Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinoma by LI Chaoqun, ZHENG Hongliang, HUANG Ping

“…A prognostic model of CARGs was constructed by incorporating 12 machine learning algorithms, and univariate and multivariate Cox regression analyses were applied to evaluate whether the 12 models as an independent prognostic factor or not. …”
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Ensemble Machine Learning Classifiers Combining CT Radiomics and Clinical-Radiological Features for Preoperative Prediction of Pathological Invasiveness in Lung Adenocarcinoma Pres... by Yunhua Li BS, Jianbang Ding MS, Kun Wu MS, Wanyin Qi BS, Shanyue Lin BS, Gangwen Chen BS, Zhichao Zuo PhD

“…Through rigorous feature engineering, we constructed a radiomic score using least absolute shrinkage and selection operator regression. We systematically evaluated both single-algorithm classifiers and ensemble approaches (including hard/soft voting and stacking), incorporating both the radiomic score and clinical-radiological features. …”
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Real-Time Cloth Simulation in Extended Reality: Comparative Study Between Unity Cloth Model and Position-Based Dynamics Model with GPU by Taeheon Kim, Jun Ma, Min Hong

“…To overcome the limitations of traditional CPU-based physics simulations, we designed and optimized highly parallelized algorithms utilizing Unity’s Compute Shader framework. …”
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Non-destructive assessment of hemp seed vigor using machine learning and deep learning models with hyperspectral imaging by Damrongvudhi Onwimol, Pongsan Chakranon, Kris Wonggasem, Papis Wongchaisuwat

“…To simplify the analysis and reduce computational complexity, a subset of key spectral wavelengths was selected using a successive projection algorithm. Deep learning models were trained on these selected wavelengths to directly learn patterns from the raw spectral data. …”
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Classification of Continuous Sky Brightness Data Using Random Forest by Rhorom Priyatikanto, Lidia Mayangsari, Rudi A. Prihandoko, Agustinus G. Admiranto

“…This study aims to develop a classification model based on Random Forest algorithm and to evaluate its performance. Using sky brightness data from 1250 nights with minute temporal resolution acquired at eight different stations in Indonesia, datasets consisting of 15 features were created to train and test the model. …”
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Machine learning frameworks to accurately predict coke reactivity index by Ayat Hussein Adhab, Morug Salih Mahdi, Krunal Vaghela, Anupam Yadav, Jayaprakash B, Mayank Kundlas, Ankur Srivastava, Jayant Jagtap, Aseel Salah Mansoor, Usama Kadem Radi, Nasr Saadoun Abd, Samim Sherzod

“…To minimize overfitting in each algorithm, K-fold cross-validation methodology is employed during the training phase. …”
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Predictive estimations of health systems resilience using machine learning by Alessandro Jatobá, Paula de Castro-Nunes, Paloma Palmieri, Omara Machado Araujo de Oliveira, Patricia Passos Simões, Valéria da Silva Fonseca, Paulo Victor Rodrigues de Carvalho

“…A comprehensive dataset was developed through rigorous data collection and preprocessing, followed by splitting the data into training and testing subsets. Various ML algorithms, including regression models and decision trees, were applied to uncover insights into the resilience of health systems over time. …”
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A generalized machine learning framework to estimate fatigue life across materials with minimal data by Dharun Vadugappatty Srinivasan, Morteza Moradi, Panagiotis Komninos, Dimitrios Zarouchas, Anastasios P. Vassilopoulos

“…An extreme gradient boosting algorithm-based ML framework encompassing Synthetic Minority Over-sampling TEchnique (SMOTE), categorical data encoding, and external loop cross-validation is developed to evaluate the fatigue life across materials. …”
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Bio-Inspired Hyperparameter Tuning of Federated Learning for Student Activity Recognition in Online Exam Environment by Ramu Shankarappa, Nandini Prasad, Ram Mohana Reddy Guddeti, Biju R. Mohan

“…The proposed PSOGA not only outperforms the proposed PSOEGA but also outperforms the benchmark algorithms considered for performance evaluation by giving an accuracy of 95.97%.…”
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Assessing the performance of machine learning and analytical hierarchy process (AHP) models for rainwater harvesting potential zone identification in hilly region, Bangladesh by Md. Mahmudul Hasan, Md. Talha, Most. Mitu Akter, Md Tasim Ferdous, Pratik Mojumder, Sujit Kumar Roy, N.M. Refat Nasher

“…Water scarcity in hilly regions presents unique challenges, particularly in Bangladesh, where obtaining fresh drinking water has become difficult to access. This study aims to evaluate the potential zones for rainwater harvesting (RWH) using machine learning (ML) algorithms and geospatial analysis. …”
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Aerodynamic Prediction and Design Optimization Using Multi-Fidelity Deep Neural Network by Bingchen Du, Ennan Shen, Jiangpeng Wu, Tongqing Guo, Zhiliang Lu, Di Zhou

“…As the insufficiency in the prediction accuracy of the optimal shapes appears when employing the non-updated MFDNN models, an update strategy is developed by tightly integrating the MFDNN models with the particle swarm optimization algorithm. To further reduce the time costs for updating models, a dual-threshold update strategy is then introduced, which can half the counts of evaluating HF data.…”
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Emotion Detection and Student Engagement in Distance Learning During Containment Due to the COVID-19 by Benyoussef Abdellaoui, Ahmed Remaida, Zineb Sabri, Younes EL BOUZEKRI EL IDRISSI, Aniss Moumen

“…The system has been implemented and tested, enabling the evaluation of student attention. Several algorithms and techniques have been used to implement our prototype as CNN. …”
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Coffee-Leaf Diseases and Pests Detection Based on YOLO Models by Jonatan Fragoso, Clécio Silva, Thuanne Paixão, Ana Beatriz Alvarez, Olacir Castro Júnior, Ruben Florez, Facundo Palomino-Quispe, Lucas Graciolli Savian, Paulo André Trazzi

“…The BRACOL dataset, annotated by an expert, was used in the experiments to guarantee the quality of the annotations and the reliability of the trained models. The evaluation of the models included quantitative and qualitative analyses, considering the mAP, F1-Score, and recall metrics. …”
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Feature fusion with attributed deepwalk for protein–protein interaction prediction by Mei-Yuan Cao, Suhaila Zainudin, Kauthar Mohd Daud

“…The fused representations are then used to train classifiers for PPI prediction. Evaluation across three datasets using multiple classifiers demonstrated that FFADW significantly improves sample clustering and performs better than existing approaches, with the XGBoost classifier showing the best results. …”
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Bag of Feature-Based Ensemble Subspace KNN Classifier in Muscle Ultrasound Diagnosis of Diabetic Peripheral Neuropathy by Kadhim K. Al-Barazanchi, Ali H. Al-Timemy, Zahid M. Kadhim

“…This work develops a computer-aided diagnostic (CAD) system based on muscle ultrasound that integrates the bag of features (BOF) and an ensemble subspace k-nearest neighbor (KNN) algorithm for DPN detection. The BOF creates a histogram of visual word occurrences to represent the muscle ultrasound images and trains an ensemble classifier through cross-validation, determining optimal parameters to improve classification accuracy for the ensemble diagnosis system. …”
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Performance of two different artificial intelligence models in dental implant planning among four different implant planning software: a comparative study by Pathompong Roongruangsilp, Walita Narkbuakaew, Pathawee Khongkhunthian

“…The impact of image rendering algorithms on model performance underscores the need for standardized preprocessing pipelines. …”
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Development and validation of computer-aided detection for colorectal neoplasms using deep learning incorporated with computed tomography colonography by Shungo Endo, Koichi Nagata, Kenichi Utano, Satoshi Nozu, Takaaki Yasuda, Ken Takabayashi, Michiaki Hirayama, Kazutomo Togashi, Hiromasa Ohira

“…Conclusions The sensitivity of CT colonography images using the AI algorithm was improved by integrating evaluations in two positions. …”
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Long-Range Wide Area Network Intrusion Detection at the Edge by Gonçalo Esteves, Filipe Fidalgo, Nuno Cruz, José Simão

“…The current work uses third-party multi-vendor sensor data obtained in the city of Lisbon for training and validating the models. The results show the efficacy of the proposed technique in evaluating received packets, logging relevant parameters in the database, and accurately identifying intrusions or expected device behaviours. …”
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Comparison of methods for tuning machine learning model hyper-parameters: with application to predicting high-need high-cost health care users by Christopher Meaney, Xuesong Wang, Jun Guan, Therese A. Stukel

“…Conclusions In our study, all HPO algorithms resulted in similar gains in model performance relative to baseline models. …”
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Image-Based Classification of Freshwater Fish Species to Support Feed Recommendation Using Random Forest by Hindayati Mustafidah, Suwarsito Suwarsito, Rahmat Setiawan, Abdul Karim

“…Evaluation results show that the system achieved a classification accuracy of 83.33%, with a precision of 83.53%, recall of 83.33%, and an F1-score of 82.86%. …”
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