Relevant bibliographies by topics / Machine learning (ML) neural nets models

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Academic literature on the topic 'Machine learning (ML) neural nets models'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Machine learning (ML) neural nets models"

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Pandey, Prachi, and Abhijitha Bandaru. "Enhancing predictive accuracy of asset returns by experimenting with ML techniques." SHS Web of Conferences 169 (2023): 01062. http://dx.doi.org/10.1051/shsconf/202316901062.

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The unparalleled success of machine learning is indisputable. It has transformed the world with unimaginable solutions to insistent problems. The remarkable accuracy that machine learning manifests for making estimations is an object of fascination for plenty of researchers all over the world. The financial industry has also benefited from the growth of this electrifying field to predict asset returns, creditworthiness of a customer, and portfolio management, among others. In this research, we spotlight how this accuracy is contingent upon the analysis of various aspects of the data. We also e
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Chandrahas, Mishra, and L. Gupta D. "Deep Machine Learning and Neural Networks: An Overview." IAES International Journal of Artificial Intelligence (IJ-AI) 6, no. 2 (2017): 66–73. https://doi.org/10.5281/zenodo.4108266.

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Deep learning is a technique of machine learning in artificial intelligence area. Deep learning in a refined "machine learning" algorithm that far surpasses a considerable lot of its forerunners in its capacities to perceive syllables and picture. Deep learning is as of now a greatly dynamic examination territory in machine learning and example acknowledgment society. It has increased colossal triumphs in an expansive zone of utilizations, for example, speech recognition, computer vision and natural language processing and numerous industry item. Neural network is used to implement t
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Madupu Venkata Vineeth and G.V. Ramana. "Detection of Phishing Websites Using Machine Learning." Metallurgical and Materials Engineering 31, no. 4 (2025): 762–68. https://doi.org/10.63278/1511.

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Phishing websites have proven to be a major security concern. Several cyberattacks risk the confidentiality, integrity, and availability of company and consumer data, and phishing is the beginning point for many of them. Many researchers have spent decades creating unique approaches to automatically detect phishing websites. While cutting-edge solutions can deliver better results, they need a lot of manual feature engineering and aren't good at identifying new phishing attacks. As a result, finding strategies that can automatically detect phishing websites and quickly manage zero-day phishing
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Lodes, Lukas, and Alexander Schiendorfer. "Certainty Groups: A Practical Approach to Distinguish Confidence Levels in Neural Networks." PHM Society European Conference 7, no. 1 (2022): 294–305. http://dx.doi.org/10.36001/phme.2022.v7i1.3331.

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Machine Learning (ML), in particular classification with deep neural nets, can be applied to a variety of industrial tasks. It can augment established methods for controlling manufacturing processes such as statistical process control (SPC) to detect non-obvious patterns in high-dimensional input data. However, due to the widespread issue of model miscalibration in neural networks, there is a need for estimating the predictive uncertainty of these models. Many established approaches for uncertainty estimation output scores that are difficult to put into actionable insight. We therefore introdu
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Senanayake, Indishe P., Kalani R. L. Pathira Arachchilage, In-Young Yeo, Mehdi Khaki, Shin-Chan Han, and Peter G. Dahlhaus. "Spatial Downscaling of Satellite-Based Soil Moisture Products Using Machine Learning Techniques: A Review." Remote Sensing 16, no. 12 (2024): 2067. http://dx.doi.org/10.3390/rs16122067.

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Soil moisture (SM) is a key variable driving hydrologic, climatic, and ecological processes. Although it is highly variable, both spatially and temporally, there is limited data availability to inform about SM conditions at adequate spatial and temporal scales over large regions. Satellite SM retrievals, especially L-band microwave remote sensing, has emerged as a feasible solution to offer spatially continuous global-scale SM information. However, the coarse spatial resolution of these L-band microwave SM retrievals poses uncertainties in many regional- and local-scale SM applications which r
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Parung, Ratu Anggriani Tangke, Hanna Arini Parhusip, and Suryasatriya Trihandaru. "Prototype of Swiftlet Nest Moisture Content Measurement Using Resistance Sensor and Machine Learning." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 8, no. 5 (2024): 674–80. https://doi.org/10.29207/resti.v8i5.5923.

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Swiftlet nests are highly valued for their health and cosmetic benefits, with moisture content crucial in determining their quality. Traditional moisture measurement methods are often slow and can potentially damage the samples. This study introduces PRORESKA, an innovative system utilizing resistance sensors and Machine Learning (ML) for non-destructive, and real-time moisture measurement. The system incorporates a voltage divider circuit to establish a correlation between resistance data and moisture content. Three mathematical models (linear, exponential, and modulated exponential) and a ne
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Alotaibi, Afnan, and Murad A. Rassam. "Enhancing the Sustainability of Deep-Learning-Based Network Intrusion Detection Classifiers against Adversarial Attacks." Sustainability 15, no. 12 (2023): 9801. http://dx.doi.org/10.3390/su15129801.

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An intrusion detection system (IDS) is an effective tool for securing networks and a dependable technique for improving a user’s internet security. It informs the administration whenever strange conduct occurs. An IDS fundamentally depends on the classification of network packets as benign or attack. Moreover, IDSs can achieve better results when built with machine learning (ML)/deep learning (DL) techniques, such as convolutional neural networks (CNNs). However, there is a limitation when building a reliable IDS using ML/DL techniques, which is their vulnerability to adversarial attacks. Such
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Lee, Sangil, Avinash Reddy Mudireddy, Deepak Kumar Pasupula, et al. "Novel Machine Learning Approach to Predict and Personalize Length of Stay for Patients Admitted with Syncope from the Emergency Departmen." Journal of Personalized Medicine 13, no. 1 (2022): 7. http://dx.doi.org/10.3390/jpm13010007.

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Background: Syncope, a common problem encountered in the emergency department (ED), has a multitude of causes ranging from benign to life-threatening. Hospitalization may be required, but the management can vary substantially depending on specific clinical characteristics. Models predicting admission and hospitalization length of stay (LoS) are lacking. The purpose of this study was to design an effective, exploratory model using machine learning (ML) technology to predict LoS for patients presenting with syncope. Methods: This was a retrospective analysis using over 4 million patients from th
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Garg, R. "PREDICTING EMERGING MARKET RETURNS WITH SIMPLE MACHINE LEARNING TECHNIQUES." Slovak international scientific journal, no. 95 (May 15, 2025): 30–35. https://doi.org/10.5281/zenodo.15427551.

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This study applies simple machine learning techniques to predict Emerging Market (EM) equity returns. It examines excess returns for major EM indices versus relevant benchmarks. Predictor variables were drawn from the same security’s historical excess returns. The intuition is that past performance can be predictive of future investment interest and that predictive relationship is unique for each security. Therefore, a variety of models need to be considered. A total of five models were applied: traditional logistic regressions, ridge, random forest, boosting and neural nets. The results
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Michaud, Eric J., Ziming Liu, and Max Tegmark. "Precision Machine Learning." Entropy 25, no. 1 (2023): 175. http://dx.doi.org/10.3390/e25010175.

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We explore unique considerations involved in fitting machine learning (ML) models to data with very high precision, as is often required for science applications. We empirically compare various function approximation methods and study how they scale with increasing parameters and data. We find that neural networks (NNs) can often outperform classical approximation methods on high-dimensional examples, by (we hypothesize) auto-discovering and exploiting modular structures therein. However, neural networks trained with common optimizers are less powerful for low-dimensional cases, which motivate
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Books on the topic "Machine learning (ML) neural nets models"

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Zhai, Xiaoming, and Joseph Krajcik, eds. Uses of Artificial Intelligence in STEM Education. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/oso/9780198882077.001.0001.

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Abstract In the age of rapid technological advancements, the integration of artificial intelligence (AI), machine learning (ML), and large language models (LLMs) in science, technology, engineering, and mathematics (STEM) education has emerged as a transformative force, reshaping pedagogical approaches and assessment methodologies. This book, comprising twenty-six chapters, delves deep into the multifaceted realm of AI-driven STEM education. It begins by exploring the challenges and opportunities of AI-based STEM education, emphasizing the intricate balance between human tasks and technologica
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Book chapters on the topic "Machine learning (ML) neural nets models"

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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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Kucharavy, Andrei. "From Deep Neural Language Models to LLMs." In Large Language Models in Cybersecurity. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54827-7_1.

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AbstractLarge Language Models (LLMs) are scaled-up instances of Deep Neural Language Models—a type of Natural Language Processing (NLP) tools trained with Machine Learning (ML). To best understand how LLMs work, we must dive into what technologies they build on top of and what makes them different. To achieve this, an overview of the history of LLMs development, starting from the 1990s, is provided before covering the counterintuitive purely probabilistic nature of the Deep Neural Language Models, continuous token embedding spaces, recurrent neural networks-based models, what self-attention brought to the table, and finally, why scaling Deep Neural Language Models led to a qualitative change, warranting a new name for the technology.
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Peeperkorn, Jari, Seppe vanden Broucke, and Jochen De Weerdt. "Can Deep Neural Networks Learn Process Model Structure? An Assessment Framework and Analysis." In Lecture Notes in Business Information Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_10.

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AbstractPredictive process monitoring concerns itself with the prediction of ongoing cases in (business) processes. Prediction tasks typically focus on remaining time, outcome, next event or full case suffix prediction. Various methods using machine and deep learning have been proposed for these tasks in recent years. Especially recurrent neural networks (RNNs) such as long short-term memory nets (LSTMs) have gained in popularity. However, no research focuses on whether such neural network-based models can truly learn the structure of underlying process models. For instance, can such neural networks effectively learn parallel behaviour or loops? Therefore, in this work, we propose an evaluation scheme complemented with new fitness, precision, and generalisation metrics, specifically tailored towards measuring the capacity of deep learning models to learn process model structure. We apply this framework to several process models with simple control-flow behaviour, on the task of next-event prediction. Our results show that, even for such simplistic models, careful tuning of overfitting countermeasures is required to allow these models to learn process model structure.
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Qiu, Waishan, Wenjing Li, Xun Liu, and Xiaokai Huang. "Subjectively Measured Streetscape Qualities for Shanghai with Large-Scale Application of Computer Vision and Machine Learning." In Proceedings of the 2021 DigitalFUTURES. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5983-6_23.

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AbstractRecently, many new studies emerged to apply computer vision (CV) to street view imagery (SVI) dataset to objectively extract the view indices of various streetscape features such as trees to proxy urban scene qualities. However, human perceptions (e.g., imageability) have a subtle relationship to visual elements which cannot be fully captured using view indices. Conversely, subjective measures using survey and interview data explain more human behaviors. However, the effectiveness of integrating subjective measures with SVI dataset has been less discussed. To address this, we integrated crowdsourcing, CV, and machine learning (ML) to subjectively measure four important perceptions suggested by classical urban design theory. We first collected experts’ rating on sample SVIs regarding the four qualities which became the training labels. CV segmentation was applied to SVI samples extracting streetscape view indices as the explanatory variables. We then trained ML models and achieved high accuracy in predicting the scores. We found a strong correlation between predicted complexity score and the density of urban amenities and services Point of Interests (POI), which validates the effectiveness of subjective measures. In addition, to test the generalizability of the proposed framework as well as to inform urban renewal strategies, we compared the measured qualities in Pudong to other five renowned urban cores worldwide. Rather than predicting perceptual scores directly from generic image features using convolution neural network, our approach follows what urban design theory suggested and confirms various streetscape features affecting multi-dimensional human perceptions. Therefore, its result provides more interpretable and actionable implications for policymakers and city planners.
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Iavarone, S., H. Yang, Z. Li, Z. X. Chen, and N. Swaminathan. "On the Use of Machine Learning for Subgrid Scale Filtered Density Function Modelling in Large Eddy Simulations of Combustion Systems." In Lecture Notes in Energy. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-16248-0_8.

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AbstractThe application of machine learning algorithms to model subgrid-scale filtered density functions (FDFs), required to estimate filtered reaction rates for Large Eddy Simulation (LES) of chemically reacting flows, is discussed in this chapter. Three test cases, i.e., a low-swirl premixed methane-air flame, a MILD combustion of methane-air mixtures, and a kerosene spray turbulent flame, are presented. The scalar statistics in these test cases may not be easily represented using the commonly used presumed shapes for modeling FDFs of mixture fraction and progress variable. Hence, the use of ML methods is explored. Particularly, deep neural network (DNN) to infer joint FDFs of mixture fraction and progress variable is reviewed here. The Direct Numerical Simulation (DNS) datasets employed to train the DNNs in each test case are described. The DNN performances are shown and compared to typical presumed probability density function (PDF) models. Finally, this chapter examines the advantages and caveats of the DNN-based approach.
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Zhang, Yang, and Yue Wu. "Introducing Machine Learning Models to Response Surface Methodologies." In Response Surface Methodology in Engineering Science [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.98191.

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Traditional response surface methodology (RSM) has utilized the ordinary least squared (OLS) technique to numerically estimate the coefficients for multiple influence factors to achieve the values of the responsive factor while considering the intersection and quadratic terms of the influencers if any. With the emergence and popularization of machine learning (ML), more competitive methods has been developed which can be adopted to complement or replace the tradition RSM method, i.e. the OLS with or without the polynomial terms. In this chapter, several commonly used regression models in the ML including the improved linear models (the least absolute shrinkage and selection operator model and the generalized linear model), the decision trees family (decision trees, random forests and gradient boosting trees), the model of the neural nets, (the multi-layer perceptrons) and the support vector machine will be introduced. Those ML models will provide a more flexible way to estimate the response surface function that is difficult to be represented by a polynomial as deployed in the traditional RSM. The advantage of the ML models in predicting precise response factor values is then demonstrated by implementation on an engineering case study. The case study has shown that the various choices of the ML models can reach a more satisfactory estimation for the responsive surface function in comparison to the RSM. The GDBT has exhibited to outperform the RSM with an accuracy improvement for 50% on unseen experimental data.
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Badar, Mohammad Sufian, Aisha Idris, Areeba Khan, Md Mustafa, and Farheen Asaf. "AI-Based Diagnosis of Novel Coronavirus Using Radiograph Images." In COVID-19: Causes, Transmission, Diagnosis, and Treatment. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815256536124010011.

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The therapeutic value of artificial intelligence (ML) in the diagnosis of viral illnesses has been illustrated by the outbreak of COVID-19. This chapter digs into the modern uses of Artificial Intelligence and Machine Learning (ML) algorithms for COVID-19 diagnosis, with a focus on chest imaging procedures like as CT and X-rays. Additionally, we explored ML's strengths, such as its capacity to analyze enormous datasets and detect patterns in medical imagery. But there are still issues to deal with, like the scarcity of data, privacy issues, and machine learning's incapacity to evaluate the severity of health conditions. However, several machine learning methods, such as decision trees, random forests, and convolutional neural networks, are reviewed in this research concerning COVID-19 diagnosis. Subsequently, we highlight the efficacy of several models in COVID-19 screening, such as XGBoost and Truncated Inception Net. Moreover, the chapter discusses potential strategies for machine learning in COVID-19 diagnosis, emphasizing the crucial role of collaboration among data scientists and healthcare experts. It is imperative to confront data bias and incorporate more comprehensive patient data than just chest imaging. All things considered, machine learning presents a potential pathway toward quick and precise COVID-19 diagnosis; nonetheless, conquering existing obstacles is necessary for ML to be widely used in healthcare institutions.
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Badhan, Ajay Kumar, Abhishek Bhattacharjee, Raman Kumar, and Princy Diwan. "Machine Learning Algorithms for Adaptive Haptic Responses." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-2307-7.ch010.

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The amalgamation of machine learning (ML) with haptic feedback has enhanced touch-based interactions between humans and computers. ML-driven haptic feedback enables context-sensitive and dynamic systems for accessibility, medical rehabilitation, VR, and remote robotics. These systems adapt to user experience, biomechanics, and environment using supervised, unsupervised, and reinforcement learning. Deep learning models like CNNs and RNNs improve user intent detection and real-time feedback. Reinforcement learning refines responses based on sensory interactions. Generative models (VAE, GAN) enable personalized haptic feedback. Bio-signal processing with EMG and EEG allows automatic adaptation to neural and muscle activity. ML-powered haptics foster inclusive design, enhancing digital accessibility for individuals with disabilities.
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Pradhan, Ashwini Kumar, Arvind Yadav, Nilamadhab Mishra, et al. "Application of Machine Learning in Healthcare IoT." In Exploration of Transformative Technologies in Healthcare 6.0. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7210-4.ch007.

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Machine learning (ML) is the most prominent topic, with many applications in video processing, language processing, visual diagnosis of illnesses, drug development, biochemistry, and biomedical. Healthcare systems and medical science-related applications are rapidly growing among these applications. Large data expansion, the Internet of Things (IoT), networked devices, and high-performance computers with graphics processing units are all contributing to the growing popularity of ML. Healthcare in IoT (HIoT), and electronic health record (EHR) data are the key data resources for neural network (NN) models. Several possible challenges, including security and quality of service improvement, are important to ML. Based on the comprehensive literature survey (CLS), ML for HIoT is examined in this research.
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Deep, Gagan, and Jyoti Verma. "The Role of AI and Machine Learning in Neuromarketing." In Advances in Marketing, Customer Relationship Management, and E-Services. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-8222-6.ch008.

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Artificial Intelligence (AI) and Machine Learning (ML) techniques are turning into a revolution in the growth of neuromarketing field and various advanced techniques that can engage the customer and provide better insights. Starting with an analysis of these technologies and their significance in the marketing context of the environment, this chapter introduces neuromarketing's applications of AI and ML. Together with the instruments and stages relevant to neuromarketing investigation, it considers the technological background, such as neural nets, deep learning, and predictions. The chapter also briefly considers practical applications also, illustrating how artificial intelligence and machine learning assess buyer behaviour and develop suitable marketing strategies and optimise customers' experiences. The campaigns mentioned and the creative planning or the specific activities that are applied are presented to the readers through perceptible case studies.
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Conference papers on the topic "Machine learning (ML) neural nets models"

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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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Hashem Dabaghian, Pedram, and Atanu Halder. "Machine Learning Based Flight Dynamic Framework for eVTOL Aircraft." In Vertical Flight Society 81st Annual Forum and Technology Display. The Vertical Flight Society, 2025. https://doi.org/10.4050/f-0081-2025-267.

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The advent of electric propulsion technology has led to a paradigm shift in aircraft design over the past few decades. This shift has expanded the possibilities for design and optimization processes more than at any previous time. To support these advancements, efficient flight dynamics simulation models that can be employed in iterative optimization and design processes are essential. Among the modules of a typical flight dynamics framework—namely, control, flight dynamics, and aerodynamics—the aerodynamics module, which includes the rotor performance model, generally demands the most computa
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ARNOLD, STEVEN M., SUBODH K. MITAL, and BRANDON L. HEARLEY. "STIFFNESS AND FATIGUE LIFE ESTIMATOR FOR POLYMER COMPOSITE LAMINATES USING MACHINE LEARNING." In Proceedings for the American Society for Composites-Thirty Eighth Technical Conference. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/asc38/36655.

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Machine learning (ML) models are increasingly being used in many engineering fields due to the advancements in ML algorithms and availability of high-speed computing power. One of the most popular ML class of models is artificial neural networks (ANN). ML is increasingly being used in the design and analysis of composite materials and structures, specifically in the constitutive modeling of composite materials with the focus on greatly accelerating multiscale analyses of composite materials and structures through development of surrogate models. Towards that end, Python-based neural nets have
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Khan, Abdul Muqtadir, Yin Luo, Esteban Ugarte, and Denis Bannikov. "Physics-Informed Machine Learning for Hydraulic Fracturing—Part I: The Backbone Model." In SPE Conference at Oman Petroleum & Energy Show. SPE, 2024. http://dx.doi.org/10.2118/218562-ms.

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Abstract The growth of machine learning (ML) approaches has sparked innovations in many applications including hydraulic fracturing design. The crucial drawback in these models is the subjectivity and expertise of the design engineers, which could risk under-realizing the true reservoir and production potential. To overcome this, we incorporate the physics of fracturing design theory into ML models through a hybridized approach. A method consolidating complete physics that integrated reservoir characteristics, fracturing diagnostics, and production performance was applied to 71 parameters of w
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Kara, Mustafa Can, Aysegul Dastan, Evan Oyler, Bret Peterson, and Rahul Dixit. "Inferring the Effect of Chemical Injections for Asphaltene Producing Deepwater Wells Using Machine Learning." In Offshore Technology Conference. OTC, 2022. http://dx.doi.org/10.4043/31955-ms.

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Abstract Asphaltene production in Deepwater wells is an important operational issue which may result in planned and unplanned shut-ins. Excessive asphaltene precipitation and deposition can cause production curtailment that adds up to significant costs yearly tooperators in Deepwater production and transportation of Deepwater asphalt base crude oil. Costly chemical injections such as xylene soaks are used to dissolve the asphaltenes in the tubing. This study proposes a new machine learning technique to increase the effectiveness of such soaks in a Deepwater well. A predictive solution is devel
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Kara, Mustafa Can, Aysegul Dastan, Evan Oyler, Bret Peterson, and Rahul Dixit. "Inferring the Effect of Chemical Injections for Asphaltene Producing Deepwater Wells Using Machine Learning." In Offshore Technology Conference. OTC, 2022. http://dx.doi.org/10.4043/31955-ms.

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Abstract Asphaltene production in Deepwater wells is an important operational issue which may result in planned and unplanned shut-ins. Excessive asphaltene precipitation and deposition can cause production curtailment that adds up to significant costs yearly tooperators in Deepwater production and transportation of Deepwater asphalt base crude oil. Costly chemical injections such as xylene soaks are used to dissolve the asphaltenes in the tubing. This study proposes a new machine learning technique to increase the effectiveness of such soaks in a Deepwater well. A predictive solution is devel
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Razak, F. Abdul, S. Postovalov, A. Knizhnik, et al. "Streamlining and Automating Drilling Fluids Advisory System with an End-To-End Machine Learning Pipeline." In GOTECH. SPE, 2025. https://doi.org/10.2118/224453-ms.

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ABSTRACT The Drilling Fluid Advisory System is a decision support tool that optimizes and recommends the amounts of additives that needs to be added to the drilling fluid to achieve desired properties in real time. This article presents an automated end-to-end pipeline that was used to prepare data, train MIMO neural nets, optimize hyper-parameters and validate the performance of these models thereby enabling on-the-fly configuration of input products, fluid systems, and output properties. The Fluid Advisory System comprising of various components, including a data pipeline for loading histori
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Buriro, A., M. Gorniak, M. Makos, et al. "Geological Insights and Machine Learning-Assisted Seismic Interpretation: Navigating Subsurface Complexity in Fold and Thrust Belt, Pakistan." In SPE Conference at Oman Petroleum & Energy Show. SPE, 2025. https://doi.org/10.2118/224948-ms.

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Abstract This study explores the integration of machine learning (ML) techniques into seismic interpretation workflows to enhance subsurface characterization within the fold and thrust belt of Pakistan. The study area spans 56 square kilometers of seismic coverage and includes three wells. The methodology is structured into four key phases: data preprocessing, fault detection, horizon extraction, and quality assessment. In the data preprocessing phase, seismic data conditioning through median filtering and graphic equalization significantly improved image clarity and resolution. These techniqu
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Dallag, Mohammed, Mustafa Bawazir, and Ali Al-Ali. "Digital Solution to Extend the Life of Wells with Continuous Corrosion Monitoring Based on Machine Learning Algorithms." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22472-ms.

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Abstract Well integrity in the oilfield is one of the challenges that petroleum engineers face, as they seek to monitor well corrosion in the field to optimize well performance. Most of these fields can be categorized as brownfields, with some of the wells considered aged and have expected integrity issues. To achieve sustainable production targets with cost-effective and safe operations from these fields requires a close monitoring of the integrity of all elements involved in the production chain. Addressing these challenges requires the engineers to coordinate and analyze several data elemen
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Santos, Domingos B. S., Gabriel F. L. Melo, and Thelmo de Araujo. "Automatic Segmentation of Skeletal Muscle Tissue in L3 CT Images Based on Random Forests and CNN Using Coarse Ground Truth Masks." In Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2024. http://dx.doi.org/10.5753/sbcas.2024.1831.

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Estimates of the composition of skeletal muscle tissue (SMT) and adipose tissues are important in the treatment of debilitating diseases, such as cancer, and in the control of overweight and obesity. Several studies have shown a high correlation between the percentage of SMT in computed tomography (CT) images corresponding to the cross-section at the level of the third lumbar vertebra (L3) and the percentage of this tissue in the whole body. A large number of models has been proposed to automatically segment CT images in order to estimate tissue compositions, many of them use supervised Machin
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Reports on the topic "Machine learning (ML) neural nets models"

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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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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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