Cognitive weaknesses or impairments on the NIH toolbox cognition battery in children and adolescents: base rates in a normative sample and proposed methods for classification by Nathan E. Cook, Nathan E. Cook, Nathan E. Cook, Grant L. Iverson, Grant L. Iverson, Grant L. Iverson, Grant L. Iverson, Justin E. Karr

“…This study examined the frequency of low NIHTB-CB scores and proposes flexible algorithms for identifying cognitive weaknesses and impairment among youth.MethodsParticipants were 1,269 youth from the NIHTB-CB normative sample who did not have a neurodevelopmental, psychiatric, or medical problem that might be associated with cognitive difficulties (53% boys and 47% girls; M = 11.8 years-old, SD = 3.0, range 7–17). …”
Get full text

AirQuaNet: A Convolutional Neural Network Model With Multi-Scale Feature Learning and Attention Mechanisms for Air Quality-Based Health Impact Prediction by Sreeni Chadalavada, Suleyman Yaman, Abdulkadir Sengur, Ravinesh C. Deo, Abdul Hafeez-Baig, Tracy Kolbe-Alexander, Niranjana Sampathila, U. Rajendra Acharya

“…It achieved outstanding results, with an R2 of 0.9997 on regression tasks and a classification accuracy of 94.21%, outperforming traditional machine learning algorithms and DL baselines. These results highlight the model’s robustness under diverse data environments and its ability for high generalization across varied temporal scales and types of contaminants. …”
Get full text

Comprehensive characterization of T cell subtypes in lung adenocarcinoma: Prognostic, predictive, and therapeutic implications by Shiquan Liu, Hao Sun, Tianye Song, Ce Liang, Lele Deng, Haiyong Zhu, Fangchao Zhao, Shujun Li

“…A T cell-related prognostic signature was developed by integrating LUAD datasets from TCGA, GSE31210, GSE50081, and GSE68465 using 10 machine learning algorithms. Further analysis linked risk scores to immune infiltration and drug sensitivity. …”
Get full text

Preoxygenation in difficult airway management: high-flow oxygenation by nasal cannula versus face mask (the PREOPTIDAM study). Protocol for a single-centre randomised study by Samir Jaber, Karim Asehnoune, Fanny Feuillet, Mickael Vourc’h, Donatien Huard, Gabrielle Baud, Arthur Guichoux, Marielle Surbled, Melanie Tissot, Anne Chiffoleau, Christophe Guitton

“…Introduction Although preoxygenation and airway management respond to precise algorithms, difficult intubation (DI) remains a daily challenge in intensive care units and in the operating rooms because of its frequent complications, including hypoxaemia. …”
Get full text

A machine learning model with crude estimation of property strategy for performance prediction of perovskite solar cells based on process optimization by Dan Li, Ernie Che Mid, Shafriza Nisha Basah, Xiaochun Liu, Jian Tang, Hongyan Cui, Huilong Su, Qianliang Xiao, Shiyin Gong

“…Nine machine learning algorithms, including decision tree (DT), random forest (RF), CatBoost, LassoLarsCV, histogram gradient boosting, extreme gradient boosting (XGBoost), K nearest neighbor, ridge regression (Ridge), and linear regression (Linear R), were employed to optimize PSC preparation and assess its impact on device performance. …”
Get full text

Multiband THz MIMO antenna with regression machine learning techniques for isolation prediction in IoT applications by Md Ashraful Haque, Kamal Hossain Nahin, Jamal Hossain Nirob, Md. Kawsar Ahmed, Narinderjit Singh Sawaran Singh, Liton Chandra Paul, Abeer D. Algarni, Mohammed ElAffendi, Ahmed A. Abd El-Latif, Abdelhamied A. Ateya

“…Furthermore, several machine learning algorithms were applied to test the design. Various metrics, including variance score, R-squared, mean squared error (MSE), mean absolute error (MAE), and root mean square error (RMSE), were used to evaluate the machine learning models. …”
Get full text

Efficient Machine Learning Models for Solar Radiation Prediction Using Ensemble Techniques: A Case Study in Low-Rainfall Arid Climates by Jimmy Aurelio Rosales Huamani, Uwe Rojas Villanueva, Christian Leonardo Rosales Ventocilla, Jose Luis Castillo Sequera, Jose Manuel Gomez Pulido

“…Finally, ensemble techniques were applied to enhance prediction accuracy by combining various ML algorithms. The best results were obtained using the Stacking Regressor (SR), achieving an <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 0.92, RMSE of 47.30 W/<inline-formula> <tex-math notation="LaTeX">$m^{2}$ </tex-math></inline-formula>, and MAE of 18.27 W/<inline-formula> <tex-math notation="LaTeX">$m^{2}$ </tex-math></inline-formula>, demonstrating high predictive performance in the target climate region.…”
Get full text

Radiomics analysis of thoracic vertebral bone marrow microenvironment changes before bone metastasis of breast cancer based on chest CT by Hao-Nan Zhu, Yi-Fan Guo, YingMin Lin, Zhi-Chao Sun, Xi Zhu, YuanZhe Li

“…Multiple machine learning algorithms were utilized to construct various radiomics models for predicting the risk of bone metastasis, and the model with optimal performance was integrated with clinical features to develop a nomogram. …”
Get full text

Deep learning-based sow posture classifier using colour and depth images by Verônica Madeira Pacheco, Tami M. Brown-Brandl, Rafael Vieira de Sousa, Gary A. Rohrer, Sudhendu Raj Sharma, Luciane Silva Martello

“…Different deep learning algorithms were developed to detect sow postures from three types of images: colour (RGB), depth (depth image transformed into greyscale), and fused (colour-depth composite images). …”
Get full text

Applications of molecular dynamics in nanomaterial design and characterization - A review by Md. Aminul Islam, S M Maksudur Rahman, Juhi Jannat Mim, Safiullah Khan, Fardin Khan, Md. Ahadul Islam Patwary, Nayem Hossain

“…Recent advances, such as improved multiscale modeling and computational algorithms and real-time simulations, shed light onto MD processes of revolutionizing nanotechnology. …”
Get full text

River total dissolved gas prediction using a hybrid greedy-stepwise feature selection and bidirectional long short-term memory model by Khabat Khosravi, Salim Heddam, Changhyun Jun, Sayed M. Bateni, Dongkyun Kim, Essam Heggy

“…Several models are developed and tested, including long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and an alternating model tree (AMT) hybridized with iterative absolute error regression (IAER) and iterative classifier optimizer (ICO) algorithms. A greedy stepwise feature selection technique (GSFST) is employed to identify the optimal model inputs. …”
Get full text

Comprehensive Splice Pattern Analysis for Previously Reported OCRL Splicing Variants and Their Phenotypic Contributions by Rini Rossanti, Eri Okada, Nana Sakakibara, Ryota Suzuki, Yuta Inoki, Yuta Ichikawa, Yu Tanaka, Hideaki Kitakado, Chika Ueda, Atsushi Kondo, Yuya Aoto, China Nagano, Tomoko Horinouchi, Tomohiko Yamamura, Shingo Ishimori, Kandai Nozu

“…We assessed the variant consequences at the mRNA level using an in vitro splicing assay with a minigene system, and examined their compatibility with in silico algorithms and correlation with disease phenotypes. …”
Get full text

Trading off Iodine and Radiation Dose in Coronary Computed Tomography by Guillaume Fahrni, Thomas Saliba, Damien Racine, Marianna Gulizia, Georgios Tzimas, Chiara Pozzessere, David C. Rotzinger

“…Radiation dose reduction strategies, such as low tube voltage protocols, prospective ECG-gating, and modern reconstruction algorithms, have significantly decreased radiation exposure, with some studies achieving sub-millisievert doses. …”
Get full text

TomaFDNet: A multiscale focused diffusion-based model for tomato disease detection by Rijun Wang, Rijun Wang, Yesheng Chen, Fulong Liang, Xiangwei Mou, Xiangwei Mou, Guanghao Zhang, Hao Jin

“…Results and DiscussionExperimental results show that TomaFDNet reaches a mean average precision (mAP) of 83.1% in detecting Early_blight, Late_blight, and Leaf_Mold on tomato leaves, outperforming classical object detection algorithms, including Faster R-CNN (mAP = 68.2%) and You Only Look Once (YOLO) series (v5: mAP = 75.5%, v7: mAP = 78.3%, v8: mAP = 78.9%, v9: mAP = 79%, v10: mAP = 77.5%, v11: mAP = 79.2%). …”
Get full text

Beneath the surface in autoimmune hemolytic anemia: pathogenetic networks, therapeutic advancements and open questions by Alessandro Costa, Alessandro Costa, Olga Mulas, Olga Mulas, Angela Maria Mereu, Mercede Schintu, Marianna Greco, Giovanni Caocci, Giovanni Caocci

“…However, therapeutic algorithms are rapidly shifting, particularly in the context of treatment-refractory disease. …”
Get full text

Machine Learning for Community-Acquired Pneumonia Diagnosis Using Routine Clinical and Laboratory Data by Sung Yoon Lim, Eunhye Cho, Bokhee Jung, Jaeyeon Lee, Miyoung Kim, Sooyoung Yoo, Seyoung Jung, Joon Yhup Lee, Sejin Nam, Hyunju Lee, Eu Suk Kim

“…RESULTS: Among the algorithms tested, eXtreme Gradient Boosting (XGBOOST) achieved the highest AUROC (0.936, 95% CI: 0.924-0.947), followed by the gradient boost (0.931, 95% CI: 0.919-0.943) and random forest (0.926, 95% CI: 0.912-0.938) models in the test set. …”
Get full text

The multiple uses of artificial intelligence in exercise programs: a narrative review by Alberto Canzone, Alberto Canzone, Giacomo Belmonte, Antonino Patti, Domenico Savio Salvatore Vicari, Domenico Savio Salvatore Vicari, Fabio Rapisarda, Valerio Giustino, Patrik Drid, Antonino Bianco

“…BackgroundArtificial intelligence is based on algorithms that enable machines to perform tasks and activities that generally require human intelligence, and its use offers innovative solutions in various fields. …”
Get full text

Can Machine Learning Enhance Intrusion Detection to Safeguard Smart City Networks from Multi-Step Cyberattacks? by Jowaria Khan, Rana Elfakharany, Hiba Saleem, Mahira Pathan, Emaan Shahzad, Salam Dhou, Fadi Aloul

“…Several machine learning algorithms are considered, namely Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), Light Gradient-Boosting Machine (LGBM), Extreme Gradient Boosting (XGB) and Random Forest (RF). …”
Get full text

Accurate detection of low concentrations of microplastics in soils via short-wave infrared hyperspectral imaging by Huan Chen, Taesung Shin, Bosoon Park, Kyoung Ro, Changyoon Jeong, Hwang-Ju Jeon, Pei-Lin Tan

“…This study evaluated the effectiveness of coupling machine learning algorithms with short-wave infrared hyperspectral imaging in detecting two types of microplastics - polyamide and polyethylene - with the maximum particle sizes of 50 and 300 ​μm, respectively, across three concentration ranges (0.01–0.10, 0.10–1.0, and 1.0–12 ​%) in soils. …”
Get full text

RGB and RGNIR image dataset for machine learning in plastic waste detectionZENODO by Owen Tamin, Ervin Gubin Moung, Jamal Ahmad Dargham, Samsul Ariffin Abdul Karim, Ashraf Osman Ibrahim, Nada Adam, Hadia Abdelgader Osman

“…Machine learning has emerged as a potential solution for plastic waste due to its ability to analyse and interpret large volumes of data using algorithms. However, developing an efficient machine learning model requires a comprehensive dataset with information on the size, shape, colour, texture, and other features of plastic waste. …”
Get full text