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PATRAS: Pattern-Aware Generalized Traffic Signal Control Based on Parallel learning

PATRAS: Pattern-Aware Generalized Traffic Signal Control Bas...

引用

Digital Twins and Parallel Intelligence (DTPI), IEEE International conference on

作者： Xingyuan Dai Yan Zhang Yiqing Tang Chen Zhao Jiaming Sun Fei Cong Yanan Lu Yisheng Lv Institute of Automation Chinese Academy of Sciences Beijing China China Telecom Digital City Technology Co. Ltd Xiongan China

The data-driven adaptive traffic signal control for real-world scenarios faces two significant challenges: adapting to diverse traffic patterns and addressing the generalization issue from the optimization environment to the deployment environment. To tackle these challenges, this paper proposes a novel approach for pattern-aware generalized traffic signal control (PATRAS) based on parallel learning. It establishes an optimization process comprising descriptive learning, predictive learning, and prescriptive learning, enabling robust and adaptable control for different traffic patterns. First, a lightweight and effective method is employed to characterize various traffic patterns using traffic flow trends. Next, for each specific traffic pattern, an offline reinforcement learning algorithm is applied to optimize the traffic signal control policy directly using collected traffic data instead of simulations. A decision support library is constructed to store and reuse these optimized policies. Lastly, a pattern-matching-based approach is used to recommend appropriate control policies from the library for the traffic pattern, guiding efficient operation of the actual transportation system. Experimental results demonstrate that the offline optimization method employed in PATRAS outperforms real-world demonstrations based on actuated control policies and other baselines in terms of generalization performance. Additionally, the recommendation method effectively improves traffic efficiency by directly applying pre-optimized policies to different traffic patterns.

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Advances in AI for Women's Healthcare: A Review of Deep learning and NLP Approaches

Advances in AI for Women's Healthcare: A Review of Deep Lear...

引用

intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Jyoti Choudhary Madhuri Thakur School of Computer Science and Engineering Lovely Professional University Punjab India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Women's health faces critical challenges, particularly in maternal and reproductive healthcare, where complications significantly contribute to global mortality rates. Despite advancements in medical science, inequities in healthcare access and diagnosis persist, especially in underserved regions. Innovative approaches leveraging artificial intelligence (AI) technologies, such as deep learning (DL) and natural language processing (NLP), are transforming healthcare by enabling early diagnosis, personalized treatment, and scalable preventive care. This paper evaluates the transformative role of these AI-driven methods in addressing critical women's health issues, including cancer detection, maternal health, and reproductive disorders, with a focus on advancements from 2018 to 2023. These years represent a period of rapid AI evolution and its integration into healthcare systems. The present study captures important challenges like restricted diversity of datasets, issues concerning transparency in models, and obstacles in actual deployment. It collates findings to give a global perspective of AI development in women's health. It is also prepared with practical future action steps for its research to lead AI-influenced innovations shaping the future of women's healthcare.

关键词： Deep learning Technological innovation Reviews Mortality Medical services Natural language processing Internet of Things Artificial intelligence Medical diagnostic imaging Gender issues

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Topology Planning for LEO Satellites Based on Deep Reinforcement learning

Topology Planning for LEO Satellites Based on Deep Reinforce...

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On-Line conference (ONCON), Industrial Electronics Society Annual

作者： Chang He Wei Liu Zhengbi Yong Zhiyuan Xu Qihao Zhou Shilei Li Dawei Shi School of Automation Beijing Institute of Technology Beijing China Institute of Telecommunication and Navigation Satellites China Academy of Space Technology Beijing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China

ISBN: (数字)9798331540319

ISBN: (纸本)9798331540326

The Low Earth Orbit (LEO) satellite network has made significant advancements in global communication services, offering low latency and extensive coverage. Effective topology planning has the dual benefit of enhancing network Quality of Service and reducing signal delay, while simultaneously optimizing resource utilization. The advent of deep reinforcement learning (DRL) methodologies offers novel approaches to inter-satellite topology planning in LEO constellations. In this work, a DRL topology planning algorithm based minimum spanning tree (DQN-MST) is proposed and compared with two traditional graph optimization algorithms. The results demonstrate that DQN-MST reduces the total network link length by 38.20% and 62.62% compared to the graph optimization algorithms, confirming the effectiveness of DQN-MST.

关键词： Satellites Network topology Low earth orbit satellites Quality of service Deep reinforcement learning Topology Planning Resource management Low latency communication optimization

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Enterprise Management Resource Scheduling Based on Cloud Computing using Velocity Aided- Grey Wolf optimization Algorithm

Enterprise Management Resource Scheduling Based on Cloud Com...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International conference on

作者： Yingying Ma Shandong institute of Commerce and Technology Shandong Jinan China

Economic globalization and international competition have led to more production enterprises adopting project management models, which involve customer or order projection. This approach may result in resource waste, labor-intensive project coordination activities, and ongoing adjustments to management procedures. To rationally utilize limited business scheduling resources, research has focused on the application of the velocity-aided- grey wolf optimization algorithm on cloud deep learning platforms. This algorithm is suitable for solving combinatorial optimization challenges and increasing the integration speed and accuracy of the algorithm by increasing the diversity of particle hosts and setting parameters. This study analyses the existing state of multi-project business management, looks at resource allocation, and creates a model for resource allocation across many projects. This method aims to rationally utilize the limited business scheduling resources in the production business. MATLAB simulates a superior configuration scheme, which validates the science and efficacy of the method studied in this research.

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Prior Prediction and Classification of Low Birth Weights (LBW) Using Machine learning

Prior Prediction and Classification of Low Birth Weights (LB...

引用

intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Priti Mishra Asha. K Sohara Banu A.R Shashikala N Vishnu Sai Vamsi P Department of CSE Sai Vidya Institute of Technology Bangalore Karnataka India Department of CSE REVA University Bangalore Karnataka India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Low birth weight (LBW) is a significant global issue with various associated dangers. LBW is an important determinant of neonatal health, affecting infant mortality and long-term outcomes. There is a strong correlation between maternal well- being during pregnancy and childbirth weight. The study of machine learning techniques for classification tasks, which effectively discriminates between LBW and NOT-LBW cases. The project's primary goal is to achieve precise predictions of LBW cases through the application of various machine learning algorithms, including XGBoost, Random Forest, Decision Trees, and SVM. These models will undergo training using supervised datasets, enabling a comparative analysis to identify the most accurate predictor among them. Along with increased accuracy, this study includes creating a flexible web interface aimed at medical professionals and the public, which allows for ideal data input to predict LBW probability. The Proposed methodology aims to contributes to obstetrics and gynecology by addressing the challenge of the lack of comprehensive medical tests for LBW and promoting early intervention for LBW in the healthcare domain.

关键词： Pregnancy Training Support vector machines Pediatrics Accuracy Surgery Predictive models Prediction algorithms Data models Random forests

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A Visionary Analysis on Ways and Techniques Used for Identification of Citrus Plant Disease

A Visionary Analysis on Ways and Techniques Used for Identif...

引用

International Symposium on Advanced Computing and Communication (ISACC)

作者： Arundhati Bora Parismita Sarma Dept. of Information Technology Gauhati University Guwahati India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

This review paper discusses the identification of different leaf diseases in plants with specific reference to the lemon group. In recent times, it has been seen that leaf diseases can be identified very accurately using different machine learning and deep learning methods from the digital images of the infected leaves. The accuracy varies depending on the tagged sample size, variation of the samples, and method used. In this paper, the authors have analyzed almost forty (40) research papers and prepared a number of tables for easy understanding of techniques used and their performance in terms of percentage of accuracy. Segmentation of leaves is performed in most techniques in the image pre-processing phase. Analysis is carried out on the given tables from different perspectives and a future way of working is formulated from this survey of research.

关键词： Deep learning Surveys Plant diseases Accuracy Reviews Computational modeling Transfer learning Neural networks Data models Object recognition

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Heterogeneous Recognition of Human Activity with CNN and RNN-based Networks using Smartphone and Smartwatch Sensors 3

Heterogeneous Recognition of Human Activity with CNN and RNN...

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3rd International conference on Big Data Analytics and Practices, IBDAP 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science North Bangkok Bangkok Thailand

ISBN: (数字)9781665469388

ISBN: (纸本)9781665469388

With ongoing advancements in remote sensor technologies, the concept of wearable sensors has gained widespread acceptance and grown ubiquitous due to its broad applicability in situations including ambient assisted living, innovative healthcare, and home appliances. In this context, the demand for Heterogeneous Human Activity Recognition (HHAR) has expanded considerably, as individuals desire their daily activities to be collected and analyzed to provide valuable information. This wearable sensing data is taken in the form of time-series data utilizing different wearable sensors, such as accelerometers, gyroscopes, and magnetometers, to offer other movement characteristics for each individual tracked from their smartphones and smartwatches. This data may be analyzed using different deep learning architectures to provide an additional platform for capturing and classifying the numerous sensor-based actions an individual could be undertaking. This research compares HHAR models based on deep learning techniques. We study the influence of smartphone and wristwatch sensor data on model performance and classification of human activities. We train and evaluate deep learning models to achieve our study objective using a publically available standard dataset (HHAR) with a 5-fold cross-validation technique. Using smartphone sensors, research findings demonstrate that the RNN-based model is superior to CNN-based models, with the most fantastic accuracy of 98.42%. © 2022 IEEE.

关键词： Smartphones

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A Hybrid Deep learning Model for Violence Detection

A Hybrid Deep Learning Model for Violence Detection

引用

intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： S Chandira Prabha S Kaviyadharshini A Annie Micheal Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Current violence detection systems often face challenges such as high computational costs, real-time processing difficulties, and limited precision in detecting violent activities in dynamic environments. This research addresses these issues by proposing a hybrid deep learning model that integrates MobileNetV2 for efficient spatial feature extraction and ConvLSTM (Convolutional Long Short-Term Memory) for capturing temporal patterns. The model effectively discriminates between aggressive and non-aggressive behaviours in real-time video streams. Using the Kaggle Violence Dataset, which comprises 1,000 video clips of violent and non-violent activities, the system achieved an accuracy of 96%, showcasing its high performance. Additionally, an integrated alert system enhances practical application by sending real-time notifications via a Telegram bot, including details such as location, timestamp, and camera ID. This solution ensures timely responses to violent incidents, addressing the core issues of computational efficiency and real-time precision, while offering scalability and applicability in diverse scenarios.

关键词： Training Accuracy Computational modeling Streaming media Feature extraction Real-time systems Data models Computational efficiency Pattern recognition Streams

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Deep Dual-Branch Network: Design and optimization of High-Accuracy Sky Segmentation

Deep Dual-Branch Network: Design and Optimization of High-Ac...

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Computer Science, Electronic Information Engineering and intelligent Control Technology (CEI), IEEE International conference on

作者： Xiangrong Fu Yang Shu Liya Tian Binzi Wang Wanyu Yang Youdong Ding Shanghai Film Academy Shanghai University Shanghai China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

This paper presents an innovative network based on deep learning for sky segmentation, a key task in image processing. Sky segmentation has various applications in computer vision, but traditional methods struggle to meet accuracy and efficiency requirements due to the diverse and complex nature of the sky in different scenes. To address this challenge, we propose a semantic-detail dual-branch sky segmentation network that draws inspiration from the visual characteristics and segmentation needs of the sky. The network aims to enhance the accuracy and detail sensitivity of the sky area through the parallel design of the semantic branch and the detail branch, along with the introduction of a feature fusion module. We train the network using the AED20K subset of datasets, which enables fine segmentation of sky regions. Through extensive experiments on multiple datasets, we demonstrate that our network achieves significant performance improvements in sky segmentation tasks, delivering excellent accuracy and outstanding sensitivity to detail.

关键词： Deep learning Image segmentation Visualization Computer vision Sensitivity Semantics Process control

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Research on the Construction of English intelligent learning Mode Based on Big Data

Research on the Construction of English Intelligent Learning...

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Education, Knowledge and Information Management (ICEKIM), International conference on

作者： Hai Wang Yinchuan University of Science and Technology Yinchuan Ningxia China

ISBN: (纸本)9781665416672

In today's era of big data, the traditional English learning model can no longer meet the personalized needs of users, and it is the general trend to reconstruct, optimize and innovate the English learning model. Therefore, based on Internet big data technology and related intelligent algorithms, this paper constructs an English learning system based on big data, so as to optimize and innovate the learning mode and create an intelligent English learning mode, with a view to further improving the English learning quality of users.

关键词： learning systems Education learning (artificial intelligence) Big Data Market research Data models Internet

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