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2nd International Workshop on Value Engineering in Artificial Intelligence, VALE 2024

作者： De Giorgis, stefano Lazzari, Nicolas Institute of Cognitive Sciences and Technologies National Research Council Rome Italy Department of Computer Science University of Pisa Pisa Italy Department of Modern Languages Literature and Cultures University of Bologna Bologna Italy

ISBN: (纸本)9783031854620

This work explores the integration of ontology-based reasoning and Machine learning techniques for explainable classification in the domain of moral and cultural values. By relying on an ontological formalization of moral values as in the Basic Human Values theory, which is based on the Description and situation Ontology Design Pattern, the sandra neuro-symbolic reasoner is used to infer values (formalized as descriptions) that are satisfied by a certain sentence. sentences, alongside their structured representation, are automatically generated using an open-source Large Language Model. The inferred descriptions are used to automatically detect the value associated with a sentence. We show that only relying on the reasoner’s inference results in explainable classification comparable to other more complex approaches. Moreover, we test how the LLMs tacit knowledge can be exploited to obtain novel formalizations of the domain. We show that combining the reasoner’s inferences with simple distributional semantics methods largely outperforms all the baselines, including complex models based on neural network architectures. © The Author(s), under exclusive license to springer Nature switzerland AG 2025.

关键词： Knowledge graph

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Recent advancements in driver’s attention prediction

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Multimedia Tools and Applications 2025年第12期84卷 10969-11000页

作者： Moradi, Morteza Palazzo, simone Rundo, Francesco spampinato, Concetto Department of Electrical Electronics and Computer Engineering University of Catania Catania95124 Italy STMicroelectronics ADG Research and Development Power and Discretes Catania95121 Italy

Ensuring the precise anticipation of a driver’s attention is crucial for upholding safety in diverse human-centric transportation scenarios. This capability proves invaluable for discerning and evaluating accident risks in driver assistance systems, as well as in autonomous driving scenarios. Beyond traditional approaches that consider contextual variables and non-visual cues, such as the driver’s mental state and brain activity patterns, recent groundbreaking advancements in deep learning-based computer vision have propelled the momentum of visual attention prediction. The fundamental concept behind these techniques is to replicate the human visual system’s ability to identify critical objects or regions capturing a driver’s attention. Notably, the integration of salient object detection and attention mechanisms has gained popularity across various application domains. This paper provides a comprehensive overview of the current state of the art in this field. The primary focus of the study is to explore the architectures and workflows of deep learning-based models dedicated to the attention prediction process. To achieve this, we examine recent works in the field, specifically selecting those that have leveraged (simulated) attention mechanisms to estimate the driver’s attention area. Our analysis delves into the contributions of each model, elucidating their architectural design and underlying organization. Additionally, we scrutinize the effectiveness of each model by explaining the reported experimental results. © The Author(s), under exclusive licence to springer science+Business Media, LLC, part of springer Nature 2024.

关键词： Forecasting

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Improving adherence to medication in an intelligent environment using reinforcement learning

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Journal of Reliable Intelligent Environments 2025年第1期11卷 1-10页

作者： Ismail, Ahsan Naeem, Muddasar Khalid, Umamah Bint Abbas, Musarat Department of Electronics Quaid-i-Azam University Islamabad Pakistan Research Center on ICT Technologies for Healthcare and Wellbeing Università Telematica Giustino Fortunato Benevento Italy

Correct and timely medication plays an important role in the treatment and recovery of a patient. Poor health outcomes are associated with the nonadherence to medication which also increases health care costs for patients and society. The medication process can be difficult for many patients such as dementia patients who face the challenge of forgetfulness in medication intake. Therefore, an intelligent and efficient patient engagement environment ensures enduring health and positive clinical outcomes. such an intelligent system can be realized employing reinforcement learning (RL) which brought significant impacts in many areas, and has useful applications in healthcare as well. This work introduces an RL-based intelligent environment that can engage the patient to improve adherence, through proper engagement alerts based on user adherence reports, and suggest him/her to improve the adherence by sending the engagement message to the user. The intelligent RL agent decides the optimal decision to send the engagement message to the user based on a patient’s behavior. The proposed system could be useful to improve adherence to medication and assist the patient in accurately following his/her medication schedule. However, the proposed system would require integration with actual patient data and electronic health records before real-world deployment. © The Author(s), under exclusive licence to springer Nature switzerland AG 2024.

关键词： Intelligent environment Medical treatment Medication adherence optimization Medication error Reinforcement learning

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Application of XGBoost algorithm and grid search hyperparameter tuning to study health effects among individuals in the industrial area

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Multimedia Tools and Applications 2025年 1-44页

作者： Johnson, susymary Perumalsamy, Deepalakshmi Department of Computer Applications Kalasalingam Academy of Research and Education Virudhunagar District Tamil Nadu Krishnankoil626126 India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar District Tamil Nadu Krishnankoil626126 India

This research focuses on utilizing data-driven machine learning techniques to study the impact of air pollution on the health of residents in industrial regions. The objective is to develop an efficient machine learning model that can accurately identify communities with a higher prevalence of health issues and diseases. A population-based survey was conducted in four geographical areas, collecting data on sociodemographic characteristics, health effects, perceived health status, and annoyance due to industries. The proposed model, XGBoost_BLR_GridCV, achieved high accuracy (ROC_AUC: 0.9215), precision of 92%, recall of 91% and f1-score of 91% in predicting the proximity of residents with chronic diseases. The results highlight the significant association between living in high proximity to industrial regions and increased rates of asthma, respiratory illness, and heart-related health problems. Comparative analysis with other tree-based models further demonstrates the effectiveness of the proposed model. © The Author(s), under exclusive licence to springer science+Business Media, LLC, part of springer Nature 2025.

关键词： Forestry

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Personalized federated few-shot node classification

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science China(Information sciences) 2025年第1期68卷 204-218页

作者： Yunfeng ZHAO Xintong HE Guoxian YU Jun WANG Yongqing ZHENG Carlotta DOMENICONI School of Software Shandong University Joint SDU-NTU Centre for Artificial Intelligence Research Shandong University Department of Mathematics National University of Singapore Department of Computer Science George Mason University

Personalized federated learning(PFL) aims to train customized models for individual clients in a decentralized setting, with the account of non-independent and identically distributed data across clients. However, most PFL methods adopt uniform classification layers for diverse clients and give rise to error-prone predictions, due to the task heterogeneity notably prominent in decentralized graph data scenarios. Although some PFL solutions setup client-specific classification layers for each client and optimize them only locally, they are corrupted with limited local training data. We propose an innovative solution called federated parameter decoupling and node augmentation(Fed PANo) to address these problems and to achieve personalized federated few-shot node classification, which is a prevalent and challenging but unexplored topic. specifically, Fed PANo first separates the local model into the GNN and classifier to handle unique client-specific task variations. The GNN is trained through federated learning to capture shared knowledge of graph nodes across clients, while the classifier is custom-designed and trained individually for each client. Additionally, a generic classifier shared among clients is adopted to encourage the GNN's grasp of shared information. Then Fed PANo further proposes the node generator along with its local and collaborative training strategies to deal with the node scarcity of clients. Extensive experimental results on benchmark datasets confirm that Fed PANo outperforms eight competitive baselines across different settings.

关键词： personalized federated learning few-shot learning node classification task heterogeneity parameter decoupling node augmentation

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An introduction to machine learning tools for the analysis of microplastics in complex matrices

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ENVIRONMENTAL sCIENCE-PROCEssEs & IMPACTs 2025年第1期27卷 10-23页

作者： Coleman, Brian R. Natl Res Council Canada Metrol Res Ctr Ottawa ON K1N 5A2 Canada

As microplastic (MP) particles continue to spread globally, their pervasive presence is increasingly problematic. Analyzing MPs in matrices as varied as soil, river water, and biosolid fertilizers is critical, as these matrices directly impact the food sources of plants, animals, and humans. Current analytical methods for quantifying and identifying MPs are limited due to labor-intensive extraction processes and the time and effort required for counting and analysis. Recently, Machine learning (ML) has been introduced to the analysis of MPs in complex matrices, significantly reducing the need for extensive extraction and increasing analysis speeds. This work aims to illuminate various ML techniques for new researchers entering this field. It highlights numerous examples in the application of these models, with a particular focus on spectroscopic techniques such as infrared and Raman spectroscopy;tools which are used to quantify and identify MPs in complex matrices. By demonstrating the effectiveness of these computer-based tools alongside the hands-on techniques currently used in the field, we are confident that these ML methodologies will soon become integral to all aspects of microplastic analysis in the environmental sciences.

关键词： Fertilizers

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Enhancing security in Distributed Drone-based Litchi Fruit Recognition and Localization systems

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computers, Materials & Continua 2025年第2期82卷 1985-1999页

作者： Liang Mao Yue Li Linlin Wang Jie Li Jiajun Tan Yang Meng Cheng Xiong Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence Application Technology Research Institute Shenzhen Polytechnic UniversityShenzhen518055China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China

This paper introduces an advanced and efficient method for distributed drone-based fruit recognition and localization, tailored to satisfy the precision and security requirements of autonomous agricultural operations. Our method incorporates depth information to ensure precise localization and utilizes a streamlined detection network centered on the RepVGG module. This module replaces the traditional C2f module, enhancing detection performance while maintaining speed. To bolster the detection of small, distant fruits in complex settings, we integrate selective Kernel Attention (sKAttention) and a specialized small-target detection layer. This adaptation allows the system to manage difficult conditions, such as variable lighting and obstructive foliage. To reinforce security, the tasks of recognition and localization are distributed among multiple drones, enhancing resilience against tampering and data manipulation. This distribution also optimizes resource allocation through collaborative processing. The model remains lightweight and is optimized for rapid and accurate detection, which is essential for real-time applications. Our proposed system, validated with a D435 depth camera, achieves a mean Average Precision (mAP) of 0.943 and a frame rate of 169 FPs, which represents a significant improvement over the baseline by 0.039 percentage points and 25 FPs, respectively. Additionally, the average localization error is reduced to 0.82 cm, highlighting the model’s high precision. These enhancements render our system highly effective for secure, autonomous fruit-picking operations, effectively addressing significant performance and cybersecurity challenges in agriculture. This approach establishes a foundation for reliable, efficient, and secure distributed fruit-picking applications, facilitating the advancement of autonomous systems in contemporary agricultural practices.

关键词： Objective detection deep learning machine learning

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OpenClinicalAI: An open and dynamic model for Alzheimer's Disease diagnosis

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EXPERT sYsTEMs WITH APPLICATIONs 2025年 261卷

作者： Huang, Yunyou Liang, Xiaoshuang Xie, Jiyue Lu, Xiangjiang Miao, Xiuxia Liu, Wenjing Zhang, Fan Kang, Guoxin Ma, Li Tang, suqin Zhan, Jianfeng Guangxi Normal Univ Key Lab Educ Blockchain & Intelligent Technol Minist Educ Guilin Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Guilin Med Univ Guilin Peoples R China Int Open Benchmark Council Delaware CA USA

Although Alzheimer's disease (AD) cannot be reversed or cured, timely diagnosis can significantly reduce the burden of treatment and care. Current research on AD diagnosis models usually regards the diagnosis task as a typical classification task with two primary assumptions: (1) All target categories are known a priori;(2) The diagnostic strategy for each patient is consistent, that is, the number and type of model input data for each patient are the same. However, real-world clinical settings are open, with complexity and uncertainty in terms of both subjects and the resources of the medical institutions. This means that diagnostic models may encounter unseen disease categories and need to dynamically develop diagnostic strategies based on the subject's specific circumstances and available medical resources. Thus, the AD diagnosis task is tangled and coupled with the diagnosis strategy formulation. To promote the application of diagnostic systems in real- world clinical settings, we propose OpenClinicalAI for direct AD diagnosis in complex and uncertain clinical settings. This is the first end-to-end model to dynamically formulate diagnostic strategies and provide diagnostic results based on the subject's conditions and available medical resources. OpenClinicalAI combines reciprocally coupled deep multi-action reinforcement learning (DMARL) for diagnostic strategy formulation and multicenter meta-learning (MCML) for open-set recognition. The experimental results show that OpenClinicalAI achieves better performance and fewer clinical examinations than the state-of-the-art model. Our method provides an opportunity to embed the AD diagnostic system into the current healthcare system to cooperate with clinicians to improve current healthcare.

关键词： Real-world clinical setting Alzheimer's disease Diagnose AI Deep learning

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Ensemble Knowledge Distillation for Federated semi-supervised Image Classification

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Tsinghua science and Technology 2025年第1期30卷 112-123页

作者： Ertong shang Hui Liu Jingyang Zhang Runqi Zhao Junzhao Du School of Computer Science and Technology Xidian UniversityXi’an 710126China Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of EducationXi’an 710126China Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province Xi’an 710126China

Federated learning is an emerging privacy-preserving distributed learning paradigm,in which many clients collaboratively train a shared global model under the orchestration of a remote *** current works on federated learning have focused on fully supervised learning settings,assuming that all the data are annotated with ground-truth ***,this work considers a more realistic and challenging setting,Federated semi-supervised learning(FssL),where clients have a large amount of unlabeled data and only the server hosts a small number of labeled *** to reasonably utilize the server-side labeled data and the client-side unlabeled data is the core challenge in this *** this paper,we propose a new FssL algorithm for image classification based on consistency regularization and ensemble knowledge distillation,called *** algorithm uses the global model as the teacher in consistency regularization methods to enhance both the accuracy and stability of client-side unsupervised learning on unlabeled ***,we introduce an additional ensemble knowledge distillation loss to mitigate model overfitting during server-side retraining on labeled *** experiments on several image classification datasets show that our EKDFssL outperforms current baseline methods.

关键词： federated learning semi-supervised learning federated semi-supervised learning knowledge distillation

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Integrating machine learning and human use experience to identify personalized pharmacotherapy in Traditional Chinese Medicine:a case study on resistant hypertension

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Journal of Traditional Chinese Medicine 2025年第1期45卷 192-200页

作者： CHE Qianzi LIU Dasheng XIANG Xinghua TIAN Yaxin XIE Feibiao XU Wenyuan LIU Jian WANG Xuejie WANG Liying BAI Weiguo HAN Xuejie YANG Wei Department of Medical Statistics Institute of Basic Research in Clinical MedicineChina Academy of Chinese Medical SciencesBeijing 100700China Department of Science and Education Medical Statistics Teaching and Research OfficeInstitute of Basic Research in Clinical MedicineChina Academy of Chinese Medical SciencesBeijing 100700China Traditional Chinese Medicine Standards Research Center Medical Statistics Teaching and Research OfficeInstitute of Basic Research in Clinical MedicineChina Academy of Chinese Medical SciencesBeijing 100700China Computer Department Xiyuan Hospital of the China Academy of Chinese Medical SciencesBeijing 100091China

OBJECTIVE:To enhance the understanding of identifying personalized pharmacotherapy options in Traditional Chinese Medicine(TCM),and further support the registration of new TCM ***:Generalized Boosted Models and XGBoost were employed to construct a classification model to identify the bad prognosis factors in resistant hypertension(RH)***,we used association analysis to explore the rules of"symptomsyndrome"and"symptom-herb"for the major influencing factors,in order to summarize prescription pattern and applicable patients of ***:Patients with major adverse cardiac events mostly have complex symptoms of phlegm,stasis,deficiency and fire intermingled with each other,and finally summarized the human experience of using Chinese herbal medicine to precisely intervene in some symptoms of RH patients on the basis of conventional Western medical ***:Machine learning algorithms can make full use of human use experience and evidence to save clinical trial resources and accelerate the development of TCM varieties.

关键词： machine learning human clinical experience personalized pharmacotherapy new drugs registration

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