In the pursuit of scalable optimization strategies for neural networks, this study addresses the computational challenges posed by SHAP-informed learning methods introduced in prior work. Specifically, we extend the SHAP-based optimization family by...
In the context of neural network optimization, this study explores the performance and computational efficiency of learning rate adjustment strategies applied with Adam and SGD optimizers. Methods evaluated include exponential annealing, step decay,...
Model interpretability is essential in machine learning, particularly for applications in critical fields like healthcare, where understanding model decisions is paramount. While SHAP (SHapley Additive exPlanations) has proven to be a robust tool for...
Ovarian cancer stands out as one of the most formidable adversaries in women’s health, largely due to its typically subtle and nonspecific early symptoms, which pose significant challenges to early detection and diagnosis. Although existing dia...
This study developed an interpretable framework, RFE-SHAP, designed for yarn quality prediction. It integrates Recursive Feature Elimination (RFE) with SHapley Additive exPlanations (SHAP) theory to refine feature selection and mitigate data redundan...
The increasing adoption of machine learning models in corporate valuation has substantially improved predictive accuracy but at the cost of interpretability, a critical limitation in regulated financial environments. This study investigates whether S...
Physiological signals have attracted growing interest in biometric recognition because they contain individual-specific characteristics that can support person identification. However, Electrodermal Activity (EDA) remains less investigated than other...
Landslides, the second largest geological hazard after earthquakes, result in significant loss of life and property. Extracting landslide information quickly and accurately is the basis of landslide disaster prevention. Fengjie County, Chongqing, Chi...
Infertility is a social stigma for individuals, and male factors cause approximately 30% of infertility. Despite this, male infertility is underrecognized and underrepresented as a disease. According to the World Health Organization (WHO), changes in...
Background and Objectives: The rising prevalence of myopia is a significant global health concern. Atropine eye drops are commonly used to slow myopia progression in children, but their long-term use raises concern about intraocular pressure (IOP). T...
The widespread deployment of Internet of Things (IoT) devices has expanded the attack surface of modern networks, making IoT botnets a persistent cybersecurity threat. Although machine learning-based intrusion detection systems (IDSs) have demonstrat...
Low-voltage series arc faults pose a significant threat to power system safety due to their random, nonlinear, and non-stationary characteristics. Traditional detection methods often suffer from low sensitivity and poor robustness under complex load...
To evaluate whether nonlinear descriptors of electroencephalogram (EEG) signals support interpretable XGBoost classification and to determine how analysis window duration affects performance. A secondary analysis of the public Bonn EEG dataset was pe...
Accurately predicting well productivity is crucial for optimizing gas production and maximizing recovery from tight gas reservoirs. Machine learning (ML) techniques have been applied to build predictive models for the well productivity, but their hig...
Retinoblastoma is a rare and aggressive form of childhood eye cancer that requires prompt diagnosis and treatment to prevent vision loss and even death. Deep learning models have shown promising results in detecting retinoblastoma from fundus images,...
Smart meter anomaly detection is critical for ensuring the security and stability of smart grids. However, existing detection methods face the following limitations: insufficient feature extraction, severe class imbalance, inefficient manual hyperpar...
Objective: The objective of this study was to identify potential robust acoustic biomarkers for functional post-thyroidectomy voice disorder (PTVD) that may support early diagnosis and personalized treatment strategies, using acoustic analysis and ex...
Ensemble learning has been widely adopted in fraud detection; however, conventional ensemble strategies rely on uniform or performance-based weighting schemes that treat explainability as a post hoc annotation rather than an architectural component....
In order to fully examine how driving behavior affects vehicle energy consumption and further cut down on excessive energy use while driving, an energy-saving evaluation method for driving behavior based on the SHAP (Shapley Additive exPlanations) sc...
Dry reforming of methane (DRM) is a key reaction for syngas production and greenhouse gas utilisation, involving multiple interacting operating variables. In this work, an interpretable machine learning approach based on CatBoost regression coupled w...
Traditional single-prediction models often exhibit limitations in meeting wind power prediction requirements in complex operational scenarios. Furthermore, the inherent “black-box” nature of deep learning models leads to limited interpret...
The sensory evaluation of Longjing tea quality relies predominantly on subjective human judgment. Straightness is a critical sensory descriptor in the morphological quality assessment of Longjing tea, which lacks objective and quantifiable criteria....
Multi-sensor fusion of optical and synthetic aperture radar (SAR) imagery is widely used for land cover classification, yet most studies treat heterogeneous sensors as a uniform feature pool, leaving class-dependent differences in sensor contribution...
The quality of sampling data critically influences landslide susceptibility prediction accuracy. Current studies commonly use a 1:1 ratio of landslide to non-landslide samples, failing to reflect natural geographical variability. This study develops...
In predicting tubing corrosion rates under multi-factor coupling, traditional methods often struggle to effectively analyze the nonlinear interactions among variables such as temperature, pressure, CO2 partial pressure, and H2S partial pressure, and...
Eco-city (EC) development depends not only on environmental governance but also on the allocation of financial and technological resources. Digital inclusive finance (DIF) may be associated with green development through improved financial accessibil...
This paper proposes a hybrid forecasting framework that combines Long Short-Term Memory (LSTM) networks with Shapley Additive Explanations (SHAPs) to quickly and accurately predict solar radiation. Historical meteorological data from the Central Weat...
This study develops an empirical audit framework for assessing the explainability–reliability gap in fraud detection: whether stable model explanations remain consistent with performance-based feature reliance under distribution shift. Using th...
Leisure time is crucial for personal development and leisure consumption. Accurate prediction of leisure time and analysis of its influencing factors creates a benefit by increasing personal leisure time. We predict leisure time and analyze its key i...
Background/Objectives: Differentiated thyroid cancer (DTC) represents over 90% of all hyroid malignancies and typically has a favorable prognosis. However, approximately 30% of patients experience recurrence within 10 years after initial treatment. C...
Accurate State of Charge (SOC) estimation is critical for optimizing the performance and longevity of lithium-ion batteries (LIBs), which are widely used in applications ranging from electric vehicles to renewable energy storage. Traditional SOC esti...
The lack of interpretability in AI-based intrusion detection systems poses a critical barrier to their adoption in forensic cybersecurity, which demands high levels of reliability and verifiable evidence. To address this challenge, the integration of...
This paper proposes a PI-Dual-STGCN fault diagnosis model based on a SHAP-LLM joint explanation framework to address issues such as the lack of transparency in the diagnostic process of deep learning models and the weak interpretability of diagnostic...
Background: Diabetes mellitus affects over 500 million people worldwide, yet many machine learning prediction models remain difficult to interpret, limiting their clinical applicability. This study proposes an explainable machine learning framework i...
Existing research lacks consensus on how digital transformation affects financial distress, and traditional linear models struggle to capture complex variable dynamics. This study examines the link from prediction and association perspectives by cons...
Accurate monitoring of soil moisture in arid agricultural regions is essential for improving crop production and the efficient management of water resources. This study focuses on Shihezi City in Xinjiang, China. We propose a novel method for soil mo...
Virtual reality (VR) technologies are increasingly applied in rehabilitation, offering interactive physical and spatial exercises. A major challenge remains the objective assessment of human movement quality (HMQA). This study aimed to identify biome...
Reliable transformer differential protection requires fast discrimination between magnetizing inrush and internal faults in the presence of residual flux, harmonic voltage, direct current (DC) bias, measurement noise, and current-transformer (CT) sat...
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...
Climate-change-induced drought can increase domestic water shortage not only by reducing runoff but also by exposing weaknesses in local sources, reservoirs, and wide-area transfer networks. This study simulates domestic water shortage in the Seomjin...
Shipping companies and maritime organizations want to improve the energy efficiency of ships and reduce fuel costs through optimization measures; however, the accurate fuel consumption prediction of fuel consumption is a prerequisite for conducting o...
Understanding the cultural and environmental influences on roadway crash patterns is essential for designing effective prevention strategies. This study applies advanced AI techniques, including Bidirectional Encoder Representations from Transformers...
Sustainability assessments in manufacturing typically report on performance but provide little evidence of which observed indicators influenced a near-term outcome, or which improvement should be prioritized by management. The published sustainable i...
Soil degradation in arid ecosystems is a major threat to sustainable development and food security, especially under accelerating climate change. Kazakhstan, where more than 70% of agricultural land suffers from salinisation, erosion, and humus loss,...
Background: Optical coherence tomography (OCT) can quantify the morphology and dimensions of a macular hole for diagnosis and treatment planning. Objective: The aim of this study was to perform automatic segmentation of macular holes (MHs) and cysts...
The need for artificial intelligence (AI) and machine learning (ML) models to optimize data center (DC) operations increases as the volume of operations management data upsurges tremendously. These strategies can assist operators in better understand...
Precise forecasting and physical elucidation of seepage behavior are crucial for maintaining the operational safety of concrete dams. Nonetheless, current monitoring methodologies frequently fail to adequately encompass nonlinear temporal relationshi...
The skeletal muscle of cattle is the main component of their muscular system, responsible for supporting and movement functions. However, there are still many unknown areas regarding the ranking of the importance of different types of cell population...
Foam concrete is a lightweight material characterized by low density and moderate mechanical strength, which can be combined with aluminum alloys to form a novel type of column. Such composite members enable rapid assembly, disassembly, and functiona...
CRISPR-Cas9 has emerged as a remarkably powerful gene editing tool and has advanced both research and gene therapy applications. Machine learning models have been developed to predict off-target cleavages. Despite progress, accuracy, stability, and i...
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