Check computer science papers: citations, claims and retractions

Automatic speech emotion recognition using recurrent neural networks with local attention

Mirsamadi, S., Barsoum, E., & Zhang, C. (2017) · 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Emotion and Mood RecognitionSpeech Recognition and Synthesis

Cited by 770

Algorithmic bias: review, synthesis, and future research directions

Kordzadeh, N., & Ghasemaghaei, M. (2022) · European Journal of Information Systems

Ethics and Social Impacts of AIAI and HR Technologies

Cited by 770

From Recognition to Cognition: Visual Commonsense Reasoning

Zellers, R., Bisk, Y., Farhadi, A., & Choi, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsVisual Attention and Saliency Detection

Cited by 769

Blockchain for the Internet of Things: A systematic literature review

Conoscenti, M., Vetro, A., & De Martin, J. C. (2016) · 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)

Blockchain Technology Applications and SecurityBlockchain Technology in Education and Learning

Cited by 769

Predictably Unequal? The Effects of Machine Learning on Credit Markets

Fuster, A., Goldsmith-Pinkham, P. S., Ramadorai, T., & Walther, A. (2017) · The Journal of Finance

Ethics and Social Impacts of AIFinancial Distress and Bankruptcy Prediction

Cited by 769

Topological sorting of large networks

Kahn, A. B. (1962) · Communications of the ACM

Complexity and Algorithms in GraphsGraph Theory and Algorithms

Cited by 769

Bridging the Gap Between Ethics and Practice: Guidelines for Reliable, Safe, and Trustworthy Human-centered AI Systems

Shneiderman, B. (2020) · ACM Transactions on Interactive Intelligent Systems

Ethics and Social Impacts of AISafety Systems Engineering in Autonomy

Cited by 768

ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements

Kulkarni, K., Lohit, S., Turaga, P., Kerviche, R., & Ashok, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Sparse and Compressive Sensing TechniquesImage and Signal Denoising Methods

Cited by 768

Natural language interfaces to databases – an introduction

Androutsopoulos, I., Ritchie, G. D., & Thanisch, P. (1995) · Natural Language Engineering

Natural Language Processing TechniquesSpeech and dialogue systems

Cited by 768

A survey of intrusion detection techniques for cyber-physical systems

Mitchell, R., & Chen, I.-R. (2014) · ACM Computing Surveys

Network Security and Intrusion DetectionSmart Grid Security and Resilience

Cited by 768

CenterMask: Real-Time Anchor-Free Instance Segmentation

Lee, Y., & Park, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 767

Evaluating Learning Algorithms: A Classification Perspective

Japkowicz, N., & Shah, M. (2011) · Cambridge University Press eBooks

Machine Learning and Data ClassificationImbalanced Data Classification Techniques

Cited by 767

Understanding quantified-selfers' practices in collecting and exploring personal data

Choe, E. K., Lee, N. B., Lee, B., Pratt, W., & Kientz, J. A. (2014) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Innovative Human-Technology InteractionMobile Health and mHealth Applications

Cited by 766

The State and Fate of Linguistic Diversity and Inclusion in the NLP World

Joshi, P., Santy, S., Budhiraja, A., Bali, K., & Choudhury, M. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Natural Language Processing TechniquesTopic Modeling

Cited by 766

Blockchain for industry 4.0: a comprehensive review

Bodkhe, U., Tanwar, S., Parekh, K., Khanpara, P., Tyagi, S., Kumar, N., & Alazab, M. (2020) · IEEE Access

Blockchain Technology Applications and SecurityBlockchain Technology in Education and Learning

Cited by 766

The current landscape of learning analytics in higher education

Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018) · Computers in Human Behavior

Online Learning and AnalyticsIntelligent Tutoring Systems and Adaptive Learning

Cited by 766

Machine Learning Operations (MLOps): Overview, Definition, and Architecture

Kreuzberger, D., Kühl, N., & Hirschl, S. (2023) · IEEE Access

Software Engineering Techniques and PracticesSoftware System Performance and Reliability

Cited by 766

Cybersecurity data science: an overview from machine learning perspective

Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020) · Journal of Big Data

Network Security and Intrusion DetectionInformation and Cyber Security

Cited by 766

Hardware Trojan attacks: Threat analysis and countermeasures

Bhunia, S., Hsiao, M. S., Banga, M., & Narasimhan, S. (2014) · Proceedings of the IEEE

Physical Unclonable Functions (PUFs) and Hardware SecuritySecurity and Verification in Computing

Cited by 766

Review of MRI-based brain tumor image segmentation using deep learning methods

Işın, A., Direkoğlu, C., & Şah, M. (2016) · Procedia Computer Science

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 766

Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model

Letham, B., Rudin, C., McCormick, T. H., & Madigan, D. (2015) · The Annals of Applied Statistics

Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare

Cited by 766

ERNIE 2.0: A Continual Pre-Training Framework for Language Understanding

Sun, Y., Wang, S., Li, Y., Feng, S., Tian, H., Wu, H., & Wang, H. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 766

Machine Learning from Theory to Algorithms: An Overview

Alzubi, J., Nayyar, A., & Kumar, A. (2018) · Journal of Physics: Conference Series

Artificial Intelligence ApplicationsMachine Learning and Algorithms

Cited by 766

Racial disparities in automated speech recognition

Koenecke, A., Nam, A., Lake, E., Nudell, J., Quartey, M., Mengesha, Z., Toups, C., Rickford, J. R., Jurafsky, D., & Goel, S. (2020) · Proceedings of the National Academy of Sciences

Speech Recognition and SynthesisEthics and Social Impacts of AI

Cited by 765