Feature Extraction Methods: A Review

Digital Image Processing Using MATLAB

González, R. C., Woods, R. E., & Eddins, S. L. (2003) · Cited by 7,079

Introduction to Machine Learning

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Approximate Calculation of Multiple Integrals

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Evaluation of Texture Features for Content-Based Image Retrieval

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Automatic Detection of Melanoma Skin Cancer using Texture Analysis

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A Computer Based Feature Extraction of Lung Nodule in Chest X-Ray Image

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FEATURE EXTRACTION OF MAMMOGRAMS

Pradeep, N., Girisha, H., Sreepathi, B., & Karibasappa, K. G. (2012) · International Journal of Bioinformatics Research · doi:10.9735/0975-3087.4.1.241-244 · Cited by 32

Chest X-ray features extraction for lung cancer classification

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Advances in Facial Expression Recognition: A Survey of Methods, Benchmarks, Models, and Datasets

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Artificial Neural Networks in Agriculture, the core of artificial intelligence: What, When, and Why

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Multi-UAV Collaborative Absolute Vision Positioning and Navigation: A Survey and Discussion

Tong, P., Yang, X., Yang, Y., Liu, W., & Wu, P. (2023) · Drones · doi:10.3390/drones7040261 · Cited by 79

Speech Emotion Recognition Using Convolution Neural Networks and Multi-Head Convolutional Transformer

Ullah, R., Asif, M., Shah, W. A., Anjam, F., Ullah, I., Khurshaid, T., Wuttisittikulkij, L., Shah, S., Ali, S. M., & Alibakhshikenari, M. (2023) · Sensors · doi:10.3390/s23136212 · Cited by 84

Estimating surface NO2 concentrations over Europe using Sentinel-5P TROPOMI observations and Machine Learning

Shetty, S., Schneider, P., Stebel, K., Hamer, P., Kylling, A., & Berntsen, T. K. (2024) · Remote Sensing of Environment · doi:10.1016/j.rse.2024.114321 · Cited by 82

Past, present and future of deep plant leaf disease recognition: A survey

George, R., Thuseethan, S., Ragel, R., Mahendrakumaran, K., Nimishan, S., Wimalasooriya, C., & Alazab, M. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.110128 · Cited by 73

Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication

Putri, L. A., Rahman, I., Puspita, M., Hidayat, S. N., Dharmawan, A. B., Rianjanu, A., Wibirama, S., Roto, R., Trıyana, K., & Wasisto, H. S. (2023) · npj Science of Food · doi:10.1038/s41538-023-00205-2 · Cited by 60

Logarithmic Learning Differential Convolutional Neural Network

Magombe, Y., Sarıgül, M., & Avcı, M. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106114 · Cited by 53

Empowering precision medicine: AI-driven schizophrenia diagnosis via EEG signals: A comprehensive review from 2002–2023

Jafari, M., Sadeghi, D., Shoeibi, A., Alinejad‐Rokny, H., Beheshti, A., López-García, D., Chen, Z., Acharya, U. R., & Górriz, J. M. (2023) · Applied Intelligence · doi:10.1007/s10489-023-05155-6 · Cited by 50

Multi-label dental disorder diagnosis based on MobileNetV2 and swin transformer using bagging ensemble classifier

Alsakar, Y. M., Elazab, N., Nader, N., Mohamed, W., Ezzat, M., & Elmogy, M. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-73297-9 · Cited by 41

Deep Learning and Transfer Learning for Automatic Cell Counting in Microscope Images of Human Cancer Cell Lines

Lavitt, F., Rijlaarsdam, D. J., van der Linden, D., Węglarz‐Tomczak, E., & Tomczak, J. M. (2021) · Applied Sciences · doi:10.3390/app11114912 · Cited by 41

A novel deep learning model based on transformer and cross modality attention for classification of sleep stages

Mostafaei, S. H., Tanha, J., & Sharafkhaneh, A. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104689 · Cited by 38

An Efficient Strategy for Blood Diseases Detection Based on Grey Wolf Optimization as Feature Selection and Machine Learning Techniques

Sallam, N. M., Saleh, A. I., Ali, H. A., & Abdelsalam, M. M. (2022) · Applied Sciences · doi:10.3390/app122110760 · Cited by 38

A novel combination belief rule base model for mechanical equipment fault diagnosis

Chen, M., Zhou, Z., Zhang, B., Hu, G., & Cao, Y. (2021) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2021.08.037 · Cited by 36

Transfer-Learning and Texture Features for Recognition of the Conditions of Construction Materials with Small Data Sets

Mengiste, E., Mannem, K. R., Prieto, S. A., & Garcia de Soto, B. (2024) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-5478 · Cited by 35

Machine learning-assisted triboelectric nanogenerator-based self-powered sensors

Zhang, R. (2024) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2024.101888 · Cited by 36

A Deep Learning based Scalable and Adaptive Feature Extraction Framework for Medical Images

Loukil, Z., Mirza, Q. K. A., Sayers, W., & Awan, I. U. (2023) · Information Systems Frontiers · doi:10.1007/s10796-023-10391-9 · Cited by 37

Hybrid feature optimized CNN for rice crop disease prediction

Vijayan, S., & Chowdhary, C. L. (2025) · Scientific Reports · doi:10.1038/s41598-025-92646-w · Cited by 33

Prediction of Parkinson’s Disease Depression Using LIME-Based Stacking Ensemble Model

Nguyen, H. V., & Byeon, H. (2023) · Mathematics · doi:10.3390/math11030708 · Cited by 37

Image Processing and Support Vector Machine (SVM) for Classifying Environmental Stress Symptoms of Pepper Seedlings Grown in a Plant Factory

Islam, S., Samsuzzaman, S., Reza, M. N., Lee, K., Ahmed, S., Cho, Y. J., Noh, D. H., & Chung, S. (2024) · Agronomy · doi:10.3390/agronomy14092043 · Cited by 32

A survey of image-based computational learning techniques for frost detection in plants

Shammi, S., Sohel, F. A., Diepeveen, D., Zander, S., & Jones, M. G. K. (2022) · Information Processing in Agriculture · doi:10.1016/j.inpa.2022.02.003 · Cited by 36

Multiclass Apple Varieties Classification Using Machine Learning with Histogram of Oriented Gradient and Color Moments

Taner, A., Mengstu, M. T., Selvi, K., Duran, H., Kabaş, Ö., Gür, İ., Karaköse, T., & Gheorghiță, N. E. (2023) · Applied Sciences · doi:10.3390/app13137682 · Cited by 36

Grid-Based Structural and Dimensional Skin Cancer Classification with Self-Featured Optimized Explainable Deep Convolutional Neural Networks

Behara, K., Bhero, E., & Agee, J. T. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25031546 · Cited by 31

Features extraction from multi-spectral remote sensing images based on multi-threshold binarization

Rusyn, B., Lutsyk, O., Kosarevych, R., Maksymyuk, T., & Gazda, J. (2023) · Scientific Reports · doi:10.1038/s41598-023-46785-7 · Cited by 32

A Deep Learning Approach to Predict Supply Chain Delivery Delay Risk Based on Macroeconomic Indicators: A Case Study in the Automotive Sector

Gabellini, M., Civolani, L., Calabrese, F., & Bortolini, M. (2024) · Applied Sciences · doi:10.3390/app14114688 · Cited by 38

Developing Multiagent E-Learning System-Based Machine Learning and Feature Selection Techniques

Hessen, S. H., Abdul-Kader, H. M., Khedr, A. E., & Salem, R. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/2941840 · Cited by 38

A new feature extraction approach of medical image based on data distribution skew

Kuwil, F. H. (2022) · Neuroscience Informatics · doi:10.1016/j.neuri.2022.100097 · Cited by 28

Deep Learning Approach for the Detection of Noise Type in Ancient Images

Pawar, P., Ainapure, B., Rashid, M. U., Ahmad, N., Alotaibi, A., & Alshamrani, S. S. (2022) · Sustainability · doi:10.3390/su141811786 · Cited by 28

Toward Automated Field Ballast Condition Evaluation: Algorithm Development Using a Vision Transformer Framework

Luo, J., Huang, H., Ding, K., Qamhia, I. I. A., Tutumluer, E., Hart, J. M., Thompson, H., & Sussmann, T. R. (2023) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981231161350 · Cited by 27

Artificial intelligence assisted common maternal fetal planes prediction from ultrasound images based on information fusion of customized convolutional neural networks

Rauf, F., Khan, M. A., Albarakati, H. M., Jabeen, K., Alsenan, S. A., Hamza, A., Teng, S., & Nam, Y. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1486995 · Cited by 29

Textile Defect Detection Using Artificial Intelligence and Computer Vision—A Preliminary Deep Learning Approach

Machado, R., Barros, L. A. M., Vieira, V., Silva, F. R. R. D. C., Costa, H., & Carvalho, V. H. (2025) · Electronics · doi:10.3390/electronics14183692 · Cited by 26

A survey of dimension reduction and classification methods for RNA-Seq data on malaria vector

Arowolo, M. O., Adebiyi, M. O., Aremu, C., & Adebiyi, A. A. (2021) · Journal Of Big Data · doi:10.1186/s40537-021-00441-x · Cited by 26

Multimodal image fusion for the detection of diabetic retinopathy using optimized explainable AI-based Light GBM classifier

Bidwai, P., Gite, S., Pahuja, N. K., Pahuja, K., Kotecha, K. V., Jain, N., & Ramanna, S. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102526 · Cited by 24

Classification of COVID-19 X-ray images using transfer learning with visual geometrical groups and novel sequential convolutional neural networks

Kumar, S., & Kumar, H. (2023) · MethodsX · doi:10.1016/j.mex.2023.102295 · Cited by 34

A systematic review and research recommendations on artificial intelligence for automated cervical cancer detection

Khare, S. K., Blanes‐Vidal, V., Booth, B. B., Petersen, L. K., & Nadimi, E. S. (2024) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1550 · Cited by 25

A REVIEW OF FEATURE EXTRACTION METHODS ON MACHINE LEARNING

Suhaidi, M., Kadir, R. A., & Tiun, S. B. (2021) · Journal of Information System and Technology Management · doi:10.35631/jistm.622005 · Cited by 22

A Review of Single Image Super Resolution Techniques using Convolutional Neural Networks

Dixit, M., & Yadav, R. N. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16786-9 · Cited by 21

Supervised Learning Aided Multiple Feature Analysis for Freshness Class Detection of Indian Gooseberry (Phyllanthus emblica)

Sarkar, T., Mukherjee, A. B., & Chatterjee, K. (2021) · Journal of The Institution of Engineers (India) Series A · doi:10.1007/s40030-021-00585-2 · Cited by 20

Cancer Diagnosis With the Aid of Artificial Intelligence Modeling Tools

Danku, A. E., Dulf, E. H., Banut, R. P., Silaghi, H., & Silaghi, C. A. (2022) · IEEE Access · doi:10.1109/access.2022.3152200 · Cited by 20

UAV Image-Based 3D Reconstruction Technology in Landslide Disasters: A Review

Chen, Y., Liu, X., Zhu, B., Zhu, D., Zuo, X., & Li, Q. (2025) · Remote Sensing · doi:10.3390/rs17173117 · Cited by 19

Toward a deeper understanding: RetNet viewed through Convolution

Li, C., & Zhang, C. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110625 · Cited by 20

A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning

Radovan, A., Mršić, L., Đambić, G., & Mihaljević, B. (2024) · Eng · doi:10.3390/eng5040172 · Cited by 24

Machine learning-based prediction of belowground biomass from aboveground biomass and soil properties

Zhao, Y., Zhang, L., Lei, S., Liao, L., & Zhang, C. (2024) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2024.106313 · Cited by 16

An efficient EGWO algorithm as feature selection for B-ALL diagnoses and its subtypes classification using peripheral blood smear images

Sallam, N. M., Saleh, A. I., Ali, H. A., & Abdelsalam, M. M. (2023) · Alexandria Engineering Journal · doi:10.1016/j.aej.2023.01.004 · Cited by 16

Performance Evaluation of Various Deep Learning Models in Gait Recognition Using the CASIA-B Dataset

Aman, N., Islam, M. R., Ahamed, M. F., & Ahsan, M. (2024) · Technologies · doi:10.3390/technologies12120264 · Cited by 15

Plant Leaf Disease Classification in Precision Farming With Hybrid Classifier: Colour, Deep and Pattern‐Based Feature Descriptors

Tripathi, M. K., Neelakantappa, M., Prashanthi, T., Sukte, C. D., Pandurang, D. D., & Bhosle, N. P. (2025) · Journal of Phytopathology · doi:10.1111/jph.70030 · Cited by 15

Deep Learning for Object Detection and Segmentation in Videos: Toward an Integration With Domain Knowledge

Ilioudi, A., Dabiri, A., Wolf, B. J., & Schutter, B. D. (2022) · IEEE Access · doi:10.1109/access.2022.3162827 · Cited by 16

Towards automated field ballast condition evaluation: Field validation of the ballast scanning vehicle capabilities

Luo, J., Ding, K., Huang, H., Qamhia, I. I. A., Tutumluer, E., Hart, J. M., Thompson, H., & Sussmann, T. R. (2024) · Transportation Geotechnics · doi:10.1016/j.trgeo.2024.101311 · Cited by 14

A Hybrid Federated Learning Model for Insurance Fraud Detection

Supriya, Y., Victor, N., Srivastava, G., & Gadekallu, T. R. (2023) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops57953.2023.10283682 · Cited by 14

Data-driven portfolio management for motion pictures industry: A new data-driven optimization methodology using a large language model as the expert

Alipour‐Vaezi, M., & Tsui, K. (2024) · Computers & Industrial Engineering · doi:10.1016/j.cie.2024.110574 · Cited by 13

Artificial Intelligence as a Catalyst for Management System Adaptability, Agility and Resilience: Mapping the Research Agenda

Popa, I., Ștefan, S. C., Josan, A., Mircioiu, C.-E., & Căruceru, N. (2025) · Systems · doi:10.3390/systems13010047 · Cited by 22

An integrated approach to apple leaf disease detection: leveraging convolutional neural networks for accurate diagnosis

Rohith, D., Saurabh, P., & Bisen, D. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20735-z · Cited by 14

A fuzzy-based multimodal approach for interpretable fake news detection

Gedara, T. M. H., Loia, V., & Tomasiello, S. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113277 · Cited by 15

Machine Learning and Deep Learning Based Hybrid Feature Extraction and Classification Model Using Digital Microscopic Bacterial Images

Kotwal, S., Rani, P., Arif, T., & Manhas, J. (2023) · SN Computer Science · doi:10.1007/s42979-023-02138-9 · Cited by 12

Wearable sensor for psychological stress monitoring of pregnant woman – State of the art

Kuncoro, C. B. D., Efendi, A., & Sakanti, M. M. (2023) · Measurement · doi:10.1016/j.measurement.2023.113556 · Cited by 12

Anomaly Detection for Skin Lesion Images Using Convolutional Neural Network and Injection of Handcrafted Features: A Method That Bypasses the Preprocessing of Dermoscopic Images

Grignaffini, F., Troiano, M., Barbuto, F., Simeoni, P., Mangini, F., D’Andrea, G., Piazzo, L., Cantisani, C., Musolff, N., Ricciuti, C., et al. (2023) · Algorithms · doi:10.3390/a16100466 · Cited by 14

Efficient-VGG16: A Novel Ensemble Method for the Classification of COVID-19 X-ray Images in Contrast to Machine and Transfer Learning

Kumar, S., & Kumar, H. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.04.122 · Cited by 17

The Development of Machine Learning Models for Radial Compressor Monitoring With Instability Detection

Carrattieri, L., Cravero, C., Marsano, D., Valenti, E., Sishtla, V., & Halbe, C. V. (2024) · Journal of Turbomachinery · doi:10.1115/1.4066918 · Cited by 12

Intelligent multi-sensor fusion and anomaly detection in vehicles via deep learning

Önsü, M. A., Şimşek, M., Fobert, M., & Kantarcı, B. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101561 · Cited by 12

A Time-Frequency Domain Feature Extraction Approach Enhanced by Computer Vision for Wire Arc Additive Manufacturing Monitoring Using Fourier and Wavelet Transform

Mattera, G., Polden, J., & Nele, L. (2024) · Journal of Advanced Manufacturing Systems · doi:10.1142/s021968672450032x · Cited by 11

Review on data-driven approaches for improving the selectivity of MOX-sensors

Djeziri, M., Benmoussa, S., Bendahan, M., & Seguin, J.-L. (2024) · Microsystem Technologies · doi:10.1007/s00542-024-05622-1 · Cited by 11

Texture‐based speciation of otitis media‐related bacterial biofilms from optical coherence tomography images using supervised classification

Zaki, F. R., Monroy, G. L., Shi, J., Sudhir, K., & Boppart, S. A. (2024) · Journal of Biophotonics · doi:10.1002/jbio.202400075 · Cited by 11

Machine Learning Models Informed by Connected Mixture Components for Short- and Medium-Term Time Series Forecasting

Gorshenin, A. K., & Vilyaev, A. L. (2024) · AI · doi:10.3390/ai5040097 · Cited by 14

An Internet-of-Things-Integrated Deep Learning Model for Fault Diagnosis in Industrial Rotating Machines

Le, T. M., Tran, H. M., Wang, K., Pham, H. V., & Dao, S. V. T. (2025) · IEEE Access · doi:10.1109/access.2025.3553155 · Cited by 15

Infrared Perspectives: Computing laptop energy dissipation via thermal imaging and the Stefan-Boltzmann equation

Sasithradevi, A., Perumal, D. A., & Persiya, J. (2024) · Thermal Science and Engineering Progress · doi:10.1016/j.tsep.2024.102742 · Cited by 11

A novel degradation model and reliability evaluation methodology based on two-phase feature extraction: An application to marine lubricating oil pump

Chen, Z., Zhao, Y., Yang, J., Wang, Y., & Dui, H. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109883 · Cited by 10

A Comprehensive Analysis of Deep Learning Techniques for Wheat Yellow Rust Disease Detection and Classification

Mishra, K., & Kaur, K. (2025) · International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) · doi:10.1109/idciot64235.2025.10915123 · Cited by 10

Classification of Mint Leaf Types Based on the Image Using Euclidean Distance and K-Means Clustering with Shape and Texture Feature Extraction

Harjanti, T. W., Setiyani, H., Trianto, J., & Rahmanto, Y. (2022) · Tech-E · doi:10.31253/te.v5i1.940 · Cited by 10

Adam golden search optimization enabled DCNN for classification of breast cancer using histopathological image

Suganthi, N., Kotagiri, S., Thirupurasundari, D. R., & Vimala, S. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106239 · Cited by 9

Comparative Analysis of Classical Machine Learning and Deep Learning Methods for Fruit Image Recognition and Classification

Salim, N. O. M., & Mohammed, A. K. (2024) · Traitement du signal · doi:10.18280/ts.410322 · Cited by 9

A comprehensive review on multi robot task assignment and path planning: Operational constraints, challenges, and potentials of deep reinforcement learning

Mahmud, S. A., Ibrahim, A. M., & Khan, M. R. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.114498 · Cited by 11

Qualitätsmanagement und vorausschauende Instandhaltung auf der Basis synthetischer Datensätze

Reuter, T., Massalsky, K., Hoyer, K., Ivanov, G., & Burkhardt, T. H. (2021) · Zeitschrift für wirtschaftlichen Fabrikbetrieb · doi:10.1515/zwf-2021-0167 · Cited by 9

Emotion Recognition Using EEG Signals in Human Brain Waves and Deep Learning Approaches

Mekruksavanich, S., Hnoohom, N., Phaphan, W., & Jitpattanakul, A. (2025) · Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON) · doi:10.1109/ectidamtncon64748.2025.10962120 · Cited by 8

Low-Dimensional Text Representations for Sentiment Analysis NLP Tasks

Akritidis, L., & Bozanis, P. (2023) · SN Computer Science · doi:10.1007/s42979-023-01913-y · Cited by 8

Combination of K-NN and PCA Algorithms on Image Classification of Fish Species

Nuraini, R., Wibowo, A., Warsito, B., Syafei, W. A., & Jaya, I. (2023) · Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) · doi:10.29207/resti.v7i5.5178 · Cited by 8

A highly efficient, scalable pipeline for fixed feature extraction from large-scale high-content imaging screens

Comolet, G., Bose, N., Winchell, J., Duren-Lubanski, A., Rusielewicz, T., Goldberg, J., Horn, G., Paull, D. J., & Migliori, B. (2024) · iScience · doi:10.1016/j.isci.2024.111434 · Cited by 7

Lithology identification using borehole images by contrast-limited adaptive histogram equalization and machine learning models

Li, E., Segarra, P., Sanchidrián, J. A., Ahmed, Z., Catalán, I., Fernández, A., & Gómez, S. (2025) · Journal of Rock Mechanics and Geotechnical Engineering · doi:10.1016/j.jrmge.2025.08.002 · Cited by 8

Revolutionizing facial emotion recognition: in-depth analysis of cutting-edge models, methodologies, and datasets

Sarvakar, K., & Rana, K. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00553-w · Cited by 10

Novel feature extraction of underwater targets by encoding hydro-acoustic signatures as image

Zare, M., & Nouri, N. M. (2023) · Applied Ocean Research · doi:10.1016/j.apor.2023.103627 · Cited by 7

A new feature extraction algorithm for measuring the spatial arrangement of texture Primitives: Distance coding diversity

Zhu, W., Yang, X., Liu, R., & Zhao, C. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.103698 · Cited by 7

Comparison of IndoBERTweet and Support Vector Machine on Sentiment Analysis of Racing Circuit Construction in Indonesia

Lee, H. M., & Sibaroni, Y. (2023) · JURNAL MEDIA INFORMATIKA BUDIDARMA · doi:10.30865/mib.v7i1.5380 · Cited by 7

SPFusion: A multi-task semantic perception infrared and visible light fusion method with quality assessment

Liang, Z., Yu, M., Sun, Y., & Dong, M. (2024) · Displays · doi:10.1016/j.displa.2024.102903 · Cited by 6

Weighted Linear Discriminant Analysis: An Effective Feature Extraction Method for Multi-Class Imbalanced Datasets

Liu, Y., & Guo, S. (2024) · Symmetry · doi:10.3390/sym16121656 · Cited by 6

Gait Phase Recognition of Hip Exoskeleton System Based on CNN and HHO-SVM Model

Wang, H., Wang, M., Li, D., Deng, F., Pan, Z. S., & Song, Y. (2024) · Electronics · doi:10.3390/electronics14010107 · Cited by 6

Predicting antibiotic resistance genes and bacterial phenotypes based on protein language models

Wang, B., Meng, R., Li, Z., Hu, M., Wang, X., Zhao, Y., Chai, Z., Jin, Y., Yue, J., Chen, W., et al. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1628952 · Cited by 9

CNN-LSTM based emotion recognition using Chebyshev moment and K-fold validation with multi-library SVM

Jayaraman, S., & Mahendran, A. (2025) · PLoS ONE · doi:10.1371/journal.pone.0320058 · Cited by 9

EnsembleSleepNet: a novel ensemble deep learning model based on transformers and attention mechanisms using multimodal data for sleep stages classification

Mostafaei, S. H., Tanha, J., & Sharafkhaneh, A. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06484-4 · Cited by 6

Solar Energy Forecasting using Artificial Neural Network

Siddhant, K., Garg, H., Saha, A., Singh, N., Choudhary, N. K., & Singh, D. K. (2022) · Students Conference on Engineering and Systems · doi:10.1109/sces55490.2022.9887754 · Cited by 6

An image watermarking scheme: combining the transform domain and deep learning modality with an improved scrambling process

Gorbal, M., Shelke, R. D., & Joshi, M. (2024) · International Journal of Computers and Applications · doi:10.1080/1206212x.2024.2313301 · Cited by 6

Evaluation of Feature Extraction Methods for Bee Audio Classification

Phan, T.-T.-H., Nguyen, H. D., & Nguyen, D.-D. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-15063-0_18 · Cited by 6

LumiLoc: A Low-Light-Optimized Visual Localization Framework for Autonomous Drones

Qu, R., Wang, Z., Liu, Y., Li, C., Jiang, H., & Chen, F. (2025) · Aerospace · doi:10.3390/aerospace12060454 · Cited by 5

Prediction and examination of the impact of the raster angle on the orthotropic elastic response of 3D-printed objects using a novel homogenization strategy based on the real clustering of RVEs

Benaissa, H. A., Zaghar, H., Moujibi, N., & Sossey-Alaoui, I. (2023) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-023-12101-w · Cited by 5

Classification and Pixel Change Detection of Brain Tumor Using Adam Kookaburra Optimization–Based Shepard Convolutional Neural Network

Abirami, S., Ramesh, K., & Vanisree, K. L. (2025) · NMR in Biomedicine · doi:10.1002/nbm.5307 · Cited by 5

Detecting Fake Images Using Neuro-Fuzzy Inference Systems: A Brief Comparative Analysis

Gedara, T. M. H., Loia, V., & Tomasiello, S. (2024) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee60900.2024.10612033 · Cited by 5

Inception-V3 Versus VGG-16: in Rice Classification Using Multilayer Perceptron

Firmansyah, I., Rosnelly, R., & Wanayumini, W. (2023) · International Conference on Information Science and Technology Innovation (ICoSTEC) · doi:10.35842/icostec.v2i1.27 · Cited by 5

Depression detection from wearables using machine learning techniques

AlMakinah, R., Canbaz, M. A., & Subaşı, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-30168-1.00010-4 · Cited by 4

Context-Aware Automated System for Image Caption to ASL Translation for Improved Accessibility in Media Applications

Aluko, O., & Menon, V. (2025) · IEEE SoutheastCon · doi:10.1109/southeastcon56624.2025.10971481 · Cited by 4

GCFA: Generative class feature fusion with agent attention for medical text classification

Wang, Y., Wang, Q., Yu, H. Q., Xie, J., Hu, F., Wang, X., & Lei, D. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103639 · Cited by 4

Application and Challenges of Machine Learning Techniques in Mining Engineering and Material Science

Shetty, V., Shedthi, B. S., & Shashishekar, C. (2023) · Journal of Mines Metals and Fuels · doi:10.18311/jmmf/2023/36099 · Cited by 4

Image feature extraction techniques: A comprehensive review

Hallur, S., & Gavade, A. B. (2025) · Franklin Open · doi:10.1016/j.fraope.2025.100366 · Cited by 19

An Empirical Study on Texture Feature Extraction Methods for Image Retriveal

Kaur, M., & Kaur, E. H. (2017) · International Journal of Advanced Research in Computer Science · doi:10.26483/ijarcs.v8i4.3740

Facial Features Extraction by Relative Geometrical Position

Hameed, S. A. A. (2014) · International Journal of Computer Applications · doi:10.5120/17263-7619

Feature Extraction Techniques for Face Recognition

Dhawan, S., & Dogra, H. (2013)

Comparison of Features Extraction Algorithms Used in the Diagnosis of Plant Diseases

Hussein, M. A., & Abbas, A. H. (2018) · Ibn AL- Haitham Journal For Pure and Applied Science · doi:10.30526/2017.ihsciconf.1785 · Cited by 5