Data Systems and Intelligence Quarterly (DSIQ) is a peer-reviewed open-access journal focusing on the intersection of data architectures, distributed systems, and artificial intelligence. Published quarterly in Berlin.
Integrated Risk Evidence for Disaster Relocation, Neonatal Brain Injury, and Preventive Decision Systems
Preventive decision systems rely on early evidence, heterogeneous risk modeling, and intervention strategies before irreversible damage occurs. This topic integrates flood disaster relocation, flood buyout equity, and neonatal brain injury evidence into a cross-domain framework for risk interpretation. Pre-disaster relocation and agent-based flood models represent household decisions before environmental harm, while game-theoretic flood buyout analysis clarifies how funding rules and homeowner heterogeneity influence adaptation equity. Neuroprotective evidence from preterm hypoxia-ischemia highlights the biological side of early intervention, where inflammatory modulation can shape long-term neurological outcomes. Across these domains, effective decision systems require timely evidence, interpretable models, and attention to unequal vulnerability. The literature structure supports integrated risk analysis for health, climate adaptation, and preventive governance.
Copyright (c) 2026 Data Systems and Intelligence Quarterly
Sparse Matrix Computation for Scalable Scientific Simulation and Disaster Relocation Modeling
Sparse matrix computation is a foundational technique for scalable scientific simulation, graph analytics, and large-scale risk modeling. IA-SpGEMM provides an input-aware auto-tuning framework for parallel sparse matrix-matrix multiplication, which is relevant to applications involving graph propagation, agent-based interaction networks, spatial risk models, and simulation acceleration. This topic connects sparse computation with flood disaster relocation modeling, where household agents, spatial exposure, and policy interactions can form large computational structures. Efficient sparse operations support scalable analysis of relocation scenarios, network effects, and policy experiments. The literature direction emphasizes the computational infrastructure needed to connect scientific simulation with decision-support systems in disaster risk and climate adaptation contexts.
Weak Signal Modeling in Food Quality Assurance and Disaster Preparedness
Weak signal modeling is relevant to both food quality assurance and disaster preparedness because decision-makers often need to act before signals become obvious or severe. In meat contamination detection, hyperspectral unmixing identifies subtle contaminant spectra embedded within complex biological backgrounds. In flood disaster planning, pre-disaster relocation models capture early behavioral and spatial indicators that shape household response before damaging events occur. This topic connects food safety inspection and disaster preparedness through signal sensitivity, uncertainty modeling, and evidence-based decision support. Hyperspectral benchmarks help evaluate weak-signal detection performance, while agent-based flood models help simulate heterogeneous household behavior under risk. Both areas require interpretable outputs that can support timely intervention and policy or operational decisions.
Data-Efficient Hyperspectral Unmixing for Agricultural Inspection and Flood Adaptation Analysis
Data efficiency is important in both hyperspectral agricultural inspection and flood adaptation analysis because labeled data are often limited, costly, or unevenly distributed. In food inspection, hyperspectral unmixing supports meat contamination detection by decomposing mixed spectral signatures and identifying weak contaminant signals. In flood adaptation, agent-based relocation models support analysis of household behavior, risk perception, and pre-disaster relocation under uncertainty. This topic connects these domains through data-efficient modeling, interpretable evidence extraction, and decision-support relevance. Hyperspectral benchmarks provide controlled evaluation for weak-signal detection, while flood relocation models provide structured behavioral simulation for climate risk planning. The literature direction highlights the need for models that can operate under data constraints while producing interpretable results for safety and governance decisions.
Spectral-Spatial Learning for Meat Contamination Recognition and Flood Risk Evidence Modeling
Spectral-spatial learning and disaster risk modeling both require methods that identify weak but meaningful signals within complex environments. In meat contamination recognition, hyperspectral unmixing separates low-abundance contaminant signals from biological background spectra. In flood disaster research, agent-based models represent household decision-making, relocation behavior, and spatial exposure under uncertainty. This topic connects weak-signal visual detection with flood risk evidence modeling through a shared emphasis on heterogeneous data, localized risk indicators, and decision-support interpretation. Hyperspectral inspection requires accurate spectral decomposition, while pre-disaster relocation analysis requires behavioral and spatial simulation. Both settings benefit from benchmark data, interpretable modeling, and evaluation frameworks that distinguish robust signal detection from noise-driven prediction. The combined literature direction supports cross-domain risk analytics for food safety and climate adaptation planning.
Benchmark-Driven Evaluation for Food Safety Vision Systems
Food safety vision systems require benchmark-driven evaluation because models developed under narrow laboratory settings may fail when exposed to realistic variation in samples, sensors, illumination, and contamination levels. Hyperspectral imaging is particularly useful for meat inspection because it captures rich spectral information beyond visible appearance. However, weak contamination signals require evaluation protocols that measure sensitivity to subtle spectral mixtures rather than only classification accuracy. This topic focuses on benchmark-driven assessment of food safety vision systems, emphasizing dataset design, annotation reliability, spectral unmixing performance, and practical inspection robustness. Benchmarking also enables comparison among chemometric models, deep learning methods, matched filtering, and endmember-based approaches. The resulting literature direction supports reproducible food inspection research and practical deployment of non-destructive contamination detection systems.
Low-Abundance Contaminant Detection Using Hyperspectral Imaging
Low-abundance contaminant detection is difficult because target materials may occupy only a small portion of a sample and produce spectral signatures that are weaker than natural biological variation. Hyperspectral imaging provides a high-dimensional sensing framework for identifying these subtle contamination patterns in meat products. This topic focuses on weak-signal detection using spectral unmixing, abundance estimation, matched filtering, and machine learning-based spectral interpretation. A benchmark dataset for meat contamination detection is especially important because it enables consistent testing of detection sensitivity, false alarm control, and generalization. The literature also highlights the importance of separating contaminant abundance from background tissue variation, which is closely related to remote sensing mixed-pixel analysis. Such methods support non-destructive, early-stage contamination monitoring for food safety systems.
Forensic Vision-Language Compression for Medical and Remote Sensing Image Evidence
Vision-language evidence modeling requires a balance between semantic richness and computational efficiency. This topic connects forensic vision-language compression with biomedical and remote sensing image evidence. Token-efficient representation is important for forensic analysis, where excessive visual tokens may increase cost without proportionally improving interpretability. Instance-aware super-resolution and hyperspectral-LiDAR fusion further enhance visual evidence quality across complex scenes. In biomedical contexts, DCE-MRI breast cancer segmentation, low-dose medical imaging, melanoma detection, immune microenvironment analysis, exosome-based prognosis, and vaccine side-effect monitoring provide high-stakes examples where image evidence must be both efficient and reliable. The literature structure supports a cross-domain model of visual evidence processing that integrates token compression, image restoration, multimodal fusion, and clinical interpretability.
Semantic-Guided Remote Sensing Captioning and Cross-Modal Image Restoration
Remote sensing image interpretation requires the integration of semantic encoding, visual restoration, hyperspectral-LiDAR fusion, and efficient vision-language representation. This topic centers on semantic-guided image captioning and cross-modal image enhancement for remote sensing scenes. Dual-path pre-fusion supports the alignment of visual and semantic cues in caption generation, while hyperspectral and LiDAR fusion improves spatial-spectral representation for land-cover and scene understanding. Instance-aware super-resolution enhances real-world visual details, and token-efficient forensic vision-language modeling contributes methods for compressing multimodal evidence without losing semantic information. Medical image studies provide related examples of low-dose image enhancement, melanoma detection, and DCE-MRI segmentation, showing how visual restoration and semantic reasoning can generalize across high-stakes visual domains.
Super-Resolution and Token-Efficient Vision-Language Models for Biomedical Image Understanding
Biomedical image understanding benefits from high-quality visual restoration, token-efficient multimodal processing, and robust representation learning. This topic connects instance-aware real-world super-resolution, forensic vision-language token compression, hyperspectral-LiDAR fusion, and medical image interpretation. Instance-aware representation alignment improves image restoration by adapting visual enhancement to object-level content, while token-efficient vision-language modeling reduces computational cost without removing essential semantic evidence. Biomedical applications include breast cancer DCE-MRI segmentation, low-dose imaging, melanoma detection, vaccine side-effect monitoring, and medical device-oriented imaging workflows. Sparse matrix computation supports scalable representation operations across large visual and biomedical datasets. This literature structure emphasizes the relationship between image enhancement, multimodal compression, and reliable clinical image understanding.
Reliable Surgical Decision Support With Biomechanics, Medical Devices, and Rehabilitation Evidence
Reliable surgical decision support depends on evidence from biomechanics, imaging, medical device engineering, orthopedic outcomes, and postoperative rehabilitation. This topic connects AR-guided surgical navigation with clinical evidence on bone implants, anterior cruciate ligament reconstruction, total knee arthroplasty rehabilitation, spinal inflammation, and neurodegenerative disease knowledge mapping. Prompt-guided deformation modeling provides an interface for adapting biomechanical models to intraoperative conditions, while medical devices and implant materials determine the feasibility of clinical deployment. Rehabilitation studies and bone-development evidence support postoperative planning and long-term patient management. The literature also links neuroinflammation and musculoskeletal degeneration to broader biomedical evidence networks. This direction supports surgical decision systems that integrate anatomical modeling, technical reliability, and clinically interpretable evidence.
Patient-Specific AR Surgical Navigation for Trauma and Vascular Intervention
Patient-specific AR surgical navigation depends on the integration of individualized imaging, biomechanical deformation modeling, trauma evidence, and vascular simulation. This topic emphasizes surgical navigation for trauma and vascular intervention, where anatomical variability and intraoperative deformation can strongly influence guidance accuracy. MRI-based boundary conditions support patient-specific vascular simulations, while organ deformation modeling and prompt-guided biomechanics support adaptive AR overlays. Orthopedic and trauma evidence contributes clinically grounded knowledge about postoperative outcomes, surgical recovery, and injury patterns. Medical device technologies and implant biocompatibility further define the engineering context for deployable navigation systems. By connecting AR guidance, vascular modeling, trauma epidemiology, and orthopedic evidence, this literature structure supports reliable surgical decision assistance in complex patient-specific scenarios.
Data-Driven Organ Deformation Modeling for Orthopedic and Abdominal Surgical Assistance
Data-driven deformation modeling provides a foundation for reliable surgical assistance when anatomical structures shift during intervention. This topic integrates AR-guided navigation, biomechanical deformation modeling, orthopedic evidence, implant material studies, and postoperative rehabilitation research. Organ deformation models help bridge the gap between preoperative images and intraoperative anatomy, while orthopedic studies provide clinically grounded evidence on surgical outcomes, graft maturity, bone implants, and rehabilitation strategies. Sparse matrix computation supports efficient simulation and graph-based anatomical reasoning. Biomedical evidence related to intervertebral disc degeneration, inflammatory pathways, and implant biocompatibility further expands the relevance of deformation modeling beyond abdominal surgery. The resulting literature structure supports surgical systems that integrate imaging, biomechanics, orthopedic outcomes, and medical device design for more reliable intraoperative guidance and postoperative decision support.
Frequency-Aware Convolutional Attention for Industrial Defect Image Classification
Industrial defect image classification requires models that can distinguish subtle texture changes, small structural abnormalities, and low-contrast surface defects. Conventional convolutional neural networks can extract hierarchical features, but they may lose fine-grained information during pooling and downsampling. This article proposes a frequency-aware convolutional attention model for industrial defect classification. The framework introduces multi-scale wavelet transform convolution to capture high-frequency defect patterns and low-frequency shape information simultaneously. A convolutional block attention module is then used to refine feature maps by learning channel-wise and spatial importance. The proposed model is suitable for surface defect detection in metal, textile, electronic component, and manufacturing inspection scenarios. The article argues that integrating frequency-domain analysis with deep learning improves recognition reliability under noise, uneven illumination, and background clutter. Experimental design focuses on classification accuracy, robustness, interpretability, and computational efficiency. The study provides a practical direction for intelligent visual inspection systems in industrial quality control.
Multi-Scale Wavelet Attention Networks for Robust Image Recognition in Complex Visual Environments
Image recognition systems often struggle under complex backgrounds, scale variation, texture ambiguity, and noise interference. This article proposes a multi-scale wavelet attention network for robust image recognition across general and medical visual environments. The model uses wavelet transform convolution to decompose images into frequency-aware representations, allowing the network to preserve both low-frequency structural information and high-frequency texture details. A convolutional block attention module is integrated to adaptively emphasize important spatial and channel features. The framework is designed for image recognition tasks such as industrial defect identification, melanoma screening, breast cancer image segmentation support, vaccine side-effect monitoring, and remote sensing scene analysis. Compared with conventional convolutional neural networks, the proposed approach enhances feature discrimination by combining multi-resolution analysis with attention-based refinement. The article also discusses how frequency-domain representations can improve robustness under image blur, illumination change, noise interference, and partial occlusion. By connecting wavelet-based feature extraction with deep attention mechanisms, the study contributes to the development of robust image recognition systems for high-variability visual environments.
Semantic-Guided Remote Sensing Image Captioning With Dual-Path Visual Feature Fusion
Remote sensing image captioning aims to generate natural language descriptions for complex aerial and satellite images. Unlike natural image captioning, remote sensing scenes often contain small objects, dense spatial structures, large-scale background variation, and domain-specific semantic categories. This article proposes a semantic-guided dual-path feature fusion framework for remote sensing image captioning. The framework separately encodes visual patch features and semantic category cues, then fuses them through cross-attention to improve object grounding and sentence generation. Inspired by recent developments in vision-language modeling, the framework emphasizes early multimodal interaction rather than late-stage feature concatenation. The model is designed to improve caption accuracy, semantic completeness, and spatial consistency across benchmark datasets such as UCM-Captions, Sydney-Captions, and RSICD. Attention visualization is used to analyze whether generated words correspond to relevant image regions. The study contributes to remote sensing vision-language research by presenting a structured approach for combining semantic guidance, visual attention, and transformer-based caption decoding.
Federated Early-Warning Modeling for Financial Credit Risk in Platform-Based Supply Chains
Platform-based supply chains generate large volumes of financial, logistics, transaction, and behavioral data that can be used for credit risk early warning. However, data ownership, privacy restrictions, and institutional fragmentation often prevent centralized risk modeling. This article proposes a federated early-warning framework for financial credit risk assessment in platform-based supply chains. The framework enables banks, core enterprises, logistics providers, and digital platforms to collaboratively train credit risk models without directly sharing raw data. It integrates traditional credit indicators with machine learning features derived from transaction frequency, repayment patterns, inventory turnover, and supply-chain relationship stability. Explainable AI methods are used to interpret federated model outputs and improve trust among participating institutions. The framework is particularly suitable for small and medium-sized enterprises whose risk profiles cannot be fully captured by financial statements alone. The article contributes to supply-chain finance research by combining privacy-preserving learning, credit risk modeling, and explainable decision support.
Multi-Task Learning and Transfer Learning for Stock Prediction: A Unified Graph-Based Framework for Heterogeneous Financial Tasks
Stock market prediction encompasses a diverse set of heterogeneous tasks, including trend prediction, volatility forecasting, volume prediction, and risk assessment, each capturing different aspects of market behavior. Traditional approaches to stock prediction typically focus on a single task in isolation, failing to exploit the rich synergies and shared representations that exist across related financial prediction tasks. This paper proposes a novel Multi-Task Stock State Space Graph (MT-S3G) framework that unifies heterogeneous financial prediction tasks through a graph-based architecture with shared representation learning and task-specific prediction heads. Our approach builds upon the Stock State Space Graph architecture introduced by Lu, Hu, and Zhang, and incorporates explanation-based bias decoupling regularization principles from Zang and Liu's work on natural language inference to improve knowledge transfer across tasks. Through extensive experiments on benchmark financial datasets, we demonstrate that MT-S3G substantially outperforms single-task baselines through positive transfer, while also enabling effective transfer learning across different markets and asset classes. Our work contributes to the growing body of research on transfer learning in finance, providing a principled framework for leveraging shared representations to improve prediction accuracy and generalization across heterogeneous financial prediction tasks.
Uncertainty Quantification and Risk-Aware Stock Prediction: A Bayesian Graph-Based State Space Approach
Stock market prediction inherently involves significant uncertainty arising from the stochastic nature of market dynamics, incomplete information, and the complex interdependencies among financial variables. While the Stock State Space Graph (S3G) model has demonstrated superior predictive accuracy by integrating state space modeling with graph-based relational reasoning, it provides only point predictions without quantifying the uncertainty associated with these predictions. This limitation is critical in financial applications where risk management and decision-making under uncertainty are paramount. This paper proposes a novel framework, Uncertainty-Aware Stock State Space Graph (UA-S3G), that extends the S3G architecture through Bayesian uncertainty quantification. Our approach distinguishes between epistemic uncertainty (model uncertainty arising from insufficient training data) and aleatoric uncertainty (inherent data noise) by employing variational inference and Monte Carlo dropout techniques. We adapt explanation-based bias decoupling regularization principles from Zang and Liu's work on natural language inference to improve uncertainty calibration in the presence of spurious correlations. Through extensive experiments on benchmark financial datasets, we demonstrate that UA-S3G produces well-calibrated prediction intervals and substantially outperforms baseline methods in risk-aware trading scenarios. Our work contributes to the growing body of research on trustworthy AI in finance, providing principled uncertainty estimates that enable more robust and reliable stock prediction systems.
Temporal Reasoning and Market Regime Detection in Stock Prediction: A Graph-Based State Space Model Approach
Financial markets exhibit dynamic behaviors characterized by distinct regimes—periods of expansion, contraction, high volatility, and crisis—that fundamentally alter the relationships between economic variables and stock returns. The Stock State Space Graph (S3G) model has demonstrated remarkable predictive performance by integrating state space modeling with graph-based relational reasoning, yet its treatment of market regimes as static latent states limits its ability to capture the nuanced temporal dynamics of regime transitions. This paper proposes a novel framework, Regime-Aware Stock State Space Graph (RA-S3G), that extends the S3G architecture through explicit regime detection, temporal reasoning mechanisms, and regime-adaptive prediction. We incorporate explanation-based bias decoupling regularization principles from Zang and Liu's work on natural language inference to enhance the model's robustness to regime-specific spurious correlations. Through extensive experiments on benchmark financial datasets spanning multiple market cycles, we demonstrate that RA-S3G substantially outperforms the baseline S3G model in regime-transition scenarios and achieves superior accuracy in predicting stock trends across diverse market conditions. Our framework provides a principled approach to temporal market modeling, enabling more accurate and robust stock prediction in real-world trading environments.
AI-Driven Organizational Learning: Dynamic Capability Formation or Accelerated Procedural Senescence?
The integration of artificial intelligence into organizational decision-making has been widely theorized as a mechanism for enhancing organizational learning—the processes through which organizations acquire, interpret, and act on information to improve performance. Yet a growing body of evidence raises a disturbing counter-narrative: rather than building dynamic organizational capabilities, the algorithmic delegation of judgment may be accelerating a form of procedural senescence—the systematic erosion of organizational knowledge, interpretive capacity, and adaptive expertise that leaves organizations more brittle and less capable of responding to novel challenges. This paper develops a comprehensive analysis of the dual potential of AI in organizational learning, arguing that the outcome—whether AI enables organizational capability building or hastens organizational forgetting—depends critically on how AI systems are designed, deployed, and governed. Drawing on three foundational references and twelve supplementary citations spanning organizational learning theory, dynamic capabilities, algorithmic management, and skill erosion research, this study examines the mechanisms through which AI systems can either enhance or undermine the three sub-processes of organizational learning: knowledge acquisition, knowledge interpretation, and behavioral adaptation. The analysis reveals that the metacognitive calibration failures documented by the MIRROR benchmark, the structural constraints on AI auditing identified by the Verification Tax, and the practical demands for explainability in human resource analytics illuminate the conditions under which AI deployment accelerates rather than arrests organizational learning decline. The paper concludes by proposing a Learning-Preserving AI Deployment framework that organizations can use to ensure that AI investment generates genuine organizational capability rather than its erosion.
A Paradigm Shift in Hepatocellular Carcinoma Treatment: An In-Depth Analysis of Etiology-Stratified First-Line Immunotherapy and Targeted Regimens
Hepatocellular carcinoma (HCC) represents a significant global health burden, with its pathogenesis intricately linked to underlying etiologies such as Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), and non-viral factors like non-alcoholic steatohepatitis (NASH). For over a decade, the multi-targeted tyrosine kinase inhibitor (TKI) sorafenib remained the standard first-line systemic therapy, despite offering modest survival benefits and substantial toxicities. The recent introduction of immune checkpoint inhibitors (ICIs) and next-generation TKIs has revolutionized the therapeutic landscape. This paper explores the efficacy and safety of these novel first-line regimens, drawing heavily upon a comprehensive network meta-analysis of 24 randomized controlled trials (RCTs). By examining overall survival (OS), progression-free survival (PFS), objective response rates (ORR), and adverse events, this analysis highlights the superiority of ICI-based combinations over traditional TKIs. Furthermore, it underscores the critical importance of viral etiology in guiding personalized treatment, demonstrating that HBV-related HCC benefits most from specific ICI plus anti-angiogenic combinations (e.g., sintilimab plus bevacizumab biosimilar), HCV-related HCC uniquely favors atezolizumab plus bevacizumab, and non-viral HCC requires dual checkpoint blockade (the STRIDE regimen) to overcome unique metabolic and immunological barriers.
Multi-Modal Physiological Data Fusion and Multi-Agent Clinical Decision Support for Intelligent Patient Monitoring
Patient monitoring is a cornerstone of clinical care, particularly in intensive care units, post-operative recovery wards, and settings managing patients with chronic diseases. Traditional bedside monitoring systems rely on single-modality physiological signals—such as electrocardiogram (ECG) for cardiac rhythm or pulse oximetry for oxygen saturation—and generate alarms based on fixed threshold exceedances. This approach produces high false alarm rates, contributes to alarm fatigue among clinical staff, and fails to detect subtle physiological deterioration patterns that precede critical events. Recent advances in multi-modal medical sensing, deep learning, and multi-agent systems offer new possibilities for continuous, accurate, and clinically actionable patient monitoring. This study proposes a Multi-Agent Intelligent Patient Monitoring System (MIPMS) that integrates multi-modality physiological data—continuous vital sign waveforms (ECG, PPG, respiratory), infrared thermal imaging for non-contact temperature mapping, and wearable inertial measurement unit (IMU) data for activity and posture assessment—through a deep learning-based fusion architecture. A multi-agent clinical decision support module decomposes the post-detection workflow into specialized tasks—vital sign pattern classification, thermal anomaly detection, alert prioritization, and clinical summary generation—each handled by a dedicated LLM-powered clinical agent. The multi-agent design enables context-aware reasoning that accounts for patient history, activity state, and clinical workflow, producing more accurate and actionable outputs than single-modality threshold-based alarm systems. Experiments conducted on three clinical monitoring datasets demonstrate that the proposed system achieves an average vital sign classification accuracy of 92.4%, a thermal anomaly detection accuracy of 89.1%, and an alert prioritization F1-score of 86.7%. The multi-agent alert system reduces the false alarm rate by 67% compared with threshold-based alarms while maintaining full sensitivity for genuine clinical deterioration events. The clinical summary generation agent achieves a semantic consistency rate of 84% with expert physician assessments. This study validates the effectiveness of combining multi-modal physiological data fusion with multi-agent clinical decision support for scalable, accurate, and interpretable intelligent patient monitoring.
A Multi-Agent Collaborative Framework for API Automated Testing Based on Large Language Models
With the continuous expansion of software system scale, the complexity of API (Application Programming Interface) testing has been escalating, making traditional manual testing approaches increasingly insufficient to meet the demands of rapid iterative development. In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in software engineering tasks, offering new technological pathways for automated testing. This study proposes a multi-stage collaborative automated testing framework for API testing driven by LLMs. The framework decomposes the complete API testing process into four stages—specification parsing, test case generation, test execution, and result verification—each handled by a specialized LLM Agent, forming a clear division of labor. Experiments conducted on three mainstream REST APIs demonstrate that the framework achieves an 87% test case generation rate and an 83% accuracy rate. Compared with end-to-end single-model approaches, the accuracy improves by approximately 11 percentage points, while the average execution time is reduced from 45 minutes to 8 minutes. This study validates the effectiveness of decomposing complex testing workflows and assigning them to specialized LLM agents, providing new insights for LLM-driven software testing research.
Deep Learning-Enhanced Industrial Visual Inspection System with Multi-Agent Collaboration for Manufacturing Quality Assurance
Quality assurance is one of the most critical stages in modern manufacturing, directly affecting product reliability and safety. Traditional visual inspection relies heavily on human inspectors, which is labor-intensive, subjective, and difficult to scale in high-throughput production lines. Recent advances in deep learning and multi-agent systems offer new possibilities for automating and enhancing visual inspection accuracy. This study proposes a Multi-Agent Deep Learning Visual Inspection System (MADL-VIS) for industrial manufacturing quality control. The system employs a two-stage detection pipeline: a deep learning-based defect detection module that identifies surface defects from visual inputs, and a multi-agent collaboration module that performs defect classification, root cause analysis, and inspection report generation. Specifically, the detection module leverages a convolutional neural network architecture with attention mechanisms to extract fine-grained defect features from product surface images. The multi-agent module decomposes the post-detection workflow into specialized tasks—defect categorization, severity scoring, cause inference, and documentation—each handled by a dedicated LLM-powered agent. Experiments conducted on three publicly available industrial inspection datasets demonstrate that the proposed system achieves an average defect detection accuracy of 91.3% and a classification accuracy of 88.7%. The multi-agent post-processing module reduces the average inspection cycle time by 38% compared with manual analysis, while the root cause inference agent achieves a consistency rate of 79% with domain expert assessments. This study validates the effectiveness of integrating deep learning-based visual detection with multi-agent collaborative analysis for automated industrial quality assurance.
Multi-Modal Deep Learning and Multi-Agent Collaboration for Intelligent Fault Diagnosis in Industrial Equipment
Unplanned equipment downtime is one of the most costly problems in modern manufacturing, causing significant production losses and safety risks. Traditional fault diagnosis in industrial settings relies on periodic manual inspections and rule-based monitoring systems, both of which struggle to detect incipient failures before they escalate into catastrophic breakdowns. Recent advances in deep learning and multi-agent systems offer new possibilities for automated, accurate, and scalable fault diagnosis. This study proposes an Intelligent Fault Diagnosis System based on Multi-Modal Deep Learning and Multi-Agent Collaboration (IFD-MDMAC). The system integrates visual data (optical camera images), thermal data (infrared thermography), and vibration data (accelerometer signals) through a multi-modal feature fusion architecture. A deep learning backbone extracts features from each modality and fuses them into a unified representation for fault detection. Downstream of fault detection, a multi-agent module decomposes the diagnosis workflow into specialized tasks—fault classification, severity assessment, remaining useful life (RUL) estimation, and maintenance recommendation—each handled by a dedicated LLM-powered agent. The multi-agent design enables structured reasoning and explainable diagnosis outputs, addressing the opacity limitation of traditional black-box deep learning models. Experiments conducted on three publicly available industrial equipment fault datasets demonstrate that the proposed system achieves an average fault detection accuracy of 93.4% and a classification accuracy of 89.6%. The multi-agent diagnosis module achieves an RUL estimation error of 8.7% and a maintenance recommendation consistency of 82% with expert maintenance engineers. The root cause inference capability reduces average diagnosis time from 45 minutes (manual) to 12 minutes (automated), representing a 73% reduction. This study validates the effectiveness of combining multi-modal deep learning perception with multi-agent cognitive diagnosis for intelligent industrial maintenance.
Multi-Modal Remote Sensing and AI-Driven Environmental Monitoring with Deep Learning-Enhanced 3D Surface Reconstruction
Environmental monitoring of natural landscapes, coastal zones, and urban surfaces is essential for understanding climate change impacts, managing natural resources, and responding to environmental disasters. Traditional remote sensing approaches rely on single-modality satellite or aerial imagery, which provides 2D visual information but lacks the depth and thermal context needed to accurately characterize complex land surface conditions. Recent advances in multi-modal sensing, 3D surface reconstruction, and large language models offer new possibilities for automated, comprehensive environmental monitoring. This study proposes an AI-Driven Multi-Modal Environmental Monitoring System (AIMES) that integrates optical remote sensing imagery, infrared thermography, and LiDAR point cloud data through a deep learning-based fusion architecture. A 3D surface reconstruction module leverages stereo phase-measuring deflectometry with deep learning-enhanced phase unwrapping to produce high-resolution digital elevation models (DEMs) of monitored terrain. A multi-agent analysis module decomposes the post-reconstruction workflow into specialized tasks—land cover classification, thermal anomaly detection, change detection, and environmental impact assessment—each handled by a dedicated LLM-powered agent. The multi-agent design enables structured reasoning and context-aware analysis that goes beyond pixel-level classification to produce actionable environmental insights. Experiments conducted on three benchmark remote sensing datasets demonstrate that the proposed system achieves an average land cover classification accuracy of 90.2%, a thermal anomaly detection accuracy of 87.8%, and a change detection F1-score of 84.5%. The multi-agent impact assessment module achieves a semantic consistency rate of 81% with expert environmental scientist evaluations, while reducing average analysis time from 3.5 hours (manual) to 22 minutes (automated). This study validates the effectiveness of combining multi-modal deep learning 3D reconstruction with multi-agent collaborative analysis for scalable, accurate, and interpretable environmental monitoring.