Home »
Academic Articles & Research Papers
Showing 20 of 1,051 academic articles
Carly D. Robinson, Michael Yeomans, Justin Reich, Chris S. Hulleman et al. • International Conference on Learning Analytics and Knowledge (LAK)
(2016)
Student intention and motivation are among the strongest predictors of persistence and completion in Massive Open Online Courses (MOOCs), but these factors are typically measured through fixed-response items that constrain student expressio...
OPENALEX
DOI: https://doi.org/10.1145/2883851.2883932
John P Pestian, Henry A. Nasrallah, Paweł Matykiewicz, Aurora J. Bennett et al. • Biomedical Informatics Insights
(2010)
Suicide is the second leading cause of death among 25-34 year olds and the third leading cause of death among 15-25 year olds in the United States. In the Emergency Department, where suicidal patients often present, estimating the risk of r...
OPENALEX
cc-by-nc
DOI: https://doi.org/10.4137/bii.s4706
PMC: 3107011
Zhiyuan Liu, Yankai Lin, Maosong Sun • Springer eBooks
(2020)
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for NLP. It also benefit related domains such as machine learning, social network analysis, semantic Web, infor...
OPENALEX
cc-by
DOI: https://doi.org/10.1007/978-981-15-5573-2
Rohit Kumar • Apress eBooks
(2017)
Taking our discussion further along from Chapter 4, on machine learning basics, we will discuss here the next topic: natural language processing, or the way machines and computers process and understand natural (human) languages....
OPENALEX
DOI: https://doi.org/10.1007/978-1-4842-3069-5_5
Arvind Chandrasekaran • International Journal on Cybernetics & Informatics
(2023)
Hints of the outbreak are detected through the modified circumstances favoring the outbreaks, like the warm weather contributing to epidermal outbreaks or the loss of sanitation leading to cholera outbreaks typically relying on the routine ...
OPENALEX
Open Access
DOI: https://doi.org/10.5121/ijci.2023.120205
Fredrik Olsson • KTH Publication Database DiVA (KTH Royal Institute of Technology)
(2009)
Active learning is a supervised machine learning technique in which the learner is in control of the data used for learning. That control is utilized by the learner to ask an oracle, typically a human with extensive knowledge of the domain ...
OPENALEX
other-oa
Tatwadarshi P. Nagarhalli, Vinod Moreshwar Vaze, Nirav Pravinsinh Rana • International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)
(2021)
The recent times have seen unprecedented growth in research and development in the field of Artificial Intelligence and its allied fields, subfields, like Machine Learning, Deep Learning and Natural Language Processing. The new found excite...
OPENALEX
DOI: https://doi.org/10.1109/icicv50876.2021.9388380
Igor I. Baskin, David Alan Winkler, Igor V. Tetko • Expert Opinion on Drug Discovery
(2016)
INTRODUCTION: Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to cho...
OPENALEX
DOI: https://doi.org/10.1080/17460441.2016.1201262
Qeethara Kadhim Al-Shayea (2011)
Artificial neural networks are finding many uses in the medical diagnosis application. The goal of this paper is to evaluate artificial neural network in disease diagnosis. Two cases are studied. The first one is acute nephritis disease; da...
OPENALEX
Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann • arXiv (Cornell University)
(2018)
Hyperbolic spaces have recently gained momentum in the context of machine learning due to their high capacity and tree-likeliness properties. However, the representational power of hyperbolic geometry is not yet on par with Euclidean geomet...
OPENALEX
Open Access
DOI: https://doi.org/10.48550/arxiv.1805.09112
Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele et al. • arXiv (Cornell University)
(2018)
In recent years, deep neural networks have revolutionized many application domains of machine learning and are key components of many critical decision or predictive processes. Therefore, it is crucial that domain specialists can understand...
OPENALEX
Open Access
DOI: https://doi.org/10.48550/arxiv.1808.04260
Nicholas Carlini, David Wagner • IEEE Symposium on Security and Privacy (S&P)
(2017)
Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples: given an input x and any target classification t, it is possible to find a new input x'...
OPENALEX
Open Access
DOI: https://doi.org/10.1109/sp.2017.49
Sebastian Kujawa, Gniewko Niedbała • Agriculture
(2021)
Artificial neural networks are one of the most important elements of machine learning and artificial intelligence. They are inspired by the human brain structure and function as if they are based on interconnected nodes in which simple proc...
OPENALEX
cc-by
DOI: https://doi.org/10.3390/agriculture11060497
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter • arXiv (Cornell University)
(2017)
Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and natural language processing via recurrent neural networks (RNNs). However, success stories of Deep Learning with standard feed-forward neural networks (FNN...
OPENALEX
Open Access
DOI: https://doi.org/10.48550/arxiv.1706.02515
Dong-Ling Deng, Xiaopeng Li, S. Das Sarma • Physical Review X
(2017)
Machine learning has recently gained attention as a possible way to understand phase transitions in many-body quantum systems. A new entanglement analysis reveals crucial properties of the data structures that encode quantum states in a neu...
OPENALEX
cc-by
DOI: https://doi.org/10.1103/physrevx.7.021021
Saad Albawi, Tareq Abed Mohammed, Saad Al-Azawi (2017)
The term Deep Learning or Deep Neural Network refers to Artificial Neural Networks (ANN) with multi layers. Over the last few decades, it has been considered to be one of the most powerful tools, and has become very popular in the literatur...
OPENALEX
DOI: https://doi.org/10.1109/icengtechnol.2017.8308186
Roheen Qamar, Baqar Ali Zardari • Mesopotamian Journal of Computer Science
(2023)
Neural networks, also known as artificial neural networks (ANNs) or artificially generated neural networks (SNNs) are a subset of machine learning that provide the foundation of deep learning techniques. Their name and form are inspired by ...
OPENALEX
cc-by
DOI: https://doi.org/10.58496/mjcsc/2023/015
Amey Thakur • International Journal for Research in Applied Science and Engineering Technology
(2021)
The purpose of this study is to familiarise the reader with the foundations of neural networks. Artificial Neural Networks (ANNs) are algorithm-based systems that are modelled after Biological Neural Networks (BNNs). Neural networks are an ...
OPENALEX
Open Access
DOI: https://doi.org/10.22214/ijraset.2021.37362
Brian David Ripley • Cambridge University Press eBooks
(1996)
This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. With unparalleled coverage and a wealth of case-studies this book gives valuable insight into both the theory and the enormously...
OPENALEX
DOI: https://doi.org/10.1017/cbo9780511812651
Paul Refenes • Wiley eBooks
(1995)
PART ONE: NEURAL NETWORKS: Introduction Design Considerations Methods for Optimal Network Design Data Modelling Considerations Testing Strategies and Metrics PART TWO: EQUITY APPLICATIONS: Modelling Stock Returns in the Framework of APT: A ...
OPENALEX