Deep Learning in Computer Vision: Principles and Applications

A Critical Review on Structural Health Monitoring: Definitions, Methods, and Perspectives

Gharehbaghi, V., Farsangi, E. N., Noori, M. N., Yang, T. Y., Li, S., Nguyễn, A., Málaga‐Chuquitaype, C., Gardoni, P., & Mirjalili, S. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09665-9 · Cited by 337

Vehicle Detection and Tracking in Adverse Weather Using a Deep Learning Framework

Hassaballah, M., Kenk, M. A., Muhammad, K., & Minaee, S. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3014013 · Cited by 287

Artificial Intelligence in News Media: Current Perceptions and Future Outlook

de‐Lima‐Santos, M., & Ceron, W. (2021) · Journalism and Media · doi:10.3390/journalmedia3010002 · Cited by 342

A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques

Salloum, S. A., Gaber, T., Vadera, S., & Shaalan, K. F. (2022) · IEEE Access · doi:10.1109/access.2022.3183083 · Cited by 275

A Survey of Explainable Artificial Intelligence for Smart Cities

Javed, A. R., Ahmed, W., Pandya, S., Maddikunta, P. K. R., Alazab, M., & Gadekallu, T. R. (2023) · Electronics · doi:10.3390/electronics12041020 · Cited by 217

Adversarial Machine Learning in Wireless Communications Using RF Data: A Review

Adesina, D. S., Hsieh, C.-C. G., Sagduyu, Y. E., & Qian, L. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3205184 · Cited by 205

Intelligent fusion-assisted skin lesion localization and classification for smart healthcare

Khan, M. A., Muhammad, K., Sharif, M. I., Akram, T., & Kadry, S. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06490-w · Cited by 128

Efficient Multi-Object Detection and Smart Navigation Using Artificial Intelligence for Visually Impaired People

Joshi, R. C., Yadav, S., Dutta, M. K., & Travieso, C. M. (2020) · Entropy · doi:10.3390/e22090941 · Cited by 131

A deep learning model for steel surface defect detection

Li, Z., Wei, X., Hassaballah, M., Li, Y., & Jiang, X. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01180-7 · Cited by 122

An automatic arrhythmia classification model based on improved Marine Predators Algorithm and Convolutions Neural Networks

Houssein, E. H., Hassaballah, M., Ibrahim, I. E., et al. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115936 · Cited by 94

TARDB-Net: triple-attention guided residual dense and BiLSTM networks for hyperspectral image classification

Cai, W., Liu, B., Wei, Z., Li, M., & Kan, J. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10188-x · Cited by 92

Skeletal Fracture Detection with Deep Learning: A Comprehensive Review

Su, Z., Adam, A., Nasrudin, M. F., Ayob, M. B., & Punganan, G. (2023) · Diagnostics · doi:10.3390/diagnostics13203245 · Cited by 103

3D image recognition using new set of fractional-order Legendre moments and deep neural networks

Ogri, O. E., Karmouni, H., Sayyouri, M., & Qjidaa, H. (2021) · Signal Processing Image Communication · doi:10.1016/j.image.2021.116410 · Cited by 71

An Exploration into Human–Computer Interaction: Hand Gesture Recognition Management in a Challenging Environment

Chang, V. I. C., Eniola, R. O., Golightly, L., & Xu, Q. A. (2023) · SN Computer Science · doi:10.1007/s42979-023-01751-y · Cited by 67

IoT-Based Bee Swarm Activity Acoustic Classification Using Deep Neural Networks

Žgank, A. (2021) · Sensors · doi:10.3390/s21030676 · Cited by 64

Human Action Recognition: A Paradigm of Best Deep Learning Features Selection and Serial Based Extended Fusion

Khan, S., Khan, M. A., Alhaisoni, M. M., Tariq, U., Yong, H.-S., Armghan, A., & Alenezi, F. S. (2021) · Sensors · doi:10.3390/s21237941 · Cited by 60

Hyperparameter optimization of deep CNN classifier for plant species identification using artificial bee colony algorithm

Erkan, U., Toktaş, A., & Üstün, D. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03631-w · Cited by 58

Near-Field Perception for Low-Speed Vehicle Automation Using Surround-View Fisheye Cameras

Eising, C., Horgan, J., & Yogamani, S. K. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3127646 · Cited by 58

SD-Net: Understanding overcrowded scenes in real-time via an efficient dilated convolutional neural network

Khan, N., Ullah, A., Haq, I. U., et al. (2020) · Journal of Real-Time Image Processing · doi:10.1007/s11554-020-01020-8 · Cited by 51

Spectral–Spatial Features Integrated Convolution Neural Network for Breast Cancer Classification

Mewada, H. K., Patel, A. V., Hassaballah, M., et al. (2020) · Sensors · doi:10.3390/s20174747 · Cited by 50

From Data Journalism to Artificial Intelligence: Challenges Faced by La Nación in Implementing Computer Vision in News Reporting

de‐Lima‐Santos, M., & Salaverría, R. (2021) · Palabra Clave · doi:10.5294/pacla.2021.24.3.7 · Cited by 48

Enhancing object detection for autonomous driving by optimizing anchor generation and addressing class imbalance

Carranza-García, M., Lara-Benítez, P., García–Gutiérrez, J., & Riquelme, J. C. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.001 · Cited by 50

Deep computer vision system for cocoa classification

Lopes, J. F., da Costa, V. G. T., Barbin, D. F., Cruz‐Tirado, J. P., Baeten, V., & Barbon, S. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13097-3 · Cited by 54

YOLOv4 algorithm for the real-time detection of fire and personal protective equipments at construction sites

Kumar, S., Gupta, H., Yadav, D., Ansari, I. A., & Verma, O. P. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11280-6 · Cited by 47

Optimized Convolutional Neural Network Models for Skin Lesion Classification

Villa-Pulgarín, J. P., Ruales-Torres, A. A., Arias-Garzón, D., Bravo-Ortíz, M. A., Arteaga-Arteaga, H. B., Mora-Rubio, A., Alzate-Grisales, J. A., Mercado-Ruiz, E., Hassaballah, M., Orozco-Arias, S., et al. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.019529 · Cited by 46

Attention‐based convolutional neural network deep learning approach for robust malware classification

Ravi, V., & Alazab, M. (2022) · Computational Intelligence · doi:10.1111/coin.12551 · Cited by 45

A Convolutional Neural Network to Perform Object Detection and Identification in Visual Large-Scale Data

Ayachi, R., Said, Y., & Atri, M. (2020) · Big Data · doi:10.1089/big.2019.0093 · Cited by 44

Deep Learning-Based Object Detection and Classification for Autonomous Vehicles in Different Weather Scenarios of Quebec, Canada

Sharma, T., Chehri, A., Fofana, I., Jadhav, S., Khare, S., Debaque, B., Duclos-Hindie, N., & Arya, D. (2024) · IEEE Access · doi:10.1109/access.2024.3354076 · Cited by 57

A Survey of Photovoltaic Panel Overlay and Fault Detection Methods

Yang, C., Sun, F., Zou, Y., et al. (2024) · Energies · doi:10.3390/en17040837 · Cited by 44

An Artificial Intelligence Based Technique for COVID-19 Diagnosis from Chest X-Ray

Bekhet, S., Hassaballah, M., Kenk, M. A., & Hameed, M. A. (2020) · Novel Intelligent and Leading Emerging Sciences Conference (NILES) · doi:10.1109/niles50944.2020.9257930 · Cited by 43

Channel Attention-Based Approach with Autoencoder Network for Human Action Recognition in Low-Resolution Frames

Dastbaravardeh, E., Askarpour, S., Anari, M. S., & Rezaee, K. (2024) · International Journal of Intelligent Systems · doi:10.1155/2024/1052344 · Cited by 43

Your Model Trains on My Data? Protecting Intellectual Property of Training Data via Membership Fingerprint Authentication

Liu, G., Xu, T., Ma, X., & Wang, C. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3155921 · Cited by 38

Reinforcement Learning with an Ensemble of Binary Action Deep Q-Networks

M. Hafiz, A., Hassaballah, M., Alqahtani, A., Alsubai, S., & Abdel Hameed, M. (2023) · Computer Systems Science and Engineering · doi:10.32604/csse.2023.031720 · Cited by 33

An Ensemble-Based Parallel Deep Learning Classifier With PSO-BP Optimization for Malware Detection

Al-Andoli, M. N., Sim, K. S., Tan, S. C., Goh, P. Y., & Lim, C. P. (2023) · IEEE Access · doi:10.1109/access.2023.3296789 · Cited by 37

Exploring the Influence of Input Feature Space on CNN‐Based Geomorphic Feature Extraction From Digital Terrain Data

Maxwell, A. E., Odom, W. E., Shobe, C. M., Doctor, D. H., Bester, M. S., & Ore, T. M. (2023) · Earth and Space Science · doi:10.1029/2023ea002845 · Cited by 38

Maximum and Leaky Maximum Propagation

Fuhl, W., & Jasneci, E. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892955 · Cited by 37

A novel sonar target detection and classification algorithm

Fan, X., Lu, L., Shi, P., & Zhang, X. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12054-4 · Cited by 33

A Survey of Deep Q-Networks used for Reinforcement Learning: State of the Art

Hafiz, A. M. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-19-1844-5_30 · Cited by 28

Optical Sensors for Industry 4.0

Santos, J. L. (2021) · IEEE Journal of Selected Topics in Quantum Electronics · doi:10.1109/jstqe.2021.3078126 · Cited by 27

State-of-the-Art in Responsible, Explainable, and Fair AI for Medical Image Analysis

Amirian, S., Gao, F., Littlefield, N. G., Hill, J. H., Yates, A. J., Plate, J. F., Pantanowitz, L., Rashidi, H. H., & Tafti, A. P. (2025) · IEEE Access · doi:10.1109/access.2025.3555543 · Cited by 34

Image Preprocessing Method in Radiographic Inspection for Automatic Detection of Ship Welding Defects

Yun, G.-H., Oh, S., & Shin, S.-C. (2021) · Applied Sciences · doi:10.3390/app12010123 · Cited by 28

A Deep Learning-Based IDS for Automotive Theft Detection for In-Vehicle CAN Bus

Khan, J. A., Lim, D., & Kim, Y. S. (2023) · IEEE Access · doi:10.1109/access.2023.3323891 · Cited by 22

Convolutional neural network with joint stepwise character/word modeling based system for scene text recognition

Harizi, R., Walha, R., Drira, F., et al. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10663-z · Cited by 22

A Novel Ensemble Based Reduced Overfitting Model with Convolutional Neural Network for Traffic Sign Recognition System

Shanmugavel, A. B., Ellappan, V., Mahendran, A., Subramanian, M., Lakshmanan, R., & Mazzara, M. (2023) · Electronics · doi:10.3390/electronics12040926 · Cited by 26

An Efficient Method for Covid-19 Detection Using Light Weight Convolutional Neural Network

Bekhet, S., Alkinani, M. H., Tabares-Soto, R., & Hassaballah, M. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.018514 · Cited by 21

Solving partial differential equations using large-data models: a literature review

Hafiz, A. M., Faiq, I., & Hassaballah, M. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10784-5 · Cited by 23

A Multi-Stream Sequence Learning Framework for Human Interaction Recognition

Haroon, U., Ullah, A., Hussain, T., et al. (2022) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2021.3138708 · Cited by 20

Derin Öğrenme Tekniklerini Kullanarak Akciğer X-Ray Görüntülerinden COVID-19 Tespiti

Bozkurt, F. (2021) · European Journal of Science and Technology · doi:10.31590/ejosat.898385 · Cited by 23

DETECTION OF PNEUMONIA FROM X-RAY IMAGES USING DEEP LEARNING TECHNIQUES

Bakır, H., Oktay, S., & Tabaru, T. E. (2023) · Journal of Scientific Reports-A · doi:10.59313/jsr-a.1219363 · Cited by 22

EMOTIONAL DESIGN

Zhou, F., Ji, Y., & Jiao, J. R. (2021) · Wiley eBooks · doi:10.1002/9781119636113.ch9 · Cited by 22

Deep learning applications in myocardial perfusion imaging, a systematic review and meta-analysis

Alskaf, E., Dutta, U., Scannell, C. M., & Chiribiri, A. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.101055 · Cited by 18

On the post-hoc explainability of deep echo state networks for time series forecasting, image and video classification

Arrieta, A. B., Gil-López, S., Laña, I., Bilbao, M. N., & Ser, J. D. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06359-y · Cited by 21

Mapping sidewalks on a neighborhood scale from street view images

Hamim, O. F., Kancharla, S. R., & Ukkusuri, S. V. (2023) · Environment and Planning B Urban Analytics and City Science · doi:10.1177/23998083231200445 · Cited by 18

Formula-Driven Supervised Learning in Computer Vision: A Literature Survey

Hafiz, A. M., Hassaballah, M., & Binbusayyis, A. (2023) · Applied Sciences · doi:10.3390/app13020723 · Cited by 15

A One‐Stage Deep Learning Model for Industrial Defect Detection

Li, Z., Wei, X., Hassaballah, M., & Jiang, X. (2023) · Advanced Theory and Simulations · doi:10.1002/adts.202200853 · Cited by 15

IS-DGM: an improved steganography method based on a deep generative model and hyper logistic map encryption via social media networks

Hameed, M. A., Hassaballah, M., & Qiao, T. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01332-w · Cited by 14

Uncertain-CAM: Uncertainty-Based Ensemble Machine Voting for Improved COVID-19 CXR Classification and Explainability

Aldhahi, W., & Sull, S. (2023) · Diagnostics · doi:10.3390/diagnostics13030441 · Cited by 15

FisheyeDistanceNet++: Self-Supervised Fisheye Distance Estimation with Self-Attention, Robust Loss Function and Camera View Generalization

Kumar, V. R., Yogamani, S. K., Milz, S., & Mäder, P. (2021) · Electronic Imaging · doi:10.2352/issn.2470-1173.2021.17.avm-181 · Cited by 14

Image classification using convolutional neural network tree ensembles

Hafiz, A. M., Bhat, R., & Hassaballah, M. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13604-6 · Cited by 15

Knowledge Representation of Training Data With Adversarial Examples Supporting Decision Boundary

Tian, Z., Wang, Z., Abdelmoniem, A. M., Liu, G., & Wang, C. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3293418 · Cited by 13

Deep learning based robust forward collision warning system with range prediction

Venkateswaran, N., Hans, W. J., & Padmapriya, N. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10703-8 · Cited by 13

Quantitative Comparison of Different Approaches for Reconstructing the Carbon‐Binder Domain from Tomographic Image Data of Cathodes in Lithium‐Ion Batteries and Its Influence on Electrochemical Properties

Prifling, B., Neumann, M., Hein, S., et al. (2022) · Energy Technology · doi:10.1002/ente.202200784 · Cited by 13

A Hybrid Deep Learning Approach to Classify the Plant Leaf Species

Rashid, J., Khan, I., Abbasi, I. A., et al. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.040356 · Cited by 12

Multi-scale and multi-column convolutional neural network for crowd density estimation

Chen, L., Wang, G., & Hou, G. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10002-8 · Cited by 13

Semantic Segmentation of Anaemic RBCs Using Multilevel Deep Convolutional Encoder-Decoder Network

Shahzad, M., Umar, A. I., Shirazi, S. H., & Shaikh, I. A. (2021) · IEEE Access · doi:10.1109/access.2021.3131768 · Cited by 12

Diagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning

Yi̇ği̇t, T., Şengöz, N., Özmen, Ö., Hemanth, D. J., & IŞIK, A. H. (2022) · Traitement du signal · doi:10.18280/ts.390311 · Cited by 14

A Non-Blind Deconvolution Semi Pipelined Approach to Understand Text in Blurry Natural Images for Edge Intelligence

Ansari, G. J., Shah, J. H., Khan, M. A., Sharif, M., Tariq, U., & Akram, T. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102675 · Cited by 13

Universal Watermark Vaccine: Universal Adversarial Perturbations for Watermark Protection

Chen, J., Liu, X., Liang, S., et al. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00228 · Cited by 11

DBCW-YOLO: A Modified YOLOv5 for the Detection of Steel Surface Defects

Han, J., Cui, G., Li, Z., & Zhao, J. (2024) · Applied Sciences · doi:10.3390/app14114594 · Cited by 12

Cross-Hole GPR for Soil Moisture Estimation Using Deep Learning

Pongrac, B., Gleich, D., Malajner, M., et al. (2023) · Remote Sensing · doi:10.3390/rs15092397 · Cited by 11

ResNet and Yolov5-enabled non-invasive meat identification for high-accuracy box label verification

Jarkas, O., Hall, J., Smith, S., Mahmud, R., Khojasteh, P., Scarsbrook, J. D., & Ko, R. K. L. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106679 · Cited by 17

Automated Inorganic Pigment Classification in Plastic Material Using Terahertz Spectroscopy

Sarjaš, A., Pongrac, B., & Gleich, D. (2021) · Sensors · doi:10.3390/s21144709 · Cited by 11

ALBERTA: ALgorithm-Based Error Resilience in Transformer Architectures

Liu, H., Singh, V., Filipiuk, M., & Hari, S. K. S. (2024) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2024.3400696 · Cited by 10

Comparative analysis of convolutional neural network and DenseNet121 transfer learning in agriculture focusing on crop leaf disease identification

Santoso, H. A., Safsalta, B. F., Febrianto, N., Saraswati, G. W., & Haw, S.-C. (2024) · Applied Computing and Informatics · doi:10.1108/aci-03-2024-0132 · Cited by 19

AUTOMATION OF POLYCYSTIC OVARY SYNDROME DIAGNOSTICS THROUGH MACHINE LEARNING ALGORITHMS IN ULTRASOUND IMAGING

Galagan, R. M., ANDREIEV, S., Stelmakh, N. V., Rafalska, Y., & Momot, A. S. (2024) · Applied Computer Science · doi:10.35784/acs-2024-24 · Cited by 10

Real-time AI-driven quality control for laboratory automation: a novel computer vision solution for the opentrons OT-2 liquid handling robot

Khan, S. U., Møller, V. K., Frandsen, R. J. N., & Mansourvar, M. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06334-3 · Cited by 12

A novel sarnede method for real-time ship detection from synthetic aperture radar image

Anil Raj, J., Idicula, S. M., & Paul, B. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12243-1 · Cited by 10

A Robust Framework for Epidemic Analysis, Prediction and Detection of COVID-19

Hassan, F., Albahli, S., Javed, A., & Irtaza, A. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.805086 · Cited by 10

Failure Forensics of Shaped PDC Cutters Using Image Analysis and Deep Learning

Liu, W., Li, J., & Gao, D. (2023) · SPE Journal · doi:10.2118/218383-pa · Cited by 10

Deep learning enhanced attributes conditional random forest for robust facial expression recognition

Liao, H., Wang, D., Fan, P., & Ding, L. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10951-8 · Cited by 10

New MDLSTM-based designs with data augmentation for offline Arabic handwriting recognition

Maalej, R., & Kherallah, M. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12339-8 · Cited by 11

Diagnosis of Alzheimer’s disease using 2D dynamic magnetic resonance imaging

Zhou, Z., Yu, L., Tian, S., & Xiao, G. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03678-9 · Cited by 10

NeuSyRE: Neuro-symbolic visual understanding and reasoning framework based on scene graph enrichment

Khan, M. J., Breslin, J. G., & Curry, E. (2023) · Semantic Web · doi:10.3233/sw-233510 · Cited by 10

HARTIV: Human Activity Recognition Using Temporal Information in Videos

Deotale, D., Verma, M., Suresh, P., Jangir, S. K., Kaur, M., Idris, S. A., & Alshazly, H. A. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.020655 · Cited by 9

A Deep Learning Approach to Monitoring Workers’ Stress at Office

Rodrigues, F., & Marchetti, J. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-27499-2_68 · Cited by 8

Remote sensing image super‐resolution based on convolutional blind denoising adaptive dense connection

Yang, X., Xie, T., Guo, Y., & Zhou, D. (2021) · IET Image Processing · doi:10.1049/ipr2.12236 · Cited by 8

LiDAR-Generated Images Derived Keypoints Assisted Point Cloud Registration Scheme in Odometry Estimation

Zhang, H., Yu, X., Sier, H., et al. (2023) · Remote Sensing · doi:10.3390/rs15205074 · Cited by 8

A GAN-Based Channel Estimation Method for MIMO OFDM Systems

Gong, C., & Hu, D. (2023) · International Conference on Communication, Image and Signal Processing (CCISP) · doi:10.1109/ccisp59915.2023.10355797 · Cited by 8

Convolutional Neural Network-Based Humerus Segmentation and Application to Bone Mineral Density Estimation from Chest X-ray Images of Critical Infants

Liu, Y.-C., Lin, Y., Tsai, P., et al. (2020) · Diagnostics · doi:10.3390/diagnostics10121028 · Cited by 8

A Deep Learning Approach to Recognize Human Activity Using Inertial Sensors and Motion Capture Systems

Jaén-Vargas, M., Leiva, K. R., Fernandes, F., Gonçalves, S., Silva, M., Lopes, D. S., & Olmedo, J. J. S. (2021) · Frontiers in artificial intelligence and applications · doi:10.3233/faia210196 · Cited by 10

Geospatial Intelligence and Artificial Intelligence for Detecting Potential Coca Paste Production Infrastructure in the Border Region of Venezuela and Colombia

Hidalgo, J. J. P., & Centeno, J. A. S. (2022) · Journal of Applied Security Research · doi:10.1080/19361610.2022.2111184 · Cited by 9

Smart Health Monitoring for Seizure Detection using Mobile Edge Computing

Ali, Z. S., Subramanian, N., & Erbad, A. (2020) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc48107.2020.9148418 · Cited by 10

Deep learning assisted distinguishing of honey seasonal changes using quadruple voltammetric electrodes

Wójcik, S., Ciepiela, F., Baś, B., & Jakubowska, M. (2022) · Talanta · doi:10.1016/j.talanta.2022.123213 · Cited by 7

Face recognition in a large dataset using a hierarchical classifier

Abbaspoor, N., & Hassanpour, H. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12382-5 · Cited by 7

All-content text recognition method for financial ticket images

Zhang, H., Dong, B., Zheng, Q., et al. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12741-2 · Cited by 7

Deep residual U-Net for automatic detection of Moroccan coastal upwelling using SST images

Snoussi, M., Tamim, A., Fellah, S. E., et al. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13692-4 · Cited by 7

SE-MD: a single-encoder multiple-decoder deep network for point cloud reconstruction from 2D images

Hafiz, A. M., Bhat, R. U. A., Parah, S. A., et al. (2023) · Pattern Analysis and Applications · doi:10.1007/s10044-023-01155-x · Cited by 7

A fast 3D object recognition algorithm using plane-constrained point pair features

Xiao, Z., Gao, J., Wu, D., Zhang, L., & Chen, X. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09525-x · Cited by 7

CyclicNet: an alternately updated network for semantic segmentation

Wu, G., & Li, Y. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09791-9 · Cited by 7

Classification of industrial surface defects based on neural architecture search

Yang, T., Zhang, T., & Huang, L. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09968-2 · Cited by 7

Overview of deep learning in medical imaging

Suzuki, K. (2017) · Radiological Physics and Technology · doi:10.1007/s12194-017-0406-5 · Cited by 1,131

Survey of Machine Learning Applications of Convolutional Neural Networks to Medical Image Analysis

Kumar, K. N. (2021) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2021.38947 · Cited by 2

3-D tracking of a moving object by an active stereo vision system

Tanaka, M., Maru, N., & Miyazaki, F. (2002) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.1994.397891 · Cited by 9

Self-monitoring to improve robustness of 3D object tracking for robotics

Mörwald, T., Zillich, M., Prankl, J., et al. (2011) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2011.6181734 · Cited by 4

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