Enhancing Hazard Detection and Risk Severity Assessment in Construction through Multinomial Naive Bayes and Regression by Akaninyene Michael Akwaisua, Anietie Ekong, Godwin Ansa

“…Multinomial Naive Bayes is employed for hazard classification due to its efficacy in handling text data, and with it, an accuracy of 0.99 was obtained. Subsequently, the trained model was evaluated to assess its performance and the severity of identified hazards are evaluated. …”
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Computed tomography enterography radiomics and machine learning for identification of Crohn’s disease by Qiao Shi, Yajing Hao, Huixian Liu, Xiaoling Liu, Weiqiang Yan, Jun Mao, Bihong T. Chen

“…A machine learning classification system was constructed by combining six selected radiomics features with eight classification algorithms. The models were trained using leave-one-out cross-validation and evaluated for accuracy. …”
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Improving Mental Health Diagnosis with Hybrid Ensemble Models: A Data-Driven Approach by Malave Sachin, Khemani Bharti, Kelkar Rucha, Balekundri Urvi, Bogar Shravani, Kolekar Areen

“…This study examines how emotional and behavioural indicators might be used to predict mental health issues using machine learning (ML) algorithms. A mental health dataset was used to train and assess a number of machines learning models, including Logistic Regression, K-Nearest Neighbours, Decision Tree, Random Forest, Gradient Boosting, XGBoost, and a Hybrid ensemble model. …”
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In-Process Monitoring of Inhomogeneous Material Characteristics Based on Machine Learning for Future Application in Additive Manufacturing by André Jaquemod, Marijana Palalić, Kamil Güzel, Hans-Christian Möhring

“…The algorithms are trained to recognize patterns, anomalies, or deviations from expected behavior, which can aid in evaluating the effect of detected defects on the machining process and the resultant component quality. …”
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The influence of Gen-AI tools application for text data augmentation: case of Lithuanian educational context data classification by Pavel Stefanovič, Urtė Radvilaitė, Birutė Pliuskuvienė, Simona Ramanauskaitė

“…All subsets were used to train several machine-learning algorithms. Additionally, the text has been processed into numerical data using two methods: bag-of-words and sBERT. …”
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Patch-Based Oil Painting Forgery Detection Based on Brushstroke Analysis Using Generative Adversarial Networks and Depth Visualization by Elhamsadat Azimi, Amirsaman Ashtari, Jaehong Ahn

“…Recently, computer vision algorithms have shown promise in image processing tasks; however, creating an automated model for painting authentication remains a challenge in art preservation and history. …”
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Constructing a Classification Model for Cervical Cancer Tumor Tissue and Normal Tissue Based on CT Radiomics by Jinghong Pei BD, Jing Yu BD, Ping Ge BD, Liman Bao BD, Haowen Pang MS, Huaiwen Zhang MS

“…To bolster model's predictive capability, the data was stratified into train data (70%) and validation data (30%). During feature selection phase, we applied Least Absolute Shrinkage and Selection Operator regression algorithm to identify most relevant features. …”
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Probabilistic regression for autonomous terrain relative navigation via multi-modal feature learning by Ickbum Kim, Sandeep Singh

“…Due to the expectations regarding novel algorithms in the context of real missions, the proposed approaches must be rigorously evaluated in extraneous scenarios and demonstrate sufficient robustness. …”
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A novel machine learning model for perimeter intrusion detection using intrusion image dataset. by Shahneela Pitafi, Toni Anwar, I Dewa Made Widia, Zubair Sharif, Boonsit Yimwadsana

“…The effectiveness of the proposed model is evaluated by comparing it to state-of-the-art techniques found in the literature. …”
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Prediction of early postoperative complications and transfusion risk after lumbar spinal stenosis surgery in geriatric patients: machine learning approach based on comprehensive ge... by Wounsuk Rhee, Sam Yeol Chang, Bong-Soon Chang, Hyoungmin Kim

“…A set of Compact models incorporating a smaller number of features was also trained, and SHAP analysis was conducted to evaluate the models’ interpretability. …”
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Prediction of COD Degradation in Fenton Oxidation Treatment of Kitchen Anaerobic Wastewater Based on IPSO-BP Neural Network by Tianpeng Zhang, Pengfei Ji, Dayong Tian, Rui Xu

“…H2O2 dosage, Fe2+ dosage, reaction time and pH value are selected as the main influencing factors of the COD degradation, and 30 groups of experimental data are selected to train the IPSO-BP neural network. The results predicted by the trained IPSO-BP neural network on 10 groups of test data are compared with the actual values, and the results predicted by BP model and genetic algorithm-BP (GA-BP) model are compared. …”
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Predicting the Open Porosity of Industrial Mortar Applied on Different Substrates: A Machine Learning Approach by Rafael Travincas, Maria Paula Mendes, Isabel Torres, Inês Flores-Colen

“…This database was then used to train and test the machine learning algorithms to predict the open porosity of the mortar. …”
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Assessment of Machine Learning Methods for Concrete Compressive Strength Prediction by Oluwafemi Omotayo, Chinwuba Arum, Catherine Ikumapayi

“…In order to focus on the prediction of concrete without any pozzolanic content, the data points containing pozzolans were dropped, leaving 526 data points which were trained and tested on the selected ML algorithms. …”
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Deep Learning for Ultrasonographic Assessment of Temporomandibular Joint Morphology by Julia Lasek, Karolina Nurzynska, Adam Piórkowski, Michał Strzelecki, Rafał Obuchowicz

“…State-of-the-art architectures were tested, and the best-performing 2D Residual U-Net was trained and validated against expert annotations. …”
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A comparative analysis of variants of machine learning and time series models in predicting women’s participation in the labor force by Rasha Elstohy, Nevein Aneis, Eman Mounir Ali

“…The dataset was then trained, tested, and evaluated. For performance validation, forecasting accuracy metrics were constructed using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), R-squared (R2), and cross-validated root mean squared error (CVRMSE). …”
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Prediction of electricity production by small wind power using artificial neural networks by Justyna Zalewska-Lesiak, Mateusz Oszczypała, Jerzy Małachowski

“…In this article, an artificial neural network method is used to evaluate the forecasting of wind energy production from a small wind turbine (SWT) installed in central Poland, reflecting inland wind conditions.MethodsA comprehensive set of algorithms and results from simulations are presented. …”
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Automated interpretation of influenza hemagglutination inhibition (HAI) assays: Is plate tilting necessary? by Garrett Wilson, Zhiping Ye, Hang Xie, Steven Vahl, Erica Dawson, Kathy Rowlen

“…The hemagglutination inhibition assay (HAI) is widely used to evaluate vaccine-induced antibody responses as well as to antigenically characterize influenza viruses. …”
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A machine learning-based recommendation framework for material extrusion fabricated triply periodic minimal surface lattice structures by Sajjad Hussain, Carman Ka Man Lee, Yung Po Tsang, Saad Waqar

“…This dataset was used to train both ML and DL algorithms. ML algorithms included Bayesian regression (BR), K-nearest neighbors (KNN), Random Forest (RF), Decision Tree (DT), and DL algorithm convolutional neural network (CNN). …”
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Construction of a Real-Time Detection for Floating Plastics in a Stream Using Video Cameras and Deep Learning by Hankyu Lee, Seohyun Byeon, Jin Hwi Kim, Jae-Ki Shin, Yongeun Park

“…Among the various YOLOv8 algorithms, YOLOv8-nano was selected to evaluate its practical applicability in real-time detection and portability. …”
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The DRAGON benchmark for clinical NLP by Joeran S. Bosma, Koen Dercksen, Luc Builtjes, Romain André, Christian Roest, Stefan J. Fransen, Constant R. Noordman, Mar Navarro-Padilla, Judith Lefkes, Natália Alves, Max J. J. de Grauw, Leander van Eekelen, Joey M. A. Spronck, Megan Schuurmans, Bram de Wilde, Ward Hendrix, Witali Aswolinskiy, Anindo Saha, Jasper J. Twilt, Daan Geijs, Jeroen Veltman, Derya Yakar, Maarten de Rooij, Francesco Ciompi, Alessa Hering, Jeroen Geerdink, Henkjan Huisman, On behalf of the DRAGON consortium

“…Abstract Artificial Intelligence can mitigate the global shortage of medical diagnostic personnel but requires large-scale annotated datasets to train clinical algorithms. Natural Language Processing (NLP), including Large Language Models (LLMs), shows great potential for annotating clinical data to facilitate algorithm development but remains underexplored due to a lack of public benchmarks. …”
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