scholarly journals AI and Deep Learning for Urban Computing

2021 ◽  
pp. 815-844
Author(s):  
Senzhang Wang ◽  
Jiannong Cao
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Urban Areas ◽  
Urban Computing ◽  
Recent Advances ◽  
Learning Techniques ◽  
Urban Safety ◽  
Significant Performance ◽  
Location Based Social Networks ◽  
Performance Gains

AbstractIn the big data era, with the large volume of available data collected by various sensors deployed in urban areas and the recent advances in AI techniques, urban computing has become increasingly important to facilitate the improvement of people’s lives, city operation systems, and the environment. In this chapter, we introduce the challenges, methodologies, and applications of AI techniques for urban computing. We first introduce the background, followed by listing key challenges from the perspective of computer science when AI techniques are applied. Then we briefly introduce the AI techniques that are widely used in urban computing, including supervised learning, semi-supervised learning, unsupervised learning, matrix factorization, graphic models, deep learning, and reinforcement learning. With the recent advances of deep-learning techniques, models such as CNN and RNN have shown significant performance gains in many applications. Thus, we briefly introduce the deep-learning models that are widely used in various urban-computing tasks. Finally, we discuss the applications of urban computing including urban planning, urban transportation, location-based social networks (LBSNs), urban safety and security, and urban-environment monitoring. For each application, we summarize major research challenges and review previous work that uses AI techniques to address them.

scholarly journals The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing

2019 ◽  
Vol 63 (11) ◽  
pp. 1658-1667
Author(s):  
M J Castro-Bleda ◽  
S España-Boquera ◽  
J Pastor-Pellicer ◽  
F Zamora-Martínez
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Supervised Learning ◽  
Image Enhancement ◽  
Super Resolution ◽  
Supervised Machine Learning ◽  
Text Documents ◽  
Learning Techniques ◽  
Printed Text

Abstract This paper presents the ‘NoisyOffice’ database. It consists of images of printed text documents with noise mainly caused by uncleanliness from a generic office, such as coffee stains and footprints on documents or folded and wrinkled sheets with degraded printed text. This corpus is intended to train and evaluate supervised learning methods for cleaning, binarization and enhancement of noisy images of grayscale text documents. As an example, several experiments of image enhancement and binarization are presented by using deep learning techniques. Also, double-resolution images are also provided for testing super-resolution methods. The corpus is freely available at UCI Machine Learning Repository. Finally, a challenge organized by Kaggle Inc. to denoise images, using the database, is described in order to show its suitability for benchmarking of image processing systems.

Gastronomic Algorithms: Artistic and Sensory Exploration of Alain Passard's Michelin Plates in the Manner of Giuseppe Arcimboldo with GANs

Leonardo ◽  
2021 ◽  
pp. 1-11
Author(s):  
Emily L. Spratt
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Artistic Collaboration ◽  
Recent Advances ◽  
Learning Techniques ◽  
Data Scientist ◽  
The Senses ◽  
Computer Programmer

Abstract Although recent advances in artificial intelligence to generate images with deep learning techniques, especially generative adversarial networks (GANs), have offered radically new opportunities for its creative applications, there has been little investigation into its use as a tool to explore the senses beyond vision alone. In an artistic collaboration that brought Chef Alain Passard, art historian and data scientist Emily Spratt, and computer programmer Thomas Fan together, photographs of the three-star Michelin plates from the Parisian restaurant Arpège were used as a springboard to explore the art of culinary presentation in the manner of the Renaissance painter Giuseppe Arcimboldo.

RECENT ADVANCES ON TECHNIQUES OF STATIC FEEDFORWARD NETWORKS WITH SUPERVISED LEARNING

1992 ◽  
Vol 03 (03) ◽  
pp. 253-290 ◽  
Author(s):  
LEI XU ◽  
STAN KLASA ◽  
ALAN YUILLE
Keyword(s):  
Supervised Learning ◽  
Basis Function ◽  
Multivariate Adaptive Regression Splines ◽  
Coulomb Energy ◽  
Multilayer Perceptrons ◽  
Learning Models ◽  
Feedforward Networks ◽  
Vast Number ◽  
Recent Advances ◽  
Learning Techniques

The rediscovery and popularization of the backpropagation training technique for multilayer perceptrons as well as the invention of the Boltzmann machine learning algorithm has given a new boost to the study on supervised learning networks. In recent years, besides widely spread applications and various further improvements of the classical backpropagation technique, many new supervised learning models, techniques as well as theories, have also been proposed in a vast number of publications. This paper tries to give a rather systematic review on the recent advances on supervised learning techniques and models for static feedforward networks. We summarize a great number of developments into four aspects: (1) Various improvements and variants made on the classical backpropagation techniques for Multilayer(static) perceptron nets, for speeding up training, avoiding local minima, increasing the generalization ability as well as for many other interesting purposes. (2) A number of other learning methods for training multilayer (static) perceptron, such as derivative estimation by perturbation, direct weight update by perturbation, genetic algorithms, recursive least square estimate and extended Kalman filters, linear programming, the policy of fixing one layer while updating another, constructing networks by converting decision tree classifiers and others. (3) Various other feedforward models which are also able to implement function approximation, probability density estimation and classification, including various models of basis function expansion (e.g. radial basis functions, restricted coulomb energy, multivariate adaptive regression splines, trigonometric and polynomial bases, projection pursuit, basis function tree and many others) and several other supervised learning models. (4) Models with complex structures, e.g. modular architecture, hierarchy architecture and others. Altogether, we try to give a global picture of the present state of supervised learning techniques (not including all the theoretical developments) for training static feedforward networks.

scholarly journals Multi-Modal Pain Intensity Assessment Based on Physiological Signals: A Deep Learning Perspective

2021 ◽  
Vol 12 ◽  
Author(s):  
Patrick Thiam ◽  
Heinke Hihn ◽  
Daniel A. Braun ◽  
Hans A. Kestler ◽  
Friedhelm Schwenker
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Pain Intensity ◽  
Supervised Learning ◽  
Pain Assessment ◽  
Deep Neural Networks ◽  
Fine Tuning ◽  
Physiological Data ◽  
Intensity Assessment ◽  
Learning Techniques

Traditional pain assessment approaches ranging from self-reporting methods, to observational scales, rely on the ability of an individual to accurately assess and successfully report observed or experienced pain episodes. Automatic pain assessment tools are therefore more than desirable in cases where this specific ability is negatively affected by various psycho-physiological dispositions, as well as distinct physical traits such as in the case of professional athletes, who usually have a higher pain tolerance as regular individuals. Hence, several approaches have been proposed during the past decades for the implementation of an autonomous and effective pain assessment system. These approaches range from more conventional supervised and semi-supervised learning techniques applied on a set of carefully hand-designed feature representations, to deep neural networks applied on preprocessed signals. Some of the most prominent advantages of deep neural networks are the ability to automatically learn relevant features, as well as the inherent adaptability of trained deep neural networks to related inference tasks. Yet, some significant drawbacks such as requiring large amounts of data to train deep models and over-fitting remain. Both of these problems are especially relevant in pain intensity assessment, where labeled data is scarce and generalization is of utmost importance. In the following work we address these shortcomings by introducing several novel multi-modal deep learning approaches (characterized by specific supervised, as well as self-supervised learning techniques) for the assessment of pain intensity based on measurable bio-physiological data. While the proposed supervised deep learning approach is able to attain state-of-the-art inference performances, our self-supervised approach is able to significantly improve the data efficiency of the proposed architecture by automatically generating physiological data and simultaneously performing a fine-tuning of the architecture, which has been previously trained on a significantly smaller amount of data.

Music Generation Using Deep Learning Techniques

2020 ◽  
Vol 17 (9) ◽  
pp. 3983-3987
Author(s):  
V. Prashant Krishnan ◽  
S. Rajarajeswari ◽  
Venkat Krishnamohan ◽  
Vivek Chandra Sheel ◽  
R. Deepak
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Generative Adversarial Networks ◽  
Sequential Data ◽  
Learning Techniques ◽  
Opinion Score ◽  
Music Generation ◽  
And Performance

This paper primarily aims to compare two deep learning techniques in the task of learning musical styles and generating novel musical content. Long Short Term Memory (LSTM), a supervised learning algorithm is used, which is a variation of the Recurrent Neural Network (RNN), frequently used for sequential data. Another technique explored is Generative Adversarial Networks (GAN), an unsupervised approach which is used to learn a distribution of a particular style, and novelly combine components to create sequences. The representation of data from the MIDI files as chord and note embedding are essential to the performance of the models. This type of embedding in the network helps it to discover structural patterns in the samples. Through the study, it is seen how a supervised learning technique performs better than the unsupervised one. A study helped in obtaining a Mean Opinion Score (MOS), which was used as an indicator of the comparative quality and performance of the respective techniques.

scholarly journals Recent Advances in Deep Learning Techniques for Face Recognition

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Md. Tahmid Hasan Fuad ◽  
Awal Ahmed Fime ◽  
Delowar Sikder ◽  
Md. Akil Raihan Iftee ◽  
Jakaria Rabbi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Recent Advances ◽  
Learning Techniques

scholarly journals An Enhanced Plant Disease Classifier Model Based on Deep Learning Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 7159-7164 ◽  
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Low Cost ◽  
Disease Diagnosis ◽  
Plant Diseases ◽  
Disease Detection ◽  
Crop Species ◽  
Learning Techniques ◽  
Proposed Model ◽  
Significant Performance

Plant disease detection is used to detect and identify symptoms of plant diseases. Detection of plant diseases through the naked eye is ineffective, especially because there are numerous diseases. Therefore, there is a need to develop low-cost methods to improve rapidity and accuracy of plant disease diagnosis. This paper presents an effective model for plant disease detection by using our developed deep learning approach. Extensive experiments were performed on the PlantVillage dataset, which contains 54,306 images categorized between 38 different classes containing 14 crop species and 26 diseases. Our proposed model demonstrated significant performance improvement in terms of accuracy, recall, precision, and F1-score compared with the existing model used for plant disease detection.

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