Check computer science papers: citations, claims and retractions

Towards blockchain-based intelligent transportation systems

Yuan, Y., & Wang, F.-Y. (2016) · 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

Blockchain Technology Applications and SecurityVehicular Ad Hoc Networks (VANETs)

Cited by 671

AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges

Sapkota, R., Roumeliotis, K. I., & Karkee, M. (2026) · Information Fusion

Artificial Intelligence ApplicationsAI-based Problem Solving and Planning

Cited by 671

Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements

Tustison, N. J., Cook, P. A., Klein, A., Song, G., Das, S. R., Duda, J. T., Kandel, B. M., van Strien, N., Stone, J. R., Gee, J. C., et al. (2014) · NeuroImage

Medical Image Segmentation TechniquesAdvanced Neuroimaging Techniques and Applications

Cited by 670

Deep Learning for Encrypted Traffic Classification: An Overview

Rezaei, S., & Liu, X. (2019) · IEEE Communications Magazine

Internet Traffic Analysis and Secure E-votingWireless Signal Modulation Classification

Cited by 670

Image registration by maximization of combined mutual information and gradient information

Pluim, J. P. W., Maintz, J. B. A., & Viergever, M. A. (2000) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesAdvanced Radiotherapy Techniques

Cited by 670

Evaluating the Factual Consistency of Abstractive Text Summarization

Kryscinski, W., McCann, B., Xiong, C., & Socher, R. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Topic ModelingAdvanced Text Analysis Techniques

Cited by 669

Is the Web as good as the lab? Comparable performance from Web and lab in cognitive/perceptual experiments

Germine, L., Nakayama, K., Duchaine, B. C., Chabris, C. F., Chatterjee, G., & Wilmer, J. B. (2012) · Psychonomic Bulletin & Review

Mobile Crowdsensing and CrowdsourcingSurvey Methodology and Nonresponse

Cited by 669

Video Rewrite: driving visual speech with audio

Bregler, C., Covell, M., & Slaney, M. (1997) · Proceedings of the 24th annual conference on Computer graphics and interactive techniques - SIGGRAPH '97

Speech and Audio ProcessingMultimedia Communication and Technology

Cited by 669

CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows

Gudovskiy, D., Ishizaka, S., & Kozuka, K. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Anomaly Detection Techniques and ApplicationsIndustrial Vision Systems and Defect Detection

Cited by 669

SecureBoost: A Lossless Federated Learning Framework

Cheng, K., Fan, T., Jin, Y., Liu, Y., Chen, T., Papadopoulos, D., & Yang, Q. (2021) · IEEE Intelligent Systems

Privacy-Preserving Technologies in DataCryptography and Data Security

Cited by 669

Internet of Things Architecture: Recent Advances, Taxonomy, Requirements, and Open Challenges

Yaqoob, I., Ahmed, E., Hashem, I. A. T., Ahmed, A. I. A., Gani, A., Imran, M., & Guizani, M. (2017) · IEEE Wireless Communications

IoT and Edge/Fog ComputingInternet of Things and AI

Cited by 668

Visual tracking and recognition using appearance-adaptive models in particle filters

Zhou, S. K., Chellappa, R., & Moghaddam, B. (2004) · IEEE Transactions on Image Processing

Video Surveillance and Tracking MethodsAdvanced Image and Video Retrieval Techniques

Cited by 668

Towards a guideline for evaluation metrics in medical image segmentation

Müller, D., Soto-Rey, I., & Kramer, F. (2022) · BMC Research Notes

Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and Education

Cited by 668

SecureBoost: A Lossless Federated Learning Framework

Cheng, K., Fan, T., Jin, Y., Liu, Y., Chen, T., Papadopoulos, D., & Yang, Q. (2019) · arXiv

Privacy-Preserving Technologies in DataCryptography and Data Security

Cited by 668

Online panels in social science research: Expanding sampling methods beyond Mechanical Turk

Chandler, J., Rosenzweig, C., Moss, A. J., Robinson, J., & Litman, L. (2019) · Behavior Research Methods

Mobile Crowdsensing and CrowdsourcingSurvey Methodology and Nonresponse

Cited by 668

DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing

Yao, S., Hu, S., Zhao, Y., Zhang, A., & Abdelzaher, T. (2017) · Proceedings of the 26th International Conference on World Wide Web

Context-Aware Activity Recognition SystemsInertial Sensor and Navigation

Cited by 667

Multi-atlas segmentation of biomedical images: A survey

Iglesias, J. E., & Sabuncu, M. R. (2015) · Medical Image Analysis

Medical Image Segmentation TechniquesMedical Imaging and Analysis

Cited by 667

Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection

Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., & Fritz, M. (2023) · Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 667

Deep learning in cancer pathology: a new generation of clinical biomarkers

Echle, A., Rindtorff, N. T., Brinker, T. J., Luedde, T., Pearson, A. T., & Kather, J. N. (2021) · British Journal of Cancer

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 667

Poincaré Embeddings for Learning Hierarchical Representations

Nickel, M., & Kiela, D. (2017) · arXiv

Advanced Graph Neural NetworksTopic Modeling

Cited by 667

Calibrating Probability with Undersampling for Unbalanced Classification

Pozzolo, A. D., Caelen, O., Johnson, R. A., & Bontempi, G. (2015) · 2015 IEEE Symposium Series on Computational Intelligence

Imbalanced Data Classification TechniquesMachine Learning and Data Classification

Cited by 667

Reinforcement learning, fast and slow

Botvinick, M., Ritter, S., Wang, J. X., Kurth-Nelson, Z., Blundell, C., & Hassabis, D. (2019) · Trends in Cognitive Sciences

Reinforcement Learning in RoboticsNeural and Behavioral Psychology Studies

Cited by 667

Learning Depth from Monocular Videos Using Direct Methods

Wang, C., Buenaposada, J. M., Zhu, R., & Lucey, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Advanced Vision and ImagingRobotics and Sensor-Based Localization

Cited by 667

Multiple Hypothesis Tracking Revisited

Kim, C., Li, F., Ciptadi, A., & Rehg, J. M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV)

Video Surveillance and Tracking MethodsAutonomous Vehicle Technology and Safety

Cited by 666