Check computer science papers: citations, claims and retractions

Deductive Verification of Chain-of-Thought Reasoning

Ling, Z., Fang, Y., Li, X., Huang, Z., Lee, M., Memisevic, R., & Su, H. (2023) · arXiv

Logic, Reasoning, and KnowledgeLogic, programming, and type systems

Cited by 44

From Lived Experience to Insight: Unpacking the Psychological Risks of Using AI Conversational Agents

Chandra, M., Naik, S., Ford, D., Okoli, E., De Choudhury, M., Ershadi, M., Ramos, G., Hernandez, J., Bhattacharjee, A., Warreth, S., et al. (2025) · Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIDigital Mental Health Interventions

Cited by 44

Machine Intelligence Techniques for Next-Generation Context-Aware Wireless Networks

Bogale, T. E., Wang, X., & Le, L. B. (2018) · arXiv

IoT and Edge/Fog ComputingSoftware-Defined Networks and 5G

Cited by 44

Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation

Hao, Z., Guo, J., Jia, D., Han, K., Tang, Y., Zhang, C., Hu, H., & Wang, Y. (2021) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 44

Learning Effects of Augmented Reality and Game-Based Learning for Science Teaching in Higher Education in the Context of Education for Sustainable Development

Czok, V., Krug, M., Müller, S., Huwer, J., & Weitzel, H. (2023) · Sustainability

Augmented Reality ApplicationsEducational Games and Gamification

Cited by 44

AntiFake: Using Adversarial Audio to Prevent Unauthorized Speech Synthesis

Yu, Z., Zhai, S., & Zhang, N. (2023) · Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security

Speech Recognition and SynthesisAdversarial Robustness in Machine Learning

Cited by 44

Compound TCP Performance for Industry 4.0 WiFi: A Cognitive Federated Learning Approach

Pokhrel, S. R., & Singh, S. (2021) · IEEE Transactions on Industrial Informatics

Network Traffic and Congestion ControlWireless Networks and Protocols

Cited by 44

Anomaly detection for a water treatment system based on one-class neural network

Aboah Boateng, E., Bruce, J. W., & Talbert, D. A. (2022) · IEEE Access

Network Security and Intrusion DetectionAnomaly Detection Techniques and Applications

Cited by 44

Quantum Approximation for Wireless Scheduling

Choi, J., Oh, S., & Kim, J. (2020) · Applied Sciences

Quantum Computing Algorithms and ArchitectureAdvanced Wireless Network Optimization

Cited by 44

Distributed learning from multiple EHR databases: Contextual embedding models for medical events

Li, Z., Roberts, K., Jiang, X., & Long, Q. (2019) · Journal of Biomedical Informatics

Machine Learning in HealthcarePrivacy-Preserving Technologies in Data

Cited by 44

Uncertainty-Guided Semi-Supervised Few-Shot Class-Incremental Learning With Knowledge Distillation

Cui, Y., Deng, W., Xu, X., Liu, Z., Liu, Z., Pietikäinen, M., & Liu, L. (2023) · IEEE Transactions on Multimedia

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 44

Development of massive open online course (MOOC) based on addie model for catering courses

Ismail, M. E., Utami, P., Ismail, I. M., Hamzah, N., & Harun, H. (2018) · Jurnal Pendidikan Vokasi

Online Learning and AnalyticsEducational Technology and E-Learning

Cited by 44

Detecting emergencies in patient portal messages using large language models and knowledge graph-based retrieval-augmented generation

Liu, S., Wright, A. P., McCoy, A. B., Huang, S. S., Steitz, B., & Wright, A. (2025) · Journal of the American Medical Informatics Association

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 44

Anomaly Detection Via Self-Organizing Map

Li, N., Jiang, K., Ma, Z., Wei, X., Hong, X., & Gong, Y. (2021) · 2021 IEEE International Conference on Image Processing (ICIP)

Anomaly Detection Techniques and ApplicationsIndustrial Vision Systems and Defect Detection

Cited by 44

Latent Relation Language Models

Hayashi, H., Hu, Z., Xiong, C., & Neubig, G. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence

Topic ModelingNatural Language Processing Techniques

Cited by 44

Empirical QoE/QoS correlation model based on multiple parameters for VoD flows

Aroussi, S., Bouabana-Tebibel, T., & Mellouk, A. (2012) · 2012 IEEE Global Communications Conference (GLOBECOM)

Image and Video Quality AssessmentNetwork Traffic and Congestion Control

Cited by 44

Automatic speech recognition for acoustical analysis and assessment of cantonese pathological voice and speech

Lee, T., Liu, Y., Huang, P.-W., Chien, J.-T., Lam, W. K., Yeung, Y. T., Law, T. K. T., Lee, K. Y. S., Kong, A. P.-H., & Law, S.-P. (2016) · 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Speech Recognition and SynthesisVoice and Speech Disorders

Cited by 44

Automatic human utility evaluation of ASR systems: does WER really predict performance?

Favre, B., Cheung, K., Kazemian, S., Lee, A., Liu, Y., Munteanu, C., Nenkova, A., Ochei, D., Penn, G., Tratz, S., et al. (2013) · Interspeech 2013

Speech Recognition and SynthesisSpeech and dialogue systems

Cited by 44

Multi-modality artificial intelligence in digital pathology

Qiao, Y., Zhao, L., Luo, C., Luo, Y., Wu, Y., Li, S., Bu, D., & Zhao, Y. (2022) · Briefings in Bioinformatics

AI in cancer detectionArtificial Intelligence in Healthcare and Education

Cited by 44

A fast image encryption scheme based on AES

Zhang, Y., Li, X., & Hou, W. (2017) · 2017 2nd International Conference on Image, Vision and Computing (ICIVC)

Chaos-based Image/Signal EncryptionCryptographic Implementations and Security

Cited by 44

Modeling multimodal cues in a deep learning-based framework for emotion recognition in the wild

Pini, S., Ahmed, O. B., Cornia, M., Baraldi, L., Cucchiara, R., & Huet, B. (2017) · Proceedings of the 19th ACM International Conference on Multimodal Interaction

Emotion and Mood RecognitionSpeech and Audio Processing

Cited by 44

Fast TreeSHAP: Accelerating SHAP value computation for trees

Yang, J. (2021) · arXiv

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 44

A review of explainable artificial intelligence from the perspectives of challenges and opportunities

Kabir, S., Hossain, M. S., & Andersson, K. (2025) · Algorithms

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 44

Identifying and Predicting Intentional Self-Harm in Electronic Health Record Clinical Notes: Deep Learning Approach

Obeid, J. S., Dahne, J., Christensen, S., Howard, S., Crawford, T., Frey, L. J., Stecker, T., & Bunnell, B. E. (2020) · JMIR Medical Informatics

Machine Learning in HealthcareMental Health via Writing

Cited by 44