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Mach. Learn. Knowl. Extr., Volume 6, Issue 2 (June 2024) – 21 articles

Cover Story (view full-size image): This study presents a “quanvolutional autoencoder” to enhance our understanding of cybersecurity data related to DDoS attacks. It uses randomized quantum circuits to improve how we analyze attack data, offering a solid alternative to traditional neural networks. The model effectively learns representations from DDoS data, with faster learning and better stability than classical methods. These findings suggest that quantum machine learning can advance data analysis and visualization in cybersecurity, highlighting the need for further research in this rapidly growing field. View this paper
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16 pages, 1625 KiB  
Article
Fine-Tuning Artificial Neural Networks to Predict Pest Numbers in Grain Crops: A Case Study in Kazakhstan
by Galiya Anarbekova, Luis Gonzaga Baca Ruiz, Akerke Akanova, Saltanat Sharipova and Nazira Ospanova
Mach. Learn. Knowl. Extr. 2024, 6(2), 1154-1169; https://doi.org/10.3390/make6020054 (registering DOI) - 26 May 2024
Viewed by 162
Abstract
This study investigates the application of different ML methods for predicting pest outbreaks in Kazakhstan for grain crops. Comprehensive data spanning from 2005 to 2022, including pest population metrics, meteorological data, and geographical parameters, were employed to train the neural network for forecasting [...] Read more.
This study investigates the application of different ML methods for predicting pest outbreaks in Kazakhstan for grain crops. Comprehensive data spanning from 2005 to 2022, including pest population metrics, meteorological data, and geographical parameters, were employed to train the neural network for forecasting the population dynamics of Phyllotreta vittula pests in Kazakhstan. By evaluating various network configurations and hyperparameters, this research considers the application of MLP, MT-ANN, LSTM, transformer, and SVR. The transformer consistently demonstrates superior predictive accuracy in terms of MSE. Additionally, this work highlights the impact of several training hyperparameters such as epochs and batch size on predictive accuracy. Interestingly, the second season exhibits unique responses, stressing the effect of some features on model performance. By advancing our understanding of fine-tuning ANNs for accurate pest prediction in grain crops, this research contributes to the development of more precise and efficient pest control strategies. In addition, the consistent dominance of the transformer model makes it suitable for its implementation in practical applications. Finally, this work contributes to sustainable agricultural practices by promoting targeted interventions and potentially reducing reliance on chemical pesticides. Full article
(This article belongs to the Section Network)
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9 pages, 1615 KiB  
Article
Evaluation of AI ChatBots for the Creation of Patient-Informed Consent Sheets
by Florian Jürgen Raimann, Vanessa Neef, Marie Charlotte Hennighausen, Kai Zacharowski and Armin Niklas Flinspach
Mach. Learn. Knowl. Extr. 2024, 6(2), 1145-1153; https://doi.org/10.3390/make6020053 - 24 May 2024
Viewed by 228
Abstract
Introduction: Large language models (LLMs), such as ChatGPT, are a topic of major public interest, and their potential benefits and threats are a subject of discussion. The potential contribution of these models to health care is widely discussed. However, few studies to date [...] Read more.
Introduction: Large language models (LLMs), such as ChatGPT, are a topic of major public interest, and their potential benefits and threats are a subject of discussion. The potential contribution of these models to health care is widely discussed. However, few studies to date have examined LLMs. For example, the potential use of LLMs in (individualized) informed consent remains unclear. Methods: We analyzed the performance of the LLMs ChatGPT 3.5, ChatGPT 4.0, and Gemini with regard to their ability to create an information sheet for six basic anesthesiologic procedures in response to corresponding questions. We performed multiple attempts to create forms for anesthesia and analyzed the results checklists based on existing standard sheets. Results: None of the LLMs tested were able to create a legally compliant information sheet for any basic anesthesiologic procedure. Overall, fewer than one-third of the risks, procedural descriptions, and preparations listed were covered by the LLMs. Conclusions: There are clear limitations of current LLMs in terms of practical application. Advantages in the generation of patient-adapted risk stratification within individual informed consent forms are not available at the moment, although the potential for further development is difficult to predict. Full article
(This article belongs to the Special Issue Large Language Models: Methods and Applications)
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19 pages, 714 KiB  
Article
Locally-Scaled Kernels and Confidence Voting
by Elizabeth Hofer and Martin v. Mohrenschildt
Mach. Learn. Knowl. Extr. 2024, 6(2), 1126-1144; https://doi.org/10.3390/make6020052 - 23 May 2024
Viewed by 206
Abstract
Classification, the task of discerning the class of an unlabeled data point using information from a set of labeled data points, is a well-studied area of machine learning with a variety of approaches. Many of these approaches are closely linked to the selection [...] Read more.
Classification, the task of discerning the class of an unlabeled data point using information from a set of labeled data points, is a well-studied area of machine learning with a variety of approaches. Many of these approaches are closely linked to the selection of metrics or the generalizing of similarities defined by kernels. These metrics or similarity measures often require their parameters to be tuned in order to achieve the highest accuracy for each dataset. For example, an extensive search is required to determine the value of K or the choice of distance metric in K-NN classification. This paper explores a method of kernel construction that when used in classification performs consistently over a variety of datasets and does not require the parameters to be tuned. Inspired by dimensionality reduction techniques (DRT), we construct a kernel-based similarity measure that captures the topological structure of the data. This work compares the accuracy of K-NN classifiers, computed with specific operating parameters that obtain the highest accuracy per dataset, to a single trial of the here-proposed kernel classifier with no specialized parameters on standard benchmark sets. The here-proposed kernel used with simple classifiers has comparable accuracy to the ‘best-case’ K-NN classifiers without requiring the tuning of operating parameters. Full article
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12 pages, 793 KiB  
Article
The Human-Centred Design of a Universal Module for Artificial Intelligence Literacy in Tertiary Education Institutions
by Daswin De Silva, Shalinka Jayatilleke, Mona El-Ayoubi, Zafar Issadeen, Harsha Moraliyage and Nishan Mills
Mach. Learn. Knowl. Extr. 2024, 6(2), 1114-1125; https://doi.org/10.3390/make6020051 - 18 May 2024
Viewed by 441
Abstract
Generative Artificial Intelligence (AI) is heralding a new era in AI for performing a spectrum of complex tasks that are indistinguishable from humans. Alongside language and text, Generative AI models have been built for all other modalities of digital data, image, video, audio, [...] Read more.
Generative Artificial Intelligence (AI) is heralding a new era in AI for performing a spectrum of complex tasks that are indistinguishable from humans. Alongside language and text, Generative AI models have been built for all other modalities of digital data, image, video, audio, and code. The full extent of Generative AI and its opportunities, challenges, contributions, and risks are still being explored by academic researchers, industry practitioners, and government policymakers. While this deep understanding of Generative AI continues to evolve, the lack of fluency, literacy, and effective interaction with Generative and conventional AI technologies are common challenges across all domains. Tertiary education institutions are uniquely positioned to address this void. In this article, we present the human-centred design of a universal AI literacy module, followed by its four primary constructs that provide core competence in AI to coursework and research students and academic and professional staff in a tertiary education setting. In comparison to related work in AI literacy, our design is inclusive due to the collaborative approach between multiple stakeholder groups and is comprehensive given the descriptive formulation of the primary constructs of this module with exemplars of how they activate core operational competence across the four groups. Full article
(This article belongs to the Section Data)
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27 pages, 6643 KiB  
Article
Assessment of Software Vulnerability Contributing Factors by Model-Agnostic Explainable AI
by Ding Li, Yan Liu and Jun Huang
Mach. Learn. Knowl. Extr. 2024, 6(2), 1087-1113; https://doi.org/10.3390/make6020050 - 16 May 2024
Viewed by 411
Abstract
Software vulnerability detection aims to proactively reduce the risk to software security and reliability. Despite advancements in deep-learning-based detection, a semantic gap still remains between learned features and human-understandable vulnerability semantics. In this paper, we present an XAI-based framework to assess program code [...] Read more.
Software vulnerability detection aims to proactively reduce the risk to software security and reliability. Despite advancements in deep-learning-based detection, a semantic gap still remains between learned features and human-understandable vulnerability semantics. In this paper, we present an XAI-based framework to assess program code in a graph context as feature representations and their effect on code vulnerability classification into multiple Common Weakness Enumeration (CWE) types. Our XAI framework is deep-learning-model-agnostic and programming-language-neutral. We rank the feature importance of 40 syntactic constructs for each of the top 20 distributed CWE types from three datasets in Java and C++. By means of four metrics of information retrieval, we measure the similarity of human-understandable CWE types using each CWE type’s feature contribution ranking learned from XAI methods. We observe that the subtle semantic difference between CWE types occurs after the variation in neighboring features’ contribution rankings. Our study shows that the XAI explanation results have approximately 78% Top-1 to 89% Top-5 similarity hit rates and a mean average precision of 0.70 compared with the baseline of CWE similarity identified by the open community experts. Our framework allows for code vulnerability patterns to be learned and contributing factors to be assessed at the same stage. Full article
(This article belongs to the Special Issue Advances in Explainable Artificial Intelligence (XAI): 2nd Edition)
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15 pages, 1886 KiB  
Article
Improving Time Series Regression Model Accuracy via Systematic Training Dataset Augmentation and Sampling
by Robin Ströbel, Marcus Mau, Alexander Puchta and Jürgen Fleischer
Mach. Learn. Knowl. Extr. 2024, 6(2), 1072-1086; https://doi.org/10.3390/make6020049 - 11 May 2024
Viewed by 392
Abstract
This study addresses a significant gap in the field of time series regression modeling by highlighting the central role of data augmentation in improving model accuracy. The primary objective is to present a detailed methodology for systematic sampling of training datasets through data [...] Read more.
This study addresses a significant gap in the field of time series regression modeling by highlighting the central role of data augmentation in improving model accuracy. The primary objective is to present a detailed methodology for systematic sampling of training datasets through data augmentation to improve the accuracy of time series regression models. Therefore, different augmentation techniques are compared to evaluate their impact on model accuracy across different datasets and model architectures. In addition, this research highlights the need for a standardized approach to creating training datasets using multiple augmentation methods. The lack of a clear framework hinders the easy integration of data augmentation into time series regression pipelines. Our systematic methodology promotes model accuracy while providing a robust foundation for practitioners to seamlessly integrate data augmentation into their modeling practices. The effectiveness of our approach is demonstrated using process data from two milling machines. Experiments show that the optimized training dataset improves the generalization ability of machine learning models in 86.67% of the evaluated scenarios. However, the prediction accuracy of models trained on a sufficient dataset remains largely unaffected. Based on these results, sophisticated sampling strategies such as Quadratic Weighting of multiple augmentation approaches may be beneficial. Full article
(This article belongs to the Section Data)
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17 pages, 9163 KiB  
Article
EyeXNet: Enhancing Abnormality Detection and Diagnosis via Eye-Tracking and X-ray Fusion
by Chihcheng Hsieh, André Luís, José Neves, Isabel Blanco Nobre, Sandra Costa Sousa, Chun Ouyang, Joaquim Jorge and Catarina Moreira
Mach. Learn. Knowl. Extr. 2024, 6(2), 1055-1071; https://doi.org/10.3390/make6020048 - 9 May 2024
Viewed by 699
Abstract
Integrating eye gaze data with chest X-ray images in deep learning (DL) has led to contradictory conclusions in the literature. Some authors assert that eye gaze data can enhance prediction accuracy, while others consider eye tracking irrelevant for predictive tasks. We argue that [...] Read more.
Integrating eye gaze data with chest X-ray images in deep learning (DL) has led to contradictory conclusions in the literature. Some authors assert that eye gaze data can enhance prediction accuracy, while others consider eye tracking irrelevant for predictive tasks. We argue that this disagreement lies in how researchers process eye-tracking data as most remain agnostic to the human component and apply the data directly to DL models without proper preprocessing. We present EyeXNet, a multimodal DL architecture that combines images and radiologists’ fixation masks to predict abnormality locations in chest X-rays. We focus on fixation maps during reporting moments as radiologists are more likely to focus on regions with abnormalities and provide more targeted regions to the predictive models. Our analysis compares radiologist fixations in both silent and reporting moments, revealing that more targeted and focused fixations occur during reporting. Our results show that integrating the fixation masks in a multimodal DL architecture outperformed the baseline model in five out of eight experiments regarding average Recall and six out of eight regarding average Precision. Incorporating fixation masks representing radiologists’ classification patterns in a multimodal DL architecture benefits lesion detection in chest X-ray (CXR) images, particularly when there is a strong correlation between fixation masks and generated proposal regions. This highlights the potential of leveraging fixation masks to enhance multimodal DL architectures for CXR image analysis. This work represents a first step towards human-centered DL, moving away from traditional data-driven and human-agnostic approaches. Full article
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46 pages, 3360 KiB  
Review
Categorical Data Clustering: A Bibliometric Analysis and Taxonomy
by Maya Cendana and Ren-Jieh Kuo
Mach. Learn. Knowl. Extr. 2024, 6(2), 1009-1054; https://doi.org/10.3390/make6020047 - 7 May 2024
Viewed by 1013
Abstract
Numerous real-world applications apply categorical data clustering to find hidden patterns in the data. The K-modes-based algorithm is a popular algorithm for solving common issues in categorical data, from outlier and noise sensitivity to local optima, utilizing metaheuristic methods. Many studies have [...] Read more.
Numerous real-world applications apply categorical data clustering to find hidden patterns in the data. The K-modes-based algorithm is a popular algorithm for solving common issues in categorical data, from outlier and noise sensitivity to local optima, utilizing metaheuristic methods. Many studies have focused on increasing clustering performance, with new methods now outperforming the traditional K-modes algorithm. It is important to investigate this evolution to help scholars understand how the existing algorithms overcome the common issues of categorical data. Using a research-area-based bibliometric analysis, this study retrieved articles from the Web of Science (WoS) Core Collection published between 2014 and 2023. This study presents a deep analysis of 64 articles to develop a new taxonomy of categorical data clustering algorithms. This study also discusses the potential challenges and opportunities in possible alternative solutions to categorical data clustering. Full article
(This article belongs to the Topic Big Data Intelligence: Methodologies and Applications)
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22 pages, 2777 KiB  
Article
Multilayer Perceptron Neural Network with Arithmetic Optimization Algorithm-Based Feature Selection for Cardiovascular Disease Prediction
by Fahad A. Alghamdi, Haitham Almanaseer, Ghaith Jaradat, Ashraf Jaradat, Mutasem K. Alsmadi, Sana Jawarneh, Abdullah S. Almurayh, Jehad Alqurni and Hayat Alfagham
Mach. Learn. Knowl. Extr. 2024, 6(2), 987-1008; https://doi.org/10.3390/make6020046 - 5 May 2024
Viewed by 622
Abstract
In the healthcare field, diagnosing disease is the most concerning issue. Various diseases including cardiovascular diseases (CVDs) significantly influence illness or death. On the other hand, early and precise diagnosis of CVDs can decrease chances of death, resulting in a better and healthier [...] Read more.
In the healthcare field, diagnosing disease is the most concerning issue. Various diseases including cardiovascular diseases (CVDs) significantly influence illness or death. On the other hand, early and precise diagnosis of CVDs can decrease chances of death, resulting in a better and healthier life for patients. Researchers have used traditional machine learning (ML) techniques for CVD prediction and classification. However, many of them are inaccurate and time-consuming due to the unavailability of quality data including imbalanced samples, inefficient data preprocessing, and the existing selection criteria. These factors lead to an overfitting or bias issue towards a certain class label in the prediction model. Therefore, an intelligent system is needed which can accurately diagnose CVDs. We proposed an automated ML model for various kinds of CVD prediction and classification. Our prediction model consists of multiple steps. Firstly, a benchmark dataset is preprocessed using filter techniques. Secondly, a novel arithmetic optimization algorithm is implemented as a feature selection technique to select the best subset of features that influence the accuracy of the prediction model. Thirdly, a classification task is implemented using a multilayer perceptron neural network to classify the instances of the dataset into two class labels, determining whether they have a CVD or not. The proposed ML model is trained on the preprocessed data and then tested and validated. Furthermore, for the comparative analysis of the model, various performance evaluation metrics are calculated including overall accuracy, precision, recall, and F1-score. As a result, it has been observed that the proposed prediction model can achieve 88.89% accuracy, which is the highest in a comparison with the traditional ML techniques. Full article
(This article belongs to the Section Learning)
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22 pages, 11463 KiB  
Article
VOD: Vision-Based Building Energy Data Outlier Detection
by Jinzhao Tian, Tianya Zhao, Zhuorui Li, Tian Li, Haipei Bie and Vivian Loftness
Mach. Learn. Knowl. Extr. 2024, 6(2), 965-986; https://doi.org/10.3390/make6020045 - 3 May 2024
Viewed by 845
Abstract
Outlier detection plays a critical role in building operation optimization and data quality maintenance. However, existing methods often struggle with the complexity and variability of building energy data, leading to poorly generalized and explainable results. To address the gap, this study introduces a [...] Read more.
Outlier detection plays a critical role in building operation optimization and data quality maintenance. However, existing methods often struggle with the complexity and variability of building energy data, leading to poorly generalized and explainable results. To address the gap, this study introduces a novel Vision-based Outlier Detection (VOD) approach, leveraging computer vision models to spot outliers in the building energy records. The models are trained to identify outliers by analyzing the load shapes in 2D time series plots derived from the energy data. The VOD approach is tested on four years of workday time-series electricity consumption data from 290 commercial buildings in the United States. Two distinct models are developed for different usage purposes, namely a classification model for broad-level outlier detection and an object detection model for the demands of precise pinpointing of outliers. The classification model is also interpreted via Grad-CAM to enhance its usage reliability. The classification model achieves an F1 score of 0.88, and the object detection model achieves an Average Precision (AP) of 0.84. VOD is a very efficient path to identifying energy consumption outliers in building operations, paving the way for the enhancement of building energy data quality, operation efficiency, and energy savings. Full article
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21 pages, 7555 KiB  
Article
Quantum-Enhanced Representation Learning: A Quanvolutional Autoencoder Approach against DDoS Threats
by Pablo Rivas, Javier Orduz, Tonni Das Jui, Casimer DeCusatis and Bikram Khanal
Mach. Learn. Knowl. Extr. 2024, 6(2), 944-964; https://doi.org/10.3390/make6020044 - 1 May 2024
Viewed by 1062
Abstract
Motivated by the growing threat of distributed denial-of-service (DDoS) attacks and the emergence of quantum computing, this study introduces a novel “quanvolutional autoencoder” architecture for learning representations. The architecture leverages the computational advantages of quantum mechanics to improve upon traditional machine learning techniques. [...] Read more.
Motivated by the growing threat of distributed denial-of-service (DDoS) attacks and the emergence of quantum computing, this study introduces a novel “quanvolutional autoencoder” architecture for learning representations. The architecture leverages the computational advantages of quantum mechanics to improve upon traditional machine learning techniques. Specifically, the quanvolutional autoencoder employs randomized quantum circuits to analyze time-series data from DDoS attacks, offering a robust alternative to classical convolutional neural networks. Experimental results suggest that the quanvolutional autoencoder performs similarly to classical models in visualizing and learning from DDoS hive plots and leads to faster convergence and learning stability. These findings suggest that quantum machine learning holds significant promise for advancing data analysis and visualization in cybersecurity. The study highlights the need for further research in this fast-growing field, particularly for unsupervised anomaly detection. Full article
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27 pages, 2009 KiB  
Review
A Comprehensive Summary of the Application of Machine Learning Techniques for CO2-Enhanced Oil Recovery Projects
by Xuejia Du, Sameer Salasakar and Ganesh Thakur
Mach. Learn. Knowl. Extr. 2024, 6(2), 917-943; https://doi.org/10.3390/make6020043 - 29 Apr 2024
Viewed by 718
Abstract
This paper focuses on the current application of machine learning (ML) in enhanced oil recovery (EOR) through CO2 injection, which exhibits promising economic and environmental benefits for climate-change mitigation strategies. Our comprehensive review explores the diverse use cases of ML techniques in [...] Read more.
This paper focuses on the current application of machine learning (ML) in enhanced oil recovery (EOR) through CO2 injection, which exhibits promising economic and environmental benefits for climate-change mitigation strategies. Our comprehensive review explores the diverse use cases of ML techniques in CO2-EOR, including aspects such as minimum miscible pressure (MMP) prediction, well location optimization, oil production and recovery factor prediction, multi-objective optimization, Pressure–Volume–Temperature (PVT) property estimation, Water Alternating Gas (WAG) analysis, and CO2-foam EOR, from 101 reviewed papers. We catalog relative information, including the input parameters, objectives, data sources, train/test/validate information, results, evaluation, and rating score for each area based on criteria such as data quality, ML-building process, and the analysis of results. We also briefly summarized the benefits and limitations of ML methods in petroleum industry applications. Our detailed and extensive study could serve as an invaluable reference for employing ML techniques in the petroleum industry. Based on the review, we found that ML techniques offer great potential in solving problems in the majority of CO2-EOR areas involving prediction and regression. With the generation of massive amounts of data in the everyday oil and gas industry, machine learning techniques can provide efficient and reliable preliminary results for the industry. Full article
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19 pages, 2051 KiB  
Perspective
Concept Paper for a Digital Expert: Systematic Derivation of (Causal) Bayesian Networks Based on Ontologies for Knowledge-Based Production Steps
by Manja Mai-Ly Pfaff-Kastner, Ken Wenzel and Steffen Ihlenfeldt
Mach. Learn. Knowl. Extr. 2024, 6(2), 898-916; https://doi.org/10.3390/make6020042 - 25 Apr 2024
Viewed by 760
Abstract
Despite increasing digitalization and automation, complex production processes often require human judgment/decision-making adaptability. Humans can abstract and transfer knowledge to new situations. People in production are an irreplaceable resource. This paper presents a new concept for digitizing human expertise and their ability to [...] Read more.
Despite increasing digitalization and automation, complex production processes often require human judgment/decision-making adaptability. Humans can abstract and transfer knowledge to new situations. People in production are an irreplaceable resource. This paper presents a new concept for digitizing human expertise and their ability to make knowledge-based decisions in the production area based on ontologies and causal Bayesian networks for further research. Dedicated approaches for the ontology-based creation of Bayesian networks exist in the literature. Therefore, we first comprehensively analyze previous studies and summarize the approaches. We then add the causal perspective, which has often not been an explicit subject of consideration. We see a research gap in the systematic and structured approach to ontology-based generation of causal graphs (CGs). At the current state of knowledge, the semantic understanding of a domain formalized in an ontology can contribute to developing a generic approach to derive a CG. The ontology functions as a knowledge base by formally representing knowledge and experience. Causal inference calculations can mathematically imitate the human decision-making process under uncertainty. Therefore, a systematic ontology-based approach to building a CG can allow digitizing the human ability to make decisions based on experience and knowledge. Full article
(This article belongs to the Section Network)
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21 pages, 3643 KiB  
Article
Enhancing Legal Sentiment Analysis: A Convolutional Neural Network–Long Short-Term Memory Document-Level Model
by Bolanle Abimbola, Enrique de La Cal Marin and Qing Tan
Mach. Learn. Knowl. Extr. 2024, 6(2), 877-897; https://doi.org/10.3390/make6020041 - 19 Apr 2024
Viewed by 1066
Abstract
This research investigates the application of deep learning in sentiment analysis of Canadian maritime case law. It offers a framework for improving maritime law and legal analytic policy-making procedures. The automation of legal document extraction takes center stage, underscoring the vital role sentiment [...] Read more.
This research investigates the application of deep learning in sentiment analysis of Canadian maritime case law. It offers a framework for improving maritime law and legal analytic policy-making procedures. The automation of legal document extraction takes center stage, underscoring the vital role sentiment analysis plays at the document level. Therefore, this study introduces a novel strategy for sentiment analysis in Canadian maritime case law, combining sentiment case law approaches with state-of-the-art deep learning techniques. The overarching goal is to systematically unearth hidden biases within case law and investigate their impact on legal outcomes. Employing Convolutional Neural Network (CNN)- and long short-term memory (LSTM)-based models, this research achieves a remarkable accuracy of 98.05% for categorizing instances. In contrast, conventional machine learning techniques such as support vector machine (SVM) yield an accuracy rate of 52.57%, naïve Bayes at 57.44%, and logistic regression at 61.86%. The superior accuracy of the CNN and LSTM model combination underscores its usefulness in legal sentiment analysis, offering promising future applications in diverse fields like legal analytics and policy design. These findings mark a significant choice for AI-powered legal tools, presenting more sophisticated and sentiment-aware options for the legal profession. Full article
(This article belongs to the Section Learning)
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35 pages, 2155 KiB  
Article
A Comprehensive Survey on Deep Learning Methods in Human Activity Recognition
by Michail Kaseris, Ioannis Kostavelis and Sotiris Malassiotis
Mach. Learn. Knowl. Extr. 2024, 6(2), 842-876; https://doi.org/10.3390/make6020040 - 18 Apr 2024
Viewed by 1113
Abstract
Human activity recognition (HAR) remains an essential field of research with increasing real-world applications ranging from healthcare to industrial environments. As the volume of publications in this domain continues to grow, staying abreast of the most pertinent and innovative methodologies can be challenging. [...] Read more.
Human activity recognition (HAR) remains an essential field of research with increasing real-world applications ranging from healthcare to industrial environments. As the volume of publications in this domain continues to grow, staying abreast of the most pertinent and innovative methodologies can be challenging. This survey provides a comprehensive overview of the state-of-the-art methods employed in HAR, embracing both classical machine learning techniques and their recent advancements. We investigate a plethora of approaches that leverage diverse input modalities including, but not limited to, accelerometer data, video sequences, and audio signals. Recognizing the challenge of navigating the vast and ever-growing HAR literature, we introduce a novel methodology that employs large language models to efficiently filter and pinpoint relevant academic papers. This not only reduces manual effort but also ensures the inclusion of the most influential works. We also provide a taxonomy of the examined literature to enable scholars to have rapid and organized access when studying HAR approaches. Through this survey, we aim to inform researchers and practitioners with a holistic understanding of the current HAR landscape, its evolution, and the promising avenues for future exploration. Full article
(This article belongs to the Section Learning)
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15 pages, 4325 KiB  
Article
Impact of Nature of Medical Data on Machine and Deep Learning for Imbalanced Datasets: Clinical Validity of SMOTE Is Questionable
by Seifollah Gholampour
Mach. Learn. Knowl. Extr. 2024, 6(2), 827-841; https://doi.org/10.3390/make6020039 - 15 Apr 2024
Viewed by 796
Abstract
Dataset imbalances pose a significant challenge to predictive modeling in both medical and financial domains, where conventional strategies, including resampling and algorithmic modifications, often fail to adequately address minority class underrepresentation. This study theoretically and practically investigates how the inherent nature of medical [...] Read more.
Dataset imbalances pose a significant challenge to predictive modeling in both medical and financial domains, where conventional strategies, including resampling and algorithmic modifications, often fail to adequately address minority class underrepresentation. This study theoretically and practically investigates how the inherent nature of medical data affects the classification of minority classes. It employs ten machine and deep learning classifiers, ranging from ensemble learners to cost-sensitive algorithms, across comparably sized medical and financial datasets. Despite these efforts, none of the classifiers achieved effective classification of the minority class in the medical dataset, with sensitivity below 5.0% and area under the curve (AUC) below 57.0%. In contrast, the similar classifiers applied to the financial dataset demonstrated strong discriminative power, with overall accuracy exceeding 95.0%, sensitivity over 73.0%, and AUC above 96.0%. This disparity underscores the unpredictable variability inherent in the nature of medical data, as exemplified by the dispersed and homogeneous distribution of the minority class among other classes in principal component analysis (PCA) graphs. The application of the synthetic minority oversampling technique (SMOTE) introduced 62 synthetic patients based on merely 20 original cases, casting doubt on its clinical validity and the representation of real-world patient variability. Furthermore, post-SMOTE feature importance analysis, utilizing SHapley Additive exPlanations (SHAP) and tree-based methods, contradicted established cerebral stroke parameters, further questioning the clinical coherence of synthetic dataset augmentation. These findings call into question the clinical validity of the SMOTE technique and underscore the urgent need for advanced modeling techniques and algorithmic innovations for predicting minority-class outcomes in medical datasets without depending on resampling strategies. This approach underscores the importance of developing methods that are not only theoretically robust but also clinically relevant and applicable to real-world clinical scenarios. Consequently, this study underscores the importance of future research efforts to bridge the gap between theoretical advancements and the practical, clinical applications of models like SMOTE in healthcare. Full article
(This article belongs to the Topic Communications Challenges in Health and Well-Being)
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27 pages, 1266 KiB  
Article
A Meta Algorithm for Interpretable Ensemble Learning: The League of Experts
by Richard Vogel, Tobias Schlosser, Robert Manthey, Marc Ritter, Matthias Vodel, Maximilian Eibl and Kristan Alexander Schneider
Mach. Learn. Knowl. Extr. 2024, 6(2), 800-826; https://doi.org/10.3390/make6020038 - 9 Apr 2024
Viewed by 894
Abstract
Background. The importance of explainable artificial intelligence and machine learning (XAI/XML) is increasingly being recognized, aiming to understand how information contributes to decisions, the method’s bias, or sensitivity to data pathologies. Efforts are often directed to post hoc explanations [...] Read more.
Background. The importance of explainable artificial intelligence and machine learning (XAI/XML) is increasingly being recognized, aiming to understand how information contributes to decisions, the method’s bias, or sensitivity to data pathologies. Efforts are often directed to post hoc explanations of black box models. These approaches add additional sources for errors without resolving their shortcomings. Less effort is directed into the design of intrinsically interpretable approaches. Methods. We introduce an intrinsically interpretable methodology motivated by ensemble learning: the League of Experts (LoE) model. We establish the theoretical framework first and then deduce a modular meta algorithm. In our description, we focus primarily on classification problems. However, LoE applies equally to regression problems. Specific to classification problems, we employ classical decision trees as classifier ensembles as a particular instance. This choice facilitates the derivation of human-understandable decision rules for the underlying classification problem, which results in a derived rule learning system denoted as RuleLoE. Results. In addition to 12 KEEL classification datasets, we employ two standard datasets from particularly relevant domains—medicine and finance—to illustrate the LoE algorithm. The performance of LoE with respect to its accuracy and rule coverage is comparable to common state-of-the-art classification methods. Moreover, LoE delivers a clearly understandable set of decision rules with adjustable complexity, describing the classification problem. Conclusions. LoE is a reliable method for classification and regression problems with an accuracy that seems to be appropriate for situations in which underlying causalities are in the center of interest rather than just accurate predictions or classifications. Full article
(This article belongs to the Special Issue Advances in Explainable Artificial Intelligence (XAI): 2nd Edition)
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11 pages, 1606 KiB  
Article
Effective Data Reduction Using Discriminative Feature Selection Based on Principal Component Analysis
by Faith Nwokoma, Justin Foreman and Cajetan M. Akujuobi
Mach. Learn. Knowl. Extr. 2024, 6(2), 789-799; https://doi.org/10.3390/make6020037 - 3 Apr 2024
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Abstract
Effective data reduction must retain the greatest possible amount of informative content of the data under examination. Feature selection is the default for dimensionality reduction, as the relevant features of a dataset are usually retained through this method. In this study, we used [...] Read more.
Effective data reduction must retain the greatest possible amount of informative content of the data under examination. Feature selection is the default for dimensionality reduction, as the relevant features of a dataset are usually retained through this method. In this study, we used unsupervised learning to discover the top-k discriminative features present in the large multivariate IoT dataset used. We used the statistics of principal component analysis to filter the relevant features based on the ranks of the features along the principal directions while also considering the coefficients of the components. The selected number of principal components was used to decide the number of features to be selected in the SVD process. A number of experiments were conducted using different benchmark datasets, and the effectiveness of the proposed method was evaluated based on the reconstruction error. The potency of the results was verified by subjecting the algorithm to a large IoT dataset, and we compared the performance based on accuracy and reconstruction error to the results of the benchmark datasets. The performance evaluation showed consistency with the results obtained with the benchmark datasets, which were of high accuracy and low reconstruction error. Full article
(This article belongs to the Topic Big Data Intelligence: Methodologies and Applications)
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19 pages, 2757 KiB  
Article
Birthweight Range Prediction and Classification: A Machine Learning-Based Sustainable Approach
by Dina A. Alabbad, Shahad Y. Ajibi, Raghad B. Alotaibi, Noura K. Alsqer, Rahaf A. Alqahtani, Noor M. Felemban, Atta Rahman, Sumayh S. Aljameel, Mohammed Imran Basheer Ahmed and Mustafa M. Youldash
Mach. Learn. Knowl. Extr. 2024, 6(2), 770-788; https://doi.org/10.3390/make6020036 - 1 Apr 2024
Viewed by 1356
Abstract
An accurate prediction of fetal birth weight is crucial in ensuring safe delivery without health complications for the mother and baby. The uncertainty surrounding the fetus’s birth situation, including its weight range, can lead to significant risks for both mother and baby. As [...] Read more.
An accurate prediction of fetal birth weight is crucial in ensuring safe delivery without health complications for the mother and baby. The uncertainty surrounding the fetus’s birth situation, including its weight range, can lead to significant risks for both mother and baby. As there is a standard birth weight range, if the fetus exceeds or falls below this range, it can result in considerable health problems. Although ultrasound imaging is commonly used to predict fetal weight, it does not always provide accurate readings, which may lead to unnecessary decisions such as early delivery and cesarian section. Besides that, no supporting system is available to predict the weight range in Saudi Arabia. Therefore, leveraging the available technologies to build a system that can serve as a second opinion for doctors and health professionals is essential. Machine learning (ML) offers significant advantages to numerous fields and can address various issues. As such, this study aims to utilize ML techniques to build a predictive model to predict the birthweight range of infants into low, normal, or high. For this purpose, two datasets were used: one from King Fahd University Hospital (KFHU), Saudi Arabia, and another publicly available dataset from the Institute of Electrical and Electronics Engineers (IEEE) data port. KFUH’s best result was obtained with the Extra Trees model, achieving an accuracy, precision, recall, and F1-score of 98%, with a specificity of 99%. On the other hand, using the Random Forest model, the IEEE dataset attained an accuracy, precision, recall, and F1-score of 96%, respectively, with a specificity of 98%. These results suggest that the proposed ML system can provide reliable predictions, which could be of significant value for doctors and health professionals in Saudi Arabia. Full article
(This article belongs to the Special Issue Sustainable Applications for Machine Learning)
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19 pages, 5712 KiB  
Article
Soil Sampling Map Optimization with a Dual Deep Learning Framework
by Tan-Hanh Pham and Kim-Doang Nguyen
Mach. Learn. Knowl. Extr. 2024, 6(2), 751-769; https://doi.org/10.3390/make6020035 - 29 Mar 2024
Viewed by 986
Abstract
Soil sampling constitutes a fundamental process in agriculture, enabling precise soil analysis and optimal fertilization. The automated selection of accurate soil sampling locations representative of a given field is critical for informed soil treatment decisions. This study leverages recent advancements in deep learning [...] Read more.
Soil sampling constitutes a fundamental process in agriculture, enabling precise soil analysis and optimal fertilization. The automated selection of accurate soil sampling locations representative of a given field is critical for informed soil treatment decisions. This study leverages recent advancements in deep learning to develop efficient tools for generating soil sampling maps. We proposed two models, namely UDL and UFN, which are the results of innovations in machine learning architecture design and integration. The models are meticulously trained on a comprehensive soil sampling dataset collected from local farms in South Dakota. The data include five key attributes: aspect, flow accumulation, slope, normalized difference vegetation index, and yield. The inputs to the models consist of multispectral images, and the ground truths are highly unbalanced binary images. To address this challenge, we innovate a feature extraction technique to find patterns and characteristics from the data before using these refined features for further processing and generating soil sampling maps. Our approach is centered around building a refiner that extracts fine features and a selector that utilizes these features to produce prediction maps containing the selected optimal soil sampling locations. Our experimental results demonstrate the superiority of our tools compared to existing methods. During testing, our proposed models exhibit outstanding performance, achieving the highest mean Intersection over Union of 60.82% and mean Dice Coefficient of 73.74%. The research not only introduces an innovative tool for soil sampling but also lays the foundation for the integration of traditional and modern soil sampling methods. This work provides a promising solution for precision agriculture and soil management. Full article
(This article belongs to the Topic Applications in Image Analysis and Pattern Recognition)
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15 pages, 911 KiB  
Article
A New and Lightweight R-Peak Detector Using the TEDA Evolving Algorithm
by Lucileide M. D. da Silva, Sérgio N. Silva, Luísa C. de Souza, Karolayne S. de Azevedo, Luiz Affonso Guedes and Marcelo A. C. Fernandes
Mach. Learn. Knowl. Extr. 2024, 6(2), 736-750; https://doi.org/10.3390/make6020034 - 29 Mar 2024
Viewed by 1201
Abstract
The literature on ECG delineation algorithms has seen significant growth in recent decades. However, several challenges still need to be addressed. This work aims to propose a lightweight R-peak-detection algorithm that does not require pre-setting and performs classification on a sample-by-sample basis. The [...] Read more.
The literature on ECG delineation algorithms has seen significant growth in recent decades. However, several challenges still need to be addressed. This work aims to propose a lightweight R-peak-detection algorithm that does not require pre-setting and performs classification on a sample-by-sample basis. The novelty of the proposed approach lies in the utilization of the typicality eccentricity detection anomaly (TEDA) algorithm for R-peak detection. The proposed method for R-peak detection consists of three phases. Firstly, the ECG signal is preprocessed by calculating the signal’s slope and applying filtering techniques. Next, the preprocessed signal is inputted into the TEDA algorithm for R-peak estimation. Finally, in the third and last step, the R-peak identification is carried out. To evaluate the effectiveness of the proposed technique, experiments were conducted on the MIT-BIH arrhythmia database (MIT-AD) for R-peak detection and validation. The results of the study demonstrated that the proposed evolutive algorithm achieved a sensitivity (Se in %), positive predictivity (+P in %), and accuracy (ACC in %) of 95.45%, 99.61%, and 95.09%, respectively, with a tolerance (TOL) of 100 milliseconds. One key advantage of the proposed technique is its low computational complexity, as it is based on a statistical framework calculated recursively. It employs the concepts of typicity and eccentricity to determine whether a given sample is normal or abnormal within the dataset. Unlike most traditional methods, it does not require signal buffering or windowing. Furthermore, the proposed technique employs simple decision rules rather than heuristic approaches, further contributing to its computational efficiency. Full article
(This article belongs to the Topic Bioinformatics and Intelligent Information Processing)
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