Special Issue "Artificial Intelligence Applications to Smart City and Smart Enterprise"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 June 2019

Special Issue Editors

Guest Editor
Prof. Eng. Donato IMPEDOVO

Department of Computer Science, University of Bari, Bari, Italy
Website | E-Mail
Interests: biometrics; automatic signature verification; artificial intelligence; pattern recognition, signal processing
Guest Editor
Prof. Giuseppe PIRLO

Department of Computer Science, University of Bari, Bari, Italy
Website | E-Mail
Interests: biometrics; automatic signature verification; artificial intelligence; pattern recognition; signal processing

Special Issue Information

Dear Colleagues,

The existence of smart cities requires a new organization structure that takes into account every aspect of how a city runs. Smart cities work under a more resource-efficient management and economy than ordinary cities. As such, advanced business models have emerged around smart cities, which lead to the creation of smart enterprises or organizations that depend on advanced software and computer applications. Smart cities and smart enterprises deal with the integration of artificial intelligence, web technologies, smart mobile platforms, telecommunications, e-commerce, e-business, and other technologies. Fields of applications are related to services for users and citizens, such as transportation, buildings, e-health, utilities, etc.

Prof. Eng. Donato IMPEDOVO
Prof. Giuseppe PIRLO
Guest Editors

Manuscript Submission Information

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Keywords

  • Application, deployment, testbed, and experiment experiences in smart cities
  • Big data for urban informatics
  • Cloud computing and network infrastructure that supports smart cities
  • Cellular networking in smart cities
  • Delay tolerant networks and systems for urban data collection
  • E-health systems
  • Environment and urban monitoring
  • Enabling wireless and mobile technologies for smart cities
  • Fault tolerance, reliability, and survivability in smart systems
  • Green computing, networking, and energy efficiency
  • Human mobility modeling and analytics
  • Mobile crowdsourcing for urban analytics
  • QoS and QoE of smart city systems, applications, and services
  • Sensing and IoT for smart cities
  • Social computing and networks
  • Software defined networking (SDN) and network function virtualization (NFV) in a smart city environment
  • Smart grid
  • Smart transportation
  • Smart buildings
  • Safety, security, and privacy for smart cities
  • Smartphone and mobile systems and applications
  • Vehicular networks
  • Collaboration and Negotiation Technologies
  • Multi-Agent Systems and Artificial Intelligence
  • e-Business and e-Commerce
  • Semantic Web
  • Intelligent Web Applications
  • Ubiquitous Computing
  • Cloud Computing
  • Collective Intelligence
  • Green Computing
  • Surveys on Sustainable Information Systems

Published Papers (3 papers)

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Research

Open AccessArticle Feature Adaptive and Cyclic Dynamic Learning Based on Infinite Term Memory Extreme Learning Machine
Appl. Sci. 2019, 9(5), 895; https://doi.org/10.3390/app9050895
Received: 28 January 2019 / Revised: 26 February 2019 / Accepted: 27 February 2019 / Published: 2 March 2019
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Abstract
Online learning is the capability of a machine-learning model to update knowledge without retraining the system when new, labeled data becomes available. Good online learning performance can be achieved through the ability to handle changing features and preserve existing knowledge for future use. [...] Read more.
Online learning is the capability of a machine-learning model to update knowledge without retraining the system when new, labeled data becomes available. Good online learning performance can be achieved through the ability to handle changing features and preserve existing knowledge for future use. This can occur in different real world applications such as Wi-Fi localization and intrusion detection. In this study, we generated a cyclic dynamic generator (CDG), which we used to convert an existing dataset into a time series dataset with cyclic and changing features. Furthermore, we developed the infinite-term memory online sequential extreme learning machine (ITM-OSELM) on the basis of the feature-adaptive online sequential extreme learning machine (FA-OSELM) transfer learning, which incorporates an external memory to preserve old knowledge. This model was compared to the FA-OSELM and online sequential extreme learning machine (OSELM) on the basis of data generated from the CDG using three datasets: UJIndoorLoc, TampereU, and KDD 99. Results corroborate that the ITM-OSELM is superior to the FA-OSELM and OSELM using a statistical t-test. In addition, the accuracy of ITM-OSELM was 91.69% while the accuracy of FA-OSELM and OSELM was 24.39% and 19.56%, respectively. Full article
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Open AccessArticle Optimization of EPB Shield Performance with Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm
Appl. Sci. 2019, 9(4), 780; https://doi.org/10.3390/app9040780
Received: 1 February 2019 / Revised: 15 February 2019 / Accepted: 18 February 2019 / Published: 22 February 2019
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Abstract
The prediction of earth pressure balance (EPB) shield performance is an essential part of project scheduling and cost estimation of tunneling projects. This paper establishes an efficient multi-objective optimization model to predict the shield performance during the tunneling process. This model integrates the [...] Read more.
The prediction of earth pressure balance (EPB) shield performance is an essential part of project scheduling and cost estimation of tunneling projects. This paper establishes an efficient multi-objective optimization model to predict the shield performance during the tunneling process. This model integrates the adaptive neuro-fuzzy inference system (ANFIS) with the genetic algorithm (GA). The hybrid model uses shield operational parameters as inputs and computes the advance rate as output. GA enhances the accuracy of ANFIS for runtime parameters tuning by multi-objective fitness function. Prior to modeling, datasets were established, and critical operating parameters were identified through principal component analysis. Then, the tunneling case for Guangzhou metro line number 9 was adopted to verify the applicability of the proposed model. Results were then compared with those of the ANFIS model. The comparison showed that the multi-objective ANFIS-GA model is more successful than the ANFIS model in predicting the advance rate with a high accuracy, which can be used to guide the tunnel performance in the field. Full article
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Open AccessArticle Improved Spatio-Temporal Residual Networks for Bus Traffic Flow Prediction
Appl. Sci. 2019, 9(4), 615; https://doi.org/10.3390/app9040615
Received: 28 December 2018 / Revised: 1 February 2019 / Accepted: 7 February 2019 / Published: 13 February 2019
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Abstract
Buses, as the most commonly used public transport, play a significant role in cities. Predicting bus traffic flow cannot only build an efficient and safe transportation network but also improve the current situation of road traffic congestion, which is very important for urban [...] Read more.
Buses, as the most commonly used public transport, play a significant role in cities. Predicting bus traffic flow cannot only build an efficient and safe transportation network but also improve the current situation of road traffic congestion, which is very important for urban development. However, bus traffic flow has complex spatial and temporal correlations, as well as specific scenario patterns compared with other modes of transportation, which is one of the biggest challenges when building models to predict bus traffic flow. In this study, we explore bus traffic flow and its specific scenario patterns, then we build improved spatio-temporal residual networks to predict bus traffic flow, which uses fully connected neural networks to capture the bus scenario patterns and improved residual networks to capture the bus traffic flow spatio-temporal correlation. Experiments on Beijing transportation smart card data demonstrate that our method achieves better results than the four baseline methods. Full article
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