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Sustainable Applications for Machine Learning
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Sustainable Applications for Machine Learning

A special issue of Machine Learning and Knowledge Extraction (ISSN 2504-4990). This special issue belongs to the section "Learning".

Deadline for manuscript submissions: 2 July 2024 | Viewed by 3384

Special Issue Editors

Dr. Danial Javaheri

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Korea University, Seoul 02841, Republic of Korea
Interests: artificial intelligence; machine learning; deep learing; cyberseucirty
Prof. Dr. Hassan Chizari

E-Mail Website
Guest Editor
Department of Technical Computing, School of Business and Technology, University of Gloucestershire, Cheltenham GL50 2RH, UK
Interests: security in IoT devices; wireless sensor networks; smart grid
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Amir Masoud Rahmani

E-Mail Website
Guest Editor
Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
Interests: internet of things, wireless networks; wearable computing; fog/cloud computing; big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The smart-everything wave and advancements in AI have caused a paradigm shift in every aspect of today's human life. Additionally, the pervasive nature of information systems has resulted in the generation of a variety of voluminous data, which should be processed, analyzed, and interpreted. While earlier approaches are no longer effective in dealing with such a sheer amount of digital data, AI offers many opportunities and solutions.

Within the realm of AI, machine learning (ML) is playing a pivotal role as its influence has enabled advanced solutions in a wide range of applications, such as autonomous systems, medical/satellite image processing, chatbots, robotics, and financial technology. Considering ML governance in numerous domains, its sustainability inevitably should be taken into consideration, now more than ever. This becomes more critical as sensitive businesses and big players such as governments, banks, giant tech, and smart factories are increasingly using ML.

This Special Issue aims to collate the latest findings on the challenges and state-of-the-art solutions in the sustainability of ML as well as its applications.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Dependability of machine learning models;
  • Acceleration of deep neural networks;
  • Privacy-preserving aspects of machine learning;
  • Reliability assessment of deep learning systems;
  • Multi-agent systems in reinforcement learning;
  • Privacy concerns in federated learning approaches;
  • Artificial neural network applications in a circular economy;
  • Sustainability of natural language processing models;
  • Optimization in machine learning;
  • Recommender systems;
  • Graph neural network analysis;
  • Reliability in ensemble learning;
  • Security aspects of generative models;
  • Ethical issues with AI/ML;
  • Machine learning applications in healthcare;
  • Computer vision applications in smart cities;
  • Machine learning for business continuity;
  • Machine learning for sustainable supply chains;
  • The role of ML/DL in Industry 4.0.

We look forward to receiving your contributions. 

Dr. Danial Javaheri
Prof. Dr. Hassan Chizari
Prof. Dr. Amir Masoud Rahmani
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. Machine Learning and Knowledge Extraction is an international peer-reviewed open access quarterly 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 1800 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

  • machine learning
  • deep learning
  • artificial neural networks
  • reinforcement learning
  • sustainable computing
  • big data analytics
  • optimization, data mining

Published Papers (2 papers)

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Research

19 pages, 2757 KiB  
Article
Birthweight Range Prediction and Classification: A Machine Learning-Based Sustainable Approach
by Dina A. Alabbad, Shahad Y. Ajibi, Raghad B. Alotaibi, Noura K. Alsqer, Rahaf A. Alqahtani, Noor M. Felemban, Atta Rahman, Sumayh S. Aljameel, Mohammed Imran Basheer Ahmed and Mustafa M. Youldash
Mach. Learn. Knowl. Extr. 2024, 6(2), 770-788; https://doi.org/10.3390/make6020036 - 01 Apr 2024
Viewed by 763
Abstract
An accurate prediction of fetal birth weight is crucial in ensuring safe delivery without health complications for the mother and baby. The uncertainty surrounding the fetus’s birth situation, including its weight range, can lead to significant risks for both mother and baby. As [...] Read more.
An accurate prediction of fetal birth weight is crucial in ensuring safe delivery without health complications for the mother and baby. The uncertainty surrounding the fetus’s birth situation, including its weight range, can lead to significant risks for both mother and baby. As there is a standard birth weight range, if the fetus exceeds or falls below this range, it can result in considerable health problems. Although ultrasound imaging is commonly used to predict fetal weight, it does not always provide accurate readings, which may lead to unnecessary decisions such as early delivery and cesarian section. Besides that, no supporting system is available to predict the weight range in Saudi Arabia. Therefore, leveraging the available technologies to build a system that can serve as a second opinion for doctors and health professionals is essential. Machine learning (ML) offers significant advantages to numerous fields and can address various issues. As such, this study aims to utilize ML techniques to build a predictive model to predict the birthweight range of infants into low, normal, or high. For this purpose, two datasets were used: one from King Fahd University Hospital (KFHU), Saudi Arabia, and another publicly available dataset from the Institute of Electrical and Electronics Engineers (IEEE) data port. KFUH’s best result was obtained with the Extra Trees model, achieving an accuracy, precision, recall, and F1-score of 98%, with a specificity of 99%. On the other hand, using the Random Forest model, the IEEE dataset attained an accuracy, precision, recall, and F1-score of 96%, respectively, with a specificity of 98%. These results suggest that the proposed ML system can provide reliable predictions, which could be of significant value for doctors and health professionals in Saudi Arabia. Full article
(This article belongs to the Special Issue Sustainable Applications for Machine Learning)
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16 pages, 1801 KiB  
Article
Effective Detection of Epileptic Seizures through EEG Signals Using Deep Learning Approaches
by Sakorn Mekruksavanich and Anuchit Jitpattanakul
Mach. Learn. Knowl. Extr. 2023, 5(4), 1937-1952; https://doi.org/10.3390/make5040094 - 11 Dec 2023
Viewed by 1816
Abstract
Epileptic seizures are a prevalent neurological condition that impacts a considerable portion of the global population. Timely and precise identification can result in as many as 70% of individuals achieving freedom from seizures. To achieve this, there is a pressing need for smart, [...] Read more.
Epileptic seizures are a prevalent neurological condition that impacts a considerable portion of the global population. Timely and precise identification can result in as many as 70% of individuals achieving freedom from seizures. To achieve this, there is a pressing need for smart, automated systems to assist medical professionals in identifying neurological disorders correctly. Previous efforts have utilized raw electroencephalography (EEG) data and machine learning techniques to classify behaviors in patients with epilepsy. However, these studies required expertise in clinical domains like radiology and clinical procedures for feature extraction. Traditional machine learning for classification relied on manual feature engineering, limiting performance. Deep learning excels at automated feature learning directly from raw data sans human effort. For example, deep neural networks now show promise in analyzing raw EEG data to detect seizures, eliminating intensive clinical or engineering needs. Though still emerging, initial studies demonstrate practical applications across medical domains. In this work, we introduce a novel deep residual model called ResNet-BiGRU-ECA, analyzing brain activity through EEG data to accurately identify epileptic seizures. To evaluate our proposed deep learning model’s efficacy, we used a publicly available benchmark dataset on epilepsy. The results of our experiments demonstrated that our suggested model surpassed both the basic model and cutting-edge deep learning models, achieving an outstanding accuracy rate of 0.998 and the top F1-score of 0.998. Full article
(This article belongs to the Special Issue Sustainable Applications for Machine Learning)
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