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

共 7176 条记录，以下是1141-1150 订阅

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Infrared Fuzzy Video data Cleaning Based on Improved YOLOv5 11th

Infrared Fuzzy Video Data Cleaning Based on Improved YOLOv5

引用

11th China conference on Command and Control, C2 2023

作者： Gong, Hua Tian, Haoyuan Chen, Congang Hu, Yutong School of Science Shenyang Ligong University Shenyang110159 China Liaoning Key Laboratory of Intelligent Optimization and Control for Ordnance Industry Shenyang110159 China Faculty of Science and Technology Beijing Normal University—Hong Kong Baptist University United International College Zhuhai519087 China National Key Laboratory of Electromagnetic Space Security Tianjing300308 China

ISBN: (纸本)9789819990207

Aiming at the problem that infrared fuzzy video data affect video quality, an improved YOLO v5 deep network model is established to carry out intelligent and accurate infrared fuzzy video data cleaning. Considering the problem of missing infrared fuzzy video data, a dataset named SYLU-INFRARED VIDEO dataSET is constructed. this dataset includes 5000 aerial and ground background infrared fuzzy video frames. Considering the spatiotemporal joint features of video data, a CBAM attention mechanism with two-dimensional attention allocation strategies is applied. this attention can automatically allocate the importance of video features extracted by YOLOv5 backbone in both spatial and channel dimensions. the experimental results show that the fusion model of CBAM and YOLO v5 enhances the cleaning effectiveness. CBAM-YOLOv5 has an increase of 17.8%, 28.4%, and 26.5% in Precision, Recall, and mAP compared to YOLOv7, respectively. © Chinese Institute of Command and Control 2024.

关键词： Antennas

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Identification of Anomalies in the Lungs by Developing a System Using Deep learning 11th

Identification of Anomalies in the Lungs by Developing a Sys...

引用

11th international conference on E-Health and Bioengineering (EHB)

作者： Ramirez, Francys Zabdiel Oyuela Reyes-Duke, Alicia Maria Espinal, Normandy Univ Tecnol Ctr Amer UNITEC Fac Engn Francisco Morazan Honduras Med Hosp Mario Catarino Rivas Cortes Honduras

ISBN: (纸本)9783031625220;9783031625237

In the field of healthcare, early and accurate detection of lung abnormalities is essential for the diagnosis and effective treatment of various respiratory diseases. this study presents the development of a system for identifying abnormalities in the lungs based on deep learning techniques. Using a diversified dataset of radiological images, a deep convolutional neural network (CNN) was trained to recognize subtle patterns and anomalous features on patients' Tomographys and computed tomography (CT) scans. the proposed system demonstrates a high accuracy with a Mean Average Precision (MAP) of 97.6 percent in detecting lung abnormalities, including nodules, infiltrates, masses, and other common pathologies. Additionally, a CNN interpretation functionality was implemented, allowing clinicians to visualize and understand the regions highlighted by the model, thereby improving confidence in clinical decisions.

关键词： AI Cancer CNN data Sheets Image Analysis Lung Roboflow

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Edge Computing empowered Cross-Platform Tool for Multifactorial Disorder Risk Assessment 18

Edge Computing empowered Cross-Platform Tool for Multifactor...

引用

18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Solomon, Dahlak Daniel Sonia Arora, Monika Vanipriya, C.H. thakur, Anjali Sharma, Nitesh Shoolini University Yogananda School of AI Computer and Data Sciences Solan India Amity University Haryana IT/Operations Amity Business School Gurugram India Sir Mvit Bangalore India

ISBN: (纸本)9789380544519

the paper presents a novel approach to multifactorial disorder risk assessment through the development and deployment of a cross-platform application empowered by edge computing technology. Multifactorial disorders pose significant challenges to public health, necessitating accurate risk assessment tools for early detection and prevention. Leveraging a dataset sourced from the Behavioral Risk Factor Surveillance System (BRFSS), selected 17 relevant features and seven diseases for analysis, converting them into binary classification variables. Our study focused on training models within the. NET Maui framework, achieving promising results with the best-performing model exhibiting a macro-accuracy of 73.93%. Additionally, this study developed a risk assessment tool framework that integrates edge computing to enhance performance and accessibility. the framework incorporates feature selection and engineering techniques, optimizing the risk assessment process. Our cross-platform application guides users through a self-check process, allowing them to input personal information and obtain risk percentage assessments. through this innovative approach, our study contributes to early detection efforts for multifactorial disorders while addressing limitations of existing risk assessment methods. Our findings underscore the potential of edge computing technology in revolutionizing healthcare applications, paving the way for more effective and accessible risk assessment tools. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： cross-platform Early detection Edge computing machine learning mobile edge computing multifactorial disorders risk assessment tool

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ScenarioQA: Evaluating Test Scenario Reasoning Capabilities of Large Language Models 27

ScenarioQA: Evaluating Test Scenario Reasoning Capabilities ...

引用

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

作者： Sinha, Shreya Paranjape, Ishaan Whitehead, Jim University of California Santa Cruz Electrical and Computer Engineering Department Santa CruzCA United States University of California Santa Cruz Computational Media Department Santa CruzCA United States

ISBN: (纸本)9798331505929

Autonomous Vehicles (AVs) have the potential of reducing car accidents and increasing accessibility to transportation. AVs need to be rigorously tested. Scenario-based testing offers a set of approaches to design high-risk tests for AVs at low cost. Since the AVs need to be tested for a large number of scenarios, automated generation approaches are needed. Pre-trained Large Language Models (LLMs) are open-input, general-purpose data generators with good learning and reasoning abilities. However, due to the black-box nature of these systems, it's difficult to get direct evidence of their abilities. In this paper, we address the open question of the reasoning capabilities of pre-trained LLMs specifically in the context of scenario-based testing of AVs. Inspired by QA benchmarks for LLM evaluations for commonsense reasoning, science reasoning, and more, we present our main contribution, ScenarioQA. this benchmark involves an LLM-based QA generation process based on an integration of methods to generate questions and corresponding answers specifically in the context of scenario-based testing. We carry out a comprehensive evaluation of this process and gain valuable insights regarding effective QA generation. In addition, we evaluate several available pre-trained LLMs for these abilities. © 2024 IEEE.

关键词： Autonomous vehicles

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intelligent detection and recognition of road cracks based on improved YOLOV8 4

Intelligent detection and recognition of road cracks based o...

引用

4th international conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Zhang, Hong Zhang, Junwei Zhan, Qingheng School of Information Engineering Minzu University of China Beijing China

ISBN: (纸本)9781510686106

Deep learning plays a vital role in road crack detection, enabling improved detection accuracy, reduced costs, and facilitated automated maintenance, thus enhancing road safety and traffic efficiency. However, most of their remarkable performance relies on complex and costly computational resources, which often cannot meet the requirements for both speed and accuracy in mobile deployment terminals. In this paper, to address the trade-off between high accuracy and real-time performance, an efficient YOLOv8-improved network is proposed. this network not only reduces network redundancy but also significantly improves inference speed, achieving a balance between high accuracy and real-time performance. this paper employs LAMP pruning techniques to optimize the model as the student model in knowledge distillation, and further designs a teacher network that integrates the BAM attention module, C2f-DynamicConv, and CARAFE upsampling operator to provide feature knowledge distillation for the pruned model. the BAM module enhances the network's sensitivity to critical information, C2f-DynamicConv expands the receptive field to enhance feature extraction capabilities, and CARAFE, based on content-adaptive upsampling, aggregates contextual information to provide richer features for prediction tasks. Experimental data shows that our model achieves a significant 69.9% improvement in FPS and a 3.98% increase in map@50 accuracy. © 2024 SPIE.

关键词： Crack detection

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Flamingo Search Algorithm with Optimal Deep learning for automated Cyber threat Detection in IoT Environments 4

Flamingo Search Algorithm with Optimal Deep Learning for Aut...

引用

4th international conference on Distributed Sensing and intelligent Systems, ICDSIS 2023

作者： Asiri, Mashael M. Department of computer Science College of Science & Art at Mohayel King Khaled University Abha Saudi Arabia

ISBN: (纸本)9781839539954

Internet of things (IoT) networks are prone to cyber attacks because of insecure wireless infrastructures, various kinds of IoT devices, large quantities of data transfer by sensors over the network and resource constraint architecture. Hence, IoT-compatible cybersecurity solutions are needed. One of the typical solutions to find cyber threats in IoT- based networks is an automatic detection system. the new trend in cyber security is Machine learning (ML)-based analytics intends at extracting security-related data to identify advanced target cyber threats and reduce the overhead of keeping static correlation rules. However, many prevailing solutions for cyber threat detection include numerous problems like high false positive rate (FPR), low scalability, high learning complexity, and poor accuracy. therefore, this study designs a new Flamingo Search Algorithm with Optimal Deep learning for automated Cyber threat Detection (FSAODL-ACTD) technique in the IoT system. the main objective of the FSAODL-ACTD technique is to automate identification of the cyberthreats in IoT networks. In the FSAODL-ACTD method, a new feature selection approach using FSA is derived. Besides, multilayer bidirectional long short term memory (MBiLSTM) model is utilized for the automated identification of the cyber threats in the IoT environment. Finally, sine cosine algorithm (SCA) is exploited for the optimal hyperparameter adjustment of the MBiLSTM method. the simulation outcomes of the FSAODL-ACTD method are tested on the cyberthreat dataset, and the outcomes ensured the supremacy of the FSAODL-ACTD approach over other current approaches. © the Institution of engineering & Technology 2023.

关键词： Cyber attacks

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An intelligent IoT Solution with Real-Time Monitoring for Road Safety 11

An Intelligent IoT Solution with Real-Time Monitoring for Ro...

引用

11th IEEE international conference on Advances in Computing and Communications, ICACC 2024

作者： Azeez, Jasna K. Mary, Bella Manoj, G. Sivasankari, G. Karunya Institute of Technology and Science Department of Electronics And Communication Tamil Nadu India

ISBN: (纸本)9798350379136

Driver drowsiness is the major reason for accidents, as per the research conducted all over India in October 2023. Paper introduces an innovative IoT solution aimed at combating accidents resulting from drowsy driving. Leveraging Raspberry Pi, alcohol sensor, GPS, ultrasonic sensor, ADC, WiFi, and IoT connectivity enables real-time monitoring. Central to its functionality is the integration of Convolution Neural Network (CNN) algorithms for accurate drowsiness detection. the Raspberry Pi serves as the central processing unit, coordinating data collection and analysis from multiple sensors. the alcohol sensor detects alcohol levels, while the ultrasonic sensor identifies obstacles on the road. GPS technology tracks the vehicle's location, aiding navigation and emergency response. ADC ensures precise sensor measurements. WiFi and IoT connectivity enable seamless data transfer to remote servers for analysis. IoT provides timely alerts to prevent accidents caused by drowsy driving. Behavioral features are non intrusive and is used for monitoring the driver. Analysis of data dumped in the server aims to refine algorithms, improve accuracy, and enhance effectiveness in preventing accidents by using the dataset for training of deep learning algorithm, ensuring continuous optimization of the system. For accident prevention, once the drowsiness is detected in the server, speed control of the vehicle and informing relatives via IoT can be considered as future work. © 2024 IEEE.

关键词： Wi-Fi

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Integrating Minecraft Education in Curriculum Design to Enhance Chemistry learning 28th

Integrating Minecraft Education in Curriculum Design to Enha...

引用

28th international conference on Knowledge Based and intelligent information and engineering Systems, KES 2024

作者： Furukado, Ryousuke Zhu, Yan Hagiwara, Goichi Faculty of Engineering Integrated System Engineering Nishinippon Institute of Technology 1-11 Aratsu Miyako-gun Fukuoka800-0394 Japan Faculty of Human Science Department of Sport Science and Health Kyushu Sangyo University 2-3-1 Matsukadai Higasgi-ku Fukuoka-shi Fukuoka813-8503 Japan

this study aimed to develop an information and communication technology-based chemistry curriculum for Japanese junior high school students, utilizing the learning platform provided by Minecraft Education. We crafted content for a series of seven lessons, aiming to evaluate the short-term educational impacts of Minecraft Education and enhance the review process, which is often neglected. Features were integrated to maximize the game's functionality, increase its entertainment value, and sustain student motivation. Future work will focus on delineating the necessary teaching skills for educators to effectively utilize Minecraft Education and on the collection and assessment of empirical data. © 2024 the Authors.

关键词： Students

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Unsupervised Semantic Segmentation with Pose Prior 11

Unsupervised Semantic Segmentation with Pose Prior

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11th international conference on Structural Health Monitoring of intelligent Infrastructure, SHMII 2022

作者： Midwinter, Max Al-Sabbag, Zaid Abbas Yeum, Chul Min Department of Civil and Environmental Engineering Faculty of Engineering University of Waterloo MontrealQCN2L 3G1 Canada

the most popular computer vision-aided visual inspection techniques in civil engineering are single image, deep neural networks, trained in a supervised learning scheme. With deeper convolutional networks and vision transformers, the need for training data will exceed the supply of and researchers’ ability to label training images. In this work, the authors will explore whether poses can be a viable inductive bias to produce a semantic segmentation with a shallow unsupervised convolutional neural network. In this work the authors utilize commercial augmented reality devices, Microsoft HoloLens 2, running simultaneous localization and mapping, to collect images and global poses of spalling defects. An unsupervised semantic segmentation, based on, and novel stochastic refinement and outlier rejection algorithm are used to generate segmentation of spalling defects. the results of experiments are positive yielding an average mIOU of 86.25%. the authors believe this result and the emergence of pose trackers (e.g., LiDARs, depth sensors), has great potential to yield a synergetic relationship between deep learning and robotics. © 2022 international conference on Structural Health Monitoring of intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.

关键词： Semantic Segmentation

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Long-term structural health monitoring of long-span suspension bridges and anomaly detection using statistical indicators 11

Long-term structural health monitoring of long-span suspensi...

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11th international conference on Structural Health Monitoring of intelligent Infrastructure, SHMII 2022

作者： Çetindemir, Oğuzhan Tepe, Esra Zülfikar, Abdullah Can Yesilyurt, Ali Apaydin, Nurdan Memişoğlu Department of Civil Engineering Gebze Technical University Kocaeli Gebze41400 Turkey Department of Computer Engineering Istanbul University-Cerrahpaşa Istanbul34320 Turkey Department of Civil Engineering Istanbul University-Cerrahpaşa Avcilar Istanbul34320 Turkey Ministry of Transport and Infrastructure Uskudar Istanbul34668 Turkey

Some research has been conducted on structural health monitoring (SHM) utilizing raw data to discover structural behavior changes, anomalies, or damage assessment. However, few of them employed statistical indicators represented as input data. Long-term monitoring has several advantages (before/after event comparison, detecting/tracking abrupt changes or aging effects), while the data collection process may have disadvantages (data storing issues, impractical computationally expensive analyses). thus, the current work proposes two unsupervised machine learning techniques for detecting anomalies using statistical indicators extracted from long-term raw dynamic measurements recorded from a three-dimensional accelerometer installed on the midspan of the Osman Gazi Bridge built in Turkey. For this purpose, one-class Support Vector Machine (SVM) and Local Outlier Factor (LOF) are used as machine learning algorithms to detect anomalous instances. Comparison between these two machine learning revealed similar anomalies. the acquired results support the development of computational methods for assessing structural anomalies based on statistical indicators of acceleration measurements. © 2022 international conference on Structural Health Monitoring of intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.

关键词： Suspension bridges

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