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Sustainability
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Industry Development Based on Deep Learning Models and AI 2.0
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Industry Development Based on Deep Learning Models and AI 2.0

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

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 4930

Special Issue Editors

Dr. Zaid Abdi Alkareem Alyasseri

E-Mail Website
Guest Editor
Information Technology Research and Development Center (ITRDC), University of Kufa, Najaf 54001, Iraq
Interests: artificial intelligence; machine and deep learning; pattern recognition; swarm intelligence; brain-computer interface; signal and image processing
Dr. Karrar Hameed Abdulkareem

E-Mail Website
Guest Editor
College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq
Interests: data science .biomedical computing; cyber security; fog computing; and artificial intelligence
Prof. Dr. Mohammed Azmi Al-Betar

E-Mail Website
Guest Editor
Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates
Interests: optimization; artificialintelligence; machine learning; deep learning; data mining; evolutionary computation

Special Issue Information

Dear Colleagues,

Sustainability is the process of living within the limits of available environmental, economic,  and social resources in ways that allow the living systems in which humans are embedded to thrive in perpetuity.  Sustainability is represented as a roadmap for developing countries in enhancing the quality of life and the message behind sustainability comes down to the kind of future we are leaving for the next generation.

Recently, sustainable development has grown significantly in importance for industries and businesses. Because of this, it has become one of the most interesting areas for many researchers in academia and industry.  Deep learning is a subfield of machine learning (ML) concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.  Deep learning has been successfully applied in various domains for achieving a better life. Compared to traditional machine learning techniques, deep learning algorithms demonstrate their ability to train models from big datasets. Additionally, those algorithms have significantly surpassed the performance of traditional methodologies for environmental, economic, social, and other fields. For that,  developing and implementing deep learning technologies will have an impact on practically every element of daily life for humans in the near future. Therefore, deep learning is a driving force to achieve sustainability goals.

The main purpose of our Special Issue focuses on recent research work on deep learning techniques for sustainability in different life domains. These techniques can be used to contribute to the demonstration of innovative methods and application areas of deep learning to solve real-world problems to achieve sustainability goals. 
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following: 

  • Artificial intelligence applications for sustainability;
  • Environmental sustainability and AI;
  • Machine learning and AI technology for sustainability;
  • Deep learning for sustainable industrialization;
  • Deep learning for sustainable waste management;
  • Deep learning for sustainable economic developments;
  • Deep learning for developing sustainable food systems;
  • Deep learning for sustainable healthcare systems;
  • Deep learning for sustainable transportation system;
  • Deep learning for sustainable Internet of Things (IoT);
  • Deep learning for sustainable energy;
  • Sustainability of artificial intelligence systems;
  • Deep learning for sustainable renewable energy and clean fuels; 
  • Deep learning for sustainable pollution monitoring and early detection.

We look forward to receiving your contributions. 

Dr. Zaid Abdi Alkareem Alyasseri
Dr. Karrar Hameed Abdulkareem
Prof. Dr. Mohammed Azmi Al-Betar
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

  • artificial intelligence
  • machine learning
  • deep learning
  • ai sustainability
  • big data analysis

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Published Papers (2 papers)

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Research

18 pages, 5172 KiB  
Article
Enhancing Rice Leaf Disease Classification: A Customized Convolutional Neural Network Approach
by Ammar Kamal Abasi, Sharif Naser Makhadmeh, Osama Ahmad Alomari, Mohammad Tubishat and Husam Jasim Mohammed
Sustainability 2023, 15(20), 15039; https://doi.org/10.3390/su152015039 - 19 Oct 2023
Cited by 8 | Viewed by 2286
Abstract
In modern agriculture, correctly identifying rice leaf diseases is crucial for maintaining crop health and promoting sustainable food production. This study presents a detailed methodology to enhance the accuracy of rice leaf disease classification. We achieve this by employing a Convolutional Neural Network [...] Read more.
In modern agriculture, correctly identifying rice leaf diseases is crucial for maintaining crop health and promoting sustainable food production. This study presents a detailed methodology to enhance the accuracy of rice leaf disease classification. We achieve this by employing a Convolutional Neural Network (CNN) model specifically designed for rice leaf images. The proposed method achieved an accuracy of 0.914 during the final epoch, demonstrating highly competitive performance compared to other models, with low loss and minimal overfitting. A comparison was conducted with Transfer Learning Inception-v3 and Transfer Learning EfficientNet-B2 models, and the proposed method showed superior accuracy and performance. With the increasing demand for precision agriculture, models like the proposed one show great potential in accurately detecting and managing diseases, ultimately leading to improved crop yields and ecological sustainability. Full article
(This article belongs to the Special Issue Industry Development Based on Deep Learning Models and AI 2.0)
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20 pages, 4932 KiB  
Article
Dynamic Clustering Strategies Boosting Deep Learning in Olive Leaf Disease Diagnosis
by Ali Hakem Alsaeedi, Ali Mohsin Al-juboori, Haider Hameed R. Al-Mahmood, Suha Mohammed Hadi, Husam Jasim Mohammed, Mohammad R. Aziz, Mayas Aljibawi and Riyadh Rahef Nuiaa
Sustainability 2023, 15(18), 13723; https://doi.org/10.3390/su151813723 - 14 Sep 2023
Cited by 1 | Viewed by 1524
Abstract
Artificial intelligence has many applications in various industries, including agriculture. It can help overcome challenges by providing efficient solutions, especially in the early stages of development. When working with tree leaves to identify the type of disease, diseases often show up through changes [...] Read more.
Artificial intelligence has many applications in various industries, including agriculture. It can help overcome challenges by providing efficient solutions, especially in the early stages of development. When working with tree leaves to identify the type of disease, diseases often show up through changes in leaf color. Therefore, it is crucial to improve the color brightness before using them in intelligent agricultural systems. Color improvement should achieve a balance where no new colors appear, as this could interfere with accurate identification and diagnosis of the disease. This is considered one of the challenges in this field. This work proposes an effective model for olive disease diagnosis, consisting of five modules: image enhancement, feature extraction, clustering, and deep neural network. In image enhancement, noise reduction, balanced colors, and CLAHE are applied to LAB color space channels to improve image quality and visual stimulus. In feature extraction, raw images of olive leaves are processed through triple convolutional layers, max pooling operations, and flattening in the CNN convolutional phase. The classification process starts by dividing the data into clusters based on density, followed by the use of a deep neural network. The proposed model was tested on over 3200 olive leaf images and compared with two deep learning algorithms (VGG16 and Alexnet). The results of accuracy and loss rate show that the proposed model achieves (98%, 0.193), while VGG16 and Alexnet reach (96%, 0.432) and (95%, 1.74), respectively. The proposed model demonstrates a robust and effective approach for olive disease diagnosis that combines image enhancement techniques and deep learning-based classification to achieve accurate and reliable results. Full article
(This article belongs to the Special Issue Industry Development Based on Deep Learning Models and AI 2.0)
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