Survival analysis using machine learning in transplantation: a practical introduction by Andrea Garcia-Lopez, Maritza Jiménez-Gómez, Andrea Gomez-Montero, Juan Camilo Gonzalez-Sierra, Santiago Cabas, Fernando Giron-Luque

“…The data were split into training, validation, and test sets using split sample (70%-30%) and cross-validation (5-folds) techniques to evaluate model performance. …”
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Interpretable machine learning modeling of temperature rise in a medium voltage switchgear using multiphysics CFD analysis by Mahmood Matin, Amir Dehghanian, Amir Hossein Zeinaddini, Hossein Darijani

“…Artificial intelligence (AI) algorithms have gained attention in various engineering fields to address similar issues. …”
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SPARQ: Efficient Entanglement Distribution and Routing in Space–Air–Ground Quantum Networks by Mohamed Shaban, Muhammad Ismail, Walid Saad

“…The results also show that the proposed DQN algorithm improves the number of resolved teleportation requests by 39% compared with shortest path baseline and the entanglement fidelity by 2% compared with an RL algorithm that is based on long short-term memory. …”
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An explainable AI-based approach for predicting undergraduate students academic performance by Fatema Tuz Johora, Md Nahid Hasan, Aditya Rajbongshi, Md Ashrafuzzaman, Farzana Akter

“…This classifier outperformed the machine learning classifiers based on the four performance evaluation metrics. Two eXplainable Artificial Intelligence (XAI) algorithms, namely SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), were integrated to provide a comprehensible prediction of the best model and determine the significant factors. …”
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Research on Data-Driven Optimal Operation of Slurry Circulation Pump by Zunyi XU, Wenhui LIU, Xuran ZHANG, Haiyan ZHANG

“…Specifically, the fuzzy C-means clustering algorithm and the combinatory evaluation method based on the weighted fusion of analytic hierarchy process and entropy weight method are applied to automatically search the optimal historical operation of the slurry circulation pump under different operating conditions, such that the SVM classifier is trained with the best historical operation record. …”
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Hyperparameters optimization of evolving spiking neural network using artificial bee colony for unsupervised anomaly detection by Rehan Rabie, Sahran Shahnorbanun, Alyasseri Zaid Abdi Alkareem, Sani Nor Samsiah, Al-Betar Mohammed Azmi

“…Further validation was provided by comparing the proposed OeSNN-ABC against five well-known optimization algorithms: particle swarm optimization, grey wolf optimization, flower pollination algorithm, whale optimization algorithm, and grid search, alongside other classifiers such as random forest, support vector machine, and k-nearest neighbor. …”
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USE OF NONSTEROIDAL ANTI-INFLAMMATORY DRUGS FOR THE TREATMENT OF AXIAL SPONDYLOARTHRITIS, INCLUDING ANKYLOSING SPONDYLITIS, MONITORING THEIR EFFICACY AND SAFETY (DRAFT GUIDELINES O... by I. Z. Gaidukova, A. P. Rebrov, E. N. Otteva, V. V. Badokin, A. G. Bochkova, O. V. Bugrova, A. A. Godzenko, A. A. Dubikov, T. V. Dubinina, O. N. Ivanova, T. V. Korotaeva, S. A. Lapshina, O. B. Nesmeyanova, I. P. Nikishina, T. A. Raskina, O. A. Rumyantseva, A. V. Smirnov, A. V. Sitalo, Sh. F. Erdes

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Machine learning model for preoperative classification of stromal subtypes in salivary gland pleomorphic adenoma based on ultrasound histogram analysis by Huan-Zhong Su, Dao-Hui Yang, Long-Cheng Hong, Yu-Hui Wu, Kun Yu, Zuo-Bing Zhang, Xiao-Dong Zhang

“…The least absolute shrinkage and selection operator (LASSO) regression identified optimal features, which were then utilized to build predictive models using logistic regression (LR) and eight ML algorithms. The effectiveness of the models was evaluated using a range of performance metrics, with a particular focus on the area under the receiver operating characteristic curve (AUC). …”
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A Novel Partitioned Random Forest Method-Based Facial Emotion Recognition by Hanif Heidari, M. Murugappan, Javeed Shaikh-Mohammed, Muhammad E. H. Chowdhury

“…Using three statistical measures Lyapunov exponents (LE), Correlation Dimension (CD), and approximate entropy (AE), we evaluated the performance of machine learning algorithms over different data lengths. …”
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MRI Delta-Radiomics and Morphological Feature-Driven TabPFN Model for Preoperative Prediction of Lymphovascular Invasion in Invasive Breast Cancer by Yunhua Li BS, Jianfeng Yang MS, Pan Xiao MS, Haibo Liu BS, Yingjun Zhou BS, Xiuqi Yang MS, Gangwen Chen BS, Zhichao Zuo PhD

“…Results The delta-radiomics model achieved an area under the curve (AUC) of 0.775. Among the evaluated machine learning models, the TabPFN algorithm demonstrated superior performance by effectively integrating the Radscore along with clinical and MR morphological features, resulting in an AUC of 0.899. …”
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Machine learning-based construction of Immunogenic cell death-related score for improving prognosis and personalized treatment in glioma by Guoyin Li, Yukui Zhao, Yubo He, Zhaoqiang Qian, Yiwen Liu, Xiaoyan Li, Lili Li, Zhiqiang Liu

“…CIBERSORT and ESTIMATE algorithms were employed to evaluate the infiltration of immune cells. …”
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Radiomics Analysis of Whole-Kidney Non-Contrast CT for Early Identification of Chronic Kidney Disease Stages 1–3 by Guirong Zhang, Pan Zhang, Yuwei Xia, Feng Shi, Yuelang Zhang, Dun Ding

“…Key features were selected through Relief, MRMR, and LASSO regression algorithms. A machine learning classifier was trained to differentiate CKD from healthy kidneys and compared with the radiologist assessments. …”
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Distributed Compensation With Parallelizability for Polarization Mode Dispersion Based on Learned Digital Backpropagation by Daobin Wang, Guangfu Li, Hui Yang, Wei Li, Ruiyang Xia, Chengqi Duan, Jianming Shang, Zanshan Zhao, Guanjun Gao

“…The proposed algorithm’s nonlinear compensation (NLC) performance is evaluated using numerical experiments on a 1,000-kilometer standard single-mode fiber link. …”
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NeuroMorse: a temporally structured dataset for neuromorphic computing by Ben Walters, Yeshwanth Bethi, Taylor Kergan, Binh Nguyen, Amirali Amirsoleimani, Jason K Eshraghian, Saeed Afshar, Mostafa Rahimi Azghadi

“…This gap may lead to evaluations that fail to fully capture the unique strengths and characteristics of neuromorphic systems. …”
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Artificial bee colony optimized random forest model for prediction of fly ash concrete compressive strength by Manish Bali, Ved Prakash Mishra, Anuradha Yenkikar

“…The method involves: • Experimental dataset generated from 168 fly ash concrete mix designs with varied input proportions. • Five regression models trained and evaluated using RMSE, MAE, and R2. • Hyperparameters of the RF model optimized using the Artificial Bee Colony algorithm.…”
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Development of a Low-Cost Sensor System for Accurate Soil Assessment and Biological Activity Profiling by Antonio Ruiz-Gonzalez, Harriet Kempson, Jim Haseloff

“…Sensing data were obtained from 31 variables, including 18 individual light wavelengths that could be used to determine seed germination rates of tomato plants. A machine learning algorithm was trained using the data obtained by characterising 75 different soil samples. …”
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Leveraging Multilingual Transformer for Multiclass Sentiment Analysis in Code-Mixed Data of Low-Resource Languages by Muhammad Kashif Nazir, Cm Nadeem Faisal, Muhammad Asif Habib, Haseeb Ahmad

“…The evaluation results showed a significant improvement in accuracy (+22.55%), precision (+21.06%), recall (+22.55%), and F-measure (+25.50%) compared to benchmark algorithms. …”
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A machine learning-based approach to predict depression in Chinese older adults with subjective cognitive decline: a longitudinal study by Jing Xu, Wenjin Zhang, Wenli Liu

“…Lasso feature selection identified 10 key variables for model training. Model performance was evaluated using ROC curves, AUC, sensitivity, specificity, accuracy, calibration, and decision curve analysis. …”
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Real-Time Posture Correction of Squat Exercise: A Deep Learning Approach for Performance Analysis and Error Correction by Prashant Rao, C. S. Asha, P. Raghavendra Rao

“…However, many individuals cannot afford professional training due to high costs, necessitating an automated workout analysis system. …”
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IoT Based Health Monitoring with Diet, Exercise and Calories recommendation Using Machine Learning by Muhammad Hassaan Naveed, Omar Bin Samin, Muhammad Bilal, Mustehsum Waseem

“…The study’s method ology includes utilizing a comprehensive dataset from Kaggle, separated into sets for testing and training, to develop and evaluate machine learning models. …”
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