Computer aided analysis of dental radiographic images

This paper is a result of a fruitful cooperation between the computer science and the dental diagnosis experiences. The study presents a new approach of applying computer algorithms to radiographic images of dental implantation used for bone regeneration. We focus here only on the contribution of the computer assistance to the clinical research as the periodontal therapy is beyond the scope of this paper. The proposed system is based on a pattern recognition approach, directed to recognize density changes in the intra-bony affected areas of patients. It comprises different modules with new algorithms specially designed to treat the patients’ radiographic images more accurately. The system includes digitizing, detecting the complicated region of interest (ROI), defining reference area to correct any projection discrepancy of the follow up images, and finally to extract the distinguishing features of the ROI as a basis for determining the rate of new bone density accumulation. This study is applied to two typical dental cases for a patient who received two different operations. The results are very encouraging and more accurate than traditional techniques reported before.

Microarray classification using statistical learning and decision tree algorithms

This considers the challenging task of cancer prediction based on microarray data for the medical community. The research was conducted on mostly common cancers (breast, colon, long, prostate and leukemia) microarray data analysis, and suggests the use of modern machine learning techniques to predict cancer.

Algorithms and applications of preference based ranking for information retrieval

In this thesis, the author designed three sets of preference based ranking algorithms for information retrieval and provided the corresponsive applications for the algorithms. The main goal is to retrieve recommended, high similar and valuable ranking results to users.

Reliable algorithms and attack detection schemes for network systems

Network and Information security and reliability is still a key issue in information technology. This thesis develops two algorithms to improve the reliability and stability of content delivery systems, and proposes three attack detection schemes with high effectiveness and accuracy in detecting network attacks.

Cache and replica lookup, indexing and synchronization in P2P environments

The major outcomes of this research project were the development of a set of decentralized algorithms to index, locate and synchronize replicated information in a networked environment. This study exploits the application specific design constraints of networked systems to improve performance, instead of relying on data structures and algorithms best suited to centralized systems.

Encoding, ensemble and accelerating strategies for linear causal model discovery

This thesis made outstanding contribution in automating the discovery of linear causal models. It introduced a highly efficient discovery algorithm, which implements new encoding, ensemble and accelerating strategies. Theoretic research and experimental work showed that this new discovery algorithm outperforms the previous system in both accuracy and efficiency.

3D depth estimation using wavelet analysis based stereo vision approach

A complete and highly robust 3D reconstruction algorithm based on stereo vision is presented. The developed system is capable of reconstructing dimensionally accurate 3D models of the objects and is very simple and cost effective due to its prominent software dependency and minimal hardware involvevment unlike existing systems.

New blind source estimation methods based on constant modulus criterion

This thesis presents two novel algorithms for blind chancel equalization (BCE) and blind source separation (BSS). Beside these, a general framework for global convergent analysis is proposed. Finally, the open problem of equalising a non-irreducible system is answered by the algorithm proposed in this thesis.

Image to text translation by multi-label classification

This thesis includes the development of an architectural framework for the proposed image to text translation system containing four components. Selection of appropriate algorithms for the first three components developed three effective multi-label classification algorithms for the fourth component, i.e. the translation component, for different problem settings.

Efficient dimensionality reduction and one-class classification for content-based image retrieval

The thesis investigates various machine learning approaches to reducing data dimensionality, and studies the impact of asymmetric data on learning in image retrieval. Efficient algorithms are proposed to reduce the data dimensionality. Integration strategies for one-class classification are designed to address asymmetric data issue and improve retrieval effectiveness.

Load balance algorithms for anycast

Increasingly, replicated anycast servers are being used to deliver network applications and service ever increasing user requests. Therefore, the strategies used to guarantee network bandwidth prerequisites and perform load balancing across the nodes of an anycast group are critical to the performance of online applications. In this paper, we model user requests, network congestion and latency, and server load using a combination of hydro-dynamics and queuing theory to develop an efficient job distribution strategy. Current, anycast research does not explicitly consider the system load of nodes within an anycast groups when distributing requests. Therefore, the performance of a heavily loaded anycast system can quickly become congested and uneven as jobs are routed to closely linked nodes which are already saturated with requests. In comparison, the nodes of further away systems remain relatively unused because of other issues such as network bandwidth and latency during these times. Our system redirects requests from busy systems to the idle, remotely linked nodes, to process requests faster in spite of slower network access. Using an empirical study, we show this technique can improve request performance, and throughput with minimal network probing overhead.

A generalized data windowing scheme for adaptive conjugate gradient algorithms

The performance of the modified adaptive conjugate gradient (CG) algorithms based on the iterative CG method for adaptive filtering is highly related to the ways of estimating the correlation matrix and the cross-correlation vector. The existing approaches of implementing the CG algorithms using the data windows of exponential form or sliding form result in either loss of convergence or increase in misadjustment. This paper presents and analyzes a new approach to the implementation of the CG algorithms for adaptive filtering by using a generalized data windowing scheme. For the new modified CG algorithms, we show that the convergence speed is accelerated, the misadjustment and tracking capability comparable to those of the recursive least squares (RLS) algorithm are achieved. Computer simulations demonstrated in the framework of linear system modeling problem show the improvements of the new modifications.

Improved 3D thinning algorithms for skeleton extraction

In this study, we focused on developing a novel 3D Thinning algorithm to extract one-voxel wide skeleton from various 3D objects aiming at preserving the topological information. The 3D Thinning algorithm was testified on computer-generated and real 3D reconstructed image sets acquired from TEMT and compared with other existing 3D Thinning algorithms. It is found that the algorithm has conserved medial axes and simultaneously topologies very well, demonstrating many advantages over the existing technologies. They are versatile, rigorous, efficient and rotation invariant.

Email categorization using (2+1)-tier classification algorithms

In this paper we have proposed a spam filtering technique using (2+1)-tier classification approach. The main focus of this paper is to reduce the false positive (FP) rate which is considered as an important research issue in spam filtering. In our approach, firstly the email message will classify using first two tier classifiers and the outputs will appear to the analyzer. The analyzer will check the labeling of the output emails and send to the corresponding mailboxes based on labeling, for the case of identical prediction. If there are any misclassifications occurred by first two tier classifiers then tier-3 classifier will invoked by the analyzer and the tier-3 will take final decision. This technique reduced the analyzing complexity of our previous work. It has also been shown that the proposed technique gives better performance in terms of reducing false positive as well as better accuracy.