Estimating Leaf Chlorophyll Fluorescence Parameters Using Partial Least Squares Regression with Fractional-Order Derivative Spectra and Effective Feature Selection by Jie Zhuang, Quan Wang

“…We developed a data-driven partial least squares regression (PLSR) model by integrating fractional-order derivative (FOD) spectral transformation with multiple feature selection methods to predict ChlF parameters. …”
Get full text

MultiRepPI: a cross-modal feature fusion-based multiple characterization framework for plant peptide-protein interaction prediction by Yu Zhiguo, Li Zixuan, Li Peng

“…First, most methods fail to adequately integrate multimodal information such as sequence, structure, and disorder properties, leading to inadequate characterization of complex interaction patterns. Second, existing models have difficulty in capturing cross-dependent features between peptides and proteins, limiting the prediction performance. …”
Get full text

A Hybrid Deep Learning and Improved SVM Framework for Real-Time Railroad Construction Personnel Detection with Multi-Scale Feature Optimization by Jianqiu Chen, Huan Xiong, Shixuan Zhou, Xiang Wang, Benxiao Lou, Longtang Ning, Qingwei Hu, Yang Tang, Guobin Gu

“…To process small sample categories, data enhancement techniques (e.g., random flip and rotation) and K-fold cross-validation are applied to optimize the model parameters. The experimental results demonstrate that the ISVM method significantly improves accuracy and real-time performance compared to traditional detection methods and single deep learning models. …”
Get full text

Empirical validation of a developmental model for binge-eating disorder in adolescents: a structural equation modeling approach by Camille Clermont, Christopher Rodrigue, Catherine Bégin

“…However, a thorough empirical validation of this model has not yet been performed. The current study aims to empirically test Tanofsky-Kraff and her colleagues’ model via structural equation modeling (SEM) and explore potential gender and age differences within this framework. …”
Get full text

Advanced Methods for Identifying Counterfeit Currency: Using Deep Learning and Machine Learning by Nama'a Hamed, Fadwa Al Azzo

Get full text

Predicting radiation pneumonitis in lung cancer using machine learning and multimodal features: a systematic review and meta-analysis of diagnostic accuracy by Zhi Chen, GuangMing Yi, XinYan Li, Bo Yi, XiaoHui Bao, Yin Zhang, XiaoYue Zhang, ZhenZhou Yang, Zhengjun Guo

“…Abstract Objectives To evaluate the diagnostic accuracy of machine learning models incorporating multimodal features for predicting radiation pneumonitis in lung cancer through a systematic review and meta-analysis. …”
Get full text

The impact on users of an unfamiliar AED following a recent training experience: A randomized cross over simulation study by Damjan Slabe, Žiga Metelko, Eva Dolenc Šparovec

Get full text

One-Class Anomaly Detection for Industrial Applications: A Comparative Survey and Experimental Study by Davide Paolini, Pierpaolo Dini, Ettore Soldaini, Sergio Saponara

Get full text

An introduction to Self-Aware Deep Learning for medical imaging and diagnosis by Paolo Dell’Aversana

Get full text

Fused YOLO and Traditional Features for Emotion Recognition From Facial Images of Tamil and Russian Speaking Children: A Cross-Cultural Study by A. Mary Mekala, M. Varalakshmi, C. P. Achyutha Gowda, Leti Manish Kumar, Elena E. Lyakso, Olga Frolova, Ruban Nersisson

“…Further, the ablation study unveils the effect of feature fusion in boosting the performance and the dominance of YOLO V5 features over the other two.…”
Get full text

Ordinal Random Tree with Rank-Oriented Feature Selection (ORT-ROFS): A Novel Approach for the Prediction of Road Traffic Accident Severity by Bita Ghasemkhani, Kadriye Filiz Balbal, Kokten Ulas Birant, Derya Birant

“…The proposed approach enhances the model performance by separately determining feature importance based on severity levels. …”
Get full text

Machine Learning-Driven Prediction of Glass-Forming Ability in Fe-Based Bulk Metallic Glasses Using Thermophysical Features and Data Augmentation by Renato Dario Bashualdo Bobadilla, Marcello Baricco, Mauro Palumbo

“…Three datasets were constructed: one based on alloy molar fractions, one using thermophysical quantities calculated via the CALPHAD method, and another utilizing Magpie-derived features. The performance of various ML models was evaluated, including support vector machines (SVM), XGBoost, and ensemble methods. …”
Get full text

TSF-MDD: A Deep Learning Approach for Electroencephalography-Based Diagnosis of Major Depressive Disorder with Temporal–Spatial–Frequency Feature Fusion by Wei Gan, Ruochen Zhao, Yujie Ma, Xiaolin Ning

“…These data are then processed by a model based on 3D-CNN and CapsNet, enabling comprehensive feature extraction across domains. …”
Get full text

Using Hybrid Feature and Classifier Fusion for an Asynchronous Brain–Computer Interface Framework Based on Steady-State Motion Visual Evoked Potentials by Bo Hu, Jun Xie, Huanqing Zhang, Junjie Liu, Hu Wang

“…Experimental results demonstrate that the fused FB(CSP + CCA)-(SVM + XGBoost) model achieves superior performance in distinguishing intentional control (IC) and non-control (NC) states compared to models using a single feature type or classifier. …”
Get full text

Framework for Race-Specific Prostate Cancer Detection Using Machine Learning Through Gene Expression Data: Feature Selection Optimization Approach by David Agustriawan, Adithama Mulia, Marlinda Vasty Overbeek, Vincent Kurniawan, Jheno Syechlo, Moeljono Widjaja, Muhammad Imran Ahmad

“…Notably, another model achieved a similarly strong performance, with 97% accuracy for White patients and 95% for African American patients, using only 9 gene features. …”
Get full text

Extracting road maps from high-resolution satellite imagery using refined DSE-LinkNet by Prativa Das, Satish Chand

“…We use a pre-trained encoder by combining the layers of the two very efficient and light-weight CNN models: DenseNet and SE-Net that makes the proposed model more expressive with faster convergence. …”
Get full text

The Fusion of Focused Spectral and Image Texture Features: A New Exploration of the Nondestructive Detection of Degeneration Degree in <i>Pleurotus geesteranus</i> by Yifan Jiang, Jin Shang, Yueyue Cai, Shiyang Liu, Ziqin Liao, Jie Pang, Yong He, Xuan Wei

“…The spectral and texture features were then fused and used to construct a classification model based on convolutional neural networks (CNN). …”
Get full text

Enhanced skin cancer diagnosis: a deep feature extraction-based framework for the multi-classification of skin cancer utilizing dermoscopy images by Hadeel Alharbi, Gabriel Avelino Sampedro, Roben A. Juanatas, Se-jung Lim

Get full text

FFA-YOLOv7: Improved YOLOv7 Based on Feature Fusion and Attention Mechanism for Wearing Violation Detection in Substation Construction Safety by Rong Chang, Bingzhen Zhang, Qianxin Zhu, Shan Zhao, Kai Yan, Yang Yang

“…Additionally, we utilized attention after feature fusion in each layer to optimize the feature map fusion effect and achieve better detection accuracy performance. …”
Get full text

Distinctive Contribution of Sound Spectral Features in Enhancing Vibration-Based Multi-Component Fault Classification Under Non-Stationary Speed Conditions by S. Sowmya, M. Saimurugan, Naveen Venkatesh

“…To begin with, speed synchronizing instantaneous frequency (IF) with two signal envelopes of vibration signals are individually fed to the machine learning (ML) classifier, such as Decision Tree (DT), Support Vector Machine-Radial Basis Function (SVM-RBF), and Artificial Neural Network (ANN), to verify the model performance. It is realized that the shaft is highly misclassified by fusing these vibration signal features. …”
Get full text