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Applied Machine Learning III

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 December 2024 | Viewed by 11618

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Prof. Dr. Grzegorz Dudek

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Department of Automatic Control, Electrical Engineering and Optoelectronics, Faculty of Electrical Engineering, Częstochowa University of Technology, Al. Armii Krajowej 17, 42-200 Częstochowa, Poland
Interests: machine learning; evolutionary computation; artificial intelligence; pattern recognition; data mining and applications in forecasting, classification, regression, and optimization problems
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Dear Colleagues,

Machine learning (ML) is one of the most exciting fields of computing today. In recent decades, ML has become an entrenched part of everyday life and has been successfully used to solve practical problems. The application area of machine learning is very broad, including engineering, industry, business, finance, medicine, and many other domains. ML covers a wide range of learning algorithms, including classic ones such as linear regression, k-nearest neighbors, or decision trees, through support vector machines and neural networks, to newly developed algorithms such as deep learning and boosted tree models. In practice, it is quite challenging to properly determine the appropriate architecture and parameters of ML models so that the resulting learner model can achieve sound performance for both learning and generalization. Practical applications of ML bring additional challenges such as dealing with big, missing, distorted, and uncertain data. In addition, interpretability is a paramount quality that ML methods should aim to achieve if they are to be applied in practice. Interpretability allows us to understand ML model operation and raises confidence in its results.

This Special Issue focuses on applications of ML models in a diverse range of fields and problems. Application papers are expected to report substantive results on a wide range of learning methods, discussing conceptualization of a problem, data representation, feature engineering, ML models, critical comparisons with existing techniques, and the interpretation of results. Specific attention will be given to recently developed ML methods, such as deep learning and boosted tree models.

Dr. Grzegorz Dudek
Guest Editor

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Published Papers (7 papers)

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Research

16 pages, 1489 KiB

Open AccessArticle

An Innovative Deep Learning Futures Price Prediction Method with Fast and Strong Generalization and High-Accuracy Research

by Lin Huo, Yanyan Xie and Jianbo Li

Appl. Sci. 2024, 14(13), 5602; https://doi.org/10.3390/app14135602 - 27 Jun 2024

Viewed by 701

Abstract

Futures commodity prices are affected by many factors, and traditional forecasting methods require close attention from professionals and suffer from high subjectivity, slowness, and low forecasting accuracy. In this paper, we propose a new method for predicting the fluctuation in futures commodity prices accurately. We solve the problem of the slow convergence of ordinary artificial bee colony algorithms by introducing a population chaotic mapping initialization operator and use the resulting chaotic mapping artificial bee colony algorithm as a trainer to learn long short-term memory neural network hyperparameters. With the combination of gate structures learned by the algorithm, the long short-term memory network can accurately characterize the basic rules of futures market prices. Finally, we conduct a series of backtesting experiments on gold and natural gas futures commodity prices to demonstrate the effectiveness of the proposed model. The experimental results show that, compared with various existing optimization models, our proposed model is able to obtain the lowest mean absolute error, mean square error, and root mean square error in the least number of iterations. In summary, the model can be used to predict the prices of a wide range of futures commodities. Full article

(This article belongs to the Special Issue Applied Machine Learning III)

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19 pages, 583 KiB

Open AccessArticle

A Knowledge-Guided Competitive Co-Evolutionary Algorithm for Feature Selection

by Junyi Zhou, Haowen Zheng, Shaole Li, Qiancheng Hao, Haoyang Zhang, Wenze Gao and Xianpeng Wang

Appl. Sci. 2024, 14(11), 4501; https://doi.org/10.3390/app14114501 - 24 May 2024

Viewed by 826

Abstract

In real-world applications, feature selection is crucial for enhancing the performance of data science and machine learning models. Typically, feature selection is a complex combinatorial optimization problem and a multi-objective optimization problem. Its primary goals are to reduce the dimensionality of the dataset and enhance the performance of machine learning algorithms. The selection of features in high-dimensional datasets is challenging due to the intricate relationships between features, which pose significant challenges to the performance and computational efficiency of algorithms. This paper introduces a Knowledge-Guided Competitive Co-Evolutionary Algorithm (KCCEA) for feature selection, especially for high-dimensional features. In the proposed algorithm, we make improvements to the foundational dominance-based multi-objective evolutionary algorithm in two aspects. First, the use of feature correlation as knowledge to guide evolution enhances the search speed and quality of traditional multi-objective evolutionary algorithm solutions. Second, a dynamically allocated competitive–cooperative evolutionary mechanism is proposed, integrating the improved knowledge-guided evolution with traditional evolutionary algorithms, further enhancing the search efficiency and diversity of solutions. Through rigorous empirical testing on various datasets, the KCCEA demonstrates superior performance compared to basic multi-objective evolutionary algorithms, providing effective solutions to multi-objective feature selection problems while enhancing the interpretability and effectiveness of prediction models. Full article

(This article belongs to the Special Issue Applied Machine Learning III)

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18 pages, 4088 KiB

Open AccessArticle

Evaluation of Machine Learning Models for Ozone Concentration Forecasting in the Metropolitan Valley of Mexico

by Rodrigo Domínguez-García and Magali Arellano-Vázquez

Appl. Sci. 2024, 14(4), 1408; https://doi.org/10.3390/app14041408 - 8 Feb 2024

Cited by 2 | Viewed by 2428

Abstract

In large and densely populated cities, the concentration of pollutants such as ozone and its dispersion is related to effects on people’s health; therefore, its forecast is of great importance to the government and the population. Given the increased computing capacity that allows for processing massive amounts of data, the use of machine learning (ML) as a tool for air quality analysis and forecasting has gotten a significant boost. This research focuses on evaluating different models, such as Random Forest (RF), Support Vector Regression (SVR), and Gradient Boosting (GB), to forecast ozone (

O_{3}

) concentration 24 h in advance, using data from the Mexico City Atmospheric Monitoring System using meteorological variables that influence the phenomenon of ozone dispersion and formation. Full article

(This article belongs to the Special Issue Applied Machine Learning III)

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16 pages, 3530 KiB

Open AccessArticle

A Novel Hybrid Spatiotemporal Missing Value Imputation Approach for Rainfall Data: An Application to the Ratnapura Area, Sri Lanka

by Shanthi Saubhagya, Chandima Tilakaratne, Pemantha Lakraj and Musa Mammadov

Appl. Sci. 2024, 14(3), 999; https://doi.org/10.3390/app14030999 - 24 Jan 2024

Cited by 1 | Viewed by 1287

Abstract

Meteorological time series, such as rainfall data, show spatiotemporal characteristics and are often faced with the problem of containing missing values. Discarding missing values or modeling data with missing values causes negative impacts on the accuracy of the final predictions. Hence, accurately estimating missing values by considering the spatiotemporal variations in data has become a crucial step in eco-hydrological modeling. The multi-layer perceptron (MLP) is a promising tool for modeling temporal variation, while spatial kriging (SK) is a promising tool for capturing spatial variations. Therefore, in this study, we propose a novel hybrid approach combining the multi-layer perceptron method and spatial kriging to impute missing values in rainfall data. The proposed approach was tested using spatiotemporal data collected from a set of nearby rainfall gauging stations in the Ratnapura area, Sri Lanka. Missing values are present in collected rainfall data consecutively for a considerably longer period. This pattern has scattered among stations discontinuously over five years. The proposed hybrid model captures the temporal variability and spatial variability of the rainfall data through MLP and SK, respectively. It integrates predictions obtained through both MLP and SK with a novel optimal weight allocation method. The performance of the model was compared with individual approaches, MLP, SK, and spatiotemporal kriging. The results indicate that the novel hybrid approach outperforms spatiotemporal kriging and the other two pure approaches. Full article

(This article belongs to the Special Issue Applied Machine Learning III)

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17 pages, 4758 KiB

Open AccessArticle

Classification of Motor Competence in Schoolchildren Using Wearable Technology and Machine Learning with Hyperparameter Optimization

by José Sulla-Torres, Alexander Calla Gamboa, Christopher Avendaño Llanque, Javier Angulo Osorio and Manuel Zúñiga Carnero

Appl. Sci. 2024, 14(2), 707; https://doi.org/10.3390/app14020707 - 14 Jan 2024

Cited by 1 | Viewed by 1233

Abstract

Determining the classification of motor competence is an essential aspect of physical activity that must be carried out during school years. The objective is to evaluate motor competence in schoolchildren using smart bands, generate percentiles of the evaluation metrics, and classify motor performance through machine learning with hyperparameter optimization. A cross-sectional descriptive study was carried out on 764 schoolchildren (451 males and 313 females) aged 6 to 17 years. Five state schools in the city of Arequipa, Peru were evaluated. Weight, height, and waist circumference were assessed, and body mass index (BMI) was calculated. The tests evaluated in the schoolchildren measured walking and running for 6 minutes. These tests were carried out using smart bands, capturing cadence, number of steps, calories consumed, speed, stride, and heart rate. As a result, the percentiles were created through the LMS method [L (asymmetry: lambda), M (median: mu), and S (coefficient of variation: sigma)]. The cut-off points considered were <P25 (below average), p25 to p75 (average), and >p75 (above average). For classification, the machine-learning algorithms random forest, decision tree, support vector machine, naive Bayes, logistic regression, k-nearest neighbor, neural network, gradient boosting, XGBboost, LightGBM, and CatBoost were used, and the hyperparameters of the models were optimized using the RandomizedSearchCV technique. In conclusion, it was possible to classify motor competence with the tests carried out on schoolchildren, significantly improving the accuracy of the machine-learning algorithms through the selected hyperparameters, with the gradient boosting classifier being the best result at 0.95 accuracy and in the ROC-AUC curves with a 0.98. The reference values proposed in this study can be used to classify the walking motor competence of schoolchildren. Finally, the mobile software product built based on the proposed model was validated using the prototype of the Software Quality Systemic Model (SQSM) based on three specific categories: functionality, reliability, and usability, obtaining 77.09%. The results obtained can be used in educational centers to achieve the suggested recommendations for physical activity in schoolchildren. Full article

(This article belongs to the Special Issue Applied Machine Learning III)

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24 pages, 6646 KiB

Open AccessArticle

Deep-Learning-Based Approach for Automated Detection of Irregular Walking Surfaces for Walkability Assessment with Wearable Sensor

by Hui R. Ng, Xin Zhong, Yunwoo Nam and Jong-Hoon Youn

Appl. Sci. 2023, 13(24), 13053; https://doi.org/10.3390/app132413053 - 7 Dec 2023

Viewed by 1495

Abstract

A neighborhood’s walkability is associated with public health, economic and environmental benefits. The state of the walking surface on sidewalks is a key factor in assessing walkability, as it promotes pedestrian movement and exercise. Yet, conventional practices for assessing sidewalks are labor-intensive and rely on subject-matter experts, rendering them subjective, inefficient and ineffective. Wearable sensors can be utilized to address these limitations. This study proposes a novel classification method that employs a long short-term memory (LSTM) network to analyze gait data gathered from a single wearable accelerometer to automatically identify irregular walking surfaces. Three different input modalities—raw acceleration data, single-stride and multi-stride hand-crafted accelerometer-based gait features—were explored and their effects on the classification performance of the proposed method were compared and analyzed. To verify the effectiveness of the proposed approach, we compared the performance of the LSTM models to the traditional baseline support vector machine (SVM) machine learning method presented in our previous study. The results from the experiment demonstrated the effectiveness of the proposed framework, thereby validating its feasibility. Both LSTM networks trained with single-stride and multi-stride gait feature modalities outperformed the baseline SVM model. The LSTM network trained with multi-stride gait features achieved the highest average AUC of 83%. The classification performance of the LSTM model trained with single-stride gait features further improved to an AUC of 88% with post-processing, making it the most effective model. The proposed classification framework serves as an unbiased, user-oriented tool for conducting sidewalk surface condition assessments. Full article

(This article belongs to the Special Issue Applied Machine Learning III)

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21 pages, 1490 KiB

Open AccessArticle

Analysis of Preprocessing Techniques for Missing Data in the Prediction of Sunflower Yield in Response to the Effects of Climate Change

by Alina Delia Călin, Adriana Mihaela Coroiu and Horea Bogdan Mureşan

Appl. Sci. 2023, 13(13), 7415; https://doi.org/10.3390/app13137415 - 22 Jun 2023

Cited by 3 | Viewed by 1787

Abstract

Machine learning is often used to predict crop yield based on the sowing date and weather parameters in non-irrigated crops. In the context of climate change, regression algorithms can help identify correlations and plan agricultural activities to maximise production. In the case of sunflower crops, we identified datasets that are not very large and have many missing values, generating a low-performance regression model. In this paper, our aim is to study and compare several approaches for missing-value imputation in order to improve our regression model. In our experiments, we compare nine imputation methods, using mean values, similar values, interpolation (linear, spline, pad), and prediction (linear regression, random forest, extreme gradient boosting regressor, and histogram gradient boosting regression). We also employ four unsupervised outlier removal algorithms and their influence on the regression model: isolation forest, minimum covariance determinant, local outlier factor and OneClass-SVM. After preprocessing, the obtained datasets are used to build regression models using the extreme gradient boosting regressor and histogram gradient boosting regression, and their performance is compared. The evaluation of the models shows an increased

R^{2}

from 0.723 when removing instances with missing data, to 0.938 for imputation using Random Forest prediction and OneClass-SVM-based outlier removal. Full article

(This article belongs to the Special Issue Applied Machine Learning III)

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