Artificial Intelligence and Computing Innovations
Artificial Intelligence and Computing Innovations (AICI) is an international, peer-reviewed, open-access quarterly journal, published twice a year in January and July, dedicated to advancing the frontiers of artificial intelligence and computing technologies. The journal provides a multidisciplinary platform for researchers, practitioners, and industry professionals to publish innovative findings that integrate AI theory, computational systems, and real-world applications. AICI focuses on promoting both theoretical and applied research in artificial intelligence, data science, and intelligent computing. The journal welcomes submissions that explore novel algorithms, architectures, and methodologies in areas such as deep learning, computer vision, natural language processing, robotics, cloud and edge computing, and intelligent decision-making. Contributions addressing emerging topics such as ethical AI, explainability, federated learning, and privacy-preserving computation are especially encouraged. The journal’s scope covers, but is not limited to, the following areas: Artificial Intelligence and Machine Learning: Deep neural networks, generative AI, reinforcement learning, and transfer learning. Data Science and Analytics: Big data frameworks, data mining, pattern recognition, and predictive modeling. Computing Systems and Architectures: Cloud-edge collaboration, distributed systems, quantum computing, and high-performance computation. Computer Vision and Multimedia Processing: Image analysis, object detection, multimodal learning, and video understanding. Natural Language Processing and Speech Intelligence: Large language models, dialogue systems, sentiment analysis, and multilingual communication. Robotics and Intelligent Control: Human–robot interaction, motion planning, and autonomous decision-making. AI Ethics, Security, and Sustainability: Fairness, interpretability, responsible innovation, and the societal impact of AI. Artificial Intelligence and Computing Innovations maintains a rigorous double-blind peer review process to ensure originality, technical quality, and relevance. The journal follows open-access principles, making all articles freely available to readers worldwide to promote rapid dissemination of scientific knowledge. With a distinguished international editorial board and a commitment to academic excellence, AICI strives to become a leading venue for cutting-edge research and cross-disciplinary dialogue in intelligent computing and AI-driven innovation.
Adaptive and Learning-Driven Resource Optimization Framework for Scalable Cloud Computing Systems
With the rapid development of cloud computing technologies in 2024, large-scale distributed systems face increasing challenges in resource allocation, latency control, and energy efficiency. This paper proposes a scalable and adaptive resource optimization framework that integrates deep learning-based prediction models with dynamic scheduling strategies. By leveraging workload forecasting and real-time feedback mechanisms, the proposed framework improves resource utilization and system stability. Experimental results demonstrate that the framework achieves significant improvements in latency reduction and energy efficiency compared with conventional approaches. The proposed method provides a robust solution for modern cloud environments characterized by high variability and heterogeneous workloads.
Spatiotemporal Deep Learning for Ocean Wave Forecasting and Vessel Motion Modeling
Reliable prediction of ocean wave dynamics and vessel motion is essential for maritime safety and operational efficiency. This paper presents a deep learning framework that integrates spatiotemporal oceanographic data with vessel kinematic signals to jointly model wave evolution and ship response. The proposed model employs a hybrid architecture combining convolutional neural networks for spatial feature extraction and transformer-based temporal modeling to capture long-range dependencies. The dataset includes 10 years of buoy-based wave measurements and onboard sensor data from cargo vessels operating in the North Pacific, totaling over 3.5 TB of multivariate time-series data. Experimental results show that the model achieves a wave height prediction RMSE of 0.21 m and improves vessel roll motion prediction accuracy by 18% compared to physics-based simulation methods. In extreme sea states (significant wave height > 5 m), the model maintains stable performance with less than 12% degradation.
Self-Supervised Temporal-Structural Learning for Industrial Fault Diagnosis under Data Imbalance
Industrial fault diagnosis in complex manufacturing systems is challenged by highly imbalanced data distributions and limited labeled samples. This study proposes a self-supervised temporal-structural learning framework that captures both temporal dynamics and inter-component dependencies in industrial sensor networks. The approach leverages contrastive learning to pretrain representations from unlabeled multivariate time-series data, followed by a graph-based encoder that models structural relationships among system components. The model is evaluated on a large-scale industrial IoT dataset comprising over 500 sensors and 8 million time-series samples collected from a real production line. Under extreme imbalance conditions (fault ratio < 2%), the proposed method achieves an F1-score of 0.81 and recall of 0.87, significantly outperforming supervised baselines such as CNN-LSTM (F1 0.69) and Random Forest (F1 0.62). The framework also demonstrates strong robustness under noise perturbations and missing data scenarios. These results indicate that self-supervised temporal-structural learning provides an effective and scalable solution for real-world industrial fault diagnosis, particularly in scenarios with sparse annotations and evolving system dynamics.
Explainable Deep Learning for Credit Risk Prediction via Multi-Modal Financial Representation Learning
Accurate credit risk prediction is critical for financial institutions in managing loan portfolios and mitigating systemic risk. This paper proposes an explainable deep learning framework that integrates structured financial records with unstructured textual data, including transaction descriptions and credit reports, through a multi-modal representation learning architecture. The model combines a Wide & Deep network with a transformer-based text encoder, enabling joint feature extraction and nonlinear interaction modeling. Experiments are conducted on a real-world dataset containing over 1.2 million loan records from U.S.-based lending platforms, with an observed default rate of 13.7%. The proposed model achieves an AUC of 0.921 and a KS statistic of 0.68, outperforming traditional logistic regression (AUC 0.842) and gradient boosting methods (AUC 0.895). Furthermore, an integrated SHAP-based interpretability module reveals key risk drivers such as debt-to-income ratio, transaction volatility, and semantic indicators of financial distress in borrower narratives. The results demonstrate that incorporating multi-modal information significantly enhances predictive performance while maintaining model transparency, making the approach suitable for regulatory-compliant financial decision-making systems.
Dual-Channel Attention-Based Multimodal Sentiment Analysis Model Integrating Text and Image Features
With the rise of multimodal data in social media, sentiment analysis based solely on text has become insufficient to capture the richness of human emotion. To address this limitation, this paper proposes a dual-channel multimodal sentiment analysis model based on attention mechanisms, named ACMSA (Attention Channel Multimodal Sentiment Analysis). The model integrates textual and visual features to improve emotional understanding and classification accuracy. Text features are extracted using the BERT model and processed through a CNN-BiGRU-Attention dual-channel architecture to capture both local and global semantic dependencies. Image features are obtained via ResNet152, enhanced by a Channel-Spatial Attention Module (CSAM) that adaptively emphasizes salient regions. The fusion of multimodal features is achieved through a Co-Attention mechanism, enabling fine-grained interaction between textual and visual representations. Experimental evaluations on the MVSA-Single and MVSA-Multi Twitter datasets demonstrate that ACMSA outperforms state-of-the-art baselines, achieving an accuracy of 77.08% and 74.42%, respectively. The results verify that attention-guided dual-channel modeling effectively strengthens cross-modal correlation and interpretability. This framework provides a robust and extensible solution for sentiment analysis in multimedia-rich environments, offering valuable implications for emotion recognition, social media monitoring, and intelligent interaction systems.
Performance Analysis of Front-End and Back-End Separation in Java Web Development
This paper conducts an in-depth investigation into the performance impact of front-end and back-end separation architecture in Java Web development and discusses corresponding optimization strategies. Front-end and back-end separation has become a widely adopted architectural paradigm in modern Web application development. By decoupling the user interface layer (front-end) from the data processing layer (back-end), this approach improves development efficiency, enhances resource management, and strengthens system scalability. The paper first introduces the fundamental concept of front-end and back-end separation and elaborates on its implementation technologies and development models, including commonly used frameworks and tools. Subsequently, it analyzes potential performance challenges introduced by this architecture, such as increased network requests and more complex front-end logic. Finally, strategies including resource optimization, code modularization, API optimization, and load balancing are proposed to mitigate these challenges and improve overall system performance.
Adaptive Instruction Tuning for Multi-Domain Dialogue Generation in Large Language Models
Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. However, adapting LLMs to multi-domain dialogue systems remains challenging due to domain distribution shifts, instruction diversity, and varying conversational contexts. This paper proposes an adaptive instruction tuning framework that enables LLMs to dynamically adjust dialogue generation strategies across heterogeneous domains. The method integrates domain-aware instruction embeddings, reinforcement-based response optimization, and context-aware decoding mechanisms. The framework improves response relevance, coherence, and adaptability across domains such as healthcare, finance, and customer service. Experimental evaluations demonstrate that the proposed method outperforms baseline instruction-tuned models in both automatic metrics and human evaluation.
Intelligent Robot Navigation Using Deep Reinforcement Learning for Dynamic Environments
Autonomous mobile robots operating in dynamic environments must effectively perceive environmental changes and make adaptive navigation decisions in real time. Traditional path planning algorithms often rely on static environment assumptions, which limits their performance in real-world scenarios where obstacles and environmental conditions continuously change. This paper proposes a deep reinforcement learning-based navigation framework that integrates perception, decision-making, and motion control into a unified learning architecture. The system employs convolutional neural networks to extract spatial features from sensor observations and utilizes a reinforcement learning policy network to generate navigation actions. A simulation-based evaluation environment is constructed to analyze navigation efficiency, collision rate, and trajectory stability. Experimental results demonstrate that the proposed framework achieves improved adaptability and higher success rates compared with classical navigation methods in dynamic environments.
Modeling Financial Risk Propagation and Systemic Contagion via Stability-Aware Graph Neural Networks
The increasing complexity and interconnectivity of global financial systems have intensified the challenge of identifying and mitigating systemic risks. Traditional econometric and network-based approaches often fail to capture nonlinear dependencies and dynamic contagion effects that characterize modern financial networks. To address this issue, this paper proposes a Graph Neural Network (GNN)-based framework for financial risk propagation and systemic stability analysis. The model leverages both structural and temporal dependencies among financial entities-such as banks, corporations, and markets-represented as nodes and edges in a dynamic graph. Through message-passing mechanisms, the proposed model aggregates information across neighboring nodes to learn latent risk representations, enabling accurate modeling of cascading failures and contagion paths. A novel stability-aware loss function is introduced to penalize high-risk clustering and enhance robustness under volatile market conditions. Experiments conducted on real-world interbank transaction and equity exposure datasets demonstrate that the GNN-based approach achieves superior performance compared to baseline methods including LSTM, VAR, and classical contagion models. The results highlight that GNNs can effectively uncover hidden risk linkages, forecast systemic vulnerability, and provide valuable insights for regulatory supervision and stress testing. This study contributes to a growing body of research bridging graph deep learning and financial system analysis, offering a scalable and interpretable paradigm for risk-aware financial modeling.
IoT-Based Intelligent Trash Can Monitoring System for Scenic Areas Using Multi-Sensor Detection and LoRa Communication
Traditional manual inspection of trash cans in scenic areas often faces low efficiency, delayed anomaly detection, and high maintenance costs due to the wide distribution of bins and complex terrain. To address these issues, this paper designs an intelligent trash can monitoring system based on the Internet of Things (IoT) that integrates multi-sensor fusion and low-power wide-area communication technology. The proposed system employs ultrasonic and pressure sensors to monitor the overflow state in real time, while temperature and orientation sensors detect fire and tilting anomalies. The STM32F103 microcontroller serves as the main control unit, responsible for data acquisition and system coordination. The collected data are transmitted via a LoRa communication module, enabling long-distance and low-power data exchange across mountainous scenic environments. The software design includes real-time sensing, data fusion, and event-triggered communication to enhance energy efficiency and response speed. Experimental validation demonstrates that the system can accurately detect overflow, fire, and tilt states, providing reliable real-time monitoring and alerting. This solution significantly improves the management efficiency of waste collection in scenic areas and supports the sustainable development of smart tourism infrastructure.
Contrastive Representation Learning for Anomaly Detection in Cloud-Based Backend Services
This study proposes an anomaly detection method based on contrastive representation learning to address the challenges of complex multi-metric coupling, dynamic dependency variation, and label scarcity in backend service systems. The method first employs a multi-scale temporal encoding module to extract dynamic features at different time granularities, capturing both short-term fluctuations and long-term trends in system operation. An adaptive dependency modeling mechanism is then constructed, which generates dynamic graph structures through feature similarity projection to characterize semantic relationships and temporal dependencies among services. On this basis, a contrastive learning objective is introduced, where structured constraints between positive and negative sample pairs enable stable feature aggregation and boundary separation under unsupervised or weakly supervised conditions. A temporal consistency regularization term is also incorporated to maintain state smoothness across time steps, enhancing robustness under non-stationary distributions. The proposed method is validated on multidimensional cloud workload datasets, and results show that it achieves high detection accuracy and stability even with incomplete labels and noisy pseudo-labels. Overall, the method not only realizes self-supervised and structure-aware anomaly detection but also demonstrates strong performance in feature representation, anomaly discrimination, and distribution shift adaptation, providing an efficient and general solution for intelligent backend operations and multi-source time-series analysis.
A Study on Robust Monitoring Methods for Backend Systems Using Structure-Aware Feature Learning
This study proposes a robust monitoring method based on structure-aware feature learning to address the challenges of complex multidimensional feature coupling, dynamically changing dependencies, and diverse anomaly patterns in backend systems. The method builds upon multi-source monitoring data, integrating temporal dynamic features and topological dependency structures to achieve unified system state representation across different time scales and structural hierarchies. First, a feature encoder is employed to extract temporal features and construct a dynamic dependency graph that captures latent structural relationships among service nodes. Second, a structure-aware mechanism and graph propagation layer are introduced to perform cross-node feature fusion and apply dependency consistency constraints, enhancing the model's stability and generalization capability in complex topological environments. Finally, variational regularization and a robust optimization objective are applied to further improve anomaly detection reliability under high noise and non-stationary distributions. Experimental results show that the proposed method outperforms existing models across multiple key metrics, including F1-Score, AUROC, and Robust Recall, while maintaining stable monitoring performance under structural perturbations, workload fluctuations, and feature heterogeneity. These results validate the effectiveness of structure-aware feature learning in complex system monitoring tasks, demonstrating its ability to accurately model anomaly propagation paths and achieve system-level semantic recognition in the feature space, thereby providing strong support for building highly reliable and interpretable backend monitoring frameworks.
Cognitive Interaction Models: Deep Learning Approaches for Intelligent and Context-Aware Human-Machine Systems
In recent years, deep learning has transformed human-computer interaction (HCI) from static rule-based systems into adaptive, context-aware, and cognitive frameworks capable of understanding human intent, emotion, and environmental context. This paper proposes a comprehensive cognitive interaction model that integrates multimodal deep neural networks to bridge perception, reasoning, and response generation across diverse human-machine scenarios. The proposed model builds upon hierarchical attention mechanisms and spatiotemporal embeddings to capture visual, auditory, and physiological cues. It introduces a shared latent space that dynamically aligns user behavior and system intent, enabling the system to interpret subtle human feedback and respond with high contextual precision. The model is trained with both supervised and self-supervised objectives, combining contrastive representation learning and reinforcement-based dialogue optimization to enhance adaptability and robustness. Experimental results demonstrate significant improvements in user satisfaction rate, emotion recognition accuracy, and task completion efficiency across multiple benchmarks, outperforming traditional HCI architectures. This framework lays the foundation for next-generation intelligent interaction systems that learn, adapt, and co-evolve with human users in dynamic, real-world environments.
An Enhanced Lightweight Convolutional Cascade Network Based on MobileNetV3 and MTCNN for Robust Face Detection in Complex Environments
In order to improve the detection accuracy of small-size and multi-angle faces in complex environments while maintaining efficiency for deployment on edge devices, this paper proposes an enhanced neural network model—MobileNetV3-MTCNN. Based on the classical MTCNN architecture, the proposed model reconstructs the feature extraction network by integrating the MobileNetV3 Bottleneck module, optimizing the balance between computational efficiency and detection precision. The improved architecture enables more effective multi-scale feature extraction and reduces redundancy in network operations. Experimental results on the WIDER_FACE dataset demonstrate that MobileNetV3-MTCNN achieves substantial gains in average precision (AP) and recall rate across easy, medium, and hard subsets compared to the original MTCNN, with increases of 4.8%, 6.2%, and 7.5% in AP respectively. Although the model parameters slightly increase, the proposed method demonstrates superior robustness and adaptability in detecting small and occluded faces under diverse lighting and pose conditions. The study confirms that the MobileNetV3-MTCNN model provides an effective and lightweight solution for real-world face detection applications, especially in densely populated and dynamic environments.
Leveraging Blockchain Technology to Enhance Transparency and Security in Engineering Project Bidding Systems
With the intensifying competition brought by economic globalization, the efficiency, fairness, and integrity of engineering project bidding systems have become critical factors influencing industry dynamics. Although electronic bidding platforms offer advantages such as enhanced transparency and reduced costs, they still face significant challenges, including fraud, illegal operations, information leakage, and limited data sharing. Blockchain technology, characterized by decentralization, immutability, transparency, and smart contracts, offers a promising solution to these issues. This study reviews the current state of engineering project bidding systems and analyzes how blockchain can address existing problems by enabling traceability, secure data sharing, and automated contract enforcement. Drawing on recent research, it proposes a blockchain-based bidding model that improves information reliability, ensures full-process supervision, and fosters trust among stakeholders. The findings suggest that integrating blockchain technology into bidding processes can fundamentally reshape project procurement practices, enhance governance, and promote the sustainable development of the engineering industry.
Insulator Defect Detection via Attention-Driven Multi-Scale Feature Fusion
Insulator defect detection is a critical task for ensuring the safe operation of power transmission systems. With the increasing adoption of unmanned aerial vehicles and intelligent inspection robots, defect detection models are required to achieve high accuracy under complex outdoor environments while maintaining low computational complexity for deployment on mobile devices. However, existing object detection models often suffer from insufficient feature extraction for small-scale defects, limited robustness to complex backgrounds, and suboptimal regression performance. To address these challenges, this paper proposes an improved YOLOv8-based insulator defect detection model that integrates an attention mechanism, multi-scale feature fusion, and an optimized bounding box regression strategy. First, a Convolutional Block Attention Module (CBAM) is embedded into the backbone network to enhance spatial and channel feature representation, improving the detection of small, occluded, and low-contrast defects. Second, the original PANet structure in YOLOv8 is replaced with a Bidirectional Feature Pyramid Network (BiFPN), enabling more effective multi-scale feature fusion while reducing redundant computation. Finally, the SIoU loss function is introduced to account for angular differences between predicted and ground truth bounding boxes, thereby improving regression convergence and localization accuracy. Experimental results on a public insulator defect dataset demonstrate that the proposed method achieves superior detection performance compared with Faster R-CNN, RetinaNet, YOLOv5, YOLOv6, and the original YOLOv8 model. The improved model attains an mAP@0.5 of 77.8%, outperforming the baseline YOLOv8 by 2.3%, while exhibiting stronger robustness in complex inspection scenarios. These results indicate that the proposed approach provides an effective and practical solution for insulator defect detection in intelligent power line inspection systems.
Image Classification via Joint Multi-Scale Convolution and Global Attention Modeling
This study focuses on the task of image classification and proposes a fusion method based on multi-scale convolution and global attention to address the limitations of traditional approaches in local feature extraction and global dependency modeling. The method first employs a multi-scale convolution module to extract image features under different receptive fields, enabling the capture of both fine-grained details and macro-structural information. Global attention is then introduced to dynamically assign weights at the feature level, strengthening dependencies across regions and ensuring effective global semantic understanding. Through the joint design of local and global modeling, the method achieves more comprehensive feature representation in complex image scenarios. In the feature fusion and aggregation stage, the results of multi-scale convolution and global attention are effectively combined, followed by classification outputs. Experiments conducted on public datasets with comparative validation and sensitivity analysis show that the proposed method outperforms common baselines such as MLP, CNN, LSTM, and Transformer in terms of AUC, ACC, Precision, and Recall, demonstrating the advantage of combining multi-scale and global attention in improving classification performance. Further hyperparameter sensitivity experiments indicate that factors such as the number of attention heads, batch size, and noise level influence model performance, highlighting the importance of proper configuration for enhanced stability. Overall, the method exhibits strong accuracy and robustness, validating the effectiveness of the fusion of multi-scale convolution and global attention.
Hybrid Deep Learning and Reinforcement Mechanisms for Adaptive Robot Navigation in Dynamic Environments
In recent years, robotic navigation has increasingly relied on deep learning-based control systems that can autonomously adapt to dynamic and uncertain environments. Traditional methods such as SLAM and PID controllers have struggled to generalize across unseen scenarios, while reinforcement learning (RL) has shown strong potential for continuous adaptation. However, pure RL methods often face convergence instability and sample inefficiency in real-world training. To address these issues, this paper proposes a Hybrid Deep Learning and Reinforcement Mechanism (HDLRM) framework that combines deep convolutional feature extraction with reinforcement-based policy adaptation. The proposed model employs a dual-branch architecture integrating a Convolutional Neural Network (CNN) for spatial understanding and a Deep Q-Network (DQN) for decision optimization. The CNN extracts semantic-rich environmental representations from multi-modal sensor inputs, while the DQN refines navigation policies through trial-based learning. A dynamic memory replay buffer and an adaptive reward modulation strategy are incorporated to stabilize training and enhance decision consistency. Experiments conducted on both simulated (Gazebo) and real-world mobile robots demonstrate superior performance in path efficiency, obstacle avoidance, and energy optimization compared to baseline methods. The HDLRM framework achieves an average success rate improvement of 11.7% and reduces collision events by 23.5% across dynamic obstacle scenarios. These results confirm that hybrid deep learning and reinforcement mechanisms can effectively improve the robustness and adaptability of autonomous robot navigation systems in complex and changing environments.
Causal Graph Learning for Interpretable Fault Identification in Complex Backend Systems
This paper addresses the challenges of complex multidimensional metric coupling, dynamic structural dependencies, and difficult-to-interpret fault propagation paths in backend systems by proposing a Causal Graph Learning–based Fault Identification (CGL-BFI) method. The approach is built upon multi-source monitoring data and identifies latent dependencies and interprets anomaly propagation mechanisms through three stages: feature causal encoding, structural inference modeling, and causal effect quantification. In the feature causal encoding stage, the model extracts and normalizes temporal features of monitoring indicators to construct a multivariate causal candidate space. In the structural inference stage, a differentiable Directed Acyclic Graph (DAG) constraint is introduced to learn directional dependencies among variables, ensuring logical consistency of the causal structure. In the causal inference stage, the method integrates Average Treatment Effect (ATE) estimation and Causal Scoring to quantify the influence strength of each metric node, enabling precise localization of root causes and propagation paths. Experimental results show that the proposed method achieves high stability and accuracy under complex network disturbances and high-noise environments, outperforming comparison models in key metrics such as AUC, F1-score, Recall, and Precision. By introducing causal structure learning, this study advances backend system anomaly detection from correlation-based analysis to mechanism-level interpretable reasoning, significantly improving the accuracy, interpretability, and robustness of fault identification in complex systems, and providing a theoretical and methodological foundation for intelligent and adaptive system operation and maintenance.
Improved Cartographer-Based SLAM with LiDAR Preprocessing for Autonomous Navigation
This study presents an enhanced Cartographer algorithm for simultaneous localization and mapping (SLAM) applied to multi-sensor robotic navigation systems. The proposed approach integrates LiDAR, odometer, and inertial measurement unit (IMU) data within a unified mapping framework to improve localization accuracy and environmental perception. To mitigate common issues such as occlusion, reflection, and measurement noise in LiDAR point clouds, the Point Cloud Library (PCL) is utilized for preprocessing to remove outliers and dropout points. An adaptive filtering mechanism based on the dynamic window method enables real-time obstacle avoidance and trajectory correction during autonomous navigation. The system is simulated under the ROS framework using Rviz and Gazebo environments, where results demonstrate superior mapping clarity and reduced drift compared with the original Cartographer algorithm. Field experiments confirm that the optimized approach effectively eliminates abnormal laser points, enhances robustness, and produces high-precision maps suitable for complex dynamic environments. Overall, this work provides a practical and extensible solution for intelligent robotic navigation and SLAM optimization in multi-source perception scenarios.
Dynamic Residual Calibration and Multi-Scale Fusion for Accurate Prediction of Non-Stationary Backend Indicators
This paper proposes a residual calibration modeling and prediction method for non-stationary backend metrics to address the challenges of non-stationarity, dynamic dependency, and distribution drift in multi-dimensional metric sequences of cloud backend systems. The method employs a dual-layer collaborative architecture consisting of a primary predictor and a residual calibrator. The primary predictor captures the overall trend and temporal evolution of system metrics, while the residual calibrator performs structured modeling and adaptive correction of prediction errors to precisely compensate for sudden fluctuations and contextual variations. At the modeling level, a multi-scale feature fusion mechanism is introduced to enhance the model's sensitivity to variations across different temporal granularities, and consistency constraints are applied to ensure smoothness and stability at both global and local levels. To handle highly dynamic load scenarios in complex cloud environments, the model further establishes a closed-loop residual feedback pathway, enabling continuous learning and self-correction under distribution shifts. This effectively suppresses prediction bias and error accumulation. The proposed approach not only achieves a coordinated representation of global trends and local perturbations in structure but also integrates residual dynamic compensation and temporal consistency into a unified optimization framework. Experimental results on representative backend metric datasets demonstrate that the model achieves low-error and high-stability performance across multi-dimensional scenarios, significantly improving prediction accuracy and robustness under non-stationary distributions, and providing a scalable solution for dynamic modeling and intelligent operations of complex systems.
Deep Learning-Based EEG Frequency Domain Analysis for Classification of Disorders of Consciousness
Disorders of Consciousness (DOC), including Minimally Conscious State (MCS) and Unresponsive Wakefulness Syndrome (UWS), are neurological conditions characterized by severe impairment in cognition and perception due to brain injury or degeneration. Traditional behavioral and imaging-based diagnosis methods are costly, subjective, and dependent on expert interpretation. To address these limitations, this study proposes a deep learning-based method that leverages EEG frequency domain features for objective DOC classification. Power Spectral Density (PSD) is extracted from EEG signals using the Welch method to capture multi-band neural oscillation characteristics. A two-dimensional Convolutional Neural Network (CNN) is then employed to automatically learn discriminative representations from the spectral maps. Dropout regularization and ReLU activation functions are integrated to enhance generalization and nonlinear feature extraction. Experimental evaluation on EEG datasets from MCS and UWS patients demonstrates that the proposed method achieves an average classification accuracy of 86.22%, significantly outperforming traditional machine learning approaches. The results confirm that PSD-based spectral encoding enhances robustness and interpretability by emphasizing frequency bands most associated with consciousness states. This framework provides an efficient, scalable, and objective solution for DOC classification, offering valuable support for clinical prognosis assessment and intelligent diagnosis.
Temporal Inconsistency in Action Recognition for Long-Duration Video Streams
Video-based action recognition systems are widely used in sports analysis, safety monitoring, and behavioral research. Most models are trained on short, curated clips that poorly reflect continuous real-world streams. This paper investigates temporal inconsistency in action predictions under long-duration recordings. Experiments show frequent boundary confusion when activities overlap or occur intermittently. Simple temporal smoothing improves visual continuity but reduces responsiveness. More principled sequence modeling remains necessary for stable deployment.
Improving Training Stability of Deep Neural Networks for Multi-Institutional Medical Image Segmentation
Deep neural networks have become standard tools for medical image segmentation. However, training stability remains sensitive to data imbalance, annotation variability, and scanner heterogeneity. This study evaluates segmentation performance on multi-institutional datasets containing 18,700 CT and MRI scans across five diagnostic categories. Variations in contrast protocols and labeling standards were found to cause unstable convergence in baseline models. A hybrid normalization and uncertainty-aware sampling strategy was applied during training. Dice coefficients improved by 6%–9% across most categories, while inter-run variance decreased substantially. Nevertheless, rare pathological cases remained difficult to segment reliably. Model robustness remains constrained by annotation consistency and domain diversity.
Controllability and User Satisfaction in AI-Based Image Editing Tools
AI-based image editing tools increasingly support automatic background removal, style transfer, and object enhancement. Despite high algorithmic accuracy, user satisfaction varies widely across application contexts. This paper examines how designers and casual users interact with intelligent editing functions during real-world tasks. Focus is placed on correction frequency, manual override behavior, and perceived controllability. Observations indicate that users often prefer slightly imperfect but adjustable outputs over fully automatic transformations. Interface transparency and reversible operations strongly influence long-term adoption.
Long-Term Performance Degradation and Maintenance in Urban Visual Surveillance Systems
Urban surveillance systems rely heavily on visual analysis pipelines for traffic monitoring, security inspection, and incident detection. Over long deployment periods, changes in lighting infrastructure, seasonal conditions, and camera aging gradually affect image quality and model reliability. This study analyzes video data collected from 420 fixed cameras operating continuously over three years. Variations in illumination intensity, color temperature, and shadow distribution were quantified and correlated with detection accuracy. Results indicate that pedestrian and vehicle detection precision declined by up to 17% in areas where nighttime lighting was upgraded without recalibration. A periodic visual normalization and model fine-tuning scheme was introduced to mitigate degradation. After deployment, performance fluctuations were reduced, but maintenance costs increased due to additional calibration requirements. Sustained visual analytics accuracy depends on coordinated hardware and software management.
Human–AI Collaboration in Online Content Moderation: An Empirical Study
Online discussion platforms rely on a combination of automated filtering and human review to manage harmful or misleading content. As user populations grow, moderation workflows become increasingly complex. This study examines moderation logs, appeal records, and escalation paths from a large community platform over 18 months. Analysis reveals that automated classifiers handled the majority of routine cases, but human reviewers remained essential for ambiguous and context-dependent content. Several workflow optimizations, including priority routing and reviewer specialization, were introduced. Review consistency improved, although reviewer burnout remained a persistent concern. Effective moderation depends on organizational design as much as on algorithmic accuracy.
Operational Evaluation of Digital Twin Systems in Continuous Industrial Production
Digital twin platforms are widely promoted for monitoring and optimizing industrial assets. These systems integrate physical sensor data, simulation models, and maintenance records into unified virtual representations. This paper reports on the deployment of a digital twin system in a continuous production environment. Data ingestion pipelines, model synchronization mechanisms, and alerting workflows were evaluated over extended operation. While predictive maintenance accuracy improved, model drift and incomplete calibration limited reliability during abnormal operating conditions. Sustained value from digital twins requires systematic model management and domain expert involvement.
Data Harmonization and Reconciliation for Transparent Digital Supply Chain Platforms
Digital platforms for supply chain monitoring aim to provide real-time visibility across procurement, transportation, and warehousing operations. In practice, fragmented data sources limit situational awareness. This study analyzes shipment, inventory, and customs records from a multinational logistics platform processing over 240 million events annually. Discrepancies in timestamp standards, identifier formats, and update frequency were found in nearly 22% of integrated records. A normalization and reconciliation layer based on probabilistic matching was implemented. Integration accuracy improved, but exception handling remained labor-intensive. Supply chain transparency depends as much on data harmonization as on sensor deployment.
User Experience Challenges in Prolonged AR-Assisted Industrial Maintenance
Augmented reality systems are increasingly used to support equipment inspection and industrial maintenance tasks. While technical feasibility has been widely demonstrated, user experience challenges remain underexplored. This paper investigates physical discomfort, attention switching, and cognitive load in prolonged AR-assisted workflows. Observations focus on visual occlusion, gesture fatigue, and information density. Field feedback suggests that interface minimalism and adaptive information layering are critical for sustained usability. Hardware performance alone does not determine long-term acceptance.