An explainable ensemble approach for advanced brain tumor classification applying Dual-GAN mechanism and feature extraction techniques over highly imbalanced data. by Priyanka Roy, Fahim Mohammad Sadique Srijon, Pankaj Bhowmik

“…This study proposes a novel deep ensemble feature extraction (DeepEFE) framework that surpasses other benchmark ML and deep learning models with an accuracy of 98.15%. …”
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LightGBM-Based Human Action Recognition Using Sensors by Yinuo Liu, Ziwei Chen

“…Finally, using feature selection and dimensionality reduction, the efficiency of the model is further improved, achieving a 70.14% increase in time efficiency without reducing the accuracy rate.…”
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Primary Fixation Feature Design Does Not Influence Total Ankle Tibial Component Stability When Implanted with Press-Fit in High Density Bone by Joshua E. Johnson PhD, Gabriel A. Clarke MS, Cesar de Cesar Netto MD, PhD, Donald D. Anderson PhD

“…Understanding how local bone density, implant fixation and design features together contribute to implant stability in TAR is necessary to improve implant performance.…”
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Noninvasive prediction of Ki-67 expression level in IDH-wildtype glioblastoma using MRI histogram analysis: comparison and combination of MRI morphological features by Qiang Liang, Qiang Liang, Qiang Liang, Qiang Li, Qiang Li, Qiang Li, Xianwang Liu, Shuqi Shao, Shuqi Shao, Shuqi Shao, Yawen Pan, Yawen Pan, Yawen Pan, Hongyu Wang, Hongyu Wang, Hongyu Wang

“…The optimal diagnostic performance was obtained by combining MRI morphological features and histogram parameters, with an AUC of 0.867.ConclusionBoth MRI morphological features and histogram parameters could predict the Ki-67 expression level in IDH-wildtype glioblastoma, and the combined model integrating MRI morphological features and histogram parameters can be an excellent imaging biomarker for noninvasively predicting Ki-67 expression levels in patients with IDH-wildtype glioblastoma.…”
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Validating attribute hierarchies in cognitive diagnosis models by Xueqin Zhang, Yu Jiang, Tao Xin, Yanlou Liu

“…Cognitive diagnosis models (CDMs) are restricted latent class models that are widely used in educational and psychological fields. …”
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Leveraging OGTT derived metabolic features to detect Binge-eating disorder in individuals with high weight: a “seek out” machine learning approach by Marianna Rania, Anna Procopio, Paolo Zaffino, Elvira Anna Carbone, Teresa Vanessa Fiorentino, Francesco Andreozzi, Cristina Segura-Garcia, Carlo Cosentino, Franco Arturi

“…The model based on the 5 h-long glucose load exhibited the best performance (sensitivity = 0.75, specificity = 0.67, F score = 0.71) diagnosing BED in 7 out of 10 cases. …”
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StarCD-Net: A Remote Sensing Change Detection Method Combining StarNet and Differential Operators by Caijia Zeng, Xiaorong Xue, Chuanlu Li, Xingbiao Xu, Siyue Zhao, Yifan Xu

“…Although deep learning models have demonstrated good performance in change detection tasks, they still face challenges when dealing with some complex scenes, such as possible insufficient feature extraction and pixel loss at the target edges. …”
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Commercial organisations head’s innovative competencies model by M. S. Blokhina

“…The model can serve as a basis for further research in this field and development of this direction of sociology.…”
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Enhanced Superpixel-Guided ResNet Framework with Optimized Deep-Weighted Averaging-Based Feature Fusion for Lung Cancer Detection in Histopathological Images by Karthikeyan Shanmugam, Harikumar Rajaguru

“…Segmentation and feature selection enhance performance, improving diagnostic accuracy. …”
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Laryngeal cancer diagnosis based on improved YOLOv8 algorithm by Xin Nie, Xueyan Zhang, Di Wang, Yuankun Liu, Lumin Xing, Wenjian Liu

“…A novel multiscale enhanced convolution module has been introduced to improve the model’s feature extraction capabilities for small-sized targets. …”
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String processing model for knowledge-driven systems by V. P. Ivashenko

“…The C \ C++ implementation of operations of the string processing model was tested. The paper gives definitions of concepts necessary for the calculation of metric features calculated over strings. …”
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Comparison оf Digital Relief Models by S. A. Antonov, S. V. Peregudov

“…Despite its popularity and at first glance similarity of the data, a number of features of each of the digital terrain models were identified. …”
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Hippocampal Functional Radiomic Features for Identification of the Cognitively Impaired Patients from Low-Back-Related Pain: A Prospective Machine Learning Study by Yang Z, Liang X, Ji Y, Zeng W, Wang Y, Zhang Y, Zhou F

“…Finally, we further analyzed the relationship between the hippocampal functional radiomic features and clinical measures, to explore the clinical significance of these features.Results: The combined radiomic features model logistic regression algorithm superior performance in distinguishing cognitively impaired patients from LBLP (AUC = 0.970, accuracy = 92.3%, sensitivity = 92.3%, specificity = 92.3%) compared to the other models. …”
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Mental Workload Detection and Assessment Through Statistical Features Extraction and Optimization Using GEL-RF Method for EEG Signals Using N-Back Dataset by Waleed Manzoor, Noman Naseer, Imran Fareed Nizami, Syed Hammad Nazeer, Husam A. Neamah

“…We propose a novel model, which integrates genetic, evolutionary and linear (GEL) feature selection techniques with a Random Forest (RF) classifier as GEL-RF model. …”
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ICAFormer: An Image Dehazing Transformer Based on Interactive Channel Attention by Yanfei Chen, Tong Yue, Pei An, Hanyu Hong, Tao Liu, Yangkai Liu, Yihui Zhou

“…To address the limitations of traditional dehazing algorithms—particularly in global feature association and local detail preservation—this study proposes a novel Transformer-based dehazing model enhanced by an interactive channel attention mechanism. …”
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SRM-YOLO for Small Object Detection in Remote Sensing Images by Bin Yao, Chengkun Zhang, Qingxiang Meng, Xiandong Sun, Xuyang Hu, Lu Wang, Xilai Li

“…In this paper, we introduce SRM-YOLO, a novel small object detection algorithm based on the YOLOv8 framework. The model incorporates the following key innovations: Reuse Fusion Structure (RFS), which enhances feature fusion; SPD-Conv, which enables effective downsampling while preserving critical information; and a specialized detection head designed for small objects. …”
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Deep learning model for gastrointestinal polyp segmentation by Zitong Wang, Zeyi Wang, Pengyu Sun

“…Our method employs an encoder-decoder structure with a pre-trained ConvNeXt model as the encoder to learn multi-scale feature representations. …”
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SSL-SurvFormer: A Self-Supervised Learning and Continuously Monotonic Transformer Network for Missing Values in Survival Analysis by Quang-Hung Le, Brijesh Patel, Donald Adjeroh, Gianfranco Doretto, Ngan Le

“…This entails a continuously <i>monotonic Transformer network</i>, empowered by <i>SSL pre-training</i>, that is designed to address the challenges presented by <i>continuous events and absent features</i> in survival prediction. Our proposed <i>continuously monotonic Transformer model</i> facilitates accurate estimation of survival probabilities, thereby bypassing the need for temporal discretization. …”
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Catalyst: Fast and flexible modeling of reaction networks. by Torkel E Loman, Yingbo Ma, Vasily Ilin, Shashi Gowda, Niklas Korsbo, Nikhil Yewale, Chris Rackauckas, Samuel A Isaacson

“…We introduce Catalyst.jl, a flexible and feature-filled Julia library for modeling and high-performance simulation of chemical reaction networks (CRNs). …”
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High-throughput method for improving rice AGB estimation based on UAV multi-source remote sensing image feature fusion and ensemble learning by Jinpeng Li, Jinpeng Li, Jinxuan Li, Jinxuan Li, Dongxue Zhao, Dongxue Zhao, Qiang Cao, Qiang Cao, Fenghua Yu, Fenghua Yu, Fenghua Yu, Yingli Cao, Yingli Cao, Yingli Cao, Shuai Feng, Shuai Feng, Shuai Feng, Tongyu Xu, Tongyu Xu, Tongyu Xu

“…Both single-type and multi-type features were used to develop individual and ensemble machine learning (ML) models for rice AGB estimation.ResultsThe findings indicate that: (i) Single-type features result in significant errors and low accuracy within the same sensor, but multi-feature fusion improves performance. …”
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