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Global Conference for Advancement in technology (GCAT)

作者： Sanjay Kumar Palit Abhiroop Nandi Ray Judhajit Sanyal Information Technology Department Calcutta Institute of Engineering and Management Kolkata India Android Application Development Mirafra Technologies Bengaluru India Data Strategy & AI Mestastop Solutions Bengaluru India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Machine learning has been shown to have widespread application in the accurate and consistent determination of disease risk for multiple diverse disease types. Among such types of machine learning models, tree-based algorithms are popular for their simplicity and effectiveness. Meta-estimation models have also demonstrated effectiveness for multiple such scenarios. The present work consequently examines and compares the performances of multiple tree-based models with significant difference in model complexity, in accurately predicting diabetes in prospective patients. Evaluation metrics such as accuracy, precision, recall and F1-score are employed to determine the optimal model for a given dataset among a handful of such models. For determination of model optimality in the context of a specific dataset, the variation in performance measures is also observed with change in factors such as the ratio between training and testing dataset sizes. Based on such assessments, optimal model performance can be isolated and such optimal models identified for diverse use cases.

关键词： Training Adaptation models Accuracy Predictive models Boosting Stability analysis Diabetes Random forests Diseases Testing

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Optimizing Intersection Signal Via Reinforcement Learning for Cooperative Adaptive Cruise Control

Optimizing Intersection Signal Via Reinforcement Learning fo...

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IEEE International Conference on Intelligent Transportation Engineering (ICITE)

作者： Shu Yang Rui Yuan Miao Wang Liang Sun Jingyuan Wu Research and Development Center of Transport Industry of Big Data Processing Technologies and Application for Comprehensive Transport Ministry of Transport China Communications Information Technology Group Co. Ltd. PRC Beijing China

ISBN: (数字)9798350314212

ISBN: (纸本)9798350314229

With the continuous advancement of urbanization, traffic congestion becomes a major challenge for social development. Intelligent driving technology can ease the traffic pressure at intersections, which is the trend of future traffic development. Cooperative Adaptive Cruise Control (CACC) is a specific application of intelligent driving technology and a necessary step toward autonomous driving. This paper proposes a signal control method for CACC homogeneous traffic flow at single intersections based on deep reinforcement learning, which considers the operation characteristics of CACC vehicles at intersections and uses a grid-based state space input to improve the model prediction accuracy. On this basis, this paper proposes a variable phase duration strategy to enhance the phase control flexibility and a platoon clearance time to reduce the vehicle travel delay. Simulation experiments show that the signal control method based on deep reinforcement learning has a significant advantage over traditional signal control methods in the CACC homogeneous traffic flow environment and is more suitable for intersections with high traffic volume. The research results of this paper not only contribute to enriching the related research field but also offer a valuable reference for improving traffic safety and efficiency at intersections.

关键词： Space vehicles Adaptive systems Accuracy Urban areas Transportation Aerospace electronics Deep reinforcement learning Delays Traffic congestion Cruise control

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Maximizing Parking Operation Revenue via Joint Control of Channels and Overselling

Maximizing Parking Operation Revenue via Joint Control of Ch...

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IEEE International Conference on Intelligent Transportation Engineering (ICITE)

作者： Liru Hua Fei Meng Cuilan Liang Shu Yang Liang Sun Jingyuan Wu China Communications Information Technology Group Co. Ltd. Research and Development Center of Transport Industry of Big Data Processing Technologies and Application for Comprehensive Transport Ministry of Transport PRC Beijing China

ISBN: (数字)9798350314212

ISBN: (纸本)9798350314229

With the acceleration of urbanization, the problem of “parking difficulty” has become a key issue that needs to be addressed in cities of all sizes. As a new parking method, reservation-based parking can effectively reduce the waiting time for parking and solve the problem of urban traffic congestion. It is widely used to alleviate parking difficulties. This article proposes a method for obtaining maximum parking revenue. Based on reservation-based parking, big data technology is used for analysis and prediction. By jointly controlling the channel ratio and overselling ratio in the reservation process, the optimal parking utilization rate and economic benefits are achieved from low to high reservation penetration rates. In summary, reservation-based parking, as an important means of solving parking difficulties, not only improves parking efficiency and alleviates urban traffic pressure but can also explore the practice of maximizing revenue using big data technology, contributing to the intelligent and green development of urban transportation.

关键词： Economics Urban areas Decision making Transportation Process control big data Aerospace electronics Traffic congestion

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Renin-angiotensin system inhibitors prescriptions in Chinese hospitalized chronic kidney disease patients

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World Journal of Clinical Cases 2024年第17期12卷 3061-3075页

作者： Chun Zhang Zhi-Yu Duan Sa-Sa Nie Zhou Zhang Xin-Ru Guo Chao-Yang Zhang Jing Dong Guang-Yan Cai Department of Nephrology The First Medical CentreChinese PLA General HospitalBeijing 100853China School of Medicine Nankai UniversityTianjin 300071China National Engineering Laboratory for Medical Big Data Application Technology Chinese PLA General HospitalBeijing 100853China

BACKGROUND Many guidelines have recommended renin-angiotensin system inhibitors(RASI)as the first-line treatment for patients with chronic kidney disease(CKD).We studied RASI prescription trends from 2010 to 2019,and analyzed the characteristics associated with RASI prescription in Chinese hospitalized CKD *** To study the prescription of renin angiotensin system inhibitors in hospitalized patients with CKD in *** It was retrospectively,cross-sectional reviewed RASI prescriptions in hospitalized CKD patients in China from 2010 to *** prescribing trends were analyzed from 2010 to 2019,and bivariate and multivariate logistic regression analyses were conducted to identify characteristics associated with RASI *** A total of 35090 CKD patients were included,with 10043(28.6%)RASI *** these patients,18919(53.9%)met the criteria for RASI treatments based on the 2012 kidney disease:Improving global outcomes *** these,7246(38.3%)patients received RASI *** prescriptions showed an initial rapid increase from 2011 to 2012,reached its peak around 2015 and 2016,and then exhibited a subsequent slight decreasing *** bivariate and multivariate analyses showed that several characteristics,including the male gender,age less than 60-year-old,nephrology department admission,lower CKD stage,history of hypertension or diabetes,proteinuria,glomerulonephritis as the CKD etiology,and non-acute kidney injury were associated with RASI *** The frequency of RASI prescriptions showed an initial increase but a slight decreasing trend in more recent *** patients with certain characteristics such as elderly age,advanced disease stage,surgery department admission,or acute kidney injury were less likely to receive RASI *** the application of RASI in hospitalized CKD patients is *** actual clinical practice needs to be *** development of related research

关键词： Chronic kidney disease Renin-angiotensin system inhibitors Prescriptions Adherence

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Azimuth Ambiguity Suppression for Sparse SAR Imaging Based on Unfolded Deep Network 15

Azimuth Ambiguity Suppression for Sparse SAR Imaging Based o...

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15th European Conference on Synthetic Aperture Radar, EUSAR 2024

作者： Wu, Yuwei Zhang, Zhe Song, Ruizhen Qiu, Xiaolan Yu, Weidong Department of Space Microwave Remote Sensing System Beijing100090 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9783800762873

Sparse synthetic aperture radar (SAR) imaging allows for a reduction in the azimuth sampling rate based on compressive sensing theory. However, it relies on the sparsity of scenes, which can lead to significant azimuth ambiguity. To address this issue, we introduce a novel network designed to suppress azimuth ambiguity for sparse SAR imaging. Our proposed deep unfolded network is based on the Iterative Thresholding Algorithm Learned (LISTA). We replace the observation matrix with Matched Filter (MF) operators to reduce computational complexity. Experiments with real SAR scene data demonstrate the effectiveness and superiority of our approach in suppressing azimuth ambiguity. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Matched filters

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Construction Equipment Performance with Cloud data Analysis for Predictive Maintenance 6

Construction Equipment Performance with Cloud Data Analysis ...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Seeni, Senthil Kumar Maheswari, P. Konidhala, Jothibabu Suguna, M. Raja Ishwarya, M.V. Srinivasan, S. Cognizant Technology Solutions Department of Mobile Application Development Buffalo GroveIL United States Srm Institute of Science and Technology Faculty of Management Studies Kattankulathur Chennai India University of Technology and Applied Sciences- Al Musannah Department of Electronics and Communication Engineering South Al Batina Musanna Oman Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Agni College of Technology Artificial intelligence and Data science Department Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381689

The research uses Internet of Things technology for predictive maintenance in the construction sector. The system addresses incorporating Internet of Things sensors into construction machinery to enable real-time monitoring of operating parameters. The study suggests a proactive maintenance strategy that may anticipate equipment breakdowns by examining the data obtained and using sophisticated prediction models. With this strategy, construction teams can arrange maintenance activities systematically, which cuts down on downtime and helps avoid expensive interruptions. The abstract emphasizes the advantages of this practice, which include reducing unexpected downtime as much as possible, extending the life of equipment, and making the most of available resources. The study proves the usefulness of the IoT-based predictive maintenance approach via empirical investigations and the analysis of case studies. This validates the strategy's efficacy, contributing to enhanced equipment performance and increased work efficiency in the construction industry. © 2024 IEEE.

关键词： Predictive maintenance

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Integrating hydrologic modeling and satellite remote sensing to assess the performance of sprinkler irrigation

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地球空间信息科学学报（英文版） 2024年第3期27卷 934-952页

作者： Chaolei Zheng Li Jia Massimo Menenti Guangcheng Hu Jing Lu Qiting Chen Min Jiang Marco Mancini Chiara Corbari Key Laboratory of Remote Sensing and Digital Earth Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina Key Laboratory of Remote Sensing and Digital Earth Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina International Research Center of Big Data for Sustainable Development Goals BeijingChina Key Laboratory of Remote Sensing and Digital Earth Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina Faculty of Civil Engineering and Earth Sciences Delft University of TechnologyDelftNetherlands Department of Civil and Environmental Engineering Politecnico di MilanoMilanoItaly

Improving irrigation water management is a key concern for the agricultural sector,and it requires extensive and comprehensive tools that provide a complete knowledge of crop water use and *** study presents a novel methodology to explicitly estimate daily gross and net crop water requirements,actual crop water use,and irrigation efficiency ofcenter pivot irrigation systems,by mainly utilizing the Sentinel-2 MultiSpectral Instrument(MSI)imagery at the farm *** model is adapted to estimate actual water use(as the sum of canopy transpiration and evaporation of water intercepted by canopy and evaporation from soil)at daily/10-m resolution,benefiting from the high-resolution Sentinel-2 data and thus to assess the irrigation efficiency at the farm *** gross irrigation water requirement is estimated from the net crop water requirement and the water loss,including the water droplet evaporation directly into the air during application before droplets fall on the canopy and canopy interception *** method was applied to a pilot farmland with two major crops(wheat and potato)in the Inner Mongolia Autonomous Region of China,where modern equipment and appropriate irrigation methods are deployed for efficient water *** estimated actual crop water use showed good agreement with the ground observations,*** determination coefficients range from 0.67 to 0.81 and root mean square errors range from 0.56mm/day to 1.24mm/day for wheat and potato when comparing the estimated evapo-transpiration with the measurement by the eddy covariance *** also showed that the losses of total irrigated volume were 25.4％for wheat and 23.7％for potato,respectively,and found that the water allocation was insufficient to meet the water requirement in this irrigated *** suggests that the amount of water applied was insufficient to meet the crop water requirement and the inherent water losses in the center pivot irrigation system,which imply the necessit

关键词： Net irrigation water requirement crop water requirement consumptive water use gross irrigation water use irrigation performance center pivot irrigation systems ETMonitor

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Dual-Branch Contrast Enhancement for Drug Repositioning

Dual-Branch Contrast Enhancement for Drug Repositioning

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Haifeng Liu Qiuyu Long Nan Zhao School of Computer and Electronic Information Nanjing Normal University China Department of Big Data Management and Application Nanjing University of Chinese Medicine China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Drug repositioning offers a promising strategy to identify novel therapeutic applications for existing drugs. Despite the frequent use of graph neural network in this domain, their efficacy is often hampered by the sparsity of known drug interaction networks. Furthermore, diseases within the same subclass frequently share structural and clinical traits, leading to similar complications and responses to specific treatments among related *** tackle the challenges of sparse drug interaction networks and leverage the shared treatment potential among similar diseases, we propose Dron, a dual-branched contrast-enhanced method for drug repositioning. Dron enhances drug and disease representations using a branched contrastive loss strategy, and improves identification accuracy by aggregating features from related categories. Validation across multiple public datasets shows that Dron significantly enhances drug repositioning performance. Molecular docking experiments on Alzheimer’s disease further confirm Dron’s effectiveness in addressing complex diseases.

关键词： Drugs Manifolds Accuracy Biological system modeling Graph neural networks Bioinformatics Alzheimer's disease Diseases

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RAUIE: A Relation-Augmented Document-Level Event Extraction Model Based on UIE

RAUIE: A Relation-Augmented Document-Level Event Extraction ...

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Artificial Intelligence, Networking and Information technology (AINIT), International Seminar on

作者： Yiyuan Liang Hong Jia Licai Wang Yangchen Huang Zhe Yang Big data research and development center North China Institute of Computing Technology Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Department of Computer Science and Engineering The Chinese University of Hong Kong China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

In document-level event extraction tasks, multiple event types often coexist within a document (the multi-event problem), and arguments may play different roles across different events (the argument intertwining problem). Existing methods often suffer from significantly reduced event extraction accuracy, mainly when focusing on scenarios with scarce annotated data and imbalanced event-type distributions. This paper emphasizes enhancing the correlation between event triggers and arguments to address these challenges. Therefore, we propose the Relation-Augmented UIE Model (RADIE), which introduces entity relationships into prompts using prompt-based learning and designs a relation-augmentation algorithm to improve the accuracy and completeness of event extraction. Experimental results demonstrate that this approach significantly outperforms baseline methods, mainly showcasing excellent performance on datasets with long-tail distributions.

关键词： Seminars Accuracy Correlation Focusing Feature extraction data mining Task analysis

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Sample-Driven Adaptive Feature Selection with Weighted Fuzzy Rough Sets

SSRN

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SSRN 2024年

作者： Yang, Xiaoling Chen, Hongmei Li, Tianrui Wan, Jihong Yao, Yiyu School of Computing and Artificial Intelligence Southwest Jiaotong University China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University China Department of Computer Science University of Regina Regina Canada

The noise in data may debase the performance of the classification model. An intuitive assumption suggests that data samples with a higher local density tend to have a greater probability of being correctly classified. Moreover, there are differences in the importance of features, which should further increase the influence of important features on the classification task. Motivated by these issues, we propose a sample-driven adaptive feature selection method to mitigate the impact of noise and obtain the importance of features. Firstly, we propose a Weighted Fuzzy Rough Sets model (WFRS) by using the more reliable samples with high local density. Inspired by the large margin theory, we further obtain a specific fuzzy relation with the goal of maximizing the within-class fuzzy relation while minimizing the between-class fuzzy relation. The approximation margin is derived by applying the WFRS model within the original feature space. Feature weights are further obtained by utilizing the information derived from the approximation margin. On this basis, the weighted feature space is obtained that compresses the features with the lower weight and stretches the features with higher weight. An evaluation function of features is constructed by capturing the uncertainty of fuzzy positive and non-negative regions in the weighted feature space. A fuzzy rough feature selection algorithm (FSW) is designed by capturing the local structure of data. The weighted features are subsequently selected to facilitate the downstream classification tasks. Experimental results show the WFRS model's robustness, the effectiveness of FSW, and its superiority compared to alternative methods. © 2024, The Authors. All rights reserved.

关键词： Rough set theory

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