Relevant bibliographies by topics / Machine Learning(ML) Techniques

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) Techniques'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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

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Jeevana, P., T. Nandini, D. Srilekha, G. Dinesh, and Mrs Archana. "Diabetic Prediction using ML Techniques." YMER Digital 21, no. 04 (2022): 585–93. http://dx.doi.org/10.37896/ymer21.04/59.

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In today's world, diabetes is a huge problem. Diabetes can cause blood sugar levels to rise, which can contribute to strokes and heart attacks. One of the most rapidly spreading diseases is this one. After speaking with a doctor and receiving a diagnosis, patients are normally required to receive their reports. Because this procedure is time-consuming and costly, we were able to fix the problem utilizing machine learning techniques. In medical organizations, many machine learning applications are both exciting and important. Machine learning is being more widely used in the medical field. Our
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Tarika, Verma, and S. Gill Nasib. "Machine Learning Techniques for Better Data Driven Decisions Revisited." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 460–64. https://doi.org/10.35940/ijeat.D6766.049420.

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The main goal of machine learning is to accurately predict the decisions to the problems without human expert intervention. These decisions depend upon patterns found and facts learnt during training tenure. However, prior incorporation of human knowledge is necessary for better prediction of the test data. The main aim is to make machines self-reliant for decision making. Providing machine with this vision makes it useful in every modern field. This makes the stepping stone to make computers behave as the humans do. Enhancing its speed and accuracy are the next step in this field. This paper
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Satarkar, Bhagyashri, Dhavalsigh Vibhute, Vishal Wadgoankar, and Yasar Sayyad. "Phishguard: ML- based phishing detection system." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–9. https://doi.org/10.55041/isjem02759.

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Spam messages in SMS and email systems pose significant security and productivity risks. Traditional detection methods, such as rule-based filters, struggle to adapt to evolving spam techniques. This paper explores machine learning techniques for spam detection, emphasizing algorithms like Naïve Bayes and Support Vector Machines (SVM), along with their applications, strengths, and limitations. Key challenges, including scalability, dataset diversity, and evolving spam patterns, are identified. The study highlights future directions such as real-time classification, deep learning models, and im
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Mahesh, T. R., and Kumar Vinoth. "Early Detection of Cancer using Machine Learning (ML) Techniques." International Journal of Information Technology, Research and Applications (IJITRA) ISSN: 2583-5343 2, no. 1 (2023): 14–21. https://doi.org/10.5281/zenodo.7780162.

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Early detection of cancer sickness leads to rapid treatment, reducing the risk of morbidity and mortality. The diagnosis of oral cancer continues to be a challenge for dental careers, particularly in the location, evaluation, and review of early-stage oral disease. Due to the lack of optimal analysis using conventional methods, oral cancer is identified and grouped using AI at an early stage. AI techniques are used to show the movement and treatment of dangerous locations and may accurately predict future disease effects. AI techniques are used to show the movement and treatment of dangerous l
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M., Alagurajan, and Vijayakumaran C. "ML Methods for Crop Yield Prediction and Estimation: An Exploration." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 3506–8. https://doi.org/10.35940/ijeat.C5775.029320.

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Machine learning Has performed a essential position within the estimation of crop yield for both farmers and consumers of the products. Machine learning techniques learn from data set related to the environment on which the estimations and estimation are to be made and the outcome of the learning process are used by farmers for corrective measures for yield optimization. This paper we explore various ML techniques utilized in crop yield estimation and provide the detailed analysis of accuracy of the techniques.
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Jin, Jennifer. "OLAP and machine learning." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (2017): 1630019. http://dx.doi.org/10.1142/s2425038416300196.

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The objective of this tutorial is to present an overview of machine learning (ML) methods. This paper outlines different types of ML as well as techniques for each kind. It covers popular applications for different types of ML. On-Line Analytic Processing (OLAP) enables users of multidimensional databases to create online comparative summaries of data. This paper goes over commercial OLAP software available as well as OLAP techniques such as “slice and dice” and “drill down and roll up.” It discusses various techniques and metrics used to evaluate how accurate a ML algorithm is.
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Hussain, Walayat, Asma Musabah Alkalbani, and Honghao Gao. "Forecasting with Machine Learning Techniques." Forecasting 3, no. 4 (2021): 868–69. http://dx.doi.org/10.3390/forecast3040052.

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Çelik, Rumeysa Hilal, Hacı Aslan Onur İşcil, Ecem Bulut, and Saliha Ece Acuner. "Learning molecular machines by machine learning." Eurasian Journal of Science Engineering and Technology 6, no. 2 (2025): 100–120. https://doi.org/10.55696/ejset.1620495.

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Proteins, often referred to as molecular machines, are essential biomolecules that perform a wide range of cellular functions, typically by forming complexes. Understanding their three-dimendional (3D) structures is key to deciphering their functions. However, a significant gap exists between the vast number of known protein sequences and the relatively limited number of experimentally determined protein structures. Unraveling the mechanisms of protein folding remains a central challenge in understanding the sequence-structure/dynamics-function relationship. In recent years, machine learning (
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Simkina, Polina. "Machine Learning Techniques for Calorimetry." Instruments 6, no. 4 (2022): 47. http://dx.doi.org/10.3390/instruments6040047.

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The Compact Muon Solenoid (CMS) is one of the general purpose detectors at the CERN Large Hadron Collider (LHC), where the products of proton–proton collisions at the center of mass energy up to 13.6 TeV are reconstructed. The electromagnetic calorimeter (ECAL) is one of the crucial components of the CMS since it reconstructs the energies and positions of electrons and photons. Even though several Machine Learning (ML) algorithms have been already used for calorimetry, with the constant advancement of the field, more and more sophisticated techniques have become available, which can be benefic
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J., Dr SIRISHA. "Assessing DDoS Detection Accuracy through Semi-Supervised Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29861.

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Despite the proliferation of advanced Machine Learning (ML) techniques in DDoS detection, this pervasive attack remains a significant menace to the Internet's integrity. Existing ML based DDoS detection methods fall into two categories: supervised and unsupervised approaches. This paper synthesizes insights from existing research endeavors, and enhance DDoS detection through machine learning methodologies, specifically focusing on semi-supervised techniques for analysis purposes. By harnessing the power of semi-supervised ML, we employ a succession of algorithms including Naive Bayes, Support
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Dissertations / Theses on the topic "Machine Learning(ML) Techniques"

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Delestrac, Paul. "Advanced Profiling Techniques For Evaluating GPU Computing Efficiency Executing ML Applications." Electronic Thesis or Diss., Université de Montpellier (2022-....), 2024. http://www.theses.fr/2024UMONS014.

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L'augmentation en complexité des applications d'Intelligence Artificielle (IA) entraîne une demande accrue en puissance de calcul et en énergie pour entraîner et exécuter des modèles d'apprentissage automatique (ML). Les processeurs graphiques (GPU), forts d'architectures améliorées (e.g., ajout de cœurs dédiés à l'IA en 2017), sont devenus le système de prédilection pour de telles tâches. Concevoir des systèmes plus efficients pour l'IA n'est possible qu'avec une connaissance approfondie des limites des systèmes existants, où matériel et logiciel sont étroitement couplés. Mais l'abstraction d
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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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Liu, Siyang. "Efficient machine learning techniques for indoor localization in wireless communication systems." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST188.

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Avec le développement rapide de l'Internet des objets (IoT), le besoin de services de localisation en intérieur, tels que la gestion des actifs, la navigation et le suivi, a également augmenté au fil du temps. Pour la localisation indoor, les systèmes de navigation par satellite tels que le GPS ont un usage limité par l'absence de visibilité directe avec les satellites.Diverses solutions ont été proposées pour la localisation en intérieur, telles que la trilatération, la triangulation et la navigation à l'estime, mais leurs performances sont limitées par les conditions du canal intérieur, tell
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MASTRO, PIETRO. "Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses." Doctoral thesis, Università degli studi della Basilicata, 2023. https://hdl.handle.net/11563/162986.

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With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, o
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Holmberg, Lars. "Human In Command Machine Learning." Licentiate thesis, Malmö universitet, Malmö högskola, Institutionen för datavetenskap och medieteknik (DVMT), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-42576.

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Machine Learning (ML) and Artificial Intelligence (AI) impact many aspects of human life, from recommending a significant other to assist the search for extraterrestrial life. The area develops rapidly and exiting unexplored design spaces are constantly laid bare. The focus in this work is one of these areas; ML systems where decisions concerning ML model training, usage and selection of target domain lay in the hands of domain experts.  This work is then on ML systems that function as a tool that augments and/or enhance human capabilities. The approach presented is denoted Human In Command ML
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Nangalia, V. "ML-EWS - Machine Learning Early Warning System : the application of machine learning to predict in-hospital patient deterioration." Thesis, University College London (University of London), 2017. http://discovery.ucl.ac.uk/1565193/.

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Preventing hospitalised patients from suffering adverse event (AEs) (unexpected cardiac, arrest, intensive care unit admission, surgery or death) is a priority in healthcare. Almost 50% of these AEs, caused by mistakes/poor standards of care, are thought to be preventable. The identification and referral of a patient at risk of an AE to a dedicated rapid response team is a key mechanism for their reduction. Focussing on variables that are routinely collected and electronically stored (blood test data, and administrative data: demographics, date and method of admission, and co-morbidities), alo
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Mattsson, Fredrik, and Anton Gustafsson. "Optimize Ranking System With Machine Learning." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-37431.

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This thesis investigates how recommendation systems has been used and can be used with the help of different machine learning algorithms. Algorithms used and presented are decision tree, random forest and singular-value decomposition(SVD). Together with Tingstad, we have tried to implement the SVD function on their recommendation engine in order to enhance the recommendation given. A trivial presentation on how the algorithms work. General information about machine learning and how we tried to implement it with Tingstad’s data. Implementations with Netflix’s and Movielens open-source dataset w
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Bozdemir, Beyza. "Privacy-preserving machine learning techniques." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS323.

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L'apprentissage automatique en tant que service (MLaaS) fait référence à un service qui permet aux entreprises de déléguer leurs tâches d'apprentissage automatique à un ou plusieurs serveurs puissants, à savoir des serveurs cloud. Néanmoins, les entreprises sont confrontées à des défis importants pour garantir la confidentialité des données et le respect des réglementations en matière de protection des données. L'exécution de tâches d'apprentissage automatique sur des données sensibles nécessite la conception de nouveaux protocoles garantissant la confidentialité des données pour les technique
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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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Tabell, Johnsson Marco, and Ala Jafar. "Efficiency Comparison Between Curriculum Reinforcement Learning & Reinforcement Learning Using ML-Agents." Thesis, Blekinge Tekniska Högskola, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20218.

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Books on the topic "Machine Learning(ML) Techniques"

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Mirjalili, Seyedali, Hossam Faris, and Ibrahim Aljarah, eds. Evolutionary Machine Learning Techniques. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-32-9990-0.

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Basuchoudhary, Atin, James T. Bang, and Tinni Sen. Machine-learning Techniques in Economics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69014-8.

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Cord, Matthieu, and Pádraig Cunningham, eds. Machine Learning Techniques for Multimedia. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75171-7.

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Richard, Forsyth, ed. Machine learning: Principles and techniques. Chapman and Hall, 1989.

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Bertino, Elisa, Sonam Bhardwaj, Fabrizio Cicala, et al. Machine Learning Techniques for Cybersecurity. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28259-1.

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Sundararajan, Srikrishnan. Multivariate Analysis and Machine Learning Techniques. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-99-0353-5.

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Devi, K. Gayathri, Kishore Balasubramanian, and Le Anh Ngoc. Machine Learning and Deep Learning Techniques for Medical Science. CRC Press, 2022. http://dx.doi.org/10.1201/9781003217497.

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Stalph, Patrick. Analysis and Design of Machine Learning Techniques. Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-04937-9.

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Özyer, Tansel, and Reda Alhajj, eds. Machine Learning Techniques for Online Social Networks. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89932-9.

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Mason, James Eric, Issa Traoré, and Isaac Woungang. Machine Learning Techniques for Gait Biometric Recognition. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29088-1.

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

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Adriaans, Pieter. "ML techniques and text analysis." In Machine Learning: ECML-93. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56602-3_164.

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Rustagi, Tanvi, and Meenu Vijarania. "Implementation of ML Techniques for Heart Prediction in Healthcare." In Machine Learning in Multimedia. CRC Press, 2024. http://dx.doi.org/10.1201/9781003477280-11.

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Moreau, Clara, Christine Deruelle, and Guillaume Auzias. "Machine Learning for Neurodevelopmental Disorders." In Machine Learning for Brain Disorders. Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3195-9_31.

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AbstractNeurodevelopmental disorders (NDDs) constitute a major health issue with >10% of the general worldwide population affected by at least one of these conditions—such as autism spectrum disorders (ASD) and attention deficit hyperactivity disorders (ADHD). Each NDD is particularly complex to dissect for several reasons, including a high prevalence of comorbidities and a substantial heterogeneity of the clinical presentation. At the genetic level, several thousands of genes have been identified (polygenicity), while a part of them was already involved in other psychiatric conditions (ple
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Kubsch, Marcus, Christina Krist, and Peter Wulff. "Pattern Recognition—Unsupervised Machine Learning." In Springer Texts in Education. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-74227-9_11.

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Mohanty, Ashima Sindhu, Priyadarsan Parida, and Krishna Chandra Patra. "Usage of ML Techniques for ASD Detection." In Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications. CRC Press, 2022. http://dx.doi.org/10.1201/9781003226147-5.

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Sharma, Ochin, Raj Gaurang Tiwari, and Heena Wadhwa. "Smart Stock Prediction Techniques Using AI and ML." In Artificial Intelligence and Machine Learning for Smart Community. CRC Press, 2023. http://dx.doi.org/10.1201/9781003409502-6.

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Pathak, Anchal, Chandra Kumar Dixit, Parin Somani, and Shashi Kant Gupta. "Prediction of Employees' Performance using Machine Learning (ML) Techniques." In Designing Workforce Management Systems for Industry 4.0. CRC Press, 2023. http://dx.doi.org/10.1201/9781003357070-11.

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Hanif, Muhammad Abdullah, Faiq Khalid, Rachmad Vidya Wicaksana Putra, et al. "Robust Computing for Machine Learning-Based Systems." In Dependable Embedded Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52017-5_20.

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AbstractThe drive for automation and constant monitoring has led to rapid development in the field of Machine Learning (ML). The high accuracy offered by the state-of-the-art ML algorithms like Deep Neural Networks (DNNs) has paved the way for these algorithms to being used even in the emerging safety-critical applications, e.g., autonomous driving and smart healthcare. However, these applications require assurance about the functionality of the underlying systems/algorithms. Therefore, the robustness of these ML algorithms to different reliability and security threats has to be thoroughly stu
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Katal, Avita. "Redefining Data Analytics with ML and Cloud." In Data Analytics using Machine Learning Techniques on Cloud Platforms. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003396772-8.

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Chauhan, Rakhi. "Introduction to Bioinformatics and Machine Learning." In Applying Machine Learning Techniques to Bioinformatics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1822-5.ch017.

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ML has revolutionised bioinformatics' difficult biological data analysis. Pattern recognition and biological process categorization help ML systems diagnose diseases, predict protein structures, and investigate gene expression. Few-shot learning and bioinformatics are effective at optimising results with limited resources, overcoming biological dataset access issues. ML and bioinformatics advance precision medicine and drug discovery while improving biological understanding. This chapter examines bioinformatics ML methods like supervised classification, clustering, and probabilistic graphical
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Conference papers on the topic "Machine Learning(ML) Techniques"

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Tiwari, Mohit, Aryan Kumar, and B. Bharathi. "Price Recommendation For E-Commerce Using ML Techniques." In 2024 Intelligent Systems and Machine Learning Conference (ISML). IEEE, 2024. https://doi.org/10.1109/isml60050.2024.11007352.

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Abbas, Syed Ashraf. "A Review on Application of Machine Learning Techniques in Seismic Analysis of Timber Structures." In 14th International Civil Engineering Conference. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-5xmu1e.

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In the last two decades, great progress in Machine Learning can be seen in various fields of structural engineering including seismic analysis. This paper focuses on the cross-filed of Machine Learning (ML) and seismic engineering and provides an overview on different ML techniques been used in seismic analysis studies, compare these techniques and their application to study the seismic response of timber structures. The comparison of common supervised ML techniques in this paper are Multi Linear Regression, Regression Tree, Regression Forest, K Nearest Neighbor, Support Vector Regression and
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Birudaraju, Hyma, K. Sailaja, Vaitla Sreedevi, R. Viswanathan, and S. Vandaarkuzhali. "Integrating Machine Learning (ML) and Deep Learning (DL) Methods to Produce Reliable Wind Power Predictions." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES). IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877637.

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Klein, Mark D., Corey D. Packard, Scott C. Gibbs, Audrey C. Levanen, Logan Canull, and Weston Early. "Multimodal synthetic image generation of terrestrial scenes with humans for AI/ML with MuSES." In Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications III, edited by Keith F. Prussing, Kimberly E. Manser, Celso De Melo, Raghuveer M. Rao, and Christopher L. Howell. SPIE, 2025. https://doi.org/10.1117/12.3054669.

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Lacava, Steven, Felipe Mercado, Valerio Viti, et al. "Synthetic scene generation for AI/ML training that accounts for system aberrations and environmental effects." In Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications III, edited by Kimberly E. Manser, Celso De Melo, Raghuveer M. Rao, and Christopher L. Howell. SPIE, 2025. https://doi.org/10.1117/12.3053899.

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Devi, Kavuluri Leela Sai Rasagna, Garnepudi Narasimha Kumar, Potturi Ashok Narayana, Kakani Venkata Ramana, K. Amarendra, and Tirupathi Rao Gullipalli. "Forest Fire Prediction and Management using AI (Artificial Intelligence), ML (Machine Learning) and Deep Learning Techniques." In 2024 8th International Conference on Inventive Systems and Control (ICISC). IEEE, 2024. http://dx.doi.org/10.1109/icisc62624.2024.00062.

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Newton-Young, Daniel, Mark White, and Peter Green. "Use of Machine Learning Techniques to Support Future Ship-Helicopter Operations Research; an Initial Investigation." In Vertical Flight Society 80th Annual Forum & Technology Display. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0080-2024-1378.

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This paper reports on the initial implementation of Machine Learning (ML) for predicting the workload experienced by a pilot when performing a recovery to a naval ship. Pilots classify their workload for each landing by providing a subjective rating, which is used to determine the ship-helicopter operating limit (SHOL). Different workload metrics have been trialed to bridge the gap between pilot subjective ratings and objective flight data. With hundreds of different helicopter, ship and airwake parameters available to examine, ML provides an approach to understanding the complex interactions
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Samouilidou, Maria E., Nikolaos Passalis, Georgios P. Georgiadis, and Michael C. Georgiadis. "Enhancing Large-Scale Production Scheduling Using Machine-Learning Techniques." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.168290.

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This study focuses on optimizing production scheduling in multi-product plants with shared resources and costly changeover operations. Specifically, two main challenges are addressed, the unknown changeover behavior of new products and the need for rapid schedule generation after unforeseen events. An innovative framework integrating Machine Learning (ML) techniques with Mixed-Integer Linear Programming (MILP) is proposed for single-stage production processes. Initially, a regression model predicts unknown changeover times based on key product attributes. Then, a representation where distances
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Vieira, Ronald E., Farzin Darihaki, Jamie Li, and Siamack A. Shirazi. "Application of Machine Learning Techniques for Sand Erosion Prediction for Elbows in Multiphase Flow." In CONFERENCE 2023. AMPP, 2023. https://doi.org/10.5006/c2023-18995.

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Abstract The aim of this work is to define, implement, test, and validate an AI methodology using existing machine learning (ML) algorithms to predict sand erosion in 90° elbows for a broad range of multiphase operating conditions. Based on information obtained from the experimental UT wall thickness loss data collected for different flow regimes (gas-sand, liquid-sand, dispersed-bubble, churn, annular, and low liquid loading multiphase flows), the methodology has been developed to predict the maximum erosion magnitudes in standard metallic elbows. In order to expand the range of application o
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Okon, Edet Ita, and Dulu Appah. "Application of Machine Learning Techniques in Reservoir Characterization." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/208248-ms.

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Abstract Application of artificial intelligence (AI) and machine learning (ML) is becoming a new addition to the traditional reservoir characterization, petrophysics and monitoring practice in oil and gas industry. Accurate reservoir characterization is the driver to optimize production performance throughout the life of a field. Developing physics-based data models are the key for applying ML techniques to solve complex reservoir problems. The main objective of this study is to apply machine learning techniques in reservoir Characterization. This was achieved via machine learning algorithm us
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Reports on the topic "Machine Learning(ML) Techniques"

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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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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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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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Weeks, Melvyn. Machine Learning for Prediction and Causal Inference. Instats Inc., 2022. http://dx.doi.org/10.61700/u0qw7udtxd5iz469.

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This seminar explores machine learning techniques for prediction and causal inference, where a researcher or decision maker needs to make a prediction or understand the impact of an intervention in a heterogenous population. For example, researchers may want to infer the effect of an economic, educational, or public health intervention, or a firm may seek to understand how a change in pricing will impact aggregate demand. In these cases, the interest may be in an average effect, but also how the effect varies over different segments of the population (i.e., heterogeneity in the effect). This s
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Weeks, Melvyn. Machine Learning for Prediction and Causal Inference. Instats Inc., 2022. http://dx.doi.org/10.61700/r1qb0f2baf6jj469.

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This seminar explores machine learning techniques for prediction and causal inference, where a researcher or decision maker needs to make a prediction or understand the impact of an intervention in a heterogenous population. For example, researchers may want to infer the effect of an economic, educational, or public health intervention, or a firm may seek to understand how a change in pricing will impact aggregate demand. In these cases, the interest may be in an average effect, but also how the effect varies over different segments of the population (i.e., heterogeneity in the effect). This s
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Niles, Kenneth, Emily Leathers, Joe Tom, et al. Leveraging artificial intelligence and machine learning (AI/ML) for levee culvert Inspections in USACE Flood Control Systems (FCS). Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49210.

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Levee inspections are essential in preventing flooding within populated regions. Risk assessments of structures are performed to identify potential failure modes to maintain the safety and health of the structure. The data collection and defect coding parts of the inspection process can be labor-intensive and time-consuming. The integration of machine learning (ML) and artificial intelligence (AI) techniques may increase accuracy of assessments and reduce time and cost. To develop a foundation for a fully autonomous inspection process, this research investigates methods to gather information f
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Clausen, Jay, Vuong Truong, Sophia Bragdon, et al. Buried-object-detection improvements incorporating environmental phenomenology into signature physics. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45625.

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The ability to detect buried objects is critical for the Army. Therefore, this report summarizes the fourth year of an ongoing study to assess environ-mental phenomenological conditions affecting probability of detection and false alarm rates for buried-object detection using thermal infrared sensors. This study used several different approaches to identify the predominant environmental variables affecting object detection: (1) multilevel statistical modeling, (2) direct image analysis, (3) physics-based thermal modeling, and (4) application of machine learning (ML) techniques. In addition, th
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Cook, Samantha, Matthew Bigl, Sandra LeGrand, Nicholas Webb, Gayle Tyree, and Ronald Treminio. Landform identification in the Chihuahuan Desert for dust source characterization applications : developing a landform reference data set. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45644.

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ERDC-Geo is a surface erodibility parameterization developed to improve dust predictions in weather forecasting models. Geomorphic landform maps used in ERDC-Geo link surface dust emission potential to landform type. Using a previously generated southwest United States landform map as training data, a classification model based on machine learning (ML) was established to generate ERDC-Geo input data. To evaluate the ability of the ML model to accurately classify landforms, an independent reference landform data set was created for areas in the Chihuahuan Desert. The reference landform data set
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Christie, Lorna. Interpretable machine learning. Parliamentary Office of Science and Technology, 2020. http://dx.doi.org/10.58248/pn633.

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Machine learning (ML, a type of artificial intelligence) is increasingly being used to support decision making in a variety of applications including recruitment and clinical diagnoses. While ML has many advantages, there are concerns that in some cases it may not be possible to explain completely how its outputs have been produced. This POSTnote gives an overview of ML and its role in decision-making. It examines the challenges of understanding how a complex ML system has reached its output, and some of the technical approaches to making ML easier to interpret. It also gives a brief overview
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Vilalta, Ricardo. Modern Machine Learning Techniques. Instats Inc., 2024. http://dx.doi.org/10.61700/6sziq6usb3koe786.

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This workshop offers a comprehensively introduction to modern machine learning techniques in Python. Designed for PhD students, professors, and professional researchers, the seminar covers a variety of valuable techniques for machine learning, from meta-learning and transfer learning, to domain adaptation, active learning, deep learning, and Bayesian networks, equipping participants with key practical skills to enhance their research capabilities.
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