Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques by Stephanie Ness, Vishwanath Eswarakrishnan, Harish Sridharan, Varun Shinde, Naga Venkata Prasad Janapareddy, Vineet Dhanawat

“…By comparing different algorithms, this research contributes to advancing the application of machine learning in network security, offering guidance on model selection and optimization for improved detection of cyber threats.…”
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Relationships between Loneliness and Occupational Dysfunction in Community-Dwelling Older Adults: A Cross-Sectional Study by Daiki Nakashima, Keisuke Fujii, Yuta Kubo, Kyosuke Yorozuya

“…Bayesian statistical modeling was used for a more stable estimation given the small sample. For model selection, we assumed a univariate analysis model of the CAOD (Model 1); a multivariate analysis model, including confounding factors in Model 1 (Model 2); and a multivariate analysis model, including random effects in Model 2 (Model 3). …”
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Integrated Multiscale Latent Variable Regression and Application to Distillation Columns by Muddu Madakyaru, Mohamed N. Nounou, Hazem N. Nounou

“…The idea behind the IMSLVR modeling algorithm is to filter the process data at different decomposition levels, model the filtered data from each level, and then select the LVR model that optimizes a model selection criterion. The performance of the developed IMSLVR algorithm is illustrated using three examples, one using synthetic data, one using simulated distillation column data, and one using experimental packed bed distillation column data. …”
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Impact of trainability on telomere dynamics of pet dogs (Canis lupus familiaris): An explorative study in aging dogs. by Julia Weixlbraun, Durga Chapagain, Jessica Svea Cornils, Steve Smith, Franz Schwarzenberger, Franz Hoelzl

“…The relative telomere length of 63 dogs was measured, using a qPCR method and a model selection approach was applied to assess which variables can explain the found telomere patterns. …”
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Structural Exploration and Conformational Transitions in MDM2 upon DHFR Interaction from Homo sapiens: A Computational Outlook for Malignancy via Epigenetic Disruption by Arundhati Banerjee, Sujay Ray

“…So, with a novel outlook, this study explores the molecular level demonstration of the best satisfactory MDM2 model selection after performing manifold modeling techniques. …”
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A Survey of Vehicle System and Energy Models by Lingyun Hua, Jian Tang, Guoming Zhu

“…This review aims to guide model selection and inspire future applications of energy consumption models for advancing sustainable automotive technologies.…”
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IAPCP: An Effective Cross-Project Defect Prediction Model via Intra-Domain Alignment and Programming-Based Distribution Adaptation by Nana Zhang, Kun Zhu, Dandan Zhu

“…The model does not require model selection and parameter tuning. Extensive experiments on a total of 82 cross-project pairs from 16 software projects demonstrate that IAPCP can achieve competitive CPDP effectiveness and efficiency compared with multiple state-of-the-art baseline models.…”
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Determinants of stock return in 10 biggest market capitalization on the indonesian stock exchange by Sri Yanthy Yosepha, Kampono Imam Yulianto, Zulfitra Zulfitra, Sahroni Sahroni, Luqman Hakim

“…Two research models are integrated into one and each goes through a model selection test stage, namely the Chow Test, Hausman Test, and Lagrange Multiplier Test using the eviews12 application. …”
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Enhancing Thermal-Hydraulic Modelling in Dual Fluid Reactor Demonstrator: The Impact of Variable Turbulent Prandtl Number by Hisham Elgendy, Sławomir Kubacki, Konrad Czerski

“…These insights underscore the importance of model selection in CFD analysis for DFRs, revealing potential hotspots and high turbulence areas that necessitate further investigation into vibration and structural safety. …”
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<italic>C&#x2099;</italic>&#x00B2; Modeling for Free-Space Optical Communications: A Review by Florian Quatresooz, Claude Oestges

“…Therefore, this work provides important insights into optical turbulence model selection, enabling accurate site characterization and informed optical terminal design.…”
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Application of the Bayesian Model Averaging in Analyzing Freeway Traffic Incident Clearance Time for Emergency Management by Yajie Zou, Bo Lin, Xiaoxue Yang, Lingtao Wu, Malik Muneeb Abid, Jinjun Tang

“…These methods commonly select a single “true” model among a majority of alternative models based on some model selection criteria. However, the conventional methods generally neglect the uncertainty related to the choice of models. …”
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High-performance traffic volume prediction: An evaluation of RNN, GRU, and CNN for accuracy and computational trade-offs by Pranolo Andri, Saifullah Shoffan, Bella Utama Agung, Wibawa Aji Prasetya, Bastian Muhammad, Hardiyanti P Cicin

“…These findings highlight crucial trade- offs between accuracy and efficiency, underscoring the need for model selection aligned with specific application requirements. …”
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Performance Analysis of Grid Topologies and RANS Turbulence Models in Predicting Aerodynamic Drag Coefficient at Zero-yaw for an Artillery Projectile by A. Ferfouri, D. D. Jerković, N. Hristov, A. V. Kari, T. Allouche

“…Furthermore, the findings indicate that the turbulence model selection impacts the zero-yaw drag prediction more than the grid topology, especially in the transonic and low supersonic cases.…”
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Integrating AutoGluon for Real-Time Monitoring and Classification of Dental Equipment Performance by Muxiu Yang, Fengzhou Li, Wenfeng Qiu

“…The intent is to enable predictive maintenance through data processing automation, model selection and performance classification. Using sensor data, such as temperature readings, vibration readings, and pressure readings from different dental tools, we trained machine learning models using AutoGluon. …”
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Predict Diabetes Using Voting Classifier and Hyper Tuning Technique by Chra Ali Kamal, Manal Ali Atiyah

“…In addition, this project methodology divided into two phases of model selection. In the first phase, two different hyper parameter techniques (Randomized Search and TPOT(autoML)) were used to increase the accuracy level for each algorithm. …”
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Predicting Crown-width of Dominant Trees on Teak Plantation from Clonal Seed Orchards in Ngawi Forest Management Unit, East Java by Ronggo Sadono

“…Non-linear regression analysis with the least squares method was used to evaluate 4 candidate models of average crown width, namely: Sigmoid, Power, Schumacher, and Gompertz models. The model selection was based on the highest coefficient of determination and the smallest standard error of the estimate and the significance of F test and T test. …”
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Enhancing Chronic Disease Prediction in IoMT-Enabled Healthcare 5.0 Using Deep Machine Learning: Alzheimer&#x2019;s Disease as a Case Study by Rabia Javed, Tahir Abbas, Tariq Shahzad, Khadija Kanwal, Sadaqat Ali Ramay, Muhammad Adnan Khan, Khmaies Ouahada

“…Our proposed model provides 96.06% accuracy, it advances our understanding of deep machine learning&#x2019;s potential for chronic disease prediction and emphasizes the need to tailor model selection to specific disease types using data from IoMT enabled devices. …”
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Uncertainty impact of isotherm models on liquid-phase adsorption thermodynamics: A bayesian inference perspective by Thiago Reschützegger, Nina Paula Gonçalves Salau

“…These findings highlight the need for uncertainty analysis in model selection and advise caution in attributing physical significance to isotherm-derived parameters. …”
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EMD-GM-ARMA Model for Mining Safety Production Situation Prediction by Menglong Wu, Yicheng Ye, Nanyan Hu, Qihu Wang, Huimin Jiang, Wen Li

“…In order to improve the prediction accuracy of mining safety production situation and remove the difficulty of model selection for nonstationary time series, a grey (GM) autoregressive moving average (ARMA) model based on the empirical mode decomposition (EMD) is proposed. …”
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Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review. by Rabia Asghar, Sanjay Kumar, Arslan Shaukat, Paul Hynds

“…To date however, no review of these techniques and their application(s) within the domain of white blood cell (WBC) classification in blood smear images has been undertaken, representing a notable knowledge gap with respect to model selection and comparison. Accordingly, the current study sought to comprehensively identify, explore and contrast ML and DL methods for classifying WBCs. …”
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