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In-depth review of integration of AI in cloud computing 3

In-depth review of integration of AI in cloud computing

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3rd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Mohamed, Nachaat Sridhara Rao, L. Sharma, Manju Sureshbaburajasekaranl Badriasulaimanalfurhood Kumar Shukla, Surendra Universiti Sains Malaysia Rabdan Academy UAE Homeland Security Abu Dhabi United Arab Emirates J.B. Institute of Engineering and Technology Dept. of Information Technology Hyderabad India Lecturer Jazan University Jazan Saudi Arabia NVIDIA Corporation Group Product Manager United States College of Computer and Information Sciences Princess Nourahbint Abdulrahman University Department of Computer Sciences Saudi Arabia Graphic Era Deemed to Be University Department of CSE Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

The successful distribution of computer services, including software, storage needs, analytics, intelligence, and many others, is reflected in cloud computing. Additionally, it provides quicker innovation while simultaneously providing flexible *** learning (DL), a subset of artificial intelligence (AI) and machine learning, is widely seen as a key technology of the Fourth Industrial Revolution (4IR or Industry 4.0). The artificial neural network (ANN)-based technique known as deep learning (DL) has gained popularity in the computing world due to its capacity for learning from data. frequently used in a variety of application fields, including cybersecurity, healthcare, visual recognition, and many more. However, because of the dynamic nature and differences in real-world environments, creating an acceptable DL model is a difficult process. issues and information Additionally, because of a lack of fundamental knowledge, DL techniques become black-box devices that hinder standard level progress.A cloud computing system in an organization offers high data security at a cheap maintenance cost. Business cooperation has increased significantly as a result of cloud technology. Use of AI has enhanced each of those procedures. The use of artificial intelligence in certain industries has a considerable impact on how successful cloud services techniques are, resulting in a consequence, the two innovations' cumulative effect boosts the prosperity of such businesses. The use of smart-device and computer vision models improve the effectiveness of public clouds. It has also demonstrated implementing AI into public cloud strategies may benefit enterprises in a variety of *** essay includes a thorough introduction and an insightful literature review. Three objectives are outlined in a methodological approach that has also been given. © 2023 IEEE.

关键词： intelligent systems

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An Education Program for Big data Security and Privacy

An Education Program for Big Data Security and Privacy

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IEEE international conference on Big data Security on Cloud (BigdataSecurity), High Performance and smart computing (HPSC) and intelligent data and Security (IDS)

作者： Bhavani Thuraisingham Kim Nimon Latifur Khan The University of Texas at Dallas The University of Texas at Tyler

ISBN: (数字)9798350389524

ISBN: (纸本)9798350389531

This paper describes the course that we developed at the University of Texas at Dallas on Big data Security and Privacy with funding from the National Science Foundation. The project started in 2017 and was completed in 2023. We have taught the course starting Fall 2020, Fall 2021, Fall 2022 and we are teaching it for the fourth time in Fall 2023. This is an extremely popular course and we limit the enrollment to 80 students. Students have some background in cyber security and big data analytics. This paper provides an overview of the organization of the course, the course content, and learning outcomes and the evaluation carried out. The members of the project include those who have expertise in (i) Cyber Security, (ii) Big data analytics, and (iii) Education Research.

关键词： data privacy Education Organizations Machine learning Learning (artificial intelligence) Big data Security

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Incentivizing smart Vehicles for Autonomous Driving HD Maps using predictive analytics

Incentivizing Smart Vehicles for Autonomous Driving HD Maps ...

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2023 international conference on Machine Vision, Image Processing and Imaging Technology, MVIPIT 2023

作者： Khattak, Muhammad Ilyas Hui, Yuan Ullah, Inam Khan, Ajmal Ahmad, Ayaz Shandong University School of Control Science and Engineering Shandong province Jinan City China Depart. of Computer Science Shandong province Jinan City China Woosong University AI & Big Data Depart. Daejeon Korea Republic of Depart. of Electrical and Computer Engineering Wah Cantt Pakistan

ISBN: (纸本)9798350306545

Recent advancements in distributed data storage, colloborative processing capabilities, and their architectural design have a profound influence on our daily lives, simplifying the process of hosting computation-intensive software applications. A single software hosting device, for instance, an Onboard Computation Unit (OBCU) of an autonomous vehicle, on the other hand, can't keep up with the growing need for diverse requirements of computing architectures and processing power of such applications. For example, the real-time handling and generation of HD maps is one such crucial and highly computation-intensive application for autonomous vehicles. For this purpose, an efficient solution is to rely on the computing power of adjacent resource-rich nodes. Hence, the use of a latency-aware predictive analytics-based computation task offloading method has a vital significance, especially with the advent of Vehicular Edge and Fog computing (VEFC). VEFC models pool redundant computational resources in close proximity. It, however, faces several challenges. The stochastic nature of vehicular networks, intricate heterogeneity at several levels, and the uncertainty of time-sensitive applications for the timely completion of tasks impede the VEFC's efficiency. We developed a computation offloading framework that timely alleviates the computing-resource-deficit devices by deploying vehicles as fog-nodes commensurate with the minimum latency requirements of advanced vehicular applications, especially computation-intensive HD map handling, and generation in real-time. Our technique is a greedy heuristic method that works in small, discrete steps instead of solving the entire optimization problem holistically. We used Monte Carlo simulation to show that our approach had an overall response time of under-300 milliseconds compared to other baseline methods. © 2023 IEEE.

关键词： Computation offloading

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The Logical Development of Internet of Things Based Elderly and Disabled Peoples Monitoring System Using smart Sensors

The Logical Development of Internet of Things Based Elderly ...

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

作者： Hubert Mary. L Shaik Mahaboob Basha Y Nandurkar Amjad Omar Safori N. Suriya Mohanalakshmi S Department of ECE Jeppiaar Institute of Technology Chennai Tirupati Andhra Pradesh Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of Journalism and Digital Media Faculty of Media Zarqa University Zarqa Jordan College of Advertising University of Business and Technology Jeddah Saudi Arabia Department of ECE Chennai Institute of Technology Chennai Department of Electronics and Communication Engineering R.M.K. Engineering College Kavaraipettai Tamil Nadu

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

As populations’ age and the need for support for individuals with disabilities increases, there is a growing demand for innovative solutions that enhance independence and quality of life. The development of a smart IoT-based monitoring system to support the quality of life of the sick and old is presented in this study. The system continuously gathers data from various sources relevant for the healthcare system using endpoint devices like wearable sensors, environmental monitors, as well as smart home devices. In this paper, a new approach is introduced called IoT based Elder/Disabled Human Assistance (IoTEHA), in which it is compared with the current method, Elder People Monitoring System (EPMS), for human activity monitoring. The objective is to give special care people more protection and peace of mind. Diagnostics are derived using advanced data analytics in conjunction with machine learning algorithms, enabling anomaly detection as well as risk prediction. Caregivers receive alerts and can intervene in real time to prevent accidents from ever occurring. It also has privacy and security features as well to keep the user data secure. In order to demonstrate the system's efficacy in enhancing the lives of the elderly and crippled, it will be strictly evaluated if it meets that requirement. Advanced data analytics and the proposed learning algorithms called IoTEHA and EPMS are employed to interpret the collected data, enabling the system to offer predictive insights and alerts. This proactive approach supports caregivers and healthcare professionals in addressing issues before they escalate, improving overall care and safety.

关键词： data privacy data analysis Soft sensors Medical services Real-time systems Internet of Things Older adults Monitoring intelligent sensors Wearable sensors

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Real Time Monitoring and Configuration Interface for smart Home Device Management Using Streamlit and Pygame

Real Time Monitoring and Configuration Interface for Smart H...

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Advances in data Engineering and intelligent computing Systems (ADICS), international conference on

作者： G Suma Mani G Venkata Krishna M.D. Anto Praveena P. Asha Mercy Paul Selvan Dept. Of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

One of the helpful tool for measuring and managing home power consumption is one of the clever and creative tool for tracking the usage of Home Power Management System. The Goal of this system is to lower the power bills, also promote energy efficiency, and provide the users active control over how much energy they use. smart meters, an intuitive and user-friendly smartphone app, and real-time energy monitoring sensors are some of the system's essential parts and these are the key parts of the system. Installing smart meters makes it possible to accurately track the power usage and provide data to the system. Users can be able to see their energy usage instantly and make smart decisions when real-time energy monitoring equipment is included. Further, the system uses advanced smart technology, analytics and machine learning algorithms to provide clients customized suggestions on how to save energy consumption. Homes may save a lot of money on electricity bills, help the environment, and have more control over how much energy they use by using our smart Home Power Management System.

关键词： Renewable energy sources Home appliances Energy consumption Power system management Real-time systems Energy efficiency smart meters

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ESPEAR: Enhanced Stream Processing for Environmental analytics and Renewable Energy Integration Using smart Dielectric Sensors

ESPEAR: Enhanced Stream Processing for Environmental Analyti...

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IT Innovation and Knowledge Discovery (ITIKD), international conference on

作者： Akey Sungheetha Rajesh Sharma R Sheila Mahapatra C H Sairam Department of Computer Science and Engineering Alliance University Bengaluru India Department of Computer Science and Engineering Alliance College of Advanced Computing Alliance University Bangalore India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

This paper presents ESPEAR (Enhanced Stream Processing for Environmental analytics and Renewable Energy), an innovative methodology integrating smart dielectric sensors with stream processing for environmental monitoring and renewable energy optimization. Building upon recent advances in green chemical reactions using inert solid dielectrics, we develop a comprehensive system that combines real-time environmental data processing with 5G millimeter-wave technology. The system implements the STAR cache-based warehouse architecture for efficient spatial data management while incorporating sustainable energy practices. Our experimental results across fourteen countries demonstrate significant improvements in processing efficiency (45%), energy consumption reduction (38%), and monitoring accuracy (92%) compared to existing methods. The implementation shows particular promise in smart city applications, achieving a 40% reduction in response latency while maintaining 99.9% system reliability.

关键词： Renewable energy sources Energy consumption smart cities Real-time systems Energy efficiency Dielectrics Environmental monitoring Reliability Optimization intelligent sensors

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ResQ – intelligent Real-Time Disaster Management Using Machine Learning and IoT

ResQ – Intelligent Real-Time Disaster Management Using Mach...

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

作者： N Duraimurugan Reshma A Reshma S Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The project, therefore, is to be a ResQ using deep learning capabilities for an enhanced response and coordination in cases of disaster. The best sources of real-time data on the weather services, government alerts, and user reports then integrate into critical updates and predictions during a disaster. The ResQ has such as features: real-time alerts, access to emergency contact information, route optimization evacuation, resource mapping, user-generated incident reports, first aid tips, offline functionality, volunteer coordination, and multi-language support. Deep learning algorithms in fact enhance predicting capabilities and thereby improve better management of disasters and effective allocation of resources. It is a means through which the effects of disasters will be reduced, rationalizing rescue procedures and saving more lives in the long term.

关键词： Deep learning Disasters Government Disaster management Prediction algorithms Real-time systems Resource management Predictive analytics Standards Resilience

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Adaptation of Seven-Layered IoT Architecture for Energy Efficiency Management in smart House 10

Adaptation of Seven-Layered IoT Architecture for Energy Effi...

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10th international Scientific conference on Computer Science, COMSCI 2022

作者： Kutseva, Milka Technical University of Sofia Plovdiv Branch Department of Computer Systems and Technologies 25 Tzanko Dustabanov Plovdiv4000 Bulgaria

ISBN: (纸本)9781665497770

This article provides an overview of the different types of Internet of Things (IoT) architectures, with more emphasis on new seven-layered model architecture for future big data-driven smart homes applied when building a system for achieving energy efficiency. One of the next evolutionary stages in Internet-based computations is the Internet of Things (IoT), which is already making a beneficial difference in a wide range of application sectors, including smart cities, sustainable living, healthcare, production, etc. We can transform the conventional grid into a more effective and intelligent energy grid with the help of IoT. The standards for various IoT technologies, including sensing, communication, and computing technologies, as well as how they relate to smart energy grids, will be the main focus of this article. Additionally, this article gives a thorough outline of the prior research on its IoT applications for smart Grid systems. We conclude by highlighting a brand-new layered architecture that is suggested for the management and processing of massive data in future smart Homes. The suggested Representational State Transfer (REST)-based architecture for future smart homes' efficient data interchange and processing activities has seven layers, including physical, fog computing, network, cloud computing, service, session, and application. © 2022 IEEE.

关键词： Fog computing

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IoT Enabled Electronic Security & Remote Monitoring Solution For Electric Bikes 3

IoT Enabled Electronic Security & Remote Monitoring Solution...

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3rd IEEE international Power and Renewable Energy conference (IPRECON)

作者： Joy, Anna Mariya Ravi, Aravind Roy, Jestin Karthikeyan, Gayathri V. Rajan, Thomas P. Mar Athanasius Coll Engn Dept Elect & Elect Engn Kothamangalam India

ISBN: (纸本)9781665491754

The Internet of Things (IoT) will be able to integrate a wide range of various and heterogeneous end systems transparently and smoothly, while also offering open access to chosen subsets of data for the creation of a wide range of digital services. The enormously wide range of devices, link layer technologies, and services that could be included in such a system makes it a very difficult undertaking to create a standard design for the IoT. This study suggests creating a short-distance shared transportation system with smart mobility. The system comprises of a charging station, a web application, a mobile application, a server, and an edge computing device that is mounted on an electric bike (e-bike). Each bicycle is regarded as a thing (Thing) that is linked to the internet, or a "Internet of Things" (IoT). The location, condition, and environmental parameters of the bike are uploaded to the web server for data analytics and display by an administrator-specific web application. Users can start utilising the bike sharing system by downloading a mobile app. The proposed system can be scaled to build a distributed shared mobility system that can fit hundreds of e-bikes.

关键词： Software analytics Visualization Internet of Things Electronic Security Electronic Mobility Remote Monitoring intelligent Transportation

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intelligent Medication Vending Machine for Enhanced Adherence

Intelligent Medication Vending Machine for Enhanced Adherenc...

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computing and data analytics (ICCDA), international conference on

作者： K Sowndarya C Linda Hepsiba Department of Data Science Bishop Heber College (Affiliated to Bharathidasan University) Tiruchirappalli India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

In the field of healthcare, non-adherence to medications is still a big issue, which greatly influences the patients' outcomes and the efficacy of the prescribed medications. This paper proposes a smart solution to ensure patients adhere to health practitioners prescribed medication regimen, the intelligent Medication Vending Machine. The one proposed here has a fairly friendly UI, especially when entering prescription schedules and doses. It also arranges the spatial storage of pills and releases them at appropriate cycles throughout the day. A detailed reminder system incorporated into the system provides timely alarms at the scheduled time and 15 minutes to the appointment at the clinic. In this case, the dissemination of the results is handled via phone and email to the corresponding guardians if the patient has not ingested a medication within 15 minutes of the set time. Moreover, it monitors the quantity of medication stock and sends email alert to the guardians a week before stock exhaustion and followed by the reminder three days to the stock exhaustion and this ensures rapid restocking. By means of the convenient and intuitive GUI, the users can edit prescription details, dosage information, and schedules swiftly. This device is expected to increase the usage of drugs significantly by patients and in the process, improve the patient's condition, therefore, reducing health costs.

关键词： Schedules Costs Hospitals Refining Medical services Internet of Things Biomedical monitoring smart devices Standards Monitoring

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