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4th International Conference for Emerging Technology, INCET 2023

作者： Chaudhari, Kalpana Joshi, Dhruvi Harugade, Pratik Jambusariya, Kritik Tiwari, Vaibhav Shah and Anchor Kutchhi Engineering College Department of Electronics and Telecommunication Engineering Mumbai India Shah and Anchor Kutchhi Engineering College Department of Computer Science Mumbai India Shah and Anchor Kutchhi Engineering College Department of Electronics and Computer Science Mumbai India

ISBN: (纸本)9798350335750

The purity of air plays a major role on mortal health of people. Poor quality of air leads to many forms of diseases, most probably in the kids. Looking at the vulnerability of air quality standards the government further decides to take important acts for prevention of such diseases. Ancient ways of tackling diseases have a short range of success due to no access to huge sets of data. For this project , we have considered using machine's algorithm to augur Air quality pointer for megacity Mumbai. Our created model can analyze closer to 93percent of aqi(air quality index), it further also predicts many oxides of carbon,nitrogen,sulfur and oxygen. Therefore,we as a country feel the need to have a machine to read the pollution levels for us to maintain the environment and to start taking respective precautions needed. In numerous artificial and civic regions at the moment,balancing the air index for human health is the biggest task right now. The burning of fossil energies, business patterns, and artificial variables all have a big impact on air pollution. We need to apply models that will record information regarding the attention of air pollutants because of the rising pollutant situations. The quality of people's lives is being impacted by the buildup of these dangerous chemicals in the air, particularly in metropolitan areas. Several lab tests have lately started using the Big Data Analytics fashion as per rise of pollutants in air. © 2023 IEEE.

关键词： Air quality

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A study of the leverage effect in Bitcoin Conditional Heteroskedasticity prediction 10

A study of the leverage effect in Bitcoin Conditional Hetero...

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10th International Conference on Artificial Intelligence and Robotics, QICAR 2024

作者： Abarghouie, Masoud Omidvari Alizadeh, Sasan H. Khademzadeh, Ahmad Department of Computer Engineering Science and Research branch Islamic Azad University Tehran Iran Faculty of Information Technology Iran Telecommunication Research Center Tehran Iran Iran Telecommunication Research Center Tehran Iran

ISBN: (纸本)9798350348873

In today's world, countries establish large companies specifically designed to effectively manage the risks associated with volatility exchange rates and leveraged assets. Bitcoin is widely recognized as the leading cryptocurrency, making it highly appealing to investors and traders. The recorded data reveals a growing number of transactions conducted using Bitcoin cryptocurrency. Consequently, accurately predicting the volatility of Bitcoin's conditional heteroskedasticity holds significant importance for the financial markets. Cryptocurrencies present a challenging situation due to their complex dynamics, extreme observations, asymmetry, and various other non-linear features. Modeling these cryptocurrencies accurately becomes difficult using statistical models, as they fail to consider the leverage effect and provide precise predictions. In this paper, we have employed a hybrid modeling approach by utilizing MLP neural network and GARCH statistical model. The experiments conducted on Bitcoin time series data have yielded promising results, showcasing a notable reduction in the prediction error of conditional heteroskedasticity volatility. ©2024 IEEE.

关键词： Forecasting

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Decoding Smart Grid Equilibrium: Insights from Machine Learning Models 10

Decoding Smart Grid Equilibrium: Insights from Machine Learn...

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10th IEEE International Conference on Power Systems, ICPS 2023

作者： Akter, Sheikh Shraboni Rakib Hossain, Md. Mahedy Hasan, S.M. Akter, Farzana Srizon, Azmain Yakin Farukuzzaman Faruk, Md. Islam, Md. Minhazul Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350318739

In the complex landscape of smart grid stability, this study undertakes a detailed examination of machine learning models, assessing their effectiveness, interpretability, and predictive prowess in this essential sector. With Logistic Regression, Decision Tree, Random Forest, Extra Tree and Support Vector Machine appearing as the algorithms with the most promising results, this study emphasizes the necessity of bridging the gap between complex algorithms and useful, understandable solutions for efficient smart grid management. Shapley and LIME values act as illuminating agents, revealing the importance of different features, while ROC curves articulate the classifiers' efficacy across various thresholds with elegance and precision. The outcomes of this investigation echo the promising capabilities of machine learning in enhancing smart grid stability, albeit necessitating cautious interpretation. These methods offer insightful revelations, underscoring their potential to not only enhance predictive accuracy but also facilitate a more nuanced understanding of the intricate variables influencing grid stability. The study underscores the imperative of bridging the gap between complex machine learning algorithms and practical, interpretable solutions to navigate the intricacies of smart grid management effectively. The findings encapsulate the resonating impact of machine learning, spotlighting its latent capability to transform the smart grid stability landscape while underscoring the essentiality of nuanced interpretation. In the expansive domain of energy management, explainable AI stands as a pivotal element, heralding a new era of clarity and innovation in the intricate world of grid management. © 2023 IEEE.

关键词： Support vector machines

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

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2024 Prognostics and System Health Management Conference, PHM 2024

作者： Cai, Congrui Lu, Zhonghai School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Embedded Systems Master Program Sweden School of Electrical Engineering and Computer Science Department of Electrical Engineering KTH Royal Institute of Technology Sweden

ISBN: (纸本)9798350360585

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms. © 2024 IEEE.

关键词： Long short-term memory

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Gated Recurrent Units and Recurrent Neural Network Based Multimodal Approach for Automatic Video Summarization

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Traitement du Signal 2023年第3期40卷 1227-1234页

作者： Kaur, Lakhwinder Aljrees, Turki Kumar, Ankit Pandey, Saroj Kumar Singh, Kamred Udham Mishra, Pankaj Kumar Singh, Teekam Department of Electronics & Telecommunication Engineering Rungta College of Engineering and Technology Bhilai490024 India College of Computer Science and Engineering University of Hafr Al Batin Hafar Al Batin39524 Saudi Arabia Department of Computer Engineering & Applications GLA University Mathura281406 India Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan School of Computing Graphic Era Hill University Dehradun248002 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India

A typical video record aggregation system requires the concurrent performance of a large number of image processing tasks, including but not limited to image acquisition, pre-processing, segmentation, feature extraction, verification, and description. These tasks must be executed with utmost precision to ensure smooth system performance. Among these tasks, feature extraction and selection are the most critical. Feature extraction involves converting the large-scale image data into smaller mathematical vectors, and this process requires great skill. Various feature extraction models are available, including wavelet, cosine, Fourier, histogram-based, and edge-based models. The key objective of any feature extraction model is to represent the image data with minimal attributes and no loss of information. In this study, we propose a novel feature-variance model that detects differences in video features and generates feature-reduced video frames. These frames are then fed into a GRU-based RNN model, which classifies them as either keyframes or non-keyframes. Keyframes are then extracted to create a summarized video, while non-keyframes are reduced. Various key-frame extraction models are also discussed in this section, followed by a detailed analysis of the proposed summarization model and its results. Finally, we present some interesting observations about the proposed model and suggest ways to improve it. © 2023 Lavoisier. All rights reserved.

关键词： Feature extraction

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Automatic Heliostat Directing System Based On Microcontroller ESP32

Automatic Heliostat Directing System Based On Microcontrolle...

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2023 International Conference on Electrical engineering and Informatics, ICEEI 2023

作者： Medyanto, Richard Sanjaya, Nicholas Liawatimena, Suryadiputra Binus Aso School of Engineering Bina Nusantara University Automotive & Robotics Program Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350381290

Solar energy is one of the most abundant sources of renewable energy in Indonesia. Solar energy is now typically harnessed using solar panels, but the low efficiency of photovoltaic cells requires the development of other alternatives. The heliostat is a sunlight directing device with mirrors that can be used in a concentrated solar power system. Current heliostats require high capital investment due to their large frames and expensive components. This research was undertaken to develop a lower cost heliostat using a smaller frame, ESP32 microcontroller, servo motor and low-cost components. The position of the sun can be determined using an algorithm based on the National Oceanic and Atmospheric Administration (NOAA) solar calculator, and the mirror is moved to maintain the sun's reflection on a target. The result of this research is a set of heliostat prototypes consisting of the frame and control system. Tests were carried out to test the performance of the designed heliostat and it was found that the heliostat has an accuracy of about 60 cm and can raise temperatures up to 3.41°C. The conclusion is that the heliostat can be used in a concentrated solar power system to heat boilers in solar power towers. © 2023 IEEE.

关键词： Solar energy

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COVID-19 Classification from X-Ray Images:An Approach to Implement Federated Learning on Decentralized Dataset

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computers, Materials & Continua 2023年第5期75卷 3883-3901页

作者： Ali Akbar Siddique S.M.Umar Talha M.Aamir Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Department of Telecommunication Engineering Sir Syed University of Engineering&TechnologyKarachi75300Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

The COVID-19 pandemic has devastated our daily lives,leaving horrific repercussions in its *** to its rapid spread,it was quite difficult for medical personnel to diagnose it in such a big *** who test positive for Covid-19 are diagnosed via a nasal PCR *** comparison,polymerase chain reaction(PCR)findings take a few hours to a few *** PCR test is expensive,although the government may bear expenses in certain ***,subsets of the population resist invasive testing like ***,chest X-rays or computerized Vomography(CT)scans are preferred in most cases,and more importantly,they are non-invasive,inexpensive,and provide a faster response *** advances in Artificial Intelligence(AI),in combination with state-of-the-art methods,have allowed for the diagnosis of COVID-19 using chest *** article proposes a method for classifying COVID-19 as positive or negative on a decentralized dataset that is based on the Federated learning *** order to build a progressive global COVID-19 classification model,two edge devices are employed to train the model on their respective localized dataset,and a 3-layered custom Convolutional Neural Network(CNN)model is used in the process of training the model,which can be deployed from the *** two edge devices then communicate their learned parameter and weight to the server,where it aggregates and updates the *** proposed model is trained using an image dataset that can be found on *** are more than 13,000 X-ray images in Kaggle Database collection,from that collection 9000 images of Normal and COVID-19 positive images are *** edge node possesses a different number of images;edge node 1 has 3200 images,while edge node 2 has *** is no association between the datasets of the various nodes that are included in the *** doing it in this manner,each of the nodes will have access to a separate image collection that has no correl

关键词： Artificial intelligence deep learning federated learning COVID-19 decentralized image dataset

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Exploring Deep Convolutional Neural Networks: A Grad-CAM Enhanced Comparative Study for Automated COVID-19 Diagnosis from Chest X-Ray Images 3

Exploring Deep Convolutional Neural Networks: A Grad-CAM Enh...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Reza, Md. Sazid Jahangir Kabir, Mir Md. Rakib Mollah, Md. Abdur Nahin, Nazmul Islam Data Science and Innovation Td School Department of Computer Science & Engineering India University of Technology Sydney Sydney Australia Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350319125

The emergence of COVID-19 in late 2019 sparked a global health crisis. Identifying infections quickly is key to providing proper care and limiting transmission. The Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test, a widely recognized primary diagnostic method for detecting COVID-19, faces challenges associated with cost and compatibility in resource-constrained environments. This issue is resolved by chest X-ray imaging since it is non-invasive and inexpensive. In this domain, research often utilizes pre-trained models on relatively small datasets of chest X-ray images, limiting the potential for optimal generalization and accuracy achievable with larger training sets. Also, these research works limit the exploration of crucial information on salient regions within CXR images, constraining the depth of analysis and interpretation. This study addresses current research challenges through a detailed comparative analysis of convolutional neural network (CNN) architectures, including pre-trained models like Inception V3, Xception, DenseNet-121, VGG-19, and a Custom CNN developed from scratch. The evaluation focuses on their effectiveness in detecting COVID-19 using a relatively vast and comprehensive dataset. The evaluation focuses on assessing the efficacy of these models in detecting COVID-19 across binary-class and multi-class classifications within a large dataset, offering valuable insights into their overall effectiveness. Grad-CAM is employed in this study, which visualizes picture salient areas affecting deep neural network predictions and improves model interpretability. The heatmaps reveal the model's decision-making processes, making their behavior more understandable to medical practitioners. With resulting accuracies of 99.13% and 97.66% in binary-class and multi-class classification, Inception V3 demonstrated the most optimal performance. This proposed approach surpasses existing benchmark methods, underscoring its superiority. © 2023 IEEE.

关键词： chest X-ray CNN COVID-19 Deep Learning DenseNet-121 Grad-CAM Inception V3 ResNet-50 RT-PCR VGG-19 Xception

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Assessing Methodological Validity and Variable Considerations for Forecasting Renewable Energy Load Requirements: A Review

Assessing Methodological Validity and Variable Consideration...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Olagunju, Ademola P. Olusanya, Olamide O. Olaleye, Taiwo Bells University of Technology Department of Electrical/Electronics and Telecommunication Engineering Ogun State Ota Nigeria Bells University of Technology Department of Computer Engineering Ogun State Ota Nigeria Federal University of Agriculture Department of Computer Science Ogun State Abeokuta Nigeria

ISBN: (纸本)9798350358155

This paper presents a systematic literature review conducted to investigate the validity of the adopted methodologies and variable considerations of forecasting renewable energy load requirements. A total of 213 studies from renowned scientific databases, including IEEE, Springer, Elsevier, ACM, and Taylor & Francis, were initially considered, resulting in a final selection of 23 primary studies using the inclusion and exclusion criterion. The review focused on analyzing the internal and external validity threats associated with the methods employed in these studies. Findings revealed several critical gaps in the methodology of the reviewed studies. Studies failed to address the bias-variance tradeoff, which is crucial for ensuring accurate predictions. A significant number of studies ignored parameter optimization in deep learning models, potentially impacting the reliability and precision of the load requirement forecasts. Studies did not conduct tests for multicollinearity of predictive attributes, which may lead to misleading results and inflated model performance. Few studies incorporated environmental and socioeconomic attributes in their datasets, which could have significant implications for energy consumption. Observations from this study highlight the importance of considering these gaps in renewable energy load requirement forecasting to enhance the accuracy and applicability of the resulting models. It emphasizes the significance of properly accounting for bias-variance tradeoffs, optimizing model parameters, and conducting tests for multicollinearity. The inclusion of environmental and socioeconomic variables in training sets is encouraged to capture the intricate relationships between these factors and energy consumption accurately. The study contributes to the improvement of renewable energy load requirement forecasting methodologies by promoting robust practices and considering comprehensive sets of variables. © 2024 IEEE.

关键词： Clean energy

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Real-time Stream Processing in IoT Environments 9

Real-time Stream Processing in IoT Environments

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

作者： Rajasekar, P. Bhosale, Rupali Shekar Indhumathi, C. Sandeep, K.V. Prasanthi Kumari, N. Rajendiran, M. School of Computing Srm Institute of Science And Technology Department of Data Science And Business Systems Tamil Nadu Kattankulathur603203 India Bharati Vidyapeeth Institute of Technology Department of Electronics And Telecommunication Engineering Maharashtra Navi Mumbai400614 India Rmk College of Engineering And Technology Department of Computer Science And Business Systems Tamil Nadu Puduvoyal601206 India Dayananda Sagar College of Engineering Department of Electronics & Telecommunication Engineering Karnataka Bengaluru560111 India School of Computer Sciences University of Petroleum And Energy Studies Uttarakhand 248007 Dehradun India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

New IoT devices generate massive real-time data. Fast, real-time stream processing is needed for IoT data insight extraction. There are pros and downsides to real-time stream processing for the Internet of Things (IoT). This article analyzes present methods and technologies. The inquiry begins with large-scale, real-time IoT data analysis. Real-time analytics and bulk processing limits are important for IoT decision-making. We then investigate IoT stream processing frameworks, methodologies, and systems' complexity. We explore Internet of Things technology selection and tradeoffs between scalability, accuracy, latency, and real-time processing system scalability. It covers real-time stream processing system design and deployment and risk mitigation. Case studies and use cases highlight real-time IoT stream processing. Healthcare, smart city, and industrial IoT operational efficiency and proactive decision-making improve with real-time data. Finally, the report suggests the Internet of Things real-time stream processing research. New IoT data analytics protocols, methods, and technologies are highlighted. We teach real-time stream processing to IoT academics, practitioners, and decision-makers. They could then develop more efficient and scalable IoT data stream optimization solutions. © 2024 IEEE.

关键词： Edge computing

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