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The state-of-art image segmentation algorithms can be applied to accurately localize objects by using deep convolutional neural networks (CNN). In this paper, we consider the anomaly detection problem encountered in a train wheel system. We propose a progressive approach to use a multi-target network to segment each component of the considered system sequentially by decoupling the segmentation and the classification task. Moreover, we use the knowledge graph approach to establish a semantic consistency matrix by quantifying the spatial relationship between various components. We show that by establishing a knowledge graph of the normally operating systems, we are able to identify a faulty component effectively.

According to a study [1] by the Ministry of Health in Singapore, since 2009, cancer, ischaemic heart disease and pneumonia together accounted for approximately 60% of the total causes of death. It has been 9 years, and Pneumonia and other Acute Upper Respiratory Infections still is one of the top 10 conditions of hospitalization. In cases of respiratory diseases such as Chronic Obstructive Pulmonary Disease (COPD), it has been found that close to 55% of cases are misdiagnosed. This is shocking as, an early diagnosis of respiratory diseases can lead to an earlier treatment intervention, ultimately lessening symptoms, slowing the progression, and improving overall quality of life. With the advent of Deep Neural Network architectures which have shown phenomenal results in the field of Computer Assisted Diagnosis (CAD), we hope to implement a Lung Classification Model using End-to-End Deep learning to classify Chest X-Ray images into one of 14 primary classes of lung diseases. Using our implementation of the Densely Connected Convolutional Neural Network model architecture, we aim to increase existing model accuracy in Lung Disease classification by iteratively reducing the search space and region of interest for different. We shall experiment on a 14-class classification model and compare the results with a binary classifier as well, to understand the performance of DenseNets on Chest X-Ray (CXR) Data with a reduced search space.

In this paper, we present an automated procedure to determine the presence of cardiomegaly on chest X-ray image based on deep learning. The proposed algorithm CardioXNet uses deep learning methods U-NET and cardiothoracic ratio for diagnosis of cardiomegaly from chest X-rays. U-NET learns the segmentation task from the ground truth data. OpenCV is used to denoise and maintain the precision of region of interest once minor errors occur. Therefore, Cardiothoracic ratio (CTR) is calculated as a criterion to determine cardiomegaly from U-net segmentations. End-to-end Dense-Net neural network is used as baseline. This study has shown that the feasibility of combing deep learning segmentation and medical criterion to automatically recognize heart disease in medical images with high accuracy and agreement with the clinical results.

Rapid advances in automated data collection tools and data storage technology has led to the wide availability of huge amount of distributed data owned by different parties. Data mining can use the distributed data to discover rules, patterns or knowledge that are normally not discovered data owned by a single party. Thus, data mining on the distributed data can lead to new insights and economic advantages. However, in recent years, privacy laws have been enacted to protect any individual sensitive information from privacy violation and misuse. To address the issue, many have proposed privacy-preserving data mining (PPDM) based on secure multi-party computation (SMC) that can mine the distributed data with privacy preservation (i.e., privacy protection). However, most SMC-based solutions are ad-hoc. They are proposed for specific applications, and thus cannot be applied to other applications directly. Another limitation of current PPDM is with only a limited set of operators such as +,−,× and log (logarithm). In data mining primitives, some functions can involve operators such as / and √ . The above issues have motivated us to investigate a general SMC-based solution to solve the current limitations of PPDM. In this thesis, we propose a general model for privacy-preserving data mining, namely as DAG. We apply a hybrid model that combines the homomorphic encryption protocol and the circuit approach in DAG model. The hybrid model has been proven efficient in computation and effective in protecting data privacy via the theoretical and experimental proofs. Specifically, our proposed research objectives are as follows: (i) We want to propose a general model of privacy-preserving data mining (i.e., DAG) that consists of a set of secure operators. The secure operators can support many mining primitives. The two-party model which is the efficient and effective model is applied to develop secure protocols in DAG. Our secure operators can provide a complete privacy under the semi-honest model. Moreover, the secure operators are efficient in computation. (ii) We will integrate DAG model into various classification problems by proposing new privacy-preserving classification algorithms. (iii) To make our DAG model that can support wider applications, we will integrate DAG into other application domains. We will integrate DAG into ant colony optimization (ACO) to solve the traveling salesman problem (TSP) by proposing a new privacypreserving traveling salesman problem (PPTSP). In this report, we present most results of the objectives mentioned above. The DAG model is general – its operators, if pipelined together, can implement various functions. It is also extendable – new secure operators can be defined to expand the functions the model supports. All secure operators of DAG are strictly proven secure via simulation paradigm (Goldreich, 2004). In addition, the error bounds and the complexities of the secure operators are derived so as to investigate accuracy and computation performance of our DAG model. We apply our DAG model into various application domains. We first apply DAG into data mining classification algorithms such as support vector machine, kernel regression, and Na¨ıve Bayes. Experiment results show that DAG generates outputs that are almost the same as those by non-private setting, where multiple parties simply disclose their data. DAG is also efficient in computation of data mining tasks. For example, in kernel regression, when training data size is 683,093, one prediction in non-private setting takes 5.93 sec, and that by our DAG model takes 12.38 sec. In the experiment of PPTSP, a salesman can find the approximate optimal traveled distance without disclosing any city locations in TSP. Various domain applications studies show that our DAG is the general model yet efficient for secure multi-party computation.