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Empowering Consumer Healthcare Through Sensor-Rich Devices using Federated Learning for Secure Resource Recommendation

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IEEE Transactions on Consumer Electronics 2025年

作者： Fathima, Afifa Salsabil Basha, Syed Muzamil Ahmed, Syed Thouheed Khan, Surbhi Bhatia Asiri, Fatima Basheer, Shakila Shukla, Madhu School of Computer Science and Engineering REVA University Karnataka Bengaluru India School of Computing and Information Technology REVA University Karnataka Bengaluru India Department of Data Science School of Science Engineering and Environment University of Salford Manchester United Kingdom Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India College of computer science Informatics and computer systems department King Khalid University Saudi Arabia Department of Information Systems College of computer and Information science Princess Nourah bint Abdulrahman University P.O. BOX 84428 Riyadh11671 Saudi Arabia the Department of CSE - AI ML & DS Marwadi University Gujarat Rajkot360003 India

When implementing zero-trust edge computing, offloading computational tasks and data access through traditional model training and usage approaches can lead to increased latency. Since the traditional methods often involve extensive communication with a central server, creating additional network hopping stations/nodes resulting in increased latency. The challenge is bound to allocate a befitting resource at a given consumer demand. In this proposed system, a federated learning model based data offloading and consumer medical resource recommendation of IoT is discussed and validated. The user/consumer group and local training models are aligned with edge servers for data preprocessing and customization with a series of resources demand creation and coordination. The consumer resource allocating priorities are fine-grained with the proposed blockchain based priority analyzer for recommendation and allocation. The computational parameter such as resource pool, average waiting time, energy consumption and transmission trust delays are observed and validated. The proposed framework fetches consumer resources logs and synchronizes the centralized training model for effective scheduling and allocation of resources with an accuracy of 94.92% under the 5G operating spectrum. The technique has demonstrated minimal latency in offloading the data request demand and resource allocation at the cloud servers. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Federated learning

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An Ensemble-based Neural Network Model for Natural Disaster in 2019

An Ensemble-based Neural Network Model for Natural Disaster ...

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ACS/IEEE International Conference on Computer Systems and Applications

作者： Vartika Bhadana Pooja Pathak Anand Singh Jalal Ashish Sharma Bhisham Sharma Imed Ben Dhaou Department of Computer Application and Engineering GLA University Mathura Department of Computer Engineering and Applications GLA University Mathura UP Department of Computer Science & Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science Hekma School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia

Recently, all have witnessed a rapid growth of COVID-19 coronavirus worldwide. The calculation of COVID-19 time-series prediction is done using many techniques like compartment models, machine learning models (ML), and deep learning models. Therefore, in this paper, the authors have proposed an ensemble-based neural network model. Neural networks (NN), along with non-linear autoregressive (NAR) functions, fitting neural networks (FITNET), or fuzzy Systems, are widely used in time-series forecasting. The responses of NAR, FITNET predictor modules are aggregated using fuzzy logic, which improves the final prediction by intelligently integrating outputs of different modules. The whole model was put to the test in terms of forecasting the coronavirus time series in India, at 13 States. In the validation data set, results of ensemble NN models with fuzzy response integration demonstrate extremely better-predicted values. Overall, the results reveal that a modular neural network with fuzzy (MNNF) beats all other approaches in performance metrics, like Root Mean Squared Error (RMSE). Prediction errors of ensemble NN i.e., MNNF are much smaller than those of classic monolithic neural networks, showing advantages of the method proposed. The model provides the prediction for the upcoming 8 days.

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River quality classification using different distances in k-nearest neighbors algorithm

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Procedia Computer Science 2022年 204卷 180-186页

作者： Nurnadiah Zamri Mohammad Ammar Pairan Wan Nur Amira Wan Azman Siti Sabariah Abas Lazim Abdullah Syibrah Naim Zamali Tarmudi Miaomiao Gao Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Besut Campus Besut Terengganu Malaysia Management Science Research Group Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Nerus Terengganu Malaysia Management Science Research Group Computer and Information Sciences University of Strathclyde Glasgow United Kingdom Universiti Technology MARA Johor Branch Segamat Johor Malaysia Faculty of Biology Medicine and Health School of Medical Siences University of Manchester Manchester United Kingdom

The practice of river quality classification usually uses Water Quality Index (WQI) to evaluate the WQI values of the river. However, due to huge data collection on river pollution with uncertain water quality parameter values, need to a different approach to classify the river quality. One of the supervised classification algorithms known as K-Nearest Neighbors (KNN) seems to give new approach for river quality classification where each data points are classified according to the k number or the closest data points neighbors. Therefore, the purpose of this paper is to apply different distances and distance-weighted in KNN for finding the most accurate river quality classification. The accuracy results are compared with Support Vector Machine (SVM) and Decision Tree (DT) algorithms. This KNN algorithm will give a different approach in classify the river quality.

关键词： Supervised classification k-nearest neighbors (KNN) river quality classification accuracy

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Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection from Point Clouds

arXiv

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arXiv 2022年

作者： Yin, Junbo Shen, Jianbing Gao, Xin Crandall, David Yang, Ruigang School of Computer Science Beijing Institute of Technology Beijing China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates Thuwal Saudi Arabia The School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States The University of Kentucky LexingtonKY40507 United States

Previous works for LiDAR-based 3D object detection mainly focus on the single-frame paradigm. In this paper, we propose to detect 3D objects by exploiting temporal information in multiple frames, i.e., the point cloud videos. We empirically categorize the temporal information into short-term and long-term patterns. To encode the short-term data, we present a Grid Message Passing Network (GMPNet), which considers each grid (i.e., the grouped points) as a node and constructs a k-NN graph with the neighbor grids. To update features for a grid, GMPNet iteratively collects information from its neighbors, thus mining the motion cues in grids from nearby frames. To further aggregate the long-term frames, we propose an Attentive Spatiotemporal Transformer GRU (AST-GRU), which contains a Spatial Transformer Attention (STA) module and a Temporal Transformer Attention (TTA) module. STA and TTA enhance the vanilla GRU to focus on small objects and better align the moving objects. Our overall framework supports both online and offline video object detection in point clouds. We implement our algorithm based on prevalent anchor-based and anchor-free detectors. The evaluation results on the challenging nuScenes benchmark show the superior performance of our method, achieving the 1st on the leaderboard without any bells and whistles, by the time the paper is submitted. Copyright © 2022, The Authors. All rights reserved.

关键词： Message passing

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A comparison of unsupervised and supervised machine learning algorithms to predict water pollutions

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Procedia Computer Science 2022年 204卷 172-179页

Clean and safe water is vital for our lives and public health. In recent decades, population growth, agriculture, industries, and climate change have worsened freshwater resource depletion and clean water pollution. Several studies have focused on water pollutions risk simulation and prediction in the presence of pollution hotspots. However, the increase and complexity of big data caused by uncertain water quality parameters led to a new efficient algorithm to trace the most accurate pollution hotspots. Therefore, this study proposes to offer different algorithms and comparative studies using Machine Learning (ML) algorithms. Ten different most widely used algorithms, including unsupervised and supervised ML, will be employed to categorize the pollution hotspots for the Terengganu River. Besides, we also validate algorithms’ accuracies by improving and changing each parameter in ML algorithms. Our results list all the accurate and efficient ML algorithms for the classification of river pollutions. These results help to facilitate river prediction using efficient and accurate algorithms in various water quality scenario.

关键词： Machine Learning Deep Learning Water Pollutions Terengganu River

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Quality Metric Guided Portrait Line Drawing Generation from Unpaired Training Data

arXiv

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arXiv 2022年

作者： Yi, Ran Liu, Yong-Jin Lai, Yu-Kun Rosin, Paul L. BNRist MOE-Key Laboratory of Pervasive Computing The Department of Computer Science and Technology Tsinghua University Beijing China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Informatics Cardiff University United Kingdom

Face portrait line drawing is a unique style of art which is highly abstract and expressive. However, due to its high semantic constraints, many existing methods learn to generate portrait drawings using paired training data, which is costly and time-consuming to obtain. In this paper, we propose a novel method to automatically transform face photos to portrait drawings using unpaired training data with two new features;i.e., our method can (1) learn to generate high quality portrait drawings in multiple styles using a single network and (2) generate portrait drawings in a "new style" unseen in the training data. To achieve these benefits, we (1) propose a novel quality metric for portrait drawings which is learned from human perception, and (2) introduce a quality loss to guide the network toward generating better looking portrait drawings. We observe that existing unpaired translation methods such as CycleGAN tend to embed invisible reconstruction information indiscriminately in the whole drawings due to significant information imbalance between the photo and portrait drawing domains, which leads to important facial features missing. To address this problem, we propose a novel asymmetric cycle mapping that enforces the reconstruction information to be visible and only embedded in the selected facial regions. Along with localized discriminators for important facial regions, our method well preserves all important facial features in the generated drawings. Generator dissection further explains that our model learns to incorporate face semantic information during drawing generation. Extensive experiments including a user study show that our model outperforms state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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From reflection to action: Combining machine learning with expert knowledge for nutrition goal recommendations 21

From reflection to action: Combining machine learning with e...

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2021 CHI Conference on Human Factors in computing Systems: Making Waves, Combining Strengths, CHI 2021

作者： Mitchell, Elliot G. Heitkemper, Elizabeth M. Burgermaster, Marissa Levine, Matthew E. Miao, Yishen Hwang, Maria L. Desai, Pooja M. Cassells, Andrea Tobin, Jonathan N. Tabak, Esteban G. Albers, David J. Smaldone, Arlene M. Mamykina, Lena Department of Biomedical Informatics Columbia University United States School of Nursing The University of Texas at Austin United States Department of Population Health Dell Medical School and Department of Nutritional Sciences The University of Texas at Austin United States Department of Computing and Mathematical Sciences California Institute of Technology United States Department of Molecular Cellular and Developmental Biology University of California Santa Barbara United States Department of Science and Math Fashion Institute of Technology United States United States and The Rockefeller University United States Courant Institute of Mathematical Sciences United States University of Colorado Anschutz Medical Campus Section of Informatics and Data Science United States Departments of Pediatrics Biomedical Engineering and Biostatistics and Informatics United States School of Nursing Columbia University United States

ISBN: (纸本)9781450380966

Self-tracking can help personalize self-management interventionsfor chronic conditions like type 2 diabetes (T2D), but refecting onpersonal data requires motivation and literacy. Machine learning(ML) methods can identify patterns, but a key challenge is making actionable suggestions based on personal health *** introduce GlucoGoalie, which combines ML with an expert system to trans-late ML output into personalized nutrition goal suggestions forindividuals with T2D. In a controlled experiment, participants with T2D found that goal suggestions were understandable and action-able. A 4-week in-the-wild deployment study showed that receiving goal suggestions augmented participants' self-discovery, choosing goals highlighted the multifaceted nature of personal preferences, and the experience of following goals demonstrated the importance of feedback and context. However, we identifed tensions between abstract goals and concrete eating experiences and found static text too ambiguous for complex concepts. We discuss implications for ML-based interventions and the need for systems that ofer more interactivity, feedback, and negotiation. © 2021 ACM.

关键词： Feedback

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Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

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Computational Intelligence and Neuroscience 2022年第0期2022卷 4690936页

作者： Veeramakali, T. Shobanadevi, A. Nayak, Nihar Ranjan Kumar, Sumit Singhal, Sunita Subramanian, Manoharan Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Sri Venkateswara College of Engineering Technology Chittoor India Indian Institute of Management Kozhikode India Department of Computer Science and Engineering Manipal University Jaipur Jaipur303007 India Department of Computer Science School of Informatics and Electrical Engineering Hachalu Hundesa Campus Ambo University Ambo Ethiopia

A key challenge in clinical recommendation systems is the problem of aberrant patient profiles in social networks. As a result of a person's abnormal profile, numerous vests might be used to make fake remarks about them, cyber bullying, or cyber-attacks. Many clinical researchers have done extensive study on this topic. The most recent studies on this topic are summarized, and an overarching framework is provided. When it comes to the methods and datasets that make up the data collection, the feature presentation and algorithm selection layers provide an overview of the various types of algorithm selections available. The categorization and evaluation of diseases and disorders has been one of the major advantages of machine learning in medical. Because it was harder to predict, it rendered it more controllable. It might range from difficult-to-find cancers in the early stages to certain other illnesses spread through the bloodstream. In healthcare, we may pick methods in machine learning depending on reliable outcomes. To do so, we must run the findings through each method. The major issue arises during information training and validation. Because the dataset is so large, eliminating mistakes might be difficult. The providers, other characteristics, various algorithms, data labelling techniques, and assessment criteria are all presented and contrasted in depth. Detecting anomalous users in medical social networks, on the other hand, is a work in progress. The result evaluation layer provides an explanation of how to evaluate and mark up the results of the various algorithm selection layers. Finally, it looks forward to more study in this area. © 2022 T. Veeramakali et al.

关键词： Diseases

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An AI-empowered indoor digital contact tracing system for COVID-19 outbreaks in residential care homes

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Infectious Disease Modelling 2024年第2期9卷 474-482页

作者： Jiahui Meng Justina Yat Wa Liu Lin Yang Man Sing Wong Hilda Tsang Boyu Yu Jincheng Yu Freddy Man-Hin Lam Daihai He Lei Yang Yan Li Gilman Kit-Hang Siu Stefanos Tyrovolas Yao Jie Xie David Man David H.K.Shum School of Nursing The Hong Kong Polytechnic UniversityHong Kong Special Administrative RegionChina Electronic and Information Engineering The Hong Kong Polytechnic UniversityHong Kong Special Administrative RegionChina Research Centre of Textiles for Future Fashion The Hong Kong Polytechnic UniversityHong Kong Special Administrative RegionChina Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic UniversityHong Kong Special Administrative RegionChina Department of Applied Mathematics The Hong Kong Polytechnic UniversityHong Kong Special Administrative RegionChina The Jockey Club School of Public Health and Primary Care The Chinese University of Hong KongHong Kong Special Administrative RegionChina Department of Rehabilitation Sciences The Hong Kong Polytechnic UniversityHong Kong Special Administrative RegionChina Department of Computing The Hong Kong Polytechnic UniversityHong Kong Special Administrative RegionChina Department of Health Technology and Informatics The Hong Kong Polytechnic UniversityHong Kong Special Administrative RegionChina Department of Nutrition and Food Studies George Mason UniversityUSA Tung Wah College Hong Kong Special Administrative RegionChina Mental Health Research Centre The Hong Kong Polytechnic UniversityHong Kong Special Administrative RegionChina

An AI-empowered indoor digital contact-tracing system was developed using a centralized architecture and advanced low-energy Bluetooth technologies for indoor positioning,with careful preservation of privacy and data *** analyzed the contact pattern data from two RCHs and investigated a COVID-19 outbreak in one study *** evaluate the effectiveness of the system in containing outbreaks with minimal contacts under quarantine,a simulation study was conducted to compare the impact of different quarantine strategies on outbreak containment within *** significant difference in contact hours between weekdays and weekends was observed for some pairs of RCH residents and staff during the two-week data collection *** significant difference between secondary cases and uninfected contacts was observed in a COVID-19 outbreak in terms of their demographics and contact *** results based on the collected contact data indicated that a threshold of accumulative contact hours one or two days prior to diagnosis of the index case could dramatically increase the efficiency of outbreak containment within RCHs by targeted isolation of the close *** study demonstrated the feasibility and efficiency of employing an AI-empowered system in indoor digital contact tracing of outbreaks in RCHs in the post-pandemic era.

关键词： COVID-19 Indoor contact tracing Contact pattern Outbreak containment Artificial intelligence

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Influence of the variables describing brain signals on the performance of the Naive Bayesian Classifier

Influence of the variables describing brain signals on the p...

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2022 Progress in Applied Electrical Engineering, PAEE 2022

作者： Kawala-Sterniuk, Aleksandra Zygarlicki, Jaroslaw Lysiak, Adam Grochowicz, Barbara Pelc, Mariusz Bauer, Waldemar Baczkowicz, Dawid Krecisz, Krzysztof Dylag, Katarzyna A. Wieczorek, Wiktoria Kahankova, Radana Martinek, Radek Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Opole Poland University of Greenwich School of Computing and Mathematical Sciences London United Kingdom Agh University of Science and Technology Department of Automatic Control and Robotics Krakow Poland Opole University of Technology Faculty of Physical Education and Physiotherapy Opole Poland St. Louis Children Hospital Krakow Poland Jagiellonian University in Krakow-Collegium Medicum Department of Pathophysiology Krakow Poland Jagiellonian University in Krakow-Collegium Medicum Department of Bioinformatics and Telemedicine Krakow Poland Vsb - Technical University Ostrava Department of Cybernetics and Biomedical Engineering-FEECS Ostrava-Poruba Czech Republic

ISBN: (纸本)9781665455152

In this paper various variables describing brain signals such as near-infrared spectroscopy (NIRS) and electroencephalography (EEG) on the Naive Bayesian Classifier were presented. Analysis of biomedical signals is a very challenging task - therefore the authors of this work decided to analyse two types of brain signals - EEG and NIRS, which have different features, character, nature and are differently prone to various artifacts occurrence. The applied classifiers can be applied for various features extraction and pattern comparisons, which is useful in diagnostic processes. © 2022 IEEE.

关键词： Near infrared spectroscopy

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