Engineering Applications of Artificial Intelligence最新文献

{"title":"Lightweight spatio-temporal residual neural network and transformer architecture with positional gating for video-based smoke and fire detection","authors":"Rafaqat Alam Khan ,&nbsp;Usama Ijaz Bajwa ,&nbsp;Rana Hammad Raza ,&nbsp;Muhammad Umar Farooq","doi":"10.1016/j.engappai.2026.113996","DOIUrl":"10.1016/j.engappai.2026.113996","url":null,"abstract":"<div><div>The occurrence of fire incidents is considered one of the common hazards which not only risks human lives, but also impacts economy and environment. Detecting fire and smoke in its initial stages is highly important to prevent them from becoming uncontrollable. Conventional sensor-based detectors have limitations such as geographic area coverage, time required to reach the sensor, and false alarm rates. However, traditional sensor-based detectors are being substituted with smart video-based detectors. These provide effective monitoring, detection and detailed analysis of smoke and fires in both indoor/outdoor environments. This study introduced a real-time automated artificial intelligence (AI)-based video model for early-stage detection of smoke and fire, effectively mitigating false alarms caused by clouds, fogs or other fire-colored backgrounds or objects. The model Dual Attention Multi-Resolution Three-Dimensional Network with Positional Gating Unit (DAMR3DNet_PGU) was trained using hybrid Spatio-Temporal Residual Neural Network and Transformer architecture (Transformer) with Positional Gating on a wide range of unique smoke and fire patterns sourced from publicly available benchmark video datasets. Experiment results illustrated significant improvements in True Positive Rate (TPR), True Negative Rate (TNR), False Positive (FP), False Negative (FN), false alarm and accuracy, when compared with various state-of-the-art methods. The efficacy of the proposed DAMR3DNet_PGU method utilizing conventional closed-circuit television (CCTV) cameras for fire and smoke detection was affirmed. The proposed technique demonstrated robust performance across multiple datasets. It achieved high accuracy rates for smoke and fire detection, while significantly reducing false negatives, false alarm and with lightweight model compared to existing approaches.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 113996"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-exposure high dynamic range reconstruction by incorporating imaging knowledge","authors":"Hu Wang ,&nbsp;Mao Ye ,&nbsp;Dengyan Luo ,&nbsp;Yan Gan","doi":"10.1016/j.engappai.2026.114177","DOIUrl":"10.1016/j.engappai.2026.114177","url":null,"abstract":"<div><div>The existing photographic equipment is not able to capture scenes of the natural world very well. Thus, the problem of reconstructing high dynamic range (HDR) images from multi-exposure low dynamic range (LDR) images arises because these images have different details. The existing methods do not fully leverage imaging knowledge in the LDR image generation pipeline, resulting in design redundancy and inefficient resource utilization. We propose a new Multi-Exposure HDR reconstruction by incorporating Imaging Knowledge (MEIK) for efficient HDR image reconstruction. Our method consists of two parts: fusion of LDR features and reconstruction of HDR feature. Due to object motion and exposure time effects, LDR features with different exposures need to be fused. A Multi-Exposure Information Aggregation (MEIA) module is proposed to fuse LDR features based on Mamba. After that, an Inverse imaging Knowledge-Driven (IKD) cluster is employed to reconstruct the HDR feature, which is a cascade of IKD blocks at different scales. The IKD block consists of three parts: HDR information recovery, imaging parameter adjustment, and noise suppression, used to simulate the mathematical formula for multi-exposure HDR imaging. Experimental results demonstrate that the proposed MEIK model outperforms existing state-of-the-art models and exhibits strong scalability.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114177"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Contextual Multimodal Federated Transformer with dual distillation for decentralized chronic obstructive pulmonary disease related lung pathology classification","authors":"Ayesha Jabbar,&nbsp;Huang Jianjun,&nbsp;Muhammad Kashif Jabbar","doi":"10.1016/j.engappai.2026.114046","DOIUrl":"10.1016/j.engappai.2026.114046","url":null,"abstract":"<div><div>The use of multimodal data within decentralized healthcare presents an opportunity to improve artificial intelligence (AI)–based classification of lung pathology related to chronic obstructive pulmonary disease (COPD) while maintaining patient privacy. Nonetheless, heterogeneous client modalities, non-independent and non-identically distributed (non-IID) data, and equity challenges in aggregating models remain unresolved. To address these issues, we propose CMF-Former-D (Contextual Multimodal Federated Transformer with Dual-Level Distillation), a privacy-preserving decentralized learning method for peer-to-peer training across heterogeneous edge devices. CMF-Former-D employs an attention-based fusion mechanism that integrates audio features from respiratory sounds and image features from chest X-rays. We further introduce PeerMesh-Distill, a server-free protocol that enables decentralized knowledge sharing with equitable distribution of model updates across heterogeneous client sites, and FairWeight-Gossip, a strategy that promotes fair update aggregation among clients. The model is dynamically adapted to client hardware and modality constraints using resource-aware configurations. CMF-Former-D achieves 98.3% accuracy, a macro-averaged F1-score (macro-F1) of 0.987, and an area under the receiver operating characteristic curve (AUROC) of 0.972 on a synthetic proxy-aligned benchmark. End-to-end multimodal inference latency is 150 ms (ms) on a central processing unit (CPU) (batch size = 1), while graphics processing unit (GPU) latency is reported separately on an edge GPU under batch size = 16. Statistical tests indicate significant improvements (<span><math><mi>p</mi></math></span>-value (p) <span><math><mrow><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>01</mn></mrow></math></span>), and client-level analysis shows reduced performance disparities across clients.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114046"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart indoor occupancy detection based on optimized camera placement, multi-view de-duplication, and large language model semantic understanding","authors":"Deli Liu,&nbsp;Xiaoping Zhou,&nbsp;Dongxiao Chen,&nbsp;Yu Li","doi":"10.1016/j.engappai.2026.114157","DOIUrl":"10.1016/j.engappai.2026.114157","url":null,"abstract":"<div><div>Accurate occupancy detection in indoor environments is essential for optimizing energy use, enhancing occupant comfort, and ensuring safety in smart buildings. This study aims to design and validate an end-to-end framework that not only counts occupants reliably but also generates rich semantic descriptions of their behaviors and spatial interactions. We propose a four-stage methodology: (1) multi-objective optimization of camera placement through field-of-view analysis and grid modeling to maximize coverage and minimize blind spots; (2) on-device human detection using a fine-tuned You Only Look Once version 8 (YOLOv8) model; (3) cross-camera identity tracking using Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) to assign unique global identifiers and eliminate duplicate counts; and (4) a multimodal large language model (LLM) that consumes annotated, identity-aware multi-view images to produce coherent natural-language summaries and structured outputs detailing occupant numbers, actions, and locations. Extensive evaluations conducted on a diverse multi-view dataset—including challenging scenarios of heavy occlusion and clothing changes—demonstrate the robustness and real-time applicability of the proposed framework. The key contribution of this work is the first demonstration of integrating identity-aware, multi-camera de-duplication with large language model–driven scene interpretation, enabling automated, actionable insights that extend beyond simple occupancy counts. This novel combination advances intelligent building management by providing both precise occupancy analytics and contextual understanding to support adaptive control and energy-efficient operation.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114157"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on a residual learning based neural-kernel framework with applications in short-term load forecasting","authors":"Wangyi Xu ,&nbsp;Yushu Xiang ,&nbsp;Xin Ma ,&nbsp;Wangpeng Li","doi":"10.1016/j.engappai.2026.113989","DOIUrl":"10.1016/j.engappai.2026.113989","url":null,"abstract":"<div><div>Short-term load forecasting is essential for power system operation, yet it remains challenging due to the non-stationary nature of load data and the difficulty of capturing complex nonlinear relationships. To address this issue, a residual learning–based neural kernel framework is proposed for short–term load forecasting. The framework integrates a Fourier kernel-based neural kernel module into a deep residual network as a residual function. The Fourier kernel enables automatic identification and separation of periodic components and long-term trends in load data, while the non-parametric property of the kernel model helps reduce model complexity. Meanwhile, the shortcut connections in the residual network effectively alleviate the vanishing gradient problem, ensuring stable and efficient model training. To further improve model performance, the Artificial Bee Colony (ABC) algorithm is employed for hyperparameter optimization, allowing efficient approximation of the global optimum. In addition, a novel Theil UII-S loss function is introduced to enhance the model’s sensitivity to abnormal load fluctuations through adaptive gradient regulation. Experimental results on four real-world power datasets demonstrate that the proposed model outperforms 23 benchmark methods in terms of prediction accuracy. Ablation studies further verify the individual contributions of the Fourier kernel, the loss function, and the ABC algorithm, providing useful insights for future research.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 113989"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Q-learning-driven adaptive rewiring for cooperative control in heterogeneous networks","authors":"Yi-Ning Weng ,&nbsp;Hsuan-Wei Lee","doi":"10.1016/j.engappai.2026.114024","DOIUrl":"10.1016/j.engappai.2026.114024","url":null,"abstract":"<div><div>Cooperation emergence in multi-agent systems represents a fundamental statistical physics problem where microscopic learning rules drive macroscopic collective behavior transitions. We propose a Q-learning-based variant of adaptive rewiring that builds on mechanisms studied in the literature. This method combines temporal difference learning with network restructuring so that agents can optimize strategies and social connections based on interaction histories. Through neighbor-specific Q-learning, agents develop sophisticated partnership management strategies that enable cooperator cluster formation, creating spatial separation between cooperative and defective regions. Using power-law networks that reflect real-world heterogeneous connectivity patterns, we evaluate emergent behaviors under varying rewiring constraint levels, revealing distinct cooperation regimes across parameter space, characterized by qualitative changes in macroscopic cooperation behavior. Our systematic analysis identifies three behavioral regimes: a permissive regime (low constraints) enabling rapid cooperative cluster formation, an intermediate regime with sensitive dependence on dilemma strength, and a patient regime (high constraints) where strategic accumulation gradually optimizes network structure. Comparative analysis against Bush–Mosteller stimulus–response learning demonstrates that Q-learning’s temporal credit assignment capabilities produce superior cooperation outcomes, particularly under intermediate rewiring constraints where long-term relationship assessment becomes crucial. Simulation results show that while moderate constraints create transition-like zones that suppress cooperation, fully adaptive rewiring enhances cooperation levels through systematic exploration of favorable network configurations. Quantitative analysis reveals that increased rewiring frequency drives large-scale cluster formation. Our results establish a new paradigm for understanding intelligence-driven cooperation pattern formation in complex adaptive systems, revealing how machine learning serves as an alternative driving force for spontaneous organization in multi-agent networks.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114024"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning-based target fencing control for delay-tolerant unmanned aerial vehicle swarm","authors":"Hao Yu,&nbsp;Xiu-xia Yang,&nbsp;Yi Zhang,&nbsp;Wen-qiang Yao","doi":"10.1016/j.engappai.2026.114069","DOIUrl":"10.1016/j.engappai.2026.114069","url":null,"abstract":"<div><div>This study focuses on the cooperative fencing mission for unmanned aerial vehicle (UAV) swarm under communication delays, proposing an adaptive self-organized control framework based on a Radial Basis Function-Brain Emotional Learning-Based Intelligent Controller (RBF-BELBIC). Firstly, a fixed-time convergent observer is developed to realize simultaneous estimation of multiple states of the target, achieving precise estimation independent of initial states through dual-channel Hurwitz polynomial configuration. Secondly, a self-organized distributed control scheme integrating consensus term, navigation term, and potential field term is constructed. This strategy enables the UAV swarm to autonomously generate a dynamic fencing convex hull around the target, eliminating the dependency on predefined geometric configurations while guaranteeing collision avoidance. Thirdly, a dual-layer intelligent robust controller driven by the RBF-BELBIC network is designed to tackle the control lag effects caused by communication delays. This architecture establishes a hierarchical structure where the RBF network serves as an upper layer for online gain optimization, and the BELBIC acts as a lower reactive control layer, thereby enabling simultaneous disturbance compensation and dynamic control policy adaptation. Closed-loop stability is analytically established using Lyapunov theory. Simulations verify that the proposed control strategy extends the tolerable delay bound by an order of magnitude over conventional methods (from 100 ms to 1000 ms). Concurrently, it reduces fencing position and velocity errors by 99.36% and 97.45%, compared to single-layer learning networks under large delays, demonstrating superior robustness in complex environments.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114069"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-path adaptive feature elevation system for detecting small targets in remote sensing imagery","authors":"Liangjun Xu,&nbsp;Hui Ma","doi":"10.1016/j.engappai.2026.114132","DOIUrl":"10.1016/j.engappai.2026.114132","url":null,"abstract":"<div><div>Detecting small targets in remote sensing imagery has long been a challenge due to factors such as weak target features and complex backgrounds. Existing methods primarily focus on improving detection efficiency, often resulting in suboptimal accuracy for small targets. This study proposes the dual-path adaptive feature elevation system (DAES-net) for detecting small targets in remote sensing imagery, which significantly enhances small target detection accuracy while maintaining reasonable detection efficiency, effectively overcoming this challenge. DAES-net first integrates a proprietary dual-path self-calibration module (DSM). This module optimizes feature fusion through global modeling and local denoising, enhancing global feature correlation while reducing redundancy to provide more precise fused features for the detection system. Second, the dynamic normalized wasserstein distance (D-NWD) loss function was designed to achieve more precise localization of minute targets. By dynamically adjusting the regression weights of the constraint terms in the normalized wasserstein distance (NWD) loss function, D-NWD implements an optimal localization strategy for small targets, thereby improving the model's localization efficiency for them. Finally, the one-time aggregated feature reuse reparameterized convolution (FRRO) was proposed. This feature reuse structure prevents information loss for small targets while accelerating model inference efficiency. Experimental results demonstrate that DAES-Net achieves the highest mean average precision (MAP) across four public small object detection datasets, outperforming existing state-of-the-art methods. This highlights the significant contribution of this research to the field of small object detection.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114132"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable and disentangled image editing by manipulating the semantic latent space in diffusion models","authors":"Tian Qiu ,&nbsp;Qianmu Li","doi":"10.1016/j.engappai.2026.114149","DOIUrl":"10.1016/j.engappai.2026.114149","url":null,"abstract":"<div><div>Diffusion models (DMs) have gained prominence in image manipulation, surpassing traditional methods like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) in quality and realism. However, performing precise attribute manipulation and interpretability directly in their high-dimensional Gaussian noise space <span><math><mi>X</mi></math></span> remains challenging. This limitation motivates a more controllable and interpretable editing framework for practical attribute-level manipulation. To overcome this, we propose leveraging an intermediate semantic latent space <span><math><mi>H</mi></math></span> along with generated semantic attention masks, achieving efficient and high-fidelity image editing with enhanced disentanglement. Our method facilitates targeted editing in the spatial domain, enabling the modification of specific attributes without affecting unrelated regions of the image. Specifically, we develop a remapper network to map textual prompt embeddings into semantic latent representations within <span><math><mi>H</mi></math></span>, ensuring editing operations closely align with textual prompts. To further improve the disentanglement and editing efficiency, we design an attention module with three different attention mask strategies applied to the adjusted latent representation. The attention mask intuitively explains the area that DMs focus on during image editing at each time step. We conduct extensive experiments on a variety of datasets, including human faces, dogs, and oil paintings. Both qualitative and quantitative results demonstrate the superiority of our approach over state-of-the-art diffusion-based editing baselines in terms of editing quality, target alignment, and reduced non-target drift.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114149"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-term cooperative path planning for stratospheric airships based on hierarchical multi-agent reinforcement learning","authors":"Chao Lv ,&nbsp;Ming Zhu ,&nbsp;Xiao Guo ,&nbsp;Jiajun Ou ,&nbsp;Baojin Zheng ,&nbsp;Liran Sun","doi":"10.1016/j.engappai.2026.114156","DOIUrl":"10.1016/j.engappai.2026.114156","url":null,"abstract":"<div><div>Stratospheric airships are increasingly used for long-term collaborative tasks, requiring efficient path planning for multiple airships. Traditional methods struggle with collaborative optimization and state space explosion in such tasks. To address these issues, this paper presents a hierarchical cooperative airship path planning (HiCAPP). This HiCAPP employs a dual-layer control architecture, with the high-level controller responsible for task allocation and the low-level controller concentrating on path planning. Experimental results show that HiCAPP outperforms traditional multi-agent reinforcement learning methods in two critical metrics: average remaining energy and average distance to the task center. Additionally, through experiments with varying numbers of agents, task durations, and disturbances, HiCAPP has demonstrated robustness and scalability. These results confirm its effectiveness in long-term cooperative monitoring tasks and highlight the advantages of hierarchical decision-making in multi-agent systems.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114156"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}