Artificial Intelligence Methodologies Research Articles

Authentic assessment in medical education: exploring AI integration and student-as-partners collaboration.

Traditional assessments often lack flexibility, personalized feedback, real-world applicability, and the ability to measure skills beyond rote memorization. These may not adequately accommodate diverse learning styles and preferences, nor do they always foster critical thinking or creativity. The inclusion of Artificial Intelligence (AI), especially Generative Pre-trained Transformers, in medical education marks a significant shift, offering both exciting opportunities and notable challenges for authentic assessment practices. Various fields, including anatomy, physiology, pharmacy, dentistry, and pathology, are anticipated to employ the metaverse for authentic assessments increasingly. This innovative approach will likely enable students to engage in immersive, project-based learning experiences, facilitating interdisciplinary collaboration and providing a platform for real-world application of knowledge and skills. This commentary paper explores how AI, authentic assessment, and Student-as-Partners (SaP) methodologies can work together to reshape assessment practices in medical education. The paper provides practical insights into effectively utilizing AI tools to create authentic assessments, offering educators actionable guidance to enhance their teaching practices. It also addresses the challenges and ethical considerations inherent in implementing AI-driven assessments, emphasizing the need for responsible and inclusive practices within medical education. Advocating for a collaborative approach between AI and SaP methodologies, the commentary proposes a robust plan to ensure ethical use while upholding academic integrity. Through navigating emerging assessment paradigms and promoting genuine evaluation of medical knowledge and proficiency, this collaborative effort aims to elevate the quality of medical education and better prepare learners for the complexities of clinical practice.

Artificial Intelligence in Medicine: from Diagnosis to Treatment

In recent years, medicine has faced the serious challenge of the covid pandemic, due to which representatives of the health care sector had to mobilize forces and resources to jointly overcome these problems. The rapid development of artificial intelligence, its learning capabilities, and in recent years the creation of a neural network opens up wide possibilities for the use of AI in medicine. Aims: To analyze the modern literature on the use of AI for diagnosis and treatment and to analyze what problems may arise with the uncontrolled introduction of artificial intelligence Methodology: When conducting a literature review, an analysis and generalization of data on the research topic from 2019 to 2024 was carried out. The literature search was carried out by keywords using the PubMed search engine. Results: The literature review demonstrated the use of artificial intelligence in medicine, which has grown significantly in recent years and continues its rapid development, which is associated with the improvement of innovative technologies. The use of artificial intelligence in diagnostics is associated with the use of a neural network, which makes it possible to identify digitized images for rapid diagnosis. The use of artificial intelligence in surgery is reflected in the application of da Vinci. Artificial intelligence has been widely used in anesthesiology. Scientific Novelty: The literature search established that the implementation of artificial intelligence in medicine creates certain challenges related to the protection of personal data, and the possibility of error is not excluded when using AI. Conclusion: The use of AI is promising for diagnosis and treatment and helps doctors quickly make a diagnosis and prescribe treatment, but certain challenges created by artificial intelligence must be solved by implementing more reliable personal data protection systems, as well as control over the information reproduced by artificial intelligence.

Explainable artificial intelligence for LDL cholesterol prediction and classification

IntroductionMonitoring LDL-C levels is essential in clinical practice because there is a direct relation between low-density lipoprotein cholesterol (LDL-C) levels and atherosclerotic heart disease risk. Therefore, measurement or estimate of LDL-C is critical. The present study aims to evaluate Artificial Intelligence (AI) and Explainable AI (XAI) methodologies in predicting LDL-C levels while emphasizing the interpretability of these predictions. Materials and methodsWe retrospectively reviewed data from the Laboratory Information System (LIS) of Ankara Etlik City Hospital (AECH). We included 60.217 patients with standard lipid profiles (total cholesterol [TC], high-density lipoprotein cholesterol, and triglycerides) paired with same-day direct LDL-C results. AI methodologies, such as Gradient Boosting (GB), Random Forests (RF), Support Vector Machines (SVM), and Decision Trees (DT), were used to predict LDL-C and compared directly measured and calculated LDL-C with formulas. XAI techniques such as Shapley additive annotation (SHAP) and locally interpretable model-agnostic explanation (LIME) were used to interpret AI models and improve their explainability. ResultsPredicted LDL-C values using AI, especially RF or GB, showed a stronger correlation with direct measurement LDL-C values than calculated LDL-C values with formulas. TC was shown to be the most influential factor in LDL-C prediction using SHAP and LIME. The agreement between the treatment groups based on NCEP ATPIII guidelines according to measured LDL-C and the LDL-C groups obtained with AI was higher than that obtained with formulas. ConclusionsIt can be concluded that AI is not only a reliable method but also an explainable method for LDL-C estimation and classification.

Explainable artificial intelligence for LDL cholesterol prediction and classification

IntroductionMonitoring LDL-C levels is essential in clinical practice because there is a direct relation between low-density lipoprotein cholesterol (LDL-C) levels and atherosclerotic heart disease risk. Therefore, measurement or estimate of LDL-C is critical. The present study aims to evaluate Artificial Intelligence (AI) and Explainable AI (XAI) methodologies in predicting LDL-C levels while emphasizing the interpretability of these predictions. Materials and methodsWe retrospectively reviewed data from the Laboratory Information System (LIS) of Ankara Etlik City Hospital (AECH). We included 60.217 patients with standard lipid profiles (total cholesterol [TC], high-density lipoprotein cholesterol, and triglycerides) paired with same-day direct LDL-C results. AI methodologies, such as Gradient Boosting (GB), Random Forests (RF), Support Vector Machines (SVM), and Decision Trees (DT), were used to predict LDL-C and compared directly measured and calculated LDL-C with formulas. XAI techniques such as Shapley additive annotation (SHAP) and locally interpretable model-agnostic explanation (LIME) were used to interpret AI models and improve their explainability. ResultsPredicted LDL-C values using AI, especially RF or GB, showed a stronger correlation with direct measurement LDL-C values than calculated LDL-C values with formulas. TC was shown to be the most influential factor in LDL-C prediction using SHAP and LIME. The agreement between the treatment groups based on NCEP ATPIII guidelines according to measured LDL-C and the LDL-C groups obtained with AI was higher than that obtained with formulas. ConclusionsIt can be concluded that AI is not only a reliable method but also an explainable method for LDL-C estimation and classification.

State-of-the-art review on the use of AI-enhanced computational mechanics in geotechnical engineering

Significant uncertainties can be found in the modelling of geotechnical materials. This can be attributed to the complex behaviour of soils and rocks amidst construction processes. Over the past decades, the field has increasingly embraced the application of artificial intelligence methodologies, thus recognising their suitability in forecasting non-linear relationships intrinsic to materials. This review offers a critical evaluation AI methodologies incorporated in computational mechanics for geotechnical engineering. The analysis categorises four pivotal areas: physical properties, mechanical properties, constitutive models, and other characteristics relevant to geotechnical materials. Among the various methodologies analysed, ANNs stand out as the most commonly used strategy, while other methods such as SVMs, LSTMs, and CNNs also see a significant level of application. The most widely used AI algorithms are Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machines (SVM), representing 35%, 19%, and 17% respectively. The most extensive AI application is in the domain of mechanical properties, accounting for 59%, followed by other applications at 16%. The efficacy of AI applications is intrinsically linked to the type of datasets employed, the selected model input. This study also outlines future research directions emphasising the need to integrate physically guided and adaptive learning mechanisms to enhance the reliability and adaptability in addressing multi-scale and multi-physics coupled mechanics problems in geotechnics.

Innovative Mathematical Insights through Artificial Intelligence (AI): Analysing Ramanujan Series and the Relationship between e and π\pi

This paper presents a comprehensive case study on the application of Artificial Intelligence (AI) in mathematics, focusing on the Ramanujan series and the intricate relationship between the mathematical constants e and π. The study explores how AI, particularly machine learning and pattern recognition techniques, can be harnessed to discover new mathematical series and patterns, thereby extending the pioneering work of the legendary mathematician Srinivasa Ramanujan. The paper begins with an overview of the Ramanujan series, illustrating their significance and applications in mathematical computations. It then delves into the specifics of AI methodologies employed to unearth new series for e and π, highlighting the algorithms and models used. Through detailed analysis and experimentation, the study demonstrates how AI can generate new series expansions for e and π, offering enhanced convergence rates and computational efficiencies. Furthermore, the paper examines the relationship between these two constants, providing insights into their interconnected nature through AI-discovered series and patterns. Practical applications of these new series in fields such as numerical methods, cryptography, and theoretical physics are also discussed. By showcasing the successful integration of AI in the realm of mathematical research, this case study underscores the potential of AI to revolutionize traditional mathematical approaches, fostering the discovery of new knowledge and the refinement of existing theories. The findings contribute to a deeper understanding of the interplay between e and π, reinforcing the profound impact of Ramanujan's work in modern mathematics and the transformative power of AI in advancing this legacy.

Enhancing Aviation Safety through AI-Driven Mental Health Management for Pilots and Air Traffic Controllers.

This article provides an overview of the mental health challenges faced by pilots and air traffic controllers (ATCs), whose stressful professional lives may negatively impact global flight safety and security. The adverse effects of mental health disorders on their flight performance pose a particular safety risk, especially in sudden unexpected startle situations. Therefore, the early detection, prediction and prevention of mental health deterioration in pilots and ATCs, particularly among those at high risk, are crucial to minimize potential air crash incidents caused by human factors. Recent research in artificial intelligence (AI) demonstrates the potential of machine and deep learning, edge and cloud computing, virtual reality and wearable multimodal physiological sensors for monitoring and predicting mental health disorders. Longitudinal monitoring and analysis of pilots' and ATCs physiological, cognitive and behavioral states could help predict individuals at risk of undisclosed or emerging mental health disorders. Utilizing AI tools and methodologies to identify and select these individuals for preventive mental health training and interventions could be a promising and effective approach to preventing potential air crash accidents attributed to human factors and related mental health problems. Based on these insights, the article advocates for the design of a multidisciplinary mental healthcare ecosystem in modern aviation using AI tools and technologies, to foster more efficient and effective mental health management, thereby enhancing flight safety and security standards. This proposed ecosystem requires the collaboration of multidisciplinary experts, including psychologists, neuroscientists, physiologists, psychiatrists, etc. to address these challenges in modern aviation.

Integrating multiple data sources for improved flight delay prediction using explainable machine learning

Flight delays negatively impact costs, customer satisfaction, and revenue in the aviation industry. As a result, it is critical to identify the factors that cause flight delays for each airport, as they can vary depending on various attributes associated with their operations.This study proposes an explainable artificial intelligence (xAI) methodology for identifying the features that affect airport delays by integrating data from multiple sources and implementing explainable artificial intelligence. The methodology incorporates operational data, airport information, geographic data, and weather data combined and used to train a series of machine learning models. Furthermore, the SHAP and Sobol techniques are used to thoroughly analyze the features that influence flight delays for the specific case of the airport in Santiago, Chile.The results show that a linear discriminant analysis model is best suited for predicting flight delays in this specific case study, and the features that have the most significant impact on delays are the international flight status, average temperature at the destination airport, wind speed, and average temperature at Santiago airport.The proposed methodology could be applied by airlines that can collect data from multiple sources and conduct similar investigations, leading to the development of a decision support system to make better-informed decisions and reduce the impact of flight delays.

Intracerebral Hemorrhage Prognosis Classification via Joint-Attention Cross-Modal Network.

Intracerebral hemorrhage (ICH) is a critical condition characterized by a high prevalence, substantial mortality rates, and unpredictable clinical outcomes, which results in a serious threat to human health. Improving the timeliness and accuracy of prognosis assessment is crucial to minimizing mortality and long-term disability associated with ICH. Due to the complexity of ICH, the diagnosis of ICH in clinical practice heavily relies on the professional expertise and clinical experience of physicians. Traditional prognostic methods largely depend on the specialized knowledge and subjective judgment of healthcare professionals. Meanwhile, existing artificial intelligence (AI) methodologies, which predominantly utilize features derived from computed tomography (CT) scans, fall short of capturing the multifaceted nature of ICH. Although existing methods are capable of integrating clinical information and CT images for prognosis, the effectiveness of this fusion process still requires improvement. To surmount these limitations, the present study introduces a novel AI framework, termed the ICH Network (ICH-Net), which employs a joint-attention cross-modal network to synergize clinical textual data with CT imaging features. The architecture of ICH-Net consists of three integral components: the Feature Extraction Module, which processes and abstracts salient characteristics from the clinical and imaging data, the Feature Fusion Module, which amalgamates the diverse data streams, and the Classification Module, which interprets the fused features to deliver prognostic predictions. Our evaluation, conducted through a rigorous five-fold cross-validation process, demonstrates that ICH-Net achieves a commendable accuracy of up to 87.77%, outperforming other state-of-the-art methods detailed within our research. This evidence underscores the potential of ICH-Net as a formidable tool in prognosticating ICH, promising a significant advancement in clinical decision-making and patient care.

Breast Cancer Molecular Subtype Prediction: A Mammography-Based AI Approach.

Breast cancer remains a leading cause of mortality among women, with molecular subtypes significantly influencing prognosis and treatment strategies. Currently, identifying the molecular subtype of cancer requires a biopsy-a specialized, expensive, and time-consuming procedure, often yielding to results that must be supported with additional biopsies due to technique errors or tumor heterogeneity. This study introduces a novel approach for predicting breast cancer molecular subtypes using mammography images and advanced artificial intelligence (AI) methodologies. Using the OPTIMAM imaging database, 1397 images from 660 patients were selected. The pretrained deep learning model ResNet-101 was employed to classify tumors into five subtypes: Luminal A, Luminal B1, Luminal B2, HER2, and Triple Negative. Various classification strategies were studied: binary classifications (one vs. all others, specific combinations) and multi-class classification (evaluating all subtypes simultaneously). To address imbalanced data, strategies like oversampling, undersampling, and data augmentation were explored. Performance was evaluated using accuracy and area under the receiver operating characteristic curve (AUC). Binary classification results showed a maximum average accuracy and AUC of 79.02% and 64.69%, respectively, while multi-class classification achieved an average AUC of 60.62% with oversampling and data augmentation. The most notable binary classification was HER2 vs. non-HER2, with an accuracy of 89.79% and an AUC of 73.31%. Binary classification for specific combinations of subtypes revealed an accuracy of 76.42% for HER2 vs. Luminal A and an AUC of 73.04% for HER2 vs. Luminal B1. These findings highlight the potential of mammography-based AI for non-invasive breast cancer subtype prediction, offering a promising alternative to biopsies and paving the way for personalized treatment plans.

Enhancing Self-Supervised Learning through Explainable Artificial Intelligence Mechanisms: A Computational Analysis

Self-supervised learning continues to drive advancements in machine learning. However, the absence of unified computational processes for benchmarking and evaluation remains a challenge. This study conducts a comprehensive analysis of state-of-the-art self-supervised learning algorithms, emphasizing their underlying mechanisms and computational intricacies. Building upon this analysis, we introduce a unified model-agnostic computation (UMAC) process, tailored to complement modern self-supervised learning algorithms. UMAC serves as a model-agnostic and global explainable artificial intelligence (XAI) methodology that is capable of systematically integrating and enhancing state-of-the-art algorithms. Through UMAC, we identify key computational mechanisms and craft a unified framework for self-supervised learning evaluation. Leveraging UMAC, we integrate an XAI methodology to enhance transparency and interpretability. Our systematic approach yields a 17.12% increase in improvement in training time complexity and a 13.1% boost in improvement in testing time complexity. Notably, improvements are observed in augmentation, encoder architecture, and auxiliary components within the network classifier. These findings underscore the importance of structured computational processes in enhancing model efficiency and fortifying algorithmic transparency in self-supervised learning, paving the way for more interpretable and efficient AI models.

A Review on Artificial Intelligence in Pharmaceutical Science

Abstract: In recent years, the use of artificial intelligence (AI) in health care has risen steadily, including a wide range of applications in the field of pharmacology. AI is now used throughout the entire continuum of pharmacology research and clinical practice and from early drug discovery to real-world data mining. The types of AI models used range from unsupervised clustering of drugs or patients aimed at identifying potential drug compounds or suitable patient populations, to supervised machine learning approaches to improve therapeutic drug monitoring. Additionally, natural language processing is increasingly used to mine electronic health records to obtain real-world data. In this mini-review, we discuss the basics of AI followed by an outline of its application in pharmacology research and clinical practice. Artificial intelligence is the upcoming technology in advance health care system. Current digitalization of medicine and availability of electronic health records (EHRs) has inspired clinical researchers and healthcare personnel to acquire artificial intelligence (AI) methodologies for big data analytics and for very large scale medical databases. The major advantage of AI is that it reduces the time that is needed for drug development and, in turn, it reduces the costs that are associated with drug development, enhances the returns on investment and may even cause a decrease in cost for the end user. A large number of researches are being carried out to improve the current available AI technology to make the pharmacy profession more efficient. The present article briefly describes the importance of AI in the process of drug development and then looks at the various AI tools that are available at the disposal of a modern-day pharmacist to aid in a more efficient functioning.

Interactive Human 3D Model with Conventional AI

Abstract: The convergence of interactive human 3D models with conventional artificial intelligence (AI) represents a groundbreaking fusion of computer graphics and intelligent systems. This interdisciplinary field combines advanced 3D modeling techniques with classical AI methodologies to create interactive virtual representations of humans. Rigorous studies in 3D modeling and animation form the basis for lifelike avatars, while conventional AI empowers these models with the ability to understand user inputs, make contextually relevant decisions, and engage in dynamic and responsive interactions. At the heart of this innovation is the emphasis on human-computer interaction (HCI), leveraging principles such as gesture recognition, voice commands, and user-friendly interfaces to enhance user experiences.

Machine Learning Identification of Nutrient Intake Variations across Age Groups in Metabolic Syndrome and Healthy Populations.

This study undertakes a comprehensive examination of the intricate link between diet nutrition, age, and metabolic syndrome (MetS), utilizing advanced artificial intelligence methodologies. Data from the National Health and Nutrition Examination Survey (NHANES) spanning from 1999 to 2018 were meticulously analyzed using machine learning (ML) techniques, specifically extreme gradient boosting (XGBoost) and the proportional hazards model (COX). Using these analytic methods, we elucidated a significant correlation between age and MetS incidence and revealed the impact of age-specific dietary patterns on MetS. The study delineated how the consumption of certain dietary components, namely retinol, beta-cryptoxanthin, vitamin C, theobromine, caffeine, lycopene, and alcohol, variably affects MetS across different age demographics. Furthermore, it was revealed that identical nutritional intakes pose diverse pathogenic risks for MetS across varying age brackets, with substances such as cholesterol, caffeine, and theobromine exhibiting differential risks contingent on age. Importantly, this investigation succeeded in developing a predictive model of high accuracy, distinguishing individuals with MetS from healthy controls, thereby highlighting the potential for precision in dietary interventions and MetS management strategies tailored to specific age groups. These findings underscore the importance of age-specific nutritional guidance and lay the foundation for future research in this area.

A Review on the Current Trends in Crop Yield Forecasting Using Artificial Intelligence

In the face of escalating global food demands and the increasing unpredictability of climate conditions, the importance of precise crop yield forecasting has never been more critical. This paper provides a comprehensive review of the current trends in leveraging Artificial Intelligence (AI) to enhance crop yield predictions, which is pivotal for strategic agricultural planning and ensuring food security. Our review covers a range of AI methodologies, including machine learning, deep learning, and hybrid models, that have been employed to predict crop yields with increasing accuracy. Recent advancements have demonstrated that machine learning techniques, such as support vector machines and random forests, are effective in modeling complex agricultural data sets with a notable degree of precision. However, deep learning approaches, including convolutional and recurrent neural networks, have started to outperform traditional machine learning models, owing to their ability to process large-scale spatial-temporal data from remote sensing and IoT-based agricultural sensors. We also explore the emergence of hybrid AI models that combine the strengths of both machine learning and deep learning technologies, providing enhanced accuracy and robustness in yield prediction under varying climatic conditions. Additionally, this review discusses the integration of AI with geographic information systems (GIS) and remote sensing technologies, which has significantly improved the spatial resolution of yield predictions. We highlight several key challenges that remain, such as data scarcity, the need for model generalization, and the integration of socioeconomic factors into yield prediction models. In conclusion, AI presents transformative potential for crop yield forecasting. By harnessing cutting-edge AI technologies and addressing existing challenges, significant strides can be made towards more sustainable and efficient agricultural practices. This paper aims to inspire continued research and innovation in this critical field.

Prediction of mechanical properties of high‐performance concrete and ultrahigh‐performance concrete using soft computing techniques: A critical review

AbstractA cement‐based material that meets the general goals of mechanical properties, workability, and durability as well as the ever‐increasing demands of environmental sustainability is produced by varying the type and quantity of individual constituents in high‐performance concrete (HPC) and ultrahigh‐performance concrete (UHPC). Expensive and time‐consuming laboratory experiments can be used to estimate the properties of concrete mixtures and elements. As an alternative, these attributes can be approximated by means of predictive models created through the application of artificial intelligence (AI) methodologies. AI approaches are among the most effective ways to solve engineering problems due to their capacity for pattern recognition and knowledge processing. Machine learning (ML) and deep learning (DL) are a subfield of AI that is gaining popularity across many scientific domains as a result of its many benefits over statistical and experimental models. These include, but are not limited to, better accuracy, faster performance, greater responsiveness in complex environments, and lower economic costs. In order to assess the critical features of the literature, a comprehensive review of ML and DL applications for HPC and UHPC was conducted in this study. This paper offers a thorough explanation of the fundamental terms and ideas of ML and DL algorithms that are frequently used to predict mechanical properties of HPC and UHPC. Engineers and researchers working with construction materials will find this paper useful in helping them choose accurate and appropriate methods for their needs.

The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review.

Stem cell research, particularly in the domain of induced pluripotent stem cell (iPSC) technology, has shown significant progress. The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has played a pivotal role in refining iPSC classification, monitoring cell functionality, and conducting genetic analysis. These enhancements are broadening the applications of iPSC technology in disease modelling, drug screening, and regenerative medicine. This review aims to explore the role of AI in the advancement of iPSC research. In December 2023, data were collected from three electronic databases (PubMed, Web of Science, and Science Direct) to investigate the application of AI technology in iPSC processing. This systematic scoping review encompassed 79 studies that met the inclusion criteria. The number of research studies in this area has increased over time, with the United States emerging as a leading contributor in this field. AI technologies have been diversely applied in iPSC technology, encompassing the classification of cell types, assessment of disease-specific phenotypes in iPSC-derived cells, and the facilitation of drug screening using iPSC. The precision of AI methodologies has improved significantly in recent years, creating a foundation for future advancements in iPSC-based technologies. Our review offers insights into the role of AI in regenerative and personalized medicine, highlighting both challenges and opportunities. Although still in its early stages, AI technologies show significant promise in advancing our understanding of disease progression and development, paving the way for future clinical applications.

LDCML: a novel ai-driven approach for privacy-preserving anonymization of quasi-identifiers

Introduction: The exponential growth of data generation has led to an escalating concern for data privacy on a global scale. This work introduces a pioneering approach to address the often overlooked data privacy leakages associated with quasi-identifiers, leveraging artificial intelligence, machine learning and data correlation analysis as foundational tools. Traditional data privacy measures predominantly focus on anonymizing sensitive attributes and exact identifiers, leaving quasi-identifiers in their raw form, potentially exposing privacy vulnerabilities.Objective: The primary objective of the presented work, is to anonymise the quasi-identifiers to enhance the overall data privacy preservation with minimal data utility degradation.Methods: In this study, the authors propose the integration of ℓ-diversity data privacy algorithms with the OPTICS clustering technique and data correlation analysis to anonymize the quasi-identifiers.Results: To assess its efficacy, the proposed approach is rigorously compared against benchmark algorithms. The datasets used are - Adult dataset and Heart Disease Dataset from the UCI machine learning repository. The comparative metrics are - Relative Distance, Information Loss, KL Divergence and Execution Time.Conclusion: The comparative performance evaluation of the proposed methodology demonstrates its superiority over established benchmark techniques, positioning it as a promising solution for the requisite data privacy-preserving model. Moreover, this analysis underscores the imperative of integrating artificial intelligence (AI) methodologies into data privacy paradigms, emphasizing the necessity of such approaches in contemporary research and application domains.

Enhancing Customer Service in Banking with AI: Intent Classification Using Distilbert

With the increasing demand for efficient and responsive customer service in the banking sector, artificial intelligence offers a promising solution. This paper presents a comparative analysis of artificial intelligence methodologies applied to intent classification within the banking sector customer service domain. Utilizing a comprehensive dataset of banking service inquiries, we evaluate several machine learning approaches, including Naive Bayes, Logistic Regression, Support Vector Machine with Linear Kernel, Random Forest, XGBoost, and the transformer-based DistilBERT model. The models are assessed based on their accuracy, precision, recall, and F1 score metrics. Our findings indicate that DistilBERT, with its distilled architecture, not only outstrips traditional models but also demonstrates exceptional performance with an accuracy and F1 score exceeding 92%. The paper delves into the advantages of employing such an efficient and powerful model in real-time customer service settings, suggesting that DistilBERT offers a substantial enhancement over conventional methods. By providing detailed insights into the model’s capabilities, we underscore the transformative impact of employing advanced AI in the financial industry to elevate customer service standards, streamline operational efficiency, and harness the power of state-of-the-art technology for improved client interactions. The results showcased in this study are indicative of the strides being made in AI applications for financial services and set a benchmark for future exploratory and practical endeavors in the field.

Enhancing Transparency and Trust in Turbofan Engine Classification: Leveraging Explainable AI

Implementing predictive maintenance is paramount in guaranteeing the dependability and efficiency of intricate industrial systems. Classification approaches have been extensively utilized to detect and anticipate potential malfunctions in acknowledged systems. Numerous machine learning algorithms exhibit effectiveness, yet they encounter interpretability issues, posing a challenge in comprehending the fundamental factors contributing to their predictions. This study investigates the utilization of machine learning algorithms for classification in the domain of predictive maintenance. This research focuses on applying Random Forest, XGBoost, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms for Turbofan Engine classification into multiple categories. We use Explainable Artificial Intelligence (XAI) methodologies to mitigate the interpretability challenges commonly associated with black-box models. We aim to utilize XAI algorithms on the most effective models to elucidate the essential characteristics and decision-making procedures underlying the classification outcomes. The findings of our experiment indicate that the Random Forest algorithm based on ensemble learning and the XGBoost algorithm based on gradient boosting exhibit superior classification accuracy. Additionally, the utilization of Explainable Artificial Intelligence (XAI) algorithms amplifies the transparency and interpretability of said models, thereby facilitating a more profound comprehension of the determinants that impact the classification results. The results of this study make a valuable contribution to predictive maintenance by demonstrating the efficacy of machine learning algorithms in multiclass classification and emphasizing the significance of explainable artificial intelligence (XAI) in furnishing practical insights for decision-making.