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Evaluating a Signalized Intersection Performance Using Unmanned Aerial data∗

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arXiv 2022年

作者： Ashqer, Mujahid I. Ashqar, Huthaifa I. Elhenawy, Mohammed Almannaa, Mohammed Aljamal, Mohammad A. Rakha, Hesham A. Bikdash, Marwan Computational Data Science and Engineering North Carolina AT State University United States Precision Systems Inc. University of Maryland Baltimore County United States CARRS-Q Queensland University of Technology Australia Civil Engineering Department King Saud University Saudi Arabia Grant Thornton LLC United States Center of Sustainable Mobility VTTI Virginia Tech United States Department of Computational Data Science and Engineering North Carolina AT State University United States

This paper presents a novel method to compute various measures of effectiveness (MOEs) at a signalized intersection using vehicle trajectory data collected by flying drones. MOEs are key parameters in determining the quality of service at signalized intersections. Specifically, this study investigates the use of drone raw data at a busy three-way signalized intersection in Athens, Greece, and builds on the open data initiative of the pNEUMA experiment. Using a microscopic approach and shockwave analysis on data extracted from realtime videos, we estimated the maximum queue length, whether, when, and where a spillback occurred, vehicle stops, vehicle travel time and delay, crash rates, fuel consumption, CO2 emissions, and fundamental diagrams. Results of the various MOEs were found to be promising, which confirms that the use of traffic data collected by drones has many applications. We also demonstrate that estimating MOEs in realtime is achievable using drone data. Such models have the ability to track individual vehicle movements within street networks and thus allow the modeler to consider any traffic conditions, ranging from highly under-saturated to highly over-saturated conditions. These microscopic models have the advantage of capturing the impact of transient vehicle behavior on various MOEs. © 2022, CC BY.

关键词： Drones

Non-adaptive and two-stage coding over the Z-channel

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arXiv 2022年

作者： Lebedev, Alexey Lebedev, Vladimir Vorobyev, Ilya Deppe, Christian Kharkevich Institute for Information Transmission Problems Russian Academy of Sciences Moscow Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russia Institute for Communications Engineering Department of Electrical and Computer Engineering Technical University of Munich Munich Germany

In this paper, we developed new coding strategies for the Z-channel. In particular, we look at the case with two-stage encoding. In this case, the encoder uses noiseless feedback once and adjusts the further encoding strategy based on the previous partial output of the channel. Nevertheless, the developed codes improve the known results with full feedback for small length and 1 error. A tool for the two-stage strategy is the development of a new optimality condition for non-adaptive codes. Copyright © 2022, The Authors. All rights reserved.

关键词： Signal encoding

Evaluating a Signalized Intersection Performance Using Unmanned Aerial data

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SSRN

SSRN 2022年

作者： Ashqer, Mujahid I. Ashqar, Huthaifa I. Elhenawy, Mohammed Almannaa, Mohammed Aljamal, Mohammad A. Rakha, Hesham A. Bikdash, Marwan Computational Data Science and Engineering at North Carolina State University United States Precision Systems Inc. University of Maryland Baltimore County United States CARRS-Q Queensland University of Technology Australia Civil Engineering Department King Saud Uni- versity Saudi Arabia Grant Thornton LLC United States Center of Sustainable Mobility VTTI Virginia Tech United States Department of Computational Data Science and Engineering at North Carolina State University United States

This paper presents a novel method to compute various measures of effectiveness (MOEs) at a signalized intersection using vehicle trajectory data collected by flying drones. MOEs are key parameters in determining the quality of service at signalized intersections. Specifically, this study investigates the use of drone raw data at a busy three-way signalized intersection in Athens, Greece, and builds on the open data initiative of the pNEUMA experiment. Using a microscopic approach and shockwave analysis on data extracted from realtime videos, we estimated the maximum queue length, whether, when, and where a spillback occurred, vehicle stops, vehicle travel time and delay, crash rates, fuel consumption, CO2 emissions, and fundamental diagrams. Results of the various MOEs were found to be promising, which confirms that the use of traffic data collected by drones has many applications. We also demonstrate that estimating MOEs in real-time is achievable using drone data. Such models have the ability to track individual vehicle movements within street networks and thus allow the modeler to consider any traffic conditions, ranging from highly under-saturated to highly over-saturated conditions. These microscopic models have the advantage of capturing the impact of transient vehicle behavior on various MOEs. © 2022, The Authors. All rights reserved.

关键词： Drones

Enhancing engineering Pedagogy through Deep Learning: Applications of Explainable AI and Neural Networks 1

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Enhancing Engineering Pedagogy through Deep Learning: Applic...

1st IEEE International Conference on Green Industrial Electronics and Sustainable Technologies, GIEST 2024

作者： Umamaheswari, U. Mamidi, Pushpa Latha Nimma, Divya Venkateswarlu, D. Al Ansari, Mohammed Saleh Valavan, Ponni Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha School of Law Chennai India S.R.K.R Engineering College Department of Engineering Mathematics & Humanities Andhra Pradesh Bhimavaram India University of Southern Mississippi Data Analyst in UMMC Computational Science United States Permanently Affiliated JNTUK Dept. of MBA AP Mylavaram India University of Bahrain College of Engineering Department of Chemical Engineering Bahrain K. Ramakrishnan College of Engineering Department of AI & DS Tamil Nadu Trichy India

ISBN: (纸本)9798331540142

Conventional engineering pedagogy frequently fails to deliver individualized learning experiences and places a strong emphasis on one-size-fits-all methods of instruction. Meeting the requirements of every individual student and improving learning outcomes become more challenging as a result. Modern instructional tools, however state-of-the-art, typically cannot offer personalized, data-driven insights. Since many systems are clear, it might be challenging for educators and learners to comprehend the recommendations made. The concept-based transformer network presented in this work is intended to improve the personalized learning experience in engineering education. Using focus, this new neural system focuses on the most relevant elements of a student's education, providing personalized recommendations and assessments. Transparency is achieved through the inclusion of semantic AI, so that teachers and students can understand and validate the recommendations made by the system The proposed approach begins with the collection of large amounts of data, which including demographic information, contact records, and measures of student performance. Pre-processing assures data accuracy and quality. Personalized learning strategies and recommendations are generated through feedforward neural networks and integrated into the instructional platform. Performance measurement and user feedback for incremental change and continuous monitoring. Key metrics such as student engagement, outcomes for learning, and satisfaction levels are used to assess the effectiveness of the model. A comparison of the proposed model with current methods demonstrates its ability to provide scalable, transparent and customizable learning solutions. © 2024 IEEE.

关键词： engineering education

A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises

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arXiv 2023年

作者： Cui, Hejie Lu, Jiaying Xu, Ran Wang, Shiyu Ma, Wenjing Yu, Yue Yu, Shaojun Kan, Xuan Ling, Chen Zhao, Liang Qin, Zhaohui S. Ho, Joyce C. Fu, Tianfan Ma, Jing Huai, Mengdi Wang, Fei Yang, Carl Department of Computer Science Emory University AtlantaGA United States Department of Biostatistics University of Michigan Ann ArborMI United States School of Computational Science and Engineering Georgia Institute of Technology AtlantaGA United States Department of Computational Science Rensselaer Polytechnic Institute TroyNY United States Department of Computer and Data Sciences Case Western Reserve University ClevelandOH United States Department of Computer Science Iowa State University AmesIA United States Department of Population Health Sciences Weill Cornell Medicine Cornell University IthacaNY United States

Objective: This comprehensive review aims to provide an overview of the current state of Healthcare Knowledge Graphs (HKGs), including their construction, utilization models, and applications across various healthcare and biomedical research domains. Methods: We thoroughly analyzed existing literature on HKGs, covering their construction methodologies, utilization techniques, and applications in basic science research, pharmaceutical research and development, clinical decision support, and public health. The review encompasses both model-free and model-based utilization approaches and the integration of HKGs with large language models (LLMs). Results: We searched Google Scholar for relevant papers on HKGs and classified them into the following topics: HKG construction, HKG utilization, and their downstream applications in various domains. We also discussed their special challenges and the promise for future work. Discussion: The review highlights the potential of HKGs to significantly impact biomedical research and clinical practice by integrating vast amounts of biomedical knowledge from multiple domains. The synergy between HKGs and LLMs offers promising opportunities for constructing more comprehensive knowledge graphs and improving the accuracy of healthcare applications. Conclusions: HKGs have emerged as a powerful tool for structuring medical knowledge, with broad applications across biomedical research, clinical decision-making, and public health. This survey serves as a roadmap for future research and development in the field of HKGs, highlighting the potential of combining knowledge graphs with advanced machine learning models for healthcare *** Codes 68T30, 68T50, 68T09 Copyright © 2023, The Authors. All rights reserved.

关键词： Knowledge graph

Comparative Effectiveness of Classification Algorithms in Predicting Diabetes

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Comparative Effectiveness of Classification Algorithms in Pr...

International Conference on computational Intelligence and Communication Networks (CICN)

作者： Fares A. Dael D. Mareyev Ibraheem Shayea S. Kulniyazova Korlan Gulnara Abitova Management Information Systems İzmir Bakirçay University Īzmir Turkey Department of Computational and Data Science Astana IT University Astana Kazakhstan Electronic and Communication Engineering Istanbul Technical University Istanbul Turkey Department of Systems Analysis and Control L.N. Gumilyov Eurasian National University Astana Kazakhstan Department of Intelligent Systems and Cybersecurity Astana IT University Astana Kazakhstan

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

Diabetes mellitus poses a significant global health challenge, with increasing prevalence, particularly in low socioeconomic regions. Accurate and early diagnosis is crucial to prevent the severe long-term complications associated with diabetes. This study conducts a comprehensive comparison of six prominent machine learning algorithms-K-Nearest Neighbors (K-NN), Naive Bayes, Support Vector Machine (SVM), Decision Trees, Random Forest, and Logistic Regression-in predicting diabetes using a dataset of 768 individuals with diverse diabetic indicators from Kaggle. Each algorithm is rigorously evaluated based on precision, recall, and F1-score to determine the most effective method for diabetes diagnosis. The results indicate that Logistic Regression outperforms the other algorithms, achieving an accuracy of 81%. This superior performance is attributed to Logistic Regression's ability to effectively delineate linear separations, which is crucial for distinguishing between diabetic and non-diabetic individuals. The study underscores the importance of feature selection and model tuning in enhancing predictive performance. The findings suggest that integrating Logistic Regression into clinical settings can significantly improve the accuracy and timeliness of diabetes diagnosis, potentially leading to better patient outcomes and reduced healthcare costs.

关键词： Support vector machines Logistic regression Accuracy Machine learning algorithms Prediction algorithms Diabetes Classification algorithms Bayes methods Medical diagnostic imaging Regression tree analysis

Investigating the relation between environment and internal structure of massive elliptical galaxies using strong lensing

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arXiv 2024年

作者： Adnan, S.M. Rafee Hasan, Muhammad Jobair Al-Imtiaz, Ahmad Robin, Sulyman H. Shwadhin, Fahim R. Shajib, Anowar J. Nahid, Mamun Hossain Tanver, Mehedi Hasan Akter, Tanjela Jahan, Nusrath Jafar, Zareef Rashid, Mamunur Biswas, Anik Chowdhury, Akbar Ahmed Feardous, Jannatul Rahaman, Ajmi Ridwan, Masuk Sharma, Rahul D. Chowdhury, Zannat Hossain, Mir Sazzat Department of Physics and Astronomy University of Louisville LouisvilleKY40208 United States Department of Physics Shahjalal University of Science and Technology Sylhet3114 Bangladesh Copernicus Astronomical Memorial of SUST Shahjalal University of Science and Technology Sylhet3114 Bangladesh Department of Electrical and Electronic Engineering Bangladesh Army University of Engineering & Technology Qadirabad Natore6431 Bangladesh Department of Astronomy & Astrophysics University of Chicago ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Department of Mathematics Shahjalal University of Science and Technology Sylhet3114 Bangladesh Center for Computational & Data Sciences Independent University Bangladesh Dhaka1229 Bangladesh Department of Computer Science and Engineering Brac University Dhaka1212 Bangladesh Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka1205 Bangladesh Department of Physical Science Independent University Bangladesh Dhaka1229 Bangladesh Department of Electrical & Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka1229 Bangladesh

Strong lensing directly probes the internal structure of the lensing galaxies. In this paper, we investigate the relation between the internal structure of massive elliptical galaxies and their environment using a sample of 15 strong lensing systems. We performed lens modeling for them using lenstronomy and constrained the mass and light distributions of the deflector galaxies. We adopt the local galaxy density as a metric for the environment and test our results against several alternative definitions of it. We robustly find that the centroid offset between the mass and light is not correlated with the local galaxy density. This result supports using centroid offsets as a probe of dark matter theories since the environment's impact on it can be treated as negligible. Although we find a strong correlation between the position angle offset and the standard definition of the local galaxy density, consistent with previous studies, the correlation becomes weaker for alternative definitions of the local galaxy density. This result weakens the support for interpreting the position angle misalignment as having originated from interaction with the environment. Furthermore, we find the 'residual shear' magnitude in the lens model to be uncorrelated with the local galaxy density, supporting the interpretation of the residual shear originating, in part, from the inadequacy in modeling the angular structure of the lensing galaxy and not solely from the structures present in the environment or along the line of sight. © 2024, CC BY.

关键词： Probes

Dimension reduction of dynamical systems on networks with leading and non-leading eigenvectors of adjacency matrices

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Physical Review Research 2022年第2期4卷 023257-023257页

作者： Naoki Masuda Prosenjit Kundu Department of Mathematics State University of New York at Buffalo New York 14260-2900 USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260-5030 USA

Dimension reduction techniques for dynamical systems on networks are considered to promote our understanding of the original high-dimensional dynamics. One strategy of dimension reduction is to derive a low-dimensional dynamical system whose behavior approximates the observables of the original dynamical system that are weighted linear summations of the state variables at the different nodes. Recently proposed methods use the leading eigenvector of the adjacency matrix of the network as the mixture weights to obtain such observables. In the present study, we explore performances of this type of one-dimensional reductions of dynamical systems on networks when we use non-leading eigenvectors of the adjacency matrix as the mixture weights. Our theory predicts that non-leading eigenvectors can be more efficient than the leading eigenvector and enables us to select the eigenvector minimizing the error. We numerically verify that the optimal non-leading eigenvector outperforms the leading eigenvector for some dynamical systems and networks. We also argue that, despite our theory, it is practically better to use the leading eigenvector as the mixture weights to avoid misplacing the bifurcation point too distantly and to be resistant against dynamical noise.

关键词： Network resilience

Enhancing engineering Pedagogy through Deep Learning: Applications of Explainable AI and Neural Networks

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Enhancing Engineering Pedagogy through Deep Learning: Applic...

Green Industrial Electronics and Sustainable Technologies (GIEST), IEEE International Conference on

作者： Umamaheswari. U Pushpa Latha Mamidi Divya Nimma D. Venkateswarlu Mohammed Saleh Al Ansari Ponni Valavan M Saveetha School of law Saveetha Institute of Medical and Technical Sciences SIMATS Chennai India Department of Engineering Mathematics & Humanities S.R.K.R Engineering College Bhimavaram Andhra Pradesh India Computational Science University of Southern Mississippi Data Analyst in UMMC USA Dept. of MBA Lakireddy Balireddy College of Engineering (Autonomous) Permanently Affiliated to JNTUK Mylavaram AP India Department of Chemical Engineering College of Engineering University of Bahrain Bahrain Department of AI & DS K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798331540142

ISBN: (纸本)9798331540159

Conventional engineering pedagogy frequently fails to deliver individualized learning experiences and places a strong emphasis on one-size-fits-all methods of instruction. Meeting the requirements of every individual student and improving learning outcomes become more challenging as a result. Modern instructional tools, however state-of-the-art, typically cannot offer personalized, data-driven insights. Since many systems are clear, it might be challenging for educators and learners to comprehend the recommendations made. The concept-based transformer network presented in this work is intended to improve the personalized learning experience in engineering education. Using focus, this new neural system focuses on the most relevant elements of a student’s education, providing personalized recommendations and assessments. Transparency is achieved through the inclusion of semantic AI, so that teachers and students can understand and validate the recommendations made by the system The proposed approach begins with the collection of large amounts of data, which including demographic information, contact records, and measures of student performance. Pre-processing assures data accuracy and quality. Personalized learning strategies and recommendations are generated through feedforward neural networks and integrated into the instructional platform. Performance measurement and user feedback for incremental change and continuous monitoring. Key metrics such as student engagement, outcomes for learning, and satisfaction levels are used to assess the effectiveness of the model. A comparison of the proposed model with current methods demonstrates its ability to provide scalable, transparent and customizable learning solutions.

关键词： Deep learning Performance evaluation Industrial electronics Accuracy Explainable AI Semantics Transformers Feedforward neural networks engineering education Monitoring