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Dissertations / Theses on the topic 'Machine learning-based collision prediction'

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Published: 7 June 2025

Last updated: 2 August 2025

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1

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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3

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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Lemus, Cárdenas Leticia. "Enhancement of vehicular ad hoc networks using machine learning-based prediction methods." Doctoral thesis, Universitat Politècnica de Catalunya, 2020. http://hdl.handle.net/10803/670020.

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Society is aware that the ecological predation of the planet is due to human activity. Therefore, it is necessary to carry out actions to reverse this damage. Besides, the trend of population growth in big cities and the uncontrolled volume of traffic cause serious problems such as traffic delays, traffic jams, increased CO2 emissions, and traffic accidents. In this sense, so-called smart cities are motivated to create a greener and safer environment where both efficient mobility and public services seek to mitigate those problems. These initiatives are supported by smart technologies such as

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Sualp, Merter. "Machine Learning Methods For Using Network Based Information In Microrna Target Prediction." Phd thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615645/index.pdf.

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Computational microRNA (miRNA) target identification in animal genomes is a challenging problem due to the imperfect pairing of the miRNA with the target site. Techniques based on sequence alone are prone to produce many false positive interactions. Therefore, integrative techniques have been developed to utilize additional genomic, structural features, and evolu- tionary conservation information for reducing the high false positive rate. We propose that the context of a putative miRNA target in a protein-protein interaction (PPI) network can be used as an additional filter in a computational

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Iqbal, Sumaiya. "Machine Learning based Protein Sequence to (un)Structure Mapping and Interaction Prediction." ScholarWorks@UNO, 2017. http://scholarworks.uno.edu/td/2379.

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Proteins are the fundamental macromolecules within a cell that carry out most of the biological functions. The computational study of protein structure and its functions, using machine learning and data analytics, is elemental in advancing the life-science research due to the fast-growing biological data and the extensive complexities involved in their analyses towards discovering meaningful insights. Mapping of protein’s primary sequence is not only limited to its structure, we extend that to its disordered component known as Intrinsically Disordered Proteins or Regions in proteins (IDPs/IDRs

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Saha, Suman. "Stock market movement prediction using machine learning techniques and graph-based approaches." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/30018.

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Machine learning techniques are preferred now than the statistical methods for stock movement prediction due to their efficiency and effectiveness. Stock market movement prediction is impacted significantly by choice of input features and prediction algorithms. We focus on a specific event of ex-dividend day and use event-specific input features of cum-dividend period for predicting price movement on the ex-dividend day. Performance improves significantly when these event-specific optimum input features are used along with machine learning models. The relative order or ranking of stocks is mo

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Wu, Wenda. "Machine Learning Based Fault Prediction for Real-time Scheduling on Shop Floor." Thesis, KTH, Industriell produktion, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-245221.

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Nowadays, scheduling on a shop floor is only focused on the availabil-ity of resources, where the potential faults are not able to be predicted. A big data analytics based fault prediction was proposed to be ap-plied in scheduling, which require a real-time decision making. To select a proper machine learning algorithm for real-time scheduling, this paper first proposes a data generation method in terms of pattern complexity and scale. Three levels of depth, an index of data complex-ity, and three levels of data attributes, an index of data scale, are used to obtain the data sets. Based on tho

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Denzel, Alexander [Verfasser], and Johannes [Akademischer Betreuer] Kästner. "Chemical structure prediction based on machine learning / Alexander Denzel ; Betreuer: Johannes Kästner." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2020. http://d-nb.info/1206184116/34.

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Blank, Clas, and Tomas Hermansson. "A Machine Learning approach to churn prediction in a subscription-based service." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240397.

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Prenumerationstjänster blir alltmer populära i dagens samhälle. En av nycklarna för att lyckas med en prenumerationsbaserad affärsmodell är att minimera kundbortfall (eng. churn), dvs. kunder som avslutar sin prenumeration inom en viss tidsperiod. I och med den ökande digitaliseringen, är det nu enklare att samla in data än någonsin tidigare. Samtidigt växer maskininlärning snabbt och blir alltmer lättillgängligt, vilket möjliggör nya infallsvinklar på problemlösning. Denna rapport kommer testa och utvärdera ett försök att förutsäga kundbortfall med hjälp av maskininlärning, baserat på kunddat

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Bagger, Toräng Malcolm, and Kasper Aldrin. "A machine learning approach to EEG based prediction of user's music preferences." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259625.

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Music has many benefits for our mood and feelings, especially so when we get to choose our own favorite music. However, accessing one's favorite music is not as easy for everyone. For motorically disabled and locked-in people, interacting with devices used for listening to music is challenging since it requires physical interaction. Machine learning classification methods used with EEG could prove useful for detecting individual musical preferences, extracted without any physical or verbal interaction. The two most common methods within EEG-based classification are Artificial neural networks (

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Chowdhury, Alok K. "Sensor-based prediction of physical activity and its impacts using machine learning." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/118664/1/Alok_Chowdhury_Thesis.pdf.

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This thesis contributed to the development of advanced learning models and multi-sensor decision fusion algorithm to improve the prediction of physical activity and its personal impacts including relative intensity and energy expenditure from wearable sensor data. It identified the optimal sensor positioning and optimal combination of multimodal sensor data for assessing physical activity and predicting its impacts. All methods of this thesis collectively deliver better algorithms and maximise the use of available sensor information to provide accurate measurement of physical activity.

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20

McCoy, Mason Eugene. "A Twitter-Based Prediction Tool for Digital Currency." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/theses/2302.

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Digital currencies (cryptocurrencies) are rapidly becoming commonplace in the global market. Trading is performed similarly to the stock market or commodities, but stock market prediction algorithms are not necessarily well-suited for predicting digital currency prices. In this work, we analyzed tweets with both an existing sentiment analysis package and a manually tailored "objective analysis," resulting in one impact value for each analysis per 15-minute period. We then used evolutionary techniques to select the most appropriate training method and the best subset of the generated features t

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Bernabè, Matteo. "Machine learning based traffic analysis and forecast for 5G Systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Mobile traffic forecasting is a relatively new research area, which is becoming of fundamental importance for next-generation networks. Proactively knowing the user demand allows the system to allocate resources and apply energy-saving decisions properly. Classical models are limited by the stationary assumption of time sequences and fail to take correlations into account. This work presents results on cellular network traffic analysis and prediction, providing a novel, robust, and precise machine learning model to efficiently and dynamically manage network resources in 5G systems.

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Hu, Hae-Jin. "Design of Comprehensible Learning Machine Systems for Protein Structure Prediction." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/22.

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With the efforts to understand the protein structure, many computational approaches have been made recently. Among them, the Support Vector Machine (SVM) methods have been recently applied and showed successful performance compared with other machine learning schemes. However, despite the high performance, the SVM approaches suffer from the problem of understandability since it is a black-box model; the predictions made by SVM cannot be interpreted as biologically meaningful way. To overcome this limitation, a new association rule based classifier PCPAR was devised based on the existing cla

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Arikatla, Govardhan, and Bhargav Chinnapottu. "Movie prediction based on movie scriptsusing Natural Language Processing and Machine Learning Algorithms." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21761.

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Paradesi, Martin Samuel Rao. "Graph-based protein-protein interaction prediction in Saccharomyces cerevisiae." Thesis, Manhattan, Kan. : Kansas State University, 2008. http://hdl.handle.net/2097/931.

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Tsardakas, Renhuldt Nikos. "Protein contact prediction based on the Tiramisu deep learning architecture." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231494.

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Experimentally determining protein structure is a hard problem, with applications in both medicine and industry. Predicting protein structure is also difficult. Predicted contacts between residues within a protein is helpful during protein structure prediction. Recent state-of-the-art models have used deep learning to improve protein contact prediction. This thesis presents a new deep learning model for protein contact prediction, TiramiProt. It is based on the Tiramisu deep learning architecture, and trained and evaluated on the same data as the PconsC4 protein contact prediction model. 228 m

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Ghafir, Ibrahim. "A machine-learning-based system for real-time advanced persistent threat detection and prediction." Thesis, Manchester Metropolitan University, 2017. http://e-space.mmu.ac.uk/618896/.

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It is widely cited that cyber attacks have become more prevalent on a global scale. In light of this, the cybercrime industry has been established for various purposes such as political, economic and socio-cultural aims. Such attacks can be used as a harmful weapon and cyberspace is often cited as a battlefield. One of the most serious types of cyber attacks is the Advanced Persistent Threat (APT), which is a new and more complex version of multi-step attack. The main aim of the APT attack is espionage and data exfiltration, which has the potential to cause significant damage and substantial f

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Messaoud, Kaouther. "Deep learning based trajectory prediction for autonomous vehicles." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS048.

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La prédiction de trajectoire des agents avoisinants d'un véhicule autonome est essentielle pour la conduite autonome afin d'effectuer une planification de trajectoire d'une manière efficace. Dans cette thèse, nous abordons la problématique de prédiction de trajectoire d'un véhicule cible dans deux environnements différents ; une autoroute et une zone urbaine (intersection, rond-point, etc.). Dans ce but, nous développons des solutions basées sur l'apprentissage automatique profond en mettant en phase les interactions entre le véhicule cibles et les éléments statiques et dynamiques de la scène.

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Postovskaya, Anna. "Rule-based machine learning for prediction of Macaca mulatta SIV-vaccination outcome using transcriptome profiles." Thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-440182.

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One of the reasons, why the development of an effective HIV vaccine remains challenging, is the lack of understanding of potential vaccination-induced protection mechanisms. In the present study, Rhesus Macaques (Macaca mulatta) gene expression profiles obtained during vaccination with promising candidate vaccines against Simian Immunodeficiency Virus (SIV) were processed with a rule-based supervised machine learning approach to analyze the effects of vaccine combination treatment. The findings from constructed rule-based classifiers suggest that the immune response against SIV builds up throu

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Osman, Yasin, and Benjamin Ghaffari. "Customer churn prediction using machine learning : A study in the B2B subscription based service context." Thesis, Blekinge Tekniska Högskola, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21872.

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The rapid growth of technological infrastructure has changed the way companies do business. Subscription based services are one of the outcomes of the ongoing digitalization, and with more and more products and services to choose from, customer churning has become a major problem and a threat to all firms. We propose a machine learning based churn prediction model for a subscription based service provider, within the domain of financial administration in the business-to-business (B2B) context. The aim of our study is to contribute knowledge within the field of churn prediction. For the propose

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De, Silva Anthony Mihirana. "Grammar based feature generation for time-series prediction." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/10278.

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The application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This thesis proposes a systematic way for generating suitable features using context-free grammar. The notion of grammar families as a compact representation to generate a broad class of features is exploited. Implementation issues and wa

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Zhao, Yuxiao. "Parking Availability Prediction based on Machine Learning Approaches: A Case Study in the Short North Area." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587147304509822.

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Martinez, Matthew, and Leon Phillip L. De. "A Smartphone-Based Gait Data Collection System for the Prediction of Falls in Elderly Adults." International Foundation for Telemetering, 2015. http://hdl.handle.net/10150/596377.

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ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NV<br>Falls prevention efforts for older adults have become increasingly important and are now a significant research effort. As part of the prevention effort, analysis of gait has become increasingly important. Data is typically collected in a laboratory setting using 3-D motion capture, which can be time consuming, invasive and requires expensive and specialized equipment as well as trained operators.

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Trivedi, Shubhendu. "A Graph Theoretic Clustering Algorithm based on the Regularity Lemma and Strategies to Exploit Clustering for Prediction." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-theses/573.

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The fact that clustering is perhaps the most used technique for exploratory data analysis is only a semaphore that underlines its fundamental importance. The general problem statement that broadly describes clustering as the identification and classification of patterns into coherent groups also implicitly indicates it's utility in other tasks such as supervised learning. In the past decade and a half there have been two developments that have altered the landscape of research in clustering: One is improved results by the increased use of graph theoretic techniques such as spectral clustering

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Geylan, Gökçe. "Training Machine Learning-based QSAR models with Conformal Prediction on Experimental Data from DNA-Encoded Chemical Libraries." Thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447354.

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DNA-encoded chemical libraries (DEL) allows an exhaustive chemical space sampling with a large-scale data consisting of compounds produced through combinatorial synthesis. This novel technology was utilized in the early drug discovery stages for robust hit identification and lead optimization. In this project, the aim was to build a Machine Learning- based QSAR model with conformal prediction for hit identification on two different target proteins, the DEL was assayed on. An initial investigation was conducted on a pilot project with 1000 compounds and the analyses and the conclusions drawn fr

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Flöjs, Amanda, and Alexandra Hägg. "Churn Prediction : Predicting User Churn for a Subscription-based Service using Statistical Analysis and Machine Learning Models." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-171678.

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Subscription-based services are becoming more popular in today’s society. Therefore, any company that engages in the subscription-based business needs to understand the user behavior and minimize the number of users canceling their subscription, i.e. minimize churn. According to marketing metrics, the probability of selling to an existing user is markedly higher than selling to a brand new user. Nonetheless, it is of great importance that more focus is directed towards preventing users from leaving the service, in other words preventing user churn. To be able to prevent user churn the company

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Omran, Abir. "Improving ligand-based modelling by combining various features." Thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-448769.

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Background: In drug discovery morphological profiles can be used to identify and establish a drug's biological activity or mechanism of action. Quantitative structure-activity relationship (QSAR) is an approach that uses the chemical structures to predict properties e.g., biological activity. Support Vector Machine (SVM) is a machine learning algorithm that can be used for classification. Confidence measures as conformal predictions can be implemented on top of machine learning algorithms. There are several methods that can be applied to improve a model’s predictive performance. Aim: The aim i

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Etminan, Ali. "Prediction of Lead Conversion With Imbalanced Data : A method based on Predictive Lead Scoring." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176433.

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An ongoing challenge for most businesses is to filter out potential customers from their audience. This thesis proposes a method that takes advantage of user data to classify po- tential customers from random visitors to a website. The method is based on the Predictive Lead Scoring method that segments customers based on their likelihood of purchasing a product. Our method, however, aims to predict user conversion, that is predicting whether a user has the potential to become a customer or not. Six supervised machine learning models have been used to carry out the classifica- tion task. To acc

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Kwame, Osei Eric. "Machine Learning-based Quality Prediction in the Froth Flotation Process of Mining : Master’s Degree Thesis in Microdata Analysis." Thesis, Högskolan Dalarna, Mikrodataanalys, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:du-31643.

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In the iron ore mining fraternity, in order to achieve the desired quality in the froth flotation processing plant, stakeholders rely on conventional laboratory test technique which usually takes more than two hours to ascertain the two variables of interest. Such a substantial dead time makes it difficult to put the inherent stochastic nature of the plant system in steady-state. Thus, the present study aims to evaluate the feasibility of using machine learning algorithms to predict the percentage of silica concentrate (SiO2) in the froth flotation processing plant in real-time. The predictive

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Mauricio-Sanchez, David, Andrade Lopes Alneu de, and higuihara Juarez Pedro Nelson. "Approaches based on tree-structures classifiers to protein fold prediction." Institute of Electrical and Electronics Engineers Inc, 2017. http://hdl.handle.net/10757/622536.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.<br>Protein fold recognition is an important task in the biological area. Different machine learning methods such as multiclass classifiers, one-vs-all and ensemble nested dichotomies were applied to this task and, in most of the cases, multiclass approaches were used. In this paper, we compare classifiers organized in tree structures to classify folds. We used a benchmark dataset containing 125 features to predict folds, comparing different superv

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Gattani, Suraj. "StackCBpred: A Stacking based Prediction of Protein-Carbohydrate Binding Sites from Sequence." ScholarWorks@UNO, 2019. https://scholarworks.uno.edu/td/2605.

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Carbohydrate-binding proteins play vital roles in many vital biological processes and study of these interactions, at residue level, are useful in treating many critical diseases. Analyzing the local sequential environments of the binding and non-binding regions to predict the protein-carbohydrate binding sites is one of the challenging problems in molecular and computational biology. Prediction of such binding sites, directly from sequences, using computational methods, can be useful to fast annotate the binding sites and guide the experimental process. Because the number of carbohydrate-bind

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Komol, Md Mostafizur Rahman. "C-ITS based prediction of driver red light running and turning behaviours." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/227694/1/Md%20Mostafizur%20Rahman_Komol_Thesis.pdf.

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Red light running is a major traffic violation. Drivers often aggressively or unintentionally violate red signal and cause traffic collisions. Moreover, Vision impairment of turning vehicles by large vehicles and road side static structures near intersections often lead to VRU crashes during their crossing at the intersection. In this research, we have developed models to predict drivers’ red light running and turning behaviour at intersections using Long Short Term Memory and Gated Recurrent Unit algorithms. We have used vehicle kinematic dataset of the C-ITS project: Ipswich Connected Vehicl

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Callh, Sebastian. "Trajectory-based Arrival Time Prediction using Gaussian Processes : A motion pattern modeling approach." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158623.

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As cities grow, efficient public transport systems are becoming increasingly important. To offer a more efficient service, public transport providers use systems that predict arrival times of buses, trains and similar vehicles, and present this information to the general public. The accuracy and reliability of these predictions are paramount, since many people depend on them, and erroneous predictions reflect badly on the public transport provider. When public transport vehicles move throughout the cities, they create motion patterns, which describe how their positions change over time. This t

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Arslan, Oktay. "Machine learning and dynamic programming algorithms for motion planning and control." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54317.

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Robot motion planning is one of the central problems in robotics, and has received considerable amount of attention not only from roboticists but also from the control and artificial intelligence (AI) communities. Despite the different types of applications and physical properties of robotic systems, many high-level tasks of autonomous systems can be decomposed into subtasks which require point-to-point navigation while avoiding infeasible regions due to the obstacles in the workspace. This dissertation aims at developing a new class of sampling-based motion planning algorithms that are fast,

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Kabir, Mitra. "Prediction of mammalian essential genes based on sequence and functional features." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/prediction-of-mammalian-essential-genes-based-on-sequence-and-functional-features(cf8eeed5-c2b3-47c3-9a8f-2cc290c90d56).html.

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Essential genes are those whose presence is imperative for an organism's survival, whereas the functions of non-essential genes may be useful but not critical. Abnormal functionality of essential genes may lead to defects or death at an early stage of life. Knowledge of essential genes is therefore key to understanding development, maintenance of major cellular processes and tissue-specific functions that are crucial for life. Existing experimental techniques for identifying essential genes are accurate, but most of them are time consuming and expensive. Predicting essential genes using comput

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Hammami, Seif Eddine. "Dynamic network resources optimization based on machine learning and cellular data mining." Thesis, Evry, Institut national des télécommunications, 2018. http://www.theses.fr/2018TELE0015/document.

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Les traces réelles de réseaux cellulaires représentent une mine d’information utile pour améliorer les performances des réseaux. Des traces comme les CDRs (Call detail records) contiennent des informations horodatées sur toutes les interactions des utilisateurs avec le réseau sont exploitées dans cette thèse. Nous avons proposé des nouvelles approches dans l’étude et l’analyse des problématiques des réseaux de télécommunications, qui sont basé sur les traces réelles et des algorithmes d’apprentissage automatique. En effet, un outil global d’analyse de données, pour la classification automatiqu

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46

Hammami, Seif Eddine. "Dynamic network resources optimization based on machine learning and cellular data mining." Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2018. http://www.theses.fr/2018TELE0015.

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Abstract:

Les traces réelles de réseaux cellulaires représentent une mine d’information utile pour améliorer les performances des réseaux. Des traces comme les CDRs (Call detail records) contiennent des informations horodatées sur toutes les interactions des utilisateurs avec le réseau sont exploitées dans cette thèse. Nous avons proposé des nouvelles approches dans l’étude et l’analyse des problématiques des réseaux de télécommunications, qui sont basé sur les traces réelles et des algorithmes d’apprentissage automatique. En effet, un outil global d’analyse de données, pour la classification automatiqu

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47

Heidaripak, Samrend. "PREDICTION OF PUBLIC BUS TRANSPORTATION PLANNING BASED ON PASSENGER COUNT AND TRAFFIC CONDITIONS." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-53408.

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Artificial intelligence has become a hot topic in the past couple of years because of its potential of solving problems. The most used subset of artificial intelligence today is machine learning, which is essentially the way a machine can learn to do tasks without getting any explicit instructions. A problem that has historically been solved by common knowledge and experience is the planning of bus transportation, which has been prone to mistakes. This thesis investigates how to extract the key features of a raw dataset and if a couple of machine learning algorithms can be applied to predict a

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Arvola, Maja. "Deep Learning for Dose Prediction in Radiation Therapy : A comparison study of state-of-the-art U-net based architectures." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447081.

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Machine learning has shown great potential as a step in automating radiotherapy treatment planning. It can be used for dose prediction and a popular deep learning architecture for this purpose is the U-net. Since it was proposed in 2015, several modifications and extensions have been proposed in the literature. In this study, three promising modifications are reviewed and implemented for dose prediction on a prostate cancer data set and compared with a 3D U-net as a baseline. The tested modifications are residual blocks, densely connected layers and attention gates. The different models are co

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Lööv, Simon. "Comparison of Undersampling Methods for Prediction of Casting Defects Based on Process Parameters." Thesis, Högskolan i Skövde, Institutionen för ingenjörsvetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20596.

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Prediction of both big and small decisions is something most companies have to make on a daily basis. The importance of having a highly accurate technique for different decision-making is not something that is new. However, even though the importance of prediction is a fact to most people, current techniques for estimation are still often highly inaccurate. The consequences of an inaccurate prediction can be huge in the differences between the misclassifications. Not just in the industry but for many different areas. Machine learning have in the recent couple of years improved significantly an

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Dsouza, Rodney Gracian. "Deep Learning Based Motion Forecasting for Autonomous Driving." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1619139403696822.

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