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Algorithms
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Machine Learning Algorithms and Optimization in the Digital Transition
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Machine Learning Algorithms and Optimization in the Digital Transition

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 23569

Special Issue Editors

Dr. Mateus Mendes

E-Mail Website
Guest Editor
1. Coimbra Institute of Engineering, Polytechnic University of Coimbra, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal
2. RCM2+ Research Centre for Asset Management and Systems Engineering, Polytechnic University of Coimbra, Rua Pedro Nunes, 3030-199 Coimbra, Portugal
Interests: machine learning; artificial intelligence; computer vision; data mining
Special Issues, Collections and Topics in MDPI journals
Dr. Balduíno Mateus

E-Mail Website
Guest Editor
1. Faculty of Engineering, Lusófona University, 1749-024 Lisboa, Portugal
2. Research Centre in Asset Management and Systems Engineering, RCM2+ Lusófona University, CampoGrande, 376, 1749-024 Lisboa, Portugal
Interests: production optimization; artificial intelligence

Special Issue Information

Dear Colleagues,

To optimize and manage modern industrial systems as well as facilities, many factors must be taken into account. Modern technologies in all sectors of activity depend on large amounts of sensors and data that facilitate multivariable analyses via algorithms to support decision making in the short, middle, and long terms. Classical and deep learning machine models have boosted the capacity to analyze large volumes of data and extract patterns that greatly contribute to informed decisions, making intelligent systems more prevalent and an important part of all organizations.

Industries and large institutions are always concerned about adjusting capacity and minimizing costs, in order to meet demand without delays or the excessive use of resources. This drives research focused on models that can provide consistent support in decision-making processes. Prediction techniques based on time series models and artificial intelligence are being used more frequently to meet these challenges and contribute to more informed decisions.

This Special Issue aims to cover the latest research, so that decisions made on the basis of the algorithms proposed are sound, adequate, and contribute to facilitate management as well as operational decisions. Original contributions on the above aspects, and related topics, are encouraged.

Dr. Mateus Mendes
Dr. Balduíno Mateus
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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

  • asset management
  • clustering
  • data analysis
  • data mining
  • decision-support systems
  • deep learning
  • fault detection
  • knowledge-based systems
  • machine learning
  • object detection
  • optimization
  • predictive maintenance
  • time series

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Related Special Issue

Published Papers (7 papers)

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Research

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17 pages, 655 KiB  
Article
A Machine Learning Framework for Condition-Based Maintenance of Marine Diesel Engines: A Case Study
by Francesco Maione, Paolo Lino, Guido Maione and Giuseppe Giannino
Algorithms 2024, 17(9), 411; https://doi.org/10.3390/a17090411 - 14 Sep 2024
Cited by 1 | Viewed by 2304
Abstract
The development of artificial intelligence-based tools is having a big impact on industry. In this context, the maintenance operations of important assets and industrial resources are changing, both from a theoretical and a practical perspective. Namely, conventional maintenance reacts to faults and breakdowns [...] Read more.
The development of artificial intelligence-based tools is having a big impact on industry. In this context, the maintenance operations of important assets and industrial resources are changing, both from a theoretical and a practical perspective. Namely, conventional maintenance reacts to faults and breakdowns as they occur or schedules the necessary inspections of systems and their parts at fixed times by using statistics on component failures, but this can be improved by a predictive maintenance based on the real component’s health status, which is inspected by appropriate sensors. In this way, maintenance time and costs are saved. Improvements can be achieved even in the marine industry, in which complex ship propulsion systems are produced for operation in many different scenarios. In more detail, data-driven models, through machine learning (ML) algorithms, generate the expected values of monitored variables for comparison with real measurements on the asset, for a diagnosis based on the difference between expectations and observations. The first step towards realization of predictive maintenance is choosing the ML algorithm. This selection is often not the consequence of an in-depth analysis of the different algorithms available in the literature. For that reason, here the authors propose a framework to support an initial implementation stage of predictive maintenance based on a benchmarking of the most suitable ML algorithms. The comparison is tested to predict failures of the oil circuit in a diesel marine engine as a case study. The algorithms are compared by considering not only the mean squared error between the algorithm predictions and the data, but also the response time, which is a crucial variable for maintenance. The results clearly indicate the framework well supports predictive maintenance and the prediction error and running time are appropriate variables to choose the most suitable ML algorithm for prediction. Moreover, the proposed framework can be used to test different algorithms, on the basis of more performance indexes, and to apply predictive maintenance to other engine components. Full article
(This article belongs to the Special Issue Machine Learning Algorithms and Optimization in the Digital Transition)
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23 pages, 1626 KiB  
Article
Is Reinforcement Learning Good at American Option Valuation?
by Peyman Kor, Reidar B. Bratvold and Aojie Hong
Algorithms 2024, 17(9), 400; https://doi.org/10.3390/a17090400 - 7 Sep 2024
Viewed by 1755
Abstract
This paper investigates algorithms for identifying the optimal policy for pricing American Options. The American Option pricing is reformulated as a Sequential Decision-Making problem with two binary actions (Exercise or Continue), transforming it into an optimal stopping time problem. Both the least square [...] Read more.
This paper investigates algorithms for identifying the optimal policy for pricing American Options. The American Option pricing is reformulated as a Sequential Decision-Making problem with two binary actions (Exercise or Continue), transforming it into an optimal stopping time problem. Both the least square Monte Carlo simulation method (LSM) and Reinforcement Learning (RL)-based methods were utilized to find the optimal policy and, hence, the fair value of the American Put Option. Both Classical Geometric Brownian Motion (GBM) and calibrated Stochastic Volatility models served as the underlying uncertain assets. The novelty of this work lies in two aspects: (1) Applying LSM- and RL-based methods to determine option prices, with a specific focus on analyzing the dynamics of “Decisions” made by each method and comparing final decisions chosen by the LSM and RL methods. (2) Assess how the RL method updates “Decisions” at each batch, revealing the evolution of the decisions during the learning process to achieve optimal policy. Full article
(This article belongs to the Special Issue Machine Learning Algorithms and Optimization in the Digital Transition)
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22 pages, 3076 KiB  
Article
Deep Learning-Based Boolean, Time Series, Error Detection, and Predictive Analysis in Container Crane Operations
by Amruta Awasthi, Lenka Krpalkova and Joseph Walsh
Algorithms 2024, 17(8), 333; https://doi.org/10.3390/a17080333 - 1 Aug 2024
Cited by 1 | Viewed by 1654
Abstract
Deep learning is crucial in marine logistics and container crane error detection, diagnosis, and prediction. A novel deep learning technique using Long Short-Term Memory (LSTM) detected and anticipated errors in a system with imbalanced data. The LSTM model was trained on real operational [...] Read more.
Deep learning is crucial in marine logistics and container crane error detection, diagnosis, and prediction. A novel deep learning technique using Long Short-Term Memory (LSTM) detected and anticipated errors in a system with imbalanced data. The LSTM model was trained on real operational error data from container cranes. The custom algorithm employs the Synthetic Minority Oversampling TEchnique (SMOTE) to balance the imbalanced data for operational data errors (i.e., too few minority class samples). Python was used to program. Pearson, Spearman, and Kendall correlation matrices and covariance matrices are presented. The model’s training and validation loss is shown, and the remaining data are predicted. The test set (30% of actual data) and forecasted data had RMSEs of 0.065. A heatmap of a confusion matrix was created using Matplotlib and Seaborn. Additionally, the error outputs for the time series for the next n seconds were projected, with the n seconds input by the user. Accuracy was 0.996, precision was 1.00, recall was 0.500, and f1 score was 0.667, according to the evaluation criteria that were produced. Experiments demonstrated that the technique is capable of identifying critical elements. Thus, future attempts will improve the model’s structure to forecast industrial big data errors. However, the advantage is that it can handle imbalanced data, which is usually what most industries have. With additional data, the model can be further improved. Full article
(This article belongs to the Special Issue Machine Learning Algorithms and Optimization in the Digital Transition)
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14 pages, 358 KiB  
Article
VMP-ER: An Efficient Virtual Machine Placement Algorithm for Energy and Resources Optimization in Cloud Data Center
by Hasanein D. Rjeib and Gabor Kecskemeti
Algorithms 2024, 17(7), 295; https://doi.org/10.3390/a17070295 - 5 Jul 2024
Viewed by 1643
Abstract
Cloud service providers deliver computing services on demand using the Infrastructure as a Service (IaaS) model. In a cloud data center, several virtual machines (VMs) can be hosted on a single physical machine (PM) with the help of virtualization. The virtual machine placement [...] Read more.
Cloud service providers deliver computing services on demand using the Infrastructure as a Service (IaaS) model. In a cloud data center, several virtual machines (VMs) can be hosted on a single physical machine (PM) with the help of virtualization. The virtual machine placement (VMP) involves assigning VMs across various physical machines, which is a crucial process impacting energy draw and resource usage in the cloud data center. Nonetheless, finding an effective settlement is challenging owing to factors like hardware heterogeneity and the scalability of cloud data centers. This paper proposes an efficient algorithm named VMP-ER aimed at optimizing power consumption and reducing resource wastage. Our algorithm achieves this by decreasing the number of running physical machines, and it gives priority to energy-efficient servers. Additionally, it improves resource utilization across physical machines, thus minimizing wastage and ensuring balanced resource allocation. Full article
(This article belongs to the Special Issue Machine Learning Algorithms and Optimization in the Digital Transition)
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21 pages, 3686 KiB  
Article
Prediction of Customer Churn Behavior in the Telecommunication Industry Using Machine Learning Models
by Victor Chang, Karl Hall, Qianwen Ariel Xu, Folakemi Ololade Amao, Meghana Ashok Ganatra and Vladlena Benson
Algorithms 2024, 17(6), 231; https://doi.org/10.3390/a17060231 - 27 May 2024
Cited by 12 | Viewed by 9163
Abstract
Customer churn is a significant concern, and the telecommunications industry has the largest annual churn rate of any major industry at over 30%. This study examines the use of ensemble learning models to analyze and forecast customer churn in the telecommunications business. Accurate [...] Read more.
Customer churn is a significant concern, and the telecommunications industry has the largest annual churn rate of any major industry at over 30%. This study examines the use of ensemble learning models to analyze and forecast customer churn in the telecommunications business. Accurate churn forecasting is essential for successful client retention initiatives to combat regular customer churn. We used innovative and improved machine learning methods, including Decision Trees, Boosted Trees, and Random Forests, to enhance model interpretability and prediction accuracy. The models were trained and evaluated systematically by using a large dataset. The Random Forest model performed best, with 91.66% predictive accuracy, 82.2% precision, and 81.8% recall. Our results highlight how well the model can identify possible churners with the help of explainable AI (XAI) techniques, allowing for focused and timely intervention strategies. To improve the transparency of the decisions made by the classifier, this study also employs explainable artificial intelligence methods such as LIME and SHAP to illustrate the results of the customer churn prediction model. Our results demonstrate how crucial it is for customer relationship managers to implement strong analytical tools to reduce attrition and promote long-term economic viability in fiercely competitive marketplaces. This study indicates that ensemble learning models have strategic implications for improving consumer loyalty and organizational profitability in addition to confirming their performance. Full article
(This article belongs to the Special Issue Machine Learning Algorithms and Optimization in the Digital Transition)
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17 pages, 424 KiB  
Article
Aiding ICD-10 Encoding of Clinical Health Records Using Improved Text Cosine Similarity and PLM-ICD
by Hugo Silva, Vítor Duque, Mário Macedo and Mateus Mendes
Algorithms 2024, 17(4), 144; https://doi.org/10.3390/a17040144 - 29 Mar 2024
Cited by 1 | Viewed by 2960
Abstract
The International Classification of Diseases, 10th edition (ICD-10), has been widely used for the classification of patient diagnostic information. This classification is usually performed by dedicated physicians with specific coding training, and it is a laborious task. Automatic classification is a challenging task [...] Read more.
The International Classification of Diseases, 10th edition (ICD-10), has been widely used for the classification of patient diagnostic information. This classification is usually performed by dedicated physicians with specific coding training, and it is a laborious task. Automatic classification is a challenging task for the domain of natural language processing. Therefore, automatic methods have been proposed to aid the classification process. This paper proposes a method where Cosine text similarity is combined with a pretrained language model, PLM-ICD, in order to increase the number of probably useful suggestions of ICD-10 codes, based on the Medical Information Mart for Intensive Care (MIMIC)-IV dataset. The results show that a strategy of using multiple runs, and bucket category search, in the Cosine method, improves the results, providing more useful suggestions. Also, the use of a strategy composed by the Cosine method and PLM-ICD, which was called PLM-ICD-C, provides better results than just the PLM-ICD. Full article
(This article belongs to the Special Issue Machine Learning Algorithms and Optimization in the Digital Transition)
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Review

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23 pages, 749 KiB  
Review
A Survey of the Applications of Text Mining for the Food Domain
by Shufeng Xiong, Wenjie Tian, Haiping Si, Guipei Zhang and Lei Shi
Algorithms 2024, 17(5), 176; https://doi.org/10.3390/a17050176 - 25 Apr 2024
Cited by 3 | Viewed by 2363
Abstract
In the food domain, text mining techniques are extensively employed to derive valuable insights from large volumes of text data, facilitating applications such as aiding food recalls, offering personalized recipes, and reinforcing food safety regulation. To provide researchers and practitioners with a comprehensive [...] Read more.
In the food domain, text mining techniques are extensively employed to derive valuable insights from large volumes of text data, facilitating applications such as aiding food recalls, offering personalized recipes, and reinforcing food safety regulation. To provide researchers and practitioners with a comprehensive understanding of the latest technology and application scenarios of text mining in the food domain, the pertinent literature is reviewed and analyzed. Initially, the fundamental concepts, principles, and primary tasks of text mining, encompassing text categorization, sentiment analysis, and entity recognition, are elucidated. Subsequently, an analysis of diverse types of data sources within the food domain and the characteristics of text data mining is conducted, spanning social media, reviews, recipe websites, and food safety reports. Furthermore, the applications of text mining in the food domain are scrutinized from the perspective of various scenarios, including leveraging consumer food reviews and feedback to enhance product quality, providing personalized recipe recommendations based on user preferences and dietary requirements, and employing text mining for food safety and fraud monitoring. Lastly, the opportunities and challenges associated with the adoption of text mining techniques in the food domain are summarized and evaluated. In conclusion, text mining holds considerable potential for application in the food domain, thereby propelling the advancement of the food industry and upholding food safety standards. Full article
(This article belongs to the Special Issue Machine Learning Algorithms and Optimization in the Digital Transition)
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