Predictive Modeling of Heart Failure Outcomes Using ECG Monitoring Indicators and Machine Learning by Jia Liu, Dan Zhu, Lingzhi Deng, Xiaoliang Chen

“…Records were randomly divided into training (70%, n = 742) and test (30%, n = 319) sets. …”
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Lightweight hybrid transformers-based dyslexia detection using cross-modality data by Abdul Rahaman Wahab Sait, Yazeed Alkhurayyif

“…We enhance Dartbooster XGBoost (DXB)-based classification using Bayesian optimization with Hyperband (BOHB) algorithm. In order to reduce computational overhead, we employ a quantization-aware training technique. …”
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Design and Analysis of a Serial Manipulator for Pick and Drop Objects for Material Handling at Uiri Metal Forming Workshop. by Behangana, Abert

“…Training programs for operators will also be developed to enhance usability and ensure safe operation. …”
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Neural networking analysis of thermally magnetized mass transfer coefficient (MTC) for Carreau fluid flow: A comparative study by Khalil Ur Rehman, Wasfi Shatanawi, Zeeshan Asghar, A.R.M. Kasim

“…Owing to 10 neurons in the hidden layer, the network is trained by the Levenberg-Marquardt algorithm. It is found that the mass transfer rate exhibits a direct relation with the Schmidt number and chemical reaction parameter. …”
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Enhancement of joint quality for laser welded dissimilar material cell-to-busbar joints using meta model-based multi-objective optimization by Andreas Andersson Lassila, Tobias Andersson, Rohollah Ghasemi, Dan Lönn

“…Artificial neural network-based meta models, trained on numerical results from computational fluid dynamics simulations of the laser welding process, are used to predict and evaluate the joint quality. …”
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Advances in Aircraft Skin Defect Detection Using Computer Vision: A Survey and Comparison of YOLOv9 and RT-DETR Performance by Nutchanon Suvittawat, Christian Kurniawan, Jetanat Datephanyawat, Jordan Tay, Zhihao Liu, De Wen Soh, Nuno Antunes Ribeiro

“…Beyond a detailed review, we experimentally evaluate the accuracy and feasibility of existing low-cost, easily deployable hardware (drone) and software solutions (computer vision algorithms). …”
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Diet Engine: A real-time food nutrition assistant system for personalized dietary guidance by Asim Moin Saad, Md. Raihanul Haque Rahi, Md. Manirul Islam, Gulam Rabbani

“…The system employs a client-server architecture, using advanced deep learning techniques like YOLOv8 (You Only Look Once version 8) and Convolutional Neural Networks (CNNs) optimized for real-time object detection with 295 layers, for training and processing image requests. Our system outperforms existing algorithms, achieving an 86 % classification accuracy on food datasets. …”
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Do We Need to Add the Type of Treatment Planning System, Dose Calculation Grid Size, and CT Density Curve to Predictive Models? by Reza Reiazi, Surendra Prajapati, Leonardo Che Fru, Dongyeon Lee, Mohammad Salehpour

“…<b>Methods:</b> This study evaluated dose calculation differences in the head and neck cancer treatment plans of 19 patients using two treatment planning systems, Pinnacle 9.10 and RayStation 11, with similar dose calculation algorithms. …”
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An improved deep learning approach for automated detection of multiclass eye diseases by Feudjio Ghislain, Saha Tchinda Beaudelaire, Romain Atangana, Tchiotsop Daniel

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Non-Destructive Detection of Current Internal Disorders and Prediction of Future Appearance in Mango Fruit Using Portable Vis-NIR Spectroscopy by Jasciane da Silva Alves, Bruna Parente de Carvalho Pires, Luana Ferreira dos Santos, Tiffany da Silva Ribeiro, Kerry Brian Walsh, Ederson Akio Kido, Sergio Tonetto de Freitas

“…After spectra were acquired of the stored fruit, the fruit cheeks were cut longitudinally to allow visual assessment of the incidence of the internal disorders. Five models were evaluated: two tree-based algorithms (J48 and random forest), one neural network (multilayer perceptron, MLP), and two SVM training algorithms (sequential minimal optimization, SMO, and LibSVM). …”
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Construction and validation of a readmission risk prediction model for elderly patients with coronary heart disease by Hanyu Luo, Benlong Wang, Rui Cao, Jun Feng

“…XGBoost, LR, RF, KNN and DT algorithms were used to build prediction models for readmission risk. …”
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A Study of a Drawing Exactness Assessment Method Using Localized Normalized Cross-Correlations in a Portrait Drawing Learning Assistant System by Yue Zhang, Zitong Kong, Nobuo Funabiki, Chen-Chien Hsu

“…The main finding of this research is that the implementation of the <i>NCC</i> algorithm within the <i>PDLAS</i> significantly enhances the accuracy of novice portrait drawings by providing detailed feedback on specific facial features, proving the system’s efficacy in art education and training.…”
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An integrated machine learning framework for developing and validating diagnostic models and drug predictions based on ulcerative colitis genes by Na An, Zhongwen Lu, Yang Li, Bing Yang, Shaozhen Ji, Xu Dong, Zhaoliang Ding

“…To build a diagnostic model for UC, we applied 113 combinations of 12 machine learning algorithms. This included 10-fold cross-validation on the training set and external validation on the test set. …”
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Predicting Neoplastic Polyp in Patients With Gallbladder Polyps Using Interpretable Machine Learning Models: Retrospective Cohort Study by Zhaobin He, Shengbiao Yang, Jianqiang Cao, Huijie Gao, Cheng Peng

“…This study employed nine ML algorithms to construct predictive models. Subsequently, model performance was evaluated and compared using several metrics, including the area under the receiver operating characteristic curve (AUC). …”
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Explainable Machine Learning for Efficient Diabetes Prediction Using Hyperparameter Tuning, SHAP Analysis, Partial Dependency, and LIME by Md. Manowarul Islam, Habibur Rahman Rifat, Md. Shamim Bin Shahid, Arnisha Akhter, Md Ashraf Uddin, Khandaker Mohammad Mohi Uddin

“…We evaluate the proposed model using two datasets and performance metrics. …”
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Out-of-hospital multimodal seizure detection: a pilot study by Martin Ballegaard, Troels Wesenberg Kjær, Ivan Chrilles Zibrandtsen, Jonas Munch Nielsen, Ástrós Eir Kristinsdóttir, Paolo Masulli, Tobias Søren Andersen

“…In one patient, we identified 15 electrographic focal impaired awareness seizures with a motor component. After training our algorithm on in-patient data, we found a sensitivity of 91% and a false alarm rate (FAR) of 18/24 hours for the detection of out-of-hospital seizures using a combination of EEG and ECG recordings. …”
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Noninvasive prediction of failure of the conservative treatment in lateral epicondylitis by clinicoradiological features and elbow MRI radiomics based on interpretable machine lear... by Jianing Cui, Ping Wang, Xiaodong Zhang, Ping Zhang, Yuming Yin, Rongjie Bai

“…Seven machine learning algorithms were evaluated to determine the optimal model for predicting the failure of conservative treatment. …”
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Early prediction of colorectal adenoma risk: leveraging large-language model for clinical electronic medical record data by Xiaoyu Yang, Jinjian Xu, Hong Ji, Jun Li, Bingqing Yang, Liye Wang

“…Area under the receiver operating characteristic curve (AUC) is the major metric for evaluating model performance. The Shapley additive explanations (SHAP) method was employed to identify the most influential risk factors.ResultsXGBoost algorithm, integrated with BGE-M3, demonstrated superior performance (AUC = 0.9847) in the validation cohort. …”
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Real-Time Estimation Method of P-Wave Slowness Based on Kalman Filtering and STC by SUN Hao, CHEN Wenhui, SUN Xuekai, YU Weidong, LI Siyi, ZHAO Peng

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Preoperative diagnosis of meningioma sinus invasion based on MRI radiomics and deep learning: a multicenter study by Yuan Gui, Wei Hu, Jialiang Ren, Fuqiang Tang, Limei Wang, Fang Zhang, Jing Zhang

“…Finally, diagnosis models were constructed using the random forest (RF) algorithm. Additionally, the diagnostic performance of different models was evaluated using receiver operating characteristic (ROC) curves, and AUC values of different models were compared using the DeLong test. …”
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