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smart Healthcare: Harnessing the Power of Machine Learning for Predictive Analysis

Smart Healthcare: Harnessing the Power of Machine Learning f...

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Engineering and Technology (PICET), Parul international conference on

作者： Aditya Barhate Praveen Kumar Prateek Verma Nayan Jikar Abhay Tale Vaibhav Hikre 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 Computer Science and Medical Engineering 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

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

smart healthcare employs cloud computing, the Internet of Things (IoT), and supercomputers to manage the healthcare system. Medical data storage volume is increasing, necessitating the adoption of Machine Learning (ML) models for predicting outcomes, particularly in the treatment of chronic illnesses. The trend of care personalization and technological integration is highlighted by the development of ML applications for clinical decision support systems, including real-time predictive analytics. Predictive analytics applications employ ML and Artificial Intelligence (AI) to forecast patient risks, outcomes, and treatment responses. Predictive analytics is more important in healthcare when it comes to detecting Alzheimer's disease, cardiovascular events, radiology, and other conditions. Therefore, the current study highlights the change in illness identification, medical imaging, and personalized medication brought about by ML and Predictive analytics in Precision Medicine (PM). However, ethical concerns and the deployment of transparent AI models still need more research. This study anticipates a time when healthcare will be quicker, better informed, and more patient-centered by using the insights provided by intelligent healthcare data. By effectively saving patient lives, the use of ML in healthcare ushers in a new era for the industry.

关键词： Ethics Solid modeling Medical services Machine learning Radiology Supercomputers Real-time systems Internet of Things Predictive analytics Biomedical imaging

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Offline Parameter Estimation of Isotropic Permanent Magnet Synchronous Motors Based on Regular Operational data and Probabilistic Error Analysis

Offline Parameter Estimation of Isotropic Permanent Magnet S...

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2024 international conference on Electrical Machines, ICEM 2024

作者： Brescia, Elia Savastio, Luigi Pio Di Nardo, Mauro De Musso, Francesco Conte, Giovanni Polichetti, Andrea Politecnico di Bari Dept. of Electr. and Inform. Engineering Bari Italy Idea 75 Bari Italy Free Energy Saving S.r.l Roma Italy

ISBN: (纸本)9798350370607

This paper proposes a novel estimation method of the stator resistance and rotor flux linkage in isotropic permanent magnet synchronous machines. Rather than using ad hoc tests or signal injection required by conventional offline and online approaches, the proposed method aims at estimating the motor parameters using data collected during the regular motor operation. To solve the rank deficiency issue that affects the parameter estimation problem, the proposed method exploits data from two different steady states of the motor. An estimation error analysis which considers the paramater variation, the measurement errors and their stochastic nature is proposed to support the choice of the motor steady states that optimize the estimation accuracy. The proposed estimation approach is validated on a dataset generated by means of a high-fidelity Simulink model also analysing the method's robustness against the variation of the algorithm parametrization, i.e. its hypothesis. © 2024 IEEE.

关键词： Stochastic systems

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Edge computing Technology: State-of-the-Art and Challenges in an Internet of Things 1st

Edge Computing Technology: State-of-the-Art and Challenges i...

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1st international conference on Artificial Intelligence for smart Community, AISC 2020

作者： Raghunathan, Deepa Krishnamoorthi, M. Research Scholar Department of Computer Science and Engineering Dr.N.G.P Institute of Technology Coimbatore India Department of Computer Science and Engineering Dr.N.G.P Institute of Technology Coimbatore India

ISBN: (纸本)9789811621826

The Internet of Things (IoT) is a network of expedients that can intuiting, collecting, managing and communicating data over the Internet without human intervention. It is a concept that connects to nearby objects without user intervention, using a wired or wireless network. In IoT, objects interact and share information to provide the user or users with advanced, intelligent services. In the field of edge computing, data engendered by IoT devices can be processed closer to where it was created, and as an alternative to sending data to data centers or data centers in more ways. New research and customer support in selecting seemly edge computing structures for particular solicitations. This paper gives a complete overview of the methods of introduced power adding structures, comparing Open Source devices through their survival method. Finally, this work sheds light on smart home application tools using Edge computing Systems and also explores the challenges posed by Edge computing Systems. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Edge computing

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A Positioning Optimization System for intelligent Pharmaceutical Production Line Based on Visual Imaging

A Positioning Optimization System for Intelligent Pharmaceut...

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2023 international conference on Power, Communication, computing and Networking Technologies, PCCNT 2023

作者： Hu, Yingkun Zeng, Xiufang Chen, Pinzhong School of Computer and Information Engineering Guangdong Polytechnic of Industry and Commerce 963 Guangzhou Avenue North Tianhe District Guangzhou510510 China School of Electronics and Information Engineering South China Normal University Foshan528225 China

ISBN: (纸本)9781450399951

On the pharmaceutical automation production line, the application of artificial intelligence technologies such as machine vision can make pharmaceutical production automated and intelligent, improve the quality and efficiency of pharmaceutical production. It can be said that machine vision is a major core technology for the automation and intelligence of pharmaceutical production equipment. This article focuses on the visual positioning technology for the filling and sealing process of traditional Chinese medicine in pharmaceutical automation production lines. The research content mainly includes two aspects: The design of visual imaging systems and the research on visual positioning algorithms for bottle mouths, stoppers, and caps. The visual imaging system uses an industrial camera combined with a circular light source, and then uses a hand eye calibration installation method to capture high-quality images of bottle mouth, cork, and cap on a movable robotic arm. Next, a series of image preprocessing operations are carried out on the original image, including Gaussian filtering and smoothing of the image to remove noise, gamma transform image enhancement and edge detection preprocessing operations, and the use of Canny edge detection to obtain images with good foreground and background contrast, appropriate brightness, and prominent edges. Finally, a circle fitting algorithm based on KMeans++ was used to locate the extracted edge images of bottle mouth, bottle stopper, and bottle cap, and the experimental results were preliminarily analyzed. © 2023 ACM.

关键词： Computer vision

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data analytics Using Machine Learning For Cyber Security Safeguards Individual Privacy

Data Analytics Using Machine Learning For Cyber Security Saf...

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Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： Sruthy S Zina Ravindran AkhilKK Gouri M. S Mohammed Waheeduddin Hussain Jothikumar R Department of Computer Science and Engineering (Cybersecurity) St. Joseph’s College of Engineering and Technology Palai Kerala Department of Computer Science and Artificial Intelligence Engineering Baselios Mathews II College of Engineering Sasthamcotta Department of Computer Science and Design Vimal Jyothi Engineering College Chemperi Department of Computer Science and Engineering Baselios Mathews II College of Engineering Lake View Kerala India Department of Information Technology Shadan College of Engineering and Technology Peerancheru Hyderabad India Department of Computer Science and Engineering Shadan College of Engineering and Technology Peerancheru Hyderabad India

The merging of data analytics and machine learning offers tremendous potential for boosting cyber security and protecting individual privacy in an age when personal data and cyber dangers are abundant. The purpose of this study was to create, test, and publish a model that takes into account this junction. Accuracy, precision, recall, F1-Score, and area under the receiver operating characteristic curve (AUC-ROC) are only a few of the indicators that show how well the framework can identify cyber threats. Further, the use of privacy-preserving methods like differential privacy highlights the moral need to guard sensitive information. Readers are given a concrete knowledge of the framework's real-world effect via vivid depictions of its practical use based on hypothetical data sets. This study serves as a helpful resource for a varied audience, from consumers worried about data privacy to organisations looking to better their cyber security defenses and legislators aiming to safeguard citizens' data. It also provides a springboard for further study in areas such as privacy enhancement, more sophisticated machine learning models, and sector-specific framework development. The digital ecosystem is always changing, but this study provides a guide for overcoming obstacles and capitalizing on possibilities at the intersection of cyber security and personal privacy.

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Boost Your Career: Real-Time Job analytics, Virtual Voice Feedback and Job Notification Using Collaborative Filtering Algorithms

Boost Your Career: Real-Time Job Analytics, Virtual Voice Fe...

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Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： Alexis B. Cabrera Criselle J. Centeno Mark Anthony S. Mercado Julianna Angelene A. Miranda Richard C. Regala John Ray M. Tenio College of Information Systems and Technology Pamantasan ng Lungsod ng Maynila (University of the City of Manila) Intramuros Manila

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

In today's rapidly evolving job market, job seekers encounter numerous challenges when trying to identify suitable employment opportunities and adequately prepare for interviews. The project titled “Boost Your Career: Real-Time Job analytics, Virtual Voice Feedback, and Job Notification using Collaborative Filtering Algorithms” seeks to empower individuals by combining advanced analytics with personalized feedback mechanisms. The platform harnesses real-time job market data to provide users with valuable insights into current employment trends and the skills that are most in demand. By employing collaborative filtering algorithms, it generates tailored job notifications that cater specifically to the preferences and qualifications of users. This feature significantly enhances the overall user experience and increases the likelihood of securing a job placement. A standout component of this platform is its virtual voice feedback system, which simulates interview scenarios, allowing users to practice their responses in a realistic environment. Through this system, users receive constructive feedback on their performance, helping them refine their speaking skills and build confidence for actual interviews. By integrating these innovative features, this project aims to create a comprehensive tool that not only assists users in discovering job opportunities but also enhances their readiness for interviews. Ultimately, “Boost Your Career” facilitates a smoother transition into the workforce, equipping job seekers with the necessary tools and insights to navigate their career paths successfully.

关键词： Engineering profession Navigation Collaborative filtering Employment Market research Real-time systems User experience Interviews Sustainable development Qualifications

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Remote sensing identification of seasonal pasture based on Sentinel-2 23

Remote sensing identification of seasonal pasture based on S...

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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： Su, Shiqi Fang, Panfei Qian, Rui Zheng, Pengfei Qiang, Zhenping College of Big Data and Intelligent Engineering Southwest Forestry University Swfu Yunnan Kunming China Southwest Forestry University Swfu Faculty of Forestry Yunnan Kunming China

ISBN: (纸本)9781665494571

In order to research the distribution of seasonal pastures, we used the Sentinel-2 remote sensing data of Golog Tibetan Autonomous Prefecture in 2017-2020 warm season (270-300d) to construct 22 spectral features and topographic features based on GEE platform. Random forest classifier was used to identify seasonal pastures. The results showed that: (1) the overall accuracy and Kappa coefficient of seasonal pasture identification were 82.41% and 0.74, respectively;The producer's accuracy and user's accuracy were (cold season pasture: 84.08%, 80.57%;Warm season pasture: 71.92%, 75.26%);(2) The total area was about 7320972.81 hm2, including 3080067.88 hm2of cold-season pasture and 4240904.93 hm2of warm season pasture. The cold season pasture was mainly distributed in Banma, Jiuzhi and Maqin counties, while the cold-season pasture was mainly distributed among Maduo, Dari and Gande counties. (3) The imagery of the end of the warm season (270-300d) during 2017-2020 were synthesized to ensure the integrity of the image of the study area as much as possible and eliminate the influence of clouds;(4) The water loss and surface exposure to herbage after grazing and treading by livestock are similar to the carbonized surface after fire. Therefore, the spectral characteristics of NDVI and NDMI which sensitive to water and CSI, NBR and NBR2 which sensitive to bare surface have good effects on the identification of seasonal pasture. (5) Most of the areas with high altitude belong to other types, indicating that elevation factors perform well in distinguishing seasonal pastures from other types of land. The results of this study can provide a reference to using remote sensing images to quickly identify seasonal pasture distribution and formulate scientific grazing management policies. © 2021 IEEE.

关键词： Water smart cities Time series analysis Vegetation mapping Land surface Fires Agriculture

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A Novel Path Planning-Aware Vehicular Task Offloading Strategy 23

A Novel Path Planning-Aware Vehicular Task Offloading Strate...

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作者： Liu, Yufei Zhao, Liang Yang, Kaiqi Li, Yunpeng School of Computing Shenyang Aerospace University Shenyang China Ciac China P&t Appliances Beijing China

ISBN: (纸本)9781665494571

In intelligent transportation system (ITS), the combination of vehicle network (VN) and mobile edge computing (MEC) can provide low delay networking services to vehicles. Since the quality of vehicle access network is mainly determined by location, the service quality of vehicle computation task unloading (CTO) should be considered in the process of vehicle path planning, so as to further improve the performance of a single vehicle in unloading throughput, delay, and cost. In this paper, with employing the SDN-based system architecture, we propose a novel path planning-aware vehicular task offloading strategy for connected vehicles in real-time by considering CTO to provide drivers a good driving experience. To achieve the efficient path planning, an effective road performance evaluation method is proposed, which combines the road capacity and the throughput of computation tasks into the driving index to evaluate the priority selected by vehicle drivers. The simulation results show that the proposed method can guarantee the driving time of the vehicle, while it can also give the driver a better quality of experience. © 2021 IEEE.

关键词： Performance evaluation Roads Simulation Systems architecture Throughput Real-time systems Path planning

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MetaKG: From data Collection to User Interaction

MetaKG: From Data Collection to User Interaction

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computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Saeed Hamood Alsamhi Ammar Hawbani Mohan Timilsina Edward Curry Insight Centre for Data Analytics University of Galway Galway Ireland School of Computer Science Shenyang Aerospace University Shenyang China

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

The emergence of the Metaverse as a new platform for human interaction could revolutionize several sectors. This paper provides a new “Metaverse Knowledge Graph” (MateKG) framework to enhance user interaction, data management, and knowledge representation in the virtual environment. The building and interacting MetaKGs is proposed to allow for efficient and successful data management, organization, and exploitation. The novel framework comprises several layers: infrastructure, data gathering, Knowledge Graph (KG), user interaction, evaluation and enhancement, and applications. We discuss several approaches, storage options, ontology designs for KG creation, and data-collecting sources. We look at customization strategies, accessibility concerns for the user interaction layer, and user interface design. Furthermore, we showcase case studies and MetaKG applications in e-commerce, smart cities, healthcare, and entertainment. We provide a starting point for creating reliable and efficient MetaKGs that serve a range of applications and domains.

关键词： Metaverse smart cities Soft sensors Buildings Entertainment industry Knowledge graphs Medical services data collection Electronic commerce Reliability

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analytics and Blockchain for data Integrity in the Pharmaceuticals Industry 4

Analytics and Blockchain for Data Integrity in the Pharmaceu...

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4th international conference on Advances in Computer Technology, Information Science and Communications, CTISC 2022

作者： Kavasidis, Isaak Lallas, Efthimios Gerogiannis, Vassilis C. Karageorgos, Anthony University of Catania Dieei Catania Italy University of Thessaly Faculty of Technology Larissa Greece University of Thessaly Department of Digital Systems Larissa Greece

ISBN: (数字)9781665458726

ISBN: (纸本)9781665458726

The data quantity explosion that we witnessed during the last two decades has lead industrial organizations to exploit this sheer amount of data for tasks that previously would seem impossible. However, larger data volumes, draw together a series of drawbacks that affect data quality and integrity and this becomes more evident in government supervised industrial settings. In many of such cases, public authorities have defined sets of principles (e.g., ALCOA+) regarding data management that industrial organizations must abide to, and either deliberate or not violation of these standards most often comes with severe legal consequences. As a matter of fact, in an effort to follow as much as humanly possible to such principles, pharmaceuticals industries invest heavily in resources to maintain high quality standards in their data urging for automated methods for calculating, monitoring and predicting ***, in complex manufacturing and production lines, data analytics provide means for real-time and continuous monitoring of large numbers of sensor variables and categorical or numerical values where higher order conclusions can be derived and taken into account when business process optimizations are considered. In this work we present an easy-to-use integrated platform for real-time raw sensor data monitoring and pre-processing in pharmaceuticals production lines combining blockchain storage for data integrity and deep-learning capabilities for data analytics. Additionally, the platform is able to calculate, monitor and predict compliance to the ALCOA+ set of principles, reducing substantially the time and effort needed to maintain and calculate such complex parameters manually. © 2022 IEEE.

关键词： data analytics

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