Relevant bibliographies by topics / Machine learning-based collision prediction

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Machine learning-based collision prediction'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Machine learning-based collision prediction"

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Li, Xiaohang, Hongda Wang, Meiting Jiang, et al. "Collision Cross Section Prediction Based on Machine Learning." Molecules 28, no. 10 (2023): 4050. http://dx.doi.org/10.3390/molecules28104050.

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Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances i
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Chang, Che-Cheng, Yee-Ming Ooi, and Bing-Herng Sieh. "IoV-Based Collision Avoidance Architecture Using Machine Learning Prediction." IEEE Access 9 (2021): 115497–505. http://dx.doi.org/10.1109/access.2021.3105619.

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Liu, Peng, Weiwei Zhang, Xuncheng Wu, Wenfeng Guo, and Wangpengfei Yu. "Driver Injury Prediction and Factor Analysis in Passenger Vehicle-to-Passenger Vehicle Collision Accidents Using Explainable Machine Learning." Vehicles 7, no. 2 (2025): 42. https://doi.org/10.3390/vehicles7020042.

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Vehicle accidents, particularly PV-PV collisions, result in significant property damage and driver injuries, causing substantial economic losses and health risks. Most existing studies focus on macro-level predictions, such as accident frequency, but lack detailed collision-level analysis, which limits the precision of severity prediction. This study investigates various accident-related factors, including environmental conditions, vehicle attributes, driver characteristics, pre-crash scenarios, and collision dynamics. Data from NHTSA’s CRSS and FARS datasets were integrated and balanced using
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Lammers, Caleb, Miles Cranmer, Sam Hadden, Shirley Ho, Norman Murray, and Daniel Tamayo. "Accelerating Giant-impact Simulations with Machine Learning." Astrophysical Journal 975, no. 2 (2024): 228. http://dx.doi.org/10.3847/1538-4357/ad7fe5.

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Abstract Constraining planet-formation models based on the observed exoplanet population requires generating large samples of synthetic planetary systems, which can be computationally prohibitive. A significant bottleneck is simulating the giant-impact phase, during which planetary embryos evolve gravitationally and combine to form planets, which may themselves experience later collisions. To accelerate giant-impact simulations, we present a machine learning (ML) approach to predicting collisional outcomes in multiplanet systems. Trained on more than 500,000 N-body simulations of three-planet
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Raj, Nitish, and Prabhat Kumar. "Leveraging HDBSCAN, LSTM and R-DTW for Proactive Detection and Collision Prediction in Maritime Traffic." Defence Science Journal 75, no. 4 (2025): 490–97. https://doi.org/10.14429/dsj.20660.

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Detecting anomalies in Automatic Identification System (AIS) data is crucial for marine safety, especially with over 60,000 vessels navigating seaways at any moment. This study proposes an enhanced approach to AIS data analysis for detecting anomalous ship behaviours and predicting collisions in maritime environments. Unlike traditional methods that rely on static threshold-based rules or simpler clustering techniques, our approach integrates advanced machine learning methods like Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) and Long Short-Term Memory (LST
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Babaoglu, Liza, and Ceni Babaoglu. "Prediction of Fatalities in Vehicle Collisions in Canada." Promet - Traffic&Transportation 33, no. 5 (2021): 661–69. http://dx.doi.org/10.7307/ptt.v33i5.3782.

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Traffic collisions affect millions around the world and are the leading cause of death for children and young adults. Thus, Canada’s road safety plan is to reduce collision injuries and fatalities with a vision of making the safest roads in the world. We aim to predict fatalities of collisions on Canadian roads, and to discover causation of fatalities through exploratory data analysis and machine learning techniques. We analyse the vehicle collisions from Canada’s National Collision Database (1999–2017.) Through data mining methodologies, we investigate association rules and key contributing f
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Abhishek, Saxena, and A. Robila Stefan. "Automated machine learning for analysis and prediction of vehicle crashes." International Journal of Informatics and Communication Technology 12, no. 1 (2023): 46–53. https://doi.org/10.11591/ijict.v12i1.pp46-5.

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This work discusses the study and development of a graphical interface and implementation of a machine learning model for vehicle traffic injury and fatality prediction for a specified date range and for a certain zip (US postal) code based on the New York City's (NYC) vehicle crash data set. While previous studies focused on accident causes, little insight has been offered into how such data may be utilized to forecast future incidents, Studies that have historically concentrated on certain road segment types, such as highways and other streets, and a specific geographic region, this stud
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Choi, Dongho, Janghyuk Yim, Minjin Baek, and Sangsun Lee. "Machine Learning-Based Vehicle Trajectory Prediction Using V2V Communications and On-Board Sensors." Electronics 10, no. 4 (2021): 420. http://dx.doi.org/10.3390/electronics10040420.

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Predicting the trajectories of surrounding vehicles is important to avoid or mitigate collision with traffic participants. However, due to limited past information and the uncertainty in future driving maneuvers, trajectory prediction is a challenging task. Recently, trajectory prediction models using machine learning algorithms have been addressed solve to this problem. In this paper, we present a trajectory prediction method based on the random forest (RF) algorithm and the long short term memory (LSTM) encoder-decoder architecture. An occupancy grid map is first defined for the region surro
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Ribeiro, Bruno, Maria João Nicolau, and Alexandre Santos. "Using Machine Learning on V2X Communications Data for VRU Collision Prediction." Sensors 23, no. 3 (2023): 1260. http://dx.doi.org/10.3390/s23031260.

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Intelligent Transportation Systems (ITSs) are systems that aim to provide innovative services for road users in order to improve traffic efficiency, mobility and safety. This aspect of safety is of utmost importance for Vulnerable Road Users (VRUs), as these users are typically more exposed to dangerous situations, and their vehicles also possess poorer safety mechanisms when in comparison to regular vehicles on the road. Implementing automatic safety solutions for VRU vehicles is challenging since they have high agility and it can be difficult to anticipate their behavior. However, if equippe
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Geng, Zhaoshi, Xiaofeng Ji, Rui Cao, Mengyuan Lu, and Wenwen Qin. "A Conflict Measures-Based Extreme Value Theory Approach to Predicting Truck Collisions and Identifying High-Risk Scenes on Two-Lane Rural Highways." Sustainability 14, no. 18 (2022): 11212. http://dx.doi.org/10.3390/su141811212.

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Collision risk identification and prediction is an effective means to prevent truck accidents. However, most existing studies focus only on highways, not on two-lane rural highways. To predict truck collision probabilities and identify high-risk scenes on two-lane rural highways, this study first calculated time to collision and post-encroachment time using high-precision trajectory data and combined them with extreme value theory to predict the truck collision probability. Subsequently, a traffic feature parameter system was constructed with the driving behavior risk parameter. Furthermore, m
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Dissertations / Theses on the topic "Machine learning-based collision prediction"

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Vergez, Lucas. "Machine learning-based automatic generation of mechanical CAD assemblies." Electronic Thesis or Diss., Paris, ENSAM, 2025. http://www.theses.fr/2025ENAME004.

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L’automatisation en Conception Assistée par Ordinateur (CAO) est une tâche complexe à cause des contraintes complexes d’ingénierie mises en œuvre durant le processus de conception. Ces travaux de thèse s’intéressent à la génération automatique d’assemblages de pièces mécaniques. Cette génération automatique peut être utilisée pour de l’aide à la conception ou de l’expansion de base de données d'assemblages mécaniques, ou de la réutilisation de modèles CAO. La méthode proposée est découpée en 3 parties. La première est la création d’un pipeline basé sur des règles métiers qui permet générer de
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Hu, Jinli. "Potential based prediction markets : a machine learning perspective." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/29000.

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A prediction market is a special type of market which offers trades for securities associated with future states that are observable at a certain time in the future. Recently, prediction markets have shown the promise of being an abstract framework for designing distributed, scalable and self-incentivized machine learning systems which could then apply to large scale problems. However, existing designs of prediction markets are far from achieving such machine learning goal, due to (1) the limited belief modelling power and also (2) an inadequate understanding of the market dynamics. This work
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Goutham, Mithun. "Machine learning based user activity prediction for smart homes." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595493258565743.

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Yaddanapudi, Suryanarayana. "Machine Learning Based Drug-Disease Relationship Prediction and Characterization." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1565349706029458.

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Börthas, Lovisa, and Sjölander Jessica Krange. "Machine Learning Based Prediction and Classification for Uplift Modeling." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-266379.

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The desire to model the true gain from targeting an individual in marketing purposes has lead to the common use of uplift modeling. Uplift modeling requires the existence of a treatment group as well as a control group and the objective hence becomes estimating the difference between the success probabilities in the two groups. Efficient methods for estimating the probabilities in uplift models are statistical machine learning methods. In this project the different uplift modeling approaches Subtraction of Two Models, Modeling Uplift Directly and the Class Variable Transformation are investiga
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Yella, Jaswanth. "Machine Learning-based Prediction and Characterization of Drug-drug Interactions." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin154399419112613.

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Wang, Jiahao. "Vehicular Traffic Flow Prediction Model Using Machine Learning-Based Model." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42288.

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Intelligent Transportation Systems (ITS) have attracted an increasing amount of attention in recent years. Thanks to the fast development of vehicular computing hardware, vehicular sensors and citywide infrastructures, many impressive applications have been proposed under the topic of ITS, such as Vehicular Cloud (VC), intelligent traffic controls, etc. These applications can bring us a safer, more efficient, and also more enjoyable transportation environment. However, an accurate and efficient traffic flow prediction system is needed to achieve these applications, which creates an opportunity
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Xia, Jing. "Bioinformatics analyses of alternative splicing, est-based and machine learning-based prediction." Thesis, Manhattan, Kan. : Kansas State University, 2008. http://hdl.handle.net/2097/1113.

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Allocco, Dominic. "Use of machine learning techniques for SNP based prediction of ancestry." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/35550.

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Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2006.<br>Includes bibliographical references (leaves 29-30).<br>Some have argued that the genetic differences between continentally defined groups are relatively small and unlikely to have biomedical significance. In this study, the extent of variation between continentally defined groups was evaluated. Small numbers of randomly selected single nucleotide polymorphisms from the International HapMap Project were used to train classifiers for prediction of ancestral continent of origin. Predictive accuracy was then tested on
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Xu, Jin. "Machine Learning – Based Dynamic Response Prediction of High – Speed Railway Bridges." Thesis, KTH, Bro- och stålbyggnad, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278538.

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Targeting heavier freights and transporting passengers with higher speeds became the strategic railway development during the past decades significantly increasing interests on railway networks. Among different components of a railway network, bridges constitute a major portion imposing considerable construction and maintenance costs. On the other hand, heavier axle loads and higher trains speeds may cause resonance occurrence on bridges; which consequently limits operational train speed and lines. Therefore, satisfaction of new expectations requires conducting a large number of dynamic assess
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Books on the topic "Machine learning-based collision prediction"

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Steinberg, Fabian. Machine Learning-based Prediction of Missing Parts for Assembly. Springer Fachmedien Wiesbaden, 2024. http://dx.doi.org/10.1007/978-3-658-45033-5.

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Steinberg, Fabian. Machine Learning-Based Prediction of Missing Parts for Assembly. Springer Fachmedien Wiesbaden GmbH, 2024.

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Chaudhuri, Arindam, and Soumya K. Ghosh. Bankruptcy Prediction through Soft Computing based Deep Learning Technique. Springer, 2017.

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Anthony Mihirana Mihirana De Silva and Philip H. W. Leong. Grammar-Based Feature Generation for Time-Series Prediction. Springer, 2015.

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Leong, Philip H. W., and Anthony Mihirana De Silva. Grammar-Based Feature Generation for Time-Series Prediction. Springer London, Limited, 2015.

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Ahmed, Omed Hassan, Pegah Malekpour Alamdari, Gholamreza Zare, and Mehdi Hosseinzadeh. Link Prediction in Data Science: Including Proximity-Based Methods and Supervised Machine Learning Models. Independently Published, 2022.

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Wikle, Christopher K. Spatial Statistics. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.710.

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The climate system consists of interactions between physical, biological, chemical, and human processes across a wide range of spatial and temporal scales. Characterizing the behavior of components of this system is crucial for scientists and decision makers. There is substantial uncertainty associated with observations of this system as well as our understanding of various system components and their interaction. Thus, inference and prediction in climate science should accommodate uncertainty in order to facilitate the decision-making process. Statistical science is designed to provide the to
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Riley, Richard D., Danielle van der Windt, Peter Croft, and Karel G. M. Moons, eds. Prognosis Research in Health Care. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780198796619.001.0001.

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What is going to happen to me, doctor?’ ‘What outcomes am I likely to experience?’ ‘Will this treatment work for me?’ Prognosis—forecasting the future—has always been a part of medical practice and caring for the sick. In modern healthcare it now has a new importance, with large financial investments being made to personalize clinical decisions and tailor treatment strategies to improve individual health outcomes based on prognostic information. Prognosis research—the study of future outcomes in people with a particular health condition—provides the critical evidence for obtaining, evaluating,
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Book chapters on the topic "Machine learning-based collision prediction"

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Abid, Khaled, Hicham Lakhlef, and Abdelmadjid Bouabdallah. "Machine Learning-Based Communication Collision Prediction and Avoidance for Mobile Networks." In Advanced Information Networking and Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99584-3_17.

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Harsha Jasni, T. C., S. Moses Santhakumar, and S. Ebin Sam. "Accident Prediction Modeling for Collision Types Using Machine Learning Tools." In Recent Advances in Transportation Systems Engineering and Management. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2273-2_48.

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Saha, Abhisek, Debasis Dan, and Soma Sanyal. "Model-Independent Prediction of Initial Geometry Parameters in Heavy Ion Collision Using Machine Learning Models." In Springer Proceedings in Physics. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0289-3_98.

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van Aswegen, J. C., H. A. Hamersma, and P. S. Els. "Collision Prediction for a Mining Collision Avoidance System." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_107.

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AbstractAccidents caused by wheeled mining machines contribute to approximately 30% of injuries and fatalities in the global mining industry. Wheeled mining machines have limited driver assist features when compared to the passenger vehicle market and are typically limited to collision avoidance by braking. These products are often subject to false positive interventions leading to production losses, increased wear, and resistance to adopt the technology by end users. This study proposes a sampling-based method to expand the collision avoidance by braking approach to include steering. The samp
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Saleh, Hadi, Anastasia Sakunova, Albo Jwaid Furqan Abbas, and Mohammed Shakir Mahmood. "Machine Learning-Based Crime Prediction." In Intelligent Decision Technologies. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3444-5_44.

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Robnik-Šikonja, Marko, and Marko Bohanec. "Perturbation-Based Explanations of Prediction Models." In Human and Machine Learning. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90403-0_9.

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Banerjee, Tania, Xiaohui Huang, Aotian Wu, Ke Chen, Anand Rangarajan, and Sanjay Ranka. "Trajectory Prediction." In Video Based Machine Learning for Traffic Intersections. CRC Press, 2023. http://dx.doi.org/10.1201/9781003431176-6.

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Sanjana Rao, G. P., K. Aditya Shastry, S. R. Sathyashree, and Shivani Sahu. "Machine Learning based Restaurant Revenue Prediction." In Evolutionary Computing and Mobile Sustainable Networks. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5258-8_35.

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Pansari, Risham Kumar, Akhtar Rasool, Rajesh Wadhvani, and Aditya Dubey. "Machine Learning-Based Stock Market Prediction." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0483-9_6.

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James, Deepa Elizabeth, and E. R. Vimina. "Machine Learning-Based Early Diabetes Prediction." In Intelligent Sustainable Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2422-3_52.

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Conference papers on the topic "Machine learning-based collision prediction"

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Deng, Weijun, Chen Li, Zhirui Yan, and Yuzhuo Yuan. "Machine Learning-Based Stroke Prediction." In International Conference on Engineering Management, Information Technology and Intelligence. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012972300004508.

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Dhondiyal, Shiv Ashish, Rohit Kumar, Himanshu Verma, Ashish Dhyani, and Sumeshwar Singh. "Machine Learning-Based Wine Quality Prediction." In 2024 Second International Conference on Advances in Information Technology (ICAIT). IEEE, 2024. http://dx.doi.org/10.1109/icait61638.2024.10690496.

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Chen, Siyuan. "Machine-Learning-Based Prediction of Obesity." In International Conference on Engineering Management, Information Technology and Intelligence. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012916000004508.

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Deng, Zhenchang, and Hao Wang. "Machine Learning-based Employee Turnover Prediction." In 2024 4th International Signal Processing, Communications and Engineering Management Conference (ISPCEM). IEEE, 2024. https://doi.org/10.1109/ispcem64498.2024.00072.

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Terence, Sebastian, Jude Immaculate, Titus, Darigi Bharath Naik, and Selvarathi Selvarathi. "Machine Learning based Bitcoin Price Prediction." In 2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI). IEEE, 2024. https://doi.org/10.1109/icdici62993.2024.10810810.

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Khan, Shaista, Vishakha Bhandarkar, Kanchan Artani, Manoj Pande, and Rajesh Nakhate. "Machine Learning-Based Prediction of Home Prices." In 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI). IEEE, 2024. https://doi.org/10.1109/idicaiei61867.2024.10842931.

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Mukherjee, Anupam, Rtwik Nambiar, and Deepjyoti Choudhury. "Machine Learning based Real Estate Price Prediction." In 2024 8th International Conference on Inventive Systems and Control (ICISC). IEEE, 2024. http://dx.doi.org/10.1109/icisc62624.2024.00067.

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Fan, Zhangyu, Bohao Liu, and Xiao Yan. "Cardiovascular Disease Prediction Based on Machine Learning." In International Conference on Engineering Management, Information Technology and Intelligence. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012939000004508.

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Huang, Weihua. "Machine Learning-Based Breast Cancer Probability Prediction." In 2024 4th International Signal Processing, Communications and Engineering Management Conference (ISPCEM). IEEE, 2024. https://doi.org/10.1109/ispcem64498.2024.00017.

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S, Arockiya Selvi, and T. Kamalakannan. "Machine Learning based Prediction of Parkinson's Diseases." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933153.

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Reports on the topic "Machine learning-based collision prediction"

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Shabalina, A., A. Carpenter, M. Rahman, C. Tennant, and L. Vidyaratne. Machine Learning Based Cavity Fault Classification and Prediction. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1735851.

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Bhurtyal, Sanjeev, Hieu Bui, Sarah Hernandez, et al. Prediction of waterborne freight activity with Automatic Identification System using machine learning. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49794.

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This paper addresses latency issues related to publicly available port-level commodity tonnage reports. Predicting commodity tonnage at the port-level, near real time vessel tracking data is used with historical WCS with a machine learning model. Commodity throughput is derived from WCS data which is released publicly approximately two years after collection. This latency presents a challenge for short-term planning and other operational uses. This study leverages near real time vessel tracking data from the AIS data set. LSTM, TCN, and TFT machine learning models are developed using the featu
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Zou, Yufei, Philip Rasch, and Hailong Wang. Hybridizing Machine Learning and Physically-based Earth System Models to Improve Prediction of Multivariate Extreme Events (AI Exploration of Wildland Fire Prediction). Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769718.

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Johnson, Lewis, Peter St. John, Delwin Elder, et al. DOE STTR Phase I Final Report Report: Machine-learning Based Prediction of Thermal Limits for Conjugated Organic Materials. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1995938.

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Chen, Yuxiang, Haoran Yang, Anna Zhao, et al. Establishing and validating a risk prediction model for peri-implantitis based on meta-analysis and machine learning methods. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2025. https://doi.org/10.37766/inplasy2025.7.0025.

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Liu, Xiaopei, Dan Liu, and Cong’e Tan. Gut microbiome-based machine learning for diagnostic prediction of liver fibrosis and cirrhosis: a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.5.0133.

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Review question / Objective: The invasive liver biopsy is the gold standard for the diagnosis of liver cirrhosis. Other non-invasive diagnostic approaches, have been used as alternatives to liver biopsy, however, these methods cannot identify the pathological grade of the lesion. Recently, studies have shown that gut microbiome-based machine learning can be used as a non-invasive diagnostic approach for liver cirrhosis or fibrosis, while it lacks evidence-based support. Therefore, we performed this systematic review and meta-analysis to evaluate its predictive diagnostic value in liver cirrhos
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Wang, Yingxuan, Cheng Yan, and Liqin Zhao. The value of radiomics-based machine learning for hepatocellular carcinoma after TACE: a systematic evaluation and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.6.0100.

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Review question / Objective: Meta-analysis was performed to predict the efficacy and survival status of patients with hepatocellular carcinoma after the application of TACE, applying clinical models, radiomic models and combined models for non-invasive assessment.We performed a Meta-analysis on the prediction of efficacy and survival status after TACE for hepatocellular carcinoma. Condition being studied: Patients were scanned using CT or MR machines, and some patients had multiple follow-up records, and imaging feature extraction software was applied to extract regions of interest and build m
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Zhang, Caiyun, David Brodylo, Mizanur Rahman, Md Atiqur Rahman, Thomas Douglas, and Xavier Comas. Using an object-based machine learning ensemble approach to upscale evapotranspiration measured from eddy covariance towers in a subtropical wetland. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48056.

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Accurate prediction of evapotranspiration (ET) in wetlands is critical for understanding the coupling effects of water, carbon, and energy cycles in terrestrial ecosystems. Multiple years of eddy covariance (EC) tower ET measurements at five representative wetland ecosystems in the subtropical Big Cypress National Preserve (BCNP), Florida (USA) provide a unique opportunity to assess the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) ET operational product MOD16A2 and upscale tower measured ET to generate local/regional wetland ET maps. We developed an object-based mac
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Slone, Scott Michael, Marissa Torres, Nathan Lamie, Samantha Cook, and Lee Perren. Automated change detection in ground-penetrating radar using machine learning in R. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49442.

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Ground-penetrating radar (GPR) is a useful technique for subsurface change detection but is limited by the need for a subject matter expert to process and interpret coincident profiles. Use of a machine learning model can automate this process to reduce the need for subject matter expert processing and interpretation. Several machine learning models were investigated for the purpose of comparing coincident GPR profiles. Based on our literature review, a Siamese Twin model using a twinned convolutional network was identified as the optimum choice. Two neural networks were tested for the interna
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10

Bailey Bond, Robert, Pu Ren, James Fong, Hao Sun, and Jerome F. Hajjar. Physics-informed Machine Learning Framework for Seismic Fragility Analysis of Steel Structures. Northeastern University, 2024. http://dx.doi.org/10.17760/d20680141.

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The seismic assessment of structures is a critical step to increase community resilience under earthquake hazards. This research aims to develop a Physics-reinforced Machine Learning (PrML) paradigm for metamodeling of nonlinear structures under seismic hazards using artificial intelligence. Structural metamodeling, a reduced-fidelity surrogate model to a more complex structural model, enables more efficient performance-based design and analysis, optimizing structural designs and ease the computational effort for reliability fragility analysis, leading to globally efficient designs while maint
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