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Algorithms
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Machine Learning Algorithms in Prediction Model
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Machine Learning Algorithms in Prediction Model

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (1 July 2023) | Viewed by 21722

Special Issue Editors

Prof. Dr. Danilo Pelusi

E-Mail Website
Guest Editor
Department of Computer Science, University of Teramo, 64100 Teramo, Italy
Interests: fuzzy logic; machine learning; evolutionary algorithms; computational intelligence; information theory
Special Issues, Collections and Topics in MDPI journals
Dr. Asit Kumar Das

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah 711103, West Bengal, India
Interests: machine learning; soft computing; natural language processing; artificial intelligence; pattern recognition; graph algorithms; information retrieval; decision support system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Predictive modelling is a statistical technique that uses historical and existing data to forecast and anticipate likely future outcomes with the goal of minimizing error. It operates by examining current and historical data and applying what it discovers to a model created to predict expected outcomes. The Autoregressive Integrated Moving Average (AIMMA) is the most commonly used statistical technique for time series forecasting (ARIMA). ARIMA models extrapolate previous values to predict future values. Today's technology has made machine learning a buzzword, and this field is developing very quickly. We use machine learning every day in applications such as Google Maps, Google Assistant, Alexa, etc.  Utilizing learning algorithms, machine learning aims to produce statistical models for data analysis and prediction. Further advances in machine learning could lead to these algorithms training themselves based on provided data and making correct predictions without having been specifically created for a specified purpose. The main goal of machine learning is to find patterns in user data and then provide predictions based on these complex patterns to address real-world applications, including image and speech recognition, traffic prediction, product recommendations, self-driving cars, email spam and malware filtering, fraud detection, medical diagnosis, automatic language translation, etc.

The majority of machine learning methods consist of creating a predictive model that uses provided data to learn the parameters of the objective function. The popularity and use of machine learning models in the modern age of massive numbers of data have been significantly influenced by the effectiveness and efficiency of the predictive model. A number of successful predictive models have been proposed with the aim of further developing machine learning, and these have enhanced the effectiveness and performance of related techniques. Different predictive models have been created in various machine learning disciplines, serving as inspiration for more general predictive techniques.

Machine learning is evolving quickly, especially with the advent of several theoretical advances, and is now widely used in many different fields. Researchers have particularly focused on predictive modelling as a crucial component of machine learning. Machine learning algorithms in prediction models are confronted by increasing difficulties as a result of the exponential growth of data volume and model complexity. Thus, discovering the core algorithm of predictive modelling in machine learning is the major goal of this Special Issue.

This Special Issue in Algorithms, “Machine Learning Algorithms in Prediction Model", hopes to considerably advance the state of the art in this field by offering a thorough examination and comparison of existing and newly developed works. We encourage authors from all over the world to submit unpublished original work. In addition, we seek extensive surveys posing thought-provoking questions as to how this field can be considerably improved. While we are particularly interested in works that address the issues listed below, we are open to any submissions that fit the Special Issue's theme.

Dr. Danilo Pelusi
Dr. Asit Kumar Das
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

  • machine learning
  • data mining
  • natural language processing
  • image processing
  • soft computing
  • artificial intelligence
  • pattern recognition

Published Papers (7 papers)

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Research

25 pages, 4162 KiB  
Article
Enhancing Ultimate Bearing Capacity Prediction of Cohesionless Soils Beneath Shallow Foundations with Grey Box and Hybrid AI Models
by Katayoon Kiany, Abolfazl Baghbani, Hossam Abuel-Naga, Hasan Baghbani, Mahyar Arabani and Mohammad Mahdi Shalchian
Algorithms 2023, 16(10), 456; https://doi.org/10.3390/a16100456 - 25 Sep 2023
Viewed by 1271
Abstract
This study examines the potential of the soft computing technique, namely, multiple linear regression (MLR), genetic programming (GP), classification and regression trees (CART) and GA-ENN (genetic algorithm-emotional neuron network), to predict the ultimate bearing capacity (UBC) of cohesionless soils beneath shallow foundations. For [...] Read more.
This study examines the potential of the soft computing technique, namely, multiple linear regression (MLR), genetic programming (GP), classification and regression trees (CART) and GA-ENN (genetic algorithm-emotional neuron network), to predict the ultimate bearing capacity (UBC) of cohesionless soils beneath shallow foundations. For the first time, two grey-box AI models, GP and CART, and one hybrid AI model, GA-ENN, were used in the literature to predict UBC. The inputs of the model are the width of footing (B), depth of footing (D), footing geometry (ratio of length to width, L/B), unit weight of sand (γd or γ′), and internal friction angle (ϕ). The results of the present model were compared with those obtained via two theoretical approaches and one AI approach reported in the literature. The statistical evaluation of results shows that the presently applied paradigm is better than the theoretical approaches and is competing well for the prediction of qu. This study shows that the developed AI models are a robust model for the qu prediction of shallow foundations on cohesionless soil. Sensitivity analysis was also carried out to determine the effect of each input parameter. The findings showed that the width and depth of the foundation and unit weight of soil (γd or γ′) played the most significant roles, while the internal friction angle and L/B showed less importance in predicting qu. Full article
(This article belongs to the Special Issue Machine Learning Algorithms in Prediction Model)
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16 pages, 4321 KiB  
Article
Machine-Learning-Based Imputation Method for Filling Missing Values in Ground Meteorological Observation Data
by Cong Li, Xupeng Ren and Guohui Zhao
Algorithms 2023, 16(9), 422; https://doi.org/10.3390/a16090422 - 02 Sep 2023
Cited by 2 | Viewed by 1526
Abstract
Ground meteorological observation data (GMOD) are the core of research on earth-related disciplines and an important reference for societal production and life. Unfortunately, due to operational issues or equipment failures, missing values may occur in GMOD. Hence, the imputation of missing data is [...] Read more.
Ground meteorological observation data (GMOD) are the core of research on earth-related disciplines and an important reference for societal production and life. Unfortunately, due to operational issues or equipment failures, missing values may occur in GMOD. Hence, the imputation of missing data is a prevalent issue during the pre-processing of GMOD. Although a large number of machine-learning methods have been applied to the field of meteorological missing value imputation and have achieved good results, they are usually aimed at specific meteorological elements, and few studies discuss imputation when multiple elements are randomly missing in the dataset. This paper designed a machine-learning-based multidimensional meteorological data imputation framework (MMDIF), which can use the predictions of machine-learning methods to impute the GMOD with random missing values in multiple attributes, and tested the effectiveness of 20 machine-learning methods on imputing missing values within 124 meteorological stations across six different climatic regions based on the MMDIF. The results show that MMDIF-RF was the most effective missing value imputation method; it is better than other methods for imputing 11 types of hourly meteorological elements. Although this paper applied MMDIF to the imputation of missing values in meteorological data, the method can also provide guidance for dataset reconstruction in other industries. Full article
(This article belongs to the Special Issue Machine Learning Algorithms in Prediction Model)
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12 pages, 482 KiB  
Article
Classification of CO Environmental Parameter for Air Pollution Monitoring with Grammatical Evolution
by Evangelos D. Spyrou, Chrysostomos Stylios and Ioannis Tsoulos
Algorithms 2023, 16(6), 300; https://doi.org/10.3390/a16060300 - 15 Jun 2023
Viewed by 1310
Abstract
Air pollution is a pressing concern in urban areas, necessitating the critical monitoring of air quality to understand its implications for public health. Internet of Things (IoT) devices are widely utilized in air pollution monitoring due to their sensor capabilities and seamless data [...] Read more.
Air pollution is a pressing concern in urban areas, necessitating the critical monitoring of air quality to understand its implications for public health. Internet of Things (IoT) devices are widely utilized in air pollution monitoring due to their sensor capabilities and seamless data transmission over the Internet. Artificial intelligence (AI) and machine learning techniques play a crucial role in classifying patterns derived from sensor data. Environmental stations offer a multitude of parameters that can be obtained to uncover hidden patterns showcasing the impact of pollution on the surrounding environment. This paper focuses on utilizing the CO parameter as an indicator of pollution in two datasets collected from wireless environmental monitoring devices in the greater Port area and the Town Hall of Igoumenitsa City in Greece. The datasets are normalized to facilitate their utilization in classification algorithms. The k-means algorithm is applied, and the elbow method is used to determine the optimal number of clusters. Subsequently, the datasets are introduced to the grammatical evolution algorithm to calculate the percentage fault. This method constructs classification programs in a human-readable format, making it suitable for analysis. Finally, the proposed method is compared against four state-of-the-art models: the Adam optimizer for optimizing artificial neural network parameters, a genetic algorithm for training an artificial neural network, the Bayes model, and the limited-memory BFGS method applied to a neural network. The comparison reveals that the GenClass method outperforms the other approaches in terms of classification error. Full article
(This article belongs to the Special Issue Machine Learning Algorithms in Prediction Model)
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34 pages, 2245 KiB  
Article
Detecting Deception Using Natural Language Processing and Machine Learning in Datasets on COVID-19 and Climate Change
by Barbara Brzic, Ivica Boticki and Marina Bagic Babac
Algorithms 2023, 16(5), 221; https://doi.org/10.3390/a16050221 - 26 Apr 2023
Cited by 4 | Viewed by 2652
Abstract
Deception in computer-mediated communication represents a threat, and there is a growing need to develop efficient methods of detecting it. Machine learning models have, through natural language processing, proven to be extremely successful at detecting lexical patterns related to deception. In this study, [...] Read more.
Deception in computer-mediated communication represents a threat, and there is a growing need to develop efficient methods of detecting it. Machine learning models have, through natural language processing, proven to be extremely successful at detecting lexical patterns related to deception. In this study, four selected machine learning models are trained and tested on data collected through a crowdsourcing platform on the topics of COVID-19 and climate change. The performance of the models was tested by analyzing n-grams (from unigrams to trigrams) and by using psycho-linguistic analysis. A selection of important features was carried out and further deepened with additional testing of the models on different subsets of the obtained features. This study concludes that the subjectivity of the collected data greatly affects the detection of hidden linguistic features of deception. The psycho-linguistic analysis alone and in combination with n-grams achieves better classification results than an n-gram analysis while testing the models on own data, but also while examining the possibility of generalization, especially on trigrams where the combined approach achieves a notably higher accuracy of up to 16%. The n-gram analysis proved to be a more robust method during the testing of the mutual applicability of the models while psycho-linguistic analysis remained most inflexible. Full article
(This article belongs to the Special Issue Machine Learning Algorithms in Prediction Model)
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27 pages, 2252 KiB  
Article
A Tri-Model Prediction Approach for COVID-19 ICU Bed Occupancy: A Case Study
by Nikolaos Stasinos, Anestis Kousis, Vangelis Sarlis, Aristeidis Mystakidis, Dimitris Rousidis, Paraskevas Koukaras, Ioannis Kotsiopoulos and Christos Tjortjis
Algorithms 2023, 16(3), 140; https://doi.org/10.3390/a16030140 - 04 Mar 2023
Cited by 2 | Viewed by 1338
Abstract
The impact of COVID-19 and the pressure it exerts on health systems worldwide motivated this study, which focuses on the case of Greece. We aim to assist decision makers as well as health professionals, by estimating the short to medium term needs in [...] Read more.
The impact of COVID-19 and the pressure it exerts on health systems worldwide motivated this study, which focuses on the case of Greece. We aim to assist decision makers as well as health professionals, by estimating the short to medium term needs in Intensive Care Unit (ICU) beds. We analyse time series of confirmed cases, hospitalised patients, ICU bed occupancy, recovered patients and deaths. We employ state-of-the-art forecasting algorithms, such as ARTXP, ARIMA, SARIMAX, and Multivariate Regression models. We combine these into three forecasting models culminating to a tri-model approach in time series analysis and compare them. The results of this study show that the combination of ARIMA with SARIMAX is more accurate for the majority of the investigated regions in short term 1-week ahead predictions, while Multivariate Regression outperforms the other two models for 2-weeks ahead predictions. Finally, for the medium term 3-weeks ahead predictions the Multivariate Regression and ARIMA with SARIMAX show the best results. We report on Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), R-squared (R2), and Mean Absolute Error (MAE) values, for one-week, two-week and three-week ahead predictions for ICU bed requirements. Such timely insights offer new capabilities for efficient management of healthcare resources. Full article
(This article belongs to the Special Issue Machine Learning Algorithms in Prediction Model)
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20 pages, 1229 KiB  
Article
Insights into Multi-Model Federated Learning: An Advanced Approach for Air Quality Index Forecasting
by Duy-Dong Le, Anh-Khoa Tran, Minh-Son Dao, Kieu-Chinh Nguyen-Ly, Hoang-Son Le, Xuan-Dao Nguyen-Thi, Thanh-Qui Pham, Van-Luong Nguyen and Bach-Yen Nguyen-Thi
Algorithms 2022, 15(11), 434; https://doi.org/10.3390/a15110434 - 17 Nov 2022
Cited by 5 | Viewed by 2710
Abstract
The air quality index (AQI) forecast in big cities is an exciting study area in smart cities and healthcare on the Internet of Things. In recent years, a large number of empirical, academic, and review papers using machine learning (ML) for air quality [...] Read more.
The air quality index (AQI) forecast in big cities is an exciting study area in smart cities and healthcare on the Internet of Things. In recent years, a large number of empirical, academic, and review papers using machine learning (ML) for air quality analysis have been published. However, most of those studies focused on traditional centralized processing on a single machine, and there had been few surveys of federated learning (FL) in this field. This overview aims to fill this gap and provide newcomers with a broader perspective to inform future research on this topic, especially for the multi-model approach. In this survey, we went over the works that previous scholars have conducted in AQI forecast both in traditional ML approaches and FL mechanisms. Our objective is to comprehend previous research on AQI prediction including methods, models, data sources, achievements, challenges, and solutions applied in the past. We also convey a new path of using multi-model FL, which has piqued the computer science community’s interest recently. Full article
(This article belongs to the Special Issue Machine Learning Algorithms in Prediction Model)
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26 pages, 855 KiB  
Article
Applying Artificial Intelligence in Cryptocurrency Markets: A Survey
by Rasoul Amirzadeh, Asef Nazari and Dhananjay Thiruvady
Algorithms 2022, 15(11), 428; https://doi.org/10.3390/a15110428 - 14 Nov 2022
Cited by 9 | Viewed by 9783
Abstract
The total capital in cryptocurrency markets is around two trillion dollars in 2022, which is almost the same as Apple’s market capitalisation at the same time. Increasingly, cryptocurrencies have become established in financial markets with an enormous number of transactions and trades happening [...] Read more.
The total capital in cryptocurrency markets is around two trillion dollars in 2022, which is almost the same as Apple’s market capitalisation at the same time. Increasingly, cryptocurrencies have become established in financial markets with an enormous number of transactions and trades happening every day. Similar to other financial systems, price prediction is one of the main challenges in cryptocurrency trading. Therefore, the application of artificial intelligence, as one of the tools of prediction, has emerged as a recently popular subject of investigation in the cryptocurrency domain. Since machine learning models, as opposed to traditional financial models, demonstrate satisfactory performance in quantitative finance, they seem ideal for coping with the price prediction problem in the complex and volatile cryptocurrency market. There have been several studies that have focused on applying machine learning for price and movement prediction and portfolio management in cryptocurrency markets, though these methods and models are in their early stages. This survey paper aims to review the current research trends in applications of supervised and reinforcement learning models in cryptocurrency price prediction. This study also highlights potential research gaps and possible areas for improvement. In addition, it emphasises potential challenges and research directions that will be of interest in the artificial intelligence and machine learning communities focusing on cryptocurrencies. Full article
(This article belongs to the Special Issue Machine Learning Algorithms in Prediction Model)
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