Machine learning systems are becoming increasingly ubiquitous. These systems’s adoption has been expanding, accelerating the shift towards a more algorithmic society, meaning that algorithmically informed decisions have greater potential for si...
Information that is complicated and ambiguous entails high cognitive load. Trying to understand such information can involve a lot of cognitive effort. An alternative to expending a lot of cognitive effort is to engage in motivated cognition, which c...
Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been ac...
Feature importance analysis is essential for interpreting machine learning models in diabetes mellitus (DM) risk prediction; however, existing interpretability methods often produce inconsistent feature rankings across models. This study proposes a u...
Machine learning (ML) engineering increasingly incorporates principles from software and requirements engineering to improve development rigor; however, key non-functional requirements (NFRs) such as interpretability and explainability remain difficu...
In the field of machine learning, a considerable amount of research is involved in the interpretability of models and their decisions. The interpretability contradicts the model quality. Random Forests are among the best quality technologies of machi...
Uncertainty is present in every single prediction of Machine Learning (ML) models. Uncertainty Quantification (UQ) is arguably relevant, in particular for safety-critical applications. Prior research focused on the development of methods to quantify...
Coal and gas outbursts constitute a major hazard for mining safety, which is critical for the sustainable development of China’s energy industry. Rapid, accurate, and reliable pre-diction is pivotal for preventing and controlling outburst incid...
Reliable assessment of livestock manure compost maturity is important for safe agricultural use, yet biological and laboratory-based indicators may not be consistently available during routine monitoring. This study developed and evaluated machine le...
In industrial machine learning, predictive performance alone is insufficient to ensure reliable deployment, as model behaviour may vary across different regions of the input space under limited data and evolving process conditions. This work investig...
In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural networks (DNN) to quantify feature importance using global and local interpretation methods. Our meth...
Introduction: Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, with coronary artery disease (CAD), also known as ischemic heart disease (IHD), responsible for approximately 13% of global deaths in 2021. Studies applying...
In recent years, machine learning, especially deep learning, has developed rapidly and has shown remarkable performance in many tasks of the smart grid field. The representation ability of machine learning algorithms is greatly improved, but with the...
The incorporation of recycled concrete aggregates (RCAs) into self-compacting concrete (SCC) represents a critical sustainable construction strategy addressing both construction waste management and natural resource conservation. However, predicting...
Small-molecule drug development faces high attrition rates driven by complex pharmacokinetics and unforeseen toxicities. While deep learning offers high predictive accuracy, its opaque “black-box” nature hinders mechanistic transparency,...
Hepatitis B is a potentially deadly liver infection caused by the hepatitis B virus. It is a serious public health problem globally. Substantial efforts have been made to apply machine learning in detecting the virus. However, the application of mode...
Delirium in hospitalized patients is a worldwide problem, causing a burden on healthcare professionals and impacting patient prognosis. A machine learning interpretation method (ML interpretation method) presents the results of machine learning predi...
Heart disease is one of the leading causes of mortality throughout the world. Among the different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non-invasive procedure. However, the following are challenges: the scarcit...
This paper investigates the role of symmetry and asymmetry in the learning process of modern machine learning models, with a specific focus on feature representation and optimization. We introduce a novel symmetry-aware learning framework that identi...
Machine learning has the potential to predict unseen data and thus improve the productivity and processes of daily life activities. Notwithstanding its adaptiveness, several sensitive applications based on such technology cannot compromise our trust...
This manuscript outlines a viable approach for training and evaluating machine learning systems for high-stakes, human-centered, or regulated applications using common Python programming tools. The accuracy and intrinsic interpretability of two types...
Customer segmentation is essential in financial services for designing targeted interventions, managing dormant portfolios, and supporting marketing re-engagement strategies. Traditional approaches such as Recency–Frequency–Monetary (RFM)...
Seismic interpretation is a fundamental process for hydrocarbon exploration. This activity comprises identifying geological information through the processing and analysis of seismic data represented by different attributes. The interpretation proces...
In modern society, vehicle accidents have been a factor that has adversely affected national development for a long time. Many countries have tried to solve this issue, and various solutions have been studied. This study aims to design a process for...
The integration of machine learning (ML) in marine engineering has been increasingly subjected to stringent regulatory scrutiny. While environmental regulations aim to reduce harmful emissions and energy consumption, there is also a growing demand fo...
Additive manufacturing (AM), especially the extrusion-based process, has many process parameters which influence the resulting part properties. Those parameters have complex interdependencies and are therefore difficult if not impossible to model ana...
Medical thermography is a noninvasive, contactless imaging technique that captures spatial temperature distributions across the human body, providing insights into vascular function, inflammation, metabolism, physiological regulation, and aging. Rece...
Psycholinguistic properties such as concept familiarity and concreteness have been investigated in relation to technological innovations in teaching and learning. Due to ongoing advances in semantic representation and machine learning technologies, t...
Radio network planning is critical for 5G deployments, particularly for temporary installations in rural areas where terrain and vegetation significantly impact signal propagation. While empirical path loss (PL) models characterize propagation enviro...
Understanding and preventing student dropout presents a decision-critical modeling problem involving heterogeneous variables, nonlinear relationships, and the need for transparent inference. This study addresses the prediction of undergraduate academ...
Although machine learning (ML) models are widely used in many fields, their prediction processes are often hard to understand. This lack of transparency makes it harder for people to trust them, especially in high-stakes fields like healthcare and fi...
Machine learning (ML) algorithms are now part of everyday life, as many technological devices use these algorithms. The spectrum of uses is wide, but it is evident that ML represents a revolution that may change almost every human activity. However,...
A skin lesion is a portion of skin that observes abnormal growth compared to other areas of the skin. The ISIC 2018 lesion dataset has seven classes. A miniature dataset version of it is also available with only two classes: malignant and benign. Mal...
High-dimensional prediction problems with complex non-linear feature interactions present significant algorithmic challenges in machine learning, particularly when dealing with imbalanced datasets and multicollinearity issues. This study proposes an...
Machine learning models are increasingly used for streamflow prediction due to their promising performance. However, their data-driven nature makes interpretation challenging. This study explores the interpretability of a Random Forest model trained...
Recently, explainability in machine and deep learning has become an important area in the field of research as well as interest, both due to the increasing use of artificial intelligence (AI) methods and understanding of the decisions made by models....
Polyvinyl alcohol (PVA) hydrogels have emerged as versatile materials due to their exceptional biocompatibility and tunable mechanical properties, showing great promise for flexible sensors, smart wound dressings, and tissue engineering applications....
Machine learning models are increasingly being used across diverse fields, including file fragment classification. As these models become more prevalent, it is crucial to understand and interpret their decision-making processes to ensure accountabili...
The Shapley value is the predominant point-valued solution concept in cooperative game theory and has recently become a foundational method in interpretable machine learning. In this domain, a prevailing strategy for circumventing the computational i...
Many repeated manual feature adjustments and much heuristic parameter tuning are required during the debugging of machine learning (ML)-based transient stability assessment (TSA) of power systems. Furthermore, the results produced by ML-based TSA are...
Glass artworks represent a significant component of cultural heritage, yet their surfaces are highly vulnerable to physicochemical weathering resulting from composition-dependent interactions with environmental factors. Understanding the complex and...
While metabolomics captures the dynamic chemical landscape of biological systems, its inherent high dimensionality and complexity pose significant analytical hurdles. Interpretable Machine Learning (IML) is revolutionizing the field by moving beyond...
Background: Acute myocardial infarctions are deadly to patients and burdensome to healthcare systems. Most recorded infarctions are patients’ first, occur out of the hospital, and often are not accompanied by cardiac comorbidities. The clinical...
A hybrid modelling framework is proposed to predict endpoint sulphur content in the ladle furnace (LF) refining process by embedding metallurgical expert knowledge into interpretable machine learning (ML). Industrial process data were extracted from...
Interpretability analysis methods, such as LIME and SHAP, are widely employed to explain the predictions of artificial intelligence models; however, they primarily function as post hoc tools and do not directly quantify the intrinsic interpretability...
Glioblastoma multiforme (GBM) is a fast-growing and highly invasive brain tumor, which tends to occur in adults between the ages of 45 and 70 and it accounts for 52 percent of all primary brain tumors. Usually, GBMs are detected by magnetic resonance...
Understanding the relationships between design factors is crucial for the development of hydrogel supercapacitors, yet the relative importance and interdependencies of material properties and operating conditions remain unclear. This study employs in...
Accurate prediction of the residual drift ratio of reinforced concrete bridge piers is challenging because conventional methods are computationally expensive, time-consuming, and unable to effectively capture complex nonlinear interactions among mult...
Electromagnetic scattering from electronic platforms degrades system performance, increases radar detectability, and intensifies electromagnetic interference in modern radar and communication systems. Electromagnetic absorbing layers offer an effecti...
The creep behavior of Ultra-High-Performance Concrete (UHPC) was investigated by machine learning (ML) and SHapley Additive exPlanations (SHAP). Important features were selected by feature importance analysis, including water-to-binder ratio, aggrega...
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