CSTFNet: A CNN and Dual Swin-Transformer Fusion Network for Remote Sensing Hyperspectral Data Fusion and Classification of Coastal Areas by Dekai Li, Harold Neira-Molina, Mengxing Huang, Syam M.S., Yu Zhang, Zhang Junfeng, Uzair Aslam Bhatti, Muhammad Asif, Nadia Sarhan, Emad Mahrous Awwad

“…In recent years, the convolutional neural network (CNN) framework and transformer models have demonstrated strong performance in HSI classification, especially in applications requiring precise change detection and analysis. However, due to the high dimensionality of HSI data and the complexity of spectral-spatial feature extraction, achieving accurate results in coastal areas remains challenging. …”
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Myocardial mechanical function measured by cardiovascular magnetic resonance in patients with heart failure by Yufan Gao, Boxin Li, Yanhe Ma, Shuo Liang, Anhong Yu, Hong Zhang, Zhigang Guo

“…ABSTRACT: Background: Strain analysis offers a valuable tool to assess myocardial mechanics, allowing for the detection of impairments in heart function. …”
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Non-Contact Oxygen Saturation Estimation Using Deep Learning Ensemble Models and Bayesian Optimization by Andrés Escobedo-Gordillo, Jorge Brieva, Ernesto Moya-Albor

“…We applied the Eulerian Video Magnification technique using the Hermite Transform (EVM-HT) as a feature detector to feed a Three-Dimensional Convolutional Neural Network (3D-CNN). …”
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Multiclass aerial image recognition using improved Black Widow Optimization with deep learning on unmanned aerial networks imaging by Mahmoud Ragab, Bandar M. Alghamdi, Sami Saeed Binyamin, Sultan Algarni, Roobaea Alroobaea, Abdullah M. Baqasah, Majed Alsafyani

“…DL uses neural networks to improve land cover recognition, simplify object detection, and enable real-time data analysis. This study proposes a Multiclass Aerial Image Recognition using Improved Black Widow Optimization with Deep Learning (MAIR-IBWODL) approach to UAV imaging. …”
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Exosome-related genes influence the progression of stroke through neuroinflammatory responses by Boyan Zhao, Boyan Zhao, Jianing Wu, Mingyang Han, Xuan Rong, Jin Jin, Shiya Liu, Cheng Zhang, Ruotian Zhang, Xin Chen, Fei Peng, Xingli Dong, Shiguang Zhao, Shiguang Zhao

“…CIBERSORT and MCPcounter were applied to assess immune infiltration, and Principal Component Analysis was utilized to explore the associations of feature genes and hub genes with immune cells. …”
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Hybrid FFT-ARMA-Burg Modeling and LSTM-Enhanced BBO Optimization for Fault Diagnosis in Induction Motors by Khadidja Boudraa, Mohammed Assam Ouali, Mohamed Ladjal

“… Induction motors (IM) are crucial in industrial systems, and fault diagnosis reliably and effectively is of importance for maintaining efficiency. The detection of a broken rotor bar(RBB) is one of the most challenging tasks in condition monitoring due to the complexity of fault features in motor current signals. …”
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Unmasking Nuances Affecting Loneliness: Using Digital Behavioural Markers to Understand Social and Emotional Loneliness in College Students by Malik Muhammad Qirtas, Evi Zafeiridi, Eleanor Bantry White, Dirk Pesch

“…The XGBoost model achieved the highest accuracy (78.48%) in predicting loneliness. Feature importance analysis found the critical role of phone usage and location-based features in distinguishing between social and emotional loneliness.…”
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Prediction of weld quality in laser welding of hardmetal and steel using high-speed imaging and machine learning methods by Mohammadhossein Norouzian, Mahan Khakpour, Marko Orosnjak, Atal Anil Kumar, Slawomir Kedziora

“…Analysis of individual image plume frames from HSI reveals distinct statistical features that are identified as unique to each welding condition. …”
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Improving Person Re-Identification Rate in Security Cameras by Orthogonal Moments and a Distance-based Criterion by Ali Dadkhah, Saeed Nasri

“…This article focuses on extracting the most distinctive features to overcome these challenges. The features of Hu moment, Zernike moment in 9th order and Legendre moment in 9th order for each image are extracted and merged into a single feature vector to form a single feature vector for each image. …”
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A metric learning method for estimating myelin content based on T2-weighted MRI from a de- and re-myelination model of multiple sclerosis. by Glen Pridham, Shahnewaz Hossain, Khalil S Rawji, Yunyan Zhang

“…Further, the area under the receiver operator characteristic curve of our unsupervised myelin feature (0.883, 95% CI: 0.874-0.891) was significantly better than any of the supervised models in detecting white matter myelin as compared to histology. …”
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Objective Pain Assessment Using Deep Learning Through EEG-Based Brain–Computer Interfaces by Abeer Al-Nafjan, Hadeel Alshehri, Mashael Aldayel

“…Deep learning models, including convolutional neural networks and recurrent neural networks, were employed to classify the wavelet features extracted through time–frequency domain analysis. …”
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Assessing data and sample complexity in unmanned aerial vehicle imagery for agricultural pattern classification by Linara Arslanova, Sören Hese, Marcel Fölsch, Friedemann Scheibler, Christiane Schmullius

“…However, there are limitations in detecting refined sub-classes of undisturbed canopy areas assigned to phenological groups, highlighting the need for class reduction and tailored feature and layer selection. …”
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Decoding Depression from Different Brain Regions Using Hybrid Machine Learning Methods by Qi Sang, Chen Chen, Zeguo Shao

“…Moreover, the analysis of the brain region segmentation revealed that the left temporal lobe exhibited higher discriminative power in detecting MDD, highlighting its important role in the neurobiology of depression. …”
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L-type (Loaf) Cervical Secretion Crystallization Directionality Biomarker Study by José María Murcia Lora, María Luisa Esparza Encina, Cristina Reig, Oscar Martínez Martínez, Jennifer Simoni, María Ángeles Martínez Calvo, Juan Luis Alcázar

“…We therefore propose this feature detection mechanism as a novel biomarker of fertility.…”
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Serum peptide biomarkers by MALDI-TOF MS coupled with machine learning for diagnosis and classification of hepato-pancreato-biliary cancers by Piya Prajumwongs, Attapol Titapun, Vasin Thanasukarn, Apiwat Jareanrat, Natcha Khuntikeo, Krit Rattanarak, Nisana Namwat, Poramate Klanrit, Arporn Wangwiwatsin, Jarin Chindaprasirt, Supinda Koonmee, Prakasit Sa-Ngiamwibool, Nattha Muangritdech, Sawanya Charoenlappanit, Janthima Jaresitthikunchai, Sittiruk Roytrakul, Watcharin Loilome

“…Visualization using heatmap, PLS-DA, and multiclass RF analysis showed clear separation between healthy individuals and HPB cancer patients, as well as among different HPB cancer subtypes. …”
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Machine learning-based stratification of prediabetes and type 2 diabetes progression by Marwa Matboli, Abdelrahman Khaled, Manar Fouad Ahmed, Manar Yehia Ahmed, Radwa Khaled, Gena M. Elmakromy, Amani Mohamed Abdel Ghani, Marwa M. El-Shafei, Marwa Ramadan M. Abdelhalim, Asmaa Mohamed Abd El Gwad

“…Abstract Background Diabetes mellitus, a global health concern with severe complications, demands early detection and precise staging for effective management. …”
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Condition Monitoring and Fault Diagnosis of Wind Turbine: A Systematic Literature Review by Musavir Hussain, Nayyar Hussain Mirjat, Faheemullah Shaikh, Lubna Luxmi Dhirani, Laveet Kumar, Ahmad K. Sleiti

“…It is therefore clear that early detection of these failures can be achieved through an appropriate condition monitoring approach. …”
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Unveiling smart contract vulnerabilities: Toward profiling smart contract vulnerabilities using enhanced genetic algorithm and generating benchmark dataset by Sepideh HajiHosseinKhani, Arash Habibi Lashkari, Ali Mizani Oskui

“…Additionally, three types of taxonomies are established, covering SC literature review, profiling techniques, and feature extraction. These taxonomies offer a systematic classification and analysis of information, enhancing the efficiency of the proposed profiling technique. …”
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Decoding Agricultural Drought Resilience: A Triple-Validated Random Forest Framework Integrating Multi-Source Remote Sensing for High-Resolution Monitoring in the North China Plain by Xianyong Meng, Song Zhang, Guoqing Wang, Jianli Ding, Chengbin Chu, Jianyun Zhang, Hao Wang

“…Particularly, it achieves 87% accuracy in mild drought (−1.0 < SPEI ≤ −0.5) and 85% in moderate drought (−1.5 < SPEI ≤ −1.0) detection. The 20-year (2000–2019) spatiotemporal analysis reveals that moderate drought events dominated the region (23.7% of total occurrences), with significant intensification during the 2010–2012 and 2014–2016 periods. …”
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Untargeted Lipidomic Biomarkers for Liver Cancer Diagnosis: A Tree-Based Machine Learning Model Enhanced by Explainable Artificial Intelligence by Cemil Colak, Fatma Hilal Yagin, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

“…Serum samples underwent untargeted lipidomic analysis with LC-QTOF-MS. Lipidomic data underwent univariate and multivariate analyses, including fold change (FC), <i>t</i>-tests, PLS-DA, and Elastic Network feature selection, to identify significant biomarker candidate lipids. …”
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