Pub Date : 2026-01-15DOI: 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.
{"title":"Attention-enhanced reinforcement learning for dynamic portfolio optimization","authors":"Pei Xue, Yuanchun Ye","doi":"10.1016/j.iswa.2025.200622","DOIUrl":"10.1016/j.iswa.2025.200622","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200622"},"PeriodicalIF":4.3,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Novel quantum tunneling and fractional calculus-based metaheuristic for robust global data optimization and its applications in engineering design","authors":"Hussam Fakhouri , Riyad Alrousan , Niveen Halalsheh , Najem Sirhan , Jamal Zraqou , Khalil Omar","doi":"10.1016/j.iswa.2025.200616","DOIUrl":"10.1016/j.iswa.2025.200616","url":null,"abstract":"<div><h3>Background:</h3><div>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.</div></div><div><h3>Contributions:</h3><div>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.</div></div><div><h3>Methodology:</h3><div>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.</div></div><div><h3>Results:</h3><div>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.</div></div><div><h3>Findings:</h3><div>The proposed fractional–quantum approach delivers fast, accurate, and robust search across heterogeneous landscapes and real-world design problems.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200616"},"PeriodicalIF":4.3,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 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.
{"title":"An efficient lightweight multi-scale CNN framework with CBAM and SPP for bearing fault diagnosis","authors":"Thanh Tung Luu , Duy An Huynh","doi":"10.1016/j.iswa.2026.200628","DOIUrl":"10.1016/j.iswa.2026.200628","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200628"},"PeriodicalIF":4.3,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Generative AI for autonomous data analytics","authors":"Mattheos Fikardos , Katerina Lepenioti , Alexandros Bousdekis , Dimitris Apostolou , Gregoris Mentzas","doi":"10.1016/j.iswa.2026.200626","DOIUrl":"10.1016/j.iswa.2026.200626","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200626"},"PeriodicalIF":4.3,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"A GCN and Graph Self-Attention Contemporary Network with Temporal Depthwise Convolutions for Gait Recognition","authors":"Md. Khaliluzzaman , Kaushik Deb , Pranab Kumar Dhar , Tetsuya Shimamura","doi":"10.1016/j.iswa.2025.200625","DOIUrl":"10.1016/j.iswa.2025.200625","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200625"},"PeriodicalIF":4.3,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-22DOI: 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%.
{"title":"Blind steganalysis-driven secure transmission validation using feature-based classification in JPEG images","authors":"Deepa D. Shankar , Adresya Suresh Azhakath","doi":"10.1016/j.iswa.2025.200623","DOIUrl":"10.1016/j.iswa.2025.200623","url":null,"abstract":"<div><div>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%.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200623"},"PeriodicalIF":4.3,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-18DOI: 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。
{"title":"Scalable and Adaptive Large-Scale Group Decision Making in Dynamic Social Networks via Graph Convolutional Neural Networks#","authors":"Elaheh Golzardi , Alireza Abdollahpouri","doi":"10.1016/j.iswa.2025.200620","DOIUrl":"10.1016/j.iswa.2025.200620","url":null,"abstract":"<div><div>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 (<span><span>Qin, Li, Liang & Pedrycz, 2026</span></span>). 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 (<span><span>Ding et al., 2025</span></span>). 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.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200620"},"PeriodicalIF":4.3,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-18DOI: 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.
{"title":"Interpretable event diagnosis in water distribution networks","authors":"André Artelt , Stelios G. Vrachimis , Demetrios G. Eliades , Ulrike Kuhl , Barbara Hammer , Marios M. Polycarpou","doi":"10.1016/j.iswa.2025.200621","DOIUrl":"10.1016/j.iswa.2025.200621","url":null,"abstract":"<div><div>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.</div><div>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 <em>counterfactual event fingerprints</em>, 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.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200621"},"PeriodicalIF":4.3,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 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.
{"title":"FireBoost: A new bio-inspired approach for feature selection based on firefly algorithm and optimized XGBoost","authors":"Nafaa Jabeur","doi":"10.1016/j.iswa.2025.200613","DOIUrl":"10.1016/j.iswa.2025.200613","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"29 ","pages":"Article 200613"},"PeriodicalIF":4.3,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-16DOI: 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.
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