Engineering Applications of Artificial Intelligence最新文献

{"title":"A novel framework for assessing determinant risk factors on cyber (dis)trust behaviors of netizens in deepfakes","authors":"Milad Taleby Ahvanooey ,&nbsp;Wojciech Mazurczyk ,&nbsp;Zefan Wang ,&nbsp;Jun Zhao","doi":"10.1016/j.engappai.2025.111319","DOIUrl":"10.1016/j.engappai.2025.111319","url":null,"abstract":"<div><div>Nowadays, Generative Artificial Intelligence (GenAI) tools or trainable agents can craft synthetic media (hereafter referred to as deepfakes) in the form of realistic texts, images, videos, and audios, incorporating events or things that never occurred in real life. These GenAI tools empower marketers and malicious actors to create deepfakes, both authorized and weaponized multimedia, which allows them to include celebrities without appearing in front of cameras or creating seductive phishing scams. Although GenAI tools can reduce the cost of content construction, they enable new risky opportunities (e.g., deepfake phishing and cyberbullying) that negatively impact netizens’ learning and (dis)trust behaviors in cyberspace. To address such risks, this study proposes a Multi-Criteria-Multi-Decision-Makers (MCMDM)-based Deepfake Risk Assessment Framework (DeepFakeR-MF) to evaluate determinant factors that impact the cyber (dis)trust behaviors of netizens in deepfakes. Moreover, DeepFakeR-MF deploys a combination of a novel optimized spherical fuzzy analytic hierarchy process method and a game theory-based MCMDM approach to prioritize and recommend alternative strategies that can be taken by five management sectors (e.g., industrial enterprises, governmental organizations, media outlets, social non-profit, and educational institutes) to mitigate GenAI-associated risks. Then, we collect 100 experts’ judgments by analyzing their responses to our questionnaire and prioritize the importance of determinant factors considering their preferences. To validate the prioritized factors on the performance of DeepFakeR-MF, we conduct a sensitivity analysis applying Monte Carlo statistical modeling. Finally, our results confirm that DeepFakeR-MF provides effective strategic alternatives for policymakers, educators, media professionals, engineers, and netizens, hopefully reducing the socio-economic risks of deepfakes.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111319"},"PeriodicalIF":7.5,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633723","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":"Deep reinforcement learning approach for hybrid renewable energy systems optimization","authors":"Inoussa Legrene ,&nbsp;Tony Wong ,&nbsp;Louis-A. Dessaint","doi":"10.1016/j.engappai.2025.111650","DOIUrl":"10.1016/j.engappai.2025.111650","url":null,"abstract":"<div><div>The sizing of hybrid renewable energy systems (HRES) is a major challenge faced in contemporary energy research. The optimal configuration based on the specific consumption requirements is essential for strategic energy planning. Effective sizing must balance the investment costs, reliability, environmental impacts, and greenhouse gas emissions while satisfying the expected energy requirements. This study proposes a novel multi-criteria sizing approach based on deep reinforcement learning (DRL). The DRL agent is guided by a reward function that integrates three essential performance metrics: energy cost (LCOE), renewable energy fraction (REF), and the loss of power supply probability (LPSP). A penalty function is also included to consider the reliance on external sources, such as diesel generators and the public grid, promoting greater autonomy and renewable usage. The DRL-based approach was implemented and tested on three distinct demand profiles, using hourly data for one year. A comparative analysis was conducted against three established methods: particle swarm optimization (PSO), multi-objective PSO (MOPSO), and non-dominated sorted genetic algorithm (NSGA-II). The results indicate that DRL significantly outperforms all the benchmark methods in terms of economic efficiency. DRL achieves a significant reduction in the energy costs, ranging from 21.33 % to 30.09 % when compared with PSO, 27.89 %–30.27 % when compared with MOPSO, and 27.63 %–28.47 % when compared with NSGA-II. These findings demonstrate that DRL presents a robust and adaptive framework for the sizing and operational control of HRES. DRL presents more autonomous, cost-effective, and scalable renewable energy solutions by minimizing the energy costs while maintaining the system reliability.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111650"},"PeriodicalIF":7.5,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633722","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 group decision making scheduling method for flexible job shop integrating blockchain and rule mining","authors":"Yingli Li,&nbsp;Ying Zhao","doi":"10.1016/j.engappai.2025.111783","DOIUrl":"10.1016/j.engappai.2025.111783","url":null,"abstract":"<div><div>The future workshop will be an intelligent one based on the Cyber-Physical System paradigm, where each device functions as an agent. Each agent will possess independent data perception and reasoning capabilities, enabling it to operate autonomously and freely join or leave the agent network. Production scheduling will be the result of multi-agent collective decision-making. Traditional centralized scheduling methods are no longer applicable to such workshops, and existing distributed scheduling approaches remain incomplete. Specifically, current distributed scheduling methods still exhibit traces of centralized scheduling and have not fully realized decentralized scheduling in a strict sense. To address this deficiency, we propose a multi-objective flexible job shop distributed scheduling method. In this method, we improve the neighborhood search algorithm by generating a high-quality initial solution, selecting effective critical operation blocks, and using rule extraction to search for the optimal solution. To achieve complete decentralization of scheduling, blockchain is introduced and redesigned for distributed processing of data. Some numerical experiments, based on well-known benchmark instances, are carried out. The results verify the feasibility and competitiveness of the scheduling method. The solution of this problem has important academic significance and engineering value for intelligent factory design.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111783"},"PeriodicalIF":7.5,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633726","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 vehicle detection method based on cross-scale feature fusion","authors":"Yuyu Meng,&nbsp;Yinbao Ma,&nbsp;Jiuyuan Huo,&nbsp;Hongrui Su","doi":"10.1016/j.engappai.2025.111749","DOIUrl":"10.1016/j.engappai.2025.111749","url":null,"abstract":"<div><div>This paper presents improved algorithms developed to enhance the detection performance of multi-scale vehicles across various lanes. The proposed methods specifically address the challenge that small-scale vehicles in video surveillance systems are susceptible to false positives and missed detections under complex conditions. These limitations ultimately lead to imbalanced detection outcomes across vehicles of different scales. Firstly, the cross-scale feature fusion structure is proposed to enhance the deep fusion capability of multi-scale features. This effectively addresses the issue of deep feature maps struggling to capture small-scale vehicle information caused by excessive downsampling. Secondly, the cross-scale feature fusion module is proposed to enable the model to dynamically capture features from multiple dimensions, facilitating a deeper understanding of both coarse- and fine-grained data and thereby significantly enhancing the performance of multi-scale vehicle detection. Additionally, the downsampling convolution is optimized using Receptive-Field Attention to improve the model's ability to understand the detailed features of multi-scale vehicles. Finally, the Wise-Intersection over Union (Wise-IoU) loss function is utilized to improve the detection performance for low-quality vehicle samples. Experiments on the VisDrone and Vehicle datasets show the number of parameters and model size of the proposed algorithm in this paper have been significantly streamlined. The algorithm can effectively balance the detection performance of multi-scale vehicles, thus obtaining higher overall detection accuracy. In addition, the test results on the DOTAv1 dataset show the proposed method has good generalization ability and cross-scene detection capability.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111749"},"PeriodicalIF":7.5,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633724","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":"Digital twin-driven reinforcement learning-based operational management for customized manufacturing","authors":"Hao Tang ,&nbsp;Minghao Cheng ,&nbsp;Uzair Aslam Bhatti ,&nbsp;Bo Xu ,&nbsp;Nan Zhou ,&nbsp;Rong Guo ,&nbsp;Bing Wei","doi":"10.1016/j.engappai.2025.111754","DOIUrl":"10.1016/j.engappai.2025.111754","url":null,"abstract":"<div><div>Due to the increasing complexity of customer demands for different batches and types of products, manufacturing operations management has been facing the challenge of uncertain product arrival times and resource processing times in customized manufacturing (CM). This paper proposes a dynamic scheduling method to solve the uncertainty in CM via the integration of the digital twin and fuzzy reinforcement learning methods. In this study, a digital twin-driven framework is first designed to describe the operation management system (OMS) hierarchies. Then a semi-Markov decision process (MDP) model with fuzzy definition is built by abstracting the stochastic scheduling process. To solve the semi-MDP model, an asynchronous multi-edge co-training method is presented to train a fuzzy deep neural network through closed-loop control of virtual commissioning, illustrating how the digital twin-driven OMS adapts to dynamic production requirements. Finally, the proposed method is verified by the performance of comparative experiment. Experimental results show that for randomly arriving products, the proposed method guarantees timely training and scheduling decisions and has the highest total system profit compared to other competing methods (Hybrid Multi-Agent System Negotiation and Ant Colony Optimization (HMA), Onto_MDP, and Deep Q Networks (DQN)). Also, the proposed method shows better scheduling performance in terms of average decision time, average training time and number of finished products when resources are abnormal.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111754"},"PeriodicalIF":7.5,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633725","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 survey on state-of-the-art deep learning applications and challenges","authors":"Mohd Halim Mohd Noor ,&nbsp;Ayokunle Olalekan Ige","doi":"10.1016/j.engappai.2025.111225","DOIUrl":"10.1016/j.engappai.2025.111225","url":null,"abstract":"<div><div>Deep learning, a branch of artificial intelligence, is a data-driven method that uses multiple layers of interconnected units or neurons to learn intricate patterns and representations directly from raw input data. Empowered by this learning capability, it has become a powerful tool for solving complex problems and is the core driver of many groundbreaking technologies and innovations. Building a deep learning model is challenging due to the algorithm’s complexity and the dynamic nature of real-world problems. Several studies have reviewed deep learning concepts and applications. However, the studies mostly focused on the types of deep learning models and convolutional neural network architectures, offering limited coverage of the state-of-the-art deep learning models and their applications in solving complex problems across different domains. Therefore, motivated by the limitations, this study aims to comprehensively review the state-of-the-art deep learning models in computer vision, natural language processing, time series analysis and pervasive computing, and robotics. We highlight the key features of the models and their effectiveness in solving the problems within each domain. Furthermore, this study presents the fundamentals of deep learning, various deep learning model types and prominent convolutional neural network architectures. Finally, challenges and future directions in deep learning research are discussed to offer a broader perspective for future researchers.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111225"},"PeriodicalIF":7.5,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633727","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":"Enhanced light detection and ranging simultaneous localization and mapping based on three-dimensional moving object tracking in dynamic scenes","authors":"Tao Gao,&nbsp;Hu Ran,&nbsp;Hongyu Chi,&nbsp;Dunwen Wei","doi":"10.1016/j.engappai.2025.111658","DOIUrl":"10.1016/j.engappai.2025.111658","url":null,"abstract":"<div><div>Moving objects can cause incorrect feature matches, significantly affecting light detection and ranging (LiDAR) odometry accuracy and mapping quality in simultaneous localization and mapping (SLAM). This paper proposes a learning-based SLAM framework that reduces the impact of moving objects on SLAM by estimating their motion through segmentation and tracking. We design a deep dynamic LiDAR odometry (DyLO) network by introducing a hierarchical attention mechanism and a dynamic mask network in point clouds to improve odometry performance in dynamic scenes. Additionally, we design a dynamic object segmentation and tracking module to extract motion information from dynamic scenes and combine it with DyLO to form a joint factor graph backend optimization for SLAM, thereby developing a complete SLAM system. Our SLAM system is validated on the public dataset with 64-line LiDAR and a self-established campus dataset with 16-line LiDAR. We compare the proposed DyLO with several outstanding iterative closest point (ICP) methods, the deep learning-based method of LiDAR odometry network (LO-Net), and the feature-based odometry approach. Compared with the outstanding LO-Net, the proposed algorithm reduces the translation error by 4.5% and the rotation error by 3.3% on average. Furthermore, our ablation experiments demonstrate that incorporating motion estimation of moving objects and backend optimization can remarkably improve the odometry accuracy.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111658"},"PeriodicalIF":7.5,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633622","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 diffusion model and knowledge distillation framework for robust coral detection in complex underwater environments","authors":"Zhaoxuan Lu ,&nbsp;Lyuchao Liao ,&nbsp;Chuang Li ,&nbsp;Xingang Xie ,&nbsp;Hui Yuan","doi":"10.1016/j.engappai.2025.111414","DOIUrl":"10.1016/j.engappai.2025.111414","url":null,"abstract":"<div><div>Coral reefs play a crucial role in marine ecosystems, but their sustainability is increasingly threatened by climate change and human activities. To aid in the protection and monitoring of these ecosystems, developing advanced artificial intelligence (AI)-based automated detection technologies is essential. This paper introduces the MambaCoral-Diffusion Detection framework (MambaCoral-Diffusion Detection, MCDD), an AI-driven approach for robust coral detection, designed to enhance performance in complex underwater environments—a critical challenge in marine engineering. Key AI contributions include integrating a diffusion model to generate realistic and diverse training data from limited and challenging underwater coral datasets, effectively alleviating the issue of data imbalance. Secondly, we adopted an innovative spatial sensing detection mechanism that enhances the accuracy of feature extraction in complex underwater environments. Finally, we introduced an efficient knowledge distillation technique that successfully transfers knowledge from complex models to more lightweight counterparts, thereby reducing computational resource requirements while maintaining efficiency and facilitating practical deployment. Experimental results show that MCDD achieves high performance on the Soft Coral dataset, reaching 91 frames per second (FPS), the mean average precision at 50% Intersection over Union (IoU) threshold of 0.843, and the mean average precision averaged across IoU thresholds from 50% to 95% of 0.566, with only 6.5 million parameters and 13.6 billion floating point operations per second (GFLOPs). These results demonstrate MCDD’s reliability and efficiency in detecting corals under complex underwater conditions, highlighting its significant potential for advancing marine research and conservation efforts. The code and dataset are available at <span><span>https://github.com/RDXiaoLu/MambaCoral-DiffDet.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111414"},"PeriodicalIF":7.5,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632344","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 survey of federated learning-based gradient compression for internet of vehicles","authors":"Ling Xing ,&nbsp;Zhaocheng Luo ,&nbsp;Jianping Gao ,&nbsp;Kaikai Deng ,&nbsp;Honghai Wu ,&nbsp;Huahong Ma","doi":"10.1016/j.engappai.2025.111662","DOIUrl":"10.1016/j.engappai.2025.111662","url":null,"abstract":"<div><div>The Federated Learning (FL) paradigm in the Internet of Vehicles (IoV) leverages vehicular networks and intelligent technologies to integrate data from traffic devices, aiming to develop a smart transportation system with high throughput, low latency, privacy protection, and collaborative multi-party training. However, frequent exchanges of model parameters between numerous vehicle nodes and roadside units (RSUs) in FL lead to uplink channel overload, which poses a significant challenge to system development. To this end, existing works introduce Gradient Compression (GC) technologies, incorporating methods such as count sketch, dynamic adjustment, and sparse matrices. These methods help to reduce communication overhead, enhance transmission efficiency, and maintain FL model training accuracy, making GC a crucial solution for overcoming communication barriers in IoV-based FL systems. In this paper, we first investigate GC technologies, systematically categorizing them by Quantization, Sparsification, and Driving Strategy-orientated compression, followed by using metrics such as efficiency, load, and latency to evaluate them. Secondly, we compare the pros and cons of different compression technologies in addressing communication problems and analyze their key characteristics. Finally, considering the deep integration of IoV and FL, we explore future research directions of the FL framework for IoV, analyze potential challenges, and propose corresponding solutions in conjunction with current mainstream deep learning models.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111662"},"PeriodicalIF":7.5,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632346","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":"Artificial intelligence-driven protocol for secure and standardized maneuver control in electrical substations","authors":"Gustavo Havila F. Campos ,&nbsp;Viviane M. Gomes Pacheco ,&nbsp;Marcio Rodrigues C. Reis ,&nbsp;Clóves Gonçalves Rodrigues ,&nbsp;Saulo Rodrigues Silva ,&nbsp;Antonio Paulo Coimbra ,&nbsp;Wesley Pacheco Calixto","doi":"10.1016/j.engappai.2025.111667","DOIUrl":"10.1016/j.engappai.2025.111667","url":null,"abstract":"<div><div>Notwithstanding recent advances in substation automation, no existing protocol integrates human–machine interaction, intelligent interlocking, operational autonomy, and artificial intelligence analysis in sequential maneuvering contexts. This study proposes an automated interface to optimize and control switching operations in electrical substations by integrating operational protocols, automated documentation generation, and artificial intelligence techniques with interactive graphical visualization. The developed solution enables sequential command execution, classification of operational events, and automatic generation of auditable reports, enhancing accuracy and traceability in operations. A total of 108 real files, corresponding to 54 events with documented failures, were analyzed and used to train and validate a recurrent convolutional neural network model. The system achieved an accuracy of 82.92% in error detection, along with reductions of 42.7% in the average operational response time and 38.5% in failure frequency. In addition to standardizing procedures, the interface demonstrated adaptability to different substation topologies and configurations, establishing itself as a scalable, secure, and efficient alternative for assisted operation environments. The results suggest that the proposed solution contributes to reducing inconsistencies, increasing decision-making autonomy, and strengthening operational safety in the power sector.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111667"},"PeriodicalIF":7.5,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144631118","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}