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8th IEEE International Smart Cities Conference, ISC2 2022

作者： Njaime, Marie Olivier, Fahed Abdallah Snoussi, Hichem Akl, Judy Chahla, Charbel Omrani, Hichem Troyes France Doctoral School of Science and Technology Lebanese University Hadath Lebanon Esch-sur-Alzette Luxembourg Lebanese University Computer and Communication Engineering Dept. Koura Lebanon Artificial Intelligence Researcher Protera Biosciences Paris France

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

Air pollution is a serious environmental danger to people, specifically those who live in urbanised regions. Air pollution is also responsible for the climate crisis. Latest researches have shown the efficiency of early alert procedures that permits citizens to decrease their exposure to air pollution. Hence, monitoring air quality has turned into an essential need in most cities. Circulation, electricity, combustible uses, and various factors contribute to air pollution. Air quality ground stations are placed across most countries to record diverse air pollutants (including NO2), but they have a limited number, constraining therefore the accuracy of ground-level NO2 at high temporal and spatial resolutions. Conversely, satellite remote sensing data measures NO2 densities at a global scale. This paper presents a Data Cleaning technique for satellite images so Transfer Learning could be applied in a further step to estimate NO2 concentrations at Luxembourg with high spatial resolutions based on a pretrained Residual Network 50 (ResNet-50). © 2022 IEEE.

关键词： Remote sensing

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Covid-19 Detection on X-Ray Image Using Deep Learning 4

Covid-19 Detection on X-Ray Image Using Deep Learning

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4th International Conference on Intelligent engineering and Management, ICIEM 2023

作者： Boina, Roja Chaturvedi, Abhay Sharma, Manish Shrivastava, Anurag Kumar, Indradeep Rao, Aln Independent Researcher MorrisvilleNC United States GLA University Department of Electronics and Communication Engineering Uttar Pradesh Mathura281406 India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Aeronautical Engineering Telangana Hyderabad India Lloyd Institute of Engineering and Technology Department of CSE Uttar Pradesh Greater Noida201308 India

ISBN: (纸本)9798350341126

An unexpected pandemic known as COVID-19 struck the entire world in the year 2020. Research in numerous sectors has been prompted to address it as a result of the lack of treatment. Understanding the new variety and developing a vaccination become more challenging as a virus evolves. Numerous nations are impacted by the rise of novel variations. Therefore, it is crucial to assess COVID-19's performance in addition to death forecasting. The proposal develops numerous analyses, visualizations, and predictive models that can forecast COVID 19 performance. The goal is to first track data visualization, later Covid 19 data are analyzed and predicted globally to raise awareness by applying machine learning techniques including linear regression, support vector regression, and Holt forecasting method. The objective of this study is to understand how machine learning methods and applications are used for various COVID-19-related tasks and investigations. An analysis of research published in Science Direct, Springer, Hindawi, and MDPI on this topic in 2020 using the search terms COVID-19, machine learning, supervised learning, and unsupervised learning. In total, 16,306 articles were retrieved, but 14 searches from these publications were used in this study. It has been demonstrated that machine learning can be useful for understanding, predicting, and differentiating COVID-19. © 2023 IEEE.

关键词： COVID-19

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The Optimization of Output of Wind Turbine with the Ongoing Grid System through BP Method Using ANN

The Optimization of Output of Wind Turbine with the Ongoing ...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： B. Prabha Cecil Donald Vijay Karthikeyan Dhandapani Srinivasa Rao Sura Raad Muhammed Sayed Muhsin Jweeg School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Chennai Campus Chennai Department of Computer Science CHRIST (Deemed to be University) Bengaluru Independent Researcher Texas United States Department of Electrical Electronics and Communication Engineering GITAM School of Technology GITAM (Deemed to be University) Visakhapatnam Andhra Pradesh India Collage of Nursing National University of Science and Technology Dhi Qar Iraq Al-Farahidi university Baghdad Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Wind turbines are intricate devices that need careful planning, evaluation, and installation to guarantee peak performance under a range of environmental circumstances. Comprehensive load calculations, performance evaluations, and iterative optimisation processes are all part of the design process. However, complex simulation techniques are required to adequately depict the non-linear behaviour of wind turbine systems because of their complicated structure. Automation of optimisation processes and simulation executions is crucial to optimise the design process and manage the large number of simulations that are needed. This work provides a thorough framework using back propagation (BP) and artificial neural networks (ANN) for simulation and optimization that will make it easier to manage and automate the execution of iterative simulations during the design and development of wind turbines. The framework’s main goals are to make design load case simulations easier and optimise activities more automatically. The framework makes it possible to optimise wind turbine systems and explore design options more effectively by automating these procedures. Three example optimisation jobs illustrate the framework’s versatility and functionality.

关键词： Performance evaluation Backpropagation Automation Artificial neural networks Wind turbines Planning Iterative methods

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The role of cloud computing technology: A savior to fight the lockdown in COVID 19 crisis, the benefits, characteristics and applications

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International Journal of Intelligent Networks 2021年 2卷 166-174页

作者： Alhomdy, Sharaf Thabit, Fursan Abdulrazzak, Fua'ad Hasan Haldorai, Anandakumar Jagtap, Sudhir Professor. Emirates International University-Yemen Ph.D Researcher. School of Computational Sciences S.R.T.M. University Nanded India Associated Professor. Department of Communication Technology and Networks Thamar University Yemen Associated Professor. Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Professor and Principle of Swami Vivekanand Campus India

The contagion of the Coronavirus (COVID-19) led to a global lockdown that put governments in emergency mode. With the total number of positive cases worldwide exceeding the 97.46 million mark, social distancing appears to be the only effective strategy to contain the virus at the moment. As a result, companies face obstacles and find it difficult to respond to this current challenge of remote working. The impact of the novel COVID-19 has created many new challenges, and many of us have had to adopt new ways of working. With the need for accessing to critical applications and the scalability of the infrastructure, cloud computing is emerging as an underlying technology. The cloud technology had a major role in fighting the epidemic;it becomes a salvation for governments and organizations in numerous fields of life, education, health, industry, communication, remote surveillance, and more information. Therefore, this study presents the benefits, characteristics and applications of cloud computing and explains how the cloud contributes to improving life in all regions of the world during COVID-19. It shows that the cloud computing helps countries in combating COVID 19, in education and health sectors, also in the economic and commercial aspects. It investigates the current state by distributing an online questionnaire to various people of academic and non-academic backgrounds in different places over the world in the ICT and education sectors. The results showed that there is an effective role for cloud computing during the epidemic. © 2021 The Author(s)

关键词： COVID-19

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5G-Vanet with Edge Technology: Cooperative Data Transmission

5G-Vanet with Edge Technology: Cooperative Data Transmission

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communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Mohammed Al-Farouni Sandeep Kumar Budhani Jatinder Kaur L. Suvonova Manish Kumar Jaishankar Bhatt Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq School of Computing Graphic Era Hill University Uttarakhand Department of Electrical and Communication Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers" National Research University Tashkent Uzbekistan Scientific Researcher Western Caspian University Baku Azerbaijan Department of Electronics and Communication Engineering GLA University Mathura Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

It is well recognized that linked cars may further enhance in-car entertainment, road safety, and transportation intelligence. 5G-VANET, or 5G-enabled Vehicular Adhoc Networks, may be used to construct software-defined communication with very high accuracy, low latency, and large capacity. Despite being essential for many 5G-VANET enabling programs, mobile data transmission between cars remains a difficult problem due to the massive amount of data required and the high cost of 5G cellular network distribution. This work focuses on cooperative information sharing in 5G-VANET facilitated by edge computing. First, we provide a software-defined cooperation data sharing architecture for 5G-VANET, allowing cellular and targeted short-range communication to operate together effectively. Through cellular connections, OpenFlow-capable cars and the Controller may communicate and exchange contextual data. The data plane (DSRC) makes it easier for nearby cars to share data cooperatively. Secondly, we define the data sharing issue on the created conflict graph as the greatest weighted autonomously set problem, and we provide an effective graph-theoretic solution. In particular, we supply a balanced greedy method that may improve the material distribution balance by taking into consideration ongoing data sharing.

关键词： Greedy algorithms Costs 5G mobile communication Simulation Information sharing Transportation computer architecture Telecommunication computing Automobiles Edge computing

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Group Spam Identification in IOT Home Automation Using Tracking Time Metadata

Group Spam Identification in IOT Home Automation Using Track...

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communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Achilov Docent Saurabh Dhanik Pardeep Kumar Jindal Saif O. Husain Diwakar Bhardwaj Varsha Mittal Department of Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan Scientific Researcher Western Caspian University Baku Azerbaijan School of Computing Graphic Era Hill University Bhimtal India Department of Electrical and Communication Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

With the stark increase due to smarter homes using online of Things, or IoT devices exchanging info far more frequently than we used to — all wirelessly. IoT devices are affected by several issues like possible cyberattacks, the unpredictability of network connectivity and data leakage since there are many types of IoT devices being used today. OBJECTIVES: To improve the privacy of data provided from web of Things (IoT) smart home devices by using machine training and computational modeling to detect anomalies. At its core the experiment contribution is how reliable Web of Things (WoT), i.e. IoT technologies transferring values from or to home things, reasons are taking different features such as average sq rate error origin and feature value in account of evaluations about internet-connected device efficacy. It is a metric that seems so important when we consider our dependency on every attached Internet of Things (IoT) device, in the network's architecture. The proposed method is applied and validated using a dataset of smart homes along with real temperature information collected for public access. Using Time series evidence collected by iot devices, the suggested method works extremely well as it can detect any anomaly or spam. This technique helps with the general health and safety of your entire structure by creating a solid base for evaluating the acceptability of Sea-of-things (SoT) gadgets in just a smart home network..

关键词： Training Data privacy Time series analysis Smart homes Smart meters Telecommunication computing Internet of Things Reliability Object recognition Tuning

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Covid-19 Detection on X-Ray Image Using Deep Learning

Covid-19 Detection on X-Ray Image Using Deep Learning

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Intelligent engineering and Management (ICIEM), International Conference on

作者： Roja Boina Abhay Chaturvedi Manish Sharma Anurag Shrivastava Indradeep Kumar Aln Rao Independent Researcher NC Morrisville USA Department of Electronics and Communication Engineering GLA University Mathura Uttar Pradesh Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Department of Aeronautical Engineering Institute of Aeronautical Engineering Hyderabad Telangana Department of CSE Lloyd Institute of Engineering and Technology Greater Noida Uttar Pradesh India

关键词：

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Scalable AI Solutions for IoT-based Healthcare Systems using Cloud Platforms

Scalable AI Solutions for IoT-based Healthcare Systems using...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Dankan Gowda V Chaithra S M Shantanu Sudhir Gujar Salman Firoz Shaikh Balaji Shesharao Ingole N Sudhakar Reddy Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka Department of Mathematics Faculty of Engineering Management and Technology(BGSIT) Adichunchanagiri University Karnataka India Department of CyberSecurity Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester NY Department of Computer Science Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester NY Independent Researcher India Department of ECE School of Engineering Mohan Babu University Tirupati India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

This research offers a solution for AI in IoT-based health care systems that can help large scale implementation of AI-based cloud platforms for real-time data processing and analysis. The IoT sensors with the cloud-enabled environment supported by the CNNs and LSTM networks for health condition monitoring allows real-time management of patient health status. Based on the experimental context, the system was able to prove that the system can actually filter large data stream and work even through the fluctuating network environment. Specifically, the performance targets such as the time taken to process the data, AI output accuracy, the ability of the system to scale and the robustness of the system were assessed. The results demonstrate that the proposed solution has accuracy of up to 96% in health predictions and is easily scalable in terms of the number of IoT devices involved. Network latency was also well controlled, whereby the lowest network latency for networks was measured in 5G networks. Such conclusions prove that the application of cloud-based AI systems can enhance the conditions of the healthcare provisioning, in terms of timely identification of patients with certain health conditions.

关键词： Accuracy 5G mobile communication Scalability Medical services Real-time systems Time measurement Internet of Things Time factors Artificial intelligence Long short term memory

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Software module development for non-invasive blood glucose measurement using an ultra-wide band and machine learning 2

Software module development for non-invasive blood glucose m...

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2nd Joint International Conference on Emerging Computing Technology and Sports, JICETS 2019

作者： Islam, Minarul Min, Chai Ly Shoumy, Nusrat Jahan Ali, Md Shawkat Khatun, Sabira Karim, Mohamad Shaiful Abdul Bari, Bifta Sama Faculty of Electrical and Electronics Engineering Technology Universiti Malaysia Pahang Pekan Pahang26600 Malaysia School of Computing and Mathematics Charles Sturt University Australia Researcher in School of Computer and Communication Engineering Universiti Malaysia Perlis Pauh Putra Arau-Perlis02600 Malaysia

Diabetes is a chronic disease and in uprising trend worldwide. There is no remedy, hence, blood glucose management is essential by screening blood glucose concentration levels (BGCL) regularly to maintain a healthy life. However, the present way of measuring BGCL is invasive by using a glucometer and drawing a blood sample directly from the human body. To overcome this discomfort-problem, a non-invasive device to measure BGCL is in demand. This paper presents an autonomous software module with a user-friendly graphical user interface (GUI) based on digital signal processing (DSP) and artificial neural network (ANN) to process, classify and recognize the BGL signature from captured ultra-wideband (UWB) signal through human blood medium. To capture the signal, a pair of UWB bio-antenna is placed in between the human earlobe. Received signals are captured and processed through GUI and undergo signal processing, ANN training, testing, and validation. An interface is developed to integrate the hardware (UWB transceiver, bio-antenna, etc.) and the developed software module to make a system. The initial system showed a consistent result with reliability and demonstrated 90.6% accuracy to detect the BGCL. The detection accuracy is 9.6% improved compared to existing work. Besides, this proposed system is cost-effective, user-friendly and suitable to be used by both doctors and home users. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Neural networks

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NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

arXiv

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arXiv 2024年

作者： Liu, Xiaoning Wu, Zongwei Li, Ao Vasluianu, Florin Zhang, Yulun Gu, Shuhang Zhang, Le Zhu, Ce Timofte, Radu Jin, Zhi Wu, Hongjun Wang, Chenxi Ling, Haitao Cai, Yuanhao Bian, Hao Zheng, Yuxin Lin, Jing Yuille, Alan Shao, Ben Guo, Jin Liu, Tianli Wu, Mohao Feng, Yixu Hou, Shuo Lin, Haotian Zhu, Yu Wu, Peng Dong, Wei Sun, Jinqiu Zhang, Yanning Yan, Qingsen Zou, Wenbin Yang, Weipeng Li, Yunxiang Wei, Qiaomu Ye, Tian Chen, Sixiang Zhang, Zhao Zhao, Suiyi Wang, Bo Luo, Yan Zuo, Zhichao Wang, Mingshen Wang, Junhu Wei, Yanyan Sun, Xiaopeng Gao, Yu Huang, Jiancheng Chen, Hongming Chen, Xiang Tang, Hui Chen, Yuanbin Zhou, Yuanbo Dai, Xinwei Qiu, Xintao Deng, Wei Gao, Qinquan Tong, Tong Li, Mingjia Hu, Jin He, Xinyu Guo, Xiaojie Sabarinathan Uma, K. Sasithradevi, A. Sathya Bama, B. Mohamed Mansoor Roomi, S. Srivatsav, V. Wang, Jinjuan Sun, Long Chen, Qiuying Shao, Jiahong Zhang, Yizhi Conde, Marcos V. Feijoo, Daniel Benito, Juan C. García, Alvaro Lee, Jaeho Kim, Seongwan Sharif, S.M.A. Khujaev, Nodirkhuja Tsoy, Roman Murtaza, Ali Khairuddin, Uswah Faudzi, Ahmad'Athif Mohd Malagi, Sampada Joshi, Amogh Akalwadi, Nikhil Desai, Chaitra Tabib, Ramesh Ashok Mudenagudi, Uma Lian, Wenyi Lian, Wenjing Kalyanshetti, Jagadeesh Aralikatti, Vijayalaxmi Ashok Yashaswini, Palani Upasi, Nitish Hegde, Dikshit Patil, Ujwala Sujata, C. Yan, Xingzhuo Hao, Wei Fu, Minghan Choksy, Pooja Sarvaiya, Anjali Upla, Kishor Raja, Kiran Yan, Hailong Zhang, Yunkai Li, Baiang Zhang, Jingyi Zheng, Huan University of Electronic Science and Technology of China China Computer Vision Lab University of Würzburg Germany Shanghai Jiao Tong University China Computer Vision Lab ETH Zurich Switzerland School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen Campus Guangdong Shenzhen518107 China Guangdong Provincial Key Laboratory of Fire Science and Technology Guangzhou510006 China Johns Hopkins University United States Tsinghua University China Zhejiang Dahua Technology Co. Ltd. China School of Computer Science Northwestern Polytechnical University China School of Software Northwestern Polytechnical University China School of Computer Science Xi'an University of Architecture and Technology China South China University of Technology China Fuzhou University China Chengdu University of Information Technology China Hong Kong University of Science and Technology Guangzhou China Hefei University of Technology China Individual Researcher Shenyang Aerospace University China Nanjing University of Science and Technology China Fuzhou University Fuzhou China Imperial Vision Technology Fuzhou China Tianjin University China Couger Inc Japan Sasi Institute of Technology & Engineering India Vellore Institute of Technology India Thiagarajar college of engineering India Coventry University United Kingdom Cidaut AI CVLab University of Wuerzburg Germany Opt-AI University Teknologi Malaysia Kuala Lumpur Malaysia Universiti Teknologi Malaysia Kuala Lumpur Malaysia KLE Technological University Karnataka Hubballi India School of Electronics and Communication Engineering KLE Technological University Karnataka Hubballi India School of Computer Science and Engineering KLE Technological University Karnataka Hubballi India Uppsala University Sweden Northeastern University United States Bosch Investment Ltd. China Fortinet Inc. University of Saskatchewan Canada Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technolog

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field. © 2024, CC BY.

关键词： Image enhancement

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