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A Comprehensive Analysis of Alcohol-Attributed Mortality in the United States 2

A Comprehensive Analysis of Alcohol-Attributed Mortality in ...

引用

2nd international conference on Computing and data Analytics, ICCDA 2024

作者： Adedigba, Deborah Hasan, Raza Ahmad, Shakeel Mahmood, Salman Solent University Department of Science and Engineering Southampton United Kingdom Nazeer Hussain University Department of Computer Science Karachi Pakistan

ISBN: (纸本)9798331542573

Alcohol related mortality remains an important public health challenge in the United States (US), with patterns that vary substantially by demographics and geography. The objective of this study is to provide a thorough analysis using state-of-the-art data mining and machine learning techniques on alcohol attributable deaths in the U.S. from 2015-2019. Driven by the urgent need for specific interventions, we used regression models followed with ensemble techniques (XGBoost) to forecast mortality rates utilizing parameters such as age, geographic region of residence, manner of death and consumption patterns. Results in accordance with empirical CDC mortality data, XGBoost outperformed all other models in predicting age-based (R2 = 0.98) and cause-specific (R2 = 0.96). Geographic patterns show that California, Texas and Florida were particularly hot spots for alcohol versus suicide related deaths. The study also found demographic disparities, with older adults being more at risk. Major research contributions are the good predictability of news on general mortality and due to age, selected causes of death as well identification these regional patterns. These findings are invaluable in shaping targeted public health initiatives and policies to address alcohol-related harm. The adoption of sophisticated predictive modelling methodology in this study contributes significantly to the field by providing an empirically driven process for investigating and confronting alcohol-attributable death burden within the U.S. © 2024 IEEE.

关键词： Prediction models

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Effectiveness of Model and data Scale Contrastive learning in Non-IID Federated learning 16

Effectiveness of Model and Data Scale Contrastive Learning i...

引用

16th international conference on Advanced Technologies for Communications, ATC 2023

作者： Ejigu, Girum Fitihamlak Tun, Ye Lin Adhikary, Apurba Kang, Sun Moo Hong, Choong Seon Kyung Hee University Department of Computer Science and Engineering Yongin Korea Republic of

ISBN: (纸本)9798350301328

All involved clients are guaranteed data privacy in a collaborative machine learning environment via Federated learning. The lack of generalization in local client models brought on by data heterogeneity, however, is one of Federated learning 's major challenges that lead to slow model convergence and communication latency. In this study, we use contrastive learning techniques that have been shown effective in both centralized and federated learning environments. For local models to have a stronger capacity for generalization, we suggest adopting contrastive loss at the model and data scales. Utilizing the CIFAR10 and CIFAR-100 datasets, we assess and contrast our suggested strategy with other industry standard approaches. © 2023 IEEE.

关键词： data privacy

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A Review of Automatic Detection of Learner States in Four Typical learning Scenarios 1

引用

4th international conference on Adaptive Instructional Systems Held as Part of the 24th international conference on Human-Computer Interaction (HCII)

作者： Wang, Guanfeng Gong, Chen Wang, Shuxia Northwestern Polytech Univ Xian Peoples R China

ISBN: (数字)9783031058875

ISBN: (纸本)9783031058875;9783031058868

Artificial intelligence technology has already been applied in the education scene, and the automatic detecting technology of learning state has attracted the attention of many researchers. This paper summarizes the main types of learning state that researchers pay attention to at present, including affect, engagement, attention, and cognitive load. Based on four typical learning scenarios: computer-based learning, mobile learning, traditional classroom-based learning, and individual computer-free learning, this paper discusses the shortcomings and development trends of detecting hardware and methods used in this field, and the social problems in obtaining a large amount of personal privacy data.

关键词： learning state Education scene Automatic detection Artificial intelligence

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Efficient-VGG16: A Novel Ensemble Method for the Classification of COVID-19 X-ray Images in Contrast to machine and Transfer learning 2

Efficient-VGG16: A Novel Ensemble Method for the Classificat...

引用

2nd international conference on machine learning and data Engineering, ICMLDE 2023

作者： Kumar, Sunil Kumar, Harish Department of Computer Engineering J. C. Bose University of Science and Technology YMCA Haryana Faridabad121006 India CSJM University UP Kanpur208024 India

Corona Virus Disease 2019 (COVID-19) is a contagious respiratory disease characterized by its high transmissibility and exponential spread, presenting persistent difficulties. This investigation sought to incorporate machine learning (ML) and transfer learning (TL) and introduced Efficient-VGG16, a novel ensemble approach that integrated the EfficientNet-B0 and VGG16 architectures to classify chest X-ray images as either COVID-19 positive or non-COVID. The COVID-Xray-5k dataset used in the research was split into different training and testing sets (with ratios of 80:20, 75:25, and 70:30), preprocessed, and then used to evaluate the approach's effectiveness. Throughout the investigation, all procedures operated effectively at the same selected image size, 224*224. Preprocessing included Gaussian blur to remove noise and relics, while Contrast Limited Adaptive Histogram Equalization (CLAHE) adjusted contrast. Among the employed ML classifiers, XgBoost attained the highest accuracy, with F1 scores of 95.78% and 71.56%, respectively. In addition, the research investigated the effectiveness of TL using pre-trained convolutional neural networks (CNNs) such as VGG16, MobileNetV3, ResNet101, Densenet201, and EfficientNet-B0. EfficientNet-B0 achieved the highest levels of accuracy and F1 score, 99.31% and 93.72%, respectively. Furthermore, the proposed Efficient-VGG16 achieved accuracy and an F1 score of 99.46% and 98.41%, respectively, and outperformed TL with EfficientNet-B0 and XgBoost. The investigation demonstrated the effectiveness of the Efficient-VGG16 for accurately classifying COVID-19. © 2024 Elsevier B.V.. All rights reserved.

关键词： Statistical tests

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Supervised machine learning Techniques applied to Medical Records Toward the Diagnosis of Rare Autoimmune Diseases 23rd

Supervised Machine Learning Techniques Applied to Medical Re...

引用

23rd international conference on Computational science, ICCS 2023

作者： Andrade Martins, Pedro Emilio de Andrade Mioto, Ana Clara Colombo Filho, Márcio Eloi Bernardi, Filipe Andrade Costa Lima, Vinícius Félix, Têmismaria Alves, Domingos Health Intelligence Laboratory Ribeirão Preto Medical School Ribeirão Preto Brazil Medical Genetics Service Porto Alegre Clinical Hospital Porto Alegre Brazil Department of Social Medicine Ribeirão Preto Medical School Ribeirão Preto Brazil

ISBN: (纸本)9783031360237

Rare autoimmune diseases provoke immune system malfunctioning, which reacts and damages the body’s cells and tissues. They have a low prevalence, classified as complex and multifactorial, with a difficult diagnosis. In this sense, this work aims to support the diagnosis of a rare autoimmune disease using the analysis of medical records from supervised machine learning methods and to identify the models with the best performance considering the characteristics of the available data set. A synthetic database was created with 1000 samples from epidemiological studies identified in the literature, simulating demographic data and symptoms from patient records for the diagnosis of Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis (MS), Systemic Lupus Erythematosus (SLE), Crohn’s Disease (CD) and Autoimmune Hepatitis (AIH). data were segmented into training (80%) and test (20%), assigning the diagnosis as the class to be predicted. The models with the highest accuracy were Logistic Regression (84%), Naive-Bayes (82.5%), and Support Vector machine (79%). Only LR obtained quality values greater than 0.8 in all metrics. SLE had the highest precision and recall for all classifiers, while ALS had the worst results. The LR accuracy model had the best performance with good quality metrics, although it was impossible to predict ALS accurately. Although a real dataset was not used, this work presents a promising approach to support the challenging diagnosis of rare autoimmune diseases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Self-supervised learning

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Structure learning in Deep Multi-Task Models 18th

Structure Learning in Deep Multi-Task Models

引用

18th international conference on Hybrid Artificial Intelligence Systems

作者： Ruiz, Carlos Alaiz, Carlos M. Dorronsoro, Jose R. Univ Autonoma Madrid Dept Comp Engn Madrid Spain Univ Autonoma Madrid Inst Ing Conocimiento Madrid Spain

ISBN: (纸本)9783031407246;9783031407253

Multi-Task learning (MTL) aims at improving the learning process by solving different tasks simultaneously. Two general approaches for neural MTL are hard and soft information sharing during training. Here we propose two new approaches to neural MTL. The first one uses a common model to enforce a soft sharing learning of the tasks considered. The second one adds a graph Laplacian term to a hard sharing neural model with the goal of detecting existing but a priori unknown task relations. We will test both tasks on real and synthetic datasets and show that either one can improve on other MTL neural models.

关键词： Deep learning

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Enhancing Pavement Condition Assessment: A Comprehensive Review of Affordable Sensing Technologies for Cycle Tracks 11

Enhancing Pavement Condition Assessment: A Comprehensive Rev...

引用

11th international conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2025

作者： Garcia, Jeziel Antonio Ayala Shah, Syed Baig, Muhammad Hassam Qureshi, Waqar Shahid Ullah, Ihsan School of Computer Science University of Galway Galway Ireland Insight SFI Research Centre for Data Analytics University of Galway Galway Ireland

ISBN: (纸本)9789897587450

This paper explores the potential of low-cost sensing technologies for assessing the condition of cycling track pavement. As cycling gains popularity, the demand for efficient pavement maintenance solutions increases. machine survey-based pavement condition assessment often relies on expensive, specialized equipment, which is not always suitable for cycle tracks due to limited budgets and accessibility, hence the need for low-cost solutions. The integration of low-cost sensing technologies and data collection, such as inertial measurement units (IMUs), low-cost imaging sensors, and crowdsourced data collection, presents a promising alternative for cycle track pavement surface assessment. This paper highlights the advantages of employing out-of-the-shelf devices such as smartphones with GPS, cameras, and IMU, or action cameras with GPS and IMU to collect images, their location, and vehicle vibration in a specific orientation. This data is then used to estimate pavement roughness, detect surface distress, and compute pavement surface condition. Literature reviews reveal a significant gap in the utilization of these technologies for cycle tracks, suggesting a promising area for further research and application. Furthermore, it proposes a software framework for data collection and visualization to combine these technologies to enhance the efficiency and reliability of pavement assessment for cycling infrastructure at a lower cost than machine-based pavement assessment and faster and more quantitative than manual surveys. This paper emphasizes the need for more practical and scalable solutions that support the maintenance of sustainable transportation systems. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Budget control

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TRANSFER learning FOR EVALUATING INTERNAL DEFECT SIZES IN INSULATING MATERIALS USING SIMULATED ULTRASONIC MEASUREMENTS 18

TRANSFER LEARNING FOR EVALUATING INTERNAL DEFECT SIZES IN IN...

引用

18th Symposium on Piezoelectricity, Acoustic Waves, and Device Applications, SPAWDA 2024

作者： Liu, Xiaojian Li, Hang Li, Zhifeng Song, Shaoheng Liu, Weihua Bai, Yixin Song, Yaqin State Key Laboratory for Strength and Vibration of Mechanical Structures School of Aerospace Engineering Xi’an Jiaotong University Xi’an710049 China China Nuclear Industry Fifth Construction Co. Ltd. Longsheng Road Jinshan District Shanghai201512 China School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Xianheng International Technology Co. Ltd China

ISBN: (纸本)9798331523732

data-driven deep learning methods are widely used in ultrasonic testing for internal defects in electrical insulation components, but their effectiveness is constrained by the scarcity of real detection signals. Many studies focus on using the finite element method to generate lots of ultrasonic signals with diverse defect types. However, the differences between simulation and real detection signals limit the performance of machine learning. In view of this, a transfer learning-based internal defect sizes evaluation strategy is proposed in this paper. FEM simulations were performed to generate void defect ultrasonic detection signals for 45 uniformly distributed diameters from 0.4 to 2.6 mm, while 5 actual void defects within the same diameter range were fabricated to obtain experimental signals. A pre-trained CNN model was initially trained on simulation data, and then the fine-tuning method was applied using experimental data, ultimately achieving a void size evaluation error of less than 6%. © 2024 IEEE.

关键词： Ultrasonic testing

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Research on Segmentation Algorithm of UAV Remote Sensing Image Based on Deep learning 4

Research on Segmentation Algorithm of UAV Remote Sensing Ima...

引用

4th international conference on Geoscience and Remote Sensing Mapping, GRSM 2022

作者： Zhou, Jian Su, Yu Ding, Qinglan Qiu, Yuhe Wang, Qing China Mobile Chengdu Institute of Research and Development Sichuan Chengdu610000 China

ISBN: (数字)9781510662179

ISBN: (纸本)9781510662162

With the rapid development of UAV technology, the research topic of remote sensing image segmentation has gradually attracted more and more attention. Whether the image can be accurately segmented is a measure of the goodness of the algorithm. In recent years, machine learning methods have been applied to a large number of fields, and deep neural network technology has also been widely used in the field of UAV remote sensing image segmentation. This paper introduces the specific applications of various deep learning methods in remote sensing image segmentation, and briefly analyzes the development of neural networks in this problem according to the research status of several typical deep neural networks in UAV remote sensing image segmentation. Research shows that on the basis of proper adjustment of optimizer, learning rate and loss function, using deep learning method to segment UAV remote sensing images, the accuracy rate can reach more than 96%. © 2023 SPIE.

关键词： Remote sensing

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Real-Time Intrusion Detection and Prevention with Neural Network in Kernel using eBPF 54

Real-Time Intrusion Detection and Prevention with Neural Net...

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54th Annual IEEE/IFIP international conference on Dependable Systems and Networks (DSN)

作者： Zhang, Junyu Chen, Pengfei He, Zilong Chen, Hongyang Li, Xiaoyun Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9798350341065;9798350341058

With the development of public cloud, real-time intrusion detection is becoming necessary. Current methods neither address the overhead of real-time network data capturing, nor effectively balance security level with performance. These issues can be addressed by offloading intrusion detection and prevention to the extended Berkeley Packet Filter (eBPF). However, current eBPF-based methods suffer from shortcomings in model performance or inference overhead. Moreover, they overlook the issues of eBPF in real-time scenarios, such as maximum eBPF instruction limitations. In this paper, we redesign the Neural Network inference mechanism to address the limitations of eBPF. Then, we propose a thread-safe parameter hot-updating mechanism without explicit spin lock. Evaluations indicate that our method achieves model performance comparable to the current best eBPF-based method while reducing memory overhead (5KB) and inference time (3000-5000ns per flow). Our method achieve F1-scores of 0.933 and 0.992 on the offline and online datasets, respectively.

关键词： Real-Time Intrusion Detection eBPF Deep learning Neural Network Quantization

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