Intelligent Systems with Applications最新文献_第5页

Optimisation of energy management in IoT devices using LSTM models: Energy consumption prediction with sleep-wake scheduling control 使用LSTM模型优化物联网设备的能量管理：睡眠-觉醒调度控制的能耗预测

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-24 DOI: 10.1016/j.iswa.2025.200624

Nahideh DerakhshanFard, Asra Rajabi Bavil Olyaei, Fahimeh RashidJafari

The Internet of Things is an enormous network of interrelated devices that makes intelligent interaction and high-level control possible in various environments, such as smart homes, smart cities, and industry, by collecting, processing, and transferring data. The majority of the low-power devices within the network utilize limited sources of energy, such as batteries, and hence energy management is a critical factor in the design and operation of the systems. Current methods, such as reinforcement and evolutionary approaches, have at times been found to provide some enhancements but lacked extensive implementation over broad systems due to computational complexity as well as their inability to adapt to changing environmental settings. The growing number of IoT devices presents challenges in energy management, making it crucial to develop accurate prediction models. This research aims to address this challenge by proposing a novel solution using Long Short-Term Memory (LSTM) networks for energy consumption forecasting. This work suggests an optimal energy usage management model based on Long Short-Term Memory networks. The model collects historical energy usage, activity scheduling, and environmental factors such as temperature and humidity. Following the preprocessing, which includes noise removal and normalisation, it predicts future energy consumption. Scheduling data and the analysis and processing of environmental conditions are done using the short-term memory, while the long-term memory helps the model identify more complex patterns in the energy consumption over time to make more accurate predictions. Based on this prediction, smart policies are made for going to sleep and waking up the devices, so that unnecessary devices are put into sleep mode and only woken up when needed. Adaptive learning algorithms also assist in adjusting to environmental conditions. Results of experiments show that the proposed method can save energy up to 58% and increase device lifetime by 30%, while the prediction of energy consumption has an accuracy of 95%.

物联网是一个由相互关联的设备组成的庞大网络，通过收集、处理和传输数据，可以在智能家居、智能城市和工业等各种环境中实现智能交互和高级控制。网络中的大多数低功耗设备利用有限的能源，如电池，因此能源管理是系统设计和运行的关键因素。目前的方法，如强化和进化方法，有时被发现提供了一些增强，但由于计算复杂性以及它们无法适应不断变化的环境设置，在广泛的系统中缺乏广泛的实施。越来越多的物联网设备给能源管理带来了挑战，因此开发准确的预测模型至关重要。本研究旨在通过提出一种使用长短期记忆（LSTM）网络进行能源消耗预测的新解决方案来解决这一挑战。本研究提出了一种基于长短期记忆网络的最佳能量使用管理模型。该模型收集历史能源使用情况、活动调度以及温度和湿度等环境因素。在预处理之后，包括去噪和归一化，它预测未来的能源消耗。调度数据和环境条件的分析和处理使用短期记忆完成，而长期记忆帮助模型识别随时间变化的能源消耗中更复杂的模式，从而做出更准确的预测。基于这一预测，智能策略被制定为进入睡眠和唤醒设备，使不需要的设备进入睡眠模式，只在需要时唤醒。自适应学习算法也有助于适应环境条件。实验结果表明，该方法节能58%，器件寿命提高30%，能耗预测准确率达95%。

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引用次数: 0

Blind steganalysis-driven secure transmission validation using feature-based classification in JPEG images 在JPEG图像中使用基于特征分类的盲隐写分析驱动的安全传输验证

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-22 DOI: 10.1016/j.iswa.2025.200623

Deepa D. Shankar , Adresya Suresh Azhakath

Information technology and digital media have significantly improved in recent years, facilitating the internet as an effective channel for communication and data transmission. Nevertheless, the rapid advancement of technology has rendered data a source of mismanagement and vulnerable to exploitation. Consequently, technologies such as data concealment were devised to mitigate exploitation. Steganalysis is a technique for data concealment. Various processes, including breaches of information security, can be mitigated by steganalysis. This work aims to encapsulate the notion of blind statistical steganalysis within image processing methodologies and ascertain the accuracy percentage of secure transmission. This work discusses the extraction of features that indicate a change during embedding. A specific percentage of text is integrated into a JPEG image of a predetermined size. The text embedding utilizes various steganographic techniques in both the spatial and transform domains. The steganographic techniques include LSB Matching, LSB Replacement, Pixel Value Differencing, and F5. Due to the blind nature of steganalysis, there are no cover images available for comparative analysis. An estimation of the cover image is obtained by a calibration concept. After embedding, the images are partitioned into 8 × 8 blocks, from which certain features are extraction for classification. This paper utilizes interblock dependent features and intrablock dependent features. Both dependencies are regarded as means to mitigate the shortcomings of each individually. The approach of machine learning is employed using a classifier to distinguish between the stego image and the cover image. This research does a comparative investigation of the classifiers SVM and SVM-PSO. Comparative research is frequently performed both with and without use cross-validation methodology. The study incorporates the concept of cross-validation for comparative analysis. There are six unique kernel functions and four sample methods for grouping. The embedding ratio employed in this investigation is 50%.

近年来，信息技术和数字媒体发展迅速，使互联网成为沟通和数据传输的有效渠道。然而，技术的迅速进步使数据成为管理不善和容易被利用的来源。因此，设计了诸如数据隐藏之类的技术来减轻利用。隐写分析是一种数据隐藏技术。各种过程，包括对信息安全的破坏，都可以通过隐写分析来缓解。这项工作旨在将盲统计隐写分析的概念封装在图像处理方法中，并确定安全传输的准确性百分比。这项工作讨论了在嵌入过程中指示变化的特征的提取。将特定百分比的文本集成到预定大小的JPEG图像中。文本嵌入利用了空间域和变换域的各种隐写技术。隐写技术包括LSB匹配、LSB替换、像素值差分和F5。由于隐写分析的盲目性，没有可用于比较分析的封面图像。利用标定概念对覆盖图像进行估计。嵌入后，将图像分割成8 × 8块，从中提取一定的特征进行分类。本文利用了块间依赖特征和块内依赖特征。这两种依赖关系都被视为减轻各自缺点的手段。采用机器学习的方法，使用分类器区分隐写图像和封面图像。本文对SVM和SVM- pso分类器进行了比较研究。比较研究经常在使用或不使用交叉验证方法的情况下进行。本研究采用交叉验证的概念进行比较分析。有六个独特的核函数和四个用于分组的示例方法。本研究采用的包埋率为50%。

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引用次数: 0

Scalable and Adaptive Large-Scale Group Decision Making in Dynamic Social Networks via Graph Convolutional Neural Networks# 基于图卷积神经网络的动态社会网络中可扩展和自适应大规模群体决策[j]

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-18 DOI: 10.1016/j.iswa.2025.200620

Elaheh Golzardi , Alireza Abdollahpouri

As social networks are constantly changing, decision-making in large groups becomes much more challenging. People form new connections, lose old ones, shift their preferences, and change how much they trust others (Qin, Li, Liang & Pedrycz, 2026). Methods that work well in stable settings often fail to keep pace here, especially when both quick adaptation and the ability to handle scale are essential (Ding et al., 2025). Our approach, called GCD-GNN (Group Consensus Decision using Graph Neural Networks), builds on graph neural networks to track these ongoing changes in structure and preferences. It processes live updates on trust levels, social ties, and preference similarities, then adjusts influence weights in real time to keep the consensus process stable. In experiments using both synthetic and real-world datasets, GCD-GNN delivered higher agreement levels, improved accuracy and precision, and faster execution compared with leading alternatives. These results point to a framework that is not only scalable, but also able to adapt effectiveness in complex, large-scale decision-making environments.

随着社交网络的不断变化，大群体的决策变得更具挑战性。人们建立新的联系，失去旧的联系，改变他们的偏好，并改变他们对他人的信任程度（秦，李，梁，Pedrycz, 2026）。在稳定环境中工作良好的方法往往无法跟上这里的步伐，特别是当快速适应和处理规模的能力都是必不可少的时候（Ding et al., 2025）。我们的方法，称为GCD-GNN（使用图神经网络的群体共识决策），建立在图神经网络的基础上，跟踪这些结构和偏好的持续变化。它处理信任水平、社会关系和偏好相似性的实时更新，然后实时调整影响权重，以保持共识过程的稳定。在使用合成数据集和真实数据集的实验中，与领先的替代方案相比，GCD-GNN提供了更高的一致性水平，提高了准确性和精度，并且执行速度更快。这些结果表明，该框架不仅具有可扩展性，而且能够适应复杂的大规模决策环境的有效性。

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引用次数: 0

Interpretable event diagnosis in water distribution networks 配水网络中的可解释事件诊断

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-18 DOI: 10.1016/j.iswa.2025.200621

André Artelt , Stelios G. Vrachimis , Demetrios G. Eliades , Ulrike Kuhl , Barbara Hammer , Marios M. Polycarpou

The increasing penetration of information and communication technologies in the design, monitoring, and control of water systems enables the use of algorithms for detecting and identifying unanticipated events (such as leakages or water contamination) using sensor measurements. However, data-driven methodologies do not always give accurate results and are often not trusted by operators, who may prefer to use their engineering judgment and experience to deal with such events.

In this work, we propose a framework for interpretable event diagnosis — an approach that assists the operators in associating the results of algorithmic event diagnosis methodologies with their own intuition and experience. This is achieved by providing contrasting (i.e., counterfactual) explanations of the results provided by fault diagnosis algorithms; their aim is to improve the understanding of the algorithm’s inner workings by the operators, thus enabling them to take a more informed decision by combining the results with their personal experiences. Specifically, we propose counterfactual event fingerprints, a representation of the difference between the current event diagnosis and the closest alternative explanation, which can be presented in a graphical way. The proposed methodology is applied and evaluated on a realistic use case using the L-Town benchmark.

信息和通信技术在水系统的设计、监测和控制方面的日益普及，使利用传感器测量来检测和识别意外事件（如泄漏或水污染）的算法成为可能。然而，数据驱动的方法并不总是能给出准确的结果，而且通常不被作业者所信任，作业者可能更愿意使用他们的工程判断和经验来处理此类事件。在这项工作中，我们提出了一个可解释事件诊断的框架-一种帮助操作员将算法事件诊断方法的结果与他们自己的直觉和经验联系起来的方法。这是通过对故障诊断算法提供的结果提供对比（即反事实）解释来实现的；他们的目标是提高操作员对算法内部工作原理的理解，从而使他们能够通过将结果与个人经验相结合来做出更明智的决策。具体来说，我们提出了反事实事件指纹，这是当前事件诊断与最接近的替代解释之间差异的表征，可以以图形方式呈现。建议的方法使用L-Town基准在实际用例中应用和评估。

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引用次数: 0

FireBoost: A new bio-inspired approach for feature selection based on firefly algorithm and optimized XGBoost FireBoost：一种基于萤火虫算法和优化的XGBoost的生物特征选择新方法

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-17 DOI: 10.1016/j.iswa.2025.200613

Nafaa Jabeur

High-dimensional data often reduce model efficiency and interpretability by introducing redundant or irrelevant features. This challenge is especially critical in domains like healthcare and cybersecurity, where both accuracy and explainability are essential. To address this, we introduce FireBoost, a novel hybrid framework that enhances classification performance through effective feature selection and optimized model training. FireBoost integrates the Firefly Algorithm (FFA) for selecting the most informative features with a customized version of XGBoost. The customized learner includes dynamic learning-rate decay, feature-specific binning, and mini-batch gradient updates. Unlike existing hybrid models, FireBoost tightly couples the selection and learning phases, enabling informed, performance-driven feature prioritization. Experiments on the METABRIC and KDD datasets demonstrate that FireBoost consistently reduces feature dimensionality while maintaining or improving classification accuracy and training speed. It outperforms standard ensemble models and shows robustness across different parameter settings. FireBoost thus provides a scalable and interpretable solution for real-world binary classification tasks involving high-dimensional data.

高维数据通常通过引入冗余或不相关的特征来降低模型的效率和可解释性。这一挑战在医疗保健和网络安全等领域尤为关键，因为这些领域的准确性和可解释性都至关重要。为了解决这个问题，我们引入了FireBoost，这是一个新的混合框架，通过有效的特征选择和优化的模型训练来提高分类性能。FireBoost集成了萤火虫算法（FFA），用于选择最具信息量的功能与定制版本的XGBoost。定制的学习器包括动态学习率衰减、特定特征分类和小批量梯度更新。与现有的混合模型不同，FireBoost将选择和学习阶段紧密结合在一起，从而实现明智的、性能驱动的功能优先级。在METABRIC和KDD数据集上的实验表明，FireBoost在保持或提高分类精度和训练速度的同时，持续地降低了特征维数。它优于标准集成模型，并在不同参数设置中显示出鲁棒性。因此，FireBoost为涉及高维数据的现实世界的二进制分类任务提供了可扩展和可解释的解决方案。

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引用次数: 0

UAV exploration for indoor navigation based on deep reinforcement learning and intrinsic curiosity 基于深度强化学习和内在好奇心的无人机室内导航探索

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-16 DOI: 10.1016/j.iswa.2025.200618

Huei-Yung Lin , Xi-Sheng Zhang , Syahrul Munir

The operational versatility of Unmanned Aerial Vehicles (UAVs) continues to drive rapid development in the field of UAV. However, a critical challenge for diverse applications — such as search and rescue or warehouse inspection — is exploring the environment autonomously. Traditional exploration approaches are often hindered in practical deployments because they require precise navigation path planning and pre-defined obstacle avoidance rules for each of the testing environments. This paper presents a UAV indoor exploration technique based on deep reinforcement learning (DRL) and intrinsic curiosity. By integrating the reward function based on the extrinsic DRL reward and the intrinsic reward, the UAV is able to autonomously establish exploration strategies and actively encourage the exploration of unknown areas. In addition, NoisyNet is introduced to assess the value of different actions during the early stages of exploration. This proposed method will significantly improve the coverage of the exploration while relying solely on visual input. The effectiveness of our proposed technique is validated through experimental comparisons with several state-of-the-art algorithms. It achieves around at least 15% more exploration coverage at the same flight time compared to others, while achieving at least 20% less exploration distance at the same exploration coverage.

无人机操作的多功能性不断推动着无人机领域的快速发展。然而，对于各种应用（如搜索和救援或仓库检查）来说，一个关键的挑战是自主探索环境。传统的探测方法在实际部署中经常受到阻碍，因为它们需要精确的导航路径规划和针对每个测试环境预先定义的避障规则。提出了一种基于深度强化学习（DRL）和内在好奇心的无人机室内探测技术。通过整合基于外在DRL奖励和内在奖励的奖励函数，无人机能够自主制定探索策略，积极鼓励对未知区域的探索。此外，引入NoisyNet来评估在探索的早期阶段不同行动的价值。该方法在完全依赖视觉输入的情况下，显著提高了探测的覆盖范围。通过与几种最先进算法的实验比较，验证了我们提出的技术的有效性。在相同的飞行时间内，与其他飞机相比，它的勘探范围至少增加了15%，而在相同的勘探范围内，它的勘探距离至少减少了20%。

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引用次数: 0

Vision transformers in precision agriculture: A comprehensive survey 精准农业中的视觉变压器：综合调查

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-13 DOI: 10.1016/j.iswa.2025.200617

Saber Mehdipour , Seyed Abolghasem Mirroshandel , Seyed Amirhossein Tabatabaei

Detecting plant diseases is a crucial aspect of modern agriculture, playing a key role in maintaining crop health and ensuring sustainable yields. Traditional approaches, though still valuable, often rely on manual inspection or conventional machine learning (ML) techniques, both of which face limitations in scalability and accuracy. The emergence of Vision Transformers (ViTs) marks a significant shift in this landscape by enabling superior modeling of long-range dependencies and offering improved scalability for complex visual tasks. This survey provides a rigorous and structured analysis of impactful studies that employ ViT-based models, along with a comprehensive categorization of existing research. It also offers a quantitative synthesis of reported performance — with accuracies ranging from 75.00% to 100.00% — highlighting clear trends in model effectiveness and identifying consistently high-performing architectures. In addition, this study examines the inductive biases of CNNs and ViTs, which is the first analysis of these architectural priors within an agricultural context. Further contributions include a comparative taxonomy of prior studies, an evaluation of dataset limitations and metric inconsistencies, and a statistical assessment of model efficiency across diverse crop-image sources. Collectively, these efforts clarify the current state of the field, identify critical research gaps, and outline key challenges — such as data diversity, interpretability, computational cost, and field adaptability — that must be addressed to advance the practical deployment of ViT technologies in precision agriculture.

植物病害检测是现代农业的一个重要方面，在保持作物健康和确保可持续产量方面发挥着关键作用。传统方法虽然仍然有价值，但通常依赖于人工检查或传统的机器学习（ML）技术，这两种方法在可扩展性和准确性方面都存在局限性。视觉转换器（vit）的出现标志着这一领域的重大转变，它支持远程依赖关系的高级建模，并为复杂的视觉任务提供改进的可伸缩性。本调查提供了一个严谨的和结构化的分析，有影响力的研究，采用基于虚拟现实的模型，以及现有研究的全面分类。它还提供了报告性能的定量综合——准确度范围从75.00%到100.00%——突出了模型有效性的清晰趋势，并确定了始终如一的高性能架构。此外，本研究考察了cnn和vit的归纳偏差，这是在农业背景下对这些建筑先验的首次分析。进一步的贡献包括对先前研究的比较分类，对数据集局限性和度量不一致性的评估，以及对不同作物图像来源的模型效率的统计评估。总的来说，这些努力澄清了该领域的现状，确定了关键的研究差距，并概述了关键挑战——例如数据多样性、可解释性、计算成本和现场适应性——必须解决这些问题，以推进ViT技术在精准农业中的实际部署。

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引用次数: 0

Enhancing token boundary detection in disfluent speech 非流利语音中token边界检测的增强

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-06 DOI: 10.1016/j.iswa.2025.200614

Manu Srivastava , Marcello Ferro , Vito Pirrelli , Gianpaolo Coro

This paper presents an open-source Automatic Speech Recognition (ASR) pipeline optimised for disfluent Italian read speech, designed to enhance both transcription accuracy and token boundary precision in low-resource settings. The study aims to address the difficulty that conventional ASR systems face in capturing the temporal irregularities of disfluent reading, which are crucial for psycholinguistic and clinical analyses of fluency. Building upon the WhisperX framework, the proposed system replaces the neural Voice Activity Detection module with an energy-based segmentation algorithm designed to preserve prosodic cues such as pauses and hesitations. A dual-alignment strategy integrates two complementary phoneme-level ASR models to correct onset–offset asymmetries, while a bias-compensation post-processing step mitigates systematic timing errors. Evaluation on the READLET (child read speech) and CLIPS (adult read speech) corpora shows consistent improvements over baseline systems, confirming enhanced robustness in boundary detection and transcription under disfluent conditions. The results demonstrate that the proposed architecture provides a general, language-independent framework for accurate alignment and disfluency-aware ASR. The approach can support downstream analyses of reading fluency and speech planning, contributing to both computational linguistics and clinical speech research.

本文提出了一种开源的自动语音识别（ASR）管道，该管道针对不流畅的意大利语读语音进行了优化，旨在提高低资源设置下的转录精度和令牌边界精度。本研究旨在解决传统的ASR系统在捕捉非流利阅读的时间不规则性方面所面临的困难，这对于流利性的心理语言学和临床分析至关重要。在WhisperX框架的基础上，该系统用基于能量的分割算法取代了神经语音活动检测模块，该算法旨在保留停顿和犹豫等韵律线索。双对齐策略集成了两个互补的音素级ASR模型来纠正初始偏移不对称，而偏置补偿后处理步骤则减轻了系统时序误差。对READLET（儿童读语）和CLIPS（成人读语）语料库的评估显示，与基线系统相比，该语料库具有一致性的改进，证实了在非流畅条件下边界检测和转录的鲁棒性增强。结果表明，所提出的体系结构为精确对齐和不流畅感知ASR提供了一个通用的、与语言无关的框架。该方法可以支持阅读流畅性和言语规划的下游分析，为计算语言学和临床言语研究做出贡献。

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引用次数: 0

A systematic review of vision transformer and explainable AI advances in multimodal facial expression recognition 系统回顾了视觉转换器和可解释人工智能在多模态面部表情识别中的进展

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-06 DOI: 10.1016/j.iswa.2025.200615

Ilya Kus , Cemal Kocak , Ayse Keles

Facial expression is one of the most important indicators used to convey human emotions. Facial expression recognition is the process of automatically detecting and classifying these expressions by computer systems. Multimodal facial expression recognition aims to perform a more accurate and comprehensive emotion analysis by combining facial expressions with different modalities such as image, speech, Electroencephalogram (EEG), or text. This study systematically reviews research conducted between 2021 and 2025 on the Vision Transformer (ViT) based approaches and Explainable Artificial Intelligence (XAI) techniques in multimodal facial expression recognition, as well as the datasets employed in these studies. The findings indicate that ViT-based models outperform conventional Convolutional Neural Networks (CNNs) by effectively capturing long-range dependencies between spatially distant facial regions, thereby enhancing emotion classification accuracy. However, significant challenges remain, including data privacy risks arising from the collection of multimodal biometric information, data imbalance and inter-modality incompatibility, high computational costs hindering real-time applications, and limited progress in model explainability. Overall, this study highlights that integrating advanced ViT architectures with robust XAI and privacy-preserving techniques can enhance the reliability, transparency, and ethical deployment of multimodal facial expression recognition systems.

面部表情是用来传达人类情感的最重要的指标之一。面部表情识别是计算机系统对面部表情进行自动检测和分类的过程。多模态面部表情识别旨在通过将面部表情与图像、语音、脑电图（EEG）或文本等不同模态相结合，进行更准确、更全面的情绪分析。本研究系统回顾了2021年至2025年间在多模态面部表情识别中基于视觉变形（ViT）的方法和可解释人工智能（XAI）技术的研究，以及这些研究中使用的数据集。研究结果表明，基于vit的模型可以有效地捕获空间距离较远的面部区域之间的远程依赖关系，从而提高情绪分类的准确性，从而优于传统的卷积神经网络（cnn）。然而，重大挑战仍然存在，包括多模态生物特征信息收集带来的数据隐私风险、数据不平衡和模态间不兼容、阻碍实时应用的高计算成本，以及模型可解释性方面的有限进展。总之，本研究强调，将先进的ViT架构与强大的XAI和隐私保护技术相结合，可以提高多模态面部表情识别系统的可靠性、透明度和道德部署。

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引用次数: 0

AL-ViT: Label-efficient Robusta coffee-bean defect detection in Thailand using active learning vision transformers AL-ViT：标签高效罗布斯塔咖啡豆缺陷检测在泰国使用主动学习视觉变压器

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-11-28 DOI: 10.1016/j.iswa.2025.200612

Sirawich Vachmanus , Wimolsiri Pridasawas , Worapan Kusakunniran , Kitti Thamrongaphichartkul , Noppanan Phinklao

In major training and export markets, the coffee bean grading process still relies heavily on manual labor to sort individual beans from large harvest volumes. This labor-intensive task is time-consuming, costly, and prone to human error, especially within Thailand’s rapidly expanding Robusta coffee sector. This study introduces AL–ViT, an end-to-end Active-Learning Vision Transformer framework that operationalizes active learning and transformer-based feature extraction within a single, production-oriented pipeline. The framework integrates a ViT-Base/16 backbone with seven active learning (AL) query strategies, random sampling, entropy-based selection, Bayesian Active Learning by Disagreement (BALD), Batch Active Learning by Diverse Gradient Embeddings (BADGE), Core-Set diversity sampling, ensemble disagreement, and a novel hybrid uncertainty–diversity strategy designed to balance informativeness and representativeness during sample acquisition. A high-resolution dataset of 2098 Robusta coffee bean images was collected under controlled-lighting conditions aligned with grading-machine setups, with only 5 % initially labeled and the remainder forming the AL pool. Across five random seeds, the hybrid strategy without MixUp augmentation achieved 97.1 % accuracy and an F1bad of 0.956 using just 850 labels (41 % of the dataset), within 0.3 percentage points of full supervision. Operational reliability, defined as 95 % accuracy, consistent with prior inspection benchmarks, was reached with only 407 labels, reflecting a 75 % reduction in annotation. Entropy sampling showed the fastest early-stage gains, whereas BADGE lagged by >1 pp; Core-Set and Ensemble provided moderate but stable results. Augmentation and calibration analyses indicated that explicit methods (MixUp, CutMix, RandAugment) offered no further benefit, with the hybrid pipeline already achieving well-calibrated probabilities. Statistical validation via paired t-tests, effect sizes, and bootstrap CIs confirmed consistent improvements of uncertainty-driven strategies over random sampling. Overall, the proposed AL–ViT framework establishes a label-efficient and practically deployable approach for agricultural quality control, achieving near-supervised accuracy at a fraction of the labeling cost.

在主要的培训和出口市场，咖啡豆分级过程仍然严重依赖人工从大量收获的咖啡豆中对单个咖啡豆进行分类。这项劳动密集型任务耗时、成本高昂，而且容易出现人为错误，尤其是在泰国迅速扩张的罗布斯塔咖啡行业。本研究介绍了AL-ViT，这是一个端到端的主动学习视觉转换器框架，可在单个面向生产的管道中实现主动学习和基于转换器的特征提取。该框架将ViT-Base/16主干与7种主动学习（AL）查询策略集成在一起：随机抽样、基于熵的选择、基于分歧的贝叶斯主动学习（BALD）、基于不同梯度嵌入的批量主动学习（BADGE）、核心集多样性抽样、集成分歧，以及一种新的混合不确定性多样性策略，旨在平衡样本获取过程中的信息性和代表性。在与分级机设置一致的受控照明条件下，收集了2098个罗布斯塔咖啡豆图像的高分辨率数据集，最初只有5%被标记，其余的形成人工智能池。在五个随机种子中，没有MixUp增强的混合策略仅使用850个标签（占数据集的41%）就实现了97.1%的准确率和0.956的F1bad，在0.3个百分点的完全监督范围内。操作可靠性，定义为95%的准确性，与先前的检查基准一致，仅达到407个标签，反映了75%的注释减少。熵采样显示了最快的早期收益，而BADGE滞后了1 pp；Core-Set和Ensemble提供了中等但稳定的结果。增强和校准分析表明，显式方法（MixUp, CutMix, RandAugment）没有进一步的好处，混合管道已经获得了很好的校准概率。通过配对t检验、效应大小和自举ci进行的统计验证证实，不确定性驱动策略比随机抽样有一致性的改进。总体而言，拟议的AL-ViT框架为农业质量控制建立了一种高效的标签和实际可部署的方法，以一小部分标签成本实现了近乎监督的准确性。

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引用次数: 0