Non-parametric feature selection and machine learning based seasonal GHI prediction for SAPV systems in India by Aadyasha Patel, Gnana Swathika O․V․

“…Seasonality-based feature selection encompasses categorizing and picking features that reflect seasonal changes in data. …”
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An ensemble deep learning framework for energy demand forecasting using genetic algorithm-based feature selection. by Mohd Sakib, Tamanna Siddiqui, Suhel Mustajab, Reemiah Muneer Alotaibi, Nouf Mohammad Alshareef, Mohammad Zunnun Khan

“…However, selecting the most appropriate features to enhance prediction accuracy and robustness remains a key challenge. …”
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The Fine Feature Extraction and Attention Re-Embedding Model Based on the Swin Transformer for Pavement Damage Classification by Shizheng Zhang, Kunpeng Wang, Zhihao Liu, Min Huang, Sheng Huang

“…Unlike the original Swin Transformer, the proposed model incorporates two key components: a fine feature extraction module and a multi-head self-attention re-embedding module. …”
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Key Generation and Testing Based on Biometrics by Alaa AbdulRaheeM, shahd Abdulrhman Hasso

“…Creating and testing a biometric key is a critical process used for security and identity verification .When using biometric traits such as fingerprints, facial features, iris patterns, earprints, and voice patterns, a unique key is created and linked to the individual's biometric identity. …”
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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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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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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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Decoding the shadows: multi-modal identity profiling in darknet markets using latent behavior feature fusion by Xuan Zhang, Pengfei Li, Xin Tong, K. P. Chow, DeYu Yuan, Si Chen, Bo Jin

“…This framework uncovers subtle behavioral patterns, such as transaction-related features, vendor posting times, pricing strategies, and product categories, which are crucial for profiling vendors whose activities are often concealed. …”
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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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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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Customized Spectro-Temporal CNN Feature Extraction and ELM-Based Classifier for Accurate Respiratory Obstruction Detection by M. Muthulakshmi, K. Venkatesan, Syarifah Bahiyah Rahayu, K. L. Nayana Sree

“…The proposed CNN-enhanced time-frequency features powered the ELM-based framework, yielding a test accuracy of 97.5% for the unseen test data considered. …”
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PneuX-Net: An Enhanced Feature Extraction and Transformation Approach for Pneumonia Detection in X-Ray Images by Kashif Munir, Muhammad Usama Tanveer, Hasan J. Alyamani, Amine Bermak, Atiq Ur Rehman

“…Random Forest (RF) builds multiple decision trees to identify complex, non-linear patterns within the feature space. Gaussian Naïve Bayes (GNB) utilizes a probabilistic framework based on Bayes’ theorem to manage uncertainty in feature distributions. …”
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Multi-Biometric Feature Extraction from Multiple Pose Estimation Algorithms for Cross-View Gait Recognition by Ausrukona Ray, Md. Zasim Uddin, Kamrul Hasan, Zinat Rahman Melody, Prodip Kumar Sarker, Md Atiqur Rahman Ahad

“…Gait recognition is a behavioral biometric technique that identifies individuals based on their unique walking patterns, enabling long-distance identification. Traditional gait recognition methods rely on appearance-based approaches that utilize background-subtracted silhouette sequences to extract gait features. …”
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A Lightweight Intrusion Detection System with Dynamic Feature Fusion Federated Learning for Vehicular Network Security by Junjun Li, Yanyan Ma, Jiahui Bai, Congming Chen, Tingting Xu, Chi Ding

“…The proposed framework employs a two-stream architecture, including a transformer-augmented autoencoder for abstract feature extraction and a lightweight CNN-LSTM–Attention model for preserving temporal and local patterns. …”
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Metode Deteksi Intrusi Menggunakan Algoritme Extreme Learning Machine dengan Correlation-based Feature Selection by Sulandri Sulandri, Achmad Basuki, Fitra Abdurrachman Bachtiar

“…However, using the ELM method alone is not able to produce good accuracy, so the ELM method needs to be added to the Correlation-Based Feature Selection (CFS) feature selection method to improve the accuracy and computational time. …”
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Geomorphologic feature and its tectonic significance in the middle and upper reaches of Yunnan section of the Lancang River Basin by Sheng Li, Yuanhua Qing, Na Li, Nannan Lu, Shuang Yang, Yufan Xu, Jingnan Liu, Ruotong Liu, Hao He

“…On the whole, the fault within the region have a controlling effect on the flow direction of the main river channel, and the patterns of the river system and the flow direction of the river channel are macroscopic responses to the tectonic uplift and lateral deformation.…”
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Enhancing Heart Attack Prediction: Feature Identification from Multiparametric Cardiac Data Using Explainable AI by Muhammad Waqar, Muhammad Bilal Shahnawaz, Sajid Saleem, Hassan Dawood, Usman Muhammad, Hussain Dawood

“…Machine learning (ML) and deep learning (DL) models have the potential to predict cardiac conditions by identifying complex patterns within data, but their “black-box” nature restricts interpretability, making it challenging for healthcare professionals to comprehend the reasoning behind predictions. …”
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