Informatica (Print ISSN 0350-5596, Online ISSN 1854-3871) is an international, peer-reviewed journal based in Europe. It publishes scholarly work in computer science, informatics, and related areas, serving researchers and practitioners across academic and applied computing.
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Dynamic Route Optimization for Logistics Using Spatio-Temporal Deep Learning with Real-Time Traffic and Weather Data
As transportation networks grow increasingly complex and data-rich, the need for intelligent, adaptive routing mechanisms has become essential for efficient and resilient mobility operations. This study presents a deep learning–enabled framework for real-time dynamic route optimization in logistics systems, addressing fundamental limitations of traditional static routing and heuristic-based decision approaches. The proposed architecture integrates long short-term memory (LSTM) networks with spatio- temporal graph convolutional networks (ST-GCN) to model nonlinear temporal evolution and spatial dependencies in traffic flows, GPS trajectories, meteorological conditions, and road network structures. By capturing these complex patterns, the predictive module generates highly accurate short-term forecasts of congestion levels and delivery delays, which are subsequently incorporated into an adaptive routing engine that continuously updates vehicle paths in response to evolving network conditions. Comprehensive preprocessing of multimodal traffic and environmental datasets, advanced feature engineering, and supervised training of the LSTM and ST-GCN models are employed. Model performance is assessed via mean absolute error (MAE), root mean square error (RMSE), and ROC–AUC. Experimental results show substantial gains over baseline predictors and conventional routing: a 45.6% reduction in MAE, a 39.5% reduction in RMSE, and an ROC–AUC of 0.91 for delay prediction, while enabling an estimated 12.3% reduction in carbon emissions. These improvements translate into measurable reductions in travel time and fuel consumption, underscoring the system’s potential to enhance operational resilience, environmental sustainability, and decision efficiency.
Copyright (c) 2026 Ahmed Abdel-Wahab Rakha, Mohammed Elsersy
Knowledge Distillation from Medical Vision Foundation Models to Lightweight Networks for Edge Deployment: A Comparative Study
Medical vision foundation models pretrained on large-scale domain-specific data achieve strong clinical performance, but their computational cost precludes deployment on resource-constrained edge devices. We investigate whether the domain-specific representations of a medical foundation model transfer more effectively through knowledge distillation (KD) than generic ImageNet-pretrained features. We compare a domain-specific foundation model (RETFound, ViT-Large, 307M parameters) against a general-purpose pretrained model (ConvNeXt-Base, 89M) as KD teachers for two lightweight students (MobileNetV3-Small, 2.5M; EfficientNet-Lite0, 4.7M) on two clinical benchmarks – HAM10000 (dermoscopy, 7 classes) and APTOS-2019 (diabetic retinopathy, 5 classes) – over a grid of distillation temperature and loss weight, with INT8 quantization and Grad-CAM analysis. Because RETFound is pretrained on retinal images, APTOS- 2019 is in-domain for it whereas HAM10000 (dermoscopy) constitutes an out-of-domain test. On balanced accuracy, the appropriate metric for these class-imbalanced datasets, ConvNeXt-Base yields the stronger student in all four settings (e.g., HAM10000 MobileNetV3: 0.826 vs. 0.763) – including on APTOS-2019, where RETFound’s retinal pretraining is in-domain – despite fewer parameters and no domain-specific pretraining; RETFound distillation can even fall below the no-distillation baseline. RETFound requires higher temperatures for effective transfer, whereas ConvNeXt is robust across a broad range. INT8 quan- tization preserves accuracy within 0.3% while compressing MobileNetV3-Small to 4.2 MB. These results indicate that a teacher’s fine-tuned quality, rather than domain-specific pretraining, predicts distillation success, and offer practical guidance for edge deployment of medical image classifiers.
Configurable Process Mining for Context-Aware Customer Journey Analysis: A Simulation-Based Comparative Study
Customer journeys frequently differ depending on the context. Customer experiences with a digital service can be influenced by factors such as location, delivery limitations, or payment methods. Process mining can reconstruct customer journeys from event logs. However, classical process discovery usually integrates all observed behavior into a single model, which may obscure context-specific variations. This study examines whether such variations can be represented more explicitly within a shared process structure using configurable process mining. A synthetic e-commerce event log was generated for two regional contexts with distinct shipping and payment policies, comprising 1,600 customer sessions, approximately 7,500 events, and 13–14 activity types. Using ProM, the Inductive Miner algorithm was applied to discover a global process model and region-specific process trees, which were then merged and configured into regional variants. Fitness and precision were used as the main evaluation metrics. In this controlled setting, the classical model achieved higher fitness (0.9438) but lower precision (0.7129), while the configured regional models achieved higher precision (0.996) with lower fitness (0.902). These findings suggest that configurable process mining may be more informative when the objective is to analyze contextual differences, whereas classical discovery remains useful for a broad understanding of the process. Real-world validation remains necessary to assess the robustness of the approach on operational customer journey logs.
Copyright (c) 2026 Imane El Aama, Hanae Sbai
Proactive Infrastructure Monitoring in Smart Cities via WSNs
Copyright (c) 2026 Hongtao Zhu, Xiaolin Hung
Calibrated Probabilistic Stacking with Linear Meta-Learning for Admission Outcome Prediction
This paper presents a two-stage probabilistic stacking ensemble for admission outcome prediction based on calibrated heterogeneous classifiers and linear decision fusion in probability space. The proposed approach combines HistGradientBoosting, ExtraTrees, and RandomForest models to generate posterior class probabilities, while probability calibration is applied to improve the consistency and comparability of probabilistic estimates. To prevent information leakage, the meta-level training space is formed exclusively from calibrated out-of-fold probability representations. At the second stage, linear meta-models are used to aggregate probabilistic outputs and produce the final decision. The method was evaluated on a real-world dataset collected from the admission campaign of Lviv Polytechnic National University. Experimental studies using holdout validation and stratified cross-validation demonstrate that the proposed ensemble achieves high and stable predictive performance while preserving the quality of probabilistic estimates. In particular, the method reached F1-scores up to 0.990 and MCC values up to 0.979 on the holdout test set, together with low LogLoss values. Comparative analysis with baseline classifiers and standard stacking approaches confirms that calibrated probabilistic fusion improves both classification quality and the reliability of posterior probability estimates in practical decision-support tasks.
Copyright (c) 2026 Khrystyna Zub, Oksana Mulesa
In memoriam: Prof. Dr. Anton Pavel Železnikar (1928-2026)
Copyright (c) 2026 Slavko Žitnik, Matjaž Gams
Deciphering COVID-19 Narratives: A Comparative Study of ML Models (RF, MNB, GB, LR, SVM) and DL Models (CNN, Bi-LSTM) for News Article Classification
The COVID-19 pandemic has provided an unprecedented amount of information in news outlets, which include scientific, health-related, political, economic, and social narratives. This study compares the effectiveness of machine learning and deep learning algorithms for classifying text data, with a certain emphasis on how well the former handle COVID-19 news narratives. The study dataset contains news articles regarding COVID-19. To achieve the primary purpose of this research is to classify COVID19 related news, we integrate multiple datasets. The analysis reveals machine learning models exhibit superior performance in text data classification. In particular, the Random Forest model reaches a 98% accuracy rate. In contrast, with regards to deep learning models, the Bidirectional Long Short-Term Memory model with FastText integration turns out to be the best option due to its exceptional accuracy. Exploratory data techniques such as topic modeling and word cloud approaches are incorporated to uncover hidden patterns in the data. Pre-trained (e.g., deep learning) and non-pre-trained ML models are implemented highlighting the versatility of ML in text classification tasks. The specific purpose to compare to the deep learning and machine learning algorithm to classification of the new article. Notably, a predictive model employing Bi-LSTM with the FastText pre-trained model achieved an impressive 94% accuracy in classifying COVID-19 news reports.
Hybrid Genetic Programming and Q-Learning for Behavior Tree Evolution in MicroRTS Tactical Scenarios
Genetic Programming (GP) can be used to evolve human-interpretable Behavior Tree controllers for realtime strategy games. Current Behavior Trees-based GP approaches compute fitness only at the end of each episode, which does not allow learning from finer-grained tactical decisions during gameplay. In this work, we integrate tabular Q-learning within the BT controller to gate actions at terminal nodes based on learned action-values, and collect additional rewards at each tick of the game that are used to augment the final fitness signal. Since the Q-table is reset at the start of each episode, our approach allows the learned information to affect fitness, while keeping the learned values from one generation separate from subsequent generations (a form of Baldwinian learning). Applied to plain-terrain MicroRTS micromanagement challenge (population of 100 BTs, 2000 generations, against a deterministic rush opponent), our approach achieves a maximum fitness of 26.44 compared to GP’s max fitness of 22.0, a relative improvement of 20.2%. Mean best-fitness was also increased by 17.8% compared to the GP-only baseline. These results are reported from a single representative run per configuration; multi-seed replication is identified as a priority for future work. The additional per-episode RL reward signal is strongly correlated with elite fitness (r = 0.89), confirming that it provides informative guidance for the evaluation. Unit coordination exhibited by the hybrid agents is also found to be more structured, with defined front-line and support roles. The agent maintains its interpretable Behavior Tree structure.
Copyright (c) 2026 Mohamed Amine Chikh Touami, Mohammed Salem, Mohamed Fayçal Khelfi
CSAF-YOLO: An Improved Smoke and Fire Object Detection Algorithm for Complex Scenes Based on YOLOv11n
Early detection of flames and smoke is crucial for fire warning systems, yet blurred smoke boundaries, small-scale targets, and complex background interference still limit existing methods. This paper proposes CSAF-YOLO, an improved detection model based on YOLOv11n. A C3-MCG module is introduced into the backbone to enhance multi-scale contextual feature representation. A DB-SAFM module is designed in the feature fusion stage to improve semantic and spatial feature alignment through a dual-branch self-attention mechanism. AWHIoU is adopted as the loss function, combined with a bi-phase focusing strategy and a scale-adaptive weighting mechanism to achieve adaptive optimisation for sampl0065s with different IoU qualities and targets of different scales. Experimental results show that CSAF-YOLO significantly improves detection performance over mainstream models, achieving 2.2% and 2.3% gains in precision and mAP50 over YOLOv11n, respectively, while maintaining a high FPS of 177.75, demonstrating strong potential for complex monitoring and real-time detection.
Copyright (c) 2026 Yuanpan Zheng, Chao Wang, Yu Zhang, Xuhang Liu, Wenbin Zhong
Quantitative and Subjective Evaluation of Latency and Packet Loss Effects on Video Streaming Quality Using Controlled Network Emulation
With the rapid growth of high-speed internet and increasingly advanced smart devices, the way we consume media has changed dramatically, with video streaming now becoming the dominant form of digital content delivery. Despite this shift, the quality of experience (QoE) still depends heavily on network conditions, especially latency (LT) and packet loss (PL). In this study, we explore the individual and combined effects of LT and PL on video quality (VQ) using a mix of objective and subjective metrics. We built a controlled experimental testbed using media player VLC to stream videos, Clumsy to simulate network impairments, and FFmpeg to compute the peak signal-to-noise ratio (PSNR). Video streams (240p-480p resolution, 128-512 kbps bitrate, 25 fps) were delivered via a wireless network (UDP) at varying LT levels (0-200 milliseconds) and PL (0-10%). Subjective quality was assessed using the mean opinion score (MOS) from 30 participants in a randomized video-viewing condition. Results show that LT had only a small effect on objective VQ, with PSNR dropping slightly (e.g., from 36.8 dB to 36.0 dB at 128 kbps as LT increased from 0 to 200 ms), but had a noticeable effect on user perception, with MOS reducing from ~4.5-4.9 to 2.8-3.5. On the other hand, PL had a much stronger impact on both measured and perceived quality. Even a 1% PL dropped PSNR by up to 3.6 dB (e.g., from 36.8 dB to 33.2 dB at 128 kbps) and noticeably lowered MOS. At higher loss rates (7-10%), PSNR significantly decreased to around 24 dB, and MOS fell below 2.0, indicating poor to unacceptable quality. The findings reveal that PL is much more detrimental to VQ than LT, although higher bitrates can offer some resilience under adverse network conditions. These results provide quantitative guidelines and practical insights for enhancing video streaming systems and improving QoE in real-world networks.
Copyright (c) 2026 Anthony Nwohiri, Adetomiwa Aribisala, Al-Bashir Muhammad, Oluwatosin Mofikoya, Korede Jacob
FSD-Net: FSD-Net: Multi-Scale Outdoor Fire and Smoke Detection via Swin Transformer-Enhanced YOLOv11s with Lightweight Feature Fusion and Inner-CIoU Loss
In this paper, a multi-scale fire and smoke detection model, named FSD-Net, is established upon the YOLOv11s model to overcome the challenges in outdoor fire scenarios, such as large variations in target shapes, complicated backgrounds, and the difficulty of detecting small objects. First, a multi-scale feature extraction module, C3Swin, is designed by integrating the Swin Transformer into the backbone network. This strengthens the model’s capability to capture fine-grained fire and smoke features. Second, we incorporate shallow features into the FPN-PAN architecture to improve small-object detection performance. Third, we developed a lightweight fusion module, GS-A2C2f, to enhance the model’s feature fusion capabilities.. Furthermore, GhostConv is employed to decrease computational complexity. An auxiliary bounding-box loss function, Inner-CIoU, is introduced to better balance detection performance and inference speed. Experimental results on a self-built dataset comprising 11,672 images evaluated following the COCO metrics, suggest that, compared to baseline model, FSD-Net achieves improvements of 2.5%, 2.3%, and 1.1% in precision, recall, and mAP@50, respectively, while lessening the number of parameters by 1.3 million and increasing FPS by 3.8%. These results confirm that FSD-Net achieves accurate fire smoke detection and localization, providing an effective solution for outdoor fire detection in complex environments.
Copyright (c) 2026 Yuanpan Zheng, Yu Zhang, Chao Wang, Xuhang Liu, Wenbin Zhong
RMAM: A Rule-based Filter Method with Modified Huffman Ranking for Variable Selection in Supervised Learning
Most existing filter-based variable selection methods compute variable scores using static mathematical formulations over fixed contingency tables and do not provide principled cut-off mechanisms to distinguish influential from redundant variables thus leaving final subset selection to end users. To address this limitation, this paper proposes a new filter-based variable selection method called Rule Mining Analysis Method (RMAM). For classification tasks involving supervised learning, this method dynamically generates rules from the training data by iteratively selecting variable values that maximize information gain with respect to the target class. Each generated rule is assigned a weight based on rule coverage and uncovered training data instances, and variable scores are subsequently computed by aggregating the weights of all rules in which they appear. To automate variable subset determination, RMAM integrates a new Modified Huffman Ranker (MHR) that computes an adaptive cut-off threshold to distinguish essential from redundant variables. Experimental evaluation across 27 UCI benchmark datasets demonstrates that RMAM reduced variable search space by an average of 75.2% relative to the original datasets thereby outperforming Information Gain and Chi-Square methods which achieved average reductions of 44.7% and 50.0%, respectively. When evaluated using Naïve Bayes classification algorithm, RMAM achieved comparable or superior predictive performance on the majority of datasets while offering substantially smaller and more interpretable variable subsets. These results demonstrate RMAM’s effectiveness as a practical variable selection approach for supervised learning applications.
Copyright (c) 2026 Talib AlShehhi, Yingjie Yang, Feng Chen, Aladdin Ayesh
Lightweight Vehicle Detection in Dense Traffic Scenarios Based on YOLOv8n with ContextECA2.0 and Adaptive Window Attention
To address the challenges of large vehicle scale variation, severe occlusion, and frequent target overlap in dense traffic scenarios, this paper proposes a lightweight vehicle detection method based on YOLOv8n. First, a novel ContextECA2.0 module is introduced at the detection head input, synergistically fusing local convolution features with global attention mechanisms to adaptively balance feature contributions across scales. Second, an Adaptive Window Attention (AWA) module dynamically adjusts the attention window size according to local target density, effectively balancing fine-grained small-object modeling and global contextual dependencies. Third, an attention-weighted SiOU loss function is introduced to dynamically weight regression gradients, improving the localization precision of overlapping targets. Extensive experiments were conducted on the large-scale VisDrone dataset (comprising 10,209 images) under an NVIDIA RTX 3090 GPU environment. Compared to the standard YOLOv8n baseline, the proposed method achieves significant improvements of 2.2% in mAP@0.5 and 1.5% in mAP@0.5:0.95, while maintaining a highly lightweight architecture (3.7M parameters) and real-time inference speed (120 FPS). The results validate the effectiveness, robustness, and practical deployability of the proposed method for dense vehicle detection in complex edge-computing traffic scenarios.
Cross-Attention Fusion of Scientific Text and Project Metadata for Explainable Technology Readiness Level Prediction
Technology Readiness Level (TRL) assessment is often constrained by document volume, evaluator subjectivity, and variation in institutional reporting practices. This paper presents a multimodal framework for TRL prediction that combines scientific project descriptions with structured project metadata through a cross-attention fusion layer. The dataset contains 18,247 technology projects collected from NASA TechPort, EU CORDIS, and US DOE repositories. NASA records provide explicit TRL labels, while CORDIS and DOE labels were derived using a milestone-based TRL rubric and annotator agreement analysis (κ=0.72). Textual descriptions were encoded with SciBERT, and structured variables were transformed through an XGBoost-based metadata encoder before being aligned in a cross-attention module. Evaluation was conducted using stratified 5-fold cross-validation with source-aware grouping to reduce leakage between related records. The proposed model reached 71.8% accuracy (MAE=0.82 TRL levels), 68.4% macro F1, and 89.2% adjacent accuracy, compared with 64.2% accuracy for the text-only SciBERT baseline and 54.1% accuracy for the structured-only XGBoost baseline. SHAP analysis identified project duration, funding amount, and technology domain as influential features, although these associations should be interpreted as predictive rather than causal. A trajectory extension produced 67.3% accuracy for 6-month TRL forecasting, with lower performance at longer horizons (62.8% at 12 months, 58.1% at 24 months). These findings support the use of multimodal TRL prediction as a decision-support mechanism, while acknowledging limitations related to label noise, cross-source bias, and external validation requirements.
BFS-CNN-ECA-GMP-GRU-MSP: An Enhanced Cross-Perspective GaitRecognition Model with Efficient Channel Attention and Cosine-ConsistentMetric Learning
Cross-view gait recognition remains vulnerable to viewpoint shifts and appearance changes, especially under carrying and clothing covariates. We propose BFS-CNN-ECA-GMP-GRU-MSP, an enhanced version of our previous BFS-CNN-GMP-GRU-MSP framework, by introducing two upgrades: multi-stage lightweight channel recalibration with Efficient Channel Attention (ECA) and cosine-consistent metric learning through cosine batch-hard triplet loss, a cosine classifier, and L2-normalized embeddings. Experiments are first conducted on CASIA-B under the same legacy closed-set protocol used in our earlier Informatica study (gallery: NM#01–04; probe: NM#05–06, BG#01–02, CL#01–02; same-view matches excluded). This protocol is retained to isolate architecture-level improvements and is interpreted as a within-protocol comparison rather than a subject-disjoint generalization benchmark. Under this setting, the proposed model reaches mean Rank-1 accuracies of 99.96% (NM), 99.74% (BG), and 98.38% (CL), improving the baseline by +2.96, +5.74, and +7.38 percentage points, respectively. To probe unseen-subject behavior more directly, we further report a supplementary subject-disjoint split (subjects 001–074 for training and 075–124 for testing), where the full model attains 97.73% (NM), 87.98% (BG), and 64.00% (CL). Under this stricter split, the clearest effect of ECA appears under clothing variation, where the full model exceeds w/o ECA by 1.82 percentage points on CL, while the ECA branch still introduces only 13 learnable parameters (k=3/5/5 for 64/128/256 channels). These results support the proposed modifications as a lightweight and effective enhancement for protocol-matched cross-view gait recognition, while broader multi-split subject-disjoint and open-set validation remains future work.
Copyright (c) 2026 Lijuan Gao, Jieran Liu
A Remaining Useful Life Prediction of Lithium-Ion Batteries via Improved Dream Optimization Algorithm-Tuned LSTM Networks
Lithium-ion batteries have been widely utilized in modern society due to their excellent performance, making the accurate prediction of their remaining useful life (RUL) of paramount importance. Current RUL prediction methods are primarily categorized into battery discharge model-based approaches and data-driven approaches. However, the charging and discharging processes of batteries are typically accompanied by nonlinear variations in internal structural parameters, which pose significant challenges for model-based prediction methods. To address this issue, this paper proposes a predictive model based on Long Short-Term Memory (LSTM) networks optimized by an Improved Dream Optimization Algorithm (IDOA). First, to overcome the limitations of the original Dream Optimization Algorithm (DOA)—namely, the tendencies to become trapped in local optima and slow convergence—an optimal point set is employed for population initialization, and an adaptive population reduction mechanism is introduced to accelerate convergence. Second, the IDOA is utilized to optimize the hyperparameters of the LSTM network, thereby enhancing its predictive capability. Finally, simulation experiments are conducted using the CALCE and NASA battery datasets via the leave-one-out cross-validation method. The performance is evaluated using the average Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) calculated over ten independent runs. Comparative analysis with the original DOA and other baseline models demonstrates that the proposed IDOA-LSTM model can accurately predict the RUL of lithium-ion batteries with higher precision. Specifically, for the four batteries in the CALCE dataset, the RMSE values are 0.0201, 0.0198, 0.0213, and 0.0186, while the MAE values are 0.0181, 0.0174, 0.0163, and 0.0154, respectively. The results indicate that the average prediction accuracy of the proposed model is improved by 53.8% compared to the unoptimized model.
Copyright (c) 2026 Suoxun Li, Jintao Liu, Yikang Zhao, Xiaoxue Wang
Tomato Leaf Disease Classification Using EfficientNet Enhanced with RefConv Structural Reparameterization, Efficient Channel Attention, and Lion Optimizer
Fine-grained classification of tomato leaf diseases faces challenges due to high inter-class similarity and insufficient model generalization ability. To address these issues, this paper proposes an improved EfficientNet architecture that fuses structural reparameterization, a lightweight channel attention mechanism, and an efficient optimization strategy. Specifically, the reparameterized refocus convolution (RefConv) module is introduced to enhance the extraction ability of subtle lesion textures. The original Squeeze-and-Excitation (SE) module is replaced with the efficient channel attention (ECA) mechanism to achieve lightweight channel feature recalibration. Meanwhile, the Lion optimizer is adopted to accelerate convergence and improve generalization performance on complex agricultural images. Experimental results show that the proposed method achieves 98.44% accuracy on the self-built tomato disease dataset, which is 4.22% higher than that of the baseline model EfficientNet-B0. Cross-species generalization tests further obtain 98.43% and 99.51% accuracy on apple and grape leaf disease datasets, respectively, verifying the robustness and transferability of the model.
Copyright (c) 2026 Lijuan Shi, Haitang Li, Shiwen Zhang
Difficulty-aware Dynamic Chain-of-Thought Prompting for Large Language Models via BM25 and Semantic Retrieval
Large language models (LLMs) have demonstrated exceptional capabilities across multiple domains and emerged as the core driving force in natural language processing. Their reasoning however can be associated with logical flaws and lack of stability when attempting to solve difficult problems that require multi-step deduction, cross-domain knowledge or implicit constraints, with redundant or insufficient exemplars in conventional prompts and poor fit with exemplar and target problems. To address these issues, we propose a dynamic Chain-of-Thought (CoT) prompting method based on problem difficulty assessment: first, the model performs zero-shot self-evaluation of the required solution steps to dynamically determine the number of exemplars; then, it integrates BM25 retrieval to select the most similar high-quality question-answer pairs, constructing precise Few-shot prompts. Experiments conducted on multiple datasets effectively resolve the two major limitations of traditional exemplar-based methods, enabling LLMs to obtain appropriately tailored exemplars for both multi-step mathematical reasoning and interdisciplinary question answering. Consequently, the accuracy of complex reasoning is improved to varying degrees across tasks.
An Explainable Multi-Scale Attention-Based Deep Learning Framework for Trustworthy Diabetic Retinopathy Detection Using Enhanced Fundus Images
Diabetic Retinopathy (DR) is a microvascular complication caused by prolonged hyperglycaemia that damages the retinal capillary network, putting people with diabetes at risk. Damage to these tiny vessels increases vascular permeability and results in microaneurysms, hemorrhages, and hard exudates. If left untreated, progression may lead to irreversible vision loss. Early detection and timely treatment substantially reduce the risk of blindness. Current clinical diagnosis relies on ophthalmologist assessment, which results in diagnostic delays and inter-observer variability. Inter-class similarity and intra-class variability in Fundus Images (FIs), due to heterogeneity in lesion morphology, retinal pigmentation, and imaging protocols, yields diagnostic ambiguity. Conventional Deep Learning (DL)-based frameworks act as black boxes, yielding high predictive accuracy but lacking interpretable evidence of the image features about their decisions, thereby limiting clinician trust and clinical adoption. To mitigate these limitations, we propose an explainable DL-based mechanism that classifies FIs into different DR severity grades. This study presents a heterogeneous feature-extraction method inspired by residual and inception networks, combined with attention mechanism to learn disease specific features, so that the model focuses on the most relevant patterns. Residual skip connections and multi-scale inception style branches, in combination with features refinement, helps classifying lesion severity with high precision. The proposed framework begins with segmentation of FIs to extract Regions of Interests (ROIs), followed by Channel-wise Contrast Limited Adaptive Histogram Equalization (C-CLAHE) combined with Difference of Gaussian (DoG) filtering, optimizing the color channels independently and standardizing contrast to highlight fine-grained retinal structures. Geometric minority oversampling is employed to ensure balanced representation of all severity categories during training. Proposed framework incorporates Swish activation to enhance gradient propagation and employs the class-aware focal loss formulation to mitigate class imbalance issue, emphasizing hard examples. Experimental results on the IDRiD and APTOS2019 combined test-set demonstrate that the proposed model achieves screening (healthy/diseased) and lesion-severity (mild/moderate/severe/proliferative) classification accuracies of 99.27% and 75.90%, respectively. Although, discriminating the four DR stages pose challenges like severe imbalance and inter-class differences among classes, which are better aided by proposed framework. On the DDR benchmark, the model achieves comprehensive DR-grading accuracy of 80.76% on 5-class classification task. The Heterogeneous Attention Residual-Inception Convolutional Network (HARIC-Net) outperforms several state-of-the-art (SOTA) DL-based architectures, including AlexNet, ResNet, DenseNet and Inception Net, along with recent domain specific hybrids in DR-grading task. HARIC-Net’s parameter counts of ~2 million underscores its computational efficiency and robust generalization capability. Gradient-weighted Class Activation Mapping (Grad-CAM), an Explainable Artificial Intelligence (XAI) technique, is used to visualize class-discriminative regions, and to provide interpretable predictions.
Copyright (c) 2026 Muhammad Nabeel Mehmood, Muhammad Hassaan Ashraf
LabAlignAgent LabAlignAgent: A LOINC-Enriched Ontology-Guided Agent forCross-Hospital Semantic Alignment of EMR Laboratory Tests
Patients often receive care from multiple hospitals, where laboratory tests are commonly represented using institution-specific names, units, and reporting conventions. This heterogeneity limits semantic interoperability across Electronic Medical Record systems. This study proposes LabAlignAgent, a LOINC-enriched ontology-guided agent for cross-hospital semantic alignment of EMR laboratory tests. The approach combines domain ontology construction, LOINC-based ontology enrichment, exact and fuzzy matching, GPTassisted semantic verification, and rule-based alignment to harmonize heterogeneous laboratory test catalogs. The evaluation used real-world laboratory test data from two Saudi hospitals: 136 alignable tests from King Fahad Specialist Hospital (KFSH) and 126 alignable tests from Saudi German Hospital (SGH). In the hospital-to-LOINC normalization task, 202 out of 262 tests, representing 77.1%, were successfully mapped and enriched with standardized LOINC concepts, including 67 additional mappings recovered through GPT-assisted reasoning beyond lexical matching alone. In the cross-hospital alignment task, 17,136 possible KFSH–SGH pairs were reduced to 4,509 semantically plausible candidate pairs using ontology-derived constraints on unit and sample type. The full LabAlignAgent workflow produced 224 high-confidence crosshospital semantic matches, compared with 67 matches obtained by the baseline prompt-based alignment without ontology enrichment. The accepted mappings were validated by a clinical pathologist, who assessed clinical equivalence using analyte, specimen, unit, method, and reference-interpretation criteria. The results show that LOINC-enriched ontology guidance and contextual semantic reasoning substantially improve laboratory test harmonization across hospitals and support more interoperable EMR integration. All datasets, ontologies, lab test names, and alignment results for each strategy are publicly available in our GitHub repository at https://github.com/SarahayadAOU/LabAlignAgent.
Metric-Wise Comparative Analysis of Hybrid CNN–SRU/LSTM and Lightweight CNN–MIL Frameworks for Deployment-Oriented Video Anomaly Detection
Video anomaly detection is a critical component of intelligent surveillance systems, where detection accuracy, temporal stability, computational efficiency, and real-world deployment feasibility must be jointly considered. Existing studies frequently rely on ROC–AUC as the primary evaluation metric, providing limited insight into practical system performance. This study presents a structured metric-wise comparative analysis of hybrid CNN–SRU/LSTM architectures and lightweight CNN-based multiple instance learning (MIL) frameworks, based on systematically collected benchmark results from datasets such as UCF-Crime and ShanghaiTech. The analysis follows a literature-driven methodology and evaluates models across multiple dimensions, including AUC, false alarm rate (FAR), temporal stability, inference speed (FPS), computational footprint, and calibration reliability using expected calibration error (ECE). Deployment-oriented factors such as latency–performance trade-offs, cross-domain robustness, and scalability under limited labeled data are also examined. Results indicate that hybrid CNN–SRU/LSTM frameworks achieve approximately 85.9% AUC across benchmark datasets with strong temporal consistency, while CNN–MIL approaches maintain competitive accuracy (≈82–84.7%) with significantly higher efficiency (up to 72 FPS) and improved calibration (ECE reduced from ~0.17 to ~0.10). Transformer-based and vision–language models achieve slightly higher accuracy (>86% AUC) but operate at substantially lower frame rates (<12 FPS) and higher memory requirements (>800 MB). These findings highlight that marginal accuracy gains often incur substantial computational cost, emphasizing multi-metric evaluation and hardware-aware model selection for practical video anomaly detection systems.
Copyright (c) 2026 Rajat Gupta, Nidhi Tyagi
Software Framework for Mitigating Programming Plagiarism and Collusion
Many mitigation strategies for programming plagiarism and collusion focus solely on either penalising students involved in such misbehaviour or manually educating students regarding the matter. This paper combines both strategies within a software framework and automates the education strategy. It informs students about plagiarism and collusion based on code similarity and instructors' expectations through an assessment submission system with three variants. Highly similar submissions are alerted, while original submissions have their similarities simulated. In addition, the quality of the submissions is also reported through static analysis. On the due date, student submissions are checked using a similarity detector that provides human-language explanations, which has two variants. Students using our framework show greater awareness of programming plagiarism and collusion, and are less likely to engage in such misbehaviours.
ATT-LCNN: An Attention-Enhanced Lightweight CNN for Acoustic Signal-Based Municipal Pipeline Leak Detection
This study focuses on the recognition of acoustic signals from municipal pipeline leaks. An original end-to-end recognition algorithm (ATT-LCNN) based on the fusion of an attention mechanism and an improved lightweight convolutional neural network (CNN) is proposed, targeting the bottlenecks of traditional methods in minor leak detection and poor generalization. The algorithm employs a preprocessing flow of "DC component removal → improved Kalman filtering → signal normalization → segmented windowing", constructs a multi-dimensional fusion feature set across time, frequency, and time-frequency domains, and embeds an improved SE-Net attention module with depth-wise separable convolution for lightweight and high-accuracy feature learning. Experiments are conducted on a self-built simulated pipeline dataset (1250 samples, covering 3 pipe diameters, 3 leakage levels, 5 propagation distances, 5 noise intensities, with 750 leak samples and 500 non-leak samples), with 5- fold cross-validation to ensure statistical robustness. Performance is benchmarked against traditional machine learning methods (SVM, Random Forest) and standard deep learning models (vanilla CNN, LSTM). The results show that the ATT-LCNN algorithm achieves a 98.7% detection rate (recall), 98.2% precision, 98.4% F1-score for minor leaks, maintains an overall accuracy of over 97.2% at a propagation distance of 20m, and reaches 97.8% accuracy under 60 dB strong noise conditions. The model has only 1.2M parameters, with a single-sample inference time of 2.3ms on CPU, meeting embedded deployment requirements. The repeated detection accuracy fluctuation range is only 0.8%, with stable performance across various pipe diameters and materials. Compared with baseline methods, ATT-LCNN improves minor leak detection accuracy by 6.3%-12.1% under high-noise conditions, balancing recognition accuracy, anti-interference capability, and real-time performance, providing technical support for intelligent leak detection in municipal pipeline networks.
Copyright (c) 2026 Mingjie Liang, Cuimei Li
Parameterized Complexity Analysis for Cryptanalysis of Composite CipherSystems: FPT Characterization and Optimized Algorithms
We present a comprehensive parameterized complexity framework for the cryptanalysis of classical cipher systems composed of multiple cryptographic transformation layers. Our main theoretical contributions are threefold. First, we prove that Cipher-Decode is fixed-parameter tractable (FPT) when parameterized by the structural complexity vector (k, ℓmax, |Σ|), yielding an algorithm with running time O∗(|Σ|k·ℓmax ). Second, we establish that the problem is para-NP-hard when parameterized solely by the number of layers k, and W[1]-hard via a complete formal reduction from k-Clique, providing a tight complexity dichotomy. Third, we design a branch-and-bound algorithm with statistical pruning based on Kullback-Leibler divergence and the Index of Coincidence, reducing the effective search space by two to six orders of magnitude relative to exhaustive search. Experimental evaluation on 47 benchmark instances spanning Vigenère, substitution, ADFGVX, and multi-layer composite ciphers confirms the theoretical scaling predictions (R2 = 0.9998) and demonstrates speedups of 115× to over 2000× against naive baselines, with direct comparisons against simulated annealing and beam search. To the best of our knowledge, this is the first rigorous FPT analysis of multi-layer classical cipher cryptanalysis.
Copyright (c) 2026 Brandon Caillahua Mendoza
Structural Limits to Proto-Life Emergence: Evidence from Maximum-Entropy Reinforcement Learning
This study investigates whether failures of proto-life emergence are primarily due to limited optimization or to insufficient model expressivity. We formulate an abstract protocell-inspired environment as a continuouscontrol Markov decision process and train a Soft Actor–Critic (SAC) agent under a maximum-entropy objective to act as a strong exploration-preserving optimizer. The environment includes protein-like concentration dynamics, membrane integrity, abstract functional role coverage, and division-like events, while expressivity is varied through three regimes controlling coupling strength, feedback structure, and internal memory. Performance is evaluated using survival rate, success rate, mean functional role coverage, and mean number of division events. Across regimes, survival reached 100%, but success increased from 67.5% in the low-expressivity regime to 97.5% in the mid regime and 100% in the high regime. Mean division count rose from 7.58 and 9.05 in the low and mid regimes to 34.58 in the high regime, indicating a qualitative transition toward growth-dominated behavior once stronger feedback and memory were available. These results show that strong entropy-regularized optimization alone does not guarantee life-like organization when essential state variables and interaction mechanisms are absent. Rather, the emergence of sustained proto-biological behavior is bounded by the representational structure of the model itself. The findings position maximum-entropy reinforcement learning as a diagnostic tool for identifying missing structural ingredients in computational origin-of-life models.
Copyright (c) 2026 Fayzeh Abdulkareem Jaber, Saeed Serwan Abdulsattar, Hani Al-Balasmeh
Exploring Public Reaction to Air Quality on Social Media: The 2022 Saharan Dust Episode in Lisbon
This study analyses the relationship between air quality and sentiments expressed on social media, focusing on the Saharan dust episode that affected Lisbon in March 2022. Environmental data were collected alongside geolocated Twitter posts (now rebranded as X) from the Lisbon area. The content of these posts was analysed using the Valence Aware Dictionary and sEntiment Reasoner algorithm to identify emotional patterns and assess correlations with fluctuations in atmospheric pollutants. A predictive analysis was also conducted using Decision Tree and Random Forest machine learning algorithms. The findings suggest that despite high levels of inhalable particulate matter (PM10) during the event, there was no significant change in online sentiments, which remained mostly neutral or slightly positive. This implies that the publics environ-mental perception may not be clearly reflected on social media, possibly due to cultural factors or the limited influence of Twitter in Portugal. The study concludes that integrating environmental and social data is feasible and valuable, but further research is needed to explore longer periods, other cities, and more advanced techniques, thus contributing to the understanding of air pollution and public perception in Portugal.
Copyright (c) 2026 Antonino Candeias, Rogério Dionísio, Fernando Ribeiro
AI-Blockchain Integrated Trusted Interaction Mechanism for Enhanced Network Security
This paper proposes an AI–blockchain integrated trusted interaction mechanism for network security. The mechanism combines a deep-learning anomaly detector with a permissioned blockchain and smart contracts to provide real-time threat analysis, decentralized authentication and tamper-resistant trust management. The AI module is a two-branch neural network that processes network-traffic features and interaction-behavior graphs and outputs an anomaly score for each event. The blockchain layer adopts a consortium architecture with PBFT-style consensus, and smart contracts implement identity verification, trust update rules and access control.Experiments are conducted on labeled network-traffic traces and an enterprise-like testbed. We compare the proposed mechanism with an AI-only baseline (deep model with centralized control) and a blockchain-only baseline (rule-based smart contracts without AI) using detection-level metrics—accuracy, precision, recall, F1-score, false positive rate (FPR) and false negative rate (FNR)—and system-level metrics—response time and throughput. Under low load, the system reaches 180 ms response time and 5000 requests/s throughput; under high load, response time is 300 ms and throughput is 4200 requests/s. Compared with the strongest baseline, throughput increases by 42.8% and FPR is reduced by 60.9%, while FNR remains low across diverse attack scenarios. These results confirm that the mechanism supports trusted interaction in dynamic network environments.
Blockchain-Enhanced Anomaly Detection Algorithm for Financial Data Sharing Platforms Using u-BlockMixup
As the financial industry advances in digitalization, the security and quality of shared data have become critical. Traditional centralized anomaly detection methods face challenges regarding single points of failure and data tampering. To address these issues, this paper proposes a Blockchain-Enhanced Anomaly Detection Algorithm using u-BlockMixup. We integrate a decentralized blockchain architecture with a semi-supervised deep learning model based on the Mean Teacher framework. Specifically, we introduce the u-BlockMixup data augmentation method, which combines supervised cross-entropy loss and unsupervised consistency loss to generate high-quality synthetic samples, thereby improving generalization on limited labeled data. Experimental results on a dataset of over 1 million financial transaction records demonstrate that the proposed method outperforms traditional Isolation Forest and Autoencoder models. The algorithm achieves an accuracy improvement of 15% and an F1 score increase of 18%, with a false positive rate reduced to 2.3%. These findings confirm that combining blockchain immutability with u-BlockMixup-enhanced machine learning significantly improves the reliability and real-time detection capabilities of financial data sharing platforms.
Dual-Mode Feature Fusion for Dynamic Gesture Recognition Using GRU-LSTM in Complex Backgrounds
To address the problems of poor gesture segmentation, incomplete feature extraction, and low recognition accuracy caused by cluttered backgrounds, light intensity fluctuations, and skin-like background interference in dynamic gesture recognition, a dual-mode feature fusion dynamic gesture recognition method based on GRU-LSTM is proposed for complex backgrounds. First, adaptive threshold segmentation and motion gesture segmentation are combined to realize fine gesture segmentation, with the Intersection over Union (IoU) and Dice coefficient reaching 92.1% and 93.5%, respectively. For feature extraction, a dual-mode fusion strategy is adopted: the MediaPipe model extracts 21 hand joint local features (processed by relative coordinate transformation and mean-variance normalization), and an improved CNN embedded with Inception and CBAM attention modules extracts global image features. A GRU-LSTM combined network is designed for sequence learning, which reduces the computational complexity by 28% compared with a single LSTM and improves the fitting accuracy by 2.7% compared with a single GRU. The Softmax-SVM combined algorithm and a three-layer MLP are used for dual-mode feature combined classification. A dedicated Seven-Step Handwashing Technique (SWT) dataset is constructed in collaboration with Hubei Provincial People's Hospital, and the proposed method achieves an average recognition accuracy of 97.73% on this dataset (with Precision 97.21%, Recall 96.89%, and F1-score 97.05%). Comparative experiments show that the method outperforms state-of-the-art (SOTA) methods (e.g., 5.2% higher accuracy than LSTM and 9.93% higher than HOG+SVM), and the inference speed reaches 28 frames per second (fps) meeting real-time recognition requirements. Experimental results confirm that the method can accurately segment and recognize dynamic gestures in complex backgrounds, and has strong robustness to light and background changes.
Multi-Objective Logistics Route Optimization Using a Physics- Informed Neural Network-Assisted Genetic Algorithm
Modern logistics distribution systems require simultaneous optimization of transportation cost, delivery time, travel distance, and energy consumption under vehicle capacity and time-window constraints. Conventional genetic algorithm (GA)-based routing approaches rely on penalty-based constraint handling and static fitness evaluation, resulting in unstable convergence and reduced scalability for high- dimensional, multi-objective vehicle routing problems (VRPs). This study proposes a Physics-Informed Neural Network Assisted Genetic Algorithm for Logistics Route Optimization (PINN-GA-RouteOpt), integrating physics-regularized learning with adaptive evolutionary search. The proposed framework embeds vehicle motion dynamics, fuel–distance nonlinear relationships, and service time-window constraints into a physics-informed neural network (PINN), where the fitness function is formulated as a composite objective minimizing transportation cost, total travel time, route distance, and energy consumption. The PINN is trained using a hybrid loss function combining supervised data loss and physics residual loss derived from motion and fuel-consumption equations, thereby generating constraint- consistent fitness landscapes. An improved GA with adaptive mutation and dynamic crossover control is employed to enhance exploration–exploitation balance and accelerate convergence. Computational experiments were conducted on benchmark logistics distribution datasets with network sizes ranging from 100 to 500 nodes and fleet sizes up to 200 vehicles. Comparative evaluation against standard GA and penalty-based multi-objective GA variants demonstrates a 22% reduction in convergence iterations, 17.1% decrease in energy consumption, 12.8% reduction in total travel distance, and 15.4% improvement in average delivery time. Additionally, Pareto front dispersion variance decreased by 19.6%, indicating improved solution stability. The results confirm that PINN-GA-RouteOpt achieves computational efficiency, constraint-aware optimization, and scalability, establishing an intelligent and energy-efficient framework for large-scale logistics distribution route planning.