Enhanced Interpretable Forecasting of Cryptocurrency Prices Using Autoencoder Features and a Hybrid CNN-LSTM Model by Wajeeha Badar, Shabana Ramzan, Ali Raza, Norma Latif Fitriyani, Muhammad Syafrudin, Seung Won Lee

“…Deep variational autoencoders (VAE) are used in the stage of preprocessing to determine noticeable patterns in datasets by learning features from historical Bitcoin price data. …”
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Morphological features of histogenic differon cells in connective tissue of guinea pigs’ lungs after sensitization with ovalbumin by S. S. Popko, V. M. Yevtushenko

“…An urgent issue of modern morphology is establishing a number of patterns of morphological changes and reactivity of connective tissue components of lungs in case of experimental sensitization with allergens. …”
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PhishingGNN: Phishing Email Detection Using Graph Attention Networks and Transformer-Based Feature Extraction by Mejdl Safran, Abdulbaset Musleh

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SwiftSession: A Novel Incremental and Adaptive Approach to Rapid Traffic Classification by Leveraging Local Features by Tieqi Xi, Qiuhua Zheng, Chuanhui Cheng, Ting Wu, Guojie Xie, Xuebiao Qian, Haochen Ye, Zhenyu Sun

“…SwiftSession extracts statistical and sequential features from the first K packets of traffic. Statistical features capture overall characteristics, while sequential features reflect communication patterns. …”
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Circadian clock features define novel subtypes among breast cancer cells and shape drug sensitivity by Carolin Ector, Jeff Didier, Sébastien De Landtsheer, Malthe S Nordentoft, Christoph Schmal, Ulrich Keilholz, Hanspeter Herzel, Achim Kramer, Thomas Sauter, Adrián E Granada

“…Furthermore, we demonstrate that the circadian clock plays a critical role in shaping pharmacological responses to various anti-cancer drugs and we identify circadian features descriptive of drug sensitivity. Collectively, our findings establish a foundation for implementing circadian-based treatment strategies in breast cancer, leveraging clock phenotypes and drug sensitivity patterns to optimize therapeutic outcomes.…”
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Improving Gaussian Naive Bayes classification on imbalanced data through coordinate-based minority feature mining by Wei Wang, Li Yan, Fen Liu, Yanxi Li

“…The algorithm transforms the dataset from absolute coordinates to RLDC-relative coordinates, revealing latent local relative density change features. Due to the imbalanced distribution, sparse feature space, and class overlap, minority class samples can exhibit distinct patterns in these transformed features. …”
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Optimizing Bearing Fault Diagnosis in Rotating Electrical Machines Using Deep Learning and Frequency Domain Features by Eduardo Quiles-Cucarella, Alejandro García-Bádenas, Ignacio Agustí-Mercader, Guillermo Escrivá-Escrivá

“…Scalograms proved particularly effective in identifying distinct vibration patterns for faults in bearings’ inner and outer races. …”
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Subcortical Brain Segmentation Based on a Novel Discriminative Dictionary Learning Method and Sparse Coding by Xiang Li, Ying Wei, Yunlong Zhou, Bin Hong

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Study on the Method of Vineyard Information Extraction Based on Spectral and Texture Features of GF-6 Satellite Imagery by Xuemei Han, Huichun Ye, Yue Zhang, Chaojia Nie, Fu Wen

“…The results indicate that three spectral features and five texture features under a 7 × 7 window have significant sensitivity to vineyard recognition. …”
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Hyperclustering: High-Order Deep/Shallow Feature Clustering for Subway Shield Tunneling Water Leakage Detection by Jianjun Xu, Xing Yuan, Lixiao Zheng, Da Lin

“…More recent efforts using deep learning models like CNNs and RNNs also face challenges in capturing the diverse relationships among features. This paper introduces HyperClustering, a new framework designed to enhance subway shield tunneling water leakage detection through multimodal deep/shallow feature fusion techniques. …”
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Attention-Guided Sample-Based Feature Enhancement Network for Crowded Pedestrian Detection Using Vision Sensors by Shuyuan Tang, Yiqing Zhou, Jintao Li, Chang Liu, Jinglin Shi

“…AGFEN improves the semantic information of high-level features by mapping it onto low-level feature details through sampling, creating an effect comparable to mask modulation. …”
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DSAT: a dynamic sparse attention transformer for steel surface defect detection with hierarchical feature fusion by Shouluan Wu, Hui Yang, Liefa Liao, Chao Song, Yating Fang, Jianglong Fu, Tan Li

“…These defects exhibit diverse morphological characteristics and complex patterns, which pose substantial challenges to traditional detection models, particularly regarding multi-scale feature extraction and information retention across network depths. …”
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Predicting Freeway Traffic Crash Severity Using XGBoost-Bayesian Network Model with Consideration of Features Interaction by Yang Yang, Kun Wang, Zhenzhou Yuan, Dan Liu

“…Furthermore, the XGBoost (eXtreme Gradient Boosting) model was established, and the SHAP (SHapley Additive exPlanation) value was introduced to interpret the XGBoost model; the importance ranking of the influence degree of each feature towards the target variables and the visualization of the global influence of each feature towards the target variables were both obtained. …”
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Kidney Ensemble-Net: Enhancing Renal Carcinoma Detection Through Probabilistic Feature Selection and Ensemble Learning by Zaib Akram, Kashif Munir, Muhammad Usama Tanveer, Atiq Ur Rehman, Amine Bermak

“…Our approach begins by acquiring spatial features from contrast-enhanced images using a Convolutional Neural Network (CNN) effectively capturing intricate patterns and structures characteristic of different carcinoma subtypes. …”
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Genomic insights into host-associated variants and transmission features of a ToBRFV isolate from Mexico by Erika Janet Zamora-Macorra, Daniel Leobardo Ochoa-Martínez, Claudia Yaritza Chavarín-Camacho, Rosemarie W. Hammond, Katia Aviña-Padilla

“…Understanding its genomic features and transmission mechanisms is critical for effective disease management. …”
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Early breast cancer detection in CT scans using convolutional neural bidirectional feature pyramid network by Tahani Jaser Alahmadi, Adeel Ahmed, Amjad Rehman, Abeer Rashad Mirdad, Bayan Al Ghofaily, Shehryar Ali

“…Despite the potential of CT scans in visualizing breast tissue in 3D with high resolution, extracting meaningful patterns from these scans is difficult due to the complex and nonlinear nature of the tissue features. …”
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Automated lung cancer detection using novel genetic TPOT feature optimization with deep learning techniques by Mohamed Hammad, Mohammed ElAffendi, Muhammad Asim, Ahmed A. Abd El-Latif, Radwa Hashiesh

“…Deep learning, particularly convolutional neural networks (CNNs), offers an automated alternative capable of learning intricate patterns from medical images. However, previous deep learning models for lung cancer detection have faced challenges such as limited data, inadequate feature extraction, interpretability issues, and susceptibility to data variability. …”
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Clinical, Dermoscopic, and Histopathological Features of Spitzoid Lesions in Adult Patients: Insights from a Retrospective Study by Riccardo Castelli, Ilaria Salvi, Ilaria Trave, Gianmaria Viglizzo, Antonio Guadagno, Francesco Cabiddu, Silvia Riva, Aurora Parodi, Emanuele Cozzani

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Attention-Enhanced CNN-LSTM Model for Exercise Oxygen Consumption Prediction with Multi-Source Temporal Features by Zhen Wang, Yingzhe Song, Lei Pang, Shanjun Li, Gang Sun

“…The baseline CNN-LSTM reached <i>R</i>² = 0.946, outperforming a plain LSTM (<i>R</i>² = 0.926) thanks to stronger local spatio-temporal feature extraction. Introducing a spatial attention mechanism raised accuracy further (<i>R</i>² = 0.962), whereas temporal attention reduced it (<i>R</i>² = 0.930), indicating that attention success depends on how well the attended features align with exercise dynamics. …”
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Swin-Panda: Behavior Recognition for Giant Pandas Based on Local Fine-Grained and Spatiotemporal Displacement Features by Xinyu Yi, Han Su, Peng Min, Mengnan He, Yimin Han, Gai Luo, Pengcheng Wu, Qingyue Min, Rong Hou, Peng Chen

“…While substantial progress has been made in the field of individual identification, behavior recognition remains underdeveloped, facing challenges such as the lack of dynamic temporal features and insufficient extraction of behavioral characteristics. …”
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