An Analysis of Social Data Credibility for Services Systems in Smart Cities – Credibility Assessment and Classification of Tweets

2017 ◽  
pp. 119-130
Author(s):  
Iman Abu Hashish ◽  
Gianmario Motta ◽  
Tianyi Ma ◽  
Kaixu Liu
Keyword(s):  
Smart Cities ◽  
Credibility Assessment ◽  
Social Data ◽  
Data Credibility

scholarly journals Deep Learning Classification of Building Types in Northern Cyprus

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Mubarak Muhammad ◽  
Sertan Serte
Keyword(s):  
Deep Learning ◽  
Real Estate ◽  
Smart Cities ◽  
House Price ◽  
Training Dataset ◽  
The Real Estate ◽  
Real Estate Sector ◽  
Northern Cyprus ◽  
Second Category

Among the areas where AI studies centered on developing models that provide real-time solutions for the real estate industry are real estate price forecasting, building age, and types and design of the building (villa, apartment, floor number). Nevertheless, within the ML sector, DL is an emerging region with an Interest increases every year. As a result, a growing number of DL research are in conferences and papers, models for real estate have begun to emerge. In this study, we present a deep learning method for classification of houses in Northern Cyprus using Convolutional neural network. This work proposes the use of Convolutional neural networks in the classification of houses images. The classification will be based on the house age, house price, number of floors in the house, house type i.e. Villa and Apartment. The first category is Villa versus Apartments class; based on the training dataset of 362 images the class result shows the overall accuracy of 96.40%. The second category is split into two classes according to age of the buildings, namely 0 to 5 years Apartments 6 to 10 years Apartments. This class is to classify the building based on their age and the result shows the accuracy of 87.42%. The third category is villa with roof versus Villa without roof apartments class which also shows the overall accuracy of 87.60%. The fourth category is Villa Price from 10,000 euro to 200,000 Versus Villa Price from 200,000 Euro to above and the result shows the accuracy of 81.84%. The last category consists of three classes namely 2 floor Apartment versus 3 floor Apartment, 2 floor Apartment versus 4 floor Apartment and 2 floor Apartment versus 5 floor Apartment which all shows the accuracy of 83.54%, 82.48% and 84.77% respectively. From the experiments carried out in this thesis and the results obtained we conclude that the main aims and objectives of this thesis which is to used Deep learning in Classification and detection of houses in Northern Cyprus and to test the performance of AlexNet for houses classification was successful. This study will be very significant in creation of smart cities and digitization of real estate sector as the world embrace the used of the vast power of Artificial Intelligence, machine learning and machine vision.

scholarly journals Prospects to create “smart cities” in Russia: Classification of problems and their solutions

2018 ◽  
Vol 13 (1) ◽  
pp. 75-89 ◽  
Author(s):  
Anna Olegovna Veselova ◽  
◽  
Anna Nikolaevna Khatskelevich ◽  
Larisa Sergeevna Ezhova ◽  
◽  
...  
Keyword(s):  
Smart Cities

scholarly journals An Ensemble Detection Model Using Multinomial Classification of Stochastic Gas Smart Meter Data to Improve Wellbeing Monitoring in Smart Cities

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 7877-7898 ◽  
Author(s):  
William Hurst ◽  
Casimiro Aday Curbelo Montanez ◽  
Nathan Shone ◽  
Dhiya Al-Jumeily
Keyword(s):  
Smart Cities ◽  
Smart Meter ◽  
Detection Model

scholarly journals An Efficient Traffic Incident Detection and Classification Framework by Leveraging the Efficacy of Model Stacking

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zafar Iqbal ◽  
Majid I. Khan ◽  
Shahid Hussain ◽  
Asad Habib
Keyword(s):  
Smart Cities ◽  
Transportation Systems ◽  
Wilcoxon Test ◽  
Incident Management ◽  
Incident Detection ◽  
Incident Type ◽  
Ensemble Technique ◽  
Traffic Incident ◽  
Type Classification

Automatic incident detection (AID) plays a vital role among all the safety-critical applications under the parasol of Intelligent Transportation Systems (ITSs) to provide timely information to passengers and other stakeholders (hospitals and rescue, police, and insurance departments) in smart cities. Moreover, accurate classification of these incidents with respect to type and severity assists the Traffic Incident Management Systems (TIMSs) and stakeholders in devising better plans for incident site management and avoiding secondary incidents. Most of the AID systems presented in the literature are incident type-specific, i.e., either they are designed for the detection of accident or congestion. While traveling along the road, one may come across different types of traffic incidents, such as accidents, congestion, and reckless driving. This necessitates that the AID system detects and classifies not only all the popular traffic incident types, but severity as well that is associated with these incidents. Therefore, this study aims to propose an efficient incident detection and classification (E-IDC) framework for smart cities, by incorporating the efficacy of model stacking, to classify the incidents with respect to their types and severity levels. The experimental results showed that the proposed E-IDC framework achieved performance gains of 5%–56% in terms of incident severity classification and 1%–14% in terms of incident type classification when applied with different classifiers. We have also applied the Wilcoxon test to benchmark the performance of our proposed framework that reflects the significance of our approach over existing individual incident predictors in terms of severity and type classification. Moreover, it has been observed that the proposed E-IDC framework outperforms the existing ensemble technique, such as XGBoost used for the classification of incidents.

Clustering and 5G-Enabled Smart Cities

2021 ◽  
pp. 1012-1050
Author(s):  
Irina Tal ◽  
Gabriel-Miro Muntean
Keyword(s):  
Heterogeneous Network ◽  
Network Architecture ◽  
Architectural Design ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Smart Cities ◽  
Clustering Algorithms ◽  
Research Area ◽  
Hoc Networks

This chapter highlights the importance of vehicular ad-hoc networks (VANETs) in the context of the 5G-enabled smarter cities and roads, a topic that attracts significant interest. In order for VANETs and its associated applications to become a reality, a very promising avenue is to bring together multiple wireless technologies in the architectural design. 5G is envisioned to have a heterogeneous network architecture. Clustering is employed in designing optimal VANET architectures that successfully use different technologies. Therefore, clustering has the potential to play an important role in the 5G-VANET-enabled solutions. This chapter presents a survey of clustering approaches in the VANET research area. The survey provides a general classification of the clustering algorithms, presents some of the most advanced and latest algorithms in VANETs, and it is among the fewest works in the literature that reviews the performance assessment of clustering algorithms.

Synergic Deep Learning for Smart Health Diagnosis of COVID-19 for Connected Living and Smart Cities

2022 ◽  
Vol 22 (3) ◽  
pp. 1-14
Author(s):  
K. Shankar ◽  
Eswaran Perumal ◽  
Mohamed Elhoseny ◽  
Fatma Taher ◽  
B. B. Gupta ◽  
...  
Keyword(s):  
Deep Learning ◽  
Smart Cities ◽  
Imaging Techniques ◽  
Input Image ◽  
Significant Loss ◽  
Deep Convolutional Neural Networks ◽  
Smart Health ◽  
And Control ◽  
Health Diagnosis

COVID-19 pandemic has led to a significant loss of global deaths, economical status, and so on. To prevent and control COVID-19, a range of smart, complex, spatially heterogeneous, control solutions, and strategies have been conducted. Earlier classification of 2019 novel coronavirus disease (COVID-19) is needed to cure and control the disease. It results in a requirement of secondary diagnosis models, since no precise automated toolkits exist. The latest finding attained using radiological imaging techniques highlighted that the images hold noticeable details regarding the COVID-19 virus. The application of recent artificial intelligence (AI) and deep learning (DL) approaches integrated to radiological images finds useful to accurately detect the disease. This article introduces a new synergic deep learning (SDL)-based smart health diagnosis of COVID-19 using Chest X-Ray Images. The SDL makes use of dual deep convolutional neural networks (DCNNs) and involves a mutual learning process from one another. Particularly, the representation of images learned by both DCNNs is provided as the input of a synergic network, which has a fully connected structure and predicts whether the pair of input images come under the identical class. Besides, the proposed SDL model involves a fuzzy bilateral filtering (FBF) model to pre-process the input image. The integration of FBL and SDL resulted in the effective classification of COVID-19. To investigate the classifier outcome of the SDL model, a detailed set of simulations takes place and ensures the effective performance of the FBF-SDL model over the compared methods.

Classification of Cybercrime Indicators in Open Social Data

2021 ◽  
pp. 317-332
Author(s):  
Ihsan Ullah ◽  
Caoilfhionn Lane ◽  
Teodora Sandra Buda ◽  
Brett Drury ◽  
Marc Mellotte ◽  
...  
Keyword(s):  
Social Data
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