Guanjin Wang

Explainable Artificial Intelligence methods have gained significant traction for their ability to elucidate the decision-making processes of black-box models, particularly in high-stakes fields such as healthcare and finance. Among these, Local Interpretable Model-agnostic Explanations (LIME) stands out as a widely adopted post-hoc, model-agnostic approach that interprets black-box predictions by constructing an interpretable surrogate model on perturbed instances to approximate the local behavior of the original model around a given instance. However, the effectiveness of LIME can depend on the quality of the training data used by the black-box model. When trained on limited or low-quality data, the black-box model may yield inaccurate predictions for perturbed samples, resulting in poorly defined local decision boundaries and consequently unreliable explanations. This limitation is especially problematic in data-scarce settings. To overcome this challenge, we propose TransLIME, a novel end-to-end explainable transfer learning framework that improves the local fidelity and stability of LIME on limited tabular datasets by transferring relevant explainability knowledge from a related auxiliary source domain with a shifted distribution. Also, in TransLIME, only representative source prototype explanations obtained through clustering are transferred to the target domain, thereby reducing cross-domain exposure of both data and explanatory information during transfer. Experimental evaluations on real-world datasets demonstrate the effectiveness of the proposed framework in improving explanation quality in target domains with limited data.

Comprehending biological reproduction and cellular metabolism is facilitated by the Enzyme Commission, which matches protein sequences to the biochemical reactions they catalyse through EC numbers. In recent years, several methods have been proposed for predicting enzyme function. However, these methods still encounter challenges. Firstly, traditional methods for manually designing enzyme features are complex and cumbersome, lacking an effective generalized method for embedding enzyme sequences. Secondly, the distribution gap between different enzymes is significant, which resulting in existing methods struggling to predict multilevel enzyme functions. Thirdly, traditional enzyme function prediction models only extract single view feature of enzyme, so there is still room for further improving the ability of these models to extract enzyme data. To address these challenges, a new multilevel enzyme function prediction model (SMENET) based on multi-view semantics is proposed. This method uses protein large language model to extract semantic information. Subsequently, this semantic information is fed into multiple information extraction network modules, followed by using Biologic Sematic Attention to integrate these views' information. Finally, a multi-view adaptive fusion network is designed to extract the best common representation between multiple semantic views. Extensive experiments were conducted on multiple datasets to validate the effectiveness of SMENET. The code and dataset of this study are available at https://github.com/zerohanwen/SMENET.

Aboriginal pregnant women and new mothers face an increased risk of mental health issues, often stemming from historical trauma, including violence and discrimination. These challenges could contribute to complex trauma and adverse perinatal outcomes, highlighting the need for culturally sensitive care. However, non-Aboriginal clinicians often face barriers due to limited cultural knowledge, exacerbated by other factors such as time constraints for training and reliance on one-time training. Large Language Models (LLMs)-based chatbots offer the potential to support self-directed learning and enhance clinicians' self-efficacy through interactive question and answer. However, LLMs also pose challenges, including hallucinatory responses, outdated knowledge, fictitious information, unverifiable references, and difficulty handling domain-specific queries. In this study, we aim to mitigate these challenges by developing a specialized chatbot for improving Aboriginal perinatal mental health question-answering. The chatbot integrates Retrieval-Augmented Generation (RAG) with a semantic search engine, enabling it to retrieve verified external knowledge and provide more accurate, contextually relevant responses without frequent retraining. We evaluate its performance against a baseline GPT-3.5-turbo model and compare LLMs integrated with different RAG techniques to assess improvements in accuracy and reliability. Clinical Relevance- This study shows the potential of the specialized RAG LLM-based chatbot to improve domain-specific, clinically relevant, and on-demand question-answering support for clinicians. By providing accurate, verified information through interactive responses, it may help bridge knowledge gaps, support self-directed learning, and complement existing training.

Fuzzy clustering has received significant attention due to its robust capability for handling uncertain data and its strong clustering performance. Recently, multi-view fuzzy clustering methods have become a popular research focus for multi-view data. However, existing methods often suffer from low computational efficiency because of the extensive distance calculations required during iterative optimization. Moreover, the challenge of adaptively and comprehensively extracting discriminative information across views to enhance clustering performance remains largely unresolved. To address these issues, this paper proposes a new adaptive multi-view fuzzy clustering method based on diverse anchors guidance (AMVFC_DAG). Based on anchor graph theory, the proposed method first introduces a new diverse anchor graph learning method with dual information exploration to capture both multi-scale common and specific information across views. To further improve clustering efficiency, the proposed method integrates this diverse anchor graph learning into a unified multi-view clustering framework with multi-scale anchors, enabling mutual enhancement between components and thereby boosting overall performance. Furthermore, adaptive scale and view weighting are incorporated to enable flexible clustering. Finally, we propose an adaptive fusion mechanism that combines the learned anchor graphs with an anchor membership matrix, to produce the final cluster assignments. Extensive experiments on multiple multi-view datasets demonstrate the proposed method's effectiveness in both clustering performance and computational efficiency.

Generative models (GMs), particularly large language models (LLMs), have garnered significant attention in machine learning and artificial intelligence for their ability to generate new data by learning the statistical properties of training data and creating data that resemble the original data. This capability offers a wide range of applications across various domains. However, the complex structures and numerous model parameters of GMs obscure the input-output processes and complicate the understanding and control of the outputs. Moreover, the purely data-driven learning mechanism limits GMs' abilities to acquire broader knowledge. There remains substantial potential for enhancing the robustness and generalization capabilities of GMs. In this work, we leverage fuzzy system, a classical modeling method, to combine both data-driven and knowledge-driven mechanisms for generative tasks. We propose a novel generative fuzzy system framework, named GenFS, which integrates the deep learning capabilities of GMs with the term-based interpretability and dual-driven mechanisms of fuzzy systems. Specifically, we propose an end-to-end GenFS-based model for sequence generation, called FuzzyS2S. A series of test studies were conducted on 12 datasets, covering three distinct categories of generative tasks: machine translation, code generation, and summary generation. The results demonstrate that FuzzyS2S outperforms the transformer in terms of accuracy and fluency. Furthermore, it exhibits better performance than state-of-the-art models T5 and CodeT5 for some application scenarios.

While Large Language Models (LLMs) have shown remarkable proficiency in text-based tasks, they struggle to interact effectively with the more realistic world without the perceptions of other modalities such as visual and audio. Multi-modal LLMs, which integrate these additional modalities, have become increasingly important across various domains. Despite the significant advancements and potential of multi-modal LLMs, there has been no comprehensive PRISMA-based systematic review that examines their applications across different domains. The objective of this work is to fill this gap by systematically reviewing and synthesising the quantitative research literature on domain-specific applications of multi-modal LLMs. This systematic review follows the PRISMA guidelines to analyse research literature published after 2022, the release of OpenAI’s ChatGPT
3.5. The literature search was conducted across several online databases, including Nature, Scopus, and Google Scholar. A total of 22 studies were identified, with 11 focusing on the medical domain, 3 on autonomous driving, and 2 on geometric analysis. The remaining studies covered a range of topics, with one each on climate, music, e-commerce, sentiment analysis, human-robot interaction, and construction. This review provides a comprehensive overview of the current state of multi-modal LLMs, highlights their domain-specific applications, and identifies gaps and future research directions.

The federated Fuzzy C-means (federated FCM) extends the traditional Fuzzy C-means (FCM) to the federated learning (FL) scenario, aiming to address the data privacy preservation issue of soft clustering in distributed environments. However, a significant challenge persists with existing federated FCM algorithms, i.e., they struggle to converge effectively in complex heterogeneous scenarios, leading to unstable clustering outcomes. Here the complex heterogeneous scenarios stem from the combination of non-independently and identically distributed (non-IID) data across different clients (statistical heterogeneity), coupled with the involvement of only some clients in each iteration (systematic heterogeneity). While prior research has attempted to address the impact of statistical heterogeneity in FL scenarios, it has overlooked the issue of system heterogeneity. In response, this paper proposes a novel federated FCM algorithm (SC-FFCM) that remains robust even in such complex heterogeneous scenarios. Firstly, the client-side clustering module of SC-FFCM adopts a Gradient-Based FCM algorithm, facilitating corrections to the direction of local optimization. Secondly, the algorithm introduces a control variates technique to rectify update bias during the iteration process, thereby mitigating the adverse effects of random client sampling and non-IID data distribution on the algorithm convergence. Finally, the proposed algorithm approximates the ideal federated FCM algorithm. Experimental studies verify the effectiveness of the proposed method. The source code of the proposed SC-FFCM algorithm is available from the following website https://github.com/Creazy-MR/SC-FFCM.

The rapid advancement of medical imaging technologies has led to an exponential increase in medical image data, making efficient retrieval from large-scale datasets critical for improving diagnostic accuracy and speed. However, two key challenges hinder this process: first, the presence of uncertain and subtle lesions in medical images that are often difficult to discern, and second, class imbalance across different case types within medical image databases. These inherent challenges significantly degrade the performance of existing hashing algorithms. In recent years, methods based on the Takagi-Sugeno-Kang fuzzy system (TSK-FS) have shown promising performance in medical image modeling. Inspired by these advances, this article proposes a novel fuzzy hashing network (FHN) based on TSK-FS to enhance retrieval performance by effectively handling both uncertainty and data imbalance in medical imaging. The FHN first introduces a novel fuzzification mechanism that incorporates the concept of a self-attention mechanism to effectively capture the complex underlying features in medical images, thereby enhancing the data discriminability in fuzzy spaces. Meanwhile, a new consequent parameter learning mechanism is developed for defuzzification by introducing the Transformer network, which aims to improve the inference efficiency and generalization capability of the FHN. Based on these two mechanisms, FHN's capability of analyzing and handling uncertain data is significantly enhanced. Furthermore, a novel hash center loss is designed to capture global relationships while emphasizing local structural information, thereby improving the handling of imbalanced data and significantly enhancing retrieval performance.

Smartphone sensor-based human activity recognition (SSHAR) generally deals with three main steps: 1) raw signal collection; 2) feature extraction; and 3) human activity classification. This study focuses on an interpretable human activity classification method to enhance SSHAR's very applicability for the application scenarios like healthcare services and personal biometric signature. To this end, by taking Takagi-Sugeno-Kang fuzzy classifiers as the subclassifiers, a novel interpretable ensemble fuzzy classifier FINE is proposed to provide linguistically interpretable fuzzy rules for classification, strong generalization and scalability for SSHAR. Since each subclassifier of FINE works on its bootstrapping subspace of original features and then is combined without an explicit aggregation, FINE has the following characteristics: 1) the diversities among all the subclassifiers are assured; 2) more generalization capabilities than the corresponding structure of each subclassifier on all the input features is justified; 3) its incremental learning can be implemented through only training an incremental subclassifier or training FINE only on incremental data. The experimental results demonstrate that FINE not only keeps at least comparable to and even better than most of the comparative methods in terms of testing performance and training time but also has both linguistically interpretable fuzzy rules and fast incremental learning capability.

Background:
Perinatal depression and anxiety significantly impact maternal and infant health, potentially leading to severe outcomes like preterm birth and suicide. Aboriginal women, despite their resilience, face elevated risks due to the long-term effects of colonization and cultural disruption. The Baby Coming You Ready (BCYR) model of care, centered on a digitized, holistic, strengths-based assessment, was co-designed to address these challenges. The successful BCYR pilot demonstrated its ability to replace traditional risk-based screens. However, some health professionals still overrely on psychological risk scores, often overlooking the contextual circumstances of Aboriginal mothers, their cultural strengths, and mitigating protective factors. This highlights the need for new tools to improve clinical decision-making.

Objective:
We explored different explainable artificial intelligence (XAI)–powered machine learning techniques for developing culturally informed, strengths-based predictive modeling of perinatal psychological distress among Aboriginal mothers. The model identifies and evaluates influential protective and risk factors while offering transparent explanations for AI-driven decisions.

Methods:
We used deidentified data from 293 Aboriginal mothers who participated in the BCYR program between September 2021 and June 2023 at 6 health care services in Perth and regional Western Australia. The original dataset includes variables spanning cultural strengths, protective factors, life events, worries, relationships, childhood experiences, family and domestic violence, and substance use. After applying feature selection and expert input, 20 variables were chosen as predictors. The Kessler-5 scale was used as an indicator of perinatal psychological distress. Several machine learning models, including random forest (RF), CatBoost (CB), light gradient-boosting machine (LightGBM), extreme gradient boosting (XGBoost), k-nearest neighbor (KNN), support vector machine (SVM), and explainable boosting machine (EBM), were developed and compared for predictive performance. To make the black-box model interpretable, post hoc explanation techniques including Shapley additive explanations and local interpretable model-agnostic explanations were applied.

Results:
The EBM outperformed other models (accuracy=0.849, 95% CI 0.8170-0.8814; F1-score=0.771, 95% CI 0.7169-0.8245; area under the curve=0.821, 95% CI 0.7829-0.8593) followed by RF (accuracy=0.829, 95% CI 0.7960-0.8617; F1-score=0.736, 95% CI 0.6859-0.7851; area under the curve=0.795, 95% CI 0.7581-0.8318). Explanations from EBM, Shapley additive explanations, and local interpretable model-agnostic explanations identified consistent patterns of key influential factors, including questions related to “Feeling Lonely,” “Blaming Herself,” “Makes Family Proud,” “Life Not Worth Living,” and “Managing Day-to-Day.” At the individual level, where responses are highly personal, these XAI techniques provided case-specific insights through visual representations, distinguishing between protective and risk factors and illustrating their impact on predictions.

Conclusions:
This study shows the potential of XAI-driven models to predict psychological distress in Aboriginal mothers and provide clear, human-interpretable explanations of how important factors interact and influence outcomes. These models may help health professionals make more informed, non-biased decisions in Aboriginal perinatal mental health screenings.