Intelligent Systems with Applications最新文献_第8页

Artificial intelligence-driven green innovation in packaging: A systematic review of adoption and diffusion challenges 包装中人工智能驱动的绿色创新：采用和扩散挑战的系统回顾

IF 4.3

Intelligent Systems with Applications Pub Date : 2025-12-01 Epub Date: 2025-09-22 DOI: 10.1016/j.iswa.2025.200589

Ye Ma, Nor Hidayati Zakaria, Basheer Al-Haimi, Chen Wu

{"title":"Artificial intelligence-driven green innovation in packaging: A systematic review of adoption and diffusion challenges","authors":"Ye Ma, Nor Hidayati Zakaria, Basheer Al-Haimi, Chen Wu","doi":"10.1016/j.iswa.2025.200589","DOIUrl":"10.1016/j.iswa.2025.200589","url":null,"abstract":"<div><div>Global concern about environmental protection has intensified the demand for sustainable packaging solutions. Integrating artificial intelligence (AI) into green innovation offers a transformative way to address these challenges. This study applies a systematic literature review (SLR) guided by the PRISMA 2020 framework to examine recent AI-powered packaging innovations. Forty-eight peer-reviewed articles, published between 2020 and 2025, were analyzed. The findings show that Machine Learning, Deep Learning, and general AI applications are the most frequently adopted technologies. Biodegradable packaging materials and smart packaging systems represent the main sustainable packaging types. AI applications are concentrated in process optimization, smart packaging monitoring, fraud detection, computer vision, and natural language processing. However, widespread adoption faces obstacles such as high costs, technical complexity, and regulatory uncertainty. Future trends highlight the importance of scalable technologies, advanced AI models, integration with the circular economy, and interdisciplinary collaboration. This review provides a structured framework to guide academics, industry practitioners, and policymakers in adopting AI-driven green innovation for sustainable packaging.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200589"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221298","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}

引用次数: 0

STL-ELM: A computationally efficient hybrid approach for predicting high volatility stock market STL-ELM：一种计算效率高的混合预测方法

IF 4.3

Intelligent Systems with Applications Pub Date : 2025-09-01 Epub Date: 2025-08-04 DOI: 10.1016/j.iswa.2025.200564

Temitope Olubanjo Kehinde , Oluyinka J. Adedokun , Morenikeji Kabirat Kareem , Joseph Akpan , Oludolapo A. Olanrewaju

{"title":"STL-ELM: A computationally efficient hybrid approach for predicting high volatility stock market","authors":"Temitope Olubanjo Kehinde , Oluyinka J. Adedokun , Morenikeji Kabirat Kareem , Joseph Akpan , Oludolapo A. Olanrewaju","doi":"10.1016/j.iswa.2025.200564","DOIUrl":"10.1016/j.iswa.2025.200564","url":null,"abstract":"<div><div>Accurate forecasting of high-volatility stock markets is critical for investors and policymakers, yet existing models struggle with computational inefficiency and noise sensitivity. This study introduces STL-ELM, a novel hybrid model combining Seasonal-Trend decomposition using LOESS (STL) and Extreme Learning Machine (ELM), to deliver unparalleled accuracy and speed. By decomposing stock data into trend, seasonal, and residual components, STL-ELM isolates multiscale features, while ELM’s lightweight architecture ensures rapid training and robust generalization, outperforming advanced techniques such as LSTM, GRU, and transformer variants in both prediction and trading simulations. With faster runtimes and minimal memory usage, STL-ELM is tailored for real-time trading applications and high-frequency financial forecasting, offering institutional investors, traders, and financial analysts a competitive edge in volatile markets. The hybrid nature of STL-ELM, which combines STL’s multiscale decomposition with ELM’s rapid learning, enhances its adaptability to various financial domains, including stocks, commodities, foreign exchange, and cryptocurrencies, by efficiently capturing domain-specific volatility patterns. This work not only sets a new standard for predictive accuracy in stock market modelling but also presents an invaluable tool for those navigating the complexities of modern financial markets.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200564"},"PeriodicalIF":4.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771355","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}

引用次数: 0

Optimized CNN-RNN architecture for rapid and accurate identification of hazardous bacteria in water samples 优化CNN-RNN架构，快速准确地识别水样中的有害细菌

IF 4.3

Intelligent Systems with Applications Pub Date : 2025-09-01 Epub Date: 2025-08-25 DOI: 10.1016/j.iswa.2025.200577

Ahmad Ihsan , Khairul Muttaqin , Nurul Fadillah , Rahmatul Fajri , Mursyidah Mursyidah

{"title":"Optimized CNN-RNN architecture for rapid and accurate identification of hazardous bacteria in water samples","authors":"Ahmad Ihsan , Khairul Muttaqin , Nurul Fadillah , Rahmatul Fajri , Mursyidah Mursyidah","doi":"10.1016/j.iswa.2025.200577","DOIUrl":"10.1016/j.iswa.2025.200577","url":null,"abstract":"<div><div>Drinking water safety is a critical global issue, as pathogenic bacteria in water can cause various severe diseases, including diarrhea and systemic infections. Rapid and accurate detection of hazardous bacteria is key to ensuring water quality, especially in regions with limited access to water treatment facilities. Conventional detection methods, such as bacterial culture, are often time-consuming and may not detect bacteria in the \"viable but non-culturable\" (VBNC) state. To address these limitations, this study proposes the development of an optimized Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) model for identifying harmful bacteria in drinking water samples. The CNN is used to extract spatial features from microscopic bacterial images, while the RNN handles temporal patterns in bacterial growth, enabling the system to detect bacteria more accurately. Experimental results show that the model, when using bacterial image staining, achieved 97.51% accuracy, 98.57% sensitivity, and 94.89% specificity. Even without image staining, the model still performed well, with 96.23% accuracy and 98.89% specificity. These findings indicate that the optimized CNN-RNN model can provide an efficient and rapid solution for detecting hazardous bacteria in drinking water. This research paves the way for further development, including the integration of IoT for real-time water quality monitoring.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200577"},"PeriodicalIF":4.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907131","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}

引用次数: 0

Review of artificial intelligence-based applications for money laundering detection 基于人工智能的洗钱检测应用综述

IF 4.3

Intelligent Systems with Applications Pub Date : 2025-09-01 Epub Date: 2025-08-15 DOI: 10.1016/j.iswa.2025.200572

Seyedmohammad Mousavian, Shah J Miah

引用次数: 0

Anomaly-based intrusion detection system based on SMOTE-IPF, Whale Optimization Algorithm, and ensemble learning 基于SMOTE-IPF、Whale优化算法和集成学习的基于异常的入侵检测系统

Intelligent Systems with Applications Pub Date : 2025-09-01 Epub Date: 2025-06-14 DOI: 10.1016/j.iswa.2025.200543

Tibebu Bekele Shana , Neetu Kumari , Mayank Agarwal , Samrat Mondal , Upaka Rathnayake

{"title":"Anomaly-based intrusion detection system based on SMOTE-IPF, Whale Optimization Algorithm, and ensemble learning","authors":"Tibebu Bekele Shana , Neetu Kumari , Mayank Agarwal , Samrat Mondal , Upaka Rathnayake","doi":"10.1016/j.iswa.2025.200543","DOIUrl":"10.1016/j.iswa.2025.200543","url":null,"abstract":"<div><div>Nowadays, cybersecurity is a major worldwide problem. Intrusion detection systems (IDS) help guarantee network security by detecting malicious entries from legitimate entries in network traffic data. IDS has considerable potential for detecting dynamic cyber threats, identifying abnormalities, and identifying malicious conduct within the network. In this paper, we propose Machine Learning (ML) models with an emphasis on the Synthetic Minority Over-sampling Technique (SMOTE) with Iterative Partitioning Filter (IPF) for class imbalance and the Whale Optimization Algorithm (WOA) for feature selection. Class imbalance often results in poorly constructed ML models prioritizing the majority class. In addition, the absence of feature selection can lead to higher computational complexity without impacting performance accuracy. This study uses Bagging, AdaBoost, Extreme Gradient Boosting (XGBoost) and Extra Trees Classifier as classification models. The two widely used datasets to assess the proposed method are NLS-KDD and UNSW-NB15. The K-Fold cross-validation technique trains this model to minimize potential overfitting. These models are evaluated based on performance metrics such as accuracy, precision, recall, and F1-score. The experimental results demonstrate that the Extra Trees Classifier significantly outperforms the baseline models and achieves accuracy values of 99.9% for the NSL-KDD dataset and 97% for the UNSW-NB 15 dataset and outperforms all evaluation measures compared to baseline models for multi-classification of the IDS.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200543"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338636","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}

引用次数: 0

On adversarial attack detection in the artificial intelligence era: Fundamentals, a taxonomy, and a review 关于人工智能时代的对抗性攻击检测：基础，分类和回顾

Intelligent Systems with Applications Pub Date : 2025-09-01 Epub Date: 2025-07-07 DOI: 10.1016/j.iswa.2025.200554

Noora Al Roken , Hakim Hacid , Ahmed Bouridane , Abir Hussain

引用次数: 0

Application of LSTM and GRU neural networks to improve peristaltic pump dosing accuracy 应用LSTM和GRU神经网络提高蠕动泵加药精度

IF 4.3

Intelligent Systems with Applications Pub Date : 2025-09-01 Epub Date: 2025-08-20 DOI: 10.1016/j.iswa.2025.200571

Davide Privitera , Stefano Bellissima , Sandro Bartolini

{"title":"Application of LSTM and GRU neural networks to improve peristaltic pump dosing accuracy","authors":"Davide Privitera , Stefano Bellissima , Sandro Bartolini","doi":"10.1016/j.iswa.2025.200571","DOIUrl":"10.1016/j.iswa.2025.200571","url":null,"abstract":"<div><div>Peristaltic pumps (PP), widely acknowledged for their benefits in pharmaceutical contexts, face challenges in achieving optimal dosing accuracy. This investigation contributes novel insights for the improvement of dosing precision, identifying how to apply AI models, specifically focusing on Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks over a realistic span of target volumes. To provide a more accurate representation of real-world performance, we consider a modified root mean square error metric (<span><math><mrow><mi>R</mi><mi>M</mi><mi>S</mi><msub><mrow><mi>E</mi></mrow><mrow><mi>P</mi><mi>P</mi></mrow></msub></mrow></math></span>) that directly compares dispensed volumes to target volumes. Based on this the study delves into two main methodologies: an iterative retraining method, called Online Training, and Pre-trained approach. Online Training shows best results, especially for volumes below 1.0 ml, achieving 38.4% improvement in <span><math><mrow><mi>R</mi><mi>M</mi><mi>S</mi><msub><mrow><mi>E</mi></mrow><mrow><mi>P</mi><mi>P</mi></mrow></msub></mrow></math></span> and 31.6% in standard deviation (<span><math><mrow><mi>S</mi><mi>T</mi><mi>D</mi></mrow></math></span>). Pre-trained models are faster and exhibit promising outcomes especially for volumes above 1.0 ml, with a three-features approach delivering the best performance (13.8% and 4.6% improvements in <span><math><mrow><mi>R</mi><mi>M</mi><mi>S</mi><msub><mrow><mi>E</mi></mrow><mrow><mi>P</mi><mi>P</mi></mrow></msub></mrow></math></span> and <span><math><mrow><mi>S</mi><mi>T</mi><mi>D</mi></mrow></math></span>, respectively). Overall, the findings highlight the effectiveness of iterative learning techniques, particularly for smaller dosage amounts, which complements the good performance of non-AI approaches for larger ones.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200571"},"PeriodicalIF":4.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895628","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}

引用次数: 0

Neural Koopman forecasting for critical transitions in infrastructure networks 基础设施网络关键转变的神经库普曼预测

IF 4.3

Intelligent Systems with Applications Pub Date : 2025-09-01 Epub Date: 2025-08-30 DOI: 10.1016/j.iswa.2025.200575

Ramen Ghosh

{"title":"Neural Koopman forecasting for critical transitions in infrastructure networks","authors":"Ramen Ghosh","doi":"10.1016/j.iswa.2025.200575","DOIUrl":"10.1016/j.iswa.2025.200575","url":null,"abstract":"<div><div>We develop a data-driven framework for long-term forecasting of stochastic dynamics on evolving networked infrastructure systems using neural approximations of Koopman operators. In real-world nonlinear systems, the exact Koopman operator is infinite-dimensional and generally unavailable in closed form, necessitating learned finite-dimensional surrogates. Focusing on applications such as traffic flow and power grid oscillations, we model the underlying dynamics as random graph-driven nonlinear processes and introduce a graph-informed neural architecture that learns approximate Koopman eigenfunctions to capture system evolution over time. Our key contribution is the joint treatment of stochastic network evolution, Koopman operator learning, and phase-transition-induced breakdowns in forecasting. We identify critical regimes—arising from graph connectivity shifts or load-induced bifurcations—where the effective forecasting horizon collapses due to spectral degeneracy in the learned Koopman operator. We establish sufficient conditions under which this collapse occurs and propose regularization techniques to mitigate representational breakdown. Numerical experiments on traffic and power networks validate the proposed method and confirm the emergence of critical behavior. These results not only highlight the challenges of forecasting near structural transitions, but also suggest that spectral collapse may serve as a diagnostic signal for detecting phase transitions in dynamic networks. Our contributions unify spectral operator theory, random dynamical systems, and neural forecasting into a control-theoretic framework for real-time intelligent infrastructure. To our knowledge, this is the first work to jointly study Koopman operator learning, stochastic network evolution, and forecasting collapse induced by graph-theoretic phase transitions.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200575"},"PeriodicalIF":4.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144921887","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}

引用次数: 0

Cancelable random masking with deep learning for secure and interpretable finger vein authentication 可取消的随机掩蔽与深度学习的安全和可解释的手指静脉认证

Intelligent Systems with Applications Pub Date : 2025-09-01 Epub Date: 2025-07-07 DOI: 10.1016/j.iswa.2025.200552

Mohamed Hammad , Abdelhamied A. Ateya , Mohammed ElAffendi , Ahmed A. Abd El-Latif

{"title":"Cancelable random masking with deep learning for secure and interpretable finger vein authentication","authors":"Mohamed Hammad , Abdelhamied A. Ateya , Mohammed ElAffendi , 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":"2025-09-01","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}

引用次数: 0

Vehicle route optimizer for waste collection and routing optimization problem 车辆路线优化器用于垃圾收集和路线优化问题

IF 4.3

Intelligent Systems with Applications Pub Date : 2025-09-01 Epub Date: 2025-07-29 DOI: 10.1016/j.iswa.2025.200521

Hussam Fakhouri , Amjad Hudaib , Faten Hamad , Sandi Fakhouri , Niveen Halalsheh , Mohannad S. Alkhalaileh

{"title":"Vehicle route optimizer for waste collection and routing optimization problem","authors":"Hussam Fakhouri , Amjad Hudaib , Faten Hamad , Sandi Fakhouri , Niveen Halalsheh , 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":"2025-09-01","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}

引用次数: 0