Browsing by Subject "Predictive models"

Now showing 1 - 7 of 7
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    A Novel Framework of Developing a Predictive Model for Powder Bed Fusion Process
    (Mary Ann Liebert, 2024) Marrey, Mallikharjun; Malekipour, Ehsan; El-Mounayri, Hazim; Faierson, Eric J.; Agarwal, Mangilal; Mechanical and Energy Engineering, Purdue School of Engineering and Technology
    The powder bed fusion (PBF) process is a metal additive manufacturing process, which can build parts with any complexity from a wide range of metallic materials. PBF process research has predominantly focused on the impact of only a few parameters on product properties due to the lack of a systematic approach for predictive modeling of a large set of process parameters simultaneously. The pivotal challenges regarding this process require a quantitative approach for mapping the material properties and process parameters onto the ultimate quality; this will then enable the optimization of those parameters. In this study, we propose a two-phase framework for studying the process parameters and developing a predictive model for 316L stainless steel material. We also discuss the correlation between process parameters that is, laser specifications and mechanical properties, and how to obtain an optimum range of volumetric energy density for producing parts with high density (>99%), as well as better ultimate mechanical properties. In this article, we introduce and test an innovative approach for developing AM predictive models, with a relatively low error percentage (i.e., around 10%), which are used for process parameter selection in accordance with user or manufacturer part performance requirements. These models are based on techniques such as support vector regression, random forest regression, and neural network. It is shown that the intelligent selection of process parameters using these models can achieve a high density of up to 99.31% with uniform microstructure, which improves hardness, impact strength, and other mechanical properties.
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    Adversarial Attacks on Deep Temporal Point Process
    (IEEE, 2022) Khorshidi, Samira; Wang, Bao; Mohler, George; Computer Science, Luddy School of Informatics, Computing, and Engineering
    Temporal point processes have many applications, from crime forecasting to modeling earthquake aftershocks sequences. Due to the flexibility and expressiveness of deep learning, neural network-based approaches have recently shown promise for modeling point process intensities. However, there is a lack of research on the robustness of such models in regards to adversarial attacks and natural shocks to systems. Precisely, while neural point processes may outperform simpler parametric models on in-sample tests, how these models perform when encountering adversarial examples or sharp non-stationary trends remains unknown. Current work proposes several white-box and blackbox adversarial attacks against temporal point processes modeled by deep neural networks. Extensive experiments confirm that predictive performance and parametric modeling of neural point processes are vulnerable to adversarial attacks. Additionally, we evaluate the vulnerability and performance of these models in the presence of non-stationary abrupt changes, using the crimes dataset, during the Covid-19 pandemic, as an example.
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    Improved Adverse Drug Event Prediction Through Information Component Guided Pharmacological Network Model (IC-PNM)
    (IEEE, 2021) Ji, Xiangmin; Wang, Lei; Hua, Liyan; Wang, Xueying; Zhang, Pengyue; Shendre, Aditi; Feng, Weixing; Li, Jin; Li, Lang; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Improving adverse drug event (ADE) prediction is highly critical in pharmacovigilance research. We propose a novel information component guided pharmacological network model (IC-PNM) to predict drug-ADE signals. This new method combines the pharmacological network model and information component, a Bayes statistics method. We use 33,947 drug-ADE pairs from the FDA Adverse Event Reporting System (FAERS) 2010 data as the training data, and the new 21,065 drug-ADE pairs from FAERS 2011-2015 as the validations samples. The IC-PNM data analysis suggests that both large and small sample size drug-ADE pairs are needed in training the predictive model for its prediction performance to reach an area under the receiver operating characteristic curve (\textAUROC)= 0.82(AUROC)=0.82. On the other hand, the IC-PNM prediction performance improved to \textAUROC= 0.91AUROC=0.91 if we removed the small sample size drug-ADE pairs from the prediction model during validation.
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    Pandemic-Aware Day-Ahead Demand Forecasting Using Ensemble Learning
    (IEEE, 2022) Arjomandi-Nezhad, Ali; Ahmadi, Amirhossein; Taheri, Saman; Fotuhi-Firuzabad, Mahmud; Moeini-Aghtaie, Moein; Lehtonen, Matti; Mechanical and Energy Engineering, Purdue School of Engineering and Technology
    Electricity demand forecast is necessary for power systems’ operation scheduling and management. However, power consumption is uncertain and depends on several factors. Moreover, since the onset of covid-19, the electricity consumption pattern went through significant changes across the globe, which made the forecasting demand more challenging. This is mainly due to the fact that pandemic-driven restrictions changed people’s lifestyles and work activities. This calls for new forecasting algorithms to more effectively handle these conditions. In this paper, ensemble-based machine learning models are utilized for this task. The lockdown temporal policies are added to the feature set in order to make the model capable of correcting itself in pandemic situations and enhance data quality for the forecasting task. Several ensemble-based machine learning models are examined for the short-term country-level demand prediction model. Besides, the quantile random forest regression is implemented for a probabilistic point of view. For case studies, the models are trained for predicting Germany’s country-level demand. The results indicate that ensemble models, especially boosting and bagging-boosting models, are capable of accurate country-level demand forecast. Besides, the majority of these models are robust against missing the pandemic policy data. However, utilizing the pandemic policy data as features increases the forecasting accuracy during the pandemic situation significantly. Furthermore, the probabilistic quantile regression demonstrated high accuracy for the aforementioned case study.
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    Predicting Opioid Prescriptions based on Patient Demographics in MIMIC-IV
    (IEEE Xplore, 2021-06) Kodela, Snigdha; Pinnamraju, Jahnavi; Gichoya, Judy W.; Purkayastha, Saptarshi; Biohealth Informatics, School of Engineering and Technology
    Opioids are widely used analgesics because of their efficacy, mild sedative and anxiolytic properties, and flexibility to administer through multiple routes. Understanding the demographics of the patients receiving these medications helps provide customized care for the susceptible group of people. We conducted a demographic evaluation of the frequently prescribed opioid drug prescriptions from the MIMIC IV database. We analyzed prescribing patterns of six commonly used opioids with demographics such as age, gender, ethnicity, marital status, and year predominantly. After conducting exploratory data analysis, we built models using Logistic Regression, Random Forest, and XGBoost to predict opioid prescriptions and demographics for those. We also analyzed the association between demographics and the frequency of prescribed medications for pain management. We found statistically significant differences in opioid prescriptions among the male and female population, married and unmarried, various ages, ethnic groups, and an association with in-hospital deaths.
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    Robustness Improvement of Computationally Efficient Cooperative Fuzzy Model Predictive-Integral Sliding Mode Control of Nonlinear Systems
    (IEEE, 2021) Farbood, Mohsen; Veysi, Mohammad; Shasadeghi, Mokhtar; Izadian, Afshin; Niknam, Taher; Aghaei, Jamshid; Engineering Technology, Purdue School of Engineering and Technology
    This paper introduces a systematic and comprehensive method to design a constrained fuzzy model predictive control (MPC) cooperated with integral sliding mode control (ISMC) based on the Takagi-Sugeno (T-S) fuzzy model for uncertain continuous-time nonlinear systems subject to external disturbances. The proposed controller benefits from the robustness, optimality, and practical constraints considerations. The robustness against the uncertainties and matched external disturbances is achieved by the proposed ISMC without iterative calculation for obtaining the robust invariant set. The MPC schemes are designed separately based on the both quadratic and non-quadratic Lyapunov functions. By the proposed MPC, the states of the system reach the desired values in the optimal, constrained, and robust manner against the unmatched external disturbances. New linear matrix inequalities (LMIs) conditions are proposed to design both the proposed MPC schemes. Also, the practical constraints on the control signals are guaranteed in the design procedure based on the invariant ellipsoid set. To evaluate the effectiveness of the suggested strategy, some simulation and experimental tests were run.
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    Sparse Multi-Task Regression and Feature Selection to Identify Brain Imaging Predictors for Memory Performance
    (IEEE, 2011) Wang, Hua; Nie, Feiping; Huang, Heng; Risacher, Shannon; Ding, Chris; Saykin, Andrew J.; Shen, Li; ADNI; Radiology and Imaging Sciences, School of Medicine
    Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions, which makes regression analysis a suitable model to study whether neuroimaging measures can help predict memory performance and track the progression of AD. Existing memory performance prediction methods via regression, however, do not take into account either the interconnected structures within imaging data or those among memory scores, which inevitably restricts their predictive capabilities. To bridge this gap, we propose a novel Sparse Multi-tAsk Regression and feaTure selection (SMART) method to jointly analyze all the imaging and clinical data under a single regression framework and with shared underlying sparse representations. Two convex regularizations are combined and used in the model to enable sparsity as well as facilitate multi-task learning. The effectiveness of the proposed method is demonstrated by both clearly improved prediction performances in all empirical test cases and a compact set of selected RAVLT-relevant MRI predictors that accord with prior studies.