Intelligent Systems with Applications最新文献

{"title":"Vehicle route optimizer for waste collection and routing optimization problem","authors":"Hussam Fakhouri ,&nbsp;Amjad Hudaib ,&nbsp;Faten Hamad ,&nbsp;Sandi Fakhouri ,&nbsp;Niveen Halalsheh ,&nbsp;Mohannad S. Alkhalaileh","doi":"10.1016/j.iswa.2025.200521","DOIUrl":"10.1016/j.iswa.2025.200521","url":null,"abstract":"<div><div>This paper introduces a novel dynamic optimization strategy called the Vehicle Route Optimizer (VRO), specifically designed to enhance the efficiency and sustainability of smart cities. Inspired by the dynamics and interactions observed in vehicle behavior and traffic systems, VRO effectively balances exploration and exploitation phases to discover optimal solutions. The algorithm has been rigorously tested using the IEEE CEC2022 benchmark suites, demonstrating its superior performance compared to 18 other optimizers. In smart cities, efficient waste management and routing are critical for reducing operational costs and minimizing environmental impact. Thus, VRO has been applied to solve the Waste Collection and Routing Optimization Problem (WCROP) in smart cities by integrating bin allocation and routing components into a single-objective optimization framework. In addressing WCROP in Smart Cities, VRO was evaluated using synthetic instances derived from PVRP-IF cases. The results show that VRO outperforms traditional hierarchical and heuristic methods in terms of total cost, computational efficiency, and solution feasibility.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200521"},"PeriodicalIF":4.3,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738302","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}

{"title":"Paranasal sinus analysis based on deep learning and machine learning techniques: A comprehensive survey","authors":"Ali Alsalama,&nbsp;Saad Harous,&nbsp;Ashraf Elnagar","doi":"10.1016/j.iswa.2025.200559","DOIUrl":"10.1016/j.iswa.2025.200559","url":null,"abstract":"<div><div>This survey provides an in-depth review of recent advancements in forensic anthropology through the application of imaging and modeling techniques for paranasal sinus structures. The focus is on exploring various studies that leverage the paranasal sinuses for the identification of individuals and demographic analysis, including age and gender estimation, especially when traditional methods such as fingerprint analysis, dental records, or DNA profiling are not feasible. Additionally, the survey aims to serve as a foundation for future work in similar analyses and segmentation tasks. These methods are especially useful in forensic contexts, such as those involving skeletonized remains where other anatomical structures are absent. The paper discusses several case studies, including the segmentation of paranasal sinuses as well as their classification for establishing biological profiles in diverse populations. The effectiveness of these 3D modeling approaches in predicting demographic characteristics such as sex, age, and ethnicity is also highlighted. Special emphasis is placed on the robustness and reliability of sinus morphology as both a forensic identifier and a tool for demographic inference.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200559"},"PeriodicalIF":0.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679030","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}

{"title":"A novel binary Stellar Oscillation Optimizer for feature selection optimization problems","authors":"Ali Rodan ,&nbsp;Sharif Naser Makhadmeh ,&nbsp;Yousef Sanjalawe ,&nbsp;Rizik M.H. Al-Sayyed ,&nbsp;Mohammed Azmi Al-Betar","doi":"10.1016/j.iswa.2025.200558","DOIUrl":"10.1016/j.iswa.2025.200558","url":null,"abstract":"<div><div>Stellar Oscillation Optimizer (SOO) takes its core inspiration from the study of stellar pulsations, a domain often referred to as asteroseismology which is formulated as an optimization algorithm for continuous domain. In this paper, the Binary version of Stellar Oscillation Optimizer (BSOO) is proposed for Feature Selection (FS) problems. BSOO introduces binary adaptations, including threshold-based encoding, controlled oscillatory movements, and a top-solution influence mechanism. In order to evaluate the BSOO, sixteen FS datasets are used with different numbers of features, samples, and class labels. Seven performance measures are also used, which are: fitness value, number of selected features, accuracy, sensitivity, specificity, Precision, and F-measure. An intensive comparative evaluation against 18 state-of-the-art optimization algorithms using the same datasets has been conducted. The results show that the proposed BSOO version is able to compete well with the other FS-based methods where it is able to overcome several methods and produce the best overall results for some datasets on different measurements. Furthermore, the convergence behavior to show the optimization behavior of BSOO during the search is investigated and visualized. Interestingly, the BSOO is able to provide a suitable trade-off between the global wide-range exploration and local nearby exploitation during the optimization process. This is proved using the statistical Wilcoxon Rank-Sum Test Results. In conclusion, this paper provides a new alternative solution for FS research community that is able to work well for many FS instances and find the optimal solution. The source code of BSOO is publicly available for both MATLAB at: <span><span>https://www.mathworks.com/matlabcentral/fileexchange/180096-bsoo-binary-stellar-oscillation-optimizer</span><svg><path></path></svg></span> and PYTHON at: <span><span>https://github.com/AliRodan/BSOO-Binary-Stellar-Oscillation-Optimizer</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200558"},"PeriodicalIF":0.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694572","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}

{"title":"Monkeypox optimizer: A TinyML bio-inspired evolutionary optimization algorithm and its engineering applications","authors":"Marwa F. Mohamed ,&nbsp;Ahmed Hamed","doi":"10.1016/j.iswa.2025.200557","DOIUrl":"10.1016/j.iswa.2025.200557","url":null,"abstract":"<div><div>High-dimensional optimization remains a key challenge in computational intelligence, especially under resource constraints. Evolutionary algorithms, which mimic the change in heritable characteristics of biological populations, have been proposed to address this. These algorithms apply selection pressure to favor better solutions over generations, and stochastic variations may occasionally introduce suboptimal candidates to preserve population diversity. However, they often struggle to balance exploration and exploitation, leading to suboptimal solutions, premature convergence, and significant computational demands, making them unsuitable for resource-constrained environments. This paper introduces Monkeypox Optimization (MO), a novel evolutionary algorithm inspired by the infection and replication lifecycle of the monkeypox virus. MO mimics the virus’s rapid spread by employing virus-to-cell infection, where the virus persistently seeks out vulnerable cells to penetrate—representing global exploration of the search space. Once inside, cell-to-cell transmission enables fast local propagation, modeling the refinement of high-potential solutions through accelerated replication. To conserve resources, MO continuously deletes the least effective virion copies, maintaining a compact and memory-efficient population. This biologically grounded design not only accelerates convergence but also aligns MO with TinyML principles, making it ideally suited for low-power, resource-constrained IoT environments. MO is benchmarked against 21 recent algorithms across 90 functions from CEC-2017, CEC-2019, and CEC-2020, and validated on three engineering design problems. Results show MO achieves up to 13% lower energy consumption and 34% shorter execution time compared to state-of-the-art competitors, while maintaining robust accuracy. A theoretical analysis reveals MO’s time complexity is <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mi>m</mi><mi>n</mi><mo>+</mo><mi>R</mi><mi>T</mi><mi>n</mi><mo>)</mo></mrow></mrow></math></span>, confirming its scalability. Statistical validation via Friedman and Fisher tests further supports MO’s performance gains.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200557"},"PeriodicalIF":0.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687060","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}

{"title":"A unified DNN weight compression framework using reweighted optimization methods","authors":"Mengchen Fan ,&nbsp;Tianyun Zhang ,&nbsp;Xiaolong Ma ,&nbsp;Jiacheng Guo ,&nbsp;Zheng Zhan ,&nbsp;Shanglin Zhou ,&nbsp;Minghai Qin ,&nbsp;Caiwen Ding ,&nbsp;Baocheng Geng ,&nbsp;Makan Fardad ,&nbsp;Yanzhi Wang","doi":"10.1016/j.iswa.2025.200556","DOIUrl":"10.1016/j.iswa.2025.200556","url":null,"abstract":"<div><div>To address the large model sizes and intensive computation requirements of deep neural networks (DNNs), weight pruning techniques have been proposed and generally fall into two categories: static regularization-based pruning and dynamic regularization-based pruning. However, the static method often leads to either complex operations or reduced accuracy, while the dynamic method requires extensive time to adjust parameters to maintain accuracy while achieving effective pruning. In this paper, we propose a unified robustness-aware framework for DNN weight pruning that dynamically updates regularization terms bounded by the designated constraint. This framework can generate both non-structured sparsity and different kinds of structured sparsity, and it incorporates adversarial training to enhance the robustness of the sparse model. We further extend our approach into an integrated framework capable of handling multiple DNN compression tasks. Experimental results show that our proposed method increases the compression rate – up to <span><math><mrow><mn>630</mn><mo>×</mo></mrow></math></span> for LeNet-5, <span><math><mrow><mn>45</mn><mo>×</mo></mrow></math></span> for AlexNet, <span><math><mrow><mn>7</mn><mo>.</mo><mn>2</mn><mo>×</mo></mrow></math></span> for MobileNet, <span><math><mrow><mn>3</mn><mo>.</mo><mn>2</mn><mo>×</mo></mrow></math></span> for ResNet-50 – while also reducing training time and simplifying hyperparameter tuning to a <strong>single penalty parameter</strong>. Additionally, our method improves model robustness by <strong>5.07%</strong> for ResNet-18 and <strong>3.34%</strong> for VGG-16 under a 16×pruning rate, outperforming the state-of-the-art ADMM-based hard constraint method.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200556"},"PeriodicalIF":0.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614543","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}

{"title":"Methodology for advanced time series demand forecasting: A hybrid model of decomposition and deep learning","authors":"Juyoung Ha ,&nbsp;Sungwon Lee ,&nbsp;Sooyeon Jeong ,&nbsp;Doohee Chung","doi":"10.1016/j.iswa.2025.200540","DOIUrl":"10.1016/j.iswa.2025.200540","url":null,"abstract":"<div><div>Advancements in data science have increasingly focused on refining time-series predictive models for effective corporate management and demand forecasting. Traditional models often struggle to capture irregular patterns in time-series data. In this study, we employ a novel hybrid model integrating Ensemble Empirical Mode Decomposition (EEMD), Least Absolute Shrinkage and Selection Operator (LASSO), and Long Short-Term Memory (LSTM) networks to address these challenges. Our approach follows a structured pipeline: EEMD decomposes time-series data into ensemble Intrinsic Mode Functions (eIMFs) to reveal complex patterns, LASSO selects the most relevant features to optimize input variables, and LSTM captures long-term dependencies for accurate demand forecasting. We evaluate our model on real-world demand data from three industries (Office Product, Packaging Materials, and Pharmaceuticals), comparing it against ARIMAX, LightGBM, LSTM, and their EEMD-enhanced variants using NRMSE, NMAE, and R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>. Results show that integrating EEMD into baseline models reduces NRMSE by an average of 27.4%, while the additional incorporation of LASSO further improves performance, achieving a total reduction of 29.1%. Compared to the standalone LSTM model, our proposed EEMD-LASSO-LSTM model demonstrates a substantial NRMSE reduction of 51.2%, highlighting its superior predictive accuracy. This innovative combination of EEMD, LASSO, and LSTM enables our proposed method to effectively capture the irregular patterns of demand, a task that has been a significant hurdle for conventional forecasting methods. The integration of EEMD, LASSO, and LSTM marks a significant advancement in time-series predictive modeling, enhancing demand forecasting and informing strategic corporate decisions.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200540"},"PeriodicalIF":4.3,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738121","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}

{"title":"Visual question answering for medical diagnosis","authors":"Nawel Ben Chaabane,&nbsp;Mohamed Bal-Ghaoui","doi":"10.1016/j.iswa.2025.200545","DOIUrl":"10.1016/j.iswa.2025.200545","url":null,"abstract":"<div><div>The use of Artificial Intelligence (AI) in medical diagnosis is a breakthrough in healthcare, improving both accuracy and efficiency. Recently, a significant advancement has been made toward the development of multimodal AI systems that can process and integrate multiple types of data or modalities. This ability is key for interpreting medical images, such as X-rays, CT, and MRI scans, as well as textual data like electronic health records (EHRs) and clinical notes. In this era, Visual Question Answering (VQA) systems have demonstrated a potential use case in the medical domain. These systems, typically based on Vision-Language Models (VLMs), can answer natural lan- guage questions based on medical images, offering precise and relevant re- sponses that help doctors make better decisions.</div><div>In this article, we evaluate existing medical VQA models along with general and trending ones to make medical diagnoses. In particular, we focus on addressing abnormality questions considered challenging in the literature. Our approach consists of evaluating the Zero-Shot (ZS) general and domain-specific capabilities of different models using two created datasets, and fine-tuning the best-found models on the training set of the abnormality dataset before evaluating their performances quantitatively and qualitatively. IdeficMed, a generative domain-specific model, achieved better consistency and VQA outcomes by only training 0.22 % of its parameters. Additionally, we employed uncertainty quantification techniques (e.g., Monte Carlo dropout) to assess the confidence of the fine-tuned models in their predictions. We also conducted a sensitivity analysis on input perturbations, such as image noise and ambiguous questions.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200545"},"PeriodicalIF":0.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703262","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}

{"title":"On adversarial attack detection in the artificial intelligence era: Fundamentals, a taxonomy, and a review","authors":"Noora Al Roken ,&nbsp;Hakim Hacid ,&nbsp;Ahmed Bouridane ,&nbsp;Abir Hussain","doi":"10.1016/j.iswa.2025.200554","DOIUrl":"10.1016/j.iswa.2025.200554","url":null,"abstract":"<div><div>The rapid advancement and sophisticated deployment of artificial intelligence tools by malicious actors have led to the rise of highly complex cyber-attacks that evolve quickly. This rapid evolution has made traditional defense systems increasingly ineffective at detecting and mitigating these hidden threats. Adversarial attacks are a prime example of such sophisticated cyber-attacks; they subtly alter attack patterns to evade detection by intelligent systems while still maintaining their harmful functionality. This paper provides a comprehensive overview of computer malware, examining both traditional concealment methods and more advanced adversarial techniques. It includes an in-depth analysis of recent research efforts aimed at detecting previously unseen adversarial attacks using both traditional and AI-driven approaches. Furthermore, this study discusses the limitations of current network intrusion detection systems and proposes directions for future research.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200554"},"PeriodicalIF":0.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580839","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}

{"title":"Cancelable random masking with deep learning for secure and interpretable finger vein authentication","authors":"Mohamed Hammad ,&nbsp;Abdelhamied A. Ateya ,&nbsp;Mohammed ElAffendi ,&nbsp;Ahmed A. Abd El-Latif","doi":"10.1016/j.iswa.2025.200552","DOIUrl":"10.1016/j.iswa.2025.200552","url":null,"abstract":"<div><div>In the area of identity verification and authentication, biometrics has emerged as a reliable means of recognizing individuals based on their unique behavioral or physical characteristics. Finger vein authentication, with its robustness, resistance to spoofing, and stable patterns, has gained significant attention as a biometric modality. This paper introduces a novel framework that integrates Cancelable Random Masking (CRM) with a lightweight deep learning model for secure and interpretable finger vein authentication. The CRM technique transforms biometric templates using cryptographic random masks, ensuring cancelability, revocability, and privacy. These transformed templates are then processed by a convolutional neural network (CNN) designed to learn discriminative features directly from masked inputs without relying on handcrafted feature extraction. Our method enhances transparency by making the transformation process interpretable and provides strong security against template inversion and adversarial attacks. Results conducted on three publicly available databases demonstrate the proposed framework’s superior performance in terms of accuracy, robustness, and resistance to spoofing and replay attacks. This is the first framework to integrate CRM within a deep learning model, satisfying all cancelable biometric criteria while enabling real-time, interpretable, and secure finger vein authentication.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200552"},"PeriodicalIF":0.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580838","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}

{"title":"Certified Accuracy and Robustness: How different architectures stand up to adversarial attacks","authors":"Azryl Elmy Sarih ,&nbsp;Nagender Aneja ,&nbsp;Ong Wee Hong","doi":"10.1016/j.iswa.2025.200555","DOIUrl":"10.1016/j.iswa.2025.200555","url":null,"abstract":"<div><div>Adversarial attacks are a concern for image classification using neural networks. Numerous methods have been created to minimize the effects of attacks, where the best defense against such attacks is through adversarial training, which has proven to be the most successful to date. Due to the nature of adversarial attacks, it is difficult to assess the capabilities of a network to defend. The standard method of assessing a network’s performance in supervised image classification tasks is based on accuracy. However, this assessment method, while still important, is insufficient when adversarial attacks are included. A new metric called certified accuracy is used to assess network performance when samples are perturbed by adversarial noise. This paper supplements certified accuracy with an abstention rate to give more insight into the network’s robustness. Abstention rate measures the percentage of the network that failed to keep its prediction unchanged as the perturbation strength increases from zero to specified strength. The study focuses on popular and good-performing CNN-based architectures, specifically EfficientNet-B7, ResNet-50, ResNet-101, Wide-ResNet-101, and transformer architectures such as CaiT and ViT-B/16. The selected architectures are trained in adversarial and standard methods and then certified on CIFAR-10 datasets perturbed with Gaussian noises of different strengths. Our results show that transformers are more resilient to adversarial attacks than CNN-based architectures by a significant margin. Transformers exhibit better certified accuracy and tolerance against stronger noises than CNN-based architectures, demonstrating good robustness with and without adversarial training. The width and depth of a network have little effect on achieving robustness against adversarial attacks, but rather, the techniques that are deployed in the network are more impactful, where attention mechanisms have been shown to improve a network’s robustness.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200555"},"PeriodicalIF":0.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597230","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}