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Supporting Elderly Care Through an AI-Driven and FHIR-Based Remote Monitoring System 21st

Supporting Elderly Care Through an AI-Driven and FHIR-Based ...

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21st international Symposium on Distributed Computing and Artificial Intelligence, DCAI 2024

作者： Martins, Rafael Pereira, Hugo Corrente, Gustavo Xavier, William Conceição, Luís Freitas, Alberto Marreiros, Goreti GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering – Polytechnic of Porto Porto Portugal LASI – Intelligent Systems Associate Laboratory Guimarães Portugal Wiseware Solutions Gafanha da Encarnação Portugal MEDCIDS – Department of Community Medicine Information and Health Decision Sciences Faculty of Medicine University of Porto Porto Portugal CINTESIS@RISE – Center for Health Technology and Services Faculty of Medicine University of Porto Porto Portugal

ISBN: (纸本)9783031809453

An increase in the elderly population has led to a growing demand for constant health care. Remote monitoring of seniors aims to provide more effective care and promote patient independence, whether they live in residential homes, or their own homes supported by a domiciliary care service. It encompasses several issues present in their lives that may benefit from being assisted and monitored remotely. There are many successful studies and products focused on one of these issues, but there are no systems, to the best of our knowledge, that serve as a broad remote monitoring solution for multiple of these topics simultaneously. This paper proposes an architecture for an intelligent health monitoring ecosystem to interact with these various aspects of the elderly’s daily life, including monitoring of vital signs, movement and medication intake, detection of falls, sleep quality analysis, access control, and patient–caregiver communication. This solution will make use of wearables and medication dispensers, and it will securely store all patient-related information into an HL7 FHIR standardized database, from where machine learning models can import data for predicting important information and generating alerts when necessary. Next steps involve the development of this proposed system and testing it with elderly volunteers living in a residential home or subscribed to its domiciliary care services. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Remote patient monitoring

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Personalized Federated learning with Feature Alignment via Knowledge Distillation 21st

Personalized Federated Learning with Feature Alignment via...

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21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Qi, Guangfei Qu, Zhihao Lyu, Shen-Huan Jia, Ninghui Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9789819601189

Personalized Federated learning (PFL) has gained significant attention for its ability to handle heterogeneous data effectively. Parameter decoupling is a typical approach to PFL. It decouples the model into a feature extractor and a classifier head, where the feature extractor is trained collaboratively to learn a common representation and the classifier head is personalized for local data. Since local training only learns personalized feature information and ignores global information, the generalization ability of the feature extractor is limited. To improve the performance of the local model, a feasible approach is to make the local feature extractor more generalized. However, prior work requires the transmission of additional feature data beyond the transmission of model parameters, which leads to privacy leakage and higher communication overhead. To address these shortcomings, we propose a PFL algorithm with feature alignment via knowledge distillation, named PFAKD. PFAKD enhances the training of local feature extractors by explicitly aligning each sample’s local features with global features, providing more detailed guidance. Meanwhile, it avoids additional communication overhead and the risk of privacy leakage. We conduct extensive experiments in heterogeneous data scenarios. PFAKD outperforms other state-of-the-art methods by up to 4.35% in terms of model accuracy. Our code is available at https://***/fei0829/PFAKD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Role of Machine learning in Climate Change Prediction and Mitigation

Role of Machine Learning in Climate Change Prediction and Mi...

引用

Machine learning and Autonomous Systems (ICMLAS), international conference on

作者： Nayan Jikar Yash Tale Abhay Tale Aditya Barhate Prateek Verma Aman Jikar Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Climate change is one of the biggest challenges of the 21st century, with profound environmental, economic, and social impacts. Machine learning (ML) has emerged as a transformative tool to address climate challenges through data-driven solutions, offering predictive capabilities for extreme weather events and enhancing the efficiency of clean-energy systems. This technology benefits the optimization of renewable energy generation, intelligent grid management, and resource utilization towards reducing greenhouse gas emissions. Moreover, ML promotes climate justice through targeted region-specific intervention and inclusive solution-making for the most vulnerable. Several technical and ethical challenges associated with data quality, scalability, and model generalization must still be overcome for ML to realize its potential. This paper reviews the role of ML in climate change prediction and mitigation discusses ongoing real-world applications and outlines the future research agenda necessary to address existing challenges. Integrating ML with emerging technologies like the Internet of Things (IoT) and explainable AI, alongside global collaboration and a focus on climate justice, will be essential in advancing effective climate action.

关键词： Climate change Renewable energy sources Ethics Prevention and mitigation Scalability data integrity Collaboration data models Internet of Things Meteorology

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Mental Health Prediction using Machine learning

Mental Health Prediction using Machine Learning

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international conference on ent Computing and Control Systems (ICICCS)

作者： Govindaraj M V Asha H Jayanth Harshitha D G Harshitha V Patil N. Mithili Devi Master of Computer Applications New Horizon College of Engineering Bengaluru India Master of Computer Application New Horizon College Of Engineering Bengaluru India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Major problems in the 21st century are mental health care problems. It can be considered as one of its important evils between men, and its source is in the largeness of awareness among the people. Using this paper, we would like to make a person realize that he is suffering from some kind of mental illness for instance, depression, anxiety, PTSD, insomnia, etc., by simply making the people aware of their symptoms using Machine learning. A sample of various ages, conditions, professions, sex, lifestyles taken from a group of respondents and collected with the help of a survey form containing questions which are often used by psychologists to understand the problem of their patient in detail has been applied machine learning algorithms on.

关键词： Surveys Technological innovation Machine learning algorithms Anxiety disorders Machine learning Mental health Predictive models Depression Socioeconomics Forecasting

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Enhancing learning Outcomes Through the Use of Conducive learning Spaces 1st

Enhancing Learning Outcomes Through the Use of Conducive Lea...

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1st international conference on Computational Technologies and Electronics, ICCTE 2023

作者： Modi, Shabina Sale, Deepali Borate, Vishal Mali, Yogesh Kisan Karmaveer Bhaurao Patil College of Engineering Satara India Dr D Y Patil College of Engineering and Innovation Talegoan Pune India G H Raisoni College of Engineering and Management Wagholi Pune India

ISBN: (纸本)9783031819346

The learning spaces or the built environment or the physical learning environment, in educational institutions impact the acquisition of 21st century skills and quality education. Proficiency in digital learning is an important competency skill required for 21st century youth. With the developing trend of e-learning, the importance or the role of learning spaces in the improvement of learning outcome is diluting. This study aims to determine the parameters for developing learning spaces to support learning and improve the learning outcomes of the learner. The research is based on the analysis of the existing literature data to find the parameters of learning spaces required for improving cognitive, affective and behavioral aspects of learning outcomes. The research findings put forward five parameters, namely wellness, stimulation, individualization, social interaction and spatial function, required for learning spaces to support learning and improve learning outcomes. The findings have been validated through a pilot survey, conducted in secondary schools in Punjab. This research will help the designers, architects and stakeholders to understand the parameters required to make learning spaces, conducive to learning. This study will further add awareness regarding the importance of learning spaces in improving the learning outcome of students. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive learning

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Empirical study to Select a Place to stay in NCR Area Using data Mining and Machine learning 3

Empirical Study to Select a Place to Stay in NCR Area Using ...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Jain, Nitin Chauhan, Surendra Singh Goel, Ajay Rastogi, Ashish Kumar Raj, Alok Agrawal, Priyanka Chandigarh University Mohali India Srm University Haryana Sonipat India Bhai Gurdas Institute of Engineering & Technology Punjab Sangrur-148001 India Iilm University Greater Noida India XenonStack Pvt. Ltd. Punjab Mohali India XenonStack Pvt. Ltd. Punjab Mohali India

ISBN: (纸本)9798331542375

From starting of the human civilization, the peoples are traveling to one place to another place for different kinds of works such as business, tourism etc. and they are need accommodation to stay there. Before the 21st century, there was a limited option to search the places for accommodation. In the digital world there are options are increasingly in comparison with the previous time. Now if someone going for a vacation for a short term, he or she can find a hotel or lodge for the same, but when students migrate one place to another or a person transfer to another city or place than he need to find a suitable place to stay for a long time. In this research paper, we try to find the best solution for this kind of problem. Here we discuss the result with some predetermined parameters. These parameters set and trained on the collected data from different type of people from different geolocations. After it, the collected data is fed to the K-Means algorithm. This algorithm is used for the making the clusters. After it by using REst API, the output of K-Means algorithms is shown on a Map. © 2025 IEEE.

关键词： K-means clustering

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CPNet: Controllable Point Cloud Generation Network Using Part-Level Information 21st

CPNet: Controllable Point Cloud Generation Network Using Par...

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21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Qin, Shun Han, WenZhuo Zhang, Jinlai Yang, Wenqi Gao, Kai Li, Jin College of Computer and Communication Engineering Changsha University of Science and Technology Changsha China College of Automotive and Mechanical Engineering Changsha University of Science and Technology Changsha China Microelectronics and SolidState Electronics Nanjing Institute of Electronic Devices Nanjing China

ISBN: (纸本)9789819601219

The generation of 3D point clouds with controllable part-level details remains a challenging task in the field of computer graphics and vision. Existing methods often produce monolithic point clouds, lacking the flexibility for part editing and transformation necessary for various applications such as object modeling, animation, and scene reconstruction. To address this limitation, we introduce the Controllable Point Cloud Generation Network (CPNet), a novel probabilistic generative model tailored for part-level point cloud generation. Our approach consists of three main components: a Segment Paerts (SP) module that establishes the foundational learning for parts, a Part selector and Transformation sampler that models the distribution of points probabilistically, and a Cross-Attention Diffusion (CAD) module with special attention mechanism designed to denoise and refine the generated point cloud. By decomposing the point cloud at the part-level, CPNet enables the generation of point clouds that can be controlled through part-level editing and replacement. The proposed method achieves state-of-the-art (SOTA) results in terms of point cloud quality and offers unparalleled control over the generation process. We demonstrate the effectiveness of CPNet through extensive experiments and comparisons with existing methods, highlighting its potential for applications in computer graphics, robotics, and other domains requiring high-quality, controllable 3D point cloud data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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20th international conference on Grid, Cloud, and Cluster Computing, GCC 2024, 3rd international conference on Quantum Technologies, ICEQT 2024, and 21st international conference on Modeling, Simulation and Visualization Methods, MSV 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

20th International Conference on Grid, Cloud, and Cluster Co...

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20th international conference on Grid, Cloud, and Cluster Computing, GCC 2024, 3rd international conference on Quantum Technologies, ICEQT 2024, and 21st international conference on Modeling, Simulation and Visualization Methods, MSV 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031858833

The proceedings contain 20 papers. The special focus in this conference is on Grid, Cloud, and Cluster Computing. The topics include: Scientific Workflow Provenance Management: System Requirements and a Reference Architecture;Enhancing data Security with Decentralized Cloud storage: An IPFS Approach;learning Robust Observable to Address Noise in Quantum Machine learning;evaluating Cost-Effective Reconfigurable Hardware for Quantum Simulation;an Efficient Quantum Solver for Multidimensional Partial Differential Equations;explaining the Design of the Quantum Fourier Transform;Optimizing Depth of Quantum Circuit for Generating GHZ states;a Comparative Analysis of Hybrid-Quantum Classical Neural Networks;NRQNN: The Role of Observable Selection in Noise-Resilient Quantum Neural Networks;studying the Impact of Quantum-Specific Hyperparameters on Hybrid Quantum-Classical Neural Networks;Eclipse Qrisp QAOA: Description and Preliminary Comparison with Qiskit Counterparts;federal Cloud Computing Adoption Case study: A Retrospective Analysis as a Precursor to Optimized Quantum Adoption Methodologies;integrating Secure Quantum Digital Signature into Quantum Communications;methodology to Accelerate Federal Agency Adoption of Quantum Technologies;understanding Public Policy Effects on Alcohol-Related Behaviors and Outcomes Using System Dynamics;Performance Investigation of Small UAV Attitude Control Based on Optimized Nonlinear Dynamic Inversion;Prediction of 1st Year Registration Renewal of General-Use JP Domain Names with the Use of Machine learning;data Clustering and Visualization with Recursive Max k-Cut Algorithm.

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Node-Level Lymph Node Automatic Segmentation in CT Images Using Deep Parallel structure-Related 3D U-Net Variant 21st

Node-Level Lymph Node Automatic Segmentation in CT Images Us...

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21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Cheng, Shi Li, Quan Zhang, Guangyu Zhang, Lei Peng, Tao School of Future Science and Engineering Soochow University Suzhou China Center of Stomatology The Second Affiliated Hospital of Soochow University Suzhou China Division of Cardiology Department of Internal Medicine Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Graduate Program of Medical Physics and Data Science Research Center Duke Kunshan University Jiangsu Kunshan China

ISBN: (纸本)9789819601240

The delineation of Lymph Nodes (LNs) is pivotal in pinpointing therapeutic targets for radiotherapy in head and neck malignancies. Nevertheless, this endeavor poses a formidable challenge, primarily stemming from the suboptimal contrast against adjacent tissues. This investigation introduces a deep learning methodology aimed at automating the segmentation of LNs within CT scans, offering the following contributions: (1) Expanding upon the 3D Unet model, we incorporate a parallel block consisting of attention gate and squeeze & excitation modules. We extensively evaluate various versions of this parallel block and achieve favorable performance. (2) To address the slow decrease in Dice loss, we introduce a lightweight boundary refinement module. Our proposed method is assessed on a dataset comprising 103 patients and 603 Lymph Nodes (LNs), with 452 nodes used for training and 151 nodes for testing. The node-level Dice similarity coefficient achieved by our method reaches an impressive 0.833. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Computerized tomography

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Smart Web Application for Recommending the Selection of Equipment for Organizing Activities in Educational Institutions

Smart Web Application for Recommending the Selection of Equi...

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international ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications engineering (ECTI-NCON)

作者： Tanarin Khongtuan Sakorn Mekruksavanich Master in Technology and Digital Data Managementc School of Information and Communication Technology University of Phayao Phayao Thailand Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

Higher education institutions strive to improve the quality of instruction and research to prepare students with essential 21st-century competencies. They promote academic and professional growth through teaching, research initiatives, community engagement, and the preservation of cultural values. One key tool in this effort is an educational resource recommendation system, critical in boosting students’ academic outcomes, stimulating engagement in learning activities, and elevating educational standards. This study presents the design and evaluation of a personalized recommendation system for online educational resources, leveraging web technologies and mining techniques integrated within a digital learning infrastructure. The proposed system enhances the intelligence and adaptability of the network-based educational platform, caters to students’ personalized learning requirements, and streamlines the process of accessing relevant information by minimizing the time required for resource discovery.

关键词： Education Decision making Web mining Boosting Research initiatives Global communication Cultural differences Recommender systems standards

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