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 m...
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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 ri...
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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 pati...
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Thisresearch 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 learni...
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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, mos...
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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 solutionssetup 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.
As microplastic (MP) particles continue to spread globally, their pervasive presence is increasingly problematic. Analyzing MPs in matrices as varied assoil, river water, and biosolid fertilizers is critical, as thes...
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As microplastic (MP) particles continue to spread globally, their pervasive presence is increasingly problematic. Analyzing MPs in matrices as varied assoil, 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 analysisspeeds. 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 techniquessuch 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.
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....
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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 autonomoussystems in contemporary agricultural practices.
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...
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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 strategiesbased on the subject'sspecific circumstances and available medical resources. Thus, the AD diagnosis task is tangled and coupled with the diagnosisstrategy 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 resultsbased 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 resultsshow 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.
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 l...
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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 learningsettings,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 datasetsshow that our EKDFssL outperforms current baseline methods.
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 XGBoos...
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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.
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