Identification of AI-Generated Rock Thin-Section Images by Feature Analysis Under Data Scarcity by Magdalena Habrat, Maciej Dwornik

“…Synthetic images were generated using a widely accessible diffusion model, based on real training data. Expert evaluation noted high realism, though some structural and rock-type differences remained detectable. …”
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PEDAGOGICAL CONDITIONS FOR FORMING PROFESSIONAL COMPETENCE OF FUTURE BACHELORS IN AGRICULTURAL ENGINEERING DURING THEIR PROFESSIONAL TRAINING by Lychova Tetiana

“…The next step in the research of the considered issue is the construction of a criterion base for forming professional competence of future bachelors in agricultural engineering during their professional training.…”
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Multi-Scale Feature Mixed Attention Network for Cloud and Snow Segmentation in Remote Sensing Images by Liling Zhao, Junyu Chen, Zichen Liao, Feng Shi

“…We opted to test the model using a high-resolution cloud and snow dataset based on WorldView2 (CSWV). This dataset contains high-resolution images of cloud and snow, which can meet the training and testing requirements of cloud and snow segmentation tasks. …”
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Comprehensive Style Transfer for Facial Images Using Enhanced Feature Attribution in Generative Adversarial Nets by Yongseon Yoo, Seonggyu Kim, Jong-Min Lee

“…Our method introduces two key innovations for enhanced feature attribution: 1) dual Gram matrix-based loss functions (G1 and G2), which operate at different stages of the generation process to capture richer style information by establishing deeper correlations between feature maps, and 2) a balanced training objective that integrates perceptual loss with cycle-consistency loss to maintain content fidelity during style transfer. …”
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Deep feature batch correction using ComBat for machine learning applications in computational pathology by Pierre Murchan, Pilib Ó Broin, Anne-Marie Baird, Orla Sheils, Stephen P Finn

“…Attention-based multiple instance learning models were trained to predict tissue-source site (TSS), as well as clinical and genetic attributes, using raw, Macenko normalized, and Combat-harmonized patch embeddings. …”
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Non-Exemplar Incremental ISAR Target Classification via Mix-Mamba Feature Adjustment Network by Ruihang Xue, Caipin Li, Wencan Peng, Xueru Bai, Feng Zhou

“…Then, the feature adjustment network facilitates knowledge transfer between base and incremental classes by dynamically maintaining a prototype for each target class. …”
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Visualizing Relaxation in Wearables: Multi-Domain Feature Fusion of HRV Using Fuzzy Recurrence Plots by Puneet Arya, Mandeep Singh, Mandeep Singh

“…HRV data were collected from 60 participants during spontaneous and slow-paced breathing sessions. Features were extracted from five domains: time, frequency, non-linear, geometric, and image-based. …”
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Automated data processing and feature engineering for deep learning and big data applications: A survey by Alhassan Mumuni, Fuseini Mumuni

“…Today, end-to-end automated data processing systems based on automated machine learning (AutoML) techniques are capable of taking raw data and transforming them into useful features for big data tasks by automating all intermediate processing stages. …”
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An Improved YOLOP Lane-Line Detection Utilizing Feature Shift Aggregation for Intelligent Agricultural Machinery by Cundeng Wang, Xiyuan Chen, Zhiyuan Jiao, Shuang Song, Zhen Ma

“…For lane-line detection tasks, we also propose an improved YOLOP lane-line detection algorithm based on feature shift aggregation. Homemade datasets were used for training and testing. …”
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SFG-Net: A Scattering Feature Guidance Network for Oriented Aircraft Detection in SAR Images by Qingyang Ke, Youming Wu, Wenchao Zhao, Qingbiao Meng, Tian Miao, Xin Gao

“…Additionally, a feature scattering center-based label assignment (FLA) strategy is introduced, which utilizes the spatial distribution of scattering information to adaptively adjust the sample coverage and ensure that strong scattering regions are prioritized during training. …”
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AggreGait: Automatic gait feature extraction for human age and gender classification with possible occlusion by Timilehin B. Aderinola, Tee Connie, Thian Song Ong, Andrew Beng Jin Teoh, Michael Kah Ong Goh

“…AggreGait integrates upper and lower body features with whole-body information for age and gender prediction. …”
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Feature fusion ensemble classification approach for epileptic seizure prediction using electroencephalographic bio-signals by Yazeed Alkhrijah, Yazeed Alkhrijah, Shehzad Khalid, Shehzad Khalid, Syed Muhammad Usman, Amina Jameel, Muhammad Zubair, Haya Aldossary, Aamir Anwar, Saad Arif

“…A non-overlapping window of 15 s is selected to segment the EEG signals, and an optimal spatial filter is applied to reduce the dimensionality. Handcrafted features, including both time and frequency domains, have been extracted and concatenated with the customized one-dimensional convolutional neural network-based features to form a comprehensive feature vector. …”
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Integrating recursive feature selection with automated machine learning framework for global wheat price prediction by Prity Kumari, N. Harshith, Athula Ginige

“…This research presents an innovative method for predicting global wheat prices by combining Recursive Feature Elimination with Cross Validation (RFECV) and Bayesian Ridge Regression in an Automated Machine Learning (AutoML) framework. …”
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Semi-supervised segmentation of cardiac chambers from LGE-CMR using feature consistency awareness by Hairui Wang, Helin Huang, Jing Wu, Nan Li, Kaihao Gu, Xiaomei Wu

“…We also applied a voxel-level contrastive learning strategy to achieve feature-level consistency, helping the model pay attention to the consistency between features overlooked in previous research. …”
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Intelligent islanding detection framework for smart grids using wavelet scalograms and HOG feature fusion by Kumaresh Pal, Kumari Namrata, Ashok Kumar Akella, Akshit Samadhiya, Ahmad Taher Azar, Mohamed Tounsi, Naglaa F. Soliman, Walid El-Shafai

“…This research addresses these critical limitations by introducing a novel, highly reliable, and robust machine learning-based islanding detection scheme. The proposed approach innovatively utilizes Histogram of Oriented Gradient (HOG) features derived from scalogram images, which are generated through Continuous Wavelet Transform (CWT) of the total harmonic distortion (THD) signals from three-phase voltages and currents. …”
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Algorithm of athletes’ fitness structure individual features’ determination with the help of multidimensional analysis (on example of basketball) by Zh.L. Kozina, M. Cieslicka, K. Prusik, R. Muszkieta, I.N. Sobko, O.A. Ryepko, T.A. Bazilyuk, S.B. Polishchuk, A.V. Osiptsov, S.A. Korol

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Estimating Maize Leaf Water Content Using Machine Learning with Diverse Multispectral Image Features by Yuchen Wang, Jianliang Wang, Jiayue Li, Jiacheng Wang, Hanzeyu Xu, Tao Liu, Juan Wang

“…This study introduces a high-precision method for estimating maize LWC utilizing UAV-based multispectral imagery combined with a Random Forest Regression (RFR) model. …”
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Escalate Prognosis of Parkinson’s Disease Employing Wavelet Features and Artificial Intelligence from Vowel Phonation by Rumana Islam, Mohammed Tarique

“…For classification purposes, two popular machine learning models, namely, support vector machine (SVM) and k-nearest neighbors (kNNs), are trained to distinguish patients with PD. <b>Results</b>: The results demonstrate that the inclusion of wavelet-based voice features enhances the performance of both the SVM and kNN models for PD detection. …”
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Construction Practices in Karachi Featuring Town Wise Study: Non-Compliance of Safety Measures and Consequences by Syed Faraz Jafri, Khawaja Sheeraz, Zubair Iqbal Rao, G. B. Pirzada

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IonoGAN: An Enhanced Model for Forecasting Quiet and Disturbed Ionospheric Features From Predicted Ionograms by Chu Qiu, Jinhui Cai, Zheng Wang, Pengdong Gao, Guojun Wang, Quan Qi, Bo Wang, Zhengwei Cheng, Jiankui Shi, Yajun Zhu, Xiao Wang, Kai Ding

“…Abstract Ionograms are radar echo graphs that depict vertical ionospheric density profiles, structures, fluctuations, and irregularities, with the F region represented by F‐trace and Spread‐F features in the graphs. In this paper, IonoGAN, an enhanced neural network based on the Generative Adversarial Network architecture, is proposed for direct prediction of ionograms and the variation of these ionospheric conditions. …”
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