Utilizing Statistical Tests for Comparing Machine Learning Algorithms by Hozan Khalid Hamarashid

“…The output of several machine learning algorithms or simulation pipelines is compared during model selection. The model that performs the best based on your performance measure becomes the last model, which can be utilized to make predictions on new data. …”
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Monitoring Murder Crime in Namibia Using Bayesian Space-Time Models by Isak Neema, Dankmar Böhning

“…The posterior mean estimate of the parameters from the model using DIC as model selection criteria show that most of the variation in the relative risk of murder is due to regional clustering, while the effect of population density and time was insignificant. …”
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Gaussian Covariance Faithful Markov Trees by Dhafer Malouche, Bala Rajaratnam

“…Faithfulness however is crucial, for instance, in model selection procedures that proceed by testing conditional independences. …”
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Modelling Soil Water Retention for Weed Seed Germination Sensitivity to Water Potential by W. John Bullied, Paul R. Bullock, Rene C. Van Acker

“…The Tani and Russo models overestimated water retention at a potential less than −0.1 MPa for all hillslope positions. Model selection and residual parameter specification are important for weed seed germination by representing water retention at the level of minimum threshold water potential for germination. …”
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Applied Comparison of Meta-analysis Techniques by Li Wang, Colin Lewis-Beck, Elyse Fritschel, Erdem Baser, Onur Baser

“…However, proper study and model selection are important to ensure the correct interpretation of results. …”
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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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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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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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