Intelligent Systems with Applications最新文献_第2页

Online clustering with interpretable drift adaptation to mixed features 可解释漂移适应混合特征的在线聚类

Intelligent Systems with Applications Pub Date : 2025-04-08 DOI: 10.1016/j.iswa.2025.200510

Flavio Corradini, Vincenzo Nucci, Marco Piangerelli, Barbara Re

引用次数: 0

Digital twins: Recent advances and future directions in engineering fields 数字孪生：工程领域的最新进展和未来方向

Intelligent Systems with Applications Pub Date : 2025-04-07 DOI: 10.1016/j.iswa.2025.200516

Kamran Iranshahi , Joshua Brun , Tim Arnold , Thomas Sergi , Ulf Christian Müller

{"title":"Digital twins: Recent advances and future directions in engineering fields","authors":"Kamran Iranshahi , Joshua Brun , Tim Arnold , Thomas Sergi , Ulf Christian Müller","doi":"10.1016/j.iswa.2025.200516","DOIUrl":"10.1016/j.iswa.2025.200516","url":null,"abstract":"<div><div>Digital Twins have emerged as a powerful in silico method for the design, operation, and maintenance of real-world assets across various domains. This review paper investigates different aspects of Digital Twins, especially their application throughout the product lifecycle and across diverse engineering domains. It provides a comprehensive overview of different services that Digital Twins can deliver at each stage of the product lifecycle in collaboration with other digital technologies (e.g., Internet of Things, etc.) using a data chain (digital thread). The review begins with an introduction to Digital Twins, highlighting their fundamental principles and significance in engineering. It then explores various domains to provide cross-domain insights into the use of Digital Twins. These domains include manufacturing, healthcare and medicine, agriculture and food supply chain, aerospace, construction and building management, automotive and transportation. Each domain-specific section explores recent advancements, showcasing innovative approaches and how Digital Twins improve decision-making, while also addressing their current challenges. Furthermore, each section peers into the future, presenting a forward-looking perspective on the challenges and evolving landscape of Digital Twins in that specific engineering sector. Finally, it provides a panoramic view of the current status of Digital Twins in engineering fields, based on evaluations by a panel of experts. The expert panel assessed the Technology Readiness Level (TRL) of Digital Twins at 4.8 on a scale from 1 to 9. They identified the integration of Digital Twins with the digital thread as the most significant current challenge.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"26 ","pages":"Article 200516"},"PeriodicalIF":0.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826360","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

Two stream GRU model with ELU activation function for sign language recognition 带ELU激活函数的两流GRU模型用于手语识别

Intelligent Systems with Applications Pub Date : 2025-04-05 DOI: 10.1016/j.iswa.2025.200513

Kasian Myagila , Devotha Godfrey Nyambo , Mussa Ally Dida

{"title":"Two stream GRU model with ELU activation function for sign language recognition","authors":"Kasian Myagila , Devotha Godfrey Nyambo , Mussa Ally Dida","doi":"10.1016/j.iswa.2025.200513","DOIUrl":"10.1016/j.iswa.2025.200513","url":null,"abstract":"<div><div>Pose Estimation features have been successfully used in human activity recognition including sign language recognition. One of the key challenges in sign language recognition is handling signer-independent modes and hand dominance of signer. This paper proposes the use of the Gated Recurrent Unit (GRU) with the ELU activation function to improve computation efficiency and to enhance model learning efficiency. In addition, the paper proposes two stream model architecture to address the challenge of left and right-hand dominance. The study developed model using a Tanzania Sign language datasets collected using mobile devices and extracted pose estimation feature using MediaPipe holistic framework. According to the results, the proposed model not only achieves an impressive overall accuracy of 95%, but also trains more efficiently than comparable algorithms. Particularly in the signer-independent mode, the two-stream approach led to substantial improvements, achieving a maximum accuracy of 92% and a minimum accuracy of 70% with significant increase on the left handed signer accuracy by 37%. The results highlight the effectiveness of the two-stream approach in overcoming challenges related to left and right-hand dominance, which often arise from signer-specific hand dominance. Additionally, the results indicate that, the proposed model can have a positive impact on limited computational resources while also enhancing the model’s overall performance.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"26 ","pages":"Article 200513"},"PeriodicalIF":0.0,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient combination of deep learning and tree-based classification models for solar panel dust detection 深度学习与基于树的分类模型的有效结合用于太阳能电池板粉尘检测

Intelligent Systems with Applications Pub Date : 2025-04-04 DOI: 10.1016/j.iswa.2025.200509

Jad Bassil , Hassan N. Noura , Ola Salman , Khaled Chahine , Mohsen Guizani

{"title":"Efficient combination of deep learning and tree-based classification models for solar panel dust detection","authors":"Jad Bassil , Hassan N. Noura , Ola Salman , Khaled Chahine , Mohsen Guizani","doi":"10.1016/j.iswa.2025.200509","DOIUrl":"10.1016/j.iswa.2025.200509","url":null,"abstract":"<div><div>Solar panels are crucial for converting sunlight into electricity. However, their efficiency and performance can significantly decline due to environmental factors, notably the buildup of dust and debris on their surfaces. This study proposes a hybrid model comprising a deep learning component for feature extraction and tree-based classifiers specifically designed to distinguish between ”dusty” and ”clean” solar panels. The objective is to develop a robust model to accurately detect dust on solar panels under various environmental conditions. Our approach leverages pre-trained deep learning models fine-tuned to detect dust on photovoltaic panels to extract relevant features. These features are then used for classification using lightweight tree-based models. Fine-tuning the pre-trained model weights significantly improves the detection performance. The results show that the combination of features extracted from EfficientNetB7 and a vision transformer achieves the highest accuracy of 97% when fed into a tree classifier. In addition, introducing a tree-based model improves all classification metrics compared to a fully dense connected layer. This work can be adapted to detect dust levels and, consequently, to help identify effective cleaning methods in an automated manner.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"26 ","pages":"Article 200509"},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Systematic review of predictive maintenance practices in the manufacturing sector 对制造部门的预测性维护实践进行系统审查

Intelligent Systems with Applications Pub Date : 2025-04-02 DOI: 10.1016/j.iswa.2025.200501

Abdeldjalil Benhanifia , Zied Ben Cheikh , Paulo Moura Oliveira , Antonio Valente , José Lima

{"title":"Systematic review of predictive maintenance practices in the manufacturing sector","authors":"Abdeldjalil Benhanifia , Zied Ben Cheikh , Paulo Moura Oliveira , Antonio Valente , José Lima","doi":"10.1016/j.iswa.2025.200501","DOIUrl":"10.1016/j.iswa.2025.200501","url":null,"abstract":"<div><div>Predictive maintenance (PDM) is emerging as a strong transformative tool within Industry 4.0, enabling significant improvements in the sustainability and efficiency of manufacturing processes. This in-depth literature review, which follows the PRISMA 2020 framework, examines how PDM is being implemented in several areas of the manufacturing industry, focusing on how it is taking advantage of technological advances such as artificial intelligence (AI) and the Internet of Things (IoT). The presented in-depth evaluation of the technological principles, implementation methods, economic consequences, and operational improvements based on academic and industrial sources and new innovations is performed. According to the studies, integrating CDM can significantly increase machine uptime and reliability while reducing maintenance costs. In addition, the transition to PDM systems that use real-time data to predict faults and plan maintenance more accurately holds out promising prospects. However, there are still gaps in the overall methodologies for measuring the return on investment of PDM implementations, suggesting an essential research direction.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"26 ","pages":"Article 200501"},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833609","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

An adaptive chaotic league championship algorithm for solving global optimization and engineering design problems 一种求解全局优化和工程设计问题的自适应混沌联赛冠军算法

Intelligent Systems with Applications Pub Date : 2025-03-28 DOI: 10.1016/j.iswa.2025.200511

Tanachapong Wangkhamhan, Jatsada Singthongchai

{"title":"An adaptive chaotic league championship algorithm for solving global optimization and engineering design problems","authors":"Tanachapong Wangkhamhan, Jatsada Singthongchai","doi":"10.1016/j.iswa.2025.200511","DOIUrl":"10.1016/j.iswa.2025.200511","url":null,"abstract":"<div><div>This paper introduces a novel approach to global numerical optimization through the development of an Adaptive Chaotic League Championship Algorithm (AC-LCA). Our methodology enhances the conventional League Championship Algorithm (LCA) by integrating an adaptive chaotic local search mechanism. This integration aims to improve the exploration and exploitation capabilities of the LCA, enabling it to effectively navigate complex search spaces and avoid premature convergence. Abundant experiments have been extensively executed on the well-known CEC2017 benchmark problem sets to validate the performance of AC-LCA. The results demonstrate significant improvements in convergence speed and solution accuracy over traditional LCA and several other state-of-the-art optimization algorithms. Notably, the adaptive chaotic component plays a critical role in fine-tuning the search process, contributing to the robustness and efficiency of the algorithm. The paper also investigates the application of AC-LCA to a set of five famous real-life engineering problems, showcasing its practicality and adaptability in diverse optimization scenarios. These applications further underline the algorithm's potential to address a wide range of complex optimization tasks, making it a valuable tool for researchers and practitioners in the field. Overall, the AC-LCA emerges as a promising new approach in global numerical optimization, offering a balance of innovative methodology and practical applicability.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"26 ","pages":"Article 200511"},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FocusAugMix: A data augmentation method for enhancing Acute Lymphoblastic Leukemia classification FocusAugMix：一种增强急性淋巴细胞白血病分类的数据增强方法

Intelligent Systems with Applications Pub Date : 2025-03-28 DOI: 10.1016/j.iswa.2025.200512

Tanzilal Mustaqim , Chastine Fatichah , Nanik Suciati , Takashi Obi , Joong-Sun Lee

{"title":"FocusAugMix: A data augmentation method for enhancing Acute Lymphoblastic Leukemia classification","authors":"Tanzilal Mustaqim , Chastine Fatichah , Nanik Suciati , Takashi Obi , Joong-Sun Lee","doi":"10.1016/j.iswa.2025.200512","DOIUrl":"10.1016/j.iswa.2025.200512","url":null,"abstract":"<div><div>The detection of various subtypes of Acute Lymphoblastic Leukemia (ALL) is crucial for precise medical identification, even though it is often hindered by the diverse appearance of leukemia cells and limited medical resources. Challenges arise from the subjectivity of evaluations and constraints in datasets, impacting the accuracy of classification. Existing methods face difficulties in achieving precise localization and building robust classification models due to the complexities in morphology and variations in subtypes, leading to challenges in accurate classification. This research proposes the FocusAugMix, a novel data augmentation method based on superpixels, which integrates Gradient-weighted Class Activation Mapping (Grad-CAM), Multi-Head Attention, and SaliencyMix to improve classification performance, especially in situations with limited datasets. The dynamic selection of superpixel contour images for each images allows this method to achieve a peak accuracy of 99.07 %, surpassing the previous method. Integrating Multi-Head Attention and Grad-CAM improves the accuracy and effectiveness of class representation in data augmentation methods for medical diagnosis.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"26 ","pages":"Article 200512"},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explainable AI based LightGBM prediction model to predict default borrower in social lending platform 基于可解释AI的LightGBM预测模型预测社交借贷平台的违约借款人

Intelligent Systems with Applications Pub Date : 2025-03-27 DOI: 10.1016/j.iswa.2025.200514

Li-Hua Li , Alok Kumar Sharma , Sheng-Tzong Cheng

引用次数: 0

Deep learning-based CAD diagnosis using CNNs 基于cnn的深度学习CAD诊断

Intelligent Systems with Applications Pub Date : 2025-03-25 DOI: 10.1016/j.iswa.2025.200507

Mohsen Amir Afzali, Hossein Ghaffarian

{"title":"Deep learning-based CAD diagnosis using CNNs","authors":"Mohsen Amir Afzali, Hossein Ghaffarian","doi":"10.1016/j.iswa.2025.200507","DOIUrl":"10.1016/j.iswa.2025.200507","url":null,"abstract":"<div><div>Coronary Artery Disease (CAD) remains a significant global health concern, necessitating accurate diagnostic methods. In this study, we propose a deep learning solution for CAD diagnosis, driven by the limitations of traditional Machine Learning (ML) techniques in effectively handling numerical data. To address this, we focus exclusively on numerical features and employ essential preprocessing steps, including converting nominal features to numerical representations, normalizing numeric values, and balancing the dataset. Subsequently, we evaluate three deep learning classifiers—Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM)—to achieve improved diagnostic accuracy. Our evaluation of the proposed methods using real data demonstrates the superiority of deep learning techniques compared to other common classifiers, such as Random Forests, Bagging, Decision Trees, and Support Vector Machines (SVM). CNNs excel in feature extraction, capturing intricate patterns associated with CAD. Although ANNs and LSTMs are valuable, they do not match the discriminative power of CNNs in this context. In summary, our study underscores the pivotal role of CNNs in CAD diagnosis, achieving a highest accuracy of 98.64 %, representing a notable improvement compared to the best results reported in previous studies. This research not only advances the scientific understanding of CAD diagnostics but also has the potential to significantly enhance clinical practice by providing more accurate and timely diagnoses, ultimately improving patient outcomes and reducing healthcare costs.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"26 ","pages":"Article 200507"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated framework of fragment-based method and generative model for lead drug molecules discovery 基于片段的先导药物分子发现方法与生成模型集成框架

Intelligent Systems with Applications Pub Date : 2025-03-21 DOI: 10.1016/j.iswa.2025.200508

Uche A.K. Chude-Okonkwo, Odifentse Lehasa

{"title":"Integrated framework of fragment-based method and generative model for lead drug molecules discovery","authors":"Uche A.K. Chude-Okonkwo, Odifentse Lehasa","doi":"10.1016/j.iswa.2025.200508","DOIUrl":"10.1016/j.iswa.2025.200508","url":null,"abstract":"<div><div>Generative models have proven valuable in generating novel lead molecules with drug-like properties. However, beyond generating drug-like molecules, the generative model should also be able to create drug molecules with structural properties and pharmacophores to modulate a specific disease. The molecular generation process should also address the multi-objective optimization challenge of producing molecules with the desired efficacy and minimal side effects. This may entail the generation of a diverse pool of molecules with the desired structural properties and pharmacophore, which would offer diverse options and paths to developing potential new drug candidates by prioritizing molecules that balance the desired properties that can cater to the needs of different individuals. Achieving this requires a generative model learning a large dataset of molecular instances with the desired chemical/structural properties. However, large sets of drug molecules are not readily available for many diseases as there are few known drug molecular instances for treating any disease. To address this challenge, this paper presents an <em>in silico</em> molecular generative framework aided by fragment-based molecules’ synthesis for generating a pool of lead molecular instances possessing structural properties and pharmacophores to treat a disease of interest. The operation of the framework is explored using Hypertension as the disease of interest and beta-blocker as the reference hypertension drug to be generated. We generated over 123 beta-blocker-like molecules and further virtual-screened them for drug-likeness, docking probability, scaffold diversity, electrostatic complementarity, and synthesis accessibility to arrive at the final lead beta-blocker-like molecules.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"26 ","pages":"Article 200508"},"PeriodicalIF":0.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0