A5020890991- Machine Learning in Healthcare
- Adversarial Robustness in Machine Learning
- Bayesian Methods and Mixture Models
- Scoliosis diagnosis and treatment
- Advanced Database Systems and Queries
- Music and Audio Processing
- Musculoskeletal pain and rehabilitation
- Data Quality and Management
- Data Management and Algorithms
- Time Series Analysis and Forecasting
- User Authentication and Security Systems
- Web Data Mining and Analysis
- Statistical Methods and Inference
- Advanced Malware Detection Techniques
- Medical Imaging and Analysis
University of Surrey
2020-2021
Due to the rapid development of Internet Things (IoT) and consequently, availability more IoT data sources, mechanisms for searching integrating sources become essential leverage all relevant improving processes services. This paper presents search framework IoTCrawler. The IoTCrawler is not only another framework, it a system systems which connects existing solutions offer interoperability overcome fragmentation. In addition its domain-independent design, features layered approach, offering...
The rapid growth in collecting and sharing sensory observation form the urban environments provides opportunities to plan manage services cities better allows citizens also observe understand changes their surrounding a way. new data creates for further application service development by creative industries start-ups. However, as size diversity of this increase, finding accessing right set timely manner is becoming more challenging. This paper describes search engine designed indexing,...
Traditional authentication mechanisms use passwords, Personal Identification Numbers (PINs) and biometrics, but these only authenticate at the point of entry. Continuous schemes instead allow systems to verify identity mitigate unauthorised access continuously. However, recent developments in generative modelling can significantly threaten continuous systems, allowing attackers craft adversarial examples gain may even limit a legitimate user from accessing protected data network. The...
Missing values exist in nearly all clinical studies because data for a variable or question are not collected available. Inadequate handling of missing can lead to biased results and loss statistical power analysis. Existing models usually do consider privacy concerns utilise the inherent correlations across multiple features impute values. In healthcare applications, we confronted with high dimensional sometimes small sample size datasets that need more effective augmentation imputation...