Multi-Domain Features and Multi-Task Learning for Steady-State Visual Evoked Potential-Based Brain–Computer Interfaces by Yeou-Jiunn Chen, Shih-Chung Chen, Chung-Min Wu

“…A sequence of convolutional neural networks is then adopted to find discriminative embedding features for classification. Finally, multi-task learning-based neural networks are used to detect the corresponding stimuli correctly. …”
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Signal Distinction Electroencephalograms (EEG) Using a Back Propagation Neural Network Based On Localized Structural Features Extractions by Najlaa Safar

“…The follow up method can be useful in several applications including time-series analysis, signal processing and speech recognition.…”
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Using spatial statistics and point‐pattern simulations to assess the spatial dependency between greater sage‐grouse and anthropogenic features by Jeffrey K. Gillan, Eva K. Strand, Jason W. Karl, Kerry P. Reese, Tamara Laninga

“…Sage‐grouse exhibited no detectable avoidance of major and minor roads. The methods used here are broadly applicable in conservation biology and wildlife management to evaluate spatial relationships between species occurrence and landscape features. …”
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Histological trends and molecular genetic features of non-small cell lung cancer in the Khanty-Mansiysk autonomous district – Ugra by A. A. Mordovskiy, A. A. Aksarin, A. M. Parsadanyan, P. P. Troyan, A. I. Pakhtusov

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Remodelling Features of Heart and Postinfarction Cardiosclerosis Type 2 Diabetes: Relationship to Gene Polymorphisms LEPR Q223R by Stanislavchuk M. A., Al Salim A.

“…Objective. To establish the features of cardiac remodeling in patients with postinfarction cardiosclerosis and type 2 diabetes based on gene polymorphism leptin receptors LEPR Q223R. …”
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Application of Machine Learning Techniques for Bearing Fault Diagnosis by Sarra Eddai, Nabih Feki, Ahmed Ghorbel, Abdelkhalak El Hami, Mohamed Haddar

“…This investigation provides a comprehensive analysis of the Case Western Reserve University (CWRU) dataset, data preprocessing procedures, feature extraction techniques, and machine learning algorithms utilized for fault detection. …”
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External validation of SpineNetV2 on a comprehensive set of radiological features for grading lumbosacral disc pathologies by Alemu Sisay Nigru, MSc, Sergio Benini, PhD, Matteo Bonetti, MD, Graziella Bragaglio, MSc, Michele Frigerio, MD, Federico Maffezzoni, MSc, Riccardo Leonardi, PhD

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DeepContainer: A Deep Learning-based Framework for Real-time Anomaly Detection in Cloud-Native Container Environments by Ke Xiong, Zhonghao Wu,  Xuzhong Jia

“…DeepContainer implements a multi-layered detection approach, combining feature engineering techniques with optimized deep learning models to identify security anomalies across diverse container workloads. …”
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Modern Trends and Recent Applications of Hyperspectral Imaging: A Review by Ming-Fang Cheng, Arvind Mukundan, Riya Karmakar, Muhamed Adil Edavana Valappil, Jumana Jouhar, Hsiang-Chen Wang

“…Future advancements are anticipated to concentrate on the integration of deep learning models for automated feature extraction and decision-making in hyperspectral imaging analysis. …”
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An automatic classification of breast cancer using fuzzy scoring based ResNet CNN model by Nisha Y., Jagadeesh Gopal

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Hyperspectral imaging and machine learning for herbicide-resistant kochia identification in sugarbeet by Bright Mensah, Kelvin Betitame, Yu Zhang, Thomas Peters, Billy Graham Ram, Mian Jalal, Xin Sun

“…Spectra data were acquired and preprocessed using standard normal variate transformation and Savitzky–Golay smoothing methods, followed by stepwise feature selection to identify wavelengths features for classification. …”
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AAV Parameters Estimation Based on Improved Time-Frequency Ridge Extraction and Hough Transform by Yongji Yu, Yonghong Ruan, Junjie Zhong

“…First-Order short-time Fourier transform synchrosqueezed transform (FSST) is proposed for extracting micro-Doppler features. Specifically, a novel AAV parameter estimation method is investigated, which is based on an improved time-frequency ridge extraction and Hough transform, following a detailed analysis of the micro-Doppler time-frequency spectrum. …”
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PS-GCN: psycholinguistic graph and sentiment semantic fused graph convolutional networks for personality detection by Wenjuan Liu, Zhengyan Sun, Subo Wei, Shunxiang Zhang, Guangli Zhu, Lei Chen

“…P-GCN is designed to capture the dependency information between psycholinguistic features, while S-GCN utilises syntactic structure analysis to gather more abundant information features and enhance semantic understanding ability. …”
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Detecting Sensitive Spectral Bands and Vegetation Indices for Potato Yield Using Handheld Spectroradiometer Data by Diego Gomez, Pablo Salvador, Juan Fernando Rodrigo, Jorge Gil

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False traffic information detection based on weak classifiers integration in vehicular ad hoc networks by Xiang-wen LIU, Ya-li SHI, ENGXia F

“…Vehicles report traffic information mutually by self-organized manner in vehicular ad hoc networks (VANET),and the message need to be identified in the open network environment.However,it is very difficult for fast moving ve-hicles to detect a lot of traffic alert information in a short time.To solve this problem,a false traffic message detection method was presented based on weak classifiers integration.Firstly,the effective features of traffic alert information was extended and segmentation rules were designed to divide the information feature set into multiple feature subsets,then the corresponding weak classifiers were used to process feature subsets respectively according to the different character-istics of the subsets' features.Simulation experiments and performance analysis show that the selected weak classifiers integration method reduces the detection time,and because of the application of combined features,the detection rate is better than the test of using only some of the characteristics.…”
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Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data by T. Radke, S. Fuchs, C. Wilms, I. Polkova, I. Polkova, I. Polkova, M. Rautenhaus, M. Rautenhaus

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Skeletal Muscle Ultrasound Radiomics and Machine Learning for the Earlier Detection of Type 2 Diabetes Mellitus by Sameed Khan, Chad L. Klochko, Sydney Cooper, Brendan Franz, Lauren Wolf, Adam Alessio, Steven B. Soliman

“…Conclusion: US radiomics and machine learning yielded promising results for the detection of T2D using skeletal muscle. Given the increasing use of shoulder US and the increasingly high number of undiagnosed patients with T2D, skeletal muscle US and radiomics analysis has the potential to serve as a supplemental noninvasive screening tool for the opportunistic earlier detection of T2D and PreD.…”
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Hybrid deep learning model for accurate and efficient android malware detection using DBN-GRU. by Heena Kauser Sk, Maria Anu V

“…The model extracts static features (permissions, API calls, intent filters) and dynamic features (system calls, network activity, inter-process communication) from Android APKs, enabling a comprehensive analysis of application behavior.The proposed model was trained and tested on the Drebin dataset, which includes 129,013 applications (5,560 malware and 123,453 benign).Performance evaluation against NMLA-AMDCEF, MalVulDroid, and LinRegDroid demonstrated that DBN-GRU achieved 98.7% accuracy, 98.5% precision, 98.9% recall, and an AUC of 0.99, outperforming conventional models.In addition, it exhibits faster preprocessing, feature extraction, and malware classification times, making it suitable for real-time deployment.By bridging static and dynamic detection methodologies, the DBN-GRU enhances malware detection capabilities while reducing false positives and computational overhead.These findings confirm the applicability of the proposed model in real-world Android security applications, offering a scalable and high-performance malware detection solution.…”
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Investigating the effects of Gaussian noise on epileptic seizure detection: The role of spectral flatness, bandwidth, and entropy by Nuri Ikizler, Gunes Ekim

“…This study investigates the effect of Gaussian noise on the classification of EEG signals from five classes in the Bonn University EEG dataset for epileptic seizure detection, using Power Spectral Density features. The EEG data are pre-processed with a low-pass filter at a cutoff frequency of 40 Hz, and a total of 11 features, including spectral flatness difference, spectral bandwidth difference, and entropy difference, are extracted. …”
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Energy-Efficient Islanding Detection Using CEEMDAN and Neural Network Integration in Photovoltaic Distribution System by Sulayman Kujabi, Emmanuel Asuming Frimpong, Francis Boafo Effah

“…Furthermore, feature permutation importance analysis highlighted the critical role of certain features in the model's performance.  …”
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