Relevant bibliographies by topics / Machine Learning (ML) model

Contents

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

Academic literature on the topic 'Machine Learning (ML) model'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Machine Learning (ML) model"

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x, Rajdeep. "Mathematics Model Used in Artificial Intelligence (AI) and Machine Learning (ML)." International Journal of Science and Research (IJSR) 13, no. 12 (2024): 1773–77. https://doi.org/10.21275/sr241227144834.

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Praveen, Halingali, Kumar Santosh, Desai Sanket, and S. Alagoudar Punith. "Survey on Applications of Machine Learning." Journal of Research and Review: Machine Learning 1, no. 2 (2025): 29–35. https://doi.org/10.5281/zenodo.14922864.

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<em>Machine learning (ML) has transformed research methodologies across technical disciplines by enabling data-driven decision-making and predictive analytics. This paper explores ML&rsquo;s core principles, issues and future developments, emphasizing its impact on optimizing research processes in fields such as engineering, materials science, and telecommunications. Key ML paradigms, including supervised, unsupervised, and reinforcement learning, are analyzed in the context of technical applications. Additionally, this study addresses challenges such as data quality and model interpretability
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Mistry, Het. "Mastering Model Selection for AI/ML Models." European Journal of Computer Science and Information Technology 13, no. 14 (2025): 55–67. https://doi.org/10.37745/ejcsit.2013/vol13n145567.

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This article presents a comprehensive framework for mastering model selection in artificial intelligence and machine learning applications across diverse domains. The article addresses the fundamental challenge of selecting models that optimally balance complexity with generalization capability, navigating the classic bias-variance tradeoff that underpins predictive performance. Beginning with theoretical foundations of regularization approaches and complexity measures, the article proceeds through data-driven selection strategies, including cross-validation techniques and advanced hyperparame
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Chittibala, Dinesh Reddy, and Srujan Reddy Jabbireddy. "Security in Machine Learning (ML) Workflows." International Journal of Computing and Engineering 5, no. 1 (2024): 52–63. http://dx.doi.org/10.47941/ijce.1714.

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Purpose: This paper addresses the comprehensive security challenges inherent in the lifecycle of machine learning (ML) systems, including data collection, processing, model training, evaluation, and deployment. The imperative for robust security mechanisms within ML workflows has become increasingly paramount in the rapidly advancing field of ML, as these challenges encompass data privacy breaches, unauthorized access, model theft, adversarial attacks, and vulnerabilities within the computational infrastructure.&#x0D; Methodology: To counteract these threats, we propose a holistic suite of str
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Shandilya, Ayush. "ML Model for Stock Classification." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 5230–39. http://dx.doi.org/10.22214/ijraset.2024.61136.

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Abstract: This study examines how effectively volatility indices can forecast stock market movements by applying different machine learning techniques. It uses a dataset from Kaggle for the TCS Company, including variables such as 'Date,' 'Symbol,' 'Series,' 'Prev Close,' 'Open,' 'High,' 'Low,' 'Last,' 'Close,' 'VWAP,' 'Volume,' 'Turnover,' 'Deliverable Volume,' and '%Deliverable.' Various models were tested and evaluated based on metrics like the AUC-ROC Curve, Accuracy, Precision, Recall, F1-Score, Cross-Validation Accuracy, and the Confusion Matrix. The findings reveal that the Linear Regre
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Gaur, Aditya. "ML Based Macroeconomic Model Simulator." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45585.

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Abstract - This AutoFi, a cutting-edge AI-driven macroeconomic simulation model, provides a robust framework for forecasting financial markets. Financial markets are deeply influenced by macroeconomic indicators such as GDP, unemployment rates, inflation, and interest rates. Accurate forecasting of these indicators is critical for optimizing investment strategies. This research presents AutoFi, a machine learning-based macroeconomic simulation model that predicts key financial indicators and asset performance over time. The proposed system incorporates historical macroeconomic data, time serie
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ARAVIND ,, P. "Brain Stroke detection Using AI/ML." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem42994.

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Brain stroke is a leading cause of disability and mortality worldwide, requiring rapid and accurate diagnosis for effective treatment. Traditional stroke diagnosis methods, such as CT and MRI scans, often rely on expert interpretation, which can be time-consuming and prone to human error. This study explores the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance the accuracy and efficiency of brain stroke detection. We propose a deep learning-based model that utilizes medical imaging data, such as CT and MRI scans, to automatically detect stroke occurre
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Chang, Chaokun, Eric Lo, and Chunxiao Ye. "Biathlon: Harnessing Model Resilience for Accelerating ML Inference Pipelines." Proceedings of the VLDB Endowment 17, no. 10 (2024): 2631–40. http://dx.doi.org/10.14778/3675034.3675052.

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Machine learning inference pipelines commonly encountered in data science and industries often require real-time responsiveness due to their user-facing nature. However, meeting this requirement becomes particularly challenging when certain input features require aggregating a large volume of data online. Recent literature on interpretable machine learning reveals that most machine learning models exhibit a notable degree of resilience to variations in input. This suggests that machine learning models can effectively accommodate approximate input features with minimal discernible impact on acc
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Dhanwate, Prof P. "Multiple Disease Prediction System Using ML." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 3612–17. http://dx.doi.org/10.22214/ijraset.2024.59642.

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Abstract: The increasing prevalence of diverse diseases presents a challenge to global healthcare systems, underscoring the need for innovative and efficient methods for early detection and preventive measures. This paper explores the application of machine learning algorithms in multiple disease prediction to enhance diagnostic accuracy and enable timely intervention. Leveraging diverse health-related data sources, including medical records and genomic information, comprehensive predictive models are developed. A multi-faceted machine learning approach integrates support vector machines, deci
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Abhishek, Shivanna. "Framework for Implementing Experiment Tracking in Machine Learning Development." Journal of Scientific and Engineering Research 11, no. 10 (2024): 118–23. https://doi.org/10.5281/zenodo.14273552.

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Machine learning (ML) projects often involve numerous experiments that need to be tracked, compared, and reproduced to ensure consistent results and effective collaboration. This paper explores the significance of experiment tracking in ML workflows, discusses best practices, and addresses challenges in implementation. We present a comprehensive framework for experiment tracking that enhances reproducibility, accountability, and collaboration within ML teams. This paper emphasizes on how systematic tracking can optimize workflows, accelerate model development, and improve the overall quality o
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Dissertations / Theses on the topic "Machine Learning (ML) model"

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John, Meenu Mary. "Design Methods and Processes for ML/DL models." Licentiate thesis, Malmö universitet, Institutionen för datavetenskap och medieteknik (DVMT), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-45026.

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Context: With the advent of Machine Learning (ML) and especially Deep Learning (DL) technology, companies are increasingly using Artificial Intelligence (AI) in systems, along with electronics and software. Nevertheless, the end-to-end process of developing, deploying and evolving ML and DL models in companies brings some challenges related to the design and scaling of these models. For example, access to and availability of data is often challenging, and activities such as collecting, cleaning, preprocessing, and storing data, as well as training, deploying and monitoring the model(s) are com
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Atienza, Nicolas. "Towards Reliable ML : Leveraging Multi-Modal Representations, Information Bottleneck and Extreme Value Theory." Electronic Thesis or Diss., université Paris-Saclay, 2025. http://www.theses.fr/2025UPASG025.

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Cette thèse de doctorat porte sur l'amélioration de la fiabilité de l'apprentissage automatique, en particulier pour les applications à forts enjeux. Les modèles d'apprentissage profond actuels, bien que très performants, restent difficiles à appréhender et à déployer de manière sûre en raison de leur opacité, de leur vulnérabilité aux attaques adverses, de leur sensibilité aux changements de distribution, et de leur inefficacité en contexte de données ou de ressources limitées. Pour surmonter ces limites, ce travail explore trois dimensions complémentaires : l'explicabilité, la robustesse et
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Garg, Anushka. "Comparing Machine Learning Algorithms and Feature Selection Techniques to Predict Undesired Behavior in Business Processesand Study of Auto ML Frameworks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285559.

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In recent years, the scope of Machine Learning algorithms and its techniques are taking up a notch in every industry (for example, recommendation systems, user behavior analytics, financial applications and many more). In practice, they play an important role in utilizing the power of the vast data we currently generate on a daily basis in our digital world.In this study, we present a comprehensive comparison of different supervised Machine Learning algorithms and feature selection techniques to build a best predictive model as an output. Thus, this predictive model helps companies predict unw
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Appelstål, Michael. "Multimodal Model for Construction Site Aversion Classification." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-421011.

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Aversion on construction sites can be everything from missingmaterial, fire hazards, or insufficient cleaning. These aversionsappear very often on construction sites and the construction companyneeds to report and take care of them in order for the site to runcorrectly. The reports consist of an image of the aversion and atext describing the aversion. Report categorization is currentlydone manually which is both time and cost-ineffective. The task for this thesis was to implement and evaluate an automaticmultimodal machine learning classifier for the reported aversionsthat utilized both the im
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Hellberg, Johan, and Kasper Johansson. "Building Models for Prediction and Forecasting of Service Quality." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-295617.

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In networked systems engineering, operational datagathered from sensors or logs can be used to build data-drivenfunctions for performance prediction, anomaly detection, andother operational tasks [1]. Future telecom services will share acommon communication and processing infrastructure in orderto achieve cost-efficient and robust operation. A critical issuewill be to ensure service quality, whereby different serviceshave very different requirements. Thanks to recent advances incomputing and networking technologies we are able to collect andprocess measurements from networking and computing de
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Hallberg, Jesper. "Searching for the charged Higgs boson in the tau nu analysis using Boosted Decision Trees." Thesis, Uppsala universitet, Högenergifysik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-301351.

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his thesis implements a multivariate analysis in the current cut- based search for the charged Higgs bosons, which are new scalar particles predicted by several extensions to the Standard Model. Heavy charged Higgs bosons (mH± mtop) produced in association with a top quark de- caying via H± → τν are considered. The final state contains a hadronic τ decay, missing transverse energy and a hadronically decaying top quark. This study is based on Monte Carlo samples simulated at CM-energy √ s = 13 TeV for signal and backgrounds. The figure of merit to measure the improvement of the new method with
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Mathias, Berggren, and Sonesson Daniel. "Design Optimization in Gas Turbines using Machine Learning : A study performed for Siemens Energy AB." Thesis, Linköpings universitet, Programvara och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-173920.

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In this thesis, the authors investigate how machine learning can be utilized for speeding up the design optimization process of gas turbines. The Finite Element Analysis (FEA) steps of the design process are examined if they can be replaced with machine learning algorithms. The study is done using a component with given constraints that are provided by Siemens Energy AB. With this component, two approaches to using machine learning are tested. One utilizes design parameters, i.e. raw floating-point numbers, such as the height and width. The other technique uses a high dimensional mesh as input
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Keisala, Simon. "Using a Character-Based Language Model for Caption Generation." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-163001.

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Using AI to automatically describe images is a challenging task. The aim of this study has been to compare the use of character-based language models with one of the current state-of-the-art token-based language models, im2txt, to generate image captions, with focus on morphological correctness. Previous work has shown that character-based language models are able to outperform token-based language models in morphologically rich languages. Other studies show that simple multi-layered LSTM-blocks are able to learn to replicate the syntax of its training data. To study the usability of character
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Giuliani, Luca. "Extending the Moving Targets Method for Injecting Constraints in Machine Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23885/.

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Informed Machine Learning is an umbrella term that comprises a set of methodologies in which domain knowledge is injected into a data-driven system in order to improve its level of accuracy, satisfy some external constraint, and in general serve the purposes of explainability and reliability. The said topid has been widely explored in the literature by means of many different techniques. Moving Targets is one such a technique particularly focused on constraint satisfaction: it is based on decomposition and bi-level optimization and proceeds by iteratively refining the target labels through a m
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Lundström, Robin. "Machine Learning for Air Flow Characterization : An application of Theory-Guided Data Science for Air Fow characterization in an Industrial Foundry." Thesis, Karlstads universitet, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-72782.

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In industrial environments, operators are exposed to polluted air which after constant exposure can cause irreversible lethal diseases such as lung cancer. The current air monitoring techniques are carried out sparely in either a single day annually or at few measurement positions for a few days.In this thesis a theory-guided data science (TGDS) model is presented. This hybrid model combines a steady state Computational Fluid Dynamics (CFD) model with a machine learning model. Both the CFD model and the machine learning algorithm was developed in Matlab. The CFD model serves as a basis for the
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Books on the topic "Machine Learning (ML) model"

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Sarang, Poornachandra. Classical Machine Learning Model Building. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45633-6.

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Mohamed, Khaled Salah. Machine Learning for Model Order Reduction. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75714-8.

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Fan, Lixin, Chee Seng Chan, and Qiang Yang, eds. Digital Watermarking for Machine Learning Model. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7554-7.

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Subrahmanian, V. S., Chiara Pulice, James F. Brown, and Jacob Bonen-Clark. A Machine Learning Based Model of Boko Haram. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-60614-5.

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Pal Chaudhuri, Parimal, Adip Dutta, Somshubhro Pal Choudhury, Dipanwita Roy Chowdhury, and Raju Hazari. New Kind of Machine Learning–Cellular Automata Model. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1501-8.

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Sturm, Jürgen. Approaches to Probabilistic Model Learning for Mobile Manipulation Robots. Springer Berlin Heidelberg, 2013.

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Olivier, Bousquet, Luxburg Ulrike von, Rätsch Gunnar, and Machine Learning Summer School (2003 : Tübingen, Germany), eds. Advanced lectures on machine learning: ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003 [and] Tübingen, Germany, August 4-16, 2003 : revised lectures. Springer, 2004.

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Widjanarko, Bambang. Pengembangan model model machine learning ketahanan pangan melalui pembentukan zona musim (ZOM) suatu wilayah: Laporan akhir hibah kompetitif penelitian sesuai prioritas nasional tahun I. Lembaga Penelitian dan Pengabdian Kepada Masyarakat, Institut Teknologi Sepuluh Nopember, 2010.

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Marrandino, Alessandro. Machine Learning with BigQuery ML: Create, Execute, and Improve Machine Learning Models in BigQuery Using Standard SQL Queries. de Gruyter GmbH, Walter, 2021.

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Sinha, Debu. Practical Machine Learning on Databricks: Seamlessly Transition ML Models and MLOps on Databricks. de Gruyter GmbH, Walter, 2023.

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Book chapters on the topic "Machine Learning (ML) model"

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Nandi, Anirban, and Aditya Kumar Pal. "Interpretable ML and Explainable ML Differences." In Interpreting Machine Learning Models. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7802-4_7.

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Geertsema, Paul. "Creating ML models." In Machine Learning for Managers. Routledge, 2023. http://dx.doi.org/10.4324/9781003330929-4.

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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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Thakkar, Mohit. "Custom Core ML Models Using Create ML." In Beginning Machine Learning in iOS. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4297-1_4.

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Molnar, Christoph, Gunnar König, Julia Herbinger, et al. "General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models." In xxAI - Beyond Explainable AI. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_4.

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AbstractAn increasing number of model-agnostic interpretation techniques for machine learning (ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) and Shapley values provide insightful model interpretations, but can lead to wrong conclusions if applied incorrectly. We highlight many general pitfalls of ML model interpretation, such as using interpretation techniques in the wrong context, interpreting models that do not generalize well, ignoring feature dependencies, interactions, uncertainty estimates and issues in high-dimensional settings, or making unjustified causal interpretations, and illustrate them with examples. We focus on pitfalls for global methods that describe the average model behavior, but many pitfalls also apply to local methods that explain individual predictions. Our paper addresses ML practitioners by raising awareness of pitfalls and identifying solutions for correct model interpretation, but also addresses ML researchers by discussing open issues for further research.
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Thakkar, Mohit. "Custom Core ML Models Using Turi Create." In Beginning Machine Learning in iOS. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4297-1_3.

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Norris, Donald J. "Exploration of ML data models: Part 1." In Machine Learning with the Raspberry Pi. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5174-4_2.

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Norris, Donald J. "Exploration of ML data models: Part 2." In Machine Learning with the Raspberry Pi. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5174-4_3.

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Brauße, Franz, Zurab Khasidashvili, and Konstantin Korovin. "SMLP: Symbolic Machine Learning Prover." In Computer Aided Verification. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65627-9_11.

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AbstractSymbolic Machine Learning Prover (SMLP) is a tool and a library for system exploration based on data samples obtained by simulating or executing the system on a number of input vectors. SMLP aims at exploring the system based on this data by taking a grey-box approach: SMLP uses symbolic reasoning for ML model exploration and optimization under verification and stability constraints, based on SMT, constraint, and neural network solvers. In addition, the model exploration is guided by probabilistic and statistical methods in a closed feedback loop with the system’s response. SMLP has been applied in industrial setting at Intel for analyzing and optimizing hardware designs at the analog level. SMLP is a general purpose tool and can be applied to any system that can be sampled and modeled by machine learning models.
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Graniero, Paolo, and Marco Gärtler. "Prediction of Batch Processes Runtime Applying Dynamic Time Warping and Survival Analysis." In Machine Learning for Cyber Physical Systems. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62746-4_6.

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AbstractBatch runs corresponding to the same recipe usually have different duration. The data collected by the sensors that equip batch production lines reflects this fact: time series with different lengths and unsynchronized events. Dynamic Time Warping (DTW) is an algorithm successfully used, in batch monitoring too, to synchronize and map to a standard time axis two series, an action called alignment. The online alignment of running batches, although interesting, gives no information on the remaining time frame of the batch, such as its total runtime, or time-to-end. We notice that this problem is similar to the one addressed by Survival Analysis (SA), a statistical technique of standard use in clinical studies to model time-to-event data. Machine Learning (ML) algorithms adapted to survival data exist, with increased predictive performance with respect to classical formulations. We apply a SA-ML-based system to the problem of predicting the time-to-end of a running batch, and show a new application of DTW. The information returned by openended DTW can be used to select relevant data samples for the SA-ML system, without negatively affecting the predictive performance and decreasing the computational cost with respect to the same SA-ML system that uses all the data available. We tested the system on a real-world dataset coming from a chemical plant.
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Conference papers on the topic "Machine Learning (ML) model"

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Askhatuly, Aidos, Dinara Berdysheva, Didar Yedilkhan, and A. Berdyshev. "Security Risks of ML Models: Adverserial Machine Learning." In 2024 IEEE 4th International Conference on Smart Information Systems and Technologies (SIST). IEEE, 2024. http://dx.doi.org/10.1109/sist61555.2024.10629452.

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Patel, Ishan, and Gheorghe Bota. "Mechanistic Model as a Bias to Machine Learning Algorithm for Confident Prediction of Corrosion." In CONFERENCE 2023. AMPP, 2023. https://doi.org/10.5006/c2023-19108.

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Abstract Bayesian network is employed to estimate a risk-based life cycle cost of corrosion for assets. It has been highly recognized that inclusion of mechanistic models to a Bayesian network can increase the confidence in estimation of corrosion rates. However, coefficients of mechanistic models are often unknown, especially when complex rate processes are involved, which discourages the usage of the model. A methodology is proposed here, to introduce a mechanistic model as a bias to a regressive machine learning (ML) algorithm. No attempts have been made to obtain phenomenological coefficie
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Hsu, Chung-Chian, Pin-Han Chen, and I.-Zhen Wu. "End-to-End Automation of ML Model Lifecycle Management using Machine Learning Operations Platforms." In 2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan). IEEE, 2024. http://dx.doi.org/10.1109/icce-taiwan62264.2024.10674445.

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Thomas, Juby, K. G. Suresh, Sateesh Kumar T. K, Vishnu Achutha Menon, and Lijo P. Thomas. "Performance Analysis of ML Models on Google App Store Data with Imbalanced Classes." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10968885.

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Li, Zhiwei, Carl Kesselman, Mike D’Arcy, Michael Pazzani, and Benjamin Yizing Xu. "Deriva-ML: A Continuous FAIRness Approach to Reproducible Machine Learning Models." In 2024 IEEE 20th International Conference on e-Science (e-Science). IEEE, 2024. http://dx.doi.org/10.1109/e-science62913.2024.10678671.

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Ananthi, S., Lokesh B, Sanjeev Chandran M, and Sakthi S. "Framework for Platform Independent Machine Learning (ML) Model Execution." In 2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2024. http://dx.doi.org/10.1109/idciot59759.2024.10467931.

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Karatekin, Tamer, Selim Sancak, Gokhan Celik, et al. "Interpretable Machine Learning in Healthcare through Generalized Additive Model with Pairwise Interactions (GA2M): Predicting Severe Retinopathy of Prematurity." In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 2019. http://dx.doi.org/10.1109/deep-ml.2019.00020.

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Vallarino, Diego. "Buy When? Survival Machine Learning Model Comparison for Purchase Timing." In 3rd International Conference on Advances in Computing & Information Technologies. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131505.

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The value of raw data is unlocked by converting it into information and knowledge that drives decision-making. Machine Learning (ML) algorithms are capable of analysing large datasets and making accurate predictions. Market segmentation, client lifetime value, and marketing techniques have all made use of machine learning. This article examines marketing machine learning techniques such as Support Vector Machines, Genetic Algorithms, Deep Learning, and K-Means. ML is used to analyse consumer behaviour, propose items, and make other customer choices about whether or not to purchase a product or
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Tseng, Tiffany, Jennifer King Chen, Mona Abdelrahman, et al. "Collaborative Machine Learning Model Building with Families Using Co-ML." In IDC '23: Interaction Design and Children. ACM, 2023. http://dx.doi.org/10.1145/3585088.3589356.

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Mohanty, Aryan, Sohini Ghosh, Adyasha Dash, and Subhashree Darshana. "Intrus-ML: An Intrusion Detection Model Based on Machine Learning." In 2023 International Conference on Communication, Circuits, and Systems (IC3S). IEEE, 2023. http://dx.doi.org/10.1109/ic3s57698.2023.10169341.

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Reports on the topic "Machine Learning (ML) model"

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Chaffa, Lucien, Martin Trépanier, and Thierry Warin. Beyond PPML: Exploring Machine Learning Alternatives for Gravity Model Estimation in International Trade. CIRANO, 2025. https://doi.org/10.54932/bfky4995.

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This study investigates the potential of machine learning (ML) methods to enhance the estimation of the gravity model, a cornerstone of international trade analysis that explains trade flows based on economic size and distance. Traditionally estimated using methods such as the Poisson Pseudo Maximum Likelihood (PPML) approach, gravity models often struggle to fully capture nonlinear relationships and intricate interactions among variables. Leveraging data from Canada and the US, one of the largest bilateral trading relationships in the world, this paper conducts a comparative analysis of tradi
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Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2320.

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Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered p
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Dutta, Sourav, Anna Wagner, Theadora Hall, and Nawa Raj Pradhan. Data-driven modeling of groundwater level using machine learning. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48452.

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This US Army Engineer Research and Development Center (ERDC), Coastal and Hydraulics Laboratory engineering technical note (CHETN) documents a preliminary study on the use of specialized machine learning (ML) methods to model the variations in groundwater level (GWL) with time. This approach uses historical groundwater observation data at seven gage locations in Wyoming, USA, available from the USGS database and historical data on several relevant meteorological variables obtained from the ERA5 reanalysis dataset produced by the Copernicus Climate Change Service (usually referred to as C3S) at
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Alonso-Robisco, Andrés, José Manuel Carbó, and José Manuel Carbó. Machine Learning methods in climate finance: a systematic review. Banco de España, 2023. http://dx.doi.org/10.53479/29594.

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Preventing the materialization of climate change is one of the main challenges of our time. The involvement of the financial sector is a fundamental pillar in this task, which has led to the emergence of a new field in the literature, climate finance. In turn, the use of Machine Learning (ML) as a tool to analyze climate finance is on the rise, due to the need to use big data to collect new climate-related information and model complex non-linear relationships. Considering the proliferation of articles in this field, and the potential for the use of ML, we propose a review of the academic lite
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Vickers, David, and Heath Spidle. PR-015-203900-R01 Reliability Detection and Accuracy of CPM Detection Systems Using Machine Learning. Pipeline Research Council International, Inc. (PRCI), 2022. http://dx.doi.org/10.55274/r0012211.

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This report documents the result of research conducted by Southwest Research Institute (SwRI�) for the Pipeline Research Council International (PRCI) into the development of a Machine Learning (ML) model for improving the detection of leaks in liquid-carrying pipelines. Operators were surveyed as to their use of CPM systems for leak detection. Several operators provided data to support the research. The data was collected, curated, and analyzed by SwRI. Several ML models were investigated. A framework was developed to allow operators to use their own data to generate ML models for their pipeli
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Burton, Simon. The Path to Safe Machine Learning for Automotive Applications. SAE International, 2023. http://dx.doi.org/10.4271/epr2023023.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Recent rapid advancement in machine learning (ML) technologies have unlocked the potential for realizing advanced vehicle functions that were previously not feasible using traditional approaches to software development. One prominent example is the area of automated driving. However, there is much discussion regarding whether ML-based vehicle functions can be engineered to be acceptably safe, with concerns related to the inherent difficulty and ambiguity of the tasks to which the technology is applied. This leads to chal
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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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Ehiabhi, Jolly, and Haifeng Wang. A Systematic Review of Machine Learning Models in Mental Health Analysis Based on Multi-Channel Multi-Modal Biometric Signals. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2023. http://dx.doi.org/10.37766/inplasy2023.2.0003.

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Review question / Objective: A systematic review of Mental health diagnosis/prognoses of mental disorders using Machine Learning techniques with information from biometric signals. A review of the trend and status of these ML techniques in mental health diagnosis and an investigation of how these signals are used to help increase the efficiency of mental health disease diagnosis. Using Machine learning techniques to classify mental health diseases as against using only expert knowledge for diagnosis. Feature Extraction from signal gotten from biometric signals that help classify sleep disorder
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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted feature
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Goulet Coulombe, Philippe, Massimiliano Marcellino, and Dalibor Stevanovic. Panel Machine Learning with Mixed-Frequency Data: Monitoring State-Level Fiscal Variables. CIRANO, 2025. https://doi.org/10.54932/qgja3449.

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We study the nowcasting of U.S. state-level fiscal variables using machine learning (ML) models and mixed-frequency predictors within a panel framework. Neural networks with continuous and categorical embeddings consistently outperform both linear and nonlinear alternatives, especially when combined with pooled panel structures. These architectures flexibly capture differences across states while benefiting from shared patterns in the panel structure. Forecast gains are especially large for volatile variables like expenditures and deficits. Pooling enhances forecast stability, and ML models ar
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