Learning Solutions Research Articles

Interpretable machine learning-based text classification method for construction quality defect reports

Efficient identification and remediation of construction defects are critical for ensuring the quality and success of engineering projects. However, the complexity of construction environments poses challenges to this endeavor. Current research predominantly relies on statistical and causal analyses of defect detection reports, yet these methods are time-consuming and error-prone due to the unstructured nature of such reports. To address this, machine learning techniques have been applied to classify defect texts rapidly and accurately. However, existing studies primarily focus on model performance enhancement, neglecting interpretability and the effect of imbalanced data. This study introduces RF-SMOTE, an oversampling technique based on Random Forest (RF), to address the limitations of traditional methods like SMOTE. Comparative analyses demonstrated the efficacy of RF-SMOTE in mitigating imbalanced data effects. Further, the application of SHAP-based interpretability methods in construction management decision-making was explored, filling gaps in existing research. Contributions include providing interpretable machine learning solutions, discussing the effect of imbalanced data, and proposing SHAP-based application scenarios.

Code-Free Machine Learning Solutions for Microscopy Image Processing: Deep Learning.

In recent years, there has been a significant expansion in the realm of processing microscopy images, thanks to the advent of machine learning techniques. These techniques offer diverse applications for image processing. Currently, numerous methods are used for processing microscopy images in the field of biology, ranging from conventional machine learning algorithms to sophisticated deep learning artificial neural networks with millions of parameters. However, a comprehensive grasp of the intricacies of these methods usually necessitates proficiency in programming and advanced mathematics. In our comprehensive review, we explore various widely used deep learning approaches tailored for the processing of microscopy images. Our emphasis is on algorithms that have gained popularity in the field of biology and have been adapted to cater to users lacking programming expertise. In essence, our target audience comprises biologists interested in exploring the potential of deep learning algorithms, even without programming skills. Throughout the review, we elucidate each algorithm's fundamental concepts and capabilities without delving into mathematical and programming complexities. Crucially, all the highlighted algorithms are accessible on open platforms without requiring code, and we provide detailed descriptions and links within our review. It's essential to recognize that addressing each specific problem demands an individualized approach. Consequently, our focus is not on comparing algorithms but on delineating the problems they are adept at solving. In practical scenarios, researchers typically select multiple algorithms suited to their tasks and experimentally determine the most effective one. It is worth noting that microscopy extends beyond the realm of biology; its applications span diverse fields such as geology and material science. Although our review predominantly centers on biomedical applications, the algorithms and principles outlined here are equally applicable to other scientific domains. Furthermore, a number of the proposed solutions can be modified for use in entirely distinct computer vision cases.

Increasing access to legal information with unsupervised solutions

AbstractAccess to justice is a significant area of legal research, especially for Socio-Legal studies. The main research topics of this area are economic or class differences, gender inequalities, or national and ethnic differences in access to justice. However, there is a less discussed aspect of access to justice: the differences between access to legal information and the differences in user groups in terms of comprehending and processing legal information. This is an important topic because there are significant differences among people's abilities to process and understand legal texts, depending on whether we are dealing with a lawyer who is an expert in the given field, a non-expert lawyer, or a citizen with a low or zero (legal) educational level. The paper argues that unsupervised machine learning solutions can help even out these differences. It presents different unsupervised solutions, mainly clustering and topic modelling, which can help to increase access to legal information. Then we present a case study in which we examine these unsupervised tools in the processing of resolutions of the Central Bank in Hungary and anonymized court decisions. The paper argues that these tools can reveal the hidden contextual regularities in unstructured legal texts, facilitating the search for legal texts even for non-legal-experts.

STUCK PIPE MITIGATION: FINDINGS, ANALYSIS, AND MACHINE LEARNING SOLUTIONS

The occurrence of stuck pipes during drilling presents significant challenges, leading to nonproductive time (NPT) and potential damage to equipment. This paper delves into the reasons behind, effects of, and methods for addressing incidents of stuck pipes in drilling actions. Extensive literature review and analysis reveal the prevalence of stuck pipe events and their diverse underlying causes, ranging from differential sticking to mechanical jamming. Additionally, the study identifies key drilling parameters and signals indicative of potential stuck pipe incidents, emphasizing the importance of early detection and intervention to prevent adverse outcomes. Proposing the utilization of machine learning algorithms and statistical models for predictive analytics as a proactive strategy to mitigate the risk of stuck pipe incidents. The paper discusses the limitations of traditional statistical models and highlights the efficacy of machine learning methods, such as artificial neural networks (ANNs) and support vector machines (SVMs), in accurately modeling drilling processes. Furthermore, practical machine learning models using real-time drilling data are presented as effective tools for early detection and timely intervention in stuck pipe incidents. The study also describes various software tools used to assess and mitigate risks in drilling operations. Overall, this paper presents prospects for improving operational efficiency and safety of drilling operations using advanced analytical methods and software solutions. Keywords: stuck pipe, type of stuck, drillstring immobilization, nonproductive time, machine learning in drilling, risk analysis software.

The Influence of Supervision by the Principal and Motivation on the Performance of Elementary School Teachers During Pandemic

Online learning is promoted as a learning solution in elementary schools during the Covid 19 pandemic. Teachers are required to be able to adapt and improve performance progressively for the optimization of learning with the support of supervision and work motivation. This study aims to analyze the effect of supervision by school principals and work motivation on the performance of elementary school teachers during the Covid 19 pandemic. This research is a quantitative research with a correlational survey involving 125 samples of elementary school teachers in the Cluster I Singawinata Teacher Working Group of Purwakarta district, West Java, Indonesia. The results showed that supervision by the principal (X1) had a significant effect on teacher performance (Y). Then work motivation (X2) has a significant effect on teacher performance (Y). And finally, supervision by the principal (X1) and work motivation (X2) have a significant effect on the performance of elementary school teachers (Y) by 46.5%. School principals need to increase supervision to teachers continuously and systematically so that teachers have the motivation to improve their competence so that they can show good performance in improving the quality of learning during the Covid 19 pandemic.

Smart infrastructure design: Machine learning solutions for securing modern cities

In the realm of securing smart cities against emerging cyber threats, this research encompasses three distinct yet interconnected initiatives. First, a pioneering data architecture design leveraging CycleGAN is proposed to counteract False Data Injection Attacks (FDIA). By cyclically transforming data distributions, CycleGAN ensures the integrity and reliability of smart city information, fortifying against malicious manipulations. Second, an innovative Internet of Things (IoT) concept is introduced, aiming to enhance real-time monitoring and context-aware threat detection within Intrusion Detection Systems (IDS). Harnessing the wealth of data generated by IoT devices, this concept provides a comprehensive understanding of network activities, fostering adaptive responses to emerging threats. Lastly, the research delves into the development of a novel IDS hyperparameter adjusting system. This system integrates the strengths of Biogeography-Based Optimization (BBO) and Whale Optimization Algorithm (WOA) to fine-tune IDS configuration parameters. Drawing inspiration from biogeographical principles and collaborative whale behavior, the hybrid optimization system balances exploration and exploitation, adapting the IDS to diverse network environments. Together, these initiatives represent a holistic approach to fortifying smart cities through cutting-edge data architecture, IoT-driven threat detection, and optimized IDS configurations, contributing to the resilience and cybersecurity of modern urban landscapes.

Enhancing federated learning robustness through randomization and mixture

Protecting data privacy is a significant challenge in machine learning (ML), and federated learning (FL) has emerged as a decentralized learning solution to address this issue. However, FL is vulnerable to poisoning attacks, which control and interrupt the learning process to substantially increase the error rate of the system. The aggregation algorithm’s robustness is crucial to prevent such attacks; however previous Byzantine-robust algorithms have exhibited vulnerability to maliciously crafted model updates. We propose three different aggregation functions namely: Switch, Layered-Switch and Weighted FedAvg. Our proposed methods involve switching between aggregation functions. We evaluate the effectiveness of these methods against a model poisoning attack to demonstrate their robustness. To observe the performance of our proposed aggregation functions, we utilized them within a federated learning framework to classify the CIFAR10, CIFAR10 and Fashion-MNIST datasets. The simulation results demonstrate that all of these methods exhibit greater robustness against the employed adversarial attack. Our approach achieves an enhancement in performance during a poisoning attack, surpassing previous Byzantine-robust algorithms by 5%, 15% and 25% on the MNIST, CIFAR10 and CIFAR100 datasets, respectively. Among the proposed aggregation functions, Weighted FedAvg has shown the highest success rate on the datasets utilized in this research.

Empowering precise advertising with Fed-GANCC: A novel federated learning approach leveraging Generative Adversarial Networks and group clustering.

In the realm of targeted advertising, the demand for precision is paramount, and the traditional centralized machine learning paradigm fails to address this necessity effectively. Two critical challenges persist in the current advertising ecosystem: the data privacy concerns leading to isolated data islands and the complexity in handling non-Independent and Identically Distributed (non-IID) data and concept drift due to the specificity and diversity in user behavior data. Current federated learning frameworks struggle to overcome these hurdles satisfactorily. This paper introduces Fed-GANCC, an innovative federated learning framework that synergizes Generative Adversarial Networks (GANs) and Group Clustering. The framework incorporates a user data augmentation algorithm predicated on adversarial generative networks to enrich user behavior data, curtail the impact of non-uniform data distribution, and enhance the applicability of the global machine learning model. Unlike traditional approaches, our framework offers user data augmentation algorithms based on adversarial generative networks, which not only enriches user behavior data but also reduces the challenges posed by non-uniform data distribution, thereby enhancing the applicability of the global machine learning (ML) model. The effectiveness of Fed-GANCC is distinctly showcased through experimental results, outperforming contemporary methods like FED-AVG and FED-SGD in terms of accuracy, loss value, and receiver operating characteristic (ROC) indicators within the same computing time. Experimental results vindicate the effectiveness of Fed-GANCC, revealing substantial enhancements in accuracy, loss value, and receiver operating characteristic (ROC) metrics compared to FED-AVG and FED-SGD given the same computational time. These outcomes underline Fed-GANCC's exceptional prowess in mitigating issues such as isolated data islands, non-IID data, and concept drift. With its novel approach to addressing the prevailing challenges in targeted advertising such as isolated data islands, non-IID data, and concept drift, the Fed-GANCC framework stands as a benchmark, paving the way for future advancements in federated learning solutions tailored for the advertising domain. The Fed-GANCC framework promises to offer pivotal insights for the future development of efficient and advanced federated learning solutions for targeted advertising.

Feature Engineering Versus Deep Learning for Scene Recognition: A Brief Survey

Scene recognition is an important computer vision task that has evolved from the study of the biological visual system. Its applications range from video surveillance, autopilot systems, to robotics. The early works were based on feature engineering that involved the computation and aggregation of global and local image descriptors. Several popular image features such as SIFT, SURF, HOG, ORB, LBP, KAZE, etc. have been proposed and applied to the task with successful results. Features can be either computed from the entire image on a global scale, or extracted from local sub-regions and aggregated across the image. Suitable classifier models are deployed that learn to classify these features. This review paper analyzes several of these handcrafted features that have been applied to the scene recognition task over the past decades, and tracks the transition from the traditional feature engineering to deep learning which forms the current state of the art in computer vision. Deep learning is now deemed to have overtaken feature engineering in several computer vision applications. Deep convolutional neural networks and vision transformers are the current state of the art for object recognition. However, scenes from urban landscapes are bound to contain similar objects posing a challenge to deep learning solutions for scene recognition. In our study, a critical analysis of feature engineering and deep learning methodologies for scene recognition is provided, and results on benchmark scene datasets are presented, concluding with a discussion on challenges and possible solutions that may facilitate more accurate scene recognition.

Trusting My Predictions: On the Value of Instance-Level Analysis

Machine Learning solutions have spread along many domains, including critical applications. The development of such models usually relies on a dataset containing labeled data. This dataset is then split into training and test sets and the accuracy of the models in replicating the test labels is assessed. This process is often iterated in a cross-validation procedure for obtaining average performance estimates. But is the average of the predictive performance on test sets enough for assessing the trustfulness of a Machine Learning model? This paper discusses the importance of knowing which individual observations of a dataset are more challenging than others and how this characteristic can be measured and used in order to improve classification performance and trustfulness. A set of strategies for measuring the hardness level of the instances of a dataset is surveyed and a Python package containing their implementation is provided.

Automated real-time prediction of geological formation tops during drilling operations: an applied machine learning solution for the Norwegian Continental Shelf

Accurate prediction of geological formation tops is a crucial task for optimizing hydrocarbon exploration and production activities. This research investigates and conducts a comprehensive comparative analysis of several advanced machine learning approaches tailored for the critical application of geological formation top prediction within the complex Norwegian Continental Shelf (NCS) region. The study evaluates and benchmarks the performance of four prominent machine learning models: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest ensemble method, and Multi-Layer Perceptron (MLP) neural network. To facilitate a rigorous assessment, the models are extensively evaluated across two distinct datasets - a dedicated test dataset and a blind dataset independent for validation. The evaluation criteria revolve around quantifying the models' predictive accuracy in successfully classifying multiple geological formation top types. Additionally, the study employs the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm as a baseline benchmarking technique to contextualize the relative performance of the machine learning models against a conventional clustering approach. Leveraging two model-agnostic feature importance analysis techniques - Permutation Feature Importance (PFI) and Shapley Additive exPlanations (SHAP), the investigation identifies and ranks the most influential input variables driving the predictive capabilities of the models. The comprehensive analysis unveils the MLP neural network model as the top-performing approach, achieving remarkable predictive accuracy with a perfect score of 0.99 on the blind validation dataset, surpassing the other machine learning techniques as well as the DBSCAN benchmark. However, the SVM model attains superior performance on the initial test dataset, with an accuracy of 0.99. Intriguingly, the PFI and SHAP analyses converge in consistently pinpointing depth (DEPT), revolution per minute (RPM), and Hook-load (HKLD) as the three most impactful parameters influencing model predictions across the different algorithms. These findings underscore the potential of sophisticated machine learning methodologies, particularly neural network-based models, to significantly enhance the accuracy of geological formation top prediction within the geologically complex NCS region. However, the study emphasizes the necessity for further extensive testing on larger datasets to validate the generalizability of the high performance observed. Overall, this research delivers an exhaustive comparative evaluation of state-of-the-art machine learning techniques, offering critical insights to guide the optimal selection, development, and real-world deployment of accurate and reliable predictive modeling strategies tailored for hydrocarbon exploration and reservoir characterization endeavors in the NCS.Graphical abstract

Deep Generative Models for Data Synthesis and Augmentation in Machine Learning

The increasing trend in deep generative modelling, which offers data scarcity and diversity solutions in machine learning, is one of the most recent developments in the field. The data-oriented approaches drive the need for the quality and variety of data sets that could be prevented due to privacy issues and limited resources. Generative deep models, basically GANs (Generative Adversarial Networks) and VAEs (Variational Automatic Encoders), appear to be the most reliable approach to the synthesis and augmentation of the data. These models employ deep learning to basically learn all by itself from raw data without anyone teaching it, which is the basis of modern artificial intelligence. Accuracy issues between overfitting and poor generalization emphasize the need for smart solutions to the problem of data shortness. Deep generative modelling works based on the data distribution, which the model learns by itself and enables a realistic sample generator. The study reviews the proficiency and complexity of GANs, VAEs, and WGANs, comparing the WGANs' capabilities with the former two. Techniques of data augmentation, e.g., repositioning, rotation, and adding Gaussian noise to the dataset, will greatly increase the diversity of the data. Regardless of the training time, all models showcase competitive inference performance and, as a result, may be satisfactorily used in real-time operations. The insights obtained shed light on ways to improve machine learning and artificial intelligence through brain data synthesis, model training, and computational efficiency.

A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs.

Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenetic, transcriptional, and post-transcriptional regulatory processes with the advancements in sequencing technology and genomics research. Therefore, they play crucial roles in the body's normal physiology and various disease outcomes. Presently, numerous unknown lncRNA sequencing data require exploration. Establishing deep learning-based prediction models for lncRNAs provides valuable insights for researchers, substantially reducing time and costs associated with trial and error and facilitating the disease-relevant lncRNA identification for prognosis analysis and targeted drug development as the era of artificial intelligence progresses. However, most lncRNA-related researchers lack awareness of the latest advancements in deep learning models and model selection and application in functional research on lncRNAs. Thus, we elucidate the concept of deep learning models, explore several prevalent deep learning algorithms and their data preferences, conduct a comprehensive review of recent literature studies with exemplary predictive performance over the past 5years in conjunction with diverse prediction functions, critically analyze and discuss the merits and limitations of current deep learning models and solutions, while also proposing prospects based on cutting-edge advancements in lncRNA research.

A sustainable and trusted solution for IoT-based federated learning using feedback behavior

Federated learning is defined as one of the solutions that use distributed clients to train and aggregate the target model without sharing the private information of clients in the network. Hence, federated learning may easily handle and tackle the generated information in a more convenient, feasible and secure manner, while involving the intelligent devices to perform testing and training at the local edges. However, the design of such federated learning solutions faces multiple difficulties due to the cost and complexity of information computation, along with a high energy consumption. Furthermore, the identification of resource-constrained devices which ensures significant power consumption along with privacy and security to the devices makes it critical to attain a sustainable federated learning environment. The aim of this paper is to integrate feedback-based and objective trust models to accurately identify the behavior of communicating devices in the network. The secure transmission of information forwarded by trusted IoT devices is computed by the objective model. In addition, the energy consumption required for measuring the authenticity of a device can be further diminished using a behavior and feedback trusted model. In addition, the past history behavior and feedback received from neighboring devices may also contribute to achieving an efficient and sustainable security mechanism with reduced communication steps and computational delay in the network. The proposed solution is evaluated to analyze the feasibility and performance in terms of energy consumption and resource utilization, while also considering various security metrics for comparison against existing methods. The proposed mechanism out-performed 87% improvement in terms of security metrics in comparison of existing approaches.

The Influence of Learning Problems on the Implementation of Students' Physical Education Values

Background: The reason this observation was carried out was to analyze the relationship between learning problems and the implementation of values and solutions in physical education for class students. The data from this research are 23 students of class XII IPS 1 at SMA N 1 SUNGAI PUA (targeted selection Purpose: Overall, the objective suggests a research inquiry into the intersection between challenges in learning and the cultivation of physical education values among students. It implies a desire to uncover how educational obstacles may hinder or shape students' adoption of these values, which could have implications for educational practices, curriculum development, and student support initiatives within the realm of physical education. Methods: This observation used qualitative methods and distributed questionnaires to each student and filled them out personally. Taking samples for this observation is based on the researcher's knowledge about the samples to be selected. Results: Based on these observation conditions, it can be concluded that problems in Physical Education learning are very important to pay attention to, so that they do not impact the application of values and solutions in Physical Education learning and the Reability results of this research are (α0.921). Conclusion: our study sheds light on the significant relationship between learning problems and the implementation of physical education values among students. Through our investigation, we have identified several key findings that underscore the importance of addressing learning challenges in the context of promoting physical education values.

Machine learning evaluation for identification of M-proteins in human serum.

Serum electrophoresis (SPEP) is a method used to analyze the distribution of the most important proteins in the blood. The major clinical question is the presence of monoclonal fraction(s) of antibodies (M-protein/paraprotein), which is essential for the diagnosis and follow-up of hematological diseases, such as multiple myeloma. Recent studies have shown that machine learning can be used to assess protein electrophoresis by, for example, examining protein glycan patterns to follow up tumor surgery. In this study we compared 26 different decision tree algorithms to identify the presence of M-proteins in human serum by using numerical data from serum protein capillary electrophoresis. For the automated detection and clustering of data, we used an anonymized data set consisting of 67,073 samples. We found five methods with superior ability to detect M-proteins: Extra Trees (ET), Random Forest (RF), Histogram Grading Boosting Regressor (HGBR), Light Gradient Boosting Method (LGBM), and Extreme Gradient Boosting (XGB). Additionally, we implemented a game theoretic approach to disclose which features in the data set that were indicative of the resulting M-protein diagnosis. The results verified the gamma globulin fraction and part of the beta globulin fraction as the most important features of the electrophoresis analysis, thereby further strengthening the reliability of our approach. Finally, we tested the algorithms for classifying the M-protein isotypes, where ET and XGB showed the best performance out of the five algorithms tested. Our results show that serum capillary electrophoresis combined with decision tree algorithms have great potential in the application of rapid and accurate identification of M-proteins. Moreover, these methods would be applicable for a variety of blood analyses, such as hemoglobinopathies, indicating a wide-range diagnostic use. However, for M-protein isotype classification, combining machine learning solutions for numerical data from capillary electrophoresis with gel electrophoresis image data would be most advantageous.

Deep unrolling networks with recurrent momentum acceleration for nonlinear inverse problems

Combining the strengths of model-based iterative algorithms and data-driven deep learning solutions, deep unrolling networks (DuNets) have become a popular tool to solve inverse imaging problems. Although DuNets have been successfully applied to many linear inverse problems, their performance tends to be impaired by nonlinear problems. Inspired by momentum acceleration techniques that are often used in optimization algorithms, we propose a recurrent momentum acceleration (RMA) framework that uses a long short-term memory recurrent neural network (LSTM-RNN) to simulate the momentum acceleration process. The RMA module leverages the ability of the LSTM-RNN to learn and retain knowledge from the previous gradients. We apply RMA to two popular DuNets—the learned proximal gradient descent (LPGD) and the learned primal-dual (LPD) methods, resulting in LPGD-RMA and LPD-RMA, respectively. We provide experimental results on two nonlinear inverse problems: a nonlinear deconvolution problem, and an electrical impedance tomography problem with limited boundary measurements. In the first experiment we have observed that the improvement due to RMA largely increases with respect to the nonlinearity of the problem. The results of the second example further demonstrate that the RMA schemes can significantly improve the performance of DuNets in strongly ill-posed problems.

Developing a gamified digital platform to promote extracurricular activities in basic and secondary schools – preliminary findings

ABSTRACT Designing learning solutions in digitally mediated contexts requires attention to the level of immersion, interactivity, personalization, and engagement promoted, to which the incorporation of active learning methods and gamified strategies can be strategic. However, more research is needed on how teachers use technology-enhanced active learning strategies and on the impact of game-design elements inclusion in learning environments. This study provides findings from the informed exploration stage of a project, consisting of the development and assessment of a digital platform to be used in Portuguese basic and secondary schools to support participation in extracurricular projects and promote its users’ sense of belonging. A survey by questionnaire was publicly shared with Portuguese schoolteachers, in June 2022, to map the current technological reality in Portuguese schools and teachers’ relationship with technologies. 215 valid responses were analysed with descriptive statistics and content analysis. The use of digital technologies was found not to have decreased after the emergency remote learning period. Teachers assumed that the most used platforms coincided with those institutionally supported and that combining the promotion of active learning methods and the formal recognition of learners’ attitudes and skills in a digital solution was something innovative and required in Portuguese schools.

Intelligence-led analytics for anti-financial crime compliance

Financial institutions are continually looking for novel ways to improve the effectiveness of their anti-financial crime controls. Increasingly, they are turning to analytics to do this by embedding analytics into their anti-financial crime processes. Historically, analytics was used to gather information and provide summarisation and insights to support assessment and optimisation of financial crime controls, but advanced analytics and machine learning solutions have transformed the way financial crime is detected and investigated. In particular, the use of ‘intelligence-led’ analytics can improve the effectiveness of an institution's anti-financial crime controls by guiding them with additional financial crime intelligence information delivered through analytics solutions.

ENHANCING LOAN APPROVAL DECISION-MAKING: AN INTERPRETABLE MACHINE LEARNING APPROACH USING LIGHTGBM FOR DIGITAL ECONOMY DEVELOPMENT

This study aims to enhance loan approval decision-making in the digital economy using an interpretable machine learning approach. The primary research question investigates how integrating an interpretable machine learning approach can improve the accuracy and transparency of loan approval processes. We employed LightGBM, a gradient-boosting framework for loan approval classification, optimized via Random Search hyperparameter tuning and validated using 10-fold cross-validation. We incorporated the Shapley Additive exPlanations (SHAP) framework to address the challenge of interpretability in machine learning. The LightGBM model outperformed conventional algorithms (Decision Tree, Random Forest, AdaBoost, and Extra Trees) in accuracy (98.13%), precision (97.78%), recall (97.17%), and F1-score (97.48%). The study demonstrates that using an interpretable machine learning approach with LightGBM and SHAP can significantly improve the accuracy and transparency of loan approval decisions. This method offers a promising avenue for financial institutions to enhance their loan approval mechanisms, ensuring more reliable, efficient, and transparent decision-making in the digital economy. The study also underscores the importance of interpretability in deploying machine learning solutions in sectors with significant socio-economic impacts.