Search Results - stack sparse autoencoder deep learning model (((suae OR sua) OR sage))

Do LLMs Offer a Robust Defense Mechanism Against Membership Inference Attacks on Graph Neural Networks?

Authors: Jnaini, Abdellah Koulali, Mohammed-Amine

Source: Computers (2073-431X); Oct2025, Vol. 14 Issue 10, p414, 20p

Subject Terms: GRAPH neural networks, PRIVACY, DEEP learning, LANGUAGE models, DATA privacy

Full stack optimization of microservice architecture: systematic review and research opportunity.

Authors: Yang, Meihong Wang, Xiaoli Cai, Binlei et al.

Source: Cluster Computing; Dec2025, Vol. 28 Issue 15, p1-29, 29p

GAM-MDR: probing miRNA–drug resistance using a graph autoencoder based on random path masking.

Authors: Zhou, Zhecheng Du, Zhenya Jiang, Xin et al.

Source: Briefings in Functional Genomics; Jul2024, Vol. 23 Issue 4, p475-483, 9p

Subject Terms: DEEP learning, GENE expression, MICRORNA, ACQUISITION of data, DISEASE progression, THERAPEUTICS

Accurate identification of snoRNA targets using variational graph autoencoder to advance the redevelopment of traditional medicines.

Authors: Wang, Zhina Chen, Yangyuan Ma, Hongming et al.

Source: Frontiers in Pharmacology; 2025, p1-12, 12p

Subject Terms: GRAPH neural networks, ARTIFICIAL intelligence, AUTOENCODERS, DRUG target, TRADITIONAL medicine

AI-Enhanced Intrusion Detection for UAV Systems: A Taxonomy and Comparative Review.

Authors: Islam, MD Sakibul Mahmoud, Ashraf Sharif Sheltami, Tarek Rahil

Source: Drones (2504-446X); Oct2025, Vol. 9 Issue 10, p682, 52p

Deep learning model for recommendation system using web of things based knowledge graph mining.

Authors: Byeon, Haewon Chunduri, Venkata Narang, Geetika et al.

Source: Service Oriented Computing & Applications; Mar2025, Vol. 19 Issue 1, p57-76, 20p

Trustworthy AI-based Performance Diagnosis Systems for Cloud Applications: A Review.

Authors: Xin, Ruyue Wang, Jingye Chen, Peng et al.

Source: ACM Computing Surveys; May2025, Vol. 57 Issue 5, p1-37, 37p

Subject Terms: ARTIFICIAL intelligence, ARTIFICIAL neural networks, SOFTWARE engineering, INFORMATION technology, HIGH performance computing, DEEP learning

A review of named entity recognition: from learning methods to modelling paradigms and tasks.

Authors: Seow, Wei Liang Chaturvedi, Iti Hogarth, Amber et al.

Source: Artificial Intelligence Review; Oct2025, Vol. 58 Issue 10, p1-87, 87p

Graph-ensemble fusion for enhanced IoT intrusion detection: leveraging GCN and deep learning.

Authors: Mittal, Kajol Khurana Batra, Payal

Source: Cluster Computing; Nov2024, Vol. 27 Issue 8, p10525-10552, 28p

Subject Terms: GRAPH neural networks, CONVOLUTIONAL neural networks, INFORMATION technology security, COMPUTER network security, DEEP learning, DATA distribution

Quantum-Inspired Cross-Attention Alignment for Turkish Scientific Abstractive Summarization.

Authors: Altay, Gönül Küçüksille, Ecir Uğur

Source: Electronics (2079-9292); Nov2025, Vol. 14 Issue 22, p4474, 38p

Subject Terms: TEXT summarization, LOW-resource languages, QUANTUM theory, MATHEMATICS, QUANTUM computing

AI-based hardware and software tools in microscopy to boost research in immunology and virology.

Authors: Morone, Diego D'Antuono, Rocco

Source: Frontiers in Immunology; 2025, p1-29, 29p

Subject Terms: ARTIFICIAL intelligence, IMMUNOLOGY, DATA analysis, FLUORESCENCE, VIROLOGY, OPEN source software, MICROSCOPY, IMAGE processing

Heterogeneous network drug-target interaction prediction model based on graph wavelet transform and multi-level contrastive learning.

Authors: Dai, Wenfeng Wang, Yanhong Yan, Shuai et al.

Source: Scientific Reports; 8/19/2025, Vol. 15 Issue 1, p1-17, 17p

Subject Terms: DRUG discovery, GRAPH neural networks, BIOINFORMATICS, DATA integration, WAVELETS (Mathematics), MACHINE learning, DATA quality, PROTEIN-protein interactions

Large language models facilitating modern molecular biology and novel drug development.

Authors: Liu, Xiao-huan Lu, Zhen-hua Wang, Tao et al.

Source: Frontiers in Pharmacology; 2025, p1-19, 19p

Subject Terms: LANGUAGE models, INFORMATION technology, ARTIFICIAL intelligence, PROTEIN structure prediction, SCIENTIFIC communication

Theoretical Bases of Methods of Counteraction to Modern Forms of Information Warfare.

Authors: Bakirov, Akhat Suleimenov, Ibragim

Source: Computers (2073-431X); Oct2025, Vol. 14 Issue 10, p410, 76p

Subject Terms: INFORMATION warfare, DISINFORMATION, INTERNET security, INFORMATION resources management, DIGITAL technology, COGNITIVE bias, NETWORK theory (Statistical physics), ARTIFICIAL intelligence

Beginner-Friendly Review of Research on R-Based Energy Forecasting: Insights from Text Mining.

Authors: Kim, Minjoong Kim, Hyeonwoo Moon, Jihoon

Source: Electronics (2079-9292); Sep2025, Vol. 14 Issue 17, p3513, 59p

Subject Terms: ENERGY consumption forecasting, TEXT mining, EMPIRICAL research, MACHINE learning, REPRODUCIBLE research, SCIENTIFIC language

Probabilistic Graph Model Based Recommendation Algorithm for Material Selection in Self-directed Learning.

Authors: Zhiyong Qiu Yingjin Cui

Source: SAGE Open; Apr-Jun2024, Vol. 14 Issue 2, p1-16, 16p

Subject Terms: SELF-managed learning (Personnel management), INFORMATION retrieval, ALGORITHMS, GRAPH theory, DATA analysis

Graph neural networks: Historical backgrounds, present revolutions, and conventionalization for the future.

Authors: Maghdid, Sozan S. Rashid, Tarik A. Askar, Shavan K.

Source: International Journal of Data Science & Analytics; Nov2025, Vol. 20 Issue 6, p5237-5299, 63p

Deep Learning Models for Diagnosis of Schizophrenia Using EEG Signals: Emerging Trends, Challenges, and Prospects.

Authors: Ranjan, Rakesh Sahana, Bikash Chandra Bhandari, Ashish Kumar

Source: Archives of Computational Methods in Engineering; May2024, Vol. 31 Issue 4, p2345-2384, 40p

Data-driven nonlinear parametric model order reduction framework using deep hierarchical variational autoencoder.

Authors: Lee, SiHun Lee, Sangmin Jang, Kijoo et al.

Source: Engineering with Computers; Aug2024, Vol. 40 Issue 4, p2385-2400, 16p

Progress and opportunities of foundation models in bioinformatics.

Authors: Li, Qing Hu, Zhihang Wang, Yixuan et al.

Source: Briefings in Bioinformatics; Nov2024, Vol. 25 Issue 6, p1-20, 20p

Subject Terms: LANGUAGE models, COMPUTATIONAL biology, ARTIFICIAL intelligence, SEQUENCE analysis, SCIENTIFIC community