25 papers · ranked by Valyu relevance
Hooman H. Rashidi, Samer Albahra, Scott Robertson, Nam K. Tran + 1 more
'Bo Hu'] One of the core elements of Machine Learning (ML) is statistics and its embedded foundational rules and without its appropriate integration, ML as we know would not exist. Various aspects of ML platforms are based on statistical rules and most notably the end results of the ML model performance cannot be…
Jonelle Angelo Cenita, Paul Richie Asuncion, Jayson Victoriano
**Special Issue on International Research Conference on Computer Engineering and Technology Education 2023 (IRCCETE 2023). Guest Associate Editors: Dr. Nelson C. Rodelas, PCpE (Computer Engineering Department, College of Engineering, University of the East-Caloocan City; nelson.rodelas@ue.edu.ph) and Engr. Ana…
Georgia Tsiliki, Cristian R. Munteanu, Jose A. Seoane, Carlos Fernandez-Lozano + 2 more
'Carlos Fernandez-Lozano' 'Haralambos Sarimveis' 'Egon L. Willighagen'] Background Predictive regression models can be created with many different modelling approaches. Choices need to be made for data set splitting, cross-validation methods, specific regression parameters and best model criteria, as they all affect…
B. D. Jenkins, A. L. Nicusan, A. Neveu, G. Lumay + 3 more
Developing fast regression models (surrogate/metamodels) from DEM data is key for practical industrial application to allow real-time evaluations. However, benchmarking different models is often overlooked in particle technology for regression tasks, as model selection is frequently not the primary research focus. This…
Fatih Gurcan, Carlos Fernandez-Lozano
Background The continuous increase in carbon dioxide (CO2) emissions from fuel vehicles generates a greenhouse effect in the atmosphere, which has a negative impact on global warming and climate change and raises serious concerns about environmental sustainability. Therefore, research on estimating and reducing vehicle…
P R Asha, M S Vijaya
Diagnosing and curing neurodegenarative disorder such as spinocerebellar ataxia is complicated when there is differences in formation of protein sequences and structures. Affinity prediction plays vital role to identify drugs for various genetic disorders. Spinocerebellar ataxia occurs but mainly it occurs due to…
Roemer J Janse, Ameen Abu-Hanna, Iacopo Vagliano, Vianda S Stel + 5 more
'Kitty J Jager' 'Giovanni Tripepi' 'Carmine Zoccali' 'Friedo W Dekker' 'Merel van Diepen'] Title: ABSTRACT An artificial intelligence boom is currently ongoing, mainly due to large language models, leading to significant interest in artificial intelligence and subsequently also in machine learning (ML). One area where…
Eduardo M. Vasconcelos, Adriano Gouveia de Souza
In statistics, researchers use Regression models for data analysis and prediction in many productive sectors (industry, business, academy, etc.). Regression models are mathematical functions representing an approximation of dependent variable Y from n independent variables Xi ∈ X. The literature presents many…
Authors not listed
Plastic mechanical recycling is the conventional technological step towards circularity. In such aspects, complex mixtures of polyolefin blends are often fed into mechanical recycling systems, resulting in moulded products with uncertain quality. To add to the difficulty of heterogeneous feedstocks, the testing of…
Authors not listed
Nonlinear regression analysis is a popular and important tool for scientists and engineers. In this article, we introduce theories and methods of nonlinear regression and its statistical inferences using the frequentist and Bayesian statistical modeling and computation. Least squares with the Gauss-Newton method is the…
Ayon Roy, Tausif Al Zubayer, Nafisa Tabassum, Muhammad Nazrul Islam + 1 more
'Abdus Sattar'] Regression analysis is a well known quantitative research method that primarily explores the relationship between one or more independent variables and a dependent variable. Conducting regression analysis manually on large datasets with multiple independent variables can be tedious. An automated system…
Aaditya Prasad Gupta
Biological systems, at all scales of organization from nucleic acids to ecosystems, are inherently complex and variable. Therefore mathematical models have become an essential tool in systems biology, linking the behavior of a system to the interaction between its components. Parameters in empirical mathematical models…
Authors not listed
We present a transferable, interpretable, and modular machine-learning framework that enhances the accuracy of density functional theory (DFT) reaction energies using physically meaningful energy-decomposition descriptors. Reaction energies computed at the DFT level with standard basis sets are first decomposed into…
Muni Lakshmi G K, Mokesh Rayalu G
Air pollution, especially elevated particulate matter concentrations, presents a substantial risk to public health and environmental sustainability in urban regions. By employing machine learning and hybrid ensemble models, this study develops a robust frame work for predicting the Air Quality Index (AQI). A multi-step…
John B. Carlin, Margarita Moreno‐Betancur
Regression methods dominate the practice of biostatistical analysis, but biostatistical training emphasises the details of regression models and methods ahead of the purposes for which such modelling might be useful. More broadly, statistics is widely understood to provide a body of techniques for "modelling data"…
Abdulaziz Albahr, Marwan Albahar, Mohammed Thanoon, Muhammad Binsawad
'Muhammad Binsawad'] Heart diseases are characterized as heterogeneous diseases comprising multiple subtypes. Early diagnosis and prognosis of heart disease are essential to facilitate the clinical management of patients. In this research, a new computational model for predicting early heart disease is proposed. The…
Kan Hatakeyama-Sato, Seigo Watanabe, Naoki Yamane, Yasuhiko Igarashi + 1 more
Materials informatics and cheminformatics struggle with data scarcity, hindering the extraction of significant relationships between structures and properties. The "Ugly Duckling" theorem, suggesting the difficulty of data processing without assumptions or prior knowledge, exacerbates this problem. Current…
Authors not listed
Phase equilibrium calculations are crucial in chemical engineering design and optimization processes. The PC-SAFT equation of state (EoS) can precisely calculate phase equilibrium, but is relatively complex and computationally intensive. Surrogate models are mathematically simple models that map or regress the…
Helle W. van den Maagdenberg, Martin Šícho, David Alencar Araripe, Sohvi Luukkonen + 9 more
Building reliable and robust quantitative structure-property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous.…
Muhammad Aslam, Ali Hussein AL-Marshadi
In this paper, a new trimmed regression model under the neutrosophic environment is introduced. The mathematical model of the new regression model along with its neutrosophic form is given. The methods to find the error sum of square and trended values are also given. The trimmed neutrosophic correlation is also…
Shreeya Banerji
Diabetes mellitus is a growing problem, especially in developing countries. People suffering from diabetes have an increased risk of developing a number of serious health problems. Consistently high blood glucose levels can lead to serious diseases affecting the heart and blood vessels, eyes, kidney, etc. In addition…
Jeffrey A. Walker
Model-averaged partial regression coefficients have been criticized for averaging over a set of models with coefficients that have different meanings from model to model. This criticism arises because statisticians since Fisher believe that the meaning of a coefficient in a regression model arises from probabilistic…
Ramyaa Ramyaa, Omid Hosseini, Giri P Krishnan, Sridevi Krishnan
Nutritional phenotyping is a promising approach to achieve personalized nutrition. While conventional statistical approaches haven’t enabled personalizing well yet, machine-learning tools may offer solutions that haven’t been evaluated yet. The primary aim of this study was to use energy balance components – input…
Saer Samanipour, Jake O'Brien, Malcolm Reid, Kevin Thomas + 1 more
The European and US chemical agencies have listed approximately 800k chemicals where knowledge on potential risks to human health and the environment are lacking. Filling these data gaps experimentally is impossible so in-silico approaches and prediction are essential. Many existing models are however limited by…
Authors not listed
Inverse problems, where we seek the values of inputs to a model that lead to a desired set of outputs, are a challenges subset of problems in science and engineering. In this work we demonstrate the use of two generative AI methods to solve inverse problems. We compare this approach to two more conventional approaches…