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Applied Sciences
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Machine Learning Techniques for the Study of Complex Systems
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Machine Learning Techniques for the Study of Complex Systems

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 (10 July 2021) | Viewed by 18950

Special Issue Editors

Dr. Alfonso Monaco

E-Mail Website
Guest Editor
Section of Bari, National Institute for Nuclear Physics (INFN), 70125 Bari, Italy
Interests: machine learning; deep learning; complex networks; big data analysis; neurodegeneratve diseases; imaging; complex systems
Special Issues, Collections and Topics in MDPI journals
Dr. Nicola Amoroso

E-Mail Website
Guest Editor
Università degli studi di Bari, Bari, Italy
Interests: machine learning; deep learning; data visualization; complex networks; imaging; segmentation; big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing availability of data is revolutionizing the way we see the world, socialize, conduct research, make politics, and do business. The amount of information available, which is impressively large, can lead to a better understanding of the complexity of natural, biological, scientific, economic, technological, and social phenomena. In order to understand the mechanism and anatomy of a complex system, it is necessary to implement modern analysis techniques that are able to model manifold scenarios and manage complex and often very extensive data. In particular, machine learning models are valuable in gaining understanding and insight of complex systems. In fact, the most important property of machine learning is the capability of representing the main complex problems to understand how complex systems perform.

Thus, we invite authors to submit unpublished work to this Special Issue on “Machine Learning Techniques for the Study of Complex Systems” in Applied Sciences. In this Special Issue, we discuss the challenges of using applied machine learning in the analysis of complex systems. The main topics of interest include but are not limited to the following:

  • Analysis of complex systems by means of the machine learning approach;
  • Machine learning techniques applied on biological, medical, economic, technological or social data;
  • Pattern detection analysis;
  • Analysis and characterization of complex and heterogeneous data through supervised or unsupervised leaning;
  • Computationally intensive methodologies applied to studying complex and extensive data;
  • Complex network analysis.

Dr. Alfonso Monaco
Dr. Nicola Amoroso
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

  • Complex systems
  • Machine learning
  • Complex network analysis
  • Pattern recognition
  • Manage complex data
  • Data mining
  • Supervised techniques
  • Unsupervised techniques

Published Papers (5 papers)

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Research

22 pages, 5911 KiB  
Article
Augmented Data and XGBoost Improvement for Sales Forecasting in the Large-Scale Retail Sector
by Alessandro Massaro, Antonio Panarese, Daniele Giannone and Angelo Galiano
Appl. Sci. 2021, 11(17), 7793; https://0-doi-org.brum.beds.ac.uk/10.3390/app11177793 - 24 Aug 2021
Cited by 9 | Viewed by 4187
Abstract
The organized large-scale retail sector has been gradually establishing itself around the world, and has increased activities exponentially in the pandemic period. This modern sales system uses Data Mining technologies processing precious information to increase profit. In this direction, the extreme gradient boosting [...] Read more.
The organized large-scale retail sector has been gradually establishing itself around the world, and has increased activities exponentially in the pandemic period. This modern sales system uses Data Mining technologies processing precious information to increase profit. In this direction, the extreme gradient boosting (XGBoost) algorithm was applied in an industrial project as a supervised learning algorithm to predict product sales including promotion condition and a multiparametric analysis. The implemented XGBoost model was trained and tested by the use of the Augmented Data (AD) technique in the event that the available data are not sufficient to achieve the desired accuracy, as for many practical cases of artificial intelligence data processing, where a large dataset is not available. The prediction was applied to a grid of segmented customers by allowing personalized services according to their purchasing behavior. The AD technique conferred a good accuracy if compared with results adopting the initial dataset with few records. An improvement of the prediction error, such as the Root Mean Square Error (RMSE) and Mean Square Error (MSE), which decreases by about an order of magnitude, was achieved. The AD technique formulated for large-scale retail sector also represents a good way to calibrate the training model. Full article
(This article belongs to the Special Issue Machine Learning Techniques for the Study of Complex Systems)
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26 pages, 649 KiB  
Article
Reinforcement Learning with Self-Attention Networks for Cryptocurrency Trading
by Carlos Betancourt and Wen-Hui Chen
Appl. Sci. 2021, 11(16), 7377; https://0-doi-org.brum.beds.ac.uk/10.3390/app11167377 - 11 Aug 2021
Cited by 7 | Viewed by 2851
Abstract
This work presents an application of self-attention networks for cryptocurrency trading. Cryptocurrencies are extremely volatile and unpredictable. Thus, cryptocurrency trading is challenging and involves higher risks than trading traditional financial assets such as stocks. To overcome the aforementioned problems, we propose a deep [...] Read more.
This work presents an application of self-attention networks for cryptocurrency trading. Cryptocurrencies are extremely volatile and unpredictable. Thus, cryptocurrency trading is challenging and involves higher risks than trading traditional financial assets such as stocks. To overcome the aforementioned problems, we propose a deep reinforcement learning (DRL) approach for cryptocurrency trading. The proposed trading system contains a self-attention network trained using an actor-critic DRL algorithm. Cryptocurrency markets contain hundreds of assets, allowing greater investment diversification, which can be accomplished if all the assets are analyzed against one another. Self-attention networks are suitable for dealing with the problem because the attention mechanism can process long sequences of data and focus on the most relevant parts of the inputs. Transaction fees are also considered in formulating the studied problem. Systems that perform trades in high frequencies cannot overlook this issue, since, after many trades, small fees can add up to significant expenses. To validate the proposed approach, a DRL environment is built using data from an important cryptocurrency market. We test our method against a state-of-the-art baseline in two different experiments. The experimental results show the proposed approach can obtain higher daily profits and has several advantages over existing methods. Full article
(This article belongs to the Special Issue Machine Learning Techniques for the Study of Complex Systems)
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17 pages, 303 KiB  
Article
What Disease Does This Patient Have? A Large-Scale Open Domain Question Answering Dataset from Medical Exams
by Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang and Peter Szolovits
Appl. Sci. 2021, 11(14), 6421; https://0-doi-org.brum.beds.ac.uk/10.3390/app11146421 - 12 Jul 2021
Cited by 55 | Viewed by 6460
Abstract
Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical [...] Read more.
Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. We implement both rule-based and popular neural methods by sequentially combining a document retriever and a machine comprehension model. Through experiments, we find that even the current best method can only achieve 36.7%, 42.0%, and 70.1% of test accuracy on the English, traditional Chinese, and simplified Chinese questions, respectively. We expect MedQA to present great challenges to existing OpenQA systems and hope that it can serve as a platform to promote much stronger OpenQA models from the NLP community in the future. Full article
(This article belongs to the Special Issue Machine Learning Techniques for the Study of Complex Systems)
14 pages, 2139 KiB  
Article
Adaptive Abnormal Oil Temperature Diagnosis Method of Transformer Based on Concept Drift
by Zhibin Zhao, Jianfeng Xu, Yanlong Zang and Ran Hu
Appl. Sci. 2021, 11(14), 6322; https://0-doi-org.brum.beds.ac.uk/10.3390/app11146322 - 08 Jul 2021
Cited by 3 | Viewed by 1625
Abstract
The diagnosis of abnormal transformer oil temperature is of great significance to guarantee the normal operation of the transformer. Due to concept drift, the oil temperature abnormal diagnosis of the oil-immersed main power transformer is usually unstable via the classic data mining method. [...] Read more.
The diagnosis of abnormal transformer oil temperature is of great significance to guarantee the normal operation of the transformer. Due to concept drift, the oil temperature abnormal diagnosis of the oil-immersed main power transformer is usually unstable via the classic data mining method. Thus, this paper proposes an adaptive abnormal oil temperature diagnosis method (AAOTD) of the transformer based on concept drift. First, the bagging ensemble learning method was used to predict the oil temperature. Then, abnormal diagnosis was performed based on the difference between the predicted oil temperature and the actual measured oil temperature. At the same time, based on the concept drift detection strategy and Adaboost ensemble learning methods, adaptive update of the base classifier in the abnormal diagnosis model was realized. Experiments validated that the algorithm proposed in this paper can significantly reduce the influence of concept drift and has higher oil temperature prediction accuracy. Furthermore, since this method only utilizes the existing power grid data resources to realize abnormal oil temperature diagnosis without extra monitoring equipment, it is an economic and efficient solution for practical scenarios in the electric power industry. Full article
(This article belongs to the Special Issue Machine Learning Techniques for the Study of Complex Systems)
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17 pages, 2408 KiB  
Article
Multi-Time-Scale Features for Accurate Respiratory Sound Classification
by Alfonso Monaco, Nicola Amoroso, Loredana Bellantuono, Ester Pantaleo, Sabina Tangaro and Roberto Bellotti
Appl. Sci. 2020, 10(23), 8606; https://0-doi-org.brum.beds.ac.uk/10.3390/app10238606 - 01 Dec 2020
Cited by 25 | Viewed by 2811
Abstract
The COVID-19 pandemic has amplified the urgency of the developments in computer-assisted medicine and, in particular, the need for automated tools supporting the clinical diagnosis and assessment of respiratory symptoms. This need was already clear to the scientific community, which launched an international [...] Read more.
The COVID-19 pandemic has amplified the urgency of the developments in computer-assisted medicine and, in particular, the need for automated tools supporting the clinical diagnosis and assessment of respiratory symptoms. This need was already clear to the scientific community, which launched an international challenge in 2017 at the International Conference on Biomedical Health Informatics (ICBHI) for the implementation of accurate algorithms for the classification of respiratory sound. In this work, we present a framework for respiratory sound classification based on two different kinds of features: (i) short-term features which summarize sound properties on a time scale of tenths of a second and (ii) long-term features which assess sounds properties on a time scale of seconds. Using the publicly available dataset provided by ICBHI, we cross-validated the classification performance of a neural network model over 6895 respiratory cycles and 126 subjects. The proposed model reached an accuracy of 85%±3% and an precision of 80%±8%, which compare well with the body of literature. The robustness of the predictions was assessed by comparison with state-of-the-art machine learning tools, such as the support vector machine, Random Forest and deep neural networks. The model presented here is therefore suitable for large-scale applications and for adoption in clinical practice. Finally, an interesting observation is that both short-term and long-term features are necessary for accurate classification, which could be the subject of future studies related to its clinical interpretation. Full article
(This article belongs to the Special Issue Machine Learning Techniques for the Study of Complex Systems)
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