Evaluating techniques for determining elasmobranch body size: a review of current methodologies by Ana S. Ferreira, Márcia A. Naré, Joana I. Robalo, Núria D. Baylina

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Towards New Approaches to Evaluate Dynamic Mosaicism in Ring Chromosome 13 Syndrome by Cristian Petter, Lilia Maria Azevedo Moreira, Mariluce Riegel

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Feature-informed machine learning for detecting material deformation and failure in aluminum pipes under bending load using acoustic emission sensors by Xiaowei Zuo, Nicholas Satterlee, Chang-Whan Lee, In-Gyu Choi, Choon-Wook Park, John S. Kang

“…Additionally, we propose a novel Feature-Informed Convolutional Neural Network (FI-CNN), which integrates the features into the CNN framework, yielding an accuracy of 92.7 %, outperforming the traditional machine learning methods. …”
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MultiRepPI: a cross-modal feature fusion-based multiple characterization framework for plant peptide-protein interaction prediction by Yu Zhiguo, Li Zixuan, Li Peng

“…In this framework, we innovatively introduce several key modules aimed at comprehensively improving the representation of peptide and protein features. First, a cross-modal encoding module (CME) is designed by fusing convolutional neural networks, recurrent neural networks, and feature enhancement mechanisms, which is capable of extracting multi-scale deep features from peptide and protein sequences, and thus better capturing their interactions at different levels. …”
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Assessment of Bone Aging—A Comparison of Different Methods for Evaluating Bone Tissue by Paweł Kamiński, Aleksander Gali, Rafał Obuchowicz, Michał Strzelecki, Adam Piórkowski, Marcin Kociołek, Elżbieta Pociask, Joanna Kwiecień, Karolina Nurzyńska

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Enhanced MRI brain tumor detection using deep learning in conjunction with explainable AI SHAP based diverse and multi feature analysis by Asif Rahman, Maqsood Hayat, Nadeem Iqbal, Fawaz Khaled Alarfaj, Salem Alkhalaf, Fahad Alturise

“…This study investigates the synergy among multiple feature representation schemes such as local Binary Patterns (LBP), Gabor filters, Discrete Wavelet Transform, Fast Fourier Transform, Convolutional Neural Networks (CNN), and Gray-Level Run Length Matrix alongside five learning algorithms namely: k-nearest Neighbor, Random Forest, Support Vector Classifier (SVC), and probabilistic neural network (PNN), and CNN. …”
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Compression-Aware Hybrid Framework for Deep Fake Detection in Low-Quality Video by Lagsoun Abdel Motalib, Oujaoura Mustapha, Hedabou Mustapha

“…Unlike end-to-end deep neural networks, our method emphasizes interpretability and computational efficiency, while maintaining high detection accuracy under diverse real-world conditions. …”
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Enhancement of eye socket recognition performance using inverse histogram fusion images and the Gabor transform by Harisu Abdullahi Shehu, Ibrahim Furkan Ince, Faruk Bulut

“…This method involves the utilization of an inverse histogram fusion image to gener-ate Gabor features from the identified eye socket regions. These Gabor features are subsequently transformed into Gabor images and employed for recognition by utilizing both traditional methods and deep-learning models. …”
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Design of upper limb muscle strength assessment system based on surface electromyography signals and joint motion by Siqi Wang, Wei Lai, Yipeng Zhang, Junyu Yao, Xingyue Gou, Hui Ye, Jun Yi, Dong Cao

“…The extracted features from the sEMG and joint motion data were analyzed using three algorithms: Random Forest (RF), Backpropagation Neural Network (BPNN), and Support Vector Machines (SVM), to predict muscle strength through regression models. …”
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Kernel machine tests of association using extrinsic and intrinsic cluster evaluation metrics. by Alexandria M Jensen, Peter DeWitt, Brianne M Bettcher, Julia Wrobel, Katerina Kechris, Debashis Ghosh

“…By incorporating notions of similarity between network community structures into a kernel distance function, the high-dimensional feature space of brain networks, defined on input pairs, can be generalized to non-linear spaces, allowing for a wider class of distance-based algorithms. …”
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Performance evaluation of enhanced deep learning classifiers for person identification and gender classification by Vasu Krishna Suravarapu, Hemprasad Yashwant Patil

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Evaluative representations of the student audience in the context of current processes in Donbas: comparison of studies by L. B. Vnukova

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Realistic synthetic dataset generation for cyber-physical systems: a performance evaluation by Yaa Acquaah, Kaushik Roy

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Evaluation method of hydrophobicity of composite insulators based on improved Mask R-CNN by SHENG Fei, CAO Liu, LIU Yulong, HUANG Jie, HUANG Yaqian, ZHU Yanqing

“…Firstly, the location and size of all water droplets in the image are determined by feature pyramid network (FPN) and the mask branch of Mask R-CNN is used to predict the hydrophobicity level of all water droplets. …”
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Identifying the effectiveness of face mask in a large population with a network-based fluid model. by Akshay Anand, Kourosh Shoele

“…In this study, a new semi-analytical flow network model based on the Kármán-Pohlhausen technique is introduced and utilized to efficiently assess mask performance across diverse facial features that represent the observed variations inside a large population. …”
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Amharic Language Image Captions Generation Using Hybridized Attention-Based Deep Neural Networks by Rodas Solomon, Mesfin Abebe

“…To address this challenge, this study proposes a hybridized attention-based deep neural network (DNN) model. The model consists of an Inception-v3 convolutional neural network (CNN) encoder to extract image features, a visual attention mechanism to capture significant features, and a bidirectional gated recurrent unit (Bi-GRU) with attention decoder to generate the image captions. …”
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MISGNet: A Multilevel Intertemporal Semantic Guidance Network for Remote Sensing Images Change Detection by Binge Cui, Chenglong Liu, Haojie Li, Jianzhi Yu

“…This leads to poor identification of identical semantic targets that have unique features. In this article, we put forth a proposal for a multilevel intertemporal semantic guidance network (MISGNet) that would effectively derive representations of semantic changes. …”
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A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation Network by Shuang Liu, Zeng Zhuang, Yanfeng Zheng, Simon Kolmanic

“…In the process of operation, they may destroy the 2D structure of the image and cannot effectively capture low-level features. Therefore, we propose a new multi-scale cross-attention method called M-VAN Unet, which is designed based on the Visual Attention Network (VAN) and can effectively learn local and global features. …”
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IAE-CDNet: A Remote Sensing Change Detection Network for Buildings With Interactive Attention-Enhanced by Zhaoyang Han, Linlin Zhang, Qingyan Meng, Chongchang Wang, Wenxu Shi, Maofan Zhao

“…To address these challenges, we propose the interactive attention-enhanced change detection network (IAE-CDNet). We design the local–global interaction attention module, which effectively establishes the interactive relationship between local and global features and realizes information interaction between branches, enhancing the ability to obtain architectural detail features. …”
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A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification by A. M. Mutawa, Sai Sruthi

“…While earlier studies primarily relied on conventional machine learning and recurrent neural networks, this work evaluates the effectiveness of transformer-based models—an area not extensively explored for this task. …”
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