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A Novel Inception Deep Learning Model for Mobile AI Stroke Prediction System: AI Research and Industry Perspectives for Connected Health

A Novel Inception Deep Learning Model for Mobile AI Stroke P...

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computing and Informatics (ICCI), international conference on

作者： Bassant M. Elbagoury Rytis Maskeliuans Marwa Zaghow Nabil Kamel Faculty of Computer and Information Sciences Faculty of Computer Science and Engineering Ain Shams University Computer Science and Artificial Intelligence Programs King Salman International University KSIU Department of Multimedia Engineering Faculty of Informatics Kaunas University of Technology Lithuania Sales Business and Services Transformation Dell technologies EMEA Egypt Computer Science and Artificial Intelligence Programs Faculty of Computer Science and Engineering King Salma International university KSIU

Artificial intelligence technologies for the mobile health and smart hospitals are important, by applying predictive Analytics and Deep Learning algorithms and developing new models. the main objective is applying artificial Intelligence methods in the medical field, especially for heart/brain stroke diseases diagnosis and prediction for emergency patients' cases. thus, to save patients' lives, also through the integration with IoT and wearable technologies, which are integrated with AI and DL algorithms that help to make sense of bio signals predictive analytics such as biomedical sensors processing. and complex diseases predictions and early detection like heart and stroke diseases. Also dealing with EMG sensors for stroke prediction. Moreover, Intelligent Mobile Health based on our previously introduced project of AI smart hospital and Mobile AI stroke prediction system for connected health and stroke emergencies. In this paper, a new deep learning model has been built and tested for Stroke EMG signal prediction by modifying the Inception-v3 architecture and other deep learning models. Also, this paper compares current results of Mobile AI health Inception model with our previous developed Mobile AI stroke engine that depends on hybrid LSTM deep learning for EMG signals prediction. Both models have achieved high accuracies. Moreover, the inception model is more stable and higher average accuracies that reaches 98%. Moreover, AI research and future industry implementations for Generative AI Dell Technologies servers is discussed.

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Cyberbullying and Hate Speech Detection on Kazakh-Language Social Networks 7

Cyberbullying and Hate Speech Detection on Kazakh-Language S...

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7th IEEE international conference on Big Data Security on Cloud, 7th IEEE international conference on High Performance and Smart computing, and 6th IEEE international conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2021

作者： Sultan, Daniyar Mussiraliyeva, Shynar Toktarova, Aigerim Nurtas, Marat Iztayev, Zhalgasbek Zhaidakbaeva, Lyazzat Shaimerdenova, Lazzat Akhmetova, Oxana Omarov, Batyrkhan Al-Farabi Kazkah National University Faculty of Information Systems Almaty Kazakhstan A. Yassawi International Kazakh-Turkish University Turkistan Kazakhstan International Information Technology University Kazakh-British Technical University Almaty Kazakhstan M.Auezov South Kazakhstan University Shymkent Kazakhstan

ISBN: (纸本)9781665439275

Currently, there is a growing number of online bullying, attacks, and propaganda of prohibited materials over the Internet. the main means of exchanging information, selecting and promoting personnel for such structures is the Internet, namely web resources, social networks and e-mail. In this regard, the task of identifying, identifying communication topics, connections, as well as monitoring behavior and predicting threats emanating from individual users, groups and network communities that generate and distribute information of a bullying network on the Internet. the article is devoted to the research and data mining related to the topic of detecting bullying on the Internet. the study examines the development of the data set aimed to train the machine learning algorithms in the Kazakh language and marking them as con-sist of bullying phrasesor not. © 2021 IEEE.

关键词： Machine learning

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the 6th international Workshop on Health Recommender systems 24

The 6th International Workshop on Health Recommender Systems

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proceedings of the 18th ACM conference on Recommender systems

作者： Hanna Hauptmann Christoph Trattner Helma Torkamaan Department of Information and Computing Sciences University of Utrecht Netherlands MediaFutures University of Bergen Norway Multi-Actor Systems Delft University of Technology Netherlands

ISBN: (纸本)9798400705052

Launched in 2016, the Health Recommender systems Workshop (HealthRecSys) rapidly became a central forum for discussing the transformative capabilities of personalized recommender systems within the health and care sectors. Despite the unforeseen pause due to the COVID-19 pandemic and other challenges, the workshop’s influence persisted through its vibrant community and publications. Our aim with the 6th HealthRecSys is to reignite these conversations and provide a forward-thinking platform that revisits the foundational elements that have contributed to the field’s growth. However, the workshop aspires to do more by infusing new perspectives and tackling the most pressing global challenges and technological innovations head-on with contemporary themes such as the impact of global health crises, generative AI models, personalized and self-managed care, and the increasing focus on health equity. HealthRecSys is dedicated to strengthening the network of researchers working on health recommender systems, drawing participants from an array of health and care domains. through our combined interactive and paper based workshop format, we aim at cultivating a cross-disciplinary community that promotes collaboration among recommender systems specialists, healthcare professionals, ethicists, and policymakers, among others.

关键词： Health recommender systems

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Developing Area Real Estate Valuation Based on Linear Regression and KNN Algorithm 6

Developing Area Real Estate Valuation Based on Linear Regres...

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6th Annual international conference on Data Science and Business Analytics, ICDSBA 2022

作者： Sun, Yuandong Peng, Guanqiao Ucla Master of Applied Stats Department of Statistics Los AngelesCA90095-1554 United States Beijing 101 Middle School Haidian Dsitrict International Department Class2-21 Beijing100091 China

ISBN: (纸本)9781665454506

Investors not only finance real estate in well-developed regions but also developing countries and cities. We use Taiwan Real Estate data from UCI Machine Learning repository and set up linear regression models to predict a House Unit Price and a KNN classification model to categorize the ranks. the findings show that the distance to the nearest MRT station and the number of convenience stores within walking distance have major impacts on the House Unit Price. Governments within developing areas should focus on infrastructure and plan to create more business centers. this focus could increase the population and draw more investors to accelerate development. © 2022 IEEE.

关键词： Houses

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Research on the Impact of Investor Sentiment, Systematic Risk on Bank Stock Prices: Empirical Study Based on VAR and Impulse Response Model 6

Research on the Impact of Investor Sentiment, Systematic Ris...

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6th Annual international conference on Data Science and Business Analytics, ICDSBA 2022

作者： Xu, Tianlin Shi, Xiangyi Feng, Chang Zhang, Shi Jinan University International School Guangzhou511400 China

ISBN: (纸本)9781665454506

this paper examines the impact of investor sentiment and systematic risk on bank stock prices in China through VAR and impulse response function by selecting indicator data reflecting investor sentiment and systematic risk from January 2011 to December 2020 in the context of behavioral finance perspective. It is found that: firstly, both investor sentiment and systematic risk have a significant positive impact on bank stock prices, however, sharp fluctuations in systematic risk can trigger panic and bring down stock prices. Second, investors can become overly optimistic about the climb in stock prices in the short term, while in the long term this sentiment is reversed, bringing stock prices back to stable levels. Based on the research results, this paper gives policy suggestions that government should strengthen expectation management and introduce investors for banks appropriately. © 2022 IEEE.

关键词： Impulse response

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Smart Grid Solutions for Sustainable Mining: A Cost-Effective Energy Management Framework with Optimization algorithms

Smart Grid Solutions for Sustainable Mining: A Cost-Effectiv...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： Sravani Parvathareddy Abid Yahya Lilian Livutse Amuhaya Ravi Samikannu Department of Electrical and Communications Systems Engineering Botswana International University of Science and Technology Botswana Department of EEE New Era Engineering College Botswana

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

this research work focuses on conceptualizing and modeling smart grid infrastructure. the project's main objective is to develop a smart grid infrastructure to improve energy management in industrial processes. Regenerate has deployed a system that uses renewable energy sources like solar photovoltaic (PV) arrays and wind turbines combined with batteries for energy storage. this research work describes a method that first creates synthetic data and then uses a polynomial function to precisely model the statistical fluctuations inherent in the output of renewable energy sources. the process is carried out using MATLAB. A simulation model created using Simulink is also applied to build a smart grid that includes dynamic load control, demand side management DSM, and various control system configurations. the simulation demonstrates that it is possible to produce electricity with minimal or no depreciation related to an increased dependency on renewable energy sources. It can also function at a higher ratio of energy output produced by traditional sources than previously credited within New Zealand while having substantially fewer overall expenses associated with carbon emissions.

关键词： Photovoltaic systems Renewable energy sources Demand side management Electricity Mathematical models Smart grids Wind turbines Load flow control Load modeling Synthetic data

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Hyper-Parameters Tuning Using Meta-Heuristic algorithms for Nurses Stress Detection

Hyper-Parameters Tuning Using Meta-Heuristic Algorithms for ...

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Cyber Resilience (ICCR), international conference on

作者： Ayat Alrosan Waleed Alomoush Merna Youssef Abdelrhman W. Nile Mohanad A. Deif Rania EL Gohary School of Computing Skyline University College Sharjah United Arab Emirates Biomedical Engineering Cairo University Giza Egypt Department of Artificial intelligence College of Information Technology Misr University for Science & Technology (MUST) 6th of October City Egypt

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

this research utilizes machine learning techniques, particularly Random Forest, to examine the impact of mental stress on nurses. Stress, arising from challenging events or demanding conditions (stressors), poses significant risks to mental and physical well-being. Real-time data collection through wearable devices includes physiological measurements, behavioral patterns, and environmental factors, creating a comprehensive method for stress monitoring. Personalized and adaptive models, crucial for accurate stress detection due to its subjective nature, are explored. the study integrates data science and machine learning to enhance efficiency in various stress detection methods, employing meta-heuristics search algorithms. Comparative analysis yielded promising results with a 96.63% accuracy and a 95.76% f1 score.

关键词： Analytical models Machine learning algorithms Computational modeling Metaheuristics Human factors Medical services Real-time systems

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Perception of IOT: Application and Challenges 6

Perception of IOT: Application and Challenges

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6th IEEE international conference on Communication and Electronics systems, ICCES 2021

作者： Mutreja, Mehar Khandelwal, Kunal Dham, Hridya Chawla, Pronika Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Haryana Faridabad India

ISBN: (纸本)9781665435871

IoT is a short form for Internet of things, which is being called the technological revolution because it is going to change the future of communications and computing drastically. A lot of researchers think that it is just an enhancement of telecommunication network or the internet but this is simply not the case. the Internet of things of things makes it easier for humans to interact with computers and learn a wide variety of services like actuators and sensors which are used to collect information, services, and various other sources of objects that are connected to the internet. IoT is also responsible for smart locating, identifying, monitoring and managing things. this is all possible due to Radio Frequency Identification System (RFID) which helps machines and computers to do the above mentioned operations through the help of radio waves. RFID are inserted into a lot of devices along with sensors which play a vital role in IoT. Sensor technology helps in detecting external data. It can be said that if one were to understand the importance of IOT systems in the modern era, which molds the cyber world and the physical world together, then it will forever change the way humans interact with smart objects. © 2021 IEEE.

关键词： Internet of things

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Non-Linearities Study and EVM Characterization of a Sub-6 Frequency Testbed for 5G Communication 6

Non-Linearities Study and EVM Characterization of a Sub-6 Fr...

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6th IEEE international conference on Advanced systems and Emergent Technologies, IC_ASET 2023

作者： Bagayogo, Mamadou Hamouda, Soumaya Barrak, Rim Sup'Com University of Carthage Mediatron Laboratory Tunis Tunisia Sup'Com University of Carthage GresCom Laboratory Tunis Tunisia

ISBN: (纸本)9798350321029

the first deployment of 5G will be on the 4G frequency bands (frequency bands below 3 GHz) and the new Sub-6 frequency bands above 3 GHz. the use of a large bandwidth and new high frequencies requires a preliminary study of the signal quality in this band. the Testbed is an ideal tool to simulate the transmission on real networks and to analyze its performance. In this paper, we setup a Sub-6 Testbed, present its architecture, study the origins of the signal degradation such as phase noise and signal power variations, and evaluate its performance by measuring the Error Vector Magnitude (EVM). the measurements are performed on a cable transmission as well as on an Over-the-Air (OTA) transmission. the results obtained show that the system has a phase noise of -84 dBc/Hz and a power reduction of 1 dBm/ GHz when the frequency increases. Besides, with 64QAM modulation, a minimum EVM of -41 dB is obtained for cable transmission and -35 dB for OTA transmission at a distance equal to 0.5 meter respectively. Moreover, the system EVM limited by the 3GPP group is achieved at a distance equal to 4, 4.3 and 5.9 meters for a 64QAM, 16QAM and QPSK OFDM signal respectively. © 2023 IEEE.

关键词： Phase noise

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Unleashing the Power of Generative Artificial Intelligence: Exploring its Boundless Potential and Overcoming Challenges in Academic Environments 6

Unleashing the Power of Generative Artificial Intelligence: ...

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6th international conference on Contemporary computing and Informatics, IC3I 2023

作者： Fegade, Atul Raut, Rajesh Deshpande, Amruta Mittal, Amit Kaul, Natashaa Khanna, Vandana Sri Balaji University BIMM Pune India Indira School of Business Studies PGDM Pune India Chitkara Business School Chitkara University Punjab India Symbiosis Center for Management and Human Resource Development Symbiosis International University Pune India Sri Sukhmani Institute of Engineering & Technology MBA Department Mohali India

ISBN: (纸本)9798350304480

Integrating generative artificial intelligence tools can allow one to manage and develop novel business methods efficiently. With the introduction of self-learning generative artificial intelligence (GAI) tools, the corporate world is trying to figure out the applications and their business implications. Major technology companies like Microsoft, Google, Facebook, etc., have shown enormous interest in investing in ventures developing generative AI tools/models. these tools/models can be experimented with, and their business use is being explored. the study aims to explore the potential uses of generative AI (GAI) in academics through its current capabilities and applications. the study also highlights the potential challenges and concerns arising from using generative AI in academics. the study has used descriptive and quantitative methods to answer the research questions. the study has used a questionnaire based on the Likert scale to measure the significance of indirect variables, viz. perceived ease of use (PEOU) and perceived trust (PT) on direct variable adoption of generative artificial intelligence (AGAI) in academics. the sample size consists of 100 students and 100 university professors. the study Mentions that perceived ease of use (PEOU) positively influences GAI (AGAI) adoption. Also, perceived trust (PT) has a predictive ability of AGIA when controlling for PEOU. the use of generative AI in a few years will become imperative in most human lives, which makes it mandatory to explore the possible ways of its adoption in the mainstream rather than avoiding or restricting its usage in academics. © 2023 IEEE.

关键词： Academic Integrity BARD ChatGPT Education Generative Adversarial Networks Generative Artificial Intelligence Plagiarism

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