Check computer science papers: citations, claims and retractions

An experimental analysis of attack classification using machine learning in IoT networks

Churcher, A., Ullah, R., Ahmad, J., ur Rehman, S., Masood, F., Gogate, M., Alqahtani, F., Nour, B., & Buchanan, W. J. (2021) · Sensors

Network Security and Intrusion DetectionInternet Traffic Analysis and Secure E-voting

Cited by 439

Robust Smartphone App Identification via Encrypted Network Traffic Analysis

Taylor, V. F., Spolaor, R., Conti, M., & Martinovic, I. (2018) · IEEE Transactions on Information Forensics and Security

Internet Traffic Analysis and Secure E-votingDigital and Cyber Forensics

Cited by 439

A deep learning approach to on-node sensor data analytics for mobile or wearable devices

Ravi, D., Wong, C., Lo, B., & Yang, G.-Z. (2017) · IEEE Journal of Biomedical and Health Informatics

Context-Aware Activity Recognition SystemsPhysical Activity and Health

Cited by 439

A probabilistic u-net for segmentation of ambiguous images

Kohl, S. A. A., Romera-Paredes, B., Meyer, C., De Fauw, J., Ledsam, J. R., Maier-Hein, K. H., Eslami, S. M. A., Rezende, D. J., & Ronneberger, O. (2018) · arXiv

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 439

Suspended accounts in retrospect: an analysis of twitter spam

Thomas, K., Grier, C., Song, D., & Paxson, V. (2011) · Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference

Spam and Phishing DetectionCybercrime and Law Enforcement Studies

Cited by 439

Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms

Alarfaj, F. K., Malik, I., Khan, H. U., Almusallam, N., Ramzan, M., & Ahmed, M. (2022) · IEEE Access

Imbalanced Data Classification TechniquesPrivacy-Preserving Technologies in Data

Cited by 439

Sentiment Analysis Is a Big Suitcase

Cambria, E., Poria, S., Gelbukh, A., & Thelwall, M. (2017) · IEEE Intelligent Systems

Sentiment Analysis and Opinion MiningEmotion and Mood Recognition

Cited by 439

The drift table: designing for ludic engagement

Gaver, W. W., Bowers, J., Boucher, A., Gellerson, H., Pennington, S., Schmidt, A., Steed, A., Villars, N., & Walker, B. (2004) · CHI '04 Extended Abstracts on Human Factors in Computing Systems

Innovative Human-Technology InteractionInteractive and Immersive Displays

Cited by 439

Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda

Tandon, A., Dhir, A., Islam, A. K. M. N., & Mäntymäki, M. (2020) · Computers in Industry

Blockchain Technology Applications and SecurityBlockchain Technology in Education and Learning

Cited by 439

Deep learning-based image recognition for autonomous driving

Fujiyoshi, H., Hirakawa, T., & Yamashita, T. (2019) · IATSS Research

Advanced Neural Network ApplicationsAutonomous Vehicle Technology and Safety

Cited by 439

Can virtual reality exposure therapy gains be generalized to real-life? A meta-analysis of studies applying behavioral assessments

Morina, N., Ijntema, H., Meyerbröker, K., & Emmelkamp, P. M. G. (2015) · Behaviour Research and Therapy

Virtual Reality Applications and ImpactsAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes

Cited by 439

The Deep Learning Revolution

Sejnowski, T. J. (2018) · The MIT Press eBooks

Artificial Intelligence ApplicationsNeural Networks and Applications

Cited by 438

Variational Continual Learning

Turner, R., Bui, T., Li, Y., & Cuong, N. (2019) · Apollo - University of Cambridge Repository

Domain Adaptation and Few-Shot LearningData Stream Mining Techniques

Cited by 438

A Geodesic Framework for Fast Interactive Image and Video Segmentation and Matting

Bai, X., & Sapiro, G. (2007) · 2007 IEEE 11th International Conference on Computer Vision

Medical Image Segmentation TechniquesVideo Surveillance and Tracking Methods

Cited by 438

Learning science in virtual reality multimedia environments: Role of methods and media

Moreno, R., & Mayer, R. E. (2002) · Journal of Educational Psychology

Virtual Reality Applications and ImpactsVisual and Cognitive Learning Processes

Cited by 438

D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry

Yang, N., von Stumberg, L., Wang, R., & Cremers, D. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Robotics and Sensor-Based LocalizationAdvanced Vision and Imaging

Cited by 438

Reduced-reference image quality assessment using a wavelet-domain natural image statistic model

Wang, Z., & Simoncelli, E. P. (2005) · SPIE Proceedings

Image and Video Quality AssessmentImage Retrieval and Classification Techniques

Cited by 438

AI for life: Trends in artificial intelligence for biotechnology

Holzinger, A., Keiblinger, K., Holub, P., Zatloukal, K., & Müller, H. (2023) · New Biotechnology

Artificial Intelligence ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 438

SNIPER: Efficient Multi-Scale Training

Singh, B., Najibi, M., & Davis, L. S. (2018) · arXiv

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 438

Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT

Shen, S., Dong, Z., Ye, J., Ma, L., Yao, Z., Gholami, A., Mahoney, M. W., & Keutzer, K. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence

Advanced Neural Network ApplicationsTopic Modeling

Cited by 437

A review of Convolutional-Neural-Network-based action recognition

Yao, G., Lei, T., & Zhong, J. (2019) · Pattern Recognition Letters

Human Pose and Action RecognitionHand Gesture Recognition Systems

Cited by 437

Fundamentals of Wearable Computers and Augmented Reality

Barfield, W. (2015) · CRC Press eBooks

Augmented Reality ApplicationsInteractive and Immersive Displays

Cited by 437

MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model

Zhang, M., Cai, Z., Pan, L., Hong, F., Guo, X., Yang, L., & Liu, Z. (2022) · arXiv

Human Motion and Animation3D Shape Modeling and Analysis

Cited by 437

Hierarchical Cross-Modal Talking Face Generation With Dynamic Pixel-Wise Loss

Chen, L., Maddox, R. K., Duan, Z., & Xu, C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Generative Adversarial Networks and Image SynthesisSpeech and Audio Processing

Cited by 437