A seed-epidermis-feature-recognition-based lightweight peanut seed selection method for embedded systems by Dehao Li, Jinlong Huang, Xincheng Li, Zhaolei Yang, Xueke An, Pengfei Xu, Yuliang Yun

“…This study developed a lightweight model for recognizing peanut seed epidermal features. The model was based on deep learning and model quantization techniques. …”
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Dynamic Feature Extraction and Semi-Supervised Soft Sensor Model Based on SCINet for Industrial and Transportation Processes by Jun Wang, Changjian Qi, Xing Luo, Shihao Deng, Qi Lei

“…To extract the dynamic information of industrial processes more fully, an unsupervised time series dynamic feature extractor was designed based on SCINet and an autoencoder, and the feature extractor was trained using complete data. …”
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A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification by Ujwalla Gawande, Mukesh Zaveri, Avichal Kapur

“…A support-vector-machine-based learning algorithm is used to train the system using the feature extracted. …”
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Optimizing Heart Disease Prediction: A Comparative Analysis of Tree-Based Ensembles With Feature Expansion and Selection by K. Aswini, Kriti Arya

“…This study examines the efficacy of tree-based ensemble machine learning models that have been improved using Feature Expansion and Selection (FES-EM). …”
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Location- and feature-based selection histories make independent, qualitatively distinct contributions to urgent visuomotor performance by Emily E Oor, Emilio Salinas, Terrence R Stanford

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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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Bag of Feature-Based Ensemble Subspace KNN Classifier in Muscle Ultrasound Diagnosis of Diabetic Peripheral Neuropathy by Kadhim K. Al-Barazanchi, Ali H. Al-Timemy, Zahid M. Kadhim

“…The result indicates that ensemble subspace KNN classification, based on the bag of features, achieved an accuracy of 97.23%. …”
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Spectral Super-Resolution Reconstruction of Multispectral Remote Sensing Images via Clustering-Based Spectral Feature by Wang Benlin, Yu Qinglin, Wang Zuo, Li Weitao, Wang Yong, Liu Huan, Gu Shuangxi, Zhang Lingling, Lv Dong

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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

“…These selected features are then used to train three base learners: Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU). …”
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Machine Learning and Deep Learning Hybrid Approach Based on Muscle Imaging Features for Diagnosis of Esophageal Cancer by Yuan Hong, Hanlin Wang, Qi Zhang, Peng Zhang, Kang Cheng, Guodong Cao, Renquan Zhang, Bo Chen

“…This study innovatively combines muscle imaging features with conventional esophageal imaging features to construct deep learning diagnostic models. …”
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Fingerprint Classification Based on Multilayer Extreme Learning Machines by Axel Quinteros, David Zabala-Blanco

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Facing depression: evaluating the efficacy of the EmpkinS-EKSpression reappraisal training augmented with facial expressions – protocol of a randomized controlled trial by Marie Keinert, Lena Schindler-Gmelch, Lydia Helene Rupp, Misha Sadeghi, Klara Capito, Malin Hager, Farnaz Rahimi, Robert Richer, Bernhard Egger, Bjoern M. Eskofier, Matthias Berking

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Classification of Leaf Diseases in Oil Palm Plants with Haar Wavelet Transform Features Based on Machine Learning by Jusman Yessi, Maulana Alfinto, Lubis Julnila Husna

“…This study aims to design a system to classify the types of leaf diseases of oil palm plants using texture feature extraction (Haar Wavelet Algorithm) and machine learning-based classification algorithms (Cubic SVM, Medium Gaussian SVM, Quadratic SVM, Cosine KNN, Fine KNN, and Weighted KNN). …”
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AnImproved Naive Bayesian Text Classification Algorithm based on Weighted Features and its Complementary Set by CHEN Kai, HUANG Ying-lai, GAO Wen-tao, ZHAO Peng

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Circuit Breaker Energy Storage State Identification Based on Quick Extraction of Vibration Signal Interval Features by Xiaofei XIA, Yufeng LU, Yi SU, Jian YANG

“…The vibration signal based circuit breaker faults diagnosis has the problem of time-consuming in feature extraction and poor real-time, which makes the method inapplicable to on-line monitoring. …”
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AI-Based Ransomware Detection: A Comprehensive Review by Jannatul Ferdous, Rafiqul Islam, Arash Mahboubi, Md Zahidul Islam

“…We then review the existing literature on the core components of AI-based ransomware detection models, including the datasets and challenges arising during data collection, data pre-processing, feature engineering techniques, model training, and performance evaluation for effective model training. …”
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Features of Implementing a Competency-Based Approach to Teaching Schoolchildren the Russian Language in the People’s Republic of China by Liu Juan

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Automatic grading of barley grain for brewery industries using convolutional neural network based on texture features by Debalke Embeyale, Yao-Tien Chen, Yaregal Assabie

“…This paper presents an automatic barley grain grading system using a deep learning approach based on the texture features of the grains. Specifically, we employed a convolutional neural network (CNN) to classify barley grains into predefined qualitycategories. …”
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An Ontology-Based Framework for Complex Urban Object Recognition through Integrating Visual Features and Interpretable Semantics by Xiao Xie, Xiran Zhou, Jingzhong Li, Weijiang Dai

“…The experimental results on lacking of the training process based on data samples show that our proposed approach can reach an accurate recognition with high-level semantics. …”
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Wood Species Identification Based on Gray Level Co-Occurrence Matrix (GLCM) Features on Macroscopic Images by Muhammad Ghiffaari Ilham Ramadhan, Bambang Sugiarto, Okta Dwi Mulya, Defti Septian Chairulsyah, Adyanto Syahrizal, Gunawan Gunawan, Riffa Haviani Laluma, Rini Nuraini Sukmana, Teguh Wiharko

“…In accordance with these situations, a computer vision-based system can address this condition. Therefore, feature extraction is necessary to extract the features of wood characteristics from the wood image. …”
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