Adverse drug reaction signal detection via the long short-term memory model by Mengqi Cao, Yanna Chi, Jinyang Yu, Yu Yang, Ruogu Meng, Jinzhu Jia, Jinzhu Jia

“…The sensitivity of Logistic Regression model based on feature variables was 86.83%, and the F1-score was 0.9063. …”
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Personalized Course Recommendation System: A Multi-Model Machine Learning Framework for Academic Success by Md Sajid Islam, A. S. M. Sanwar Hosen

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Clinical Characteristics and a Novel Prediction Nomogram (EASTAR) for Patients with Hemorrhagic Fever with Renal Syndrome: A Multicenter Retrospective Study by Ke Ma, Ting Wu, Wei Guo, Jun Wang, Quan Ming, Jun Zhu, Hongwu Wang, Guang Chen, Xiaojing Wang, Weiming Yan, Xiaoping Luo, Tao Chen, Qin Ning

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Automatic detection of cognitive events using machine learning and understanding models’ interpretations of human cognition by Quang Dang, Murat Kucukosmanoglu, Michael Anoruo, Golshan Kargosha, Sarah Conklin, Justin Brooks

“…Five models achieve MCC between 0.43 and 0.75. We report three key findings: (1) the generalized model reduces the specificity to enhance the sensitivity, illustrating the trade-off from specialization to generalization; (2) the permutation feature importance analyses show that both pupil dilation and gaze position contribute to model predictions, with task-specific models focusing on task-specific structure patterns to predict while the generalized model is using human cognitive responses; and (3) in an online simulation environment, models performance decreases by approximately 0.05 on MCC. …”
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3-D–2-D Hybrid Lightweight CNN Model: Enhancing Canopy Feature Retrieval in Hyperspectral Imaging for Accurate Plant Species Classification by Chinsu Lin, Hung-Yi Chien, Keng-Hao Liu

“…Key challenges include performance limitations when handling many species, the impact of feature similarity on identification, overfitting and scalability issues, and the tradeoff between data cost and performance. …”
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HFC-YOLO11: A Lightweight Model for the Accurate Recognition of Tiny Remote Sensing Targets by Jinyin Bai, Wei Zhu, Zongzhe Nie, Xin Yang, Qinglin Xu, Dong Li

“…To address critical challenges in tiny object detection within remote sensing imagery, including resolution–semantic imbalance, inefficient feature fusion, and insufficient localization accuracy, this study proposes Hierarchical Feature Compensation You Only Look Once 11 (HFC-YOLO11), a lightweight detection model based on hierarchical feature compensation. …”
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Enhancing Pulmonary Disease Prediction Using Large Language Models With Feature Summarization and Hybrid Retrieval-Augmented Generation: Multicenter Methodological Study Based on R... by Ronghao Li, Shuai Mao, Congmin Zhu, Yingliang Yang, Chunting Tan, Li Li, Xiangdong Mu, Honglei Liu, Yuqing Yang

“…ObjectiveThis paper explores LLMs with new prompt engineering technology to enhance model interpretability and improve the prediction performance of pulmonary disease based on a traditional deep learning model. …”
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K-Means Clustering and Classification of Breast Cancer Images Using Histogram of Oriented Gradients Features and Convolutional Neural Network Models: Diagnostic Image Analysis Stud... by Said Salloum

“…The model's performance was evaluated using standard metrics such as accuracy, precision, recall, and F1 ResultsThe CNN achieved a classification accuracy of 98%, with precision, recall, and F1 ConclusionsThe combination of HOG feature extraction and CNN-based classification demonstrated high performance in breast cancer detection. …”
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Artificial intelligence-driven cybersecurity: enhancing malicious domain detection using attention-based deep learning model with optimization algorithms by Fatimah Alhayan, Asma Alshuhail, Ahmed Omer Ahmed Ismail, Othman Alrusaini, Sultan Alahmari, Abdulsamad Ebrahim Yahya, Monir Abdullah, Samah Al Zanin

“…Initially, the min–max scaling method is utilized in the pre-processing phase to convert input data into an appropriate design. For feature selection (FS), the proposed EMDD-ADLMOA technique utilizes the quantum-inspired firefly algorithm (QIFA) model. …”
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Cloud-based real-time enhancement for disease prediction using Confluent Cloud, Apache Kafka, feature optimization, and explainable artificial intelligence by Abdulaziz AlMohimeed

“…A second Python script to consume health attributes from a Kafka topic and apply a stacking model to predict CKD in real-time. The results showed that the stacking model with features selected by GA recorded the best performance with 100 accuracy. …”
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Establishment and validation of a model for predicting the SSIGN score and prognosis of patients with clear cell renal cell carcinoma based on CT radiomic features and clinical ind... by ZHI Kaiyue, YANG Zhitao, ZHANG Shuo, ZHU He, WANG Yanmei, ZHAO Lianzi, WANG Ning, ZHAO Xia, LI Xianjun, CHENG Nan, WANG Yicong, CHEN Chengcheng, WANG Nan, NIE Pei

“…Objective To establish and validate a model for predicting the SSIGN score and prognosis of patients with clear cell renal cell carcinoma (ccRCC) based on CT radiomic features and clinical indices. …”
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Ensemble Machine Learning Models Utilizing a Hybrid Recursive Feature Elimination (RFE) Technique for Detecting GPS Spoofing Attacks Against Unmanned Aerial Vehicles by Raghad Al-Syouf, Omar Y. Aljarrah, Raed Bani-Hani, Abdallah Alma’aitah

“…For the UAV location GPS spoofing dataset, the bagging classifier emerged as the top performer, achieving 99.16% accuracy and 0.002 s processing time compared to other well-known ML models. …”
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Improving Early Detection of Dementia: Extra Trees-Based Classification Model Using Inter-Relation-Based Features and K-Means Synthetic Minority Oversampling Technique by Yanawut Chaiyo, Worasak Rueangsirarak, Georgi Hristov, Punnarumol Temdee

“…To address this challenge, this study proposes a novel approach in which readily available biochemical and physiological features from electronic health records are employed to develop a machine learning-based binary classification model, improving accessibility and early detection. …”
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Global miniaturization of broadband antennas by prescreening and machine learning by Slawomir Koziel, Anna Pietrenko-Dabrowska, Ubaid Ullah

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Interpretable Machine Learning for Serum-Based Metabolomics in Breast Cancer Diagnostics: Insights from Multi-Objective Feature Selection-Driven LightGBM-SHAP Models by Emek Guldogan, Fatma Hilal Yagin, Hasan Ucuzal, Sarah A. Alzakari, Amel Ali Alhussan, Luca Paolo Ardigò

“…SHAP analysis ranked metabolites based on their contribution to model predictions. <i>Results:</i> Compared to other feature selection approaches, the MOFS approach was more robust in terms of predictive performance, and metabolites identified by this method were used in subsequent analyses for biomarker discovery. …”
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An interpretable and stacking ensemble model for predicting heat and mass transfer of desiccant wheel by Mengyang Li, Liu Chen

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An Efficient Optimal CapsNet Model-Based Computer-Aided Diagnosis for Gastrointestinal Cancer Classification by Fahdah A Almarshad, Prasanalakshmi Balaji, Liyakathunisa Syed, Eman Aljohani, Santhi Muttipoll Dharmarajlu, Thavavel Vaiyapuri, Nourah Ali AlAseem

“…Besides, the SOADL-GCC technique uses a capsule network (CapsNet) model for deriving the feature vectors from preprocessed images. …”
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Transfer learning based hybrid feature learning framework for enhanced skin cancer diagnosis using deep feature integration by Maridu Bhargavi, Sivadi Balakrishna

“…However, current diagnostic methods lack feature representation and model generalization. Among the primary challenges in automated skin cancer classification are addressing differences in lesion appearance, occlusions, and data class imbalance that impact model performance and reliability. …”
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Superpixel guided spectral-spatial feature extraction and weighted feature fusion for hyperspectral image classification with limited training samples by Yao Li, Liyi Zhang, Lei Chen, Yunpeng Ma

“…Abstract Deep learning is a double-edged sword. The powerful feature learning ability of deep models can effectively improve classification accuracy. …”
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Early Remaining Useful Life Prediction for Lithium-Ion Batteries Using a Gaussian Process Regression Model Based on Degradation Pattern Recognition by Linlin Fu, Bo Jiang, Jiangong Zhu, Xuezhe Wei, Haifeng Dai

“…The model hyperparameters are further optimized through the particle swarm optimization (PSO) algorithm to improve the adaptability and generalization capability of the predictive models. …”
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