Deep learning for insider threat detection: Review, challenges and opportunities
Insider threats, as one type of the most challenging threats in cyberspace, usually cause
significant loss to organizations. While the problem of insider threat detection has been …
significant loss to organizations. While the problem of insider threat detection has been …
[HTML][HTML] EAGA-MLP—an enhanced and adaptive hybrid classification model for diabetes diagnosis
Disease diagnosis is a critical task which needs to be done with extreme precision. In recent
times, medical data mining is gaining popularity in complex healthcare problems based …
times, medical data mining is gaining popularity in complex healthcare problems based …
Evaluation of machine learning methods developed for prediction of diabetes complications: a systematic review
Background: With the rising prevalence of diabetes, machine learning (ML) models have
been increasingly used for prediction of diabetes and its complications, due to their ability to …
been increasingly used for prediction of diabetes and its complications, due to their ability to …
Data-gru: Dual-attention time-aware gated recurrent unit for irregular multivariate time series
Due to the discrepancy of diseases and symptoms, patients usually visit hospitals irregularly
and different physiological variables are examined at each visit, producing large amounts of …
and different physiological variables are examined at each visit, producing large amounts of …
Artificial intelligence for social good: A survey
Artificial intelligence for social good (AI4SG) is a research theme that aims to use and
advance artificial intelligence to address societal issues and improve the well-being of the …
advance artificial intelligence to address societal issues and improve the well-being of the …
[HTML][HTML] Continuous diagnosis and prognosis by controlling the update process of deep neural networks
Continuous diagnosis and prognosis are essential for critical patients. They can provide
more opportunities for timely treatment and rational allocation. Although deep-learning …
more opportunities for timely treatment and rational allocation. Although deep-learning …
Safe: A neural survival analysis model for fraud early detection
Many online platforms have deployed anti-fraud systems to detect and prevent fraudulent
activities. However, there is usually a gap between the time that a user commits a fraudulent …
activities. However, there is usually a gap between the time that a user commits a fraudulent …
Adaptive risk-aware sharable and individual subspace learning for cancer survival analysis with multi-modality data
Z Zhao, Q Feng, Y Zhang, Z Ning - Briefings in Bioinformatics, 2023 - academic.oup.com
Biomedical multi-modality data (also named multi-omics data) refer to data that span
different types and derive from multiple sources in clinical practices (eg gene sequences …
different types and derive from multiple sources in clinical practices (eg gene sequences …
Bayesian data integration and variable selection for pan-cancer survival prediction using protein expression data
Accurate prognostic prediction using molecular information is a challenging area of
research, which is essential to develop precision medicine. In this paper, we develop …
research, which is essential to develop precision medicine. In this paper, we develop …
Cardiac complication risk profiling for cancer survivors via multi-view multi-task learning
Complication risk profiling is a key challenge in the healthcare domain due to the complex
interaction between heterogeneous entities (eg, visit, disease, medication) in clinical data …
interaction between heterogeneous entities (eg, visit, disease, medication) in clinical data …