Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Application of Machine Learning in Intelligent Infrastructures and Smart Cities
share announcement

Application of Machine Learning in Intelligent Infrastructures and Smart Cities

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

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 3683

Special Issue Editors

Dr. Srimannarayana Grandhi

E-Mail Website
Guest Editor
School of Engineering and Technology, Central Queensland University, Melbourne, QLD 4701, Australia
Interests: artificial intelligence; blockchain technology; enterprise systems; knowledge management; multicriteria decision making
Special Issues, Collections and Topics in MDPI journals
Dr. Santoso Wibowo

E-Mail Website
Guest Editor
School of Engineering and Technology, Central Queensland University, Melbourne, QLD 4701, Australia
Interests: artificial intelligence; deep learning; machine learning; intelligent information systems; performance evaluation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As a part of smart cities initiatives, several governments have acquired intelligent systems to improve the living standards of their citizens by providing them with intelligent services for enhancing livability, workability and sustainability. These intelligent systems collect huge amounts of data and offer further opportunities for governments to analyse these data and make data-driven decisions. Therefore, this Special Issue aims to present new approaches for analysing huge volumes of data and recognising hidden patterns and experimental results in the area of smart cities, intelligent systems and machine learning, from both theoretical and experimental perspectives. 

Areas relevant to smart cities include, but are not limited to, intelligent systems and architectures, embedded systems, intelligent infrastructure, machine learning, deep learning, artificial intelligence, AI tools and applications, big data, data cleansing, data mining and knowledge discovery, genetic algorithms, neural networks, clustering, decision trees, data set training, model building and improvement, supervised and unsupervised learning, predictive analysis, output performance, pattern recognition, data visualisation, automated monitoring and decision making, scientific experiments, problem solving and other topics in the context of data analysis and decision making.

This Special Issue will publish high-quality, original research papers detailing the research and outcomes in the overlapping fields of:

  • Smart cities, intelligent infrastructure and intelligent systems;
  • Artificial intelligence, machine learning and deep learning;
  • Automated planning and scheduling technologies;
  • Big data;
  • Information retrieval and data mining;
  • Knowledge discovery;
  • Decision support.

Dr. Srimannarayana Grandhi
Dr. Santoso Wibowo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart cities
  • artificial intelligence
  • deep learning
  • machine learning
  • intelligent systems

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 126005 KiB  
Article
Early Fire Detection System by Using Automatic Synthetic Dataset Generation Model Based on Digital Twins
by Hyeon-Cheol Kim, Hoang-Khanh Lam, Suk-Hwan Lee and Soo-Yol Ok
Appl. Sci. 2024, 14(5), 1801; https://doi.org/10.3390/app14051801 - 22 Feb 2024
Viewed by 608
Abstract
Fire is amorphous and occurs differently depending on the space, environment, and material of the fire. In particular, the early detection of fires is a very important task in preventing large-scale accidents; however, there are currently almost no learnable early fire datasets for [...] Read more.
Fire is amorphous and occurs differently depending on the space, environment, and material of the fire. In particular, the early detection of fires is a very important task in preventing large-scale accidents; however, there are currently almost no learnable early fire datasets for machine learning. This paper proposes an early fire detection system optimized for certain spaces using a digital-twin-based automatic fire learning data generation model for each space. The proposed method first automatically generates realistic particle-simulation-based synthetic fire data on an RGB-D image matched to the view angle of a monitoring camera to build a digital twin environment of the real space. In other words, our method generates synthetic fire data according to various fire situations in each specific space and then performs transfer learning using a state-of-the-art detection model with these datasets and distributes them to AIoT devices in the real space. Synthetic fire data generation optimized for a space can increase the accuracy and reduce the false detection rate of existing fire detection models that are not adaptive to space. Full article
(This article belongs to the Special Issue Application of Machine Learning in Intelligent Infrastructures and Smart Cities)
►▼ Show Figures

Figure 1

13 pages, 2204 KiB  
Article
Efficient Fire Detection with E-EFNet: A Lightweight Deep Learning-Based Approach for Edge Devices
by Haleem Farman, Moustafa M. Nasralla, Sohaib Bin Altaf Khattak and Bilal Jan
Appl. Sci. 2023, 13(23), 12941; https://doi.org/10.3390/app132312941 - 04 Dec 2023
Viewed by 998
Abstract
Fire detection employing vision sensors has drawn significant attention within the computer vision community, primarily due to its practicality and utility. Previous research predominantly relied on basic color features, a methodology that has since been surpassed by adopting deep learning models for enhanced [...] Read more.
Fire detection employing vision sensors has drawn significant attention within the computer vision community, primarily due to its practicality and utility. Previous research predominantly relied on basic color features, a methodology that has since been surpassed by adopting deep learning models for enhanced accuracy. Nevertheless, the persistence of false alarms and increased computational demands remains challenging. Furthermore, contemporary feed-forward neural networks face difficulties stemming from their initialization and weight allocation processes, often resulting in vanishing-gradient issues that hinder convergence. This investigation recognizes the considerable challenges and introduces the cost-effective Encoded EfficientNet (E-EFNet) model. This model demonstrates exceptional proficiency in fire recognition while concurrently mitigating the incidence of false alarms. E-EFNet leverages the lightweight EfficientNetB0 as a foundational feature extractor, augmented by a series of stacked autoencoders for refined feature extraction before the final classification phase. In contrast to conventional linear connections, E-EFNet adopts dense connections, significantly enhancing its effectiveness in identifying fire-related scenes. We employ a randomized weight initialization strategy to mitigate the vexing problem of vanishing gradients and expedite convergence. Comprehensive evaluation against contemporary state-of-the-art benchmarks reaffirms E-EFNet’s superior recognition capabilities. The proposed model outperformed state-of-the-art approaches in accuracy over the Foggia and Yar datasets by achieving a higher accuracy of 0.31 and 0.40, respectively, and its adaptability for efficient inferencing on edge devices. Our study thoroughly assesses various deep models before ultimately selecting E-EFNet as the optimal solution for these pressing challenges in fire detection. Full article
(This article belongs to the Special Issue Application of Machine Learning in Intelligent Infrastructures and Smart Cities)
►▼ Show Figures

Figure 1

28 pages, 4912 KiB  
Article
A Blockchain-Based Model for the Prevention of Superannuation Fraud: A Study of Australian Super Funds
by Chalani Mapa Mudiyanselage, Pethigamage Perera and Sriamannarayana Grandhi
Appl. Sci. 2023, 13(17), 9949; https://doi.org/10.3390/app13179949 - 03 Sep 2023
Cited by 2 | Viewed by 1518
Abstract
Superannuation is the fund set aside by employers to provide their employees with a dignified retirement. Studies highlight that issues can arise with retirement funds from employers, such as failure to make required contributions to an employee’s superannuation fund, incorrect payments, or debiting [...] Read more.
Superannuation is the fund set aside by employers to provide their employees with a dignified retirement. Studies highlight that issues can arise with retirement funds from employers, such as failure to make required contributions to an employee’s superannuation fund, incorrect payments, or debiting the wrong fund, contrary to legal or contractual obligations. To address these issues, the Australian Government has implemented laws and regulations to ensure employers fulfil their contribution obligations. Despite these safeguards and highly secured information systems, there has been a significant increase in fraudulent activity in the finance sector, and there have been several instances of employers not making contributions, misusing retirement funds, or reporting incorrectly in their systems. The current process restricts employees from viewing recent data until the contributions reach their super fund, which opens the doors for fraud. Recently, blockchain technology has gained popularity because of its ability to improve security and prevent fraud across many sectors, including finance. Prior studies have shed limited light on how superannuation fraud can be prevented. Moreover, there is limited literature on the possibility of utilizing blockchain technology to address this issue. Therefore, this paper aims to review the current superannuation contribution process and identify the factors contributing to non-payment, incorrect payments, misallocation of funds and communication gaps. This study presents a novel process model and develops a blockchain-based application to mitigate fraudulent practices. This research provides valuable insights into the design of innovative process models that utilize blockchain technology to address superannuation challenges. Furthermore, the paper presents a sample simulated smart contract to explore additional implications and advancements in this domain. Full article
(This article belongs to the Special Issue Application of Machine Learning in Intelligent Infrastructures and Smart Cities)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop