Using Machine Learning for Analysis of Wideband Acoustic Immittance and Assessment of Middle Ear Function in Infants by Shan Peng, Yukun Zhao, Xinyi Yao, Huilin Yin, Bei Ma, Ke Liu, Gang Li, Yang Cao

“…Furthermore, we developed a program based on ML models with an interactive GUI interface. …”
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Sustainable phytoprotection: a smart monitoring and recommendation framework using Puma Optimization for potato pathogen detection by Amal H. Alharbi, Faris H. Rizk, Khaled Sh. Gaber, Marwa M. Eid, Marwa M. Eid, El-Sayed M. El-kenawy, El-Sayed M. El-kenawy, Pushan Kumar Dutta, Doaa Sami Khafaga

“…The system is trained and evaluated on a real-world dataset derived from structured field experiments, comprising 52 instances and 42 agronomic, microbial, and ecological variables. …”
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A large-scale ENIGMA multisite replication study of brain age in depression by Laura K.M. Han, Richard Dinga, Ramona Leenings, Tim Hahn, James H. Cole, Lyubomir I. Aftanas, Alyssa R. Amod, Bianca Besteher, Romain Colle, Emmanuelle Corruble, Baptiste Couvy-Duchesne, Konstantin V. Danilenko, Paola Fuentes-Claramonte, Ali Saffet Gonul, Ian H. Gotlib, Roberto Goya-Maldonado, Nynke A. Groenewold, Paul Hamilton, Naho Ichikawa, Jonathan C. Ipser, Eri Itai, Sheri-Michelle Koopowitz, Meng Li, Go Okada, Yasumasa Okamoto, Olga S. Churikova, Evgeny A. Osipov, Brenda W.J.H. Penninx, Edith Pomarol-Clotet, Elena Rodríguez-Cano, Matthew D. Sacchet, Hotaka Shinzato, Kang Sim, Dan J. Stein, Aslihan Uyar-Demir, Dick J. Veltman, Lianne Schmaal

“…However, the estimated neuroimaging-derived “brain age gap” has varied from study to study, likely driven by differences in training and testing sample (size), age range, and used modality/features. …”
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Breeding of a new mid-to-late ripening apple cultivar Wangyuebingcui by XU Haihong, YANG Kai, LI Guangjin, ZHANG Guoqiang, TIAN Ge, AI Bohao, LI Xuqiang, WANG Junya

“…Juvenile branches present reddish-brown coloration featuring sparse lenticels and moderate pubescence, maturing to grey-green in 2-3 year growth with brown, slightly protuberant lenticels. …”
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Understanding Polymers Through Transfer Learning and Explainable AI by Luis A. Miccio

“…Given the challenges imposed by data scarcity in polymer science, transfer learning offers a promising solution by using learnt features of models pre-trained on other datasets. We conducted a comparative analysis of direct modelling and transfer learning-based approaches using a polyacrylates’ glass transitions dataset as a proof-of-concept study. …”
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Underwater acoustic target recognition under working conditions mismatch by WANG Haitao, JIN Anqi, YANG Shuang, ZENG Xiangyang

“…Auditory features are used as inputs to the system, and knowledge distillation is utilized to learn the intrinsic connection of target features under different working conditions. …”
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Classification of first embryonic division stages of multiple Caenorhabditis species by deep learning by Dhruv Khatri, Prachi Negi, Chaitanya A. Athale

“…Thus, deep learning networks can be used to generalize the morphological changes across species of nematode embryos, capturing chronology based on low-level intracellular features with biological relevance.…”
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Towards Species-Specific Coral Classification in Reef Monitoring Efforts by Hyeong Jang, Jonathan Leidig, Greg Wolffe

“…This paper details the architecture, parameterization, and effectiveness of these models as trained on a curated set of images. The models were then evaluated using one square kilometer maps of the seafloor to assess their practicability for automating several image-based analysis tasks on a widespread scale. …”
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How deep learning identifies and learns aspects of plant for classification by Devi V. Sowmya, Pooja Govu, Ramya Sande, Priya Nakka Vishnu

“…Convolutional Neural Network (CNN) of supervised learning algorithm used along with the Keras and Tensorflow the features were analysed and deployed in Streamlit. Accomplishing an accuracy of 95% on a dataset which was trained over 10,000 plant samples with conventional strategies. …”
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Integrating miRNA profiling and machine learning for improved prostate cancer diagnosis by Shweta Singh, Abhay Kumar Pathak, Sukhad Kural, Lalit Kumar, Madan Gopal Bhardwaj, Mahima Yadav, Sameer Trivedi, Parimal Das, Manjari Gupta, Garima Jain

“…Abstract Prostate cancer (PCa) diagnosis remains challenging due to overlapping clinical features with benign prostatic hyperplasia (BPH) and limitations of existing diagnostic tools like PSA tests, which yield high false-positive rates. …”
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Tailored knowledge distillation with automated loss function learning. by Sheng Ran, Tao Huang, Wuyue Yang

“…Building upon our proposed generic loss networks for logits and intermediate features, we derive a dynamic optimization strategy to adjust losses based on the student models' changing states for enhanced performance and adaptability. …”
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Modeling and Implementing Two-Stage AdaBoost for Real-Time Vehicle License Plate Detection by Moon Kyou Song, Md. Mostafa Kamal Sarker

“…In this paper, we propose a real-time and robust method for LPD systems using the two-stage adaptive boosting (AdaBoost) algorithm combined with different image preprocessing techniques. Haar-like features are used to compute and select features from LP images. …”
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Dynamic emotion intensity estimation from physiological signals facilitating interpretation via appraisal theory. by Isabel Barradas, Reinhard Tschiesner, Angelika Peer

“…Using data-extracted physiological features, we train intrasubject and intersubject intensity models using a genetic algorithm, which outperform traditional sliding-window linear regression, providing a robust basis for interpretation. …”
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Reliability analysis in curriculum development for social science education driven by machine learning by Rui Mao

“…Lasso was considered a regression technique that selected relevant features for prediction but eliminated redundant features. …”
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Vaccines against Covid-19: Comparison, Limitations, the Decrease of Pandemic and the Perspective of Viral Respiratory by E. P. Kharchenko

“…The aim is to compare their features for objective substantiation of their application. …”
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Large Area Crops Mapping by Phenological Horizon Attention Transformer (PHAT) Method Using MODIS Time-Series Imagery by Quanshan Gao, Taixia Wu, Hongzhao Tang, JingYu Yang, Shudong Wang

“…The PHAT model was therefore trained using the phenological features of endmembers to obtain the spatial distribution of crops, and to resolve the issue of varying time-series curves for the same crop across large areas. …”
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Mapping Wetlands with High-Resolution Planet SuperDove Satellite Imagery: An Assessment of Machine Learning Models Across the Diverse Waterscapes of New Zealand by Md. Saiful Islam Khan, Maria C. Vega-Corredor, Matthew D. Wilson

“…In this research, our motivation was to test whether high-spatial-resolution PlanetScope imagery can be used with pixel-based machine learning to support the mapping and monitoring of wetlands at a national scale. (2) Methods: This study compared four machine learning classification models—Random Forest (RF), XGBoost (XGB), Histogram-Based Gradient Boosting (HGB) and a Multi-Layer Perceptron Classifier (MLPC)—to detect and map wetland areas across New Zealand. …”
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Detection and Defense: Student-Teacher Network for Adversarial Robustness by Kyoungchan Park, Pilsung Kang

“…Focusing on the fact that distortion in the hidden layer features is inevitable for the success of adversarial attacks, we train the student network to predict the undistorted hidden layer features of the teacher network (target DNN). …”
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Machine Learning Prediction of Tritium‐Helium Groundwater Ages in the Central Valley, California, USA by Abdullah Azhar, Indrasis Chakraborty, Ate Visser, Yang Liu, Jory Chapin Lerback, Erik Oerter

“…The ML models were trained on 63 features, including location, well construction information, landscape characteristics, and climate variables, water chemistry, and stable isotopes. …”
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Classification of Continuous Sky Brightness Data Using Random Forest by Rhorom Priyatikanto, Lidia Mayangsari, Rudi A. Prihandoko, Agustinus G. Admiranto

“…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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