Relevant bibliographies by topics / Hybrid machine learning models

Contents

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

Academic literature on the topic 'Hybrid machine learning models'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Hybrid machine learning models"

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Lok, Lai Kai, Vazeerudeen Abdul Hameed, and Muhammad Ehsan Rana. "Hybrid machine learning approach for anomaly detection." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 1016–24. https://doi.org/10.11591/ijeecs.v27.i2.pp1016-1024.

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This research aims to improve anomaly detection performance by developing two variants of hybrid models combining supervised and unsupervised machine learning techniques. Supervised models cannot detect new or unseen types of anomaly. Hence in variant 1, a supervised model that detects normal samples is followed by an unsupervised learning model to screen anomaly. The unsupervised model is weak in differentiating between noise and fraud. Hence in variant 2, the hybrid model incorporates an unsupervised model that detects anomaly is followed by a supervised model to validate an anomaly. Three d
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Pulicharla, Mohan Raja. "Hybrid Quantum-Classical Machine Learning Models: Powering the Future of AI." Journal of Science & Technology 4, no. 1 (2023): 40–65. http://dx.doi.org/10.55662/jst.2023.4102.

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The burgeoning field of machine learning has transformed numerous sectors, revolutionizing everything from image recognition to financial forecasting. However, classical machine learning algorithms often encounter limitations when dealing with complex, high-dimensional problems. This is where the nascent field of quantum machine learning (QML) emerges, offering a paradigm shift with its unique computational capabilities. By harnessing the principles of quantum mechanics, QML promises to solve problems intractable for classical methods, like simulating complex molecules or optimizing financial
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Rodrigues, Sandy, Gerhard Mütter, Helena Geirinhas Ramos, and F. Morgado-Dias. "Machine Learning Photovoltaic String Analyzer." Entropy 22, no. 2 (2020): 205. http://dx.doi.org/10.3390/e22020205.

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Photovoltaic (PV) system energy production is non-linear because it is influenced by the random nature of weather conditions. The use of machine learning techniques to model the PV system energy production is recommended since there is no known way to deal well with non-linear data. In order to detect PV system faults, the machine learning models should provide accurate outputs. The aim of this work is to accurately predict the DC energy of six PV strings of a utility-scale PV system and to accurately detect PV string faults by benchmarking the results of four machine learning methodologies kn
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Masrom, Suraya, Rahayu Abdul Rahman, Masurah Mohamad, Abdullah Sani Abd Rahman, and Norhayati Baharun. "Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 3 (2022): 1153. http://dx.doi.org/10.11591/ijai.v11.i3.pp1153-1163.

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This paper addresses the performances of machine learning classification models for the detection of tax avoidance problems. The machine learning models employed automated features selection with hybrid two metaheuristics algorithms namely particle swarm optimization (PSO) and genetic algorithm (GA). Dealing with a real dataset on the tax avoidance cases among companies in Malaysia, has created a stumbling block for the conventional machine learning models to achieve higher accuracy in the detection process as the associations among all of the features in the datasets are extremely low. This p
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Suraya, Masrom, Abdul Rahman Rahayu, Mohamad Masurah, Sani Abd Rahman Abdullah, and Baharun Norhayati. "Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms." International Journal of Artificial Intelligence (IJ-AI) 11, no. 3 (2022): 1153–63. https://doi.org/10.11591/ijai.v11.i3.pp1153-1163.

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This paper addresses the performances of machine learning classification models for the detection of tax avoidance problems. The machine learning models employed automated features selection with hybrid two metaheuristics algorithms namely particle swarm optimization (PSO) and genetic algorithm (GA). Dealing with a real dataset on the tax avoidance cases among companies in Malaysia, has created a stumbling block for the conventional machine learning models to achieve higher accuracy in the detection process as the associations among all of the features in the datasets are extremely low. This p
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Chou, Jui-Sheng, Chih-Fong Tsai, and Yu-Hsin Lu. "PROJECT DISPUTE PREDICTION BY HYBRID MACHINE LEARNING TECHNIQUES." Journal of Civil Engineering and Management 19, no. 4 (2013): 505–17. http://dx.doi.org/10.3846/13923730.2013.768544.

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This study compares several well-known machine learning techniques for public-private partnership (PPP) project dispute problems. Single and hybrid classification techniques are applied to construct models for PPP project dispute prediction. The single classification techniques utilized are multilayer perceptron (MLP) neural networks, decision trees (DTs), support vector machines, the naïve Bayes classifier, and k-nearest neighbor. Two types of hybrid learning models are developed. One combines clustering and classification techniques and the other combines multiple classification techniques.
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Chaubey, Mangalam. "Diabetes Mellitus Prediction using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 4786–90. http://dx.doi.org/10.22214/ijraset.2023.52755.

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Abstract: Diabetes is a chronic metabolic disorder affecting millions of people worldwide, and machine learning has shown great potential in predicting the disease using medical and demographic features from patient data. In this paper, we propose a hybrid model of Support Vector Machines (SVM) and XGBoost for diabetes prediction, which combines the strengths of both algorithms to achieve higher accuracy and better performance. We evaluate the proposed model using the Pima Indian diabetes dataset and compare its performance with other machine learning models. To improve the performance of the
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Kim, Yeonuk, Monica Garcia, T. Andrew Black, and Mark S. Johnson. "Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge." PLOS One 20, no. 7 (2025): e0328798. https://doi.org/10.1371/journal.pone.0328798.

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Physics-informed machine learning techniques have emerged to tackle challenges inherent in pure machine learning (ML) approaches. One such technique, the hybrid approach, has been introduced to estimate terrestrial evapotranspiration (ET), a crucial variable linking water, energy, and carbon cycles. A key advantage of these hybrid ET models is their improved performance, particularly under extreme conditions, compared to ET estimates relying solely on ML. However, the mechanisms driving their improved performance are not well understood. To address this gap, we developed six hybrid approaches
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Lok, Lai Kai, Vazeerudeen Abdul Hameed, and Muhammad Ehsan Rana. "Hybrid machine learning approach for anomaly detection." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 1016. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp1016-1024.

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This research aims to <span lang="EN-US">improve anomaly detection performance by developing two variants of hybrid models combining supervised and unsupervised machine learning techniques. Supervised models cannot detect new or unseen types of anomaly. Hence in variant 1, a supervised model that detects normal samples is followed by an unsupervised learning model to screen anomaly. The unsupervised model is weak in differentiating between noise and fraud. Hence in variant 2, the hybrid model incorporates an unsupervised model that detects anomaly is followed by a supervised model to val
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Sengaliappan, Dr M. "Flood Prediction using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41892.

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Due to urbanization and climate change, flooding has increased in frequency and severity, upsetting lives and seriously damaging property. Flood Susceptibility Modeling (FSM), which employs sophisticated machine learning approaches, helps identify flood-prone locations and the elements that contribute to these risks in order to solve this problem. This study explores hybrid FSM models that integrate the Index of Entropy (IOE) with Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF) to offer a dependable approach for flood prediction and prevention. To assess the predictive
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Dissertations / Theses on the topic "Hybrid machine learning models"

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Sayin, Günel Burcu. "Towards Reliable Hybrid Human-Machine Classifiers." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/349843.

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In this thesis, we focus on building reliable hybrid human-machine classifiers to be deployed in cost-sensitive classification tasks. The objective is to assess ML quality in hybrid classification contexts and design the appropriate metrics, thereby knowing whether we can trust the model predictions and identifying the subset of items on which the model is well-calibrated and trustworthy. We start by discussing the key concepts, research questions, challenges, and architecture to design and implement an effective hybrid classification service. We then present a deeper investigation of each ser
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Abdullah, Siti Norbaiti binti. "Machine learning approach for crude oil price prediction." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/machine-learning-approach-for-crude-oil-price-prediction(949fa2d5-1a4d-416a-8e7c-dd66da95398e).html.

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Crude oil prices impact the world economy and are thus of interest to economic experts and politicians. Oil price’s volatile behaviour, which has moulded today’s world economy, society and politics, has motivated and continues to excite researchers for further study. This volatile behaviour is predicted to prompt more new and interesting research challenges. In the present research, machine learning and computational intelligence utilising historical quantitative data, with the linguistic element of online news services, are used to predict crude oil prices via five different models: (1) the H
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ISAKSSON, LARS JOHANNES. "HYBRID DEEP LEARNING AND RADIOMICS MODELS FOR ASSESSMENT OF CLINICALLY RELEVANT PROSTATE CANCER." Doctoral thesis, Università degli Studi di Milano, 2022. https://hdl.handle.net/2434/946529.

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Precision medicine holds the potential to revolutionize healthcare by providing every patient with personalized treatments and decisions tailored to his or her individual needs. This might be enabled by the large influx of potentially diagnostic information from new sources such as genetics and modern imaging techniques, provided the relevant information can be extracted. One such framework that has started to demonstrate promise in radiology, especially in the assessment of cancer, is radiomics; the practice of characterizing images by extracting a substantial amount of quantitative mathemati
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Tian, Xiaoguang. "Hybrid Models in Automobile Insurance: Technology Adoption and Customer Relations." Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1538717/.

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Customer relationship management (CRM), a primary activity in the business value chain to relate to the customer, involves solicitation, analysis, and the use of the knowledge about the customer to provide goods and services through effective and efficient methods. It is a wise strategy and source of competitive advantage for customer behavior understanding and business performance management. The use of information technology (IT) in CRM allows companies to simplify their processes, to integrate product or service related decision making with the business strategies, and to optimize their ope
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Ramachandra, Rao Sanjay Kamath. "Question Answering with Hybrid Data and Models." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASS024.

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La recherche de réponses à des questions relève de deux disciplines : le traitement du langage naturel et la recherche d’information. L’émergence de l’apprentissage profond dans plusieurs domaines de recherche tels que la vision par ordinateur, le traitement du langage naturel etc. a conduit à l’émergence de modèles de bout en bout. Les travaux actuels de l’état de l’art en question-réponse (QR) visent à mettre en oeuvre de tels modèles. Dans le cadre du projet GoASQ, l’objectif est d’étudier, comparer et combiner différentes approches pour répondre à des questions formulées en langage naturel
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Chowdhury, Ziaul Islam, and Iskanter Bensenousi. "Evaluation of different machine learning models for the prediction of electric or hybrid vehicle buyers and identification of the characteristics of the buyers in the EU." Thesis, Blekinge Tekniska Högskola, Institutionen för industriell ekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20712.

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The main goal of this thesis is to evaluate different machine learning models in order to classify buyers of an electric or a hybrid vehicle and to identify the characteristics of the buyers in the European Union. Machine learning algorithms and techniques were adopted to analyze the dataset and to create models that could predict, with a certain accuracy, the customer’s willingness to buy an EV. Identification of the characteristics of the buyers were based on the identified most important features from the machine learning models and statistical analysis. The research consisted of explorator
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Black, Kevin P. "Interactive Machine Assistance: A Case Study in Linking Corpora and Dictionaries." BYU ScholarsArchive, 2015. https://scholarsarchive.byu.edu/etd/5620.

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Machine learning can provide assistance to humans in making decisions, including linguistic decisions such as determining the part of speech of a word. Supervised machine learning methods derive patterns indicative of possible labels (decisions) from annotated example data. For many problems, including most language analysis problems, acquiring annotated data requires human annotators who are trained to understand the problem and to disambiguate among multiple possible labels. Hence, the availability of experts can limit the scope and quantity of annotated data. Machine-learned pre-annotation
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Torregrosa, jordan Sergio. "Approches Hybrides et Méthodes d'Intelligence Artificielle Basées sur la Simulation Numérique pour l'Optimisation des Systèmes Aérodynamiques Complexes." Electronic Thesis or Diss., Paris, HESAM, 2024. http://www.theses.fr/2024HESAE002.

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La conception industrielle d'un composant est un processus complexe, long et coûteux, contraint par des spécifications physiques, stylistiques et de développement précises en fonction de ses conditions et de son environnement d'utilisation futurs. En effet, un composant industriel est défini et caractérisé par de nombreux paramètres qui doivent être optimisés pour satisfaire au mieux toutes ces spécifications. Cependant, la complexité de ce problème d'optimisation multiparamétrique sous contraintes est telle que sa résolution analytique est compromise.Dans le passé, un tel problème était résol
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Johansson, Åke, and Joel Wikner. "Learning-Based Motion Planning and Control of a UGV With Unknown and Changing Dynamics." Thesis, Linköpings universitet, Reglerteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176923.

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Research about unmanned ground vehicles (UGVs) has received an increased amount of attention in recent years, partly due to the many applications of UGVs in areas where it is inconvenient or impossible to have human operators, such as in mines or urban search and rescue. Two closely linked problems that arise when developing such vehicles are motion planning and control of the UGV. This thesis explores these subjects for a UGV with an unknown, and possibly time-variant, dynamical model. A framework is developed that includes three components: a machine learning algorithm to estimate the unknow
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Vasquez, Capacho John William. "Chronicle Based Alarm Management." Thesis, Toulouse, INSA, 2017. http://www.theses.fr/2017ISAT0032/document.

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La sécurité des installations industrielles implique une gestion intégrée de tous les facteurs pouvant causer des incidents. La gestion d’alarmes est une condition qui peut être formulée comme un problème de reconnaissance de motifs pour lequel les motifs temporels sont utilisés pour caractériser différentes situations typiques, en particulier liées au phases de démarrage et d'arrêt. Dans cette thèse, nous proposons une nouvelle approche de gestion des alarmes basée sur un processus de diagnostic. En considérant les alarmes et les actions des procédures d'exploitation standard comme des événem
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Books on the topic "Hybrid machine learning models"

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Nandi, Anirban, and Aditya Kumar Pal. Interpreting Machine Learning Models. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7802-4.

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Bolc, Leonard. Computational Models of Learning. Springer Berlin Heidelberg, 1987.

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Galindez Olascoaga, Laura Isabel, Wannes Meert, and Marian Verhelst. Hardware-Aware Probabilistic Machine Learning Models. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74042-9.

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Singh, Pramod. Deploy Machine Learning Models to Production. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6546-8.

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Zhang, Zhihua. Statistical Machine Learning: Foundations, Methodologies and Models. John Wiley & Sons, Limited, 2017.

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Rendell, Larry. Representations and models for concept learning. Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1987.

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Veit-Haibach, Patrick, and Ken Herrmann, eds. Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00119-2.

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Ehteram, Mohammad, Zohreh Sheikh Khozani, Saeed Soltani-Mohammadi, and Maliheh Abbaszadeh. Estimating Ore Grade Using Evolutionary Machine Learning Models. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8106-7.

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Zhang, Le, Chen Chen, Zeju Li, and Greg Slabaugh, eds. Generative Machine Learning Models in Medical Image Computing. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80965-1.

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Bisong, Ekaba. Building Machine Learning and Deep Learning Models on Google Cloud Platform. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4470-8.

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Book chapters on the topic "Hybrid machine learning models"

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Akshay, B. R., Sini Raj Pulari, T. S. Murugesh, and Shriram K. Vasudevan. "Breast cancer classification with hybrid ML models." In Machine Learning. CRC Press, 2024. http://dx.doi.org/10.1201/9781032676685-5.

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Pandey, Amritanshu, Sumaiya Thaseen, Ch Aswani Kumar, and Gang Li. "Identification of Botnet Attacks Using Hybrid Machine Learning Models." In Hybrid Intelligent Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49336-3_25.

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Muhammad, Abdullahi Uwaisu, Xiaodong Li, and Jun Feng. "Using LSTM GRU and Hybrid Models for Streamflow Forecasting." In Machine Learning and Intelligent Communications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32388-2_44.

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Malik, Anurag, Yazid Tikhamarine, Rawshan Ali, Alban Kuriqi, and Anil Kumar. "Meteorological Drought Prediction Using Hybrid Machine Learning Models." In Integrated Drought Management, Volume 2. CRC Press, 2023. http://dx.doi.org/10.1201/9781003276548-9.

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Sontakke, Atharv, Mrunali Yewale, Sejal Zambare, Sakshi Tendulkar, and Anagha Chaudhari. "Credit Card Fraud Detection Using Machine Learning and Predictive Models: A Comparative Study." In Hybrid Intelligent Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96305-7_16.

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Echajei, Sahar, Hanane Ferjouchia, and Mostafa Rachik. "Enhancing Diabetes Risk Prediction with Hybrid Machine Learning Models." In Lecture Notes in Information Systems and Organisation. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-75329-9_34.

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Lakshmi, K., M. Umme Salma, and Sangeetha Shathish. "Gestational Diabetes Prediction Using Hybrid Probabilistic Machine Learning Models." In Bioinformatics and Beyond. CRC Press, 2025. https://doi.org/10.1201/9781003508403-6.

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Diedrich, Alexander, Kaja Balzereit, and Oliver Niggemann. "First Approaches to Automatically Diagnose and Reconfigure Hybrid Cyber-Physical Systems." In Machine Learning for Cyber Physical Systems. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62746-4_12.

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AbstractMaintaining modern production machinery requires a significant amount of time and money. Still, plants suffer from expensive production stops and downtime due to faults within individual components. Often, plants are too complex and generate too much data to make manual analysis and diagnosis feasible. Instead, faults often occur unnoticed, resulting in a production stop. It is then the task of highly-skilled engineers to recognise and analyse symptoms and devise a diagnosis. Modern algorithms are more effective and help to detect and isolate faults faster and more precise, thus leadin
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Pandey, Hemlatha, Tejal Lalitkumar Karnavat, Mandadapu Naga Sai Sandilya, Shashwat Katiyar, and Hemant Rathore. "Intrusion Detection System Based on Machine and Deep Learning Models: A Comparative and Exhaustive Study." In Hybrid Intelligent Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96305-7_38.

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Hu, Yong, Meng Yu, Guanxiang Yin, Fei Du, Meng Wang, and Yuejin Zhang. "Short-Term Traffic Flow Prediction Based on Hybrid Model." In Machine Learning for Cyber Security. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62463-7_13.

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Conference papers on the topic "Hybrid machine learning models"

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Huang, Ziyue. "Evaluating Hybrid Machine Learning Models Bankruptcy Prediction." In 2025 7th International Symposium on Computational and Business Intelligence (ISCBI). IEEE, 2025. https://doi.org/10.1109/iscbi64586.2025.11015371.

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Kumar, Dhriti, and Chaya Ravindra. "Transfer learning and hybrid models for the classification of eye diseases on retinal images." In Applications of Machine Learning 2024, edited by Barath Narayanan, Michael E. Zelinski, Tarek M. Taha, Abdul A. Awwal, and Khan M. Iftekharuddin. SPIE, 2024. http://dx.doi.org/10.1117/12.3027368.

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Li, Ziyan. "A Hybrid Approach to Spam Detection Using UNet and Diffusion Models." In International Conference on Data Analysis and Machine Learning. SCITEPRESS - Science and Technology Publications, 2024. https://doi.org/10.5220/0013517700004619.

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Srinidhi, Srivageesh K., K. S. Vishal, U. Someswara Shashank, and Meena Belwal. "Quantum Machine Learning Compiler for Hybrid Quantum-Classical Models." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725839.

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Onuoha, Michael, Aditya Kulkarni, and Navrati Saxena. "Improving COVID-19 Forecasts using Hybrid Machine Learning Models." In 2024 5th International Conference on Communications, Information, Electronic and Energy Systems (CIEES). IEEE, 2024. https://doi.org/10.1109/ciees62939.2024.10811297.

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Shahhoseyni, Shabnam, Arijit Chakraborty, Mohammad Reza Boskabadi, Venkat Venkatasubramanian, and Seyed Soheil Mansouri. "Hybrid machine-learning for dynamic plant-wide biomanufacturing." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.174465.

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This study focuses on biomanufacturing case study, i.e. Lovastatin production, employing a hybrid modeling framework that combines mechanistic and data-driven approaches. A time-series dataset was generated using the KT-Biologics I (KTB1) plantwide model, a dynamic simulation of continuous biomanufacturing. The dataset captures critical parameters such as nutrient concentrations and API production. The AI-DARWIN framework was used to develop interpretable machine learning models with constrained functional forms, ensuring both accuracy and clarity. The resulting polynomial-based models reveal
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Agrawal, Shashwat, Gopal Kumar Gupta, Pandi Kirupa Gopalakrishna, Vanitha Sivasankaran Balasubramaniam, Lagan Goel, and Siddhey Mahadik. "Hybrid Machine Learning Models: Combining Strengths of Supervised and Unsupervised Learning Approaches." In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2024. https://doi.org/10.1109/ic3i61595.2024.10829140.

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Selvam, Kayalvizhi, Manimozhi Sekar, and R. Arulraj. "Hybrid Machine Learning Models for Improved Short-Term Wind Forecasting." In 2024 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia). IEEE, 2024. https://doi.org/10.1109/isgtasia61245.2024.10876297.

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Ebadinezhad, Sahar, Nooshin Nooraei Nia, Nasratullah Shirzad, and Nwabueze Kenneth Osemeha. "Enhancing Intrusion Detection Systems Using RNN, LSTM, and Hybrid RNN-LSTM Models." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10968214.

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A, Abirami, Lakshmi Priya S, Lekshmi Prriya T, and Madhanraj J. "A Hybrid Approach for Diabetic Retinopathy Classification Using EfficientNetB5 and ResNet50 Models." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10969009.

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Reports on the topic "Hybrid machine learning models"

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Geza, Mangistu, T. Tesfa, Liangping Li, and M. Qiao. Toward Hybrid Physics -Machine Learning to improve Land Surface Model predictions. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769785.

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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum k
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Pasupuleti, Murali Krishna. Decision Theory and Model-Based AI: Probabilistic Learning, Inference, and Explainability. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv525.

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Abstract Decision theory and model-based AI provide the foundation for probabilistic learning, optimal inference, and explainable decision-making, enabling AI systems to reason under uncertainty, optimize long-term outcomes, and provide interpretable predictions. This research explores Bayesian inference, probabilistic graphical models, reinforcement learning (RL), and causal inference, analyzing their role in AI-driven decision systems across various domains, including healthcare, finance, robotics, and autonomous systems. The study contrasts model-based and model-free approaches in decision-
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Pasupuleti, Murali Krishna. Mathematical Modeling for Machine Learning: Theory, Simulation, and Scientific Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv125.

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Abstract Mathematical modeling serves as a fundamental framework for advancing machine learning (ML) and artificial intelligence (AI) by integrating theoretical, computational, and simulation-based approaches. This research explores how numerical optimization, differential equations, variational inference, and scientific computing contribute to the development of scalable, interpretable, and efficient AI systems. Key topics include convex and non-convex optimization, physics-informed machine learning (PIML), partial differential equation (PDE)-constrained AI, and Bayesian modeling for uncertai
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Davoudi Kakhki, Fatemeh, and Maria Chierichetti. Exploring the Relationship Between Mandatory Helmet Use Regulations and Adult Cyclists’ Behavior in California Using Hybrid Machine Learning Models. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2024.

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In California, bike fatalities increased by 8.1% from 2015 to 2016. Even though the benefits of wearing helmets in protecting cyclists against trauma in cycling crash has been determined, the use of helmets is still limited, and there is opposition against mandatory helmet use, particularly for adults. Therefore, exploring perceptions of adult cyclists regarding mandatory helmet use is a key element in understanding cyclists’ behavior, and determining the impact of mandatory helmet use on their cycling rate. The goal of this research is to identify sociodemographic characteristics and cycling
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Skryzalin, Jacek, Kenneth Goss, and Benjamin Jackson. Securing machine learning models. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1661020.

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Nowinska, Agnieszka Urszula, and Gisele Msann. AI disruption in chartering in Danish Shipping. Aalborg University Open Publishing, 2025. https://doi.org/10.54337/aau.bk2_2025.

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Our research highlights the current state and trends of artificial intelligence (AI) adoption in Denmark’s chartering, particularly in the dry bulk and tanker segments. Companies in the dry bulk sector are leading AI adoption, with the tanker segment closely following and adoption rates in our sample appear higher than national averages reported by consultancies. Most firms are in either the experimental phase or transitioning toward more integrated AI systems, often opting for hybrid models that allow them to maintain internal control over key processes. Factors such as company size and matur
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Szunyogh, Istvan, Edward Ott, and Brian Hunt. Machine-Learning-Assisted Hybrid Earth System Modelling. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769745.

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Martinez, Carianne, Jessica Jones, Drew Levin, Nathaniel Trask, and Patrick Finley. Physics-Informed Machine Learning for Epidemiological Models. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1706217.

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Lavender, Samantha, and Trent Tinker, eds. Testbed-19: Machine Learning Models Engineering Report. Open Geospatial Consortium, Inc., 2024. http://dx.doi.org/10.62973/23-033.

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