Expert Systems最新文献

{"title":"Explainable AI, Augmented Reality and Metaverse Convergence in Smart City: Technologies, Challenges and Opportunities","authors":"Moumita Chanda,&nbsp;Olive Mazumder,&nbsp;Tawfikur Rahman,&nbsp;Nibedita Deb,&nbsp;Jannatul Ferdous,&nbsp;Kashfi Shormita Kushal","doi":"10.1111/exsy.70402","DOIUrl":"https://doi.org/10.1111/exsy.70402","url":null,"abstract":"<div>\u0000 \u0000 <p>Disruptive technologies such as the Metaverse, Augmented Reality (AR), and Explainable AI (XAI) are driving future transformation and are increasingly used by computers to provide data-driven solutions for smart city challenges like power, transportation, healthcare and education. However, a significant number of automated systems suffer from a loss of control over decision interpretability, making it difficult for people to understand and trust their outputs. This review paper aims to provide readers with a thorough understanding of the concepts, characteristics, and uses of XAI, AR, and the Metaverse in the context of smart cities, including a detailed examination of their applications, problems and solutions. It also seeks to offer a comprehensive examination of the latest research in this area to direct future studies. To achieve this, 145 articles published over the past decade were meticulously analysed. A qualitative assessment was also conducted utilising the Delphi technique to evaluate the examined literature. The main outcome of this research establishes that XAI, AR, and the Metaverse are interrelated components that drive smart city development by offering transparency, improving user experiences, and promoting cooperation and decision-making. Furthermore, these technologies are identified as essential for automated systems, public data security, decision interpretability, and remote connection with people, bringing the world closer to a global village.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"43 10","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TFE-AOD: Detecting Depressed Radical Co-Occurrence Individuals in Social Networks by Combining Temporal Fine-Grained Emotions and Aggressive Outbursts","authors":"Ming Yin,&nbsp;Yujie Yang,&nbsp;Peng Qin,&nbsp;Ziyao Zhu,&nbsp;Jijiao Jiang","doi":"10.1111/exsy.70396","DOIUrl":"https://doi.org/10.1111/exsy.70396","url":null,"abstract":"<div>\u0000 \u0000 <p>Depressed individuals with co-occurring radical tendencies pose greater potential risk than general depression sufferers. Previous studies largely detect depression sufferers or classify depressive symptoms in social networks, ignoring the radicalisation process under long-term complex emotions. Thus, we propose TFE-AOD, a property-specific method that incorporates temporal fine-grained emotions into aggressive outbursts to enhance detection of depression individuals with radical tendencies. The proposed method first classifies fine-grained emotions via an attention-based Transformer, introducing irony (e.g., self-mockery, sarcasm, struggle and powerlessness) to capture explicit depressive traits. It then combines fine-grained emotions with nocturnal activity to identify depression in long text sequences, leveraging time-dependent localisation to align with symptom accumulation. Nocturnal activity reflects the higher nighttime complaint propensity of depressed individuals. Finally, a knowledge-enhanced pretrained model detects co-occurrence by incorporating aggressive outburst features (alcohol abuse, drug dependency, covert hateful mentality, violence exposure). Experiments on Sina Weibo (SWDD) and a self-constructed Douban dataset show that TFE-AOD outperforms state-of-the-art models in detecting this co-occurrence.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"43 10","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An inception-ResNet deep learning approach to classify tumours in the ovary as benign and malignant","authors":"Ashwini Kodipalli,&nbsp;Srirupa Guha,&nbsp;Santosh Dasar,&nbsp;Taha Ismail","doi":"10.1111/exsy.13215","DOIUrl":"10.1111/exsy.13215","url":null,"abstract":"<p>The classification of tumours into benign and malignant continues to date to be a very relevant and significant research topic in the cancer research domain. With the advent of Computer Vision and rapid developments in the fields of deep learning, as well as medical devices and instruments, researchers are therefore utilizing the state-of-the-art deep learning architectures to discover patterns in the medical image data and thereby use this information to detect tumours and classify them as benign or malignant. In this paper, we propose a custom state-of-the-art deep learning architecture, the Inception-ResNet v2 for the classification of ovarian tumours into the two categories of benign and malignant based on a custom dataset with a validation accuracy of 97.5% and a test accuracy of 67%. Furthermore, a quantum convolutional neural network (QCNN) was also implemented with an accuracy of 92% on the validation dataset.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"43 9","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46952939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image Inpainting in 30 Years: A Survey","authors":"Zhenhua Yu,&nbsp;Hengxiang Zhao,&nbsp;Wenchao Zhang,&nbsp;Wei Song,&nbsp;Yu Zheng,&nbsp;Michele Nappi,&nbsp;Junxin Chen","doi":"10.1111/exsy.70386","DOIUrl":"https://doi.org/10.1111/exsy.70386","url":null,"abstract":"<div>\u0000 \u0000 <p>As a fundamental task in restoring continuous visual signals, image inpainting plays a critical role in autonomous driving perception, medical imaging, video editing and digital heritage preservation. Driven by deep learning and large-scale generative models, the field has transitioned from low-level texture synthesis to high-level semantic generation, yielding major breakthroughs in structural fidelity and visual realism. Centring on the generative paradigm as the architectural trajectory, this survey systematically categorizes the 30-year evolution of image inpainting into three distinct technological generations: traditional prior-driven synthesis, deep learning data-driven reconstruction and modern foundation model-driven generation. Despite this progress, highly competitive methods still struggle with large-scale missing regions, global consistency in complex scenes, fine-grained micro-details and alignment with human visual perception. To address these gaps, we critically evaluate the technical paradigms and main bottlenecks within each of these evolutionary stages. We categorize and compare mainstream breakthroughs across high-resolution restoration, text-guided synthesis and complex scene generation. Furthermore, we compile standard benchmarks, evaluation metrics and quantitative performance comparisons of representative algorithms. Finally, we dissect open challenges—focusing on cross-scene generalization and evaluation metric alignment—and outline future trajectories, particularly the integration of inpainting with text-guided foundation models, providing a definitive reference for future theoretical and engineering advancements.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"43 9","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A New Multi-Objective Ensemble System for Geoscience Resource Price Forecasting: Evidence from the Green Metal Market","authors":"Wendong Yang,&nbsp;Xinyi Zang,&nbsp;Junyuan Li,&nbsp;Yan Hao","doi":"10.1111/exsy.70387","DOIUrl":"https://doi.org/10.1111/exsy.70387","url":null,"abstract":"<div>\u0000 \u0000 <p>Green metals are an important type of geoscience resource and also essential components for building a clean energy future. However, their substantial price fluctuations make accurate price forecasting a crucial decision-making enabler for energy transitions. Previous studies have paid limited attention to the adaptive selection of effective subseries-level predictors. Consequently, this paper develops a novel multi-objective ensemble system with an adaptive selection mechanism for geoscience resource market management, called DPBPAPSME. Four modules are designed in DPBPAPSME. Specifically, the data preprocessing module reduces the negative impact of noise and modeling complexity of the original time series. The basic predictor module introduces four different artificial intelligence models to overcome the shortcomings of using a specific predictor. The adaptive predictor selection module overcomes the limitations of traditional subjective and objective indicator selection, adaptively identifies effective predictors, and improves the prediction precision and robustness. Furthermore, a multi-objective ensemble module optimizes and adjusts the weight coefficients of the predictors of the selected subseries and fully integrates the complementary forecasting information provided by the selected predictors. Three green metals, platinum, zinc, and copper, were used in the main experiments, and an additional gold dataset was employed to examine the generalizability of the proposed system. The mean absolute percentage errors of DPBPAPSME on the three datasets were 0.161292%, 0.143030%, and 0.091245%, respectively. The results indicate that the proposed DPBPAPSME system is a promising tool for the management of the geoscience resource market.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"43 9","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Topic-Driven Mixture of Experts With Low-Rank Adaptation for Multi-Trait Scoring of Teachers' Reflective Essays","authors":"Jiacheng Gu,&nbsp;Gong Wang,&nbsp;Fei Jiang,&nbsp;Zhiying Wang,&nbsp;Haibo Peng","doi":"10.1111/exsy.70381","DOIUrl":"https://doi.org/10.1111/exsy.70381","url":null,"abstract":"<div>\u0000 \u0000 <p>Current methods for multi-trait essay scoring mainly focus on style-based traits such as grammar and structure, with limited attention to multiple content-level traits. This limitation prevents them from effectively assessing the fine-grained content quality of essays, resulting in evaluation inaccuracies where high-scoring essays may reflect formulaic writing rather than insightful content. In this paper, we propose a novel multi-trait scoring task based on teachers' reflective essays, aiming to evaluate instructional insight conveyed through concrete content. To explore content-oriented scoring of teaching reflection, we first construct a real-world dataset, PTRE, consisting of 1820 reflective essays written by pre-service teachers. Each essay is annotated with scores for four instructional content traits: Textual Expression, Problem Awareness, Comprehensive Thinking and Continuous Growth. Additionally, the dataset includes partial sentence-level annotations highlighting trait-relevant content as supporting evidence for each trait. We further design TMoLE, a topic-driven mixture of experts framework for content-oriented multi-trait scoring. It learns trait-specific topic distributions guided by seed words derived from sentence-level annotations under expert guidance, ensuring semantic alignment with each scoring trait. Using these distributions as routing signals, TMoLE dynamically activates experts implemented with low-rank adaptation to precisely capture trait representations and improve multi-trait scoring. Experimental results on the constructed PTRE dataset show that TMoLE achieves an average Quadratic Weighted Kappa (QWK) of 0.6138. It achieves the highest average QWK among the compared methods and the best results on Textual Expression, Problem Awareness and Continuous Growth, supporting its effectiveness for content-oriented multi-trait scoring.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"43 9","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148617298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Review on Large-Scale Hardware and Software Platforms for Neuromorphic Computing","authors":"Partha Pratim Ray","doi":"10.1111/exsy.70385","DOIUrl":"https://doi.org/10.1111/exsy.70385","url":null,"abstract":"<div>\u0000 \u0000 <p>Neuromorphic computing is emerging as a promising paradigm for sustainable edge intelligence by enabling event-driven, low-latency and energy-aware computation close to sensors. However, the field remains fragmented across device technologies, mixed-signal and digital hardware platforms, spiking neural network models, software frameworks, event-stream processing tools, interoperability standards and benchmarking practices. This review provides a cross-layer synthesis of contemporary neuromorphic computing platforms with particular emphasis on their relevance to scalable and sustainable edge deployment. The article organizes the neuromorphic ecosystem into interconnected layers spanning materials and devices, hardware architectures, software and interoperability tools, benchmark resources and application domains. Mixed-signal platforms are analysed in terms of analog efficiency, accelerated neural dynamics, biological plausibility, calibration requirements, variability and reproducibility challenges. Digital neuromorphic processors are examined with respect to programmability, deterministic execution, routing fabrics, memory organization, software integration and deployment readiness. The review further discusses software frameworks, simulators, event-data libraries, hardware-mapping tools and intermediate representations that support model development, portability and cross-platform evaluation. A central finding is that neuromorphic systems cannot be compared meaningfully using isolated metrics such as neuron count, chip power, latency, or throughput alone; fair evaluation requires explicit reporting of workload, event rate, model topology, mapping strategy, software stack, measurement boundary and deployment context. Accordingly, the article proposes a benchmarking and reporting perspective for neuromorphic edge intelligence that links accuracy, latency, energy efficiency, robustness and reproducibility. Thus, this review clarifies current progress, unresolved challenges and future directions for sustainable edge sensing, robotics, healthcare monitoring, smart infrastructure, industrial automation and distributed intelligent systems.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"43 9","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CSG-Net: A Causal-Aware Structural Graph Network for Multimodal Music Emotion Recognition","authors":"Lu Chen,&nbsp;Yu Liu","doi":"10.1111/exsy.70384","DOIUrl":"https://doi.org/10.1111/exsy.70384","url":null,"abstract":"<div>\u0000 \u0000 <p>Multimodal Music Emotion Recognition (MMER) aims to bridge the ‘affective semantic gap’ between machine perception and human emotion by fusing information from audio and lyrics. However, state-of-the-art models encounter a performance plateau, a problem we trace back to three persistent, structural challenges: (1) the neglect of music's intrinsic narrative structure, where models predominantly treat music as linear feature sequences; (2) a biased dependency on certain modalities, especially the ‘Lyrical Dominance Bias’, where models over-rely on explicit lyrical cues, particularly in cases of emotional conflict; and (3) data modality incompatibility, where existing frameworks struggle to process both lyrical songs and instrumental pieces uniformly. To systematically address these issues, we introduce the Causal-aware Structural Graph Network (CSG-Net). CSG-Net contributes to the field by uniquely integrating structural and causal priors into a unified MMER framework. First, we propose an adaptive structural topology module to parameterise the hierarchical logic of emotional progression and repetition, thereby moving beyond the limitations of linear modelling. Second, we pioneer the use of causal inference in this domain, designing a training and inference mechanism based on counterfactual intervention. This allows the model to disentangle and suppress the direct, often biasing, influence of lyrics, forcing it to learn the more robust causal relationship between audio and text. Finally, through an efficient random lyric masking strategy, CSG-Net achieves seamless compatibility with different musical forms within a single model. Extensive experiments demonstrate that CSG-Net significantly outperforms 14 highly competitive baselines, establishing a new state-of-the-art specifically on the MER-500 and MuSe-CaR benchmarks under standard evaluation protocols, achieving an absolute accuracy improvement of +2.7% on MER-500 and +2.2% on MuSe-CaR (<i>p</i> &lt; 0.005). Notably, the model limits performance degradation to only 12% during zero-shot transfer to purely instrumental data (compared to &gt; 22% in baselines), providing consistent quantitative evidence of its superior cross-modal generalisation and robustness.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"43 9","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148617186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AISyst: AI-Powered Interactive Visual System to Assist With Fidelity Assessment of Synthetic Tabular Data","authors":"Liqun Liu,&nbsp;Leonid Bogachev,&nbsp;Netochukwu Onyiaji,&nbsp;Lukas Čironis,&nbsp;János Gyarmati-Szabó,&nbsp;Roy A. Ruddle","doi":"10.1111/exsy.70373","DOIUrl":"https://doi.org/10.1111/exsy.70373","url":null,"abstract":"<p>Evaluating synthetic data produced by generative models remains a critical challenge in sensitive domains such as healthcare and finance. Ensuring that such data is ‘faithful’ to real data is essential for downstream applications and decision-making, including regulatory compliance. This paper introduces an AI-powered interactive visual system—AIS<span>yst</span>—designed to assess the fidelity of synthetic tabular datasets. The system supports multilevel comparisons with real datasets, spanning multivariate resemblance analyses based on dimensionality reduction through suitable two-dimensional projections, bivariate correlation and univariate similarity. AIS<span>yst</span> also integrates an AI assistant by leveraging state-of-the-art large language models to summarize key findings and generate suggestions for improving synthetic data generation models. We validated the capabilities of AIS<span>yst</span> through three case studies, supported by feedback from industrial AI experts who endorsed its broader deployment.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"43 9","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70373","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MSPCIFormer: A Multi-Scale Patching Channel-Independent Transformer for Cryptocurrency Price Forecasting","authors":"Huali Zhao,&nbsp;Martin Crane,&nbsp;Marija Bezbradica","doi":"10.1111/exsy.70343","DOIUrl":"https://doi.org/10.1111/exsy.70343","url":null,"abstract":"<p>Forecasting cryptocurrency prices remains challenging due to extreme volatility, regime-dependent dynamics, and unstable cross-asset correlations. Statistical methods such as ARIMA and GARCH assume stationarity and linear dependence structures, making them inadequate for capturing non-linear temporal patterns in high volatile cryptocurrency data. Conventional machine learning methods often require hand-crafted features and fail to capture the sequential temporal dependencies inherent in price series. Recurrent deep learning approaches such as recurrent neural networks (RNNs) and LSTMs address the issues but suffer from limited parallelization, vanishing gradients, and difficulty in learning multi-scale temporal patterns. The advances in Transformer have demonstrated strong capability in capturing long-range temporal dependencies through self-attention while enabling parallel computation. However, canonical Transformer still struggle with noisy, volatile financial time series due to computational complexity and sensitivity to irrelevant temporal patterns. These limitations motivate the exploration of Transformer-based architectures. Our study makes three key contributions. First, we propose MSPCIFormer, a novel Transformer-based architecture that integrates multi-scale patching with channel-independent (CI) modelling to capture heterogeneous temporal dynamics while mitigating noise from time-varying inter-asset correlations. Second, we conduct comprehensive experiments comparing MSPCIFormer with state-of-the-art Transformer models and strong time-series forecasting baselines, evaluated using both statistical and economic metrics across multiple forecasting horizons. Third, we establish a unified evaluation framework incorporating both Hold-out and Walk-forward evaluation framework with economical metrics for regime-robustness testing. Empirical results demonstrate that MSPCIFormer achieves the best or tied-best predictive accuracy among all Transformer-based baselines across three cryptocurrency assets, while maintaining competitive and stable performance across diverse market regimes.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"43 9","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70343","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}