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Sustainability
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Machine Learning Methods and IoT for Sustainability
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Machine Learning Methods and IoT for Sustainability

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 3205

Special Issue Editors

Dr. Bhaskar B. Gardas

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Mumbai, M.H Saboo Siddik College of Engineering, Byculla, Mumbai 4000 08, India
Interests: big data analytics; circular economy; cloud computing adoption; cloud-IoT adoption; education; e-mobility; environmental management; green/sustainable supply chain management; sustainable HRM; healthcare; human factors; human-technology interaction; interdisciplinary research; Industry 4.0
Dr. Nima Jafari Navimipour

E-Mail Website
Guest Editor
Department of Computer Engineering, Kadir Has University, Istanbul, Turkey
Interests: AI; machine leaarning; IoT; cloud computing; edge computing
Special Issues, Collections and Topics in MDPI journals
Dr. Vaibhav S. Narwane

E-Mail Website
Guest Editor
Department of Mechanical Engineering, SomaiyaVidyaviharUniversity, K J SomaiyaCollege of Engineering, Vidyavihar (East), Mumbai 4000 77, India
Interests: smart manufacturing; artificial intelligence; machine learning; big data analytics; sustainable supply chain management; agricultural supply chain; cloud computing; IoT application; Industry-4.0; circular economy; quantum computing; interdisciplinary research; decision support system
Dr. Nilesh P. Ghongade

E-Mail Website
Guest Editor
Mechanical Engineering Department, University of Mumbai, M.H. Saboo Siddik College of Engineering, Byculla, Mumbai, Maharashtra 400008, India
Interests: application of CAD; additive manufacturing; bioengineering; sustainability; MCDM; healthcare

Special Issue Information

Dear Colleagues,

In the contemporary era, many challenges are being faced by all the economies such as global warming, and environmental deterioration which are due to inefficient socio-ecological transformation. The huge growth in the population and prosperity of the economies are considered to be significant issues that pose a threat to societies, economies, and natural systems. One solution to address or mitigate these issues and improve the performance of the systems with a focus on sustainability is by embracing technological advancements such as Artificial Intelligence (AI), IoT, Blockchain, and ML concepts.

The topics of interest within the scope of this focused section include but are not limited to:

  1. Challenges, and benefits of IoT, AI, and ML methods for achieving sustainability.
  2. Application areas: Smart cities with a focus on smart mobility, smart environment, and energy systems, healthcare, supply chain management, human resources, and the agricultural sector.
  3. Evaluation of the business performance taking into account the sustainability aspects.
  4. Application of multi-criteria decision-making (MCDM) approaches for analysing research problems in the case area.
  5. Human factors impact on the adoption of technology and organisational performance.

Dr. Bhaskar B. Gardas
Dr. Nima Jafari Navimipour
Dr. Vaibhav S. Narwane
Dr. Nilesh P. Ghongade
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. Sustainability 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

  • IoT
  • machine learning
  • decision-making
  • sustainability
  • environmental management

Published Papers (2 papers)

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Research

17 pages, 7000 KiB  
Article
Improved Artificial Ecosystem Optimizer with Deep-Learning-Based Insect Detection and Classification for Agricultural Sector
by Mohammed Aljebreen, Hanan Abdullah Mengash, Fadoua Kouki and Abdelwahed Motwakel
Sustainability 2023, 15(20), 14770; https://doi.org/10.3390/su152014770 - 11 Oct 2023
Viewed by 768
Abstract
The agricultural industry has the potential to meet the increasing food production requirements and supply nutritious and healthy food products. Since the Internet of Things (IoT) phenomenon has achieved considerable growth in recent years, IoT-based systems have been established for pest detection so [...] Read more.
The agricultural industry has the potential to meet the increasing food production requirements and supply nutritious and healthy food products. Since the Internet of Things (IoT) phenomenon has achieved considerable growth in recent years, IoT-based systems have been established for pest detection so as to mitigate the loss of crops and reduce serious damage by employing pesticides. In the event of pest attack, the detection of crop insects is a tedious process for farmers since a considerable proportion of crop yield is affected and the quality of pest detection is diminished. Based on morphological features, conventional insect detection is an option, although the process has a disadvantage, i.e., it necessitates highly trained taxonomists to accurately recognize the insects. In recent times, automated detection of insect categories has become a complex problem and has gained considerable interest since it is mainly carried out by agriculture specialists. Advanced technologies in deep learning (DL) and machine learning (ML) domains have effectively reached optimum performance in terms of pest detection and classification. Therefore, the current research article focuses on the design of the improved artificial-ecosystem-based optimizer with deep-learning-based insect detection and classification (IAEODL-IDC) technique in IoT-based agricultural sector. The purpose of the proposed IAEODL-IDC technique lies in the effectual identification and classification of different types of insects. In order to accomplish this objective, IoT-based sensors are used to capture the images from the agricultural environment. In addition to this, the proposed IAEODL-IDC method applies the median filtering (MF)-based noise removal process. The IAEODL-IDC technique uses the MobileNetv2 approach as well as for feature vector generation. The IAEO system is utilized for optimal hyperparameter tuning of the MobileNetv2 approach. Furthermore, the gated recurrent unit (GRU) methodology is exploited for effective recognition and classification of insects. An extensive range of simulations were conducted to exhibit the improved performance of the proposed IAEODL-IDC methodology. The simulation results validated the remarkable results of the IAEODL-IDC algorithm with recent systems. Full article
(This article belongs to the Special Issue Machine Learning Methods and IoT for Sustainability)
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28 pages, 2918 KiB  
Article
The Management of IoT-Based Organizational and Industrial Digitalization Using Machine Learning Methods
by Aoqi Xu, Mehdi Darbandi, Danial Javaheri, Nima Jafari Navimipour, Senay Yalcin and Anas A. Salameh
Sustainability 2023, 15(7), 5932; https://doi.org/10.3390/su15075932 - 29 Mar 2023
Cited by 1 | Viewed by 1876
Abstract
Recently, the widespread adoption of the Internet of Things (IoT) model has led to the development of intelligent and sustainable industries that support the economic security of modern societies. These industries can offer their participants a higher standard of living and working services [...] Read more.
Recently, the widespread adoption of the Internet of Things (IoT) model has led to the development of intelligent and sustainable industries that support the economic security of modern societies. These industries can offer their participants a higher standard of living and working services via digitalization. The IoT also includes ubiquitous technology for extracting context information to deliver valuable services to customers. With the growth of connected things, the related designs often suffer from high latency and network overheads, resulting in unresponsiveness. The continuous transmission of enormous amounts of sensor data from IoT nodes is problematic because IoT-based sensor nodes are highly energy-constrained. Recently, the research community in the field of IoT and digitalization has labored to build efficient platforms using machine learning (ML) algorithms. ML models that run directly on edge devices are intensely interesting in the context of IoT applications. The use of intelligence ML algorithms in the IoT can automate training, learning, and problem-solving while enabling decision-making based on past data. Therefore, the primary aim of this research is to provide a systematic procedure to review the state-of-the-art on this scope and offer a roadmap for future studies; thus, a structure is introduced for industry sustainability, based on ML methods. The publications were reviewed using a systematic approach that divided the papers into four categories: reinforcement learning, semi-supervised learning, unsupervised learning, and supervised learning. The results showed that ML models could manage IoT-enabled industries efficiently and provide better results compared to other models, with significant differences in learning time and performance. The study findings are considered from a variety of angles concerning the industrial sector’s capacity management of the new elements of Industry 4.0 by combining the industry IoT and ML. Additionally, unique and relevant instructions are provided for the designers of expert intelligent production systems in industrial domains. Full article
(This article belongs to the Special Issue Machine Learning Methods and IoT for Sustainability)
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