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Federated learning paradigm in E-health systems: An overview 4

Federated Learning paradigm in E-health systems: An overview

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4th international conference on pattern Analysis and Intelligent Systems, PAIS 2022

作者： Sellamna, Abdellatif Boukhamla, Akram Zine Eddine Benkaddour, Mohammed Kamel University of Kasdi Merbah Laboratoire d'Intelligence Artificielle et des Technologies de l'Information Ouargla Algeria

ISBN: (数字)9781665461610

ISBN: (纸本)9781665461610

Every day, professionals generate and use massive healthcare data to save, treat and ameliorate the lives of patients. the healthcare industry has adopted cloud-based solutions to solve several problems in a cost-effective manner. therefore, privacy and security mechanisms should be deployed to protect valuable medical information from unauthorized access. Much of the work in literature in recent years has focused on using artificial intelligence techniques such as deep learning and federated learning to solve various problems in the health field. Federated learning (FL) is a special technique for machine learning for privacy preservation. this study aims to compare the traditional centralized training approach and FL to show the advantages of using FL in the medical field and prove that FL can be adopted for security and data latency in e-health systems. the results obtained showed the feasibility of FL when compared to traditional methods used in the aspect of securing data and latency in the medical field. © 2022 IEEE.

关键词： Cost effectiveness

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Significant CC400 Functional Brain Parcellations Based LeNet5 Convolutional Neural Network for Autism Spectrum Disorder Detection 5th

Significant CC400 Functional Brain Parcellations Based LeNet...

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5th international conference on Recent Trends in Image Processing and pattern recognition (RTIP2R)

作者： Othmani, Alice Bizet, thibaut Pellerin, Tanguy Hamdi, Badr Bock, Marc-Antoine Dev, Soumyabrata Univ Paris Est LISSI UPEC F-94400 Vitry Sur Seine France EFREI Paris Engn Sch F-94800 Villejuif France Univ Coll Dublin Sch Comp Sci Dublin Ireland

ISBN: (纸本)9783031235986;9783031235993

machine learning and computer vision have opened new pathways to investigate imaging data captured from different sensors. Numerous application areas are getting benefit from these advancements and one of these areas is medical imaging. Despite rapid advancements in machine learning based medical condition diagnosis systems (CADs), some ailments and disorders are hard to diagnose/classify due to the absence or the lack of consensus on biomarkers for specific disorders, like the Autism Spectrum Disorder (ASD). In this study, the challenging problem of classification of ASD using the magnetic resonance imaging (MRI) data is tackled. Hence, we propose an interpretable deep neural network based approach for ASD detection from MRI images. Our proposed explanation method is based on the selection of four regions of interest from the MRI images. the four significant CC400 functional brain parcellations are then concatenated and fed to a LeNet-5-based convolutional neural network to predict ASD. the performances of the proposed approach are evaluated on ABIDE dataset and promising results are achieved. three augmented datasets are considered and an accuracy of 95% is achieved by using LeNet-5 which outperforms VGG16 and ResNet-50. the achieved accuracy outperforms also the existing deep neural networks based approaches on ABIDE dataset. the use of the four significant CC400 functional brain parcellations makes our approach more interpretable and more accurate.

关键词： Deep learning Convolutional Neural Network Autism Spectrum Disorder ABIDE I MRI images

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Construction of an Intelligent Identification System for Concrete Bridge Cracks by Integrating machine learning and Neural Network Models 4

Construction of an Intelligent Identification System for Con...

引用

4th international conference on Industrial IoT, Big data and Supply Chain, IIoTBDSC 2023

作者： Zhang, Zanpeng Xu, Meng Luo, Lingyu Hong, Zhong China Merchants Chongqing Communications Technology Research and Design Institute Co. Ltd. Chongqing400067 China China Merchants Chongqing Highway Engineering Testing Center Co. Ltd. Chongqing400067 China Chongqing Jiangbei District Heji Primary School Chongqing400000 China Tibet Autonomous Region Key Highway Construction Project Management Center Lhasa850000 China

ISBN: (纸本)9798350341690

With the increasing use time and frequency of bridges, they will inevitably be subjected to a large number of traffic vehicles and various human damage. In addition, the severe erosion of the natural environment will cause different situations and degrees of damage to the surface of the beam. According to relevant data, cracks are the main factor causing damage to concrete bridges. therefore, this paper combines machine learning and NN (Neural Network) model to build an intelligent identification system for concrete bridge cracks. through the system, we can clearly see the recognition of the model on the bridge crack picture. the numerical representation on the diagonal of the Confusion matrix identifies the correct type, and the numerical representation not on the diagonal represents the wrong type. And the research results show that the classification accuracy of this algorithm for the verification set is high, close to the training set, and the Loss function is close to the Loss function of the training set on the basis of the overall fluctuation, so there is no over fitting phenomenon, the uncomfortable training results are good, and the training time is very similar. the algorithm proposed in this paper can accurately identify concrete surface crack images in a short period of time, indicating that the algorithm can be used for crack localization. © 2023 IEEE.

关键词： Neural network models

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data analysis of Global Perceived Stress Scores during Covid-19 4

Data analysis of Global Perceived Stress Scores during Covid...

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4th international conference on Circuits, Control, Communication and Computing, I4C 2022

作者： Muttaraju, Chakita Kabber, Anusha Parimala, S. Anagani, Prajwal Srinivasa, Gowri PES University Department of Computer Science and Engineering Bengaluru India PES University PES Center for Pattern Recognition Dept. of CSE Bengaluru India

ISBN: (纸本)9798350397475

the Covid-19 Pandemic that broke out in late December 2019 has had a widespread negative effect on the mental health of people around the world. this work aims to elicit features that had a major influence on mental health during the pandemic to better understand preventive measures and remedial actions that can be taken to help individuals in need. Along with factors such as demographic age, gender, marital status, and employment status, additional information such as the effect of media used as a source of information, coping methods, trust in the country's government, and healthcare organizations was analyzed to find their correlation (if any) to the perceived stress of the individual. machine learning techniques such as XGBoost, AdaBoost, Decision Trees, Ordinal regression, k-Nearest Neighbors, Lasso and Ridge regression were used to arrive at a relationship between the perceived stress scores and the features considered. On interpreting results from the different models, we conclude that the main factor influencing stress scores was loneliness followed by features indicating trust in government, compliance with Covid-19 preventive measures and concerns regarding the pandemic. © 2022 IEEE.

关键词： Decision trees

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machine learning Improved Chan-Taylor Co-Weighted Localization Algorithm 4

Machine Learning Improved Chan-Taylor Co-Weighted Localizati...

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4th international conference on Frontiers Technology of Information and Computer, ICFTIC 2022

作者： Tong, QingSheng Zhao, Shuang Wang, DaYu Yang, HongWei Changchun University of Science and Technology Electronics and Communication Engineering Jilin Province Changchun City China Changchun Public Security Bureau Jilin Province Changchun City China

ISBN: (纸本)9798350321951

With the rise of the Internet of things and smart cities, indoor positioning technology becomes more and more important. Among the existing network technologies, ultra-wide band (UWB) has the advantages of high transmission rate, and strong penetration ability. However, its signal propagation is susceptible to N on- Line-of-Sight (NLOS) effects. therefore, this paper focuses on indoor positioning in NLOS environments, and studies the Chan-Taylor co-weighted positioning algorithm improved by machine learning. the main research contents include: 1. A machine learning method is proposed to identify and classify NLOS signals. Using a sliding window to dynamically calculate the variance and mean of multiple groups of TDOA measurements in a short period of time as the input to the recognition algorithm, and classify them into three categories: Line-of-Sight (LOS), low NLOS error, and high NLOS error. 2. Based on the Chan- Taylor co-location algorithm, the algorithm introduces the concept of weighting coefficient, optimizes the observation data, and finally obtains the target position coordinates. the simulation results show that the positioning accuracy and system stability of the algorithm are improved compared with the Chan algorithm and the Chan-Taylor co-location algorithm. © 2022 IEEE.

关键词： machine learning

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IoT and machine learning-Based Covid-19 Healthcare Monitoring System Using Face recognition 4th

IoT and Machine Learning-Based Covid-19 Healthcare Monitorin...

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4th international conference on machine learning, Image Processing, Network Security and data Sciences (MIND)

作者： Vaswani, Chahat Chimaniya, Shalini Ranjan, Rajnish K. Bhawsar, Yachana Govt Womens Polytechn Coll Bhopal 462016 India LNCT Univ Bhopal 462042 India

ISBN: (纸本)9783031243660;9783031243677

After facing the horrible COVID-19 pandemic, steadily life is getting back to normal again. this pandemic came with opportunities as well, especially for researchers to come out with novel ideas and handle the situation. Many researchers have contributed with their dedicated research work with the help of recent technology to overcome similar circumstances. this paper presents a novel idea for proper monitoring and detecting normal/abnormal health using AI-based models. Proper monitoring and detection of symptoms are essential to ensure the health of members. this model is devised using several IoTs components and various ML (machine learning) techniques have been used to get comparative enhanced results. the hardware used Raspberry Pi 4 model B, which is the main hardware connected to several sensors like MLX906014 non-contact thermal sensor and MAX30100 pulse oximeter and heart rate sensor to measure body temperature without contact and to calculate the level of oxygen in the blood and measuring pulse rate respectively. Additionally, a Camera module for facilitating face recognition features for devices has been used. AnAlert will be sent to Admin if someone has an abnormal temperature and oxygen level. the Firebase database is used to store information and it gets updated in real-time. People's health history can be further analyzed through graphs for visualization and monitored by the administrator.

关键词： machine learning Internet of things Sensors Support Vector machine Face recognition

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machine learning Model for Medical data Classification for Accurate Brain Tumor Cell Detection 4th

Machine Learning Model for Medical Data Classification for A...

引用

4th international conference on Innovative Product Design and Intelligent Manufacturing System, IPDIMS 2022

作者： Navabothu, Gnana Sri Sai Sujith Sakode, Himanshu Gottipati, Jagathi Rama Devi, Polagani Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada520007 India

ISBN: (纸本)9789819916641

the health industry has grown into a large organization, and a vast number of medical data can be produced every day by the healthcare industry to collect information for the prediction of future diseases that can be produced by a patient using treatment history and health information. In a variety of medical diagnostic applications, automated defect identification in medical imaging has become an arising sector. Automated brain tumor identification and categorization are the focus of our work. In most cases, CT or MRI scans are used to examine the anatomy of the brain. the main objective of brain tumor detection is to help in clinical diagnosis. the goal is to design a machine learning method that ensures the existence of a tumor and to provide a reliable technique of tumor diagnosis that aids medical staff to provide proper medication to the patient. We get deep convolutional neural network (CNN) models that have already been trained using the transfer learning method to extract features from brain MRI scans. the Visual Geometry Group (VGG 16) is the most notable model when it comes to image recognition architectures as it surpasses and outperforms other traditional convolutional neural network models. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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A Review on the Applications of machine learning and Deep learning Algorithms for Image recognition

A Review on the Applications of Machine Learning and Deep Le...

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Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Ketan Wanjari Prateek Verma Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Sawangi (Meghe) Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Sawangi (Meghe) Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Modern image recognition has experienced dramatic improvements because of machine learning and Deep learning algorithms together. this study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. this paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. the latest image processing techniques include ViTs alongside GANs and Few-Shot learning but developers need to achieve better results in future improvements. the main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. this paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition.

关键词： Deep learning Ethics data privacy Image recognition machine learning algorithms Reviews Social networking (online) Vectors Security Stakeholders

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IPMV 2022 - 2022 4th international conference on Image Processing and machine Vision

IPMV 2022 - 2022 4th International Conference on Image Proce...

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4th international conference on Image Processing and machine Vision, IPMV 2022

ISBN: (纸本)9781450395823

the proceedings contain 19 papers. the topics discussed include: infrared dim and small target detection based on total variation and multiple noise constraints modeling;infrared small target detection algorithm with complex background based on YOLO-NWD;masked face recognition with 3D facial geometric attributes;group sparse-based discriminative feature learning for face recognition;CLAMOT: 3D detection and tracking via multi-modal feature aggregation;research on the application of three-dimensional digital model in the protection and inheritance of traditional architecture: take the example of the ma tau wall of Huizhou architecture;hierarchical iris image super resolution based on wavelet transform;an improved dark channel prior defogging algorithm based on transmissivity image segmentation;image processing based scoring system for small arms firing in the military domain;and a quantitative comparison of automated cleaning techniques for web scraped image data of ‘smart cities’.

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Intelligent Monitoring and Management of Trade Logistics Based on Big data and Computer Vision Technology 4

Intelligent Monitoring and Management of Trade Logistics Bas...

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4th international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Gao, Zhongpo Kang, Chengwen Wu, Xufeng School of Economics Harbin University of Commerce Harbin150028 China School of Economics and Management Harbin University Harbin150086 China

Technologies such as big data and computer vision are increasingly being applied in trade logistics, and their integration provides strong support for achieving modernization and intelligence in the logistics industry. this article utilizes various sensors, global positioning systems, radio frequency identification and other technologies to obtain real-time information on the location, temperature, and transportation status of goods. Video surveillance and image recognition methods can be used, combined with computer vision technology, to monitor the loading and unloading process of goods and the condition of transportation vehicles in real time. this article studies an intelligent monitoring algorithm that achieves anomaly detection, warning prompts, and ensures the safety and smoothness of transportation. Exploration of transportation path optimization in batch 1 experimental group (big data analysis) saved 3 hours of time and cost by 500 yuan. this article explores the intelligent monitoring and management of trade logistics based on big data and computer vision technology, which can help improve transportation efficiency and reduce costs. © 2024 the Authors.

关键词： Radio frequency identification (RFID)

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