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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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A Framework for Dress Code Monitoring System using Transfer learning from Pre-Trained YOLOv4 Model 11

A Framework for Dress Code Monitoring System using Transfer ...

引用

11th international conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET - SIP 2023

作者： Agarwal, Divyangini Gupta, Prashant Eapen, Naived George Department of Data Science Lavasa India

ISBN: (纸本)9798350348422

Maintaining a proper dress code in organizations or any environment is very important. It not only imbibes a sense of discipline but also reflects the personality and qualities of people as individuals. To follow this practice, some organizations like educational institutions and a few corporations have made it mandatory for the personnel to maintain proper attire as per their regulations. Manual checks are performed to adhere to the organizations' regulations which becomes tedious and erroneous most of the times. Having an automated system not only saves time but also there is very little scope of mistakes and errors. Taking this into context, the main aim and idea behind the project is to propose a model for detecting the dress code in such workplaces and educational institutions where the attire needs to be regularly monitored. the model detects Business Formals (Blazer, Shirt & Pants) worn by the personnel, for which CNN has been considered, along with YOLOv4, for performing the detection, due to its nature of giving the highest accuracy in comparison to the other object-detection models. Providing the Mean Average Precision of around 81%, it becomes evident that the model performs quite well in performing the detections. © 2023 IEEE.

关键词： Object detection

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Advanced Cardiovascular Health Management through Comprehensive Risk Assessment and Predictive Modeling of Cardiac Arrest 11

Advanced Cardiovascular Health Management Through Comprehens...

引用

11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Guharoy, Rabel Dubey, Brahmanand Mohapatra, Srikanta Kumar Sidharth, Suhel Rashtriya Raksha University Sitaics Gandhinagar India Nit Durgapur Department of Cse Durgapur India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Bvdu Det Department of Aiml Navi Mumbai India

ISBN: (纸本)9798350350357

Cardiovascular diseases, with cardiac arrest being a prominent example, continue to be a leading global health concern. In this context, effective risk assessment and predictive modeling play a pivotal role in enhancing early diagnosis and tailored management of cardiac arrest. this research paper presents an extensive investigation into the comprehensive risk assessment and predictive modeling of cardiac arrest, incorporating clinical and demographic features as essential components for a holistic approach to cardiovascular health management. Our study focuses on a set of 13 key features. these features are commonly recognized in cardiology as influential factors in cardiac arrest diagnosis and prognosis. Utilizing new-fangled machine learning techniques, we construct predictive models that offer risk assessments for cardiac arrest, enabling early identification of individuals at risk of a heart attack or other cardiovascular events. the critical role of these models in this healthcare context, facilitating improved patient care and tailored interventions for individuals susceptible to cardiac arrest. the research culminates in a multifaceted approach to cardiac arrest risk assessment and predictive modeling, harnessing the synergy of clinical and demographic data. Our findings underscore the significance of early intervention and individualized care, further advancing the goal of reducing the global burden of cardiovascular diseases, particularly in the context of cardiac arrest. © 2024 IEEE.

关键词： Cardiology

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HAXM: A Hybrid Scheme to Predict Breast Cancer Using Adaboost and XGBoost with Multi-Layer Perceptron Classifier 4

HAXM: A Hybrid Scheme to Predict Breast Cancer Using Adaboos...

引用

4th IEEE international conference on Computing, Communication, and intelligent Systems, ICCCIS 2023

作者： Guchhait, Anusmita Verma, Aashish Tarun, Sashi Apex Institute of Technology CSE Chandigarh University Punjab Mohali India Akal College of Engineering and Technology CSE Eternal University Baru Sahib Himachal Pradesh India

ISBN: (纸本)9798350306118

Millions of women around the world are affected by breast cancer, which is a devastating and common disease. Early prediction is crucial for improving outcomes as it can be difficult to effectively treat at later stages. By improving prognosis, diagnosis, and treatment options, Treatment for breast cancer might undergo a radical change if artificial intelligence (AI) is implemented and will be used in clinical decision-making, risk assessment, and personalized treatment. AI-powered solutions, like computer-aided prediction systems and machine learning algorithms, can result in accurate and sooner diagnosis of breast cancer. To distinguish between cancerous and non-cancerous breast tumors, doctors and pathologists needed some automated technologies. the data gathered by the medical fields can be used to make judgments and execute diagnoses using machine learning algorithms. this paper compares different machine learning algorithms' performances, including Logistic Regression, Decision Tree, K Nearest Neighbours (k-NN) and Support Vector Machine (SVM) along with Naive Bayes, plus ensemble machine learning algorithms like AdaBoost, Random Forest, and XGBoost with our proposed hybrid machine learning model called HAXM. the primary objective is to assess the efficiency and effectiveness of each algorithm based on various criteria, including the Fl-score, the AUC-ROC curve, recall, accuracy, and precision, regarding the correctness of data classification. the trial findings show that HAXM has the lowest mistake rate and the greatest accuracy, at 99.41 percent. © 2023 IEEE.

关键词： Efficiency

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Open-Circuit Fault Diagnosis Based on Deep learning for Four-Level Active Neutral-Point Clamped Inverters 11

Open-Circuit Fault Diagnosis Based on Deep Learning for Four...

引用

11th international conference on Power Electronics - ECCE Asia, ICPE 2023-ECCE Asia

作者： Apsari, Dyan Puspita Jung, Jiwon Lee, Dong-Choon Department of Electrical Engineering Yeungnam University Gyeongsan Korea Republic of

ISBN: (纸本)9788957083505

In this paper, a deep learning-based fault diagnosis has been proposed for improving reliability when detect faulty switches of four-level active neutral point clamped (ANPC) inverters. the proposed method detects an open-circuit fault in a single switching device by training a 1-D Convolutional Neural Network (1-D CNN) to extract the periodic feature of pole voltage, which can be done without adding any algorithm for feature extraction. Due to the increased number of switches and conduction paths compared to a two-level inverter, the other method requires complex mathematical equations, threshold settings, and theoretical knowledge of the topology. However, the proposed data-driven deep learning-based method can be made simpler and more robust under different system conditions without using mathematical equations. Simulation results have validated the feasibility of the proposed method that show the diagnostic average accuracy rate reached 99.53 % with conditions that were untrained in the deep learning model. © 2018 IEEE.

关键词： Failure analysis

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learning-Based State Estimation for automated Lane-Changing 27

Learning-Based State Estimation for Automated Lane-Changing

引用

27th IEEE international conference on intelligent Transportation Systems, ITSC 2024

作者： Ha, Won Yong Chakraborty, Sayan Lin, Xiaoyi Ozbay, Kaan Jiang, Zhong-Ping New York University Control and Networks Lab Department of Electrical and Computer Engineering 370 Jay Street BrooklynNY11201 United States C2SMARTER Center New York University Department of Civil and Urban Engineering 6 MetroTech Center BrooklynNY11201 United States

ISBN: (纸本)9798331505929

this paper introduces a learning-based optimal control strategy enhanced with nonmodel-based state estimation to manage the complexities of lane-changing maneuvers in autonomous vehicles. Traditional approaches often depend on comprehensive system state information, which may not always be accessible or accurate due to dynamic traffic environments and sensor limitations. Our methodology dynamically adapts to these uncertainties and sensor noise by iteratively refining its control policy based on real-time sensor data and reconstructed states. We implemented an experimental setup featuring a scaled vehicle equipped with GPS, IMUs, and cameras, all processed through an Nvidia Jetson AGX Xavier board. this approach is pivotal as it addresses the limitations of simulations, which often fail to capture the complexity of dynamic real-world conditions. the results from real-world experiments demon-strate that our learning-based control system achieves smoother and more consistent lane-changing behavior compared to traditional direct measurement approaches. this paper underscores the effectiveness of integrating Adaptive Dynamic Program-ming (ADP) with state estimation techniques, as demonstrated through small-scale experiments. these experiments are crucial as they provide a practical validation platform that simulates real-world complexities, representing a significant advancement in the control systems used for autonomous driving. © 2024 IEEE.

关键词： State estimation

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learning Based on Generative AI with Image Synthesis and data Augmentation Techniques 11

Learning Based on Generative AI with Image Synthesis and Dat...

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11th IEEE international conference on Social Networks Analysis, Management and Security, SNAMS 2024

作者： Jain, Alok Roy, Dharmendra Kumar Ayasrah, Firas Tayseer William, P. Lakshmi, G. Prasanna CA United States Hyderabad Institute of Technology and Management Hyderabad India College of Education Humanities and Science Al Ain University Al Ain United Arab Emirates Sanjivani College of Engineering Deaprtment of Information Technology Kopargaon India Amity University Dubai United Arab Emirates School of Computer Science and Engineering Sandip University Maharashtra Nashik India

ISBN: (纸本)9798331518349

Generative artificial intelligence enables improved image synthesis and data augmentation in computer vision. GANs and VAEs are essential. VAEs learn using data distributions, whereas GANs employ generator-discriminator architecture to generate realistic pictures. these technologies have improved data augmentation and produced a wide diversity of synthetic data. this study uses GANs to create a visual creative generative AI model. the study process includes data collection, model creation, participant training, and final evaluation. Generative artificial intelligence improves statistical model performance and durability during data augmentation. this study examines the scientific and ethical implications of this technological accomplishment. Generative artificial intelligence's effects on several fields are also examined. © 2024 IEEE.

关键词： Generative adversarial networks

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USCNet: Seismic P-wave arrival time pickup based on deep learning 11

USCNet: Seismic P-wave arrival time pickup based on deep lea...

引用

11th international conference on Environmental and engineering Geophysics, ICEEG 2024

作者： Xu, Lei Shen, Tong Jiang, Xuan Country School of Information and Engineering Southwest University of Science and Technology Mianyang China

the accurate pickup of seismic P-waves has important application value for real-time seismic data processing such as earthquake activity monitoring and earthquake disaster warning. However, existing low signal-to-noise ratio seismic data pickup algorithms often face problems of low accuracy and poor robustness. To address these issues, we designed a seismic P-wave picking network called USCNet. this network combines the advantages of U-Net and SegNet networks. the U-Net network performs well in picking accuracy, but requires a large number of learning parameters;SegNet networks are trained with fewer learning parameters, but have lower picking accuracy. the USCNet network integrates the CBAM attention mechanism, which can deeply extract key information from seismic data, thereby significantly improving the picking accuracy. We use the Stanford Earthquake dataset (STEAD) as the data source. the experimental results show that USCNet performs significantly better than U-Net and SegNet networks on this dataset, with significant improvements in metrics such as Mean (μ), Variance (δ), Precision (P), and Recall (R), as well as significantly improved picking performance. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Seismic waves

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Breast Cancer Biomarker Analysis Using Gene Co-expression Networks 11th

Breast Cancer Biomarker Analysis Using Gene Co-expression Ne...

引用

11th international Work-conference on Bioinformatics and Biomedical engineering (IWBBIO)

作者： Lopez-Fernandez, Aurelio Gallejones-Eskubi, Janire Saz-Navarro, Dulcenombre M. Gomez-Vela, Francisco A. Univ Pablo de Olavide Intelligent Data Anal Grp DATAi Ctra Utrera Km 1 Seville 41013 Spain Galdakano Usansolo Univ Hosp Hlth Syst Res Inst Biosistemak Galdakano 48960 Spain

ISBN: (纸本)9783031646355;9783031646362

Gene co-expression networks have emerged as a robust tool for conducting comprehensive analyses of gene expression patterns. these networks, constructed through inference algorithms, facilitate the exploration of various biological processes and enable the identification of novel biomarkers from which to explore new lines of disease research. this work found that breast cancer stromal cells are strongly dysregulated in genes related to modifications in cellular structures that hold stromal tissue cells together, inflammatory responses, and molecules implicated in immune system regulation. Finally, ANAPC11, LRFN5, COL8A2, TEX11, DOCK9, CPLX1, LONP2, and LAT2 biomarkers were suggested in the context of stromal breast tumors.

关键词： Gene co-expression network Bioinformatics Biomarkers Breast cancer

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Use of Social Media in Flood Assessment in Bangladesh 11

Use of Social Media in Flood Assessment in Bangladesh

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11th IEEE international conference on intelligent Systems, IS 2022

作者： Shubho, Fahimul Hoque Sarker, Emon Rafi, Abdullah Al Rahman, Parinda Mahmud Habib, Mohammad Tanvir Ihsan, Tahrima Rahman, Rashedur M. North South University Department of Electrical and Computer Engineering Bashundhara Dhaka1229 Bangladesh

ISBN: (纸本)9781665456562

Widespread floods are one of the most destructive natural phenomena that frequently occur in Bangladesh. It devastates human life, food security, shelter, and social and financial losses. Due to Bangladesh's current economic situation, installing and maintaining comprehensive flood gauges for national flood assessments will not be viable. Hence, online social media platforms can be a valuable avenue to get real-world data to perform flood assessments. this study investigates whether the abundance of data collected from social media in Bangladesh could be used to achieve a reliable and consistent flood assessment in Bangladesh. the data gathered from online platforms are related to flooding and exist in the form of videos, images, and text. the data collected is analyzed with a Machine learning approach. the digital data of images and video frames were converted into numeric values using the VGG-16 architecture. A Convolutional Neural Network takes in the numeric data produced by the VGG-16 for classification. the classification made accuracy of 92% for the image-based model and 87% for the text-based model. © 2022 IEEE.

关键词： data mining

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Quantum-Based Prediction Model for Carbon Neutrality 11

Quantum-Based Prediction Model for Carbon Neutrality

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11th international conference on Advanced Informatics: Concept, theory and Application, ICAICTA 2024

作者： Munawar, Ghifari Surendro, Kridanto School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Politeknik Negeri Bandung Department of Computer Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798331520311

Carbon neutrality is a global target pursued by cities worldwide to achieve a balance between carbon emissions and removals, reaching a net-zero carbon state. Mitigation measures are being implemented to reduce emissions and enhance carbon sequestration, aiming to meet the targets set for 2050 or 2060. However, challenges posed by urban sprawl and increasing urbanization raise concerns about the feasibility of achieving carbon neutrality. Various studies have been conducted to project the attainment of this goal by developing prediction models. Machine learning (ML) prediction models use socio-economic, energy, and technological data to forecast carbon neutrality. these models consider factors like GDP per capita, urbanization rate, total energy consumption, and forest stock volume, formulating scenarios based on policy documents and historical data. Some models have incorporated optimization methods like the sparrow search algorithm, genetic neural network, and aquila optimizer to improve prediction accuracy. However, classical optimization methods have limitations, such as susceptibility to getting trapped in local optima, which can affect model performance. Quantum-based optimization methods, particularly quantum annealing (QA), are emerging as potential solutions to address these challenges by leveraging the principles of quantum mechanics to optimize complex problem spaces. QA enhances ML processes like feature selection, hyperparameter optimization, and regression model optimization. this study provides a review of pipeline processes from state-of-the-art methods, as well as their potential quantum-based enhancements, to achieve more precise predictive models. © 2024 IEEE.

关键词： Carbon sequestration

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