
Learning Models ◽  
Quality Of Data

In the last couple of years, artificial neural networks have gained considerable momentum. Their results could be enhanced if the number of layers could be made deeper. Of late, a lot of data has been generated, which has led to big data. This comes along with many challenges like quality, which is one of the most important ones. Deep learning models can improve the quality of data. In this chapter, an attempt has been made to review deep supervised and deep unsupervised learning algorithms and the various activation functions used. Challenges in deep learning have also been discussed.

scholarly journals A Novel Machine Learning Approach for Sentiment Analysis on Twitter Incorporating the Universal Language Model Fine-Tuning and SVM

2022 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Barakat AlBadani ◽  
Ronghua Shi ◽  
Jian Dong
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Detection Efficiency ◽  
Language Model ◽  
Fine Tuning ◽  
Support Vector ◽  
Learning Approach ◽  
Universal Language

Twitter sentiment detectors (TSDs) provide a better solution to evaluate the quality of service and product than other traditional technologies. The classification accuracy and detection performance of TSDs, which are extremely reliant on the performance of the classification techniques, are used, and the quality of input features is provided. However, the time required is a big problem for the existing machine learning methods, which leads to a challenge for all enterprises that aim to transform their businesses to be processed by automated workflows. Deep learning techniques have been utilized in several real-world applications in different fields such as sentiment analysis. Deep learning approaches use different algorithms to obtain information from raw data such as texts or tweets and represent them in certain types of models. These models are used to infer information about new datasets that have not been modeled yet. We present a new effective method of sentiment analysis using deep learning architectures by combining the “universal language model fine-tuning” (ULMFiT) with support vector machine (SVM) to increase the detection efficiency and accuracy. The method introduces a new deep learning approach for Twitter sentiment analysis to detect the attitudes of people toward certain products based on their comments. The extensive results on three datasets illustrate that our model achieves the state-of-the-art results over all datasets. For example, the accuracy performance is 99.78% when it is applied on the Twitter US Airlines dataset.

scholarly journals On the Relationship between Ideological and Political Education and the Management of College Students

2020 ◽  
Vol 4 (2) ◽  
pp. 34
Author(s):  
Lingzhi Li
Keyword(s):  
College Students ◽  
Educational Policy ◽  
Colleges And Universities ◽  
Education Level ◽  
Ideological And Political Education ◽  
Management Level ◽  
Student Management ◽  
Continuous Progress ◽  
The Relationship

<p>With the continuous progress of our society and the improvement of education level, the benefits of Ideology and Politics education are gradually recognized by people. Ideology and Politics education has always been an important educational policy in China's colleges and universities. It is believed that it can greatly improve the comprehensive quality of students and help students to establish correct values. Therefore, the Ideology and Politics education mode of college students is gradually popularized. However, many colleges and universities sometimes ignore the downward trend of student management level. This situation will lead to the mode of Ideology and Politics education in varsity not be effectively applied. This paper will discuss the relationship between the student management and the idea of Ideology and Politics education, and finally come to a conclusion.</p>

scholarly journals Formal Medical Knowledge Representation Supports Deep Learning Algorithms, Bioinformatics Pipelines, Genomics Data Analysis, and Big Data Processes

2019 ◽  
Vol 28 (01) ◽  
pp. 152-155
Author(s):  
Ferdinand Dhombres ◽  
Jean Charlet ◽  
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Knowledge Representation ◽  
Semantic Annotation ◽  
Medical Knowledge ◽  
Added Value ◽  
Association Mining ◽  
Learning Approaches ◽  
Semantic Representations ◽  
Research Areas

Objective: To select, present, and summarize the best papers published in 2018 in the field of Knowledge Representation and Management (KRM). Methods: A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers published in 2018 in KRM, based on PubMed and ISI Web Of Knowledge queries. Results: Four best papers were selected among the 962 publications retrieved following the Yearbook review process. The research areas in 2018 were mainly related to the ontology-based data integration for phenotype-genotype association mining, the design of ontologies and their application, and the semantic annotation of clinical texts. Conclusion: In the KRM selection for 2018, research on semantic representations demonstrated their added value for enhanced deep learning approaches in text mining and for designing novel bioinformatics pipelines based on graph databases. In addition, the ontology structure can enrich the analyses of whole genome expression data. Finally, semantic representations demonstrated promising results to process phenotypic big data.

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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