Effective and Secure Healthcare Machine Learning System with Explanations Based on High Quality Crowdsourcing Data

Affordable cloud computing technologies allow users to efficiently outsource, store, and manage their Personal Health Records (PHRs) and share with their caregivers or physicians. With this exponential growth of the stored large scale clinical data and the growing need for personalized care, researchers are keen on developing data mining methodologies to learn efficient hidden patterns in such data. While studies have shown that those progresses can significantly improve the performance of various healthcare applications for clinical decision making and personalized medicine, the collected medical datasets are highly ambiguous and noisy. Thus, it is essential to develop a better tool for disease progression and survival rate predictions, where dataset needs to be cleaned before it is used for predictions and useful feature selection techniques need to be employed before prediction models can be constructed. In addition, having predictions without explanations prevent medical personnel and patients from adopting such healthcare deep learning models. Thus, any prediction models must come with some explanations. Finally, despite the efficiency of machine learning systems and their outstanding prediction performance, it is still a risk to reuse pre-trained models since most machine learning modules that are contributed and maintained by third parties lack proper checking to ensure that they are robust to various adversarial attacks. We need to design mechanisms for detection such attacks. In this thesis, we focus on addressing all the above issues: (i) Privacy Preserving Disease Treatment & Complication Prediction System (PDTCPS): A privacy-preserving disease treatment, complication prediction scheme (PDTCPS) is proposed, which allows authorized users to conduct searches for disease diagnosis, personalized treatments, and prediction of potential complications. (ii) Incentivizing High Quality Crowdsourcing Data For Disease Prediction: A new incentive model with individual rationality and platform profitability features is developed to encourage different hospitals to share high quality data so that better prediction models can be constructed. We also explore how data cleaning and feature selection techniques affect the performance of the prediction models. (iii) Explainable Deep Learning Based Medical Diagnostic System: A deep learning based medical diagnosis system (DL-MDS) is present which integrates heterogeneous medical data sources to produce better disease diagnosis with explanations for authorized users who submit their personalized health related queries. (iv) Attacks on RNN based Healthcare Learning Systems and Their Detection & Defense Mechanisms: Potential attacks on Recurrent Neural Network (RNN) based ML systems are identified and low-cost detection & defense schemes are designed to prevent such adversarial attacks. Finally, we conduct extensive experiments using both synthetic and real-world datasets to validate the feasibility and practicality of our proposed systems

Facial Recognition in Uncontrolled Conditions for Information Security

With the increasing use of computers nowadays, information security is becoming an important issue for private companies and government organizations. Various security technologies have been developed, such as authentication, authorization, and auditing. However, once a user logs on, it is assumed that the system would be controlled by the same person. To address this flaw, we developed a demonstration system that uses facial recognition technology to periodically verify the identity of the user. If the authenticated user's face disappears, the system automatically performs a log-off or screen-lock operation. This paper presents our further efforts in developing image preprocessing algorithms and dealing with angled facial images. The objective is to improve the accuracy of facial recognition under uncontrolled conditions. To compare the results with others, the frontal pose subset of the Face Recognition Technology (FERET) database was used for the test. The experiments showed that the proposed algorithms provided promising results.</p

A Novel Relocalization Method-Based Dynamic Steel Billet Flaw Detection and Marking System

In the current steel production process, occasional flaws within the billet are somewhat inevitable. Overlooking these flaws can compromise the quality of the resulting steel products. To address and mark these flaws for further handling, Magnetic Particle Testing (MT) in conjunction with machine vision is commonly utilized. This method identifies flaws on the billet’s surface and subsequently marks them via a device, eliminating the need for manual intervention. However, certain processes, such as magnetic particle cleaning, require substantial spacing between the vision system and the marking device. This extended distance can lead to shifts in the billet position, thereby potentially affecting the precision of flaw marking. In response to this challenge, we developed a detection-marking system consisting of 2D cameras, a manipulator, and an integrated 3D camera to accurately pinpoint the flaw’s location. Importantly, this system can be integrated into active production lines without causing disruptions. Experimental assessments on dynamic billets substantiated the system’s efficacy and feasibility

Protective Effect of <i>Lycium ruthenicum</i> Polyphenols on Oxidative Stress against Acrylamide Induced Liver Injury in Rats

Acrylamide (ACR) is formed during tobacco and carbohydrate-rich food heating and is widely applied in many industries, with a range of toxic effects. The antioxidant properties of Lycium ruthenicum polyphenols (LRP) have been established before. This study aimed to research the protective effect of LRP against ACR-induced liver injury in SD rats. Rats were divided into six groups: Control, ACR (40 mg/kg/day, i.g.), LRP (50, 100, and 200 mg/kg/day, i.g.) plus ACR, and LRP groups. After 19 days, we evaluated oxidative status and mitochondrial functions in the rat’s liver. The results showed that glutathione (GSH) and superoxide dismutase (SOD) levels increased after LRP pretreatment. In contrast, each intervention group reduced reactive oxygen species (ROS) and malondialdehyde (MDA) levels compared to the ACR group. Meanwhile, alanine aminotransferase (ALT), aspartate aminotransferase (AST), liver mitochondrial ATPase activity, mRNA expression of mitochondrial complex I, III, and expression of nuclear factor-erythroid 2-related factor 2 (Nrf2) and its downstream proteins were all increased. This study suggested that LRP could reduce ACR-induced liver injury through potent antioxidant activity. LRP is recommended as oxidative stress reliever against hepatotoxicity

Integrating Inflammation-Responsive Prodrug with Electrospun Nanofibers for Anti-Inflammation Application

Chronic inflammation plays a side effect on tissue regeneration, greatly inhibiting the repair or regeneration of tissues. Conventional local delivery of anti-inflammation drugs through physical encapsulation into carriers face the challenges of uncontrolled release. The construction of an inflammation-responsive prodrug to release anti-inflammation drugs depending on the occurrence of inflammation to regulate chronic inflammation is of high need. Here, we construct nanofiber-based scaffolds to regulate the inflammation response of chronic inflammation during tissue regeneration. An inflammation-sensitive prodrug is synthesized by free radical polymerization of the indomethacin-containing precursor, which is prepared by the esterification of N-(2-hydroxyethyl) acrylamide with the anti-inflammation drug indomethacin. Then, anti-inflammation scaffolds are constructed by loading the prodrug in poly(&epsilon;-caprolactone)/gelatin electrospun nanofibers. Cholesterol esterase, mimicking the inflammation environment, is adopted to catalyze the hydrolysis of the ester bonds, both in the prodrug and the nanofibers matrix, leading to the generation of indomethacin and the subsequent release to the surrounding. In contrast, only a minor amount of the drug is released from the scaffold, just based on the mechanism of hydrolysis in the absence of cholesterol esterase. Furthermore, the inflammation-responsive nanofiber scaffold can effectively inhibit the cytokines secreted from RAW264.7 macrophage cells induced by lipopolysaccharide in vitro studies, highlighting the great potential of these electrospun nanofiber scaffolds to be applied for regulating the chronic inflammation in tissue regeneration