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A Review Paper on Machine Learning Techniques and Its applications in Health Care Sector 1

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2nd International conference on Ubiquitous Computing and Intelligent Information systems (ICUIS)

作者： Gautam, Priya Dehraj, Pooja Noida Inst Engn & Technol Greater Noida India

ISBN: (数字)9789811925412

ISBN: (纸本)9789811925412;9789811925405

Healthcare data analysis has seen a significant rise in recent years and thus becoming a prominent research field. Healthcare data types include sensor data, Clinical data, and Omics data. Clinical data are generally collected whereas a patient is being treated whereas multiple wearable and wireless sensor devices are used for collecting the sensor data. Omics data includes transcriptome, genome, and proteome data types, all of which are complicated and high-dimensional. Since the data type varies and manually dealing with the raw data becomes a challenging task. This is where Machine Learning emerges as a significant tool for analyzing the data. To precisely forecast the healthcare data results, Machine Learning utilizes varied statistical techniques and advanced algorithms. For analysis ML utilizes different algorithms such as Reinforcement Learning, Supervised Learning, and Unsupervised Learning. This paper discusses the Machine learning algorithms of many forms that can be used to analyse and survey enormous volumes of healthcare data.

关键词： Classification algorithm Disease diagnostics Healthcare data Machine learning

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RSM-PA:Research on Route Selection Method of Inter-domain Routing System Based on Path Availability 23

RSM-PA:Research on Route Selection Method of Inter-domain Ro...

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23rd IEEE International conference on High Performance Computing and Communications, 7th IEEE International conference on Data Science and systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in sensor, Cloud and Big Data systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zhu, Huihu Qiu, Han Zhu, Junhu Wang, Qingxian State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665494571

The inter-domain routing system is the key infrastructure of the Internet. However, BGP does not consider the link state in the routing selection process. As a result, when the inter-domain routing system faces security incidents such as disasters and attacks, it is easy to cause the continuous propagation of link failures, resulting in a large-scale network disconnection and posing a considerable threat to the security of the Internet. The previous research mainly aims to enhance the protocol's safety, cannot change the original failure propagation mechanism, and cannot fundamentally solve cascading failure propagation. In response to this problem, this paper introduces SDN and proposes RSM-PA. This method perceives the available path topology by sharing the link state information in the region, provides a verification mechanism for the availability of the route in the routing process to avoid the link failure in the region due to overload. The comparison of routing results before and after the link state changes under the same experimental conditions shows that RSM-PA can effectively adjust the routing strategy according to the path availability, and lay a solid foundation for suppressing the propagation of cascading failures. © 2021 IEEE.

关键词： Network topology Heuristic algorithms Power system protection Routing Real-time systems Routing protocols Internet

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Airspace Geofencing Volume Sizing with an advanced Air Mobility Vehicle Performance Model 41

Airspace Geofencing Volume Sizing with an Advanced Air Mobil...

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41st IEEE/AIAA Digital Avionics systems conference, DASC 2022

作者： Kim, Joseph Liberko, Nicholas Atkins, Ella University of Michigan Robotics Institute United States Collins Aerospace Mission Systems University of Michigan Aiaa Fellow Aerospace Engineering Department and Robotics Department United States

ISBN: (数字)9781665486071

ISBN: (纸本)9781665486071

Airspace geofencing is a key enabler for versatile and safe low-altitude Unmanned Aircraft System (UAS) Traffic Management (UTM). Geofenced airspace volume sizing analysis is necessary to assure efficiency and safety for a broad suite of emerging UAS applications. Airspace safety buffer sizes can be determined and verified by modeling vehicle dynamics as well as quantifying guidance, navigation and control uncertainties. This paper proposes a methodology to design safety buffer sizes given specific vehicle and sensor properties and wind. These parameters are then translated to geofence safety buffer dimensions for straight and turning flight to statistically guarantee an aircraft will stay inside its flight trajectory based keep-in geofence. Vehicle, sensor, and wind parameters are tabulated and analyzed over a prescribed flight trajectory using a Computational Fluid Dynamics (CFD) analysis. Case studies using a small UAS model and a full-size advanced air mobility (AAM) aircraft model provide comparison of geofence volume sizings. © 2022 IEEE.

关键词： Air mobility

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Convolutional Neural Network Classification of Vulnerable Road Users based on Micro-Doppler Signatures using an Automotive Radar 25

Convolutional Neural Network Classification of Vulnerable Ro...

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IEEE 25th International conference on Intelligent Transportation systems (ITSC)

作者： Wachtel, Diogo Edler, Johannes Schroder, Sabine Queiroz, Samuel Huber, Werner TH Ingolstadt Ctr Automot Res Integrated Safety Syst & Measurem Inst Automated Driving CARISSMA IAD Ingolstadt Germany Friedrich Alexander Univ Erlangen Nurnberg FAU Erlangen Germany Inst Tecnol Aeronaut Sao Jose Dos Campos Brazil

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

advanced driver assistance systems (ADAS) are growing in quantity and complexity, requiring even more sensor effectiveness and computational resources. Even though cameras and LiDAR sensors can provide a steady solution in ideal conditions, they are unreliable for adverse weather conditions, such as rain and dense fog. Therefore, radar technology is used for those circumstances is an option to be used for those circumstances. However, its classification accuracy must be enhanced to compete with the other sensor types. This work presents a solution to enhance radar classification quality, adapting a Convolutional Neural Network (CNN) structure to the input of micro-Doppler images of vulnerable road users (VRUs), such as pedestrians, cyclists, and e-scooter drivers. Moreover, two CNNs were trained, each for a particular region of interest (ROIs), one in the near field of the radar until 15m, and the second one up to 25m. The proposed solution presents a promising overall accuracy for its first trial. Nevertheless, it must be further extended to more complex scenes, including more participants, obstacles, and weather conditions.

关键词： radar applications convolutional neural networks object classification

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Finite Element Analysis Study of FBG Magnetostrictive Composite Flux sensor Geometry for Electric Motor Air-gap Flux Monitoring 11

Finite Element Analysis Study of FBG Magnetostrictive Compos...

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11th Mediterranean conference on Embedded Computing (MECO) / 3rd Summer School on Cyber-Physical + systems and Internet of Things (CPS and IoT)

作者： Suryandi, Asep Andi Tuohy, Paul Djurovic, Sinisa Univ Manchester PTIP TIRBR Natl Res & Innovat Agcy BRIN Dept Elect & Elect Engn Manchester Lancs England Univ Manchester Dept Elect & Elect Engn Manchester Lancs England

ISBN: (纸本)9781665468282

With the progressively more prominent usage of electric motors in a wide range of applications, more advanced monitoring of their operating parameters is becoming increasingly important. The in-service observation of the airgap magnetic flux can be very useful for monitoring the status of permanent magnets, which are key to most modern motors' performance. This was recently shown to be possible through the in-situ application of magnetostrictive composite fibre optic flux sensors. This paper reports an electromagnetic finite element model study exploring the performance of various such sensor configurations and positions in the air-gap of a commercial motor design. The aim of the model study is to identify sensor design and positioning features that can yield an enhanced response in observation of rotor flux. It is shown that rectangular cross-sectional flux sensor geometries provide a considerable advantage in this respect.

关键词： PM motors Finite Element Analysis FBG sensor permanent magnets magnetostrictive composite material terfenol-D powder

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System Architecture for a Wireless sensor Network for Sustainable Agriculture, Forestry and Green Spaces in Smart Cities

System Architecture for a Wireless Sensor Network for Sustai...

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IEEE International Workshop on Intelligent Data Acquisition and advanced Computing systems: Technology and applications

作者： Sebastian Rosewick Felix Schorlemer Frank Kunemund Department of Computer Science Fachhochschule Dortmund

This paper presents a wireless sensor network employing heterogeneous mobile robot platforms designed for data-acquisition and research in agriculture, forestry and green spaces in smart cities. Where conventional sensor networks struggle with a lack in flexibility, little variation in their reading points and no possibilities in mobile manipulation, this approach offers new capabilities in accomplishing complex tasks in outdoor and indoor scenarios. A wheel driven robot platform in combination with multiple UAVs 1 1 Unmanned Aerial Vehicle forms the basis for the mobile sensor network which can make use of a wide variety of sensors and actuators. The system benefits from new developments in controlling multi-agent systems as well as machine-learning-algorithms for local and independent data-analysis.

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Factors that Influence E-Satisfaction and Their Impact on Online Purchase Intention of Telemedicine applications 9

Factors that Influence E-Satisfaction and Their Impact on On...

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9th IEEE International conference on Computing, Engineering and Design, ICCED 2023

作者： Gui, Anderes So, Idris Gautama Novantia, Dea Princes, Elfindah Sianipar, Nesti Fronika Haron, Hasnah Bina Nusantara University Information System Program School of Information Systems Jakarta Indonesia Bina Nusantara University Binus Business School Management Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program-Master of Information Systems Management Information Systems Management Department Jakarta Indonesia Bina Nusantara University Faculty of Engineering Food Technology Department Jakarta Indonesia Universiti Sains Islam Malaysia Faculty of Economics and Muamalat Nilai Malaysia

ISBN: (纸本)9798350370126

This study embarked on a rigorous examination of the factors driving user satisfaction and usage behavior in the context of telehealth applications. Utilizing a well- structured Google Form survey, distributed through an array of social media platforms, including WhatsApp, Discord, Instagram, and Line, the research gathered 251 responses. The purposive sampling technique was employed to ensure that the data captured the experiences of individuals who had actively engaged with telehealth services. One hypothesis, relating to the influence of Online Reviews, was found to be unsupported. This surprising result suggests that, contrary to expectations, the opinions and reviews shared online do not significantly affect users' satisfaction or their behavioral decisions in the context of telehealth applications. This finding highlights the need for a more nuanced understanding of the factors influencing user preferences and choices in this rapidly evolving sector. Conversely, other hypotheses examining factors such as Social Influence, Facilitating Conditions, Perceived Reliability, Price Value, and Purchase Intention were substantiated. The positive influence of these factors on customer usage behavior underscores their significance in shaping user experiences and satisfaction within the telehealth ecosystem. Furthermore, the study posits that facilitating conditions should be closely aligned with technological advancements, as this alignment is conducive to the development of enhanced application features. This insight suggests that telehealth providers should continuously innovate and integrate advanced technologies to ensure that the facilitating conditions meet the ever-evolving needs and expectations of users. © 2023 IEEE.

关键词： Sales

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In vivo compound eye imaging using full-field optical coherence tomography 3

In vivo compound eye imaging using full-field optical cohere...

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conference on Unconventional Optical Imaging iii Part of SPIE Photonics Europe conference

作者： Kumar, Pawan Nijas, Mohamed John, Renu Indian Inst Technol Hyderabad Dept Biomed Engn Med Opt & Sensor Lab Kandi 502284 Telangana India

ISBN: (纸本)9781510651494;9781510651487

The anatomical structures of the eye possessed by vertebrates and arthropods are of two major types when considering the natural image-forming systems. Study of these vision systems gives an opportunity to understand the solution to a specific problem developed by the Nature. Also, the understanding of the eye systems in the animal world is fascinating and important in the development of bioinspired manufacturing of dioptric systems for many advanced and sophisticated instruments. The techniques used to study anatomical features of a compound eye like electron microscopies (SEM, TEM), micro computed tomography (mu CT), histology etc. have limitations, making them unsuitable for in vivo studies. In vivo imaging tools especially with high resolution imaging capability can provide an insight of visual ecology in greater detail. It also reduces the cost and the amount of time required to perform experiments. Full-field optical coherence tomography (FF-OCT) is an expansion of OCT technique that uses high numerical aperture microscope objective to get highly resolved en face tomographic image of a biological sample in the in vivo condition. We report the application of an in-house developed time domain full-field optical coherence tomography (TD FF-OCT) system for depth resolved en face and three dimensional, in vivo imaging of dragonfly's prominent compound eye.

关键词： Full field optical coherence tomography Compound eye imaging Dragonfly

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WaveTune: Harnessing Radar sensors to Compose Music Beats with Body Gestures

WaveTune: Harnessing Radar Sensors to Compose Music Beats wi...

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International conference on Distributed Computing in sensor systems (DCOSS)

作者： Suchdeep Singh Juneja Girish Vaidya Marco Zuniga Delft University of Technology Delft Netherlands Amsterdam Institute for Advanced Metropolitan Solutions Amsterdam Netherlands

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

In the field of music technology, the ability to connect body movements with music generation has led to highly engaging applications. Many existing solutions use cameras or wearable devices to capture physical movements and translate them into commands for a music generation tool. While the camera-based systems are privacy-invasive and demand good lighting conditions, the wearable devices adversely impact the fluidity of movements. We propose WaveTune, a privacy-friendly and device-free interface for creating music beats through body gestures. Wavetune has the following components: (i) a millimeter-wave radar as the frontend sensor that captures body movements, (ii) a gesture recognition algorithm that performs gesture delimitation and identification in real-time, and (iii) a music generation component that maintains a seamless and pleasant experience. WaveTune also provides an option to continuously map random dynamic movements into musical parameters to encourage further interaction. We recruited 24 users to train and test WaveTune’s ability to map body gestures to musical commands. To the best of our knowledge, WaveTune is the first mmWave system that allows a layered composition of music beats.

关键词： Performance evaluation Urban areas Music Gesture recognition Millimeter wave radar Cameras Real-time systems

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Wireless Body Area Network (WBAN) Centric Healthcare System for Continuous Monitoring 9

Wireless Body Area Network (WBAN) Centric Healthcare System ...

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9th International conference on advanced Computing and Communication systems, ICACCS 2023

作者： Kumar, M. Anand Valli Mayil, V. Jeyalakshmi, T. Ratha Ghanshala, Anshul Gupta, Anshika Graphic Era University Dept of Computer Applications Dehradun India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh India Jain University School of Sciences Department of Data Science Banglore India The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350397376

Stroke is one the most dangerous disease all across the world which would lead to serious disability or death among all aged people due to changes in environmental factors. Recent reports indicate that a mere 75 million people will suffer from stroke-related disabilities by the year 2030. It was also identified that a stroke occurs every 40 seconds on average across the globe. One out of every five stroke-related patients would die in a year. A Recent health survey indicates that most developed countries face a high number of stroke patients in very recent years. There is also a rapid growth in the number of stroke cases in developing countries. To prevent the high-risk factors related to stroke diseases, there is much need for continuous observation of physiological signals in real-time and to analyze the data. One of the main strategies is wearable technology that employs wireless sensor networks to link numerous medical devices and associated systems. They have to ability to function independently across several platforms. Wearable devices are used in most medical applications because of their noteworthy qualities, including adaptability, versatility, inexpensiveness, and efficiency in various platforms. Integration of the Internet of Things (IoT) with wearable devices and Fog Computing provides a more flexible mechanism to deploy a continuous healthcare monitoring system to monitor vital symptoms and to detect the early phase of stroke factors instantly. To predict stroke-related disorders, this research proposes a Wireless Body Area Network (WBAN)-centric healthcare system for continuous monitoring and examination of physiological signals. Initially, the proposed model predicts the nature of the symptoms based on the parameters collected by the IoT sensors. Deep learning techniques are then applied to predict the risk factors that are critical to the stroke patient during the emergency condition. The key contribution of this research work is to provide an emerg

关键词： Internet of things

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