Algorithms

11 pages, 5355 KiB

Open AccessArticle

Adaptive-Step Perturb-and-Observe Algorithm for Multidimensional Phase Noise Stabilization in Fiber-Based Multi-Arm Mach–Zehnder Interferometers

by H. Abarzúa, C. Melo, S. E. Restrepo, S. Vergara, D. Sbarbaro, G. Cañas, G. Lima, G. Saavedra and J. Cariñe

Algorithms 2024, 17(12), 534; https://doi.org/10.3390/a17120534 (registering DOI) - 21 Nov 2024

Abstract

Fiber-optic Mach–Zehnder interferometers are widely used in research areas such as telecommunications, spectroscopy, and quantum information. These optical structures are known to be affected by phase fluctuations that are usually modeled as multiparametric noise. This multidimensional noise must be stabilized or compensated for [...] Read more.

Fiber-optic Mach–Zehnder interferometers are widely used in research areas such as telecommunications, spectroscopy, and quantum information. These optical structures are known to be affected by phase fluctuations that are usually modeled as multiparametric noise. This multidimensional noise must be stabilized or compensated for to enable fiber-optic Mach–Zehnder architectures for practical applications. In this work, we study the effectiveness of a modified Perturb-and-Observe (P&O) algorithm to control multidimensional phase noise in fiber-based multi-arm Mach–Zehnder interferometers. We demonstrate the feasibility of stabilizing multidimensional phase noise by numerical simulations using a simple feedback control scheme and analyze the algorithm’s performance for systems up to dimension

8 \times 8

. We achieved minimal steady-state errors that guarantee high optical visibility in complex optical systems with

N \times N

matrices (with

N = [2, 3, 4, 5, 6, 7, 8]

). Full article

(This article belongs to the Section Algorithms for Multidisciplinary Applications)

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19 pages, 1117 KiB

Open AccessArticle

Automatic Simplification of Lithuanian Administrative Texts

by Justina Mandravickaitė, Eglė Rimkienė, Danguolė Kotryna Kapkan, Danguolė Kalinauskaitė and Tomas Krilavičius

Algorithms 2024, 17(11), 533; https://doi.org/10.3390/a17110533 - 20 Nov 2024

Abstract

Text simplification reduces the complexity of text while preserving essential information, thus making it more accessible to a broad range of readers, including individuals with cognitive disorders, non-native speakers, children, and the general public. In this paper, we present experiments on text simplification [...] Read more.

Text simplification reduces the complexity of text while preserving essential information, thus making it more accessible to a broad range of readers, including individuals with cognitive disorders, non-native speakers, children, and the general public. In this paper, we present experiments on text simplification for the Lithuanian language, aiming to simplify administrative texts to a Plain Language level. We fine-tuned mT5 and mBART models for this task and evaluated the effectiveness of ChatGPT as well. We assessed simplification results via both quantitative metrics and qualitative evaluation. Our findings indicated that mBART performed the best as it achieved the best scores across all evaluation metrics. The qualitative analysis further supported these findings. ChatGPT experiments showed that it responded quite well to a short and simple prompt to simplify the given text; however, it ignored most of the rules given in a more elaborate prompt. Finally, our analysis revealed that BERTScore and ROUGE aligned moderately well with human evaluations, while BLEU and readability scores indicated lower or even negative correlations Full article

(This article belongs to the Section Evolutionary Algorithms and Machine Learning)

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13 pages, 1085 KiB

Open AccessArticle

Exponential Functions Permit Estimation of Anaerobic Work Capacity and Critical Power from Less than 2 Min All-Out Test

by Ming-Chang Tsai, Scott Thomas and Marc Klimstra

Algorithms 2024, 17(11), 532; https://doi.org/10.3390/a17110532 - 20 Nov 2024

Abstract

The Critical Power Model (CPM) is key for assessing athletes’ aerobic and anaerobic energy systems but typically involves lengthy, exhausting protocols. The 3 min all-out test (3MT) simplifies CPM assessment, yet its duration remains demanding. Exponential decay models, specifically mono- and bi-exponential functions, [...] Read more.

The Critical Power Model (CPM) is key for assessing athletes’ aerobic and anaerobic energy systems but typically involves lengthy, exhausting protocols. The 3 min all-out test (3MT) simplifies CPM assessment, yet its duration remains demanding. Exponential decay models, specifically mono- and bi-exponential functions, offer a more efficient alternative by accurately capturing the nonlinear energy dynamics in high-intensity efforts. This study explores shortening the 3MT using these functions to reduce athlete strain while preserving the accuracy of critical power (CP) and work capacity (

W^{'}

) estimates. Seventy-six competitive cyclists and triathletes completed a 3MT on a cycle ergometer, with CP and

W^{'}

calculated at shorter intervals. Results showed that a 90 s test using the bi-exponential model yielded CP and

W^{'}

values similar to those of the full 3MT. Meanwhile, the mono-exponential model required at least 135 s. Bland–Altman and linear regression analyses confirmed that a 120 s test with the mono-exponential model reliably estimated CP and

W^{'}

with minimal physical strain. These findings support a shortened, less-demanding 3MT as a valid alternative for CPM assessment. Full article

(This article belongs to the Section Analysis of Algorithms and Complexity Theory)

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18 pages, 1508 KiB

Open AccessArticle

Adversarial Validation in Image Classification Datasets by Means of Cumulative Spectral Gradient

by Diego Renza, Ernesto Moya-Albor and Adrian Chavarro

Algorithms 2024, 17(11), 531; https://doi.org/10.3390/a17110531 - 19 Nov 2024

Abstract

The main objective of a machine learning (ML) system is to obtain a trained model from input data in such a way that it allows predictions to be made on new i.i.d. (Independently and Identically Distributed) data with the lowest possible error. However, [...] Read more.

The main objective of a machine learning (ML) system is to obtain a trained model from input data in such a way that it allows predictions to be made on new i.i.d. (Independently and Identically Distributed) data with the lowest possible error. However, how can we assess whether the training and test data have a similar distribution? To answer this question, this paper presents a proposal to determine the degree of distribution shift of two datasets. To this end, a metric for evaluating complexity in datasets is used, which can be applied in multi-class problems, comparing each pair of classes of the two sets. The proposed methodology has been applied to three well-known datasets: MNIST, CIFAR-10 and CIFAR-100, together with corrupted versions of these. Through this methodology, it is possible to evaluate which types of modification have a greater impact on the generalization of the models without the need to train multiple models multiple times, also allowing us to determine which classes are more affected by corruption. Full article

(This article belongs to the Special Issue Machine Learning Algorithms for Image Understanding and Analysis)

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20 pages, 3221 KiB

Open AccessArticle

A VIKOR-Based Sequential Three-Way Classification Ranking Method

by Wentao Xu, Jin Qian, Yueyang Wu, Shaowei Yan, Yongting Ni and Guangjin Yang

Algorithms 2024, 17(11), 530; https://doi.org/10.3390/a17110530 - 19 Nov 2024

Abstract

VIKOR uses the idea of overall utility maximization and individual regret minimization to afford a compromise result for multi-attribute decision-making problems with conflicting attributes. Many researchers have proposed corresponding improvements and expansions to make it more suitable for sorting optimization in their respective [...] Read more.

VIKOR uses the idea of overall utility maximization and individual regret minimization to afford a compromise result for multi-attribute decision-making problems with conflicting attributes. Many researchers have proposed corresponding improvements and expansions to make it more suitable for sorting optimization in their respective research fields. However, these improvements and extensions only rank the alternatives without classifying them. For this purpose, this text introduces the three-way sequential decisions method and combines it with the VIKOR method to design a three-way VIKOR method that can deal with both ranking and classification. By using the final negative ideal solution (NIS) and the final positive ideal solution (PIS) for all alternatives, the individual regret value and group utility value of each alternative were calculated. Different three-way VIKOR models were obtained by four different combinations of individual regret value and group utility value. In the ranking process, the characteristics of VIKOR method are introduced, and the subjective preference of decision makers is considered by using individual regret, group utility, and decision index values. In the classification process, the corresponding alternatives are divided into the corresponding decision domains by sequential three-way decisions, and the risk of direct acceptance or rejection is avoided by putting the uncertain alternatives into the boundary region to delay the decision. The alternative is divided into decision domains through sequential three-way decisions, sorted according to the collation rules in the same decision domain, and the final sorting results are obtained according to the collation rules in different decision domains. Finally, the effectiveness and correctness of the proposed method are verified by a project investment example, and the results are compared and evaluated. The experimental results show that the proposed method has a significant correlation with the results of other methods, ad is effective and feasible, and is simpler and more effective in dealing with some problems. Errors caused by misclassification is reduced by sequential three-way decisions. Full article

(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)

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28 pages, 6900 KiB

Open AccessArticle

A New Approach to Recognize Faces Amidst Challenges: Fusion Between the Opposite Frequencies of the Multi-Resolution Features

by Regina Lionnie, Julpri Andika and Mudrik Alaydrus

Algorithms 2024, 17(11), 529; https://doi.org/10.3390/a17110529 - 17 Nov 2024

Abstract

This paper proposes a new approach to pixel-level fusion using the opposite frequency from the discrete wavelet transform with Gaussian or Difference of Gaussian. The low-frequency from discrete wavelet transform sub-band was fused with the Difference of Gaussian, while the high-frequency sub-bands were [...] Read more.

This paper proposes a new approach to pixel-level fusion using the opposite frequency from the discrete wavelet transform with Gaussian or Difference of Gaussian. The low-frequency from discrete wavelet transform sub-band was fused with the Difference of Gaussian, while the high-frequency sub-bands were fused with Gaussian. The final fusion was reconstructed using an inverse discrete wavelet transform into one enhanced reconstructed image. These enhanced images were utilized to improve recognition performance in the face recognition system. The proposed method was tested against benchmark face datasets such as The Database of Faces (AT&T), the Extended Yale B Face Dataset, the BeautyREC Face Dataset, and the FEI Face Dataset. The results showed that our proposed method was robust and accurate against challenges such as lighting conditions, facial expressions, head pose, 180-degree rotation of the face profile, dark images, acquisition with time gap, and conditions where the person uses attributes such as glasses. The proposed method is comparable to state-of-the-art methods and generates high recognition performance (more than 99% accuracy). Full article

(This article belongs to the Section Algorithms for Multidisciplinary Applications)

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19 pages, 5212 KiB

Open AccessArticle

Assessment of Solar Energy Generation Toward Net-Zero Energy Buildings

by Rayan Khalil, Guilherme Vieira Hollweg, Akhtar Hussain, Wencong Su and Van-Hai Bui

Algorithms 2024, 17(11), 528; https://doi.org/10.3390/a17110528 - 16 Nov 2024

Abstract

With the continuous rise in the energy consumption of buildings, the study and integration of net-zero energy buildings (NZEBs) are essential for mitigating the harmful effects associated with this trend. However, developing an energy management system for such buildings is challenging due to [...] Read more.

With the continuous rise in the energy consumption of buildings, the study and integration of net-zero energy buildings (NZEBs) are essential for mitigating the harmful effects associated with this trend. However, developing an energy management system for such buildings is challenging due to uncertainties surrounding NZEBs. This paper introduces an optimization framework comprising two major stages: (i) renewable energy prediction and (ii) multi-objective optimization. A prediction model is developed to accurately forecast photovoltaic (PV) system output, while a multi-objective optimization model is designed to identify the most efficient ways to produce cooling, heating, and electricity at minimal operational costs. These two stages not only help mitigate uncertainties in NZEBs but also reduce dependence on imported power from the utility grid. Finally, to facilitate the deployment of the proposed framework, a graphical user interface (GUI) has been developed, providing a user-friendly environment for building operators to determine optimal scheduling and oversee the entire system. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence/Machine Learning Techniques for Safe Operation and Control in Power and Sustainable Energy Systems)

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22 pages, 3297 KiB

Open AccessArticle

Sleep Apnea Classification Using the Mean Euler–Poincaré Characteristic and AI Techniques

by Moises Ramos-Martinez, Felipe D. J. Sorcia-Vázquez, Gerardo Ortiz-Torres, Mario Martínez García, Mayra G. Mena-Enriquez, Estela Sarmiento-Bustos, Juan Carlos Mixteco-Sánchez, Erasmo Misael Rentería-Vargas, Jesús E. Valdez-Resendiz and Jesse Yoe Rumbo-Morales

Algorithms 2024, 17(11), 527; https://doi.org/10.3390/a17110527 - 15 Nov 2024

Abstract

Sleep apnea is a sleep disorder that disrupts breathing during sleep. This study aims to classify sleep apnea using a machine learning approach and a Euler–Poincaré characteristic (EPC) model derived from electrocardiogram (ECG) signals. An ensemble K-nearest neighbors classifier and a feedforward neural [...] Read more.

Sleep apnea is a sleep disorder that disrupts breathing during sleep. This study aims to classify sleep apnea using a machine learning approach and a Euler–Poincaré characteristic (EPC) model derived from electrocardiogram (ECG) signals. An ensemble K-nearest neighbors classifier and a feedforward neural network were implemented using the EPC model as inputs. ECG signals were preprocessed with a polynomial-based scheme to reduce noise, and the processed signals were transformed into a non-Gaussian physiological random field (NGPRF) for EPC model extraction from excursion sets. The classifiers were then applied to the EPC model inputs. Using the Apnea-ECG dataset, the proposed method achieved an accuracy of 98.5%, sensitivity of 94.5%, and specificity of 100%. Combining machine learning methods and geometrical features can effectively diagnose sleep apnea from single-lead ECG signals. The EPC model enhances clinical decision-making for evaluating this disease. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms for Medicine (2nd Edition))

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22 pages, 2446 KiB

Open AccessReview

A Comprehensive Review of Autonomous Driving Algorithms: Tackling Adverse Weather Conditions, Unpredictable Traffic Violations, Blind Spot Monitoring, and Emergency Maneuvers

by Cong Xu and Ravi Sankar

Algorithms 2024, 17(11), 526; https://doi.org/10.3390/a17110526 - 15 Nov 2024

Abstract

With the rapid development of autonomous driving technology, ensuring the safety and reliability of vehicles under various complex and adverse conditions has become increasingly important. Although autonomous driving algorithms perform well in regular driving scenarios, they still face significant challenges when dealing with [...] Read more.

With the rapid development of autonomous driving technology, ensuring the safety and reliability of vehicles under various complex and adverse conditions has become increasingly important. Although autonomous driving algorithms perform well in regular driving scenarios, they still face significant challenges when dealing with adverse weather conditions, unpredictable traffic rule violations (such as jaywalking and aggressive lane changes), inadequate blind spot monitoring, and emergency handling. This review aims to comprehensively analyze these critical issues, systematically review current research progress and solutions, and propose further optimization suggestions. By deeply analyzing the logic of autonomous driving algorithms in these complex situations, we hope to provide strong support for enhancing the safety and reliability of autonomous driving technology. Additionally, we will comprehensively analyze the limitations of existing driving technologies and compare Advanced Driver Assistance Systems (ADASs) with Full Self-Driving (FSD) to gain a thorough understanding of the current state and future development directions of autonomous driving technology. Full article

(This article belongs to the Special Issue Algorithms for Smart Cities (2nd Edition))

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31 pages, 7153 KiB

Open AccessArticle

You Only Look Once Version 5 and Deep Simple Online and Real-Time Tracking Algorithms for Real-Time Customer Behavior Tracking and Retail Optimization

by Mohamed Shili, Osama Sohaib and Salah Hammedi

Algorithms 2024, 17(11), 525; https://doi.org/10.3390/a17110525 - 15 Nov 2024

Abstract

The speedy progress of computer vision and machine learning engineering has inaugurated novel means for improving the purchasing experiment in brick-and-mortar stores. This paper examines the utilization of YOLOv (You Only Look Once) and DeepSORT (Deep Simple Online and Real-Time Tracking) algorithms for [...] Read more.

The speedy progress of computer vision and machine learning engineering has inaugurated novel means for improving the purchasing experiment in brick-and-mortar stores. This paper examines the utilization of YOLOv (You Only Look Once) and DeepSORT (Deep Simple Online and Real-Time Tracking) algorithms for the real-time detection and analysis of the purchasing penchant in brick-and-mortar market surroundings. By leveraging these algorithms, stores can track customer behavior, identify popular products, and monitor high-traffic areas, enabling businesses to adapt quickly to customer preferences and optimize store layout and inventory management. The methodology involves the integration of YOLOv5 for accurate and rapid object detection combined with DeepSORT for the effective tracking of customer movements and interactions with products. Information collected in in-store cameras and sensors is handled to detect tendencies in customer behavior, like repeatedly inspected products, periods expended in specific intervals, and product handling. The results indicate a modest improvement in customer engagement, with conversion rates increasing by approximately 3 percentage points, and a decline in inventory waste levels, from 88% to 75%, after system implementation. This study provides essential insights into the further integration of algorithm technology in physical retail locations and demonstrates the revolutionary potential of real-time behavior tracking in the retail industry. This research determines the foundation for future developments in functional strategies and customer experience optimization by offering a solid framework for creating intelligent retail systems. Full article

(This article belongs to the Section Evolutionary Algorithms and Machine Learning)

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17 pages, 681 KiB

Open AccessArticle

Subsampling Algorithms for Irregularly Spaced Autoregressive Models

by Jiaqi Liu, Ziyang Wang, HaiYing Wang and Nalini Ravishanker

Algorithms 2024, 17(11), 524; https://doi.org/10.3390/a17110524 - 15 Nov 2024

Abstract

With the exponential growth of data across diverse fields, applying conventional statistical methods directly to large-scale datasets has become computationally infeasible. To overcome this challenge, subsampling algorithms are widely used to perform statistical analyses on smaller, more manageable subsets of the data. The [...] Read more.

With the exponential growth of data across diverse fields, applying conventional statistical methods directly to large-scale datasets has become computationally infeasible. To overcome this challenge, subsampling algorithms are widely used to perform statistical analyses on smaller, more manageable subsets of the data. The effectiveness of these methods depends on their ability to identify and select data points that improve the estimation efficiency according to some optimality criteria. While much of the existing research has focused on subsampling techniques for independent data, there is considerable potential for developing methods tailored to dependent data, particularly in time-dependent contexts. In this study, we extend subsampling techniques to irregularly spaced time series data which are modeled by irregularly spaced autoregressive models. We present frameworks for various subsampling approaches, including optimal subsampling under A-optimality, information-based optimal subdata selection, and sequential thinning on streaming data. These methods use A-optimality or D-optimality criteria to assess the usefulness of each data point and prioritize the inclusion of the most informative ones. We then assess the performance of these subsampling methods using numerical simulations, providing insights into their suitability and effectiveness for handling irregularly spaced long time series. Numerical results show that our algorithms have promising performance. Their estimation efficiency can be ten times as high as that of the uniform sampling estimator. They also significantly reduce the computational time and can be up to forty times faster than the full-data estimator. Full article

(This article belongs to the Section Algorithms for Multidisciplinary Applications)

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21 pages, 388 KiB

Open AccessArticle

The Nelder–Mead Simplex Algorithm Is Sixty Years Old: New Convergence Results and Open Questions

by Aurél Galántai

Algorithms 2024, 17(11), 523; https://doi.org/10.3390/a17110523 - 14 Nov 2024

Abstract

We investigate and compare two versions of the Nelder–Mead simplex algorithm for function minimization. Two types of convergence are studied: the convergence of function values at the simplex vertices and convergence of the simplex sequence. For the first type of convergence, we generalize [...] Read more.

We investigate and compare two versions of the Nelder–Mead simplex algorithm for function minimization. Two types of convergence are studied: the convergence of function values at the simplex vertices and convergence of the simplex sequence. For the first type of convergence, we generalize the main result of Lagarias, Reeds, Wright and Wright (1998). For the second type of convergence, we also improve recent results which indicate that the Lagarias et al.’s version of the Nelder–Mead algorithm has better convergence properties than the original Nelder–Mead method. This paper concludes with some open questions. Full article

(This article belongs to the Special Issue Numerical Optimization and Algorithms: 2nd Edition)

5 pages, 188 KiB

Open AccessEditorial

Metaheuristic Algorithms in Optimal Design of Engineering Problems

by Łukasz Knypiński, Ramesh Devarapalli and Marcin Kamiński

Algorithms 2024, 17(11), 522; https://doi.org/10.3390/a17110522 - 14 Nov 2024

Abstract

Metaheuristic optimization algorithms (MOAs) are widely used to optimize the design process of engineering problems [...] Full article

(This article belongs to the Special Issue Metaheuristic Algorithms in Optimal Design of Engineering Problems)

16 pages, 8588 KiB

Open AccessArticle

Quotient Network-A Network Similar to ResNet but Learning Quotients

by Peng Hui, Jiamuyang Zhao, Changxin Li and Qingzhen Zhu

Algorithms 2024, 17(11), 521; https://doi.org/10.3390/a17110521 - 13 Nov 2024

Abstract

The emergence of ResNet provides a powerful tool for training extremely deep networks. The core idea behind it is to change the learning goals of the network. It no longer learns new features from scratch but learns the difference between the target and [...] Read more.

The emergence of ResNet provides a powerful tool for training extremely deep networks. The core idea behind it is to change the learning goals of the network. It no longer learns new features from scratch but learns the difference between the target and existing features. However, the difference between the two kinds of features does not have an independent and clear meaning, and the amount of learning is based on the absolute rather than the relative difference, which is sensitive to the size of existing features. We propose a new network that perfectly solves these two problems while still having the advantages of ResNet. Specifically, it chooses to learn the quotient of the target features with the existing features, so we call it the quotient network. In order to enable this network to learn successfully and achieve higher performance, we propose some design rules for this network so that it can be trained efficiently and achieve better performance than ResNet. Experiments on the CIFAR10, CIFAR100, and SVHN datasets prove that this network can stably achieve considerable improvements over ResNet by simply making tiny corresponding changes to the original ResNet network without adding new parameters. Full article

(This article belongs to the Topic AI and Computational Methods for Modelling, Simulations and Optimizing of Advanced Systems: Innovations in Complexity)

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25 pages, 3540 KiB

Open AccessArticle

Minimum-Energy Scheduling of Flexible Job-Shop Through Optimization and Comprehensive Heuristic

by Oludolapo Akanni Olanrewaju, Fabio Luiz Peres Krykhtine and Felix Mora-Camino

Algorithms 2024, 17(11), 520; https://doi.org/10.3390/a17110520 - 12 Nov 2024

Abstract

This study considers a flexible job-shop scheduling problem where energy cost savings are the primary objective and where the classical objective of the minimization of the make-span is replaced by the satisfaction of due times for each job. An original two-level mixed-integer formulation [...] Read more.

This study considers a flexible job-shop scheduling problem where energy cost savings are the primary objective and where the classical objective of the minimization of the make-span is replaced by the satisfaction of due times for each job. An original two-level mixed-integer formulation of this optimization problem is proposed, where the processed flows of material and their timing are explicitly considered. Its exact solution is discussed, and, considering its computational complexity, a comprehensive heuristic, balancing energy performance and due time constraint satisfaction, is developed to provide acceptable solutions in polynomial time to the minimum-energy flexible job-shop scheduling problem, even when considering its dynamic environment. The proposed approach is illustrated through a small-scale example. Full article

(This article belongs to the Special Issue Scheduling Theory and Algorithms for Sustainable Manufacturing)

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26 pages, 862 KiB

Open AccessArticle

Can the Plantar Pressure and Temperature Data Trend Show the Presence of Diabetes? A Comparative Study of a Variety of Machine Learning Techniques

by Eduardo A. Gerlein, Francisco Calderón, Martha Zequera-Díaz and Roozbeh Naemi

Algorithms 2024, 17(11), 519; https://doi.org/10.3390/a17110519 - 12 Nov 2024

Abstract

This study aimed to explore the potential of predicting diabetes by analyzing trends in plantar thermal and plantar pressure data, either individually or in combination, using various machine learning techniques. A total of twenty-six participants, comprising thirteen individuals diagnosed with diabetes and thirteen [...] Read more.

This study aimed to explore the potential of predicting diabetes by analyzing trends in plantar thermal and plantar pressure data, either individually or in combination, using various machine learning techniques. A total of twenty-six participants, comprising thirteen individuals diagnosed with diabetes and thirteen healthy individuals, walked along a 20 m path. In-shoe plantar pressure data were collected and the plantar temperature was measured both immediately before and after the walk. Each participant completed the trial three times, and the average data between the trials were calculated. The research was divided into three experiments: the first evaluated the correlations between the plantar pressure and temperature data; the second focused on predicting diabetes using each data type independently; and the third combined both data types and assessed the effect of such to enhance the predictive accuracy. For the experiments, 20 regression models and 16 classification algorithms were employed, and the performance was evaluated using a five-fold cross-validation strategy. The outcomes of the initial set of experiments indicated that the machine learning models were significant correlations between the thermal data and pressure estimates. This was consistent with the findings from the prior correlation analysis, which showed weak relationships between these two data modalities. However, a shift in focus towards predicting diabetes by aggregating the temperature and pressure data led to encouraging results, demonstrating the effectiveness of this approach in accurately predicting the presence of diabetes. The analysis revealed that, while several classifiers demonstrated reasonable metrics when using standalone variables, the integration of thermal and pressure data significantly improved the predictive accuracy. Specifically, when only plantar pressure data were used, the Logistic Regression model achieved the highest accuracy at 68.75%. Those predictions based solely on temperature data showed the Naive Bayes model as the lead with an accuracy of 87.5%. Notably, the highest accuracy of 93.75% was observed when both the temperature and pressure data were combined, with the Extra Trees Classifier performing the best. These results suggest that combining temperature and pressure data enhances the model’s predictive accuracy. This can indicate the importance of multimodal data integration and their potentials in diabetes prediction. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (2nd Edition))

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A 25 m walkway; the designated area avoided “quick” twisting movements. Full article ">Figure 2
Thermal image capture setup. Full article ">Figure 3
Regions of interest on the feet were marked for thermal and pressure measurements: hallux, 1st metatarsus, 3rd metatarsus, 5th metatarsus, midfoot (proximal to 5th metatarsus apophysis), medial arch on proximal 1st metatarsus, and heel. Full article ">Figure 4
Correlation index matrix representing the relationship between temperature and pressure features across different individuals. The matrix shows correlation coefficients for pressure and temperature data between the left and right feet of each individual, as well as across different individuals. Full article ">Figure 5
Performance comparison between the Extra Trees Classifier and Random Forest Classifier. (a,c) display the feature importance plots, with (a) highlighting the Extra Trees Classifier’s focus on thermal data and (c) illustrating the Random Forest Classifier’s balanced consideration of both temperature and pressure features. (b,d) depict the decision boundaries for the Extra Trees Classifier and Random Forest Classifier, respectively, showing how the models classify diabetic (1) and non-diabetic (0) cases based on these features. Random Forest’s mixed use of temperature and pressure data underscores its more comprehensive approach to predicting diabetes. Full article ">

21 pages, 623 KiB

Open AccessArticle

Attribute Relevance Score: A Novel Measure for Identifying Attribute Importance

by Pablo Neirz, Hector Allende and Carolina Saavedra

Algorithms 2024, 17(11), 518; https://doi.org/10.3390/a17110518 - 9 Nov 2024

Abstract

This study introduces a novel measure for evaluating attribute relevance, specifically designed to accurately identify attributes that are intrinsically related to a phenomenon, while being sensitive to the asymmetry of those relationships and noise conditions. Traditional variable selection techniques, such as filter and [...] Read more.

This study introduces a novel measure for evaluating attribute relevance, specifically designed to accurately identify attributes that are intrinsically related to a phenomenon, while being sensitive to the asymmetry of those relationships and noise conditions. Traditional variable selection techniques, such as filter and wrapper methods, often fall short in capturing these complexities. Our methodology, grounded in decision trees but extendable to other machine learning models, was rigorously evaluated across various data scenarios. The results demonstrate that our measure effectively distinguishes relevant from irrelevant attributes and highlights how relevance is influenced by noise, providing a more nuanced understanding compared to established methods such as Pearson, Spearman, Kendall, MIC, MAS, MEV, GMIC, and

P h i_{k}

. This research underscores the importance of phenomenon-centric explainability, reproducibility, and robust attribute relevance evaluation in the development of predictive models. By enhancing both the interpretability and contextual accuracy of models, our approach not only supports more informed decision making but also contributes to a deeper understanding of the underlying mechanisms in diverse application domains, such as biomedical research, financial modeling, astronomy, and others. Full article

(This article belongs to the Special Issue Algorithms for Feature Selection (2nd Edition))

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13 pages, 1056 KiB

Open AccessArticle

A Framework for Evaluating Dynamic Directed Brain Connectivity Estimation Methods Using Synthetic EEG Signal Generation

by Zoran Šverko, Saša Vlahinić and Peter Rogelj

Algorithms 2024, 17(11), 517; https://doi.org/10.3390/a17110517 - 9 Nov 2024

Abstract

This study presents a method for generating synthetic electroencephalography (EEG) signals to test dynamic directed brain connectivity estimation methods. Current methods for evaluating dynamic brain connectivity estimation techniques face challenges due to the lack of ground truth in real EEG signals. To [...] Read more.

This study presents a method for generating synthetic electroencephalography (EEG) signals to test dynamic directed brain connectivity estimation methods. Current methods for evaluating dynamic brain connectivity estimation techniques face challenges due to the lack of ground truth in real EEG signals. To address this, we propose a framework for generating synthetic EEG signals with predefined dynamic connectivity changes. Our approach allows for evaluating and optimizing dynamic connectivity estimation methods, particularly Granger causality (GC). We demonstrate the framework’s utility by identifying optimal window sizes and regression orders for GC analysis. The findings could guide the development of more accurate dynamic connectivity techniques. Full article

(This article belongs to the Special Issue Artificial Intelligence and Signal Processing: Circuits and Systems)

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16 pages, 2594 KiB

Open AccessArticle

Topological Reinforcement Adaptive Algorithm (TOREADA) Application to the Alerting of Convulsive Seizures and Validation with Monte Carlo Numerical Simulations

by Stiliyan Kalitzin

Algorithms 2024, 17(11), 516; https://doi.org/10.3390/a17110516 - 8 Nov 2024

Abstract

The detection of adverse events—for example, convulsive epileptic seizures—can be critical for patients suffering from a variety of pathological syndromes. Algorithms using remote sensing modalities, such as a video camera input, can be effective for real-time alerting, but the broad variability of environments [...] Read more.

The detection of adverse events—for example, convulsive epileptic seizures—can be critical for patients suffering from a variety of pathological syndromes. Algorithms using remote sensing modalities, such as a video camera input, can be effective for real-time alerting, but the broad variability of environments and numerous nonstationary factors may limit their precision. In this work, we address the issue of adaptive reinforcement that can provide flexible applications in alerting devices. The general concept of our approach is the topological reinforced adaptive algorithm (TOREADA). Three essential steps—embedding, assessment, and envelope—act iteratively during the operation of the system, thus providing continuous, on-the-fly, reinforced learning. We apply this concept in the case of detecting convulsive epileptic seizures, where three parameters define the decision manifold. Monte Carlo-type simulations validate the effectiveness and robustness of the approach. We show that the adaptive procedure finds the correct detection parameters, providing optimal accuracy from a large variety of initial states. With respect to the separation quality between simulated seizure and normal epochs, the detection reinforcement algorithm is robust within the broad margins of signal-generation scenarios. We conclude that our technique is applicable to a large variety of event detection systems. Full article

(This article belongs to the Section Algorithms for Multidisciplinary Applications)

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