scholarly journals DEEP LEARNING: APPLICATION OF THE LSTMMODEL IN THE CATEGORIZATION OF TWEETS ON VIOLENCE IN THE CITY OF BELÉM

2019 ◽  
Author(s):  
Jonatã Paulino da Costa ◽  
Aurea Milene Teixeira Barbosa dos Santos ◽  
Eulália Carvalho da Mata ◽  
Marcelino Silva da Silva ◽  
Priscila Siqueira Aranha ◽  
...  
Keyword(s):  
Deep Learning ◽  
The City

Predictive Analytics for Smart Parking: A Deep Learning Approach in Forecasting of IoT Data

2021 ◽  
Vol 21 (3) ◽  
pp. 1-21
Author(s):  
Francesco Piccialli ◽  
Fabio Giampaolo ◽  
Edoardo Prezioso ◽  
Danilo Crisci ◽  
Salvatore Cuomo
Keyword(s):  
Deep Learning ◽  
Smart City ◽  
Predictive Analytics ◽  
Positive Impact ◽  
Search Time ◽  
Building Blocks ◽  
Ensemble Technique ◽  
Smart Parking ◽  
Polluting Emissions ◽  
The City

Nowadays, a sustainable and smart city focuses on energy efficiency and the reduction of polluting emissions through smart mobility projects and initiatives to “sensitize” infrastructure. Smart parking is one of the building blocks of intelligent mobility, innovative mobility that aims to be flexible, integrated, and sustainable and consequently integrated into a Smart City. By using the Internet of Things (IoT) sensors located in the parking areas or the underground car parks in combination with a mobile application, which indicates to citizens the free places in the different areas of the city and guides them toward the chosen parking, it is possible to reduce air pollution and fluidifying noise traffic. In this article, we present and discuss an innovative Deep Learning-based ensemble technique in forecasting the parking space occupancy to reduce the search time for parking and to optimize the flow of cars in particularly congested areas, with an overall positive impact on traffic in urban centres. A genetic algorithm has also been used to optimize predictors parameters. The main goal is to design an intelligent IoT-based service that can predict, in the next few hours, the parking spaces occupancy of a street. The proposed approach has been assessed on a real IoT dataset composed by over than 15M of collected sensor records. Obtained results demonstrate that our method outperforms both single predictors and the widely used strategy of the mean providing inherently robust predictions.

scholarly journals Research on Urban Renewal Public Space Design Based on Convolutional Neural Network Model

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jixin Wan ◽  
Huosai Shi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Public Space ◽  
Urban Space ◽  
Design Method ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Space Design ◽  
Generation Mode ◽  
The City

By establishing a database of urban space cases, machine learning algorithms and deep learning algorithms can be used to train computers to learn how to design urban spaces. Based on the basic concepts of machine learning and deep learning and their procedural logic, this paper explores the generation mode of traffic road network, neighborhood space form, and building function layout of urban space and uses the northern extension of the central green axis of the city as an application case to confirm its feasibility in order to seek a set of artificial intelligence-based urban space generation design method and provide a new idea for the innovative development of urban design methods.

Potential classification of Smart Village – Smart Economy with Deep Learning methods

2021 ◽  
Vol 2 (3) ◽  
pp. 147-162
Author(s):  
Runanto Runanto ◽  
Muhammad Fahmi Mislahudin ◽  
Fauzan Azmi Alfiansyah ◽  
Maudy Khairunnisa Maisun Taqiyyah ◽  
Eneng Tita Tosida
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Development Program ◽  
City Development ◽  
Target Class ◽  
Learning Methods ◽  
Development Goals ◽  
The Village ◽  
The City

Development gap in the city and village is still happening on Indonesia. It happened because of the massive urbanization factors. Poverty in the Indonesian villages are relatively higher than on the urbans. In order to reach the maximal city development, Ministry of Village, Development of Disadvantaged Regions and Transmigration of Indonesia created a sustainable village development program namely Village’s Sustainable Development Goals (SDGs) and optimized the village potential data. This study aimed to design the smart village – smart economy classification system by using deep learning methods on village potential data on Indonesia at 2020. The method used in this study is data mining processes namely KDD (Knowledge Discovery and Data mining). The result in this study showed the best models were obtained which consisting of 2 hidden layers and each layer is 128, 128 layers which using target class from the process of calculating the score is able to reach 94.93% of the accuracy from the training process and 96% on the testing process and succeeded to classify the potentials of smart village – smart economy.

scholarly journals DEEP NEURAL NETWORKS FOR ABOVE-GROUND DETECTION IN VERY HIGH SPATIAL RESOLUTION DIGITAL ELEVATION MODELS

2015 ◽  
Vol II-3/W4 ◽  
pp. 103-110 ◽  
Author(s):  
D. Marmanis ◽  
F. Adam ◽  
M. Datcu ◽  
T. Esch ◽  
U. Stilla
Keyword(s):  
Deep Learning ◽  
Urban Environments ◽  
Convergence Time ◽  
Classification Problems ◽  
Learning Techniques ◽  
Digital Elevation ◽  
Very High Spatial Resolution ◽  
Novel Method ◽  
Dongying City ◽  
The City

Deep Learning techniques have lately received increased attention for achieving state-of-the-art results in many classification problems, including various vision tasks. In this work, we implement a Deep Learning technique for classifying above-ground objects within urban environments by using a Multilayer Perceptron model and VHSR DEM data. In this context, we propose a novel method called M-ramp which significantly improves the classifier’s estimations by neglecting artefacts, minimizing convergence time and improving overall accuracy. We support the importance of using the M-ramp model in DEM classification by conducting a set of experiments with both quantitative and qualitative results. Precisely, we initially train our algorithm with random DEM tiles and their respective point-labels, considering less than 0.1% over the test area, depicting the city center of Munich (25 km<sup>2</sup>). Furthermore with no additional training, we classify two much larger unseen extents of the greater Munich area (424 km<sup>2</sup>) and Dongying city, China (257 km<sup>2</sup>) and evaluate their respective results for proving knowledge-transferability. Through the use of M-ramp, we were able to accelerate the convergence by a magnitude of 8 and achieve a decrease in above-ground relative error by 24.8% and 5.5% over the different datasets.

scholarly journals Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States

2017 ◽  
Vol 114 (50) ◽  
pp. 13108-13113 ◽  
Author(s):  
Timnit Gebru ◽  
Jonathan Krause ◽  
Yilun Wang ◽  
Duyun Chen ◽  
Jia Deng ◽  
...  
Keyword(s):  
United States ◽  
Deep Learning ◽  
The United States ◽  
Motor Vehicles ◽  
Google Street View ◽  
Street View ◽  
Fine Spatial Resolution ◽  
Gender Education ◽  
The City ◽  
Us Cities

The United States spends more than $250 million each year on the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed several years. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may become an increasingly practical supplement to the ACS. Here, we present a method that estimates socioeconomic characteristics of regions spanning 200 US cities by using 50 million images of street scenes gathered with Google Street View cars. Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Data from this census of motor vehicles, which enumerated 22 million automobiles in total (8% of all automobiles in the United States), were used to accurately estimate income, race, education, and voting patterns at the zip code and precinct level. (The average US precinct contains ∼1,000 people.) The resulting associations are surprisingly simple and powerful. For instance, if the number of sedans encountered during a drive through a city is higher than the number of pickup trucks, the city is likely to vote for a Democrat during the next presidential election (88% chance); otherwise, it is likely to vote Republican (82%). Our results suggest that automated systems for monitoring demographics may effectively complement labor-intensive approaches, with the potential to measure demographics with fine spatial resolution, in close to real time.

scholarly journals Modified AlexNet Convolution Neural Network For Covid-19 Detection Using Chest X-ray Images

2020 ◽  
pp. 119-130
Author(s):  
Shadman Q. Salih ◽  
Hawre Kh. Abdulla ◽  
Zanear Sh. Ahmed ◽  
Nigar M. Shafiq Surameery ◽  
Rasper Dh. Rashid
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Imaging Techniques ◽  
Convolution Neural Network ◽  
World Health ◽  
X Ray ◽  
Chest X Ray ◽  
Health Organization ◽  
Fully Connected ◽  
The City

First outbreak of COVID-19 was in the city of Wuhan in China in Dec.2019 and then it becomes a pandemic disease all around the world. World Health Organization (WHO) confirmed more than 5.5 million cases and 341,155 deaths from the disease till the time of writing this paper. This new worldwide disease forced researchers to make more precise way to diagnose COVID-19. In the last decade, medical imaging techniques show its efficiency in helping radiologists to detect and diagnose the diseases. Deep learning and transfer learning algorithms are good techniques to detect disease from different image source types such as X-Ray and CT scan images. In this work we used a deep learning technique based on Convolution Neural Network (CNN) to detect and diagnose COVID-19 disease using Chest X-ray images.  Moreover, the modified AlexNet architecture is proposed in different scenarios were differing from each other in terms of the type of the pooling layers and/or the number of the neurons that have used in the second fully connected layer. The used chest X-ray images are gathered from two COVID-19 X-ray image datasets and one dataset includes large number of normal and pneumonia X-ray images. With the proposed models we obtained the same or even better result than the original AlexNet with having a smaller number of neurons in the second fully connected layer.

scholarly journals Shop Weatherly – A Weather based Smart E-Commerce System Using CNN

2021 ◽  
Vol 11 (4) ◽  
pp. 2785-2800
Author(s):  
Jawaria Sallar ◽  
Sallar Khan ◽  
Shariq Ahmed ◽  
Parshan Kumar ◽  
Hasham Faridy ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Customer Satisfaction ◽  
Convolutional Neural Network ◽  
Online Shopping ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Free Environment ◽  
The City

In this current era of modern online shopping, people want to spend as little time as possible when it comes to buying products, therefore they prefer online shopping. People go shopping when the weather gets changed. For travelers, there is no such E-commerce platform that can recommend clothes according to any city weather. Even when people want to gift clothes to someone living in another country there is no such platform that gives recommendation of clothes according to that city's weather. They usually face problems when they want to buy weather-based products from various E-commerce platforms where they see mixed clothes of all types of weather which is very time-consuming, they become so confused most of the time that they think about whether they should buy or not. In this paper, we proposed a novel idea by using Convolutional Neural Network Algorithm of deep learning for developing an e-commerce platform that is unique in a way that it recommends clothes according to the city weather which provides hassle-free environment eventually saves customer's time thereby increasing customer satisfaction.

scholarly journals Automatic Fault Location Identification and Isolation Method for Smart Distribution Network in Surabaya City

2021 ◽  
Vol 2117 (1) ◽  
pp. 012025
Author(s):  
N H Rohiem ◽  
A Soeprijanto ◽  
O Penangsang ◽  
N P U Putra ◽  
R Defianti ◽  
...  
Keyword(s):  
Deep Learning ◽  
Distribution System ◽  
Fault Location ◽  
Network System ◽  
Learning Method ◽  
Current Profile ◽  
Isolation Method ◽  
Disturbed Areas ◽  
Smart Distribution Network ◽  
The City

Abstract There are various types of fault that can occur in the distribution system network, so it is necessary to identify the location of the fault and isolate the fault in the area of the fault. The city of Surabaya is in preparation for the development of a smart city, so it is necessary to prepare a smart distribution system network system that can identify locations and isolate disturbed areas automatically. This paper describes the reconfiguration process to improve the value of losses in the system which results in a decrease in the value of total line losses after reconfiguration of 313.46 kW from 8 scenarios and includes the effect of adding solar energy to the existing network. The process of identifying the fault location and the isolation process on the Surabaya distribution system network in this paper uses the deep learning method. The fault location is determined based on the voltage and current profile of each bus in the system, while the isolation process is carried out by opening the switch closest to the fault area. In this process, deep learning can provide accurate fault location and isolation results for 6 fault tests.

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