Enhanced Extraction of Activation Time and Contractility From Myocardial Strain Data Using Parameter Space Features and Computational Simulations by Borut Kirn

“…Each pair generated a simulated strain pattern, and by scanning the grid, we identified cohorts of similar strain patterns for each simulation. …”
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Multi-Dimensional AE Signal Features in Eccentrically Loaded Concrete Structures: A Machine Learning Classification for Damage Progression by Shilong Ding, Alipujiang Jierula, Abudusaimaiti Kali, Tong Han, Tae-Min Oh

“…This study employed K-means clustering algorithm and Gaussian mixture models (GMMs) to analyze AE signal features from reinforced concrete (RC) columns undergoing failure under the eccentric compression loading of different eccentricity. …”
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MAFONN-EP: A Minimal Angular Feature Oriented Neural Network based Emotion Prediction system in image processing by L.B. Krithika, G.G. Lakshmi Priya

“…Particular features are selected by employing the Cuckoo Search based Particle Swarm Optimization (CS-PSO) technique that also reduces the feature dimensionality. …”
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Hybrid feature selection for real-time road surface classification on low-end hardware: A machine learning approach by Cong Ngo Van, Duc-Nghia Tran, Ton That Long, Nguyen Gia Minh Thao, Duc-Tan Tran

“…This paper proposed a hybrid filter-wrapper algorithm for feature selection based on an index of important features used in the pavement classification task; it takes advantage of two pure methods: the fast speed of the filter and the efficiency of the wrapper method. …”
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Hidden features: CD36/SR-B2, a master regulator of macrophage phenotype/function through metabolism by Yuge Chen, Xuejia Zhang, Xuejia Zhang, Shengbin Huang, Shengbin Huang, Maria Febbraio

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Historical Manuscripts Analysis: A Deep Learning System for Writer Identification Using Intelligent Feature Selection with Vision Transformers by Merouane Boudraa, Akram Bennour, Mouaaz Nahas, Rashiq Rafiq Marie, Mohammed Al-Sarem

“…Leveraging vision transformer models, our methodology effectively learns complex patterns and features from extracted patches, enabling precise identification of writers across historical manuscripts. …”
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Behavioral Phenotype, Electroclinical Features, and Treatment Options in Twins with Lrp2 Candidate Variants (Donnay–Barrow/Foar Syndrome) by Alessia Mingarelli, Giovanni Battista Pipitone, Giacomo Torini, Maria Grazia Patricelli, Martina Totaro, Clara Colonna, Paola Carrera, Federico Raviglione

“…During follow-up, at the age of 7, the main clinical features of the patients included insomnia, autistic features, severe psychomotor delay, and absent speech. …”
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Prediction Model and Knowledge Discovery for Roof Stress in Mined-Out Areas Integrating 3D Scanning Image Features by Yong Yang, Kepeng Hou, Huafen Sun, Linning Guo, Yalei Zhe

“…To address these issues, this study innovatively integrates 3D laser scanning image features into the prediction of roof stress in mined-out areas. …”
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An electrical load forecasting model based on a novel closed loop neural networks and interaction gain feature selection by Gholamreza Memarzadeh, Faezeh Amirteimoury, Hossein Noori, Farshid Keynia

“…This multi-step process begins with the wavelet transform, which decomposes the load data into distinct frequency components, allowing for a detailed analysis of underlying patterns. Then, MI-IG was employed for feature selection, ensuring that only the most informative and relevant variables are included in the forecasting model. …”
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DSCnet: detection of drug and alcohol addiction mechanisms based on multi-angle feature learning from the hybrid representation of EEG by Jing Wu, Nan Zhang, Qilei Ye, Xiaorui Zheng, Minmin Shao, Xian Chen, Hui Huang

“…This enables the model to capture neural activity patterns related to addiction mechanisms. DSCnet uses a multi-angle feature extraction strategy, emphasizing information from various perspectives.ResultsOn the drug addiction dataset, DSCnet achieved 85.11% accuracy, 85.13% precision, 85.12% recall, and 85.12% F1-score. …”
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Comparative analysis of deep neural network architectures for renewable energy forecasting: enhancing accuracy with meteorological and time-based features by Sunawar Khan, Tehseen Mazhar, Muhammad Amir Khan, Tariq Shahzad, Wasim Ahmad, Afsha Bibi, Mamoon M. Saeed, Habib Hamam

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Enhanced air quality prediction using adaptive residual Bi-LSTM with pyramid dilation and optimal weighted feature selection by R. Sudha, Ajith Damodaran, Gunaselvi Manohar

“…After extracting the weighted features, classification is carried out using an Adaptive Residual Bi-LSTM network combined with Pyramid Dilation (ARBi-LSTM-PD), which significantly increase the model’s potential to identify complex patterns within the data. …”
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Sleep Stage Classification Using Variational Mode Decomposition and Wrapper-Based Feature Selection From the Single Channel EEG by Vipin Prakash Yadav, M. A. Aswathy, Sahebgoud Hanamantray Karaddi, Sana Pavankumar Reddy, G. Pradeep Reddy, Aman Kumar

“…Furthermore, the optimum feature subset was selected using the Wrapper-Based Feature Selector (WBFS). …”
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Study of component features of essential oils of <i>Rosmarinus officinalis</i> and <i>Salvia sclarea</i> of Crimean origin by R. E. Ermachenkov, A. L. Markov, M. M. Agaev, E. V. Burtseva, I. I. Terninko

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Forecasting Stock Market Volatility Using Housing Market Indicators: A Reinforcement Learning-Based Feature Selection Approach by Pourya Zareeihemat, Samira Mohamadi, Jamal Valipour, Seyed Vahid Moravvej

“…The RL component actively refines feature selection through continuous data interaction, ensuring the model captures the most significant features and effectively mitigates the risk of overfitting. …”
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Stereo Matching Network with Transformer-CNN Feature Fusion and ConvGRU Refinement for High-resolution Satellite Stereo Images by M. Yang, S. Jiang, W. Jiang, Q. Li

“…First, in the feature extraction stage, we use two independent Transformer and CNN modules to extract global and local features of stereo image pairs. …”
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Evaluating cognitive decline detection in aging populations with single-channel EEG features based on two studies and meta-analysis by Lior Molcho, Neta B. Maimon, Talya Zeimer, Ofir Chibotero, Sarit Rabinowicz, Vered Armoni, Noa Bar On, Nathan Intrator, Ady Sasson

“…Abstract Timely detection of cognitive decline is paramount for effective intervention, prompting researchers to leverage EEG pattern analysis, focusing particularly on cognitive load, to establish reliable markers for early detection and intervention. …”
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Smart Grid Intrusion Detection for IEC 60870-5-104 With Feature Optimization, Privacy Protection, and Honeypot-Firewall Integration by Pedamallu Sai Mrudula, Rayappa David Amar Raj, Archana Pallakonda, Yanamala Rama Muni Reddy, K. Krishna Prakasha, V. Anandkumar

“…Furthermore, the proposed framework includes a federated learning-based scheme that utilizes differential privacy and homomorphic encryption to ensure the privacy and integrity of the data to enhance model interpretability and efficiency with feature ranking to provide insights into attack patterns and anomaly characteristics. …”
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Exploring the relationship between features calculated from contextual embeddings and EEG band power during sentence reading in Chinese by Yao Wang, Yao Wang, Yao Wang, Tiantian Xue, Tiantian Xue, Tiantian Xue, Xingyu Yang, Xingyu Yang, Xingyu Yang

“…Building on this framework, we hypothesize that cumulative distance metrics between contextual embeddings of adjacent linguistic units (words/Chinese characters) in sentence contexts may quantitatively reflect neural activation intensity during reading comprehension.MethodsUsing large-scale EEG datasets collected during reading tasks, we systematically investigated the relationship between these computationally derived distance features and frequency-specific band power measures associated with neural activity.ResultsIn conclusion, gamma-band power exhibited associations with various NLP features in the ChineseEEG dataset, whereas no comparable gamma-specific effects were observed in the ZuCo1.0 dataset. …”
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Computer-aided diagnosis of Haematologic disorders detection based on spatial feature learning networks using blood cell images by Jamal Alsamri, Hamed Alqahtani, Ali M. Al-Sharafi, Abdulbasit A. Darem, Khalid Nazim, Abdul Sattar, Menwa Alshammeri, Ahmad A. Alzahrani, Marwa Obayya

“…Machine learning (ML) and deep learning (DL) have aided the classification and collection of patterns in data, foremost in the growth of AI methods employed in numerous haematology fields. …”
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