Intelligent Systems with Applications最新文献_第9页

Visual question answering for medical diagnosis 用于医学诊断的视觉问答

Intelligent Systems with Applications Pub Date : 2025-09-01 Epub Date: 2025-07-09 DOI: 10.1016/j.iswa.2025.200545

Nawel Ben Chaabane, Mohamed Bal-Ghaoui

{"title":"Visual question answering for medical diagnosis","authors":"Nawel Ben Chaabane, 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":"2025-09-01","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}

引用次数: 0

Formal concept views for explainable boosting: A lattice-theoretic framework for Extreme Gradient Boosting and Gradient Boosting Models 可解释提升的形式概念观点：极端梯度提升和梯度提升模型的格理论框架

IF 4.3

Intelligent Systems with Applications Pub Date : 2025-09-01 Epub Date: 2025-08-26 DOI: 10.1016/j.iswa.2025.200569

Sherif Eneye Shuaib , Pakwan Riyapan , Jirapond Muangprathub

{"title":"Formal concept views for explainable boosting: A lattice-theoretic framework for Extreme Gradient Boosting and Gradient Boosting Models","authors":"Sherif Eneye Shuaib , Pakwan Riyapan , Jirapond Muangprathub","doi":"10.1016/j.iswa.2025.200569","DOIUrl":"10.1016/j.iswa.2025.200569","url":null,"abstract":"<div><div>Tree-based ensemble methods, such as Extreme Gradient Boosting (XGBoost) and Gradient Boosting models (GBM), are widely used for supervised learning due to their strong predictive capabilities. However, their complex architectures often hinder interpretability. This paper extends a lattice-theoretic framework originally developed for Random Forests to boosting algorithms, enabling a structured analysis of their internal logic via formal concept analysis (FCA).</div><div>We formally adapt four conceptual views: leaf, tree, tree predicate, and interordinal predicate to account for the sequential learning and optimization processes unique to boosting. Using the binary-class version of the car evaluation dataset from the OpenML CC18 benchmark suite, we conduct a systematic parameter study to examine how hyperparameters, such as tree depth and the number of trees, affect both model performance and conceptual complexity. Random Forest results from prior literature are used as a comparative baseline.</div><div>The results show that XGBoost yields the highest test accuracy, while GBM demonstrates greater stability in generalization error. Conceptually, boosting methods generate more compact and interpretable leaf views but preserve rich structural information in higher-level views. In contrast, Random Forests tend to produce denser and more redundant concept lattices. These trade-offs highlight how boosting methods, when interpreted through FCA, can strike a balance between performance and transparency.</div><div>Overall, this work contributes to explainable AI by demonstrating how lattice-based conceptual views can be systematically extended to complex boosting models, offering interpretable insights without sacrificing predictive power.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200569"},"PeriodicalIF":4.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907137","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

Machine learning alloying design of biodegradable zinc alloy for bone implants using XGBoost and Bayesian optimization 基于XGBoost和贝叶斯优化的生物可降解锌合金骨植入物的机器学习合金化设计

Intelligent Systems with Applications Pub Date : 2025-09-01 Epub Date: 2025-06-21 DOI: 10.1016/j.iswa.2025.200549

Mohanad Deif , Hani Attar , Mohammad Aljaidi , Ayoub Alsarhan , Dimah Al-Fraihat , Ahmed Solyman

{"title":"Machine learning alloying design of biodegradable zinc alloy for bone implants using XGBoost and Bayesian optimization","authors":"Mohanad Deif , Hani Attar , Mohammad Aljaidi , Ayoub Alsarhan , Dimah Al-Fraihat , Ahmed Solyman","doi":"10.1016/j.iswa.2025.200549","DOIUrl":"10.1016/j.iswa.2025.200549","url":null,"abstract":"<div><div>Developing implants using biodegradable materials eliminates the need for secondary surgery, improves both mechanical and biological properties, and enhances biocompatibility. This study proposes a machine learning approach based on Bayesian optimization (BO) and an eXtreme Gradient Boosting (XGBoost) algorithm to design a biodegradable Zinc (Zn) alloy and forecast percentage of elements in the Zn alloy for bone implants. The dataset employed in this study comprised 1182 samples of Zn alloys obtained from supplementary articles from Google Scholar and the mat web database. For forecasting the mechanical parameters Yield Stress (YS), Ductility, and Ultimate Tensile Strength (UTS), the suggested method got maximum R<sup>2</sup> values of 0.85, 0.87, and 0.81 demonstrating its exceptional predictive capacity. In addition, the model created a Zn biodegradable alloy with UTS of 363.55 Mpa, YS of 318.93 Mpa, and Ductility of 14 %, which are regarded as good mechanical characteristics meet bone implant criteria. The BO-XGBoost model can expedite the production of the proper alloy for several medical applications, saving time, money, and effort.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200549"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490496","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

A real-time semantic segmentation network leveraging spatial and contextual features for enhanced scene understanding 利用空间和上下文特征增强场景理解的实时语义分割网络

Intelligent Systems with Applications Pub Date : 2025-09-01 Epub Date: 2025-06-24 DOI: 10.1016/j.iswa.2025.200542

Haifeng Sima , Meng Gao , Lanlan Liu

{"title":"A real-time semantic segmentation network leveraging spatial and contextual features for enhanced scene understanding","authors":"Haifeng Sima , Meng Gao , Lanlan Liu","doi":"10.1016/j.iswa.2025.200542","DOIUrl":"10.1016/j.iswa.2025.200542","url":null,"abstract":"<div><div>Real-time semantic segmentation of images requires both rich contextual and accurate spatial information. However, Multiple downsampling in deep convolutional neural networks often lead to loss of such information, resulting in reduced segmentation accuracy. To address the above problems, we propose SPCONet, a lightweight real-time semantic segmentation network that integrates spatial and contextual features. The network incorporates three key modules: (1) a Spatial Feature Aggregation Module (SFAM) that captures fine spatial details from shallow layers using spatially separable convolutions with multiple kernel sizes; (2) a Contextual Information Retrieval Module (CIRM) that extracts semantic context from deeper layers using dynamic convolution; (3) an Attention Fusion Module (AFM) that combines spatial and contextual features via local and global attention mechanisms. Quantitative experiments show that SPCONet achieves 77.5% and 75.3% mIoU at 74 FPS and 82 FPS on the Cityscapes and CamVid datasets, respectively. These results suggest that SPCONet provides an effective balance between segmentation accuracy and real-time inference capability.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200542"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522421","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

Emotion recognition and forecasting from wearable data via cluster-guided attention with cross-species pretraining 基于聚类引导注意力和跨物种预训练的可穿戴数据情感识别和预测

IF 4.3

Intelligent Systems with Applications Pub Date : 2025-09-01 Epub Date: 2025-07-30 DOI: 10.1016/j.iswa.2025.200560

Wonjik Kim , Gaku Kutsuzawa , Michiyo Maruyama

{"title":"Emotion recognition and forecasting from wearable data via cluster-guided attention with cross-species pretraining","authors":"Wonjik Kim , Gaku Kutsuzawa , Michiyo Maruyama","doi":"10.1016/j.iswa.2025.200560","DOIUrl":"10.1016/j.iswa.2025.200560","url":null,"abstract":"<div><div>Wearable devices enable the continuous acquisition of physiological signals, offering the potential for real-time emotion monitoring in daily life. However, emotion recognition remains challenging due to individual differences, label ambiguity, and limited annotated data. This study proposes a lightweight, cluster-guided attention model for binary emotion recognition (positive vs. negative) and forecasting (up to two hours ahead) from wearable signals such as heart rate and step count. To improve generalization, we leverage unsupervised clustering in the latent space and integrate cross-species pretraining using structured behavioral and physiological data from mice. Our framework reduces annotation burden through an emoji-based self-report interface and performs both within- and across-subject validation. Experimental results on human wearable data demonstrate that our method outperforms classical and lightweight deep learning baselines in both accuracy and macro-F1 score, achieving approximately 74.4% accuracy (macro-F1: 71.5%) for current emotion recognition, 72.9% accuracy (macro-F1: 70.7%) for 1-h forecasting, and 65.5% accuracy (macro-F1: 63.0%) for 2-h forecasting. Moreover, mouse-based pretraining yields consistent performance gains, especially at longer-horizon prediction tasks. These findings suggest that biologically informed attention mechanisms and cross-domain knowledge transfer can significantly enhance emotion modeling from low-resource wearable data.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200560"},"PeriodicalIF":4.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750473","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

Certified Accuracy and Robustness: How different architectures stand up to adversarial attacks 认证的准确性和健壮性：不同的架构如何经受对抗性攻击

Intelligent Systems with Applications Pub Date : 2025-09-01 Epub Date: 2025-07-07 DOI: 10.1016/j.iswa.2025.200555

Azryl Elmy Sarih , Nagender Aneja , Ong Wee Hong

{"title":"Certified Accuracy and Robustness: How different architectures stand up to adversarial attacks","authors":"Azryl Elmy Sarih , Nagender Aneja , 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":"2025-09-01","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}

引用次数: 0

Situation-aware Cyber–Physical–Social System for Cultural Heritage 情境感知文化遗产的网络-物理-社会系统

Intelligent Systems with Applications Pub Date : 2025-09-01 Epub Date: 2025-06-13 DOI: 10.1016/j.iswa.2025.200544

Francesco Colace , Giuseppe D’Aniello , Massimo De Santo , Rosario Gaeta , Gabriel Zuchtriegel

{"title":"Situation-aware Cyber–Physical–Social System for Cultural Heritage","authors":"Francesco Colace , Giuseppe D’Aniello , Massimo De Santo , Rosario Gaeta , Gabriel Zuchtriegel","doi":"10.1016/j.iswa.2025.200544","DOIUrl":"10.1016/j.iswa.2025.200544","url":null,"abstract":"<div><div>The safeguard of cultural heritage (CH) is one of the most of interest issues for all the countries, like Italy, known for their thousand-year history. Cultural properties have to be maintained regularly and effectively so that the condition of such properties remains good at all times. Human operators have always been the ones in charge of monitoring and maintaining these properties, with domain experts capable of understanding when and how the maintenance has to be done. In our paper, we define a CH asset as a Cyber–Physical–Social System. We designed and proposed a prototype of a Situation-aware Cyber–Physical–Social System (CPSS) for Cultural Heritage, capable of supporting the human operator situation awareness. The CPSS is a Machine Learning (ML) and expert based system equipped with modules for capturing information, which are then processed with ML techniques to identify asset maintenance issues, understanding how they will evolve, and what are the priorities in the maintenance activity to be performed. We propose three case studies relating respectively to: four structures in the archaeological site of Pompeii, three in the archaeological site of Paestum, and three related to the area the archaeological site of the Colosseum, in Rome, for the safeguarding of which the system uses vulnerability indexes, calculated using prior knowledge related to these structures, maintenance issues detected from aerial photos using a YoloV7 detection model, and context space theory with weather and anthropogenic flow data. We showed how it was possible to identify critical and dangerous situations for these zones, with vulnerability indexes capable of mitigating damaged and dangerous areas to be left in that state with the advent of adverse weather phenomena, which indeed from the photos appeared damaged and flooded.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200544"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298090","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

Paranasal sinus analysis based on deep learning and machine learning techniques: A comprehensive survey 基于深度学习和机器学习技术的鼻窦分析：综合调查

Intelligent Systems with Applications Pub Date : 2025-09-01 Epub Date: 2025-07-18 DOI: 10.1016/j.iswa.2025.200559

Ali Alsalama, Saad Harous, Ashraf Elnagar

{"title":"Paranasal sinus analysis based on deep learning and machine learning techniques: A comprehensive survey","authors":"Ali Alsalama, Saad Harous, 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":"2025-09-01","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}

引用次数: 0

Can large language models autonomously generate unique and profound insights in fundamental analysis? 大型语言模型能否在基础分析中自主地产生独特而深刻的见解？

IF 4.3

Intelligent Systems with Applications Pub Date : 2025-09-01 Epub Date: 2025-08-12 DOI: 10.1016/j.iswa.2025.200566

Tao Xu , Zhe Piao , Tadashi Mukai , Yuri Murayama , Kiyoshi Izumi

{"title":"Can large language models autonomously generate unique and profound insights in fundamental analysis?","authors":"Tao Xu , Zhe Piao , Tadashi Mukai , Yuri Murayama , Kiyoshi Izumi","doi":"10.1016/j.iswa.2025.200566","DOIUrl":"10.1016/j.iswa.2025.200566","url":null,"abstract":"<div><div>Fundamental analysis plays a critical role in equity investing, but its complexity has long limited the involvement of artificial intelligence (AI). Recent advances in large language models (LLMs), however, have opened new possibilities for AI to handle fundamental analysis. Despite this potential, leveraging LLMs to generate practically useful outputs remains a non-trivial challenge, and existing research is still in its early stages. This paper aims to enhance the performance of LLMs in fundamental analysis in a novel way, drawing inspiration from the practices of human analysts. We first propose a novel Autonomous Fundamental Analysis System (AutoFAS), which enables LLM agents to perform analyses on various topics of target companies. Next, we allow LLM agents to autonomously conduct research on specified companies with AutoFAS by exploring various topics they deem important, mimicking the experience accumulation of human analysts. Then, when presented with new research topics, the agents generate reports by referring to their accumulated analyses. Experiments show that, with AutoFAS, LLM agents can autonomously and logically explore various facets of target companies. The evaluation of their analysis on new research topics demonstrates that by drawing on accumulated analyses, they can naturally produce more unique and profound insights. This resembles the human process of generating novel ideas. Our work highlights a promising direction for applying LLMs in complex fundamental analysis, bridging the gap between human expertise and LLMs’ analysis.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200566"},"PeriodicalIF":4.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861088","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

Multi-modal expert system for automated durian ripeness classification using deep learning 基于深度学习的榴莲成熟度自动分类多模式专家系统

IF 4.3

Intelligent Systems with Applications Pub Date : 2025-09-01 Epub Date: 2025-07-31 DOI: 10.1016/j.iswa.2025.200563

Santi Sukkasem, Watchareewan Jitsakul, Phayung Meesad

{"title":"Multi-modal expert system for automated durian ripeness classification using deep learning","authors":"Santi Sukkasem, Watchareewan Jitsakul, Phayung Meesad","doi":"10.1016/j.iswa.2025.200563","DOIUrl":"10.1016/j.iswa.2025.200563","url":null,"abstract":"<div><div>Accurate classification of durian ripeness is essential for quality control and minimizing post-harvest losses. Manual inspection remains subjective and inconsistent, prompting the need for automated methods. We present a multi-modal approach that integrates Convolutional Neural Networks (CNNs) for image-based classification and Recurrent Neural Networks (RNNs) for automatic textual descriptions. Trained on 16,000 annotated images across four ripeness stages, the model achieved high classification accuracy (MobileNetV2: 95.50%) and superior captioning performance (ResNet101 + Bi-GRU: BLEU 0.9974, METEOR 0.9949, ROUGE 0.9164). While weighted summation fusion demonstrated superior performance, concatenation was ultimately chosen for its simplicity and real-world deployment feasibility. Statistical validation using one-way ANOVA (<span><math><mrow><mi>p</mi><mo><</mo><mn>0</mn><mo>.</mo><mn>05</mn></mrow></math></span>) confirmed the significance of the findings. These results highlight the potential of the proposed multi-modal approach as a practical and interpretable framework for automated durian ripeness assessment.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200563"},"PeriodicalIF":4.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144766986","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