Optimal features assisted multi-attention fusion for robust fire recognition in adverse conditions by Inam Ullah, Nada Alzaben, Yousef Ibrahim Daradkeh, Mi Young Lee

“…This novel progressive attention-over-attention framework achieves state-of-the-art (SOTA) performance while maintaining computational efficiency. Our approach introduces three key innovations: Firstly, Convolutional Self-Attention (CSA), integrating global self-attention with convolution through dynamic kernels and trainable filters for enhanced low-level fire feature processing. …”
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Features of distillation separation of multicomponent mixtures by A. K. Frolkova, A. V. Frolkova, V. M. Raeva, V. I. Zhuchkov

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Multi-Domain Feature Incorporation of Lightweight Convolutional Neural Networks and Handcrafted Features for Lung and Colon Cancer Diagnosis by Omneya Attallah

“…This study presents a computer-aided diagnostic (CAD) framework that integrates multi-domain features through a hybrid methodology. …”
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A complex roadside object detection model based on multi-scale feature pyramid network by Zhihao Zheng, Jianguang Zhao, Jingjing Fan, Ruirui Bai, Jiana Zhao, Jianan Liu

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Developing Innovative Feature Extraction Techniques from the Emotion Recognition Field on Motor Imagery Using Brain–Computer Interface EEG Signals by Amr F. Mohamed, Vacius Jusas

“…Statistical, wavelet analysis, Hjorth parameters, higher-order spectra, fractal dimensions (Katz, Higuchi, and Petrosian), and a five-dimensional combination of all five feature sets were implemented. …”
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Improving the Performance of Electrotactile Brain–Computer Interface Using Machine Learning Methods on Multi-Channel Features of Somatosensory Event-Related Potentials by Marija Novičić, Olivera Djordjević, Vera Miler-Jerković, Ljubica Konstantinović, Andrej M. Savić

“…Exploring and optimizing the parameters of sERP elicitation, as well as feature extraction and classification methods, is crucial for addressing the accuracy versus speed trade-off in various assistive BCI applications where the tactile modality may have added value.…”
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Improved bio-inspired with machine learning computing approach for thyroid prediction by Divya Kesavulu, Kannadasan R

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Joint Exploitation of Physical-Layer and Artificial Features for Privacy-Preserving Distributed Source Camera Identification by Hui Tian, Haibao Chen, Yuyan Zhao, Jiawei Zhang

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Development and Validation of Predictive Models for Differentiating Resectable Stage III Peripheral SCLC from NSCLC Using Radiomic Features and Clinical Parameters by Junjie Zhang MD, Ligang Hao MD, Qiuxu Zhang MD, Lina Zheng MD, Qian Xu PhD, Fengxiao Gao MD

“…Conclusion The integration of clinical parameters and radiomics features within the combined model may hold significant potential for the preoperative differentiation of stage III peripheral SCLC from NSCLC.…”
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18F-FDG PET/CT metabolic parameters are correlated with clinical features and valuable in clinical stratification management in patients of castleman disease by Guolin Wang, Qianhe Xu, Yinuo Liu, Huatao Wang, Fei Yang, Zhenfeng Liu, Xinhui Su

“…The correlation between these metabolic parameters and clinical features were studied using a univariate analysis. …”
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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. …”
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Enhanced safety assessment on tunnel excavation via refined rock mass parameter identification by Hongwei Huang, Tongjun Yang, Jiayao Chen, Zhongkai Huang, Chen Wu, Jianhong Man

“…This study employs computer vision and deep learning techniques to execute the refined extraction and quantification of rock mass information in tunnel faces. …”
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Analysis of the Relationship Between Scale Invariant Feature Transform Keypoint Properties and Their Invariance to Geometrical Transformation Applied to Cone-Beam Computed Tomograp... by Diletta Pennati, Leonardo Bocchi

“…In particular, this work was focused on images obtained with Cone-Beam Computed Tomography (CBCT) technology. Besides fine-tuning SIFT parameters on a case-by-case basis, the novelty of this work consists of finding the optimal SIFT parameters on the basis of the keypoints stability. …”
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Exploring feature sparsity for out-of-distribution detection by Qichao Chen, Kuan Li, Zhiyuan Chen, Tomas Maul, Jianping Yin

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Relationship between high-resolution computed tomography quantitative imaging analysis and physiological and clinical features in antisynthetase syndrome-related interstitial lung... by Donald P Tashkin, Rohit Aggarwal, Chester V Oddis, Grace Kim, Fereidoun Abtin, Daniela Markovic, Christina Charles-Schoeman, Galina Marder, Jonathan Goldin, Paul F Dellaripa, Swamy Venuturupalli, Gary Matt Hunninghake, Tracy J Doyle, Siamak Moghadam-Kia, Sangmee Sharon Bae, Cato Chan, Daniel Sullivan, Jeremy Falk

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Nomogram combining dual-energy computed tomography features and radiomics for differentiating parotid warthin tumor from pleomorphic adenoma: a retrospective study by Zhiwei Gong, Jianying Li, Yilin Han, Shiyu Chen, Lijun Wang

“…IntroductionAccurate differentiation between pleomorphic adenomas (PA) and Warthin tumors (WT) in the parotid gland is challenging owing to overlapping imaging features. This study aimed to evaluate a nomogram combining dual-energy computed tomography (DECT) quantitative parameters and radiomics to enhance diagnostic precision.MethodsThis retrospective study included 120 patients with pathologically confirmed PA or WT, randomly divided into training and test sets (7:3). …”
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Features of the magnetorefractive effect in Co–Si nanocomposites by A. N. Yurasov, R. Kulgunina, M. M. Yashin, M. A. Simdyanova

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COVID-19 biobank: features of the cytokine profile by N. N. Sushentseva, O. S. Popov, S. V. Apalko, A. Yu. Anisenkova, S. V. Azarenko, K. V. Smantserev, D. N. Khobotnikov, T. V. Gladysheva, E. V. Minina, S. V. Strelyukhina, S. P. Urazov, D. Pavlovich, S. R. Fridman, S. G. Shcherbak

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Innovative deep learning classifiers for breast cancer detection through hybrid feature extraction techniques by S. Vijayalakshmi, Binay Kumar Pandey, Digvijay Pandey, Mesfin Esayas Lelisho

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Estimation of the High-Frequency Feature Slope in Gravitational Wave Signals from Core Collapse Supernovae Using Machine Learning by Alejandro Casallas-Lagos, Javier M. Antelis, Claudia Moreno, Ramiro Franco-Hernández

“…We conducted an in-depth exploration of the use of different machine learning (ML) for regression algorithms, including Linear, Ridge, LASSO, Bayesian Ridge, Decision Tree, and a variety of Deep Neural Network (DNN) architectures, to estimate the slope of the high-frequency feature (HFF), a prominent emergent feature found in the gravitational wave (GW) signals of core collapse supernovae (CCSN). …”
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