Journal of Computing and Electronic Information Management
Journal of Computing and Electronic Information Management (JCEIM) is a peer-reviewed journal published by Darcy & Roy Press. The mission of the journal is to provide an academic forum for discussing the issues of computing, electronics, information management across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
A Review of Colorectal Polyp Segmentation Methods
Colorectal cancer is a malignant tumor with a persistently high incidence worldwide, and the majority of cases arise from the malignant transformation of polyps. However, polyps in colonoscopic images exhibit significant variations in morphology and scale, and the boundaries between polyps and surrounding tissues are often indistinct, which makes manual detection particularly challenging. On the one hand, the diagnostic process relies heavily on physicians’ experience, which may lead to missed diagnoses and misdiagnoses. On the other hand, the limited number of experienced clinicians makes it difficult to meet patients’ needs in a timely manner. This diagnostic uncertainty, combined with the scarcity of medical resources, highlights the critical value of AI-assisted diagnostic systems. Developing high-performance polyp segmentation algorithms that accurately locate and delineate polyp regions can improve diagnostic efficiency and provide effective decision support for clinicians. Recent advances in deep learning have significantly improved colorectal polyp segmentation. Regarding network architectures, models have evolved from early U-shaped designs to Transformer-based architectures that capture global context. More recently, Mamba-like networks and dual-branch architectures have also been proposed. In terms of research paradigms, this paper focuses on the application of methods such as multi-scale feature fusion, attention mechanisms, and diffusion models. In addition, this study summarizes commonly used datasets in the field of polyp segmentation and provides a detailed description of evaluation metrics closely related to segmentation performance. Finally, this paper systematically analyzes the limitations of current colorectal polyp segmentation methods in clinical practice. Based on this analysis, it discusses potential directions and trends for future algorithm development, providing a reference for further research.
Copyright (c) 2026 Journal of Computing and Electronic Information Management https://creativecommons.org/licenses/by-nc/4.0
Neural Methods for Adaptive Voltage and Frequency Scaling in Energy Aware Processors
Dynamic Voltage and Frequency Scaling (DVFS) represents one of the most effective runtime mechanisms for balancing computational performance against energy consumption in modern processors. Traditional rule-based and heuristic-driven DVFS governors struggle to adapt to increasingly heterogeneous and unpredictable workload patterns characteristic of contemporary computing environments, including mobile devices, data centers, and edge deployments. This review examines the growing body of research applying neural methods—including recurrent neural networks, convolutional neural networks, deep reinforcement learning (DRL), and federated learning—to the adaptive DVFS problem. We survey the evolution from classical prediction-based approaches to policy-learning frameworks capable of jointly optimizing energy efficiency and Quality of Service (QoS) constraints. Our analysis reveals that DRL-based approaches consistently achieve energy reductions of 25–35% over fixed-frequency baselines while maintaining QoS violation rates below 4%, outperforming both traditional governors and supervised prediction models. We conclude by identifying open research directions in multi-objective optimization, on-device continual learning, and heterogeneous processor support.
Optimization of precast Component Production Scheduling: A Comparative Study of Genetic Algorithm and Simulated Annealing
The production of precast components is a core link in the industrialization of construction, and the rationality of production scheduling directly determines project costs, construction progress, and resource utilization efficiency. Faced with challenges such as intricate production processes, multiple resource constraints, and the need for flexible response to market demands, traditional scheduling methods are increasingly incompetent. To address these issues, this study explores the application of two intelligent optimization algorithms—Genetic Algorithm (GA) and Simulated Annealing (SA)—in precast component production scheduling. Based on a systematic analysis of the production process and constraints of precast components, corresponding mathematical models are established for each algorithm. Through case studies using real production data from precast component manufacturers, the effectiveness of the two algorithms is verified. The results show that both GA and SA can significantly optimize the production sequence, shorten the maximum makespan, and improve equipment utilization. Specifically, GA achieves a makespan of 45 hours with a more efficient convergence rate, while SA effectively avoids local optima and obtains a makespan of 46.8 hours. This research provides a scientific and flexible scheduling solution for precast component manufacturers and enriches the application of intelligent algorithms in the construction industry.
A lightweight bauxite detection and identification algorithm
To address the issues of low efficiency and poor accuracy of manual methods in bauxite sorting, this paper proposes a lightweight ore identification and detection algorithm, CL-YOLOv11, based on an improved YOLOv11. This algorithm incorporates a C3k2-GD module integrating GhostModule and dynamic convolution in the backbone network to enhance adaptive feature extraction while reducing redundant parameters. Furthermore, a lightweight shared detail enhancement detection head (LSDECD) is constructed in the detection head, improving the accuracy of capturing small targets and texture edges by introducing detail enhancement convolution and learnable dynamic factors. Experimental results show that compared to the original model, CL-YOLOv11 reduces the number of parameters by approximately 33%, decreases floating-point computation by 23%, and significantly improves inference speed. It achieves a balance between high accuracy and high efficiency while maintaining a high detection accuracy of 93.60%, providing an effective technical solution for automated intelligent bauxite sorting.
Unstructured Road Obstacle Avoidance Path Planning Algorithm Based on BEV Grid Map
In view of the narrow, unstructured road surfaces, irregular distribution of obstacles, and the large body and steering lag characteristics of mining trucks, traditional path planning algorithms tend to generate edge-hugging paths or infeasible broken paths, posing collision risks. Based on high-precision BEV grid maps generated by LiDAR-camera multi-sensor fusion, this paper proposes an improved hybrid A* obstacle avoidance path planning algorithm that combines safety potential fields with kinematic constraints. The algorithm constructs an environmental safety potential field through Euclidean distance transformation to quantify collision risks in the grid space; it expands the A* algorithm's state space and improves the cost function by introducing steering penalties and maximum steering angle limits to ensure kinematic feasibility of the path; finally, cubic B-spline curves are used to smooth the discrete path, producing a curvature-continuous trajectory. Simulation results show that, compared with the traditional A* algorithm, the proposed algorithm increases the minimum safety distance between the path and obstacles by more than 17 times, effectively eliminates broken paths, and its planning time meets the real-time control requirements for low-speed operation of mining trucks, providing safe and smooth local navigation trajectories for autonomous mining vehicles on unstructured roads.
Random Forest Landslide Susceptibility Assessment Based on Weighted Factor and Bayesian Mean Particle Swarm Optimization
To address the overfitting problem of the random forest (RF) model in landslide susceptibility assessment, and to improve both the model's prediction accuracy and hyperparameter optimization efficiency, a random forest model combining weighted factors and Bayesian mean particle swarm optimization (BW-MPSO-RF) is proposed. First, the contribution of landslide influencing factors is quantified by random forest feature importance and SHAP value, and a weighted evaluation index system is constructed, with higher contribution factors given higher weights. Second, mean particle swarm optimization (MPSO) and Bayesian optimization algorithm are integrated. MPSO is used to complete the global coarse search of hyperparameters, and Bayesian optimization is used to achieve the local fine search, forming a two-layer hyperparameter adaptive optimization strategy. Finally, taking Lanzhou as the study area, the research is carried out from four aspects: model construction, construction of weighted index system, accuracy verification, and evaluation result analysis. The results show that the AUC value of the BW-MPSO-RF model reaches 0.9427, and the prediction accuracy is 94.16%. Compared with MPSO-RF, PSO-RF, GA-RF and traditional RF models, the prediction accuracy and stability are significantly improved. This model can effectively solve the overfitting problem of RF, and the hyperparameter optimization efficiency is 32.6% higher than that of MPSO alone, providing more efficient and accurate technical support for landslide disaster risk management.
Based on Non-Inertial Frame Modeling for High-Dynamic Companion Flight Control Research
This paper investigates the intelligent companion flight control problem for mobile platforms (UGVs) and unmanned aerial vehicles (UAVs) under highly dynamic conditions. To address the Coriolis, Euler, and centrifugal fictitious forces introduced by a non-inertial reference frame and rapidly varying disturbances, an integrated approach of "non-inertial relative dynamics modeling + parameter preview estimation based on the Unscented Kalman Filter (UKF) + Continuous Gain Scheduling (CGS) MPC" is proposed. First, a complete and reproducible set of relative kinematics and dynamics under platform-fixed connections is presented, unifying gravity and thrust modeling. Subsequently, a unified UKF for platform angular velocity/angular acceleration and apparent acceleration is constructed, with its output fed into the MPC as time-varying parameters and tightening radii. Finally, a continuously varying weight scheduling law dependent on non-inertia intensity and uncertainty is proposed, balancing tracking accuracy and robustness. Simulation results indicate that, compared to fixed-weight MPC and simplified models that ignore fictitious forces, the proposed method significantly reduces peak errors and constraint violations under high-maneuvering and strong-noise conditions while maintaining control smoothness and real-time performance.
MSDA-SparseInst: Real-Time Instance Segmentation via Multi-Scale Feature Fusion and Dual-Branch Attention
Real-time instance segmentation is crucial for urban road-scene understanding, where accurate pixel-level perception is required under complex backgrounds, occlusion, and large scale variation. However, existing efficient methods often struggle to balance segmentation accuracy and inference speed, especially for small distant objects and densely distributed instances. To address this issue, this paper proposes MSDA-SparseInst, a real-time instance segmentation framework based on SparseInst. Specifically, an improved backbone is adopted to enhance feature extraction, a Multi-scale Dilated Feature Aggregation (MDFA) module is introduced to strengthen cross-scale contextual modeling, and a lightweight dual-branch attention strategy composed of GCSA and GGCA is designed to refine decoder features. Experimental results on the Cityscapes validation set show that the proposed method achieves 21.8 AP, 43.4 AP50, and 18.1 AP75 at 30.7 FPS, improving the baseline SparseInst by 3.0 AP while maintaining real-time performance. The results demonstrate that MSDA-SparseInst provides a better trade-off between segmentation accuracy and efficiency for urban road-scene instance segmentation.
A Happiness Prediction Model Based on Performance-Diversity Dual-Constraint Dynamic Weighted Ensemble
To address the problems of insufficient generalization ability of traditional single machine learning models in happiness prediction tasks and the failure of fixed-weight ensemble models to adapt to sample-level prediction differences, this paper proposes a Performance-Diversity Dual-constraint Dynamic Weighting Ensemble (PD-DWE) model. First, a diversified base model pool including Random Forest, K-Nearest Neighbors, Support Vector Machine, Gradient Boosting Tree, XGBoost, Multilayer Perceptron, and LightGBM is constructed, and hyperparameter optimization of the base models is completed through a two-stage random search. Second, based on the 10-fold stratified cross-validation framework, high-quality model selection is performed using an F1-score threshold in each fold, and diversity constraint is achieved by combining predictive correlation filtering. Finally, a dynamic weight calculation strategy integrating the global performance of models and sample-level prediction confidence is designed, and the optimal classification threshold is adaptively searched to complete the final prediction. Experimental results show that the proposed PD-DWE model achieves an F1-score of 79.1% on the test set, representing a 12.43% relative improvement compared with the optimal single model, and an AUC value of 69.61%. Its comprehensive prediction performance is significantly superior to that of single machine learning models, providing a more stable and efficient technical solution for the quantitative prediction of happiness.
Collision-Free Trajectory Optimization of Manipulator Based on Black-Winged Kite Algorithm
Aiming at the requirement of global obstacle avoidance for each link of the manipulator, an obstacle avoidance trajectory planning method based on the Black-winged Kite Algorithm (BKA) was proposed. Firstly, the envelope model of the obstacle and the manipulator was simplified, and the collision detection problem was transformed into the problem of calculating whether the distance between the center of the obstacle envelope model and each joint was too large. Secondly, the trajectory planning was carried out by using cubic polynomial, and the trajectory curve was adjusted by adjusting the cubic coefficient to meet the obstacle avoidance demand. Finally, the BKA was used to optimize the time of the motion trajectory, and the running time of the manipulator was reduced under the premise of meeting the working demand. The simulation results of MATLAB showed that this method could plan a trajectory that could meet the collision-free requirements of each link of the robot arm, and the movement time of the robot arm was greatly shortened, which could ensure that the robot arm could complete the task smoothly and efficiently in actual production.
A Method for Localization and Dense Mapping in Indoor Dynamic Environments Based on an Improved ORB-SLAM2 Algorithm
To address the difficulty of ORB-SLAM2 in constructing dense maps in complex indoor dynamic environments, this paper proposes an improved method for localization and dense mapping in dynamic indoor scenes. The proposed approach integrates a semantic segmentation module based on DeepLabV3+ and introduces an additional semantic thread to obtain scene semantic information. In the tracking process, semantic segmentation results are combined with a dual depth-consistency checking strategy to identify and remove dynamic feature points while preserving reliable static features. Furthermore, the original keyframe selection mechanism is enhanced by incorporating relative pose variations between consecutive frames as an additional criterion, which helps reduce redundant keyframes and improve localization accuracy. A dense mapping thread is also introduced to fuse multi-frame point cloud data and generate a dense three-dimensional map of indoor environments. Experiments conducted on the TUM RGB-D Dataset demonstrate that the proposed method significantly improves trajectory accuracy. The average localization error is reduced by more than 95% in highly dynamic sequences and by 39.60% in low-dynamic sequences. The results show that the proposed approach achieves more accurate pose estimation and dense map reconstruction, providing effective support for mobile robot localization and navigation in indoor dynamic environments.
Research on Physically-Constrained Continuous-Time Dynamical Models and Precise Smartphone Battery Life Prediction
Addressing the challenge of balancing prediction accuracy and interpretability in smartphone battery life forecasting, this study constructs a physically-driven continuous-time dynamical model and a dynamically calibrated Time-to-End-of-Life (TTE) prediction system. The study first decomposes battery output power into external loads and internal losses based on the law of energy conservation, coupling it with a first-order lumped-parameter thermal model. To address user behavior randomness, a continuous-time Markov chain simulation mode switching is introduced, utilizing an Unscented Kalman Filter (UKF) for online parameter calibration. Empirical results demonstrate that this model accurately describes the evolution trajectory of the state of charge (SOC) under various load and temperature conditions. Specifically, the framework achieves precise runtime estimation across diverse scenarios—predicting TTE from 10.69 hours in compute-heavy modes to 23.46 hours in light-load modes—and identifies CPU operations as the primary energy consumption driver, while revealing GPS impact is negligible. This study provides a systematic quantitative solution for energy efficiency management and high-precision runtime prediction in mobile devices.
Band-Focused EdgeNeXt: A Lightweight Architecture for Tibetan Dialect Classification via Spectral Attention and Dual-Pooling Fusion
This study enhances Tibetan dialect classification in low-resource scenarios by proposing a Frequency Band-Focused SE Block and GAP+GMP dual-pooling fusion. The SE Block dynamically weights critical features (e.g., Ü-Tsang F2 formant) while resisting noise. Dual-pooling resolves feature smoothing/loss issues, with progressive stochastic depth boosting generalization. On a 26,762-spectrogram dataset (Ü-Tsang/Amdo/Khams), the 5.8M-parameter model achieves 99.4% accuracy, surpassing EdgeNeXt/RepViT/DilatedFormer by 0.6%/4.0%/0.5%, halving misclassification rates. Signal-to-noise ratio =5dB tests confirm robustness for edge-computing deployment.
Accelerating Deep Learning Inference for Brain Tumor Segmentation: A Review of Architectures, Frameworks, and Clinical Translation
Brain tumor segmentation has transitioned from an accuracy-dominant research agenda to a constrained multi-objective optimization problem in which segmentation quality, latency, memory footprint, robustness to missing modalities, and deployment reproducibility must be optimized jointly rather than sequentially. Focusing on developments published from 2023 to 2026, this review analyzes inference acceleration from a system perspective that links architectural evolution, model-compression and compilation strategies, and hardware-aware deployment constraints within a single causal framework. Specifically, we synthesize evidence on the shift from CNN/nnU-Net baselines to Transformer hybrids and linear-time State Space Model families, evaluate the practical effects of mixed precision, quantization, pruning, distillation, and compiled runtimes, and examine how modality-missing robustness interacts with graph stability and therefore with real-world compiler efficiency. On the basis of this synthesis, we formulate a reproducible evaluation protocol for acceleration claims that reduces cross-paper comparability errors and makes reported latency evidence clinically interpretable. We conclude with a forward-looking engineering roadmap indicating how the field can move from benchmark-centric speed demonstrations to reliable real-time segmentation systems suitable for heterogeneous hospital infrastructure.
A Review of Deep Learning Methods for Brain Tumor Segmentation with Missing Modalities
Multi-modality imaging significantly enhances the accuracy and reliability of brain tumor segmentation by providing complementary biological information. However, in clinical practice, obtaining a complete set of Magnetic Resonance Imaging (MRI) modalities is often hindered by equipment, time, and cost constraints. The challenge of missing modalities thus becomes a major obstacle to achieving high-performance segmentation. This paper systematically reviews emerging methods addressing this issue, with a focus on their network architectures. The main strategies include data synthesis for generating missing scans, hetero-modal segmentation utilizing flexible architectures for variable inputs, and Knowledge Distillation (KD), which transfers knowledge from models trained on complete datasets. Building on these foundations, we analyze the novelty, strengths, and limitations of each method. To provide context, we also introduce commonly used MRI datasets. Ultimately, this review aims to deliver a comprehensive performance evaluation of compensation techniques and outline promising future directions for overcoming this persistent clinical challenge.
YOLO-RSLW: Research on Optimization of Lightweight YOLO11 Object Detection Algorithm
To address the challenges of excessive parameters and high computational complexity in YOLO-series models under resource-constrained environments, this paper proposes an improved YOLO11 framework named YOLO-RSLW, which focuses on the synergistic optimization of lightweight design and accuracy enhancement. The proposed approach systematically reconstructs the model architecture by targeting two critical components: feature fusion and detection head design, thereby significantly reducing model complexity while improving detection performance. Specifically, a lightweight RepNCSPELAN4Lighter module is designed based on reparameterization techniques] to replace the original C3k2 modules in both the backbone and neck networks. The SPPF and C2PSA modules are removed, and an SPPELAN module is introduced to enhance feature representation capability. An efficient LiteShiftHead is proposed to optimize the detection pipeline through task-decoupled group-wise processing and lightweight convolutions. Furthermore, the WIoU loss function is adopted to replace the original loss computation, enhancing the accuracy and robustness of bounding box regression. Experimental results on the DOTAv1.5 aerial image dataset demonstrate that the improved model reduces the parameter count by 37.3% (down to 5.92M), while achieving a 1.1 percentage point improvement in both mAP50 and mAP50-95 (with mAP50 reaching 38.2%), realizing a synergistic optimization of accuracy and efficiency. Ablation studies validate the effectiveness of each proposed module, among which RepNCSPELAN4Lighter and LiteShiftHead contribute most significantly to model compression, while the WIoU loss further improves localization accuracy. This study provides a more efficient detection solution for embedded platforms and real-time vision applications.
ECG Classification Algorithm Based on Deep Ensemble Learning
Cardiovascular disease has become a major global public health concern due to its high mortality rate. As a core non-invasive diagnostic tool, electrocardiogram (ECG) automatic classification is critical for the early screening of heart diseases. To address the problems of low efficiency in manual diagnosis, the inability of single deep learning models to simultaneously capture morphological and temporal features in ECG classification, and insufficient robustness under class-imbalanced scenarios, this paper proposes a deep ensemble learning algorithm named CBR-Stacking. The algorithm uses 1D-CNN, CNN-BiLSTM, and ResNet1D as heterogeneous base models to capture local morphological features, local-temporal fused features, and deep residual features of ECG signals, respectively. Following the Stacking ensemble strategy, the outputs of base models are taken as meta-features and fed into a logistic regression meta-classifier to obtain the final classification decision. Meanwhile, Z-score normalization and class weight method are adopted for data preprocessing to alleviate amplitude differences and class imbalance. Experimental results on the PTB myocardial infarction dataset and MIT-BIH arrhythmia dataset show that the CBR-Stacking model achieves accuracies of 99.55% and 98.86% in binary classification and five-class classification tasks, respectively, outperforming single base models and traditional machine learning methods in all evaluation metrics. Moreover, the time consumption of the model ensemble stage accounts for less than 0.1%, ensuring stable running efficiency. The proposed model effectively integrates the complementary advantages of multi-architecture deep learning models, improving the accuracy, robustness, and generalization ability of ECG classification, and provides an efficient and reliable solution for intelligent auxiliary diagnosis of electrocardiograms.
Graph Neural Networks for Modeling Complex Dependencies in Global Supply Chain Networks
Global supply chain networks exhibit intricate dependencies characterized by multi-tier supplier relationships, dynamic demand propagation, cascading disruption effects, and complex material flows across geographically distributed entities. Traditional analytical approaches struggle to capture these interdependencies due to their reliance on simplified assumptions about network structure and information flow. Graph neural networks (GNN) have emerged as a powerful framework for modeling complex relational data by learning representations that encode both node attributes and graph topology through message passing mechanisms. This review examines the application of GNN to supply chain network modeling, focusing on how these methods capture dependency structures, predict disruption cascades, optimize network configurations, and facilitate decision-making under uncertainty. We explore fundamental GNN architectures including graph convolutional networks (GCN), graph attention networks (GAT), and graph recurrent networks, analyzing their suitability for different supply chain modeling tasks. The paper investigates how GNN approaches model multi-tier visibility, demand forecasting with network effects, risk propagation analysis, and supplier relationship dynamics. Key applications examined include disruption prediction, inventory optimization across network echelons, supplier selection considering indirect dependencies, and resilience assessment for global supply chains. This comprehensive analysis reveals that GNN methods demonstrate superior performance in capturing non-local dependencies and modeling complex interaction patterns compared to traditional supply chain analytics. The paper concludes by identifying critical research directions including temporal GNN for dynamic supply chain evolution, heterogeneous GNN for multi-modal supply chain data
Research on Fire Target Detection Algorithm Based on YOLOv11 Architecture
Fire is a highly hazardous public safety event that not only causes significant casualties and property damage but also results in a profound negative impact on the ecological environment. Consequently, investigating methods to enhance the accuracy and detection speed of fire detection models holds substantial social and economic significance. Traditionally, fire detection technology has primarily relied on physical sensor components. Although these devices demonstrate high sensitivity in practical applications, their overall detection efficiency remains limited, and they are frequently susceptible to interference from complex external environments. With the continuous advancement of computer hardware performance and the increasing maturity of object detection technology based on computer vision algorithms, deep learning models with robust generalization capabilities and high detection precision have emerged as superior alternatives. This study explores a fire detection algorithm based on a one-stage object detection framework, specifically focusing on the You Only Look Once version 11 architecture. This advanced model can automatically extract fire characteristics from input images, facilitating an end-to-end detection methodology that significantly improves both the speed and accuracy of fire identification. By implementing optimization strategies such as cosine learning rate scheduling and mixed precision training, the research aims to achieve a balance between lightweight deployment and high-performance detection. Experimental results indicate that the proposed approach effectively identifies fire targets in diverse scenarios, providing a reliable technical foundation for real-time monitoring and early warning systems in large buildings, warehouses, and forest areas.
Skin Lesion Segmentation via Improved U-Net with Spatial Group-wise Enhancement and Multi-scale Parallel Feature Fusion
Accurate segmentation of skin lesions is a prerequisite for automated dermatological diagnosis. While the U-Net architecture is widely used for medical image segmentation, its performance is often limited by background noise interference and the loss of multi-scale context in deep layers. This paper proposes an improved U-Net-based model tailored for skin lesion segmentation. We integrate a lightweight Spatial Group-wise Enhancement (SGE) attention mechanism into the encoder to suppress non-pathological textures and noise. Furthermore, a Multi-scale Parallel Feature Fusion (MPFF) module is introduced at the deep stages of the network to aggregate multi-scale semantic information and preserve high-frequency boundary details. Experimental results on the ISIC benchmarks show that our proposed model significantly outperforms the baseline U-Net in terms of Dice Score and Intersection over Union (IoU), providing a robust tool for clinical skin lesion analysis.
Muskrat Optimization Algorithm (MOA): A Nature-Inspired Metaheuristic for Complex Optimization Problems.
This paper proposes a novel metaheuristic optimization method, named the Muskrat Optimization Algorithm (MOA), for solving complex optimization problems. Inspired by the foraging and nesting behaviors of muskrats, MOA simulates their cooperative characteristics and spatial memory mechanism. The population is modeled as muskrats with three distinct search paths, and the optimization process is divided into two main phases: foraging and nesting. In the foraging phase, an opportunistic strategy is applied in the early stage to enhance global exploration, followed by a defensive strategy to improve convergence stability. In the nesting phase, a tentative nesting strategy is first adopted to maintain population diversity, and then a refined nesting strategy is employed to strengthen local exploitation. A dynamic memory updating mechanism further balances exploration and exploitation by adjusting historical search information. The performance of MOA is evaluated on the CEC2017 benchmark suite and three constrained engineering design problems. Experimental results demonstrate that MOA achieves competitive convergence accuracy, robustness, and overall optimization performance.
OverLoCK-GPH: A Bio-Inspired Object Detector with Graph-Prior Modulation and Hybrid Instance Refinement
MASK R-CNN is a visual model based on convolutional neural networks and applied to object detection. In the Mask R-CNN architecture, the Backbone typically employs ResNet. Through continuous convolution and downsampling, it extracts texture and semantic features of the image equally layer by layer, resulting in a large amount of background noise being mistaken for useful information, which interferes with the localization of the target. In addition, the Neck adopts a simple top-down additive fusion. This fusion is static and linear, and is limited by the local receptive field of the convolutional kernel, resulting in a lack of spatial relationships in FPN, incomplete object detection, and inaccurate localization. This paper proposes an enhanced detection framework named OverLoCK-GPH. Firstly, we utilize the Overview-Net of OverLoCK to generate a global context prior, and inject it into the features at each level through a novel prior-guided feature pyramid network, achieving dynamic weight modulation in space. Secondly, we introduce the Graph Attention Block at the high-level feature extraction stage, which captures long-range semantic dependencies by modeling pixels as graph nodes. Finally, we designed a Hybrid Instance Refinement Head for detection, which suppresses background noise at the ROI level through a channel attention mechanism. Experiments demonstrate that this method significantly outperforms the benchmark model in complex scenarios, effectively addressing the issues of missed and false detections of fuzzy targets.
Steel Surface Defect Detection Using an Enhanced YOLOv8s with PPA Mechanism and AFPN
Accurate and real-time detection of steel surface defects is essential for industrial quality control, yet it remains challenging due to extreme scale variations and complex background noise. Conventional object detection models often suffer from feature degradation and spatial information conflicts when processing these microscopic and multi-scale defects. To overcome these limitations, we propose an enhanced YOLOv8s-based detector. First, a Paralleled Patch-Aware Attention (PPA) module is integrated to extract multi-scale defect features, adaptively emphasizing critical textures while suppressing irrelevant industrial background noise. Second, we optimize the neck architecture by introducing an Asymptotic Feature Pyramid Network (AFPN) equipped with an Adaptive Spatial Fusion (ASF) mechanism. This structure progressively fuses non-adjacent hierarchical features, effectively mitigating semantic gaps and spatial information conflicts during multi-level aggregation. Extensive experiments conducted on the NEU-DET dataset demonstrate that the proposed method significantly outperforms the baseline YOLOv8s and other state-of-the-art models. The enhanced model achieves a superior balance between detection precision (mAP@0.5) and real-time inference speed, making it highly suitable for practical industrial inspection tasks.
Asymptotic Feature Pyramid Network (AFPN)
Latent Progressive Diffusion Model for Super-Resolution Reconstruction of Oil and Gas Well Perforation Images
To address the challenges in super-resolution (SR) reconstruction of oil and gas well perforation images—such as excessive GPU memory consumption during high-resolution training, susceptibility to losing complex textural details, severe artifact generation, low computational efficiency of the original Denoising Diffusion Probabilistic Model (DDPM) in pixel space, and the failure of naive progressive diffusion to resolve computational costs from a dimensional perspective—this paper proposes a latent progressive denoising diffusion probabilistic model for super-resolution (LP-DDPMSR). The model integrates the dimensional compression advantages of the Latent Diffusion Model (LDM) with the staged training strategy of progressive diffusion. By mapping perforation images into a low-dimensional latent space via a pre-trained Variational Autoencoder (VAE) for the diffusion process, the approach fundamentally alleviates the GPU memory burden caused by pixel-level computations. A Latent Perforation Hierarchical Feature Enhancement Sub-Network (L-PFEN) is designed to specifically optimize the latent representation of high-frequency and low-frequency features in perforation images. The Latent Perforation-Adapted U-Net (L-PA-U-Net) is enhanced by embedding a Time-Aware adaptive Cross-Attention (TACA) mechanism, which strengthens feature modeling of critical perforation regions within the latent space. Furthermore, a three-stage progressive training framework in the latent space is established to achieve a smooth resolution escalation from 64×64 to 256×256. Experimental results demonstrate that, under 2×, 4×, and 8× magnification factors for oil and gas well perforation images, LP-DDPMSR outperforms Bicubic, ESRGAN, DDPMSR, and LDM-based methods in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). At 8× magnification, the model achieves a PSNR of 20.83 dB, an SSIM of 0.8217, and a Fréchet Inception Distance (FID) of 45.29. Compared to the pixel-space progressive model DDPMSR, LP-DDPMSR reduces training GPU memory consumption by 68.5% and improves training efficiency by 62.3%. While ensuring high-fidelity restoration of perforation image details, the model significantly enhances computational efficiency, providing more effective technical support for oil and gas well perforation quality assessment.
The Impact of Artificial Intelligence on Customer Personalization Strategies in E-Commerce
With the rapid development of digital economy and the in-depth popularization of e-commerce, customer personalization has become a core competitive factor for e-commerce platforms to improve user stickiness and transaction conversion rates. Artificial Intelligence (AI), as a disruptive technological force, is reshaping the theoretical framework and practical path of e-commerce customer personalization strategies with its capabilities of massive data analysis, deep feature mining and intelligent decision-making. This paper explores the multi-dimensional impact of AI on e-commerce customer personalization strategies: first, it analyzes the inherent demand for AI-driven transformation of personalization strategies in the current e-commerce development stage, and sorts out the core AI technologies applied in the field of customer personalization. Second, it constructs the implementation framework of AI-based e-commerce customer personalization strategies, and expounds the optimization paths of personalized recommendation, personalized marketing and personalized service supported by AI. Finally, it discusses the practical challenges faced by the application of AI in e-commerce personalization, such as data privacy risks and algorithmic bias, and proposes targeted solution strategies and future research directions. This paper provides a systematic theoretical reference and practical implementation scheme for e-commerce enterprises to optimize customer personalization strategies with the help of AI technology, and has important guiding significance for the high-quality development of the e-commerce industry.
Multi-temporal InSAR Analysis of Land Subsidence in the Shaqu Coal Mine Based on a Hybrid Cloud Processing Framework
This study addresses the demand for monitoring intensive surface subsidence in the Shaqu No. 1 and No. 2 coal mines, Shanxi Province, by implementing a hybrid cloud-based InSAR analytical framework. The workflow utilizes the ASF HyP3 cloud platform for automated Sentinel-1 data preprocessing, including interferogram generation and phase unwrapping, integrated with the open-source MintPy toolbox for Small Baseline Subset time-series modeling. The results demonstrate that this approach significantly reduces the dependency on local computational resources while effectively capturing the spatiotemporal evolution of moving subsidence basins. The findings provide critical technical support for operational safety and geohazard mitigation in complex mining environments.
Research on the Strawberry Disease and Pest Identification Algorithm Based on the YOLOv8 Framework
As an important economic crop, strawberries are highly susceptible to various pests and diseases during their growth, seriously affecting yield and quality. Traditional manual detection methods are time-consuming, labor-intensive, and difficult to apply promptly and comprehensively. To promote the development of intelligent pest and disease detection systems, improve strawberry cultivation efficiency, and ensure strawberry quality, a lightweight Yolo-CAD model based on Yolov8n was proposed, designed to address issues such as multi-labeling and inaccurate detection. The model uses ADown[10] as the downsampling module to reduce feature loss during pooling and optimizes Yolov8's Neck based on the Cross-Scale Feature Fusion Module (CCFM), integrating multi-scale features and contextual information to enhance the model's adaptability to scale variations and improve its ability to detect small objects. Experimental results show that the enhanced Yolo-CAD model achieves a mean accuracy (mAP50) of 76.33%, a precision of 76.19%, and a recall of 71.20% on the validation set. Compared to SSD, Retinanet, Yolov3, Yolov5n, Yolov8n, Yolov9t, and Yolov10n, the average precision (mAP50) was improved by 11.58, 6.22, 7.22, 6.25, 5.08, 5.44, and 6.21 percentage points, respectively. Moreover, the size of Yolo-CAD model is only 3.37MB, 43.65% smaller than Yolov8n, making it more suitable for deployment on mobile platforms with limited computational power. The proposed Yolo-CAD model has shown promising results in identifying and detecting strawberry pests and diseases, and can provide technical support for pest prevention and automated harvesting.
Research on Rotated Target Detection Method for Maritime Vessels in UAV Aerial Images Based on Deep Learning
Aerial imagery captured by unmanned aerial vehicles (UAVs) over maritime scenes is characterized by a wide field of view, homogeneous backgrounds, and the presence of densely packed vessels with diverse orientations. Traditional horizontal bounding box detection methods applied in such scenarios often introduce excessive background noise and fail to accurately represent vessel orientations, resulting in inadequate differentiation of densely moored vessels. To address this issue, this paper proposes an improved rotated object detection method. Building upon the YOLOv8-OBB model with inherent rotation detection capability, the proposed method incorporates a Coordinate Attention (CA) module to enhance feature extraction for elongated vessel targets, optimizes the Feature Pyramid Network (FPN) structure with cross-level skip connections to strengthen multi-scale feature fusion, and employs a Skew Intersection over Union (SkewIoU) loss function to improve the regression accuracy of rotated bounding boxes. For comprehensive performance evaluation, experiments are conducted on the public maritime dataset HRSC2016 and a self-constructed nearshore vessel subset. Results indicate that the improved model achieves a mean Average Precision (mAP@0.5) of 90.5% on the HRSC2016 test set, representing a 1.3 percentage point improvement over the baseline model (89.2%). Ablation studies demonstrate the consistent contributions of each enhancement module, and the model exhibits significantly improved robustness in detecting dense and small-scale vessels. This work provides a more accurate solution for maritime surveillance, search and rescue, and related applications.
VB-CP-ABE: An Improved CP-ABE Scheme Based on Blockchain Collaboration and Fine-Grained Version Control
To address the shortcomings of data privacy protection in fruit traceability systems and the high revocation overhead of traditional CP-ABE schemes when personnel changes are frequent, this paper proposes an improved traceability system based on blockchain collaboration and version control. First, a two-layer "on-chain-off-chain" storage architecture is designed: IPFS is used to store AES-encrypted traceability data, while a consortium blockchain stores access policies, ciphertext indexes, and a global attribute version table, effectively alleviating on-chain storage pressure while ensuring data immutability. Second, a VB-CP-ABE algorithm based on version control is constructed. This algorithm cryptographically binds user attribute private keys to dynamic version numbers stored on the blockchain. When permission needs to be revoked, the version number of a specific attribute on the chain is updated via a smart contract, ensuring that the newly generated or published ciphertext cannot cancel the hash entries when decrypting with the old version private key, thus achieving immediate forward revocation. For users who still need to retain permissions, only incremental issuance of new version private key components for the affected attributes is required. Simulation results show that the proposed scheme outperforms comparative schemes in key generation, encryption, and decryption efficiency while ensuring access control security, demonstrating significant practical application value.
Federated Learning Approaches for Privacy-Preserving Big Data Analytics
The blistering growth of big data analytics in industries has transformed how decision-making is done and increased the risk to privacy of centralized machine learning, in which aggregation of sensitive raw data can put information at risk of breaches and inference attacks. The Federated Learning (FL) system provides a decentralized framework that allows performing collaborative model training, but retains data centred on the client devices or institutional servers, thus fulfilling the stringent regulatory standard of regulations like GDPR and HIPAA. The current review is a synthesis of 127 recent papers (20232025) that assess five main privacy-sensible FL methods, namely Standard Federated Averaging (FedAvg), Differential Privacy-enhanced FL (DP-FL, Secure Aggregation, Homomorphic Encryption-based FL (HE-FL) and Hybrid FL frameworks. One of them, DP-FL, is the most widely used method (about 40% of deployments) and it offers a good privacy-utility trade-off with common accuracy degradations of 1-5%. Hybrid designs, particularly those combining differential privacy and secure aggregation, provide defense-in-depth protection, little performance loss (1-4%), and most rapidly increasing deployment rates (34%/year), especially in regulated markets. FL has a high level of practical impact in fundamental areas: healthcare (35% of the applications, e.g., multi-institutional medical imaging and disease prediction), finance (28%, e.g., fraud detection and risk assessment), IoT/smart cities (20%, e.g., traffic optimization and predictive maintenance), and mobile/enterprise systems. The longstanding issues, such as non-IID data heterogeneity, communication overhead, security threats (poisoning and inference attacks), system heterogeneity, and scalability, are addressed with inventions, such as adaptive aggregation, gradient compression, hierarchical architectures, and Byzantine-robust mechanisms. In recent developments to 2026, the focus of development is toward personalized FL, greater adversarial robustness and connection with large language models, making hybrid and personalized FL the best approach to creating secure, scalable, privacy-preserving analytics in the increasingly decentralized world of big data.