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Objective QoE Models for Cloud-Based First Person Shooter Game over Mobile Networks

Objective QoE Models for Cloud-Based First Person Shooter Ga...

引用

Consumer Communications and Networking Conference, CCNC IEEE

作者： Henrique Souza Rossi Karan Mitra Christer Åhlund Irina Cotanis Niclas Örgen Per Johansson Department of Computer Science Electrical and Space Engineering Pervasive and Mobile Computing Luleå University of Technology Infovista AB Sweden

Mobile cloud gaming (MCG) lets users play cloud games (CG) on mobile devices anywhere via mobile networks. However, the stochastic nature of network quality of service (QoS) can result in varying user quality of experience (QoE). Understanding, modeling, and predicting the impact of mobile networks' QoS on users' QoE is crucial. This helps stakeholders optimize networks, and game developers efficiently create cloud-hosted games provisioned over mobile networks. This paper investigates the impact of QoS on users' QoE and proposes, develops and validates novel models for predicting QoE for MCG in mobile networks using realistic subjective tests. In particular, we propose and develop three QoE models using multiple, polynomial, and non-linear regression. Our results validate that multiple regression (with R 2 =0.79, RMSE=0.45) can model complex relationships between QoS factors that impact QoE. Multiple polynomial regression achieved the overall fit with (R 2 =0.94, RMSE=0.24). Lastly, the non-linear model achieved a good RMSE of 0.24. To select the best model out of the three, we applied the F-test and determined that polynomial regression had the best statistical fit.

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Advanced Machine Learning Algorithms for 1Predictive Analytics in Healthcare to Enhance Patient Outcomes with Data-Driven Insights

Advanced Machine Learning Algorithms for 1Predictive Analyti...

引用

2024 IEEE International Conference on Recent Advances in Science and engineering Technology, ICRASET 2024

作者： Pandiarajan, Rajkumar Jagannathan, J. Ramesh, P.S. Ponmalar, A. Sudha School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India R.M.K. Engineering College Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350388602

This research aims to explore the use of modern complex defensive machine learning algorithms in the provision of predictive analytics for health improvement. Incorporating electronic health records, medical image information, and genomic data the work proposes and assesses multiple machine learning models including deep learning, ensemble learning, and reinforcement learning. The main findings shown very high performance in disease prediction, in chronic illness and in the application of efficiency improvements in hospitals as a whole. Overall, such threats as data quality, data privacy, and model interpretability have been discussed in the research and emphasize the importance of the machine learning approach in healthcare. It is highlighted that further development and integration of the interdisciplinary approaches as well as the use of the transparent and interpretable artificial intelligence and machine learning models can be urgent to increase the acceptance among the healthcare staff. This paper enhances the literature on applying machine learning in healthcare systems adding to the repertoire of knowledge for improved patient care. © 2024 IEEE.

关键词： Electronic health record

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Machine Learning based Enhancement of Trading and Business Enterprises 2

Machine Learning based Enhancement of Trading and Business E...

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2nd International Conference on Sustainable computing and Data Communication Systems, ICSCDS 2023

作者： Aruna, M. Jayakarthik, R. Pimo, S. John Khan, Bhola Upadhyaya, Makarand Kuriakose, Neenu Nmit Department of Electrical and Electronics Engineering Bangalore India Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science India St. Xavier's Catholic College of Engineering Department of CSE Nagercoil Chunkankadai Tamil Nadu Kanyakumari629 003 India Yobe State University Department of Economics Damaturu Nigeria University of Bahrain College of Business Bahrain Department of Computer Science Ernakulam India

ISBN: (纸本)9781665491990

Techniques for machine learning (ML) are advancing at breakneck pace, both in academic world and business world. However, various businesses are at different stages of development when it comes to effectively using ML approaches. As a result, the current state of machine learning usage in businesses are analysed. When compared to bigger businesses, small and medium-sized companies (also known as 'SMEs') have a harder time using machine learning technology, according to research findings. However, ML technologies are becoming more popular in business domains. In order to determine the variables that permit success and the factors that contribute to success, 18 organizations in variety of sectors are considered in this qualitative empirical research. The findings indicate that small and medium-sized enterprises, in particular, struggle to use machine learning technology owing to a lack of knowledge of ML. Nevertheless, this deficiency in resources may be compensated by partners and necessary instruments. Many strategies that might close the gap for SMEs are covered in this study. © 2023 IEEE.

关键词： Machine learning

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Plug-in Electric Vehicles Charging Load Profile Estimation

Plug-in Electric Vehicles Charging Load Profile Estimation

引用

International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Mohsen Ghavami Chanan Singh Department of Electrical and Computer Engineering Texas A&M University College Station USA

ISBN: (数字)9798350372786

ISBN: (纸本)9798350372793

Plug-in Electric Vehicles (PEVs) are rapidly expanding in the transportation sector and are an emerging component of the power grid uncertain load. The high level PEV penetration will impose additional demand and will have a negative impact on the power grid. A complete load profile model is required to ensure a realistic and implementable plan related to PEVs and their impact on the power grid. In this paper, a methodology is proposed to estimate the PEV charging load and available storage profile model using the travel patterns derived from the national household travel survey (NHTS). The most recent published national household travel survey (2017 NHTS) is used to approximate the travel patterns of PEVs. The charging profile model proposed in this section aims to be utilized for reliability analysis purposes.

关键词： Surveys Plug-in electric vehicles Power demand Transportation Reliability theory Probabilistic logic Power grids

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Calculation and Validation of Weather-Informed Renewable Generation in the US based on ERA5 Hourly Weather Measurements

Calculation and Validation of Weather-Informed Renewable Gen...

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Power and Energy Conference at Illinois (PECI)

作者： Farnaz Safdarian Jordan Cook Seung Jun Lee Thomas J. Overbye Department of Electrical and Computer Engineering Texas A&M University College Station TX

Due to the benefits of direct inclusion of weather measurements in the power flow studies compared to using cumulative utility capacity factors, we introduce a methodology for the estimation of renewable energy output from detailed ERA5 data based on the U.S. Energy Information Administration generator data and power models and then validate the calculated results of each generator, using publicly available resources. Validation is performed by comparing our estimations against publicly available data for the largest renewable generators in the U.S. The analysis reveals strong correlations with reference capacity factors, underscoring the effectiveness of our approach. This validation not only supports the proposed strategy but also highlights its potential for improving renewable energy models.

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Securing Smart Transportation: Blockchain and IoT for Sustainable Cities

Securing Smart Transportation: Blockchain and IoT for Sustai...

引用

IEEE International Smart Cities Conference (ISC2)

作者： Abdoh Jabbari department of Electrical and Electronics Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia

ISBN: (数字)9798350364316

ISBN: (纸本)9798350364323

Residents of “smart cities” have access to cuttingedge services designed to enhance their daily lives. But it has been noted that there would be particular challenges with the gathering, storing, processing, and analyzing of diverse data that citizens typically bear. Threats to sensor data, products and services, and applications for smart cities may have been introduced by Industry 4.0 factors such as the expansion of the World Wide Web of Things, the use of cloud social media, as well as other similar developments. Data security issues arise as a result of these vulnerabilities. To address the issue of data vulnerability, we provide a decentralized data management platform for safe and intelligent transportation that integrates blockchain technology with the World of Things within an environmentally friendly smart city setting. In order to guarantee efficiency and adaptability, a smart mobility system necessitates the development of an integrated transportation network. Following an overview of relevant background material, this paper presents a data architecture based on Hyperledger Fabric that facilitates a trustworthy, secure smart transportation system.

关键词： Cloud computing Smart cities Social networking (online) Distributed ledger Fabrics Blockchains Smart transportation Fourth Industrial Revolution Internet of Things Web sites

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Real-Time Cell Viability Measurements Using Lab-on-CMOS-Capacitance Sensor Array

Real-Time Cell Viability Measurements Using Lab-on-CMOS-Capa...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Prithwish Dasgupta Kyle Nielsen Pamela Abshire Sheung Lu Department of Electrical and Computer Engineering University of Maryland College Park MD United States

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

We report advances in packaging and testing for existing Complementary Metal Oxide Semiconductor (CMOS) chips that extend their longevity and reusability, thus increasing their effectiveness in monitoring cell viability and facilitating concurrent visual inspection. We created several Printed Circuit Board (PCB) designs aimed at mitigating packaging failures while facilitating data collection using a microcontroller ensuring the creation of a reliable and replaceable cell viability measurement device. Using 3D Modeling software and programming Field Programmable Gate Arrays (FPGA), we developed a rapidly interchangeable test platform and established a data readout system. These advancements notably enhance research efficiency and data quality by minimizing downtime and improving the correlation of capacitance measurements with direct visual observations of cell behavior.

关键词： Visualization Semiconductor device measurement Three-dimensional displays Printed circuits Packaging Software Software reliability

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Machine Vision Applications in Robotics and Automation 9

Machine Vision Applications in Robotics and Automation

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9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Bharathi, G.P. Kavitha, P. Abhang, Laxman Baburao Rathinasabapathi, G. Sharma, Vipin Sathish, T. Sri Sai Ram Institute of Technology Department of Computer And Communication Engineering West Tambaram Tamil Nadu Chennai600045 India R.M.K Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Kavaraipettai601206 India Pravara Rural Engineering College Department of Automation And Robotics Maharashtra Loni413736 India Panimalar Engineering College Department of Mechanical Engineering Tamil Nadu Chennai600123 India Medi-Caps University Department of Mechanical Engineering Madhya Pradesh Indore453331 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350365092

The extensive incorporation of machine vision into the fields of robotics and automation in a variety of different ways. The various uses of machine vision and the revolutionary impact it has on the capabilities of robotic systems. This investigation is fueled by improvements in imaging technology and artificial intelligence. In the abstract, major features such as object detection, navigation, and human-robot interaction are discussed in depth. The abstract also demonstrates how machine vision algorithms contribute to enhanced precision, efficiency, and adaptability in automation processes. The purpose of thisis to shed light on the role that machine vision plays in revolutionising industrial automation, collaborative robotics, and autonomous systems by conducting an examination of real-world implementations and case studies. This all-encompassing investigation not only highlights the current status of machine vision applications but also forecasts the future trajectory of this technology, thereby establishing it as a foundational component for the development of robotics and automation in a variety of fields. © 2024 IEEE.

关键词： Navigation systems

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Application of Synthetic Geomagnetic Storms on Transformer Thermal Assessment

Application of Synthetic Geomagnetic Storms on Transformer T...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Rida Fatima Adam B. Birchfield Department of Electrical and Computer Engineering Texas A&M University College Station Texas

The lack of extensive data on severe geomagnetic storms has necessitated the need to generate synthetic time-series electric field, particularly to assess and predict the im-pact of such extreme storms on the resilience of the electric power grid. Due to geomagnetic disturbances, geomagnetically induced currents (GIC) can lead to half-cycle saturation of the transformers, placing the equipment at the potential risk of experiencing increased hotspot heating. This paper describes a methodology for generating extreme synthetic geomagnetic storms through an iterative process of temporally and spatially varying the fragments of the NERC benchmark event by scaling the time duration, magnitude, and direction of the storm. In addition, the results of the thermal sensitivity of the transformers to the assumed quasi-de electric fields simulated on a large-scale synthetic electric grid are presented. Further engineering assessments may utilize the computed extreme GIC scenarios.

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Non-Invasive and Cost-Effective Monitoring of Street Lighting Photometric Performance

Non-Invasive and Cost-Effective Monitoring of Street Lightin...

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International Conf on electrical and Electronic engineering (ICEEE)

作者： Esrat Khan Leen Ba'ba' Ruqaia Alshatri Saba Parvez Abdalla Gad Mohamed Jama Electrical Computer and Biomedical Engineering Department College of Engineering Abu Dhabi University Abu Dhabi UAE

ISBN: (数字)9798350362541

ISBN: (纸本)9798350362558

The United Arab Emirates has a large and sophisticated street lighting infrastructure; therefore, the photometric performance of the street lighting systems must be prioritized. Locating the malfunctions in a large street lighting infrastructure can be time-consuming and arduous. In this paper, a low-cost street lighting performance monitoring system is designed, implemented, and tested. The proposed system comprises three main components: an illuminance sensor, a spectrometer, and a GNSS receiver. These components build a light and robust system to mount on vehicles' rooftops. Additionally, a comprehensive data analysis and inference engine is developed to determine the photometric performance of the lighting poles by identifying the number and the position of the defective luminaires, under-performing luminaires, poles obstructed by vegetation, and lighting poles that suffer from a complete power outage due to electrical faults in lighting control cabinets. Lastly, the field survey results are demonstrated in reports and interactive online visualization using Google Maps, in which the obtained information can be converted into performance metrics and indexes overlayed on urban maps.

关键词： Photography Measurement Fault diagnosis Web services Lighting Vegetation mapping Data visualization Satellite images Batteries Monitoring

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