Intelligent Systems with Applications最新文献

IndiSegNet: Real-time semantic segmentation for unstructured road scenes in intelligent transportation systems IndiSegNet：智能交通系统中非结构化道路场景的实时语义分割

IF 4.3

Intelligent Systems with Applications

Pub Date : 2026-01-17 DOI: 10.1016/j.iswa.2026.200629

Pritam Chakraborty , Anjan Bandyopadhyay , Kushagra Agrawal , Jin Zhang , Man-Fai Leung

Autonomous driving in developing regions demands perception systems that can operate reliably in unstructured road environments marked by heterogeneous traffic, weak or missing lane geometry, frequent occlusions, and strong appearance variability. Existing semantic segmentation models, although successful in structured Western datasets, exhibit poor generalization to such chaotic conditions and are often too computationally heavy for real-time deployment on low-power edge hardware. To address these gaps, this paper focuses on the challenge of achieving fast, accurate, and resource-efficient segmentation tailored to complex Indian road scenes. We propose IndiSegNet, a lightweight architecture designed explicitly for this setting. The model introduces two novel components—Multi-Scale Contextual Features (MSCF) for capturing irregular object scales and Encoded Features Refining (EFR) for enhancing thin-structure and boundary detail, resulting in a more stable representation for unstructured environments. IndiSegNet achieves 67.2% mIoU on IDD, 78.9% on Cityscapes, and 74.6% on CamVid, while sustaining 112 FPS on Jetson Nano, outperforming standard baselines by 12%–18% IoU on safety-critical classes such as pedestrians, riders, and vehicles. Real-world evaluation across urban, monsoonal, rural, and mountainous regions shows less than 2.5% variance in mIoU with consistent inference speeds above 108 FPS. These results demonstrate that IndiSegNet offers a practical and hardware-efficient solution for high-speed autonomous navigation in the challenging traffic conditions of developing regions.

在发展中地区，自动驾驶需要能够在非结构化道路环境中可靠运行的感知系统，这些环境的特点是交通不均匀、车道几何形状薄弱或缺失、频繁闭塞以及外观变异性强。现有的语义分割模型，虽然在结构化的西方数据集上取得了成功，但在这种混乱的条件下表现出较差的泛化能力，并且对于在低功耗边缘硬件上的实时部署来说，计算量往往太大。为了解决这些差距，本文将重点放在实现针对复杂印度道路场景的快速、准确和资源高效分割的挑战上。我们提出了IndiSegNet，这是一个专门为这种设置设计的轻量级架构。该模型引入了两个新组件——用于捕获不规则对象尺度的多尺度上下文特征（MSCF）和用于增强薄结构和边界细节的编码特征精炼（EFR），从而对非结构化环境进行更稳定的表示。IndiSegNet在IDD上的mIoU达到67.2%，在cityscape上达到78.9%，在CamVid上达到74.6%，而在Jetson Nano上保持112 FPS，在行人、骑手和车辆等安全关键类别上的IoU比标准基准高出12%-18%。在城市、季风、农村和山区的实际评估中，mIoU的差异小于2.5%，推理速度一致高于108 FPS。这些结果表明，IndiSegNet为发展中地区具有挑战性的交通条件下的高速自主导航提供了实用且硬件高效的解决方案。

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引用次数: 0

Attention-enhanced reinforcement learning for dynamic portfolio optimization 动态投资组合优化的注意力增强强化学习

IF 4.3

Intelligent Systems with Applications

Pub Date : 2026-01-15 DOI: 10.1016/j.iswa.2025.200622

Pei Xue, Yuanchun Ye

We propose a deep reinforcement learning framework for dynamic portfolio optimization that combines a Dirichlet policy with cross-sectional attention mechanisms. The Dirichlet distribution enforces feasibility by construction, accommodates tradability masks, and provides a coherent geometry for exploration. Our architecture integrates per-asset temporal encoders with a global attention layer, allowing the policy to adaptively weight sectoral co-movements, factor spillovers, and other cross-asset dependencies. We evaluate the framework on a comprehensive S&P 500 panel from 2000 to 2025 using purged walk-forward backtesting to prevent look-ahead bias. Empirical results show that attention-enhanced Dirichlet policies deliver higher terminal wealth, Sharpe and Sortino ratios than equal-weight and reinforcement learning baselines, while maintaining realistic turnover and drawdown profiles. Our findings highlight that principled action parameterization and attention-based representation learning materially improve both the stability and interpretability of reinforcement learning methods for portfolio allocation.

我们提出了一个用于动态投资组合优化的深度强化学习框架，该框架将Dirichlet策略与横截面注意机制相结合。狄利克雷分布通过构造加强了可行性，容纳了可交易掩模，并为勘探提供了连贯的几何形状。我们的架构将每个资产的时间编码器与全局关注层集成在一起，允许策略自适应地权衡部门协同运动、因素溢出和其他跨资产依赖关系。我们在2000年至2025年的综合标准普尔500指数面板上评估了框架，使用清除的向前回溯测试来防止前瞻性偏见。实证结果表明，与等权重和强化学习基线相比，注意力增强的狄利克雷政策提供了更高的终端财富、夏普和索蒂诺比率，同时保持了现实的周转和收缩概况。我们的研究结果强调，有原则的动作参数化和基于注意的表示学习极大地提高了强化学习方法在投资组合分配中的稳定性和可解释性。

引用次数: 0

Novel quantum tunneling and fractional calculus-based metaheuristic for robust global data optimization and its applications in engineering design 基于分数阶演算的新型量子隧道和元启发式鲁棒全局数据优化方法及其在工程设计中的应用

IF 4.3

Intelligent Systems with Applications

Pub Date : 2026-01-09 DOI: 10.1016/j.iswa.2025.200616

Hussam Fakhouri , Riyad Alrousan , Niveen Halalsheh , Najem Sirhan , Jamal Zraqou , Khalil Omar

Background:

Bound-constrained single-objective optimization and constrained engineering design often feature heterogeneous landscapes and barrier-like structures, motivating search procedures that are scale-aware, robust near constraints, and economical in tuning.

Contributions:

We introduce Quantum Tunneling and Fractional Calculus-Based Metaheuristic (QTFM), a physics-inspired metaheuristic that is parameter-lean and employs bounded, range-aware operators to reduce sensitivity to tuning and to prevent erratic steps close to constraints.

Methodology:

QTFM couples fractional-step dynamics for scale-aware exploitation with a quantum-tunneling jump for barrier crossing, and augments these with a wavefunction-collapse local search that averages a small neighborhood and applies minimal perturbations to accelerate refinement without sacrificing diversity.

Results:

On the IEEE Congress on Evolutionary Computation CEC 2022 single-objective bound-constrained suite, QTFM ranked first on ten of twelve functions; it reached the best optimum on F1 and achieved the best mean values on F2–F8 and F10–F11 with stable standard deviations. In three constrained engineering problems, QTFM produced the lowest mean and the best-found solution for the robotic gripper design, and the lowest mean for the planetary gear train and three-bar truss design.

Findings:

The proposed fractional–quantum approach delivers fast, accurate, and robust search across heterogeneous landscapes and real-world design problems.

背景：受约束的单目标优化和受约束的工程设计通常具有异质景观和类似障碍物的结构，激励搜索过程具有规模意识、鲁棒性和经济性。贡献：我们引入了量子隧道和基于分数微积分的元启发式（QTFM），这是一种物理启发的元启发式，它是参数精益的，并采用有界的范围感知算子来降低对调谐的敏感性，并防止接近约束的不稳定步骤。方法：QTFM将分数阶动力学与量子隧道跃迁结合起来，用于规模感知开发，并通过波函数坍缩局部搜索来增强这些功能，该搜索可以平均小邻域，并应用最小的扰动来加速改进，而不会牺牲多样性。结果：在IEEE进化计算大会CEC 2022单目标约束集上，QTFM在12项功能中有10项排名第一；在F1上达到最佳，在F2-F8和F10-F11上达到最佳均值，标准差稳定。在三个约束工程问题中，QTFM给出了机器人夹持器设计的最小均值和最优解，以及行星齿轮传动和三杆桁架设计的最小均值。研究结果：提出的分数量子方法提供了跨异质景观和现实世界设计问题的快速、准确和强大的搜索。

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引用次数: 0

An efficient lightweight multi-scale CNN framework with CBAM and SPP for bearing fault diagnosis 基于CBAM和SPP的轴承故障诊断的高效轻量级多尺度CNN框架

IF 4.3

Intelligent Systems with Applications

Pub Date : 2026-01-08 DOI: 10.1016/j.iswa.2026.200628

Thanh Tung Luu , Duy An Huynh

Rolling bearing degradation produces vibration signatures that vary across operating conditions, posing challenges for reliable fault diagnosis. This study proposes an adaptive and lightweight diagnostic framework combining a Depthwise Separable Multi-Scale CNN (DSMSCNN) with Convolutional Block Attention Module (CBAM) and Spatial Pyramid Pooling (SPP) to extract fault-frequency invariant features across different mechanical domains. Wavelet-based time–frequency maps are utilized to suppress noise and preserve multi-resolution spectral characteristics. The multi-scale separable convolutions adaptively capture discriminative frequency patterns, while CBAM highlights informative spectral regions and SPP enhances scale robustness without fixed input sizes. Experiments on the CWRU and HUST bearing datasets demonstrate over 99 % accuracy with significantly fewer parameters than conventional CNNs. The results confirm that the proposed DSMSCNN-CBAM-SPP framework effectively captures invariant fault-frequency features, offering a compact and adaptive solution for intelligent bearing fault diagnosis and real-time predictive maintenance in a noisy environment.

滚动轴承退化会产生不同运行条件下的振动特征，这对可靠的故障诊断提出了挑战。本文提出了一种基于深度可分离多尺度CNN （DSMSCNN）、卷积块注意模块（CBAM）和空间金字塔池（SPP）的自适应轻量级诊断框架，用于提取不同机械领域的故障频率不变特征。基于小波的时频图用于抑制噪声和保持多分辨率频谱特征。多尺度可分离卷积自适应捕获判别频率模式，而CBAM突出信息频谱区域，SPP增强了不固定输入大小的尺度鲁棒性。在CWRU和HUST轴承数据集上的实验表明，与传统cnn相比，该方法的准确率超过99%，参数显著减少。结果表明，所提出的DSMSCNN-CBAM-SPP框架能够有效捕获不变的故障频率特征，为噪声环境下的轴承智能故障诊断和实时预测性维护提供了一种紧凑、自适应的解决方案。

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引用次数: 0

Personalized two-stage comparison-based framework for low-to-mid-intensity facial expression recognition in real-world scenarios 现实场景中低强度面部表情识别的个性化两阶段比较框架

IF 4.3

Intelligent Systems with Applications

Pub Date : 2026-01-08 DOI: 10.1016/j.iswa.2026.200627

Junyao Zhang , Kei Shimonishi , Kazuaki Kondo , Yuichi Nakamura

We evaluate a personalized, two-stage comparison-based FER framework on two datasets of low-to-mid-intensity, near-neutral expressions. The framework consistently outperforms FaceReader and Py-Feat. On the natural-transition younger-adult dataset (Dataset A, n = 9), mean accuracy is 90.22% ± 3.53%, with within-subject median gains of +16.46 percentage points (pp) over FaceReader (95% CI [+11.33, +33.90], p = 0.00195, r = 1.00) and +8.17 pp over Py-Feat (95% CI [+3.39, +21.58], p = 0.00195, r = 1.00). On the older adults dataset (Dataset B, n = 78), mean accuracy is 75.58% ± 9.04%, exceeding FaceReader by +15.47 pp (95% CI [+13.44, +17.21], p = 2.77 × 10^–14, r = 0.980) and Py-Feat by +17.67 pp (95% CI [+15.13, +19.34], p = 3.02 × 10^–14, r = 0.985). Component analyses are above chance on both datasets (B-stage medians 92.90% and 99.51%), and polarity-specific asymmetries emerge in the C-stage (A: positive > negative, Δ = +4.23 pp, two-sided p = 0.0273; B: negative > positive, Δ = -7.72 pp, p = 0.00442). On a subset of Dataset A emphasizing subtle transitions, the system maintains [78.61%, 85.38%] accuracy where human annotation accuracy ranges [50.00%, 71.47%]. Grad-CAM highlights eyebrows, forehead, and mouth regions consistent with expressive cues. Collectively, these findings demonstrate statistically significant and practically meaningful advantages for low-to-mid-intensity expression recognition and intensity ranking.

我们在两个低到中等强度、接近中性表达的数据集上评估了一个个性化的、基于两阶段比较的FER框架。该框架始终优于FaceReader和Py-Feat。在自然过渡的年轻人-成年人数据集（数据集A， n = 9）上，平均准确率为90.22%±3.53%，比FaceReader （95% CI [+11.33, +33.90], p = 0.00195, r = 1.00）和Py-Feat （95% CI [+3.39, +21.58], p = 0.00195, r = 1.00）的受试者内中位增益+16.46个百分点（pp）。在老年人数据集（数据集B， n = 78）上，平均准确率为75.58%±9.04%，超过FaceReader +15.47 pp （95% CI [+13.44, +17.21], p = 2.77 × 10-14, r = 0.980）和Py-Feat +17.67 pp （95% CI [+15.13, +19.34], p = 3.02 × 10-14, r = 0.985）。成分分析在两个数据集上都高于偶然（B期中位数为92.90%和99.51%），并且极性特异性不对称出现在c期（A：阳性>；阴性，Δ = +4.23 pp，双面p = 0.0273； B：阴性>；阳性，Δ = -7.72 pp, p = 0.00442）。在强调微妙过渡的Dataset a子集上，系统保持了[78.61%，85.38%]的准确率，而人类标注的准确率范围为[50.00%,71.47%]。Grad-CAM突出眉毛、前额和嘴部与表达线索一致。综上所述，这些发现显示了在低到中强度表达识别和强度排序方面具有统计学意义和实际意义的优势。

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引用次数: 0

Generative AI for autonomous data analytics 自主数据分析的生成式人工智能

IF 4.3

Intelligent Systems with Applications

Pub Date : 2026-01-02 DOI: 10.1016/j.iswa.2026.200626

Mattheos Fikardos , Katerina Lepenioti , Alexandros Bousdekis , Dimitris Apostolou , Gregoris Mentzas

Recent advancements in Large Language Models (LLMs) and Generative Artificial Intelligence (GenAI) have revolutionised software engineering (SE), augmenting practitioners across the SE lifecycle. In this paper, we focus on the application of GenAI within data analytics—considered a subdomain of SE—to address the growing need for reliable, user-friendly tools that bridge the gap between human expertise and automated analytical processes. In our work, we transform a conventional API-based analytics platform into a set of tools that can be used by AI agents and formulate a process to facilitate the communication between the data analyst, the agents and the platform. The result is a chat-based interface that allows analysts to query and execute analytical workflows using natural language, thereby reducing cognitive overhead and technical barriers. To validate our approach, we instantiated the proposed framework with open-source models and achieved a mean overall score increase of 7.2 % compared to other baselines. Complementary user-study data demonstrate that the chat-based analytics interface yielded superior task efficiency and higher user preference scores compared to the traditional form-based baseline.

大型语言模型（llm）和生成式人工智能（GenAI）的最新进展已经彻底改变了软件工程（SE），增加了整个SE生命周期的实践者。在本文中，我们关注GenAI在数据分析中的应用（被认为是se的子领域），以满足对可靠的、用户友好的工具日益增长的需求，这些工具可以弥合人类专业知识和自动化分析过程之间的差距。在我们的工作中，我们将传统的基于api的分析平台转化为一组AI代理可以使用的工具，并制定了一个流程来促进数据分析师、代理和平台之间的沟通。结果是一个基于聊天的界面，它允许分析人员使用自然语言查询和执行分析工作流，从而减少认知开销和技术障碍。为了验证我们的方法，我们用开源模型实例化了提出的框架，与其他基线相比，平均总分增加了7.2%。补充的用户研究数据表明，与传统的基于表单的基线相比，基于聊天的分析界面产生了卓越的任务效率和更高的用户偏好得分。

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引用次数: 0

A GCN and Graph Self-Attention Contemporary Network with Temporal Depthwise Convolutions for Gait Recognition 基于时序深度卷积的GCN和图自关注当代网络步态识别

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-31 DOI: 10.1016/j.iswa.2025.200625

Md. Khaliluzzaman , Kaushik Deb , Pranab Kumar Dhar , Tetsuya Shimamura

Skeleton-based gait recognition has significantly improved due to the advent of graph convolutional networks (GCNs). Nevertheless, the classical ST-GCN has a key drawback: limited receptive fields fail to learn the global correlations of joints, restricting its ability to extract global dependencies effectively. To address this, we present the GSCTN method, a GCN and self-attention contemporary network with temporal convolution. This method combines GCN with a self-attention mechanism using a learnable weighted fusion. By combining local joint details from GCN with the larger context from self-attention, GSCTN creates a strong representation of skeleton movements. Our approach uses decoupled self-attention (DSA) techniques that fragment the tightly coupled (TiC) SA module into two learnable components, unary and pairwise SA, to model joint relationships separately. The unary SA shows an extensive relationship between the single key joint and all additional query joints. The paired SA captures the local gait features from each pair of body joints. We also present a Depthwise Multi-scale Temporal Convolutional Network (DMS-TCN) that smoothly captures the temporal nature of joint movements. DMS-TCN efficiently handles both short-term and long-term motion patterns. To boost the model’s ability to converge spatial and temporal joints dynamically, we applied Global Aware Attention (GAA) to the GSCTN module. We tested our method on the OUMVLP-Pose, CASIA-B, and GREW datasets. The suggested method exhibits remarkable accuracy on widely used CASIA-B datasets, with 97.9% for normal walking, 94.8% for carrying a bag, and 91.91% for clothing conditions. Meanwhile, the OUMVLP-Pose and GREW datasets exhibit a rank-1 accuracy of 93.5% and 75.7%, respectively. Our experimental results demonstrate that the proposed model is a holistic approach for gait recognition by utilizing GCN, DSA, and GAA with DMS-TCN to capture both inter-domain and spatial aspects of human locomotion.

由于图卷积网络（GCNs）的出现，基于骨骼的步态识别得到了显著改善。然而，经典的ST-GCN有一个关键的缺点：有限的接受域无法学习关节的全局相关性，限制了它有效提取全局依赖关系的能力。为了解决这个问题，我们提出了GSCTN方法，一种具有时间卷积的GCN和自关注当代网络。该方法使用可学习的加权融合将GCN与自关注机制相结合。通过将来自GCN的局部关节细节与来自自我关注的更大上下文相结合，GSCTN创建了骨骼运动的强大表示。我们的方法使用解耦自注意（DSA）技术，将紧耦合（TiC）自注意模块分割为两个可学习的组件，一元自注意和成对自注意，分别对联合关系建模。一元SA显示了单键连接和所有附加查询连接之间的广泛关系。配对的SA捕获每对身体关节的局部步态特征。我们还提出了一种深度多尺度时间卷积网络（DMS-TCN），可以平滑地捕获关节运动的时间性质。DMS-TCN有效地处理短期和长期的运动模式。为了提高模型动态收敛空间和时间节点的能力，我们将全局感知注意（GAA）应用于GSCTN模块。我们在OUMVLP-Pose、CASIA-B和grow数据集上测试了我们的方法。该方法在广泛使用的CASIA-B数据集上显示出显著的准确率，正常行走的准确率为97.9%，携带包的准确率为94.8%，穿着的准确率为91.91%。同时，OUMVLP-Pose和grow数据集的rank-1精度分别为93.5%和75.7%。我们的实验结果表明，该模型是一种全面的步态识别方法，利用GCN、DSA和GAA与DMS-TCN来捕获人类运动的域间和空间方面。

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引用次数: 0

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