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.

Spam filtering using classification algorithms

This thesis proposes an innovative adaptive multi-classifier spam filtering model, with a grey-list analyser and a dynamic feature selection method, to overcome false-positive problems in email classification. It also presents additional techniques to minimize the added complexity. Empirical evidence indicates the success of this model over existing approaches.

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.

Reliable downloading algorithms for bittorrent-like systems

In this paper we study a reliable downloading algorithm for BitTorrent-like systems, and attest it in mathematics. BitTorrent-like systems have become immensely popular peer-to-peer file distribution tools in the internet in recent years. We analyze them in theory and point out some of their limitations especially in reliability, and propose an algorithm to resolve these problems by using the redundant copies in neighbors in P2P networks and can further optimize the downloading speed in some condition. Our preliminary simulations show that the proposed reliable algorithm works well; the improved BitTorrent-like systems are very stable and reliable.

Localized relaxational dynamics of succinonitrile

Succinonitrile (N≡C—CH₂—CH₂—C≡N) is a good ionic conductor, when doped with an ionic compound, at room temperature, where it is in its plastic crystalline phase (Long et al. Solid State Ionics 2003, 161, 105; Alarco et al. Nat. Mater. 2004, 3, 476). We report on the relaxational dynamics of the plastic phase near the two first-order phase transitions and on the effect of dissolving a salt in the plastic matrix by quasi-elastic neutron scattering. At 240 K, the three observed relaxations are localized and we can describe their dynamics (τ ≈ 1.7, 17, and 140 ps) to a certain extent from a model using a single molecule that was proposed by B_ée et al. allowing for all conformations in its unit cell (space group IM3M). The extent of the localized motion as observed is however larger than that predicted by the model and suggests that the isomerization of succinonitrile is correlated with a jump to the nearest neighbor site in the unit cell. The salt containing system is known to be a good ionic conductor, and our results show that the effect of the ions on the succinonitrile matrix is homogeneous. Because the isomerizations and rotations are governed by intermolecular interactions, the dissolved ions have an effect over an extended range. Due to the addition of the salt, the dynamics of one of the components (τ ≈ 17 ps) shows more diffusive character at 300 K. The calculated upper limit of the corresponding diffusion constant of succinonitrile in the electrolyte is a factor 30 higher than what is reported for the ions. Our results suggest that the succinonitrile diffusion is caused by nearest neighbor jumps that are localized on the observed length and time scales.

Comparison of performance parameters for conventional and localized surface plasmon resonance graphene biosensors

This paper investigates the enhancement of the sensitivity and adsorption efficiency of a localized surface plasmon resonance (LSPR) biosensor that includes a layer of graphene sheet on top of the gold layer. For this purpose, biomolecular interactions of biotin-streptavidin with the graphene layer on the gold thin film are monitored. The performance of the LSPR graphene biosensor is theoretically and numerically assessed in terms of sensitivity and adsorption efficiency under varying conditions, including the thickness of biomolecule layer, number of graphene layers and operating wavelength. Enhanced sensitivity and improved adsorption efficiency are obtained for the LSPR graphene biosensor in comparison with its conventional counterpart. It is found that the LSPR graphene biosensor has better sensitivity with lower operating wavelength and larger number of graphene layers.

Variable incidence angle localized surface plasmon resonance graphene biosensor

This paper investigates the enhancement of sensitivity of variable incidence angle LSPR biosensor by monitoring biomolecular interactions of biotin-streptavidin with gold thin film. The investigation is carried out by means of introducing an additional layer of graphene sheet on top of gold layer (graphene biosensor) and using different coupling configuration of laser beam. The sensitivity, which is indicated by the shift of plasmon resonance angle, increases with graphene deposited onto the gold layers and is linearly related with the number of graphene layers. In addition, an investigation of the shift of plasmon dip is carried out for two different analyte interfaces: air and water. It is found that graphene biosensor has better sensitivity for triangular prism, higher prism angle, and water interface. The evaluation approach involves a plot of a reflectivity curve as a function of the angle of incidence while the operating wavelength is kept fixed.

Design of multiple sequence alignment algorithms on parallel, distributed memory supercomputers

The challenge of comparing two or more genomes that have undergone recombination and substantial amounts of segmental loss and gain has recently been addressed for small numbers of genomes. However, datasets of hundreds of genomes are now common and their sizes will only increase in the future. Multiple sequence alignment of hundreds of genomes remains an intractable problem due to quadratic increases in compute time and memory footprint. To date, most alignment algorithms are designed for commodity clusters without parallelism. Hence, we propose the design of a multiple sequence alignment algorithm on massively parallel, distributed memory supercomputers to enable research into comparative genomics on large data sets. Following the methodology of the sequential progressiveMauve algorithm, we design data structures including sequences and sorted k-mer lists on the IBM Blue Gene/P supercomputer (BG/P). Preliminary results show that we can reduce the memory footprint so that we can potentially align over 250 bacterial genomes on a single BG/P compute node. We verify our results on a dataset of E.coli, Shigella and S.pneumoniae genomes. Our implementation returns results matching those of the original algorithm but in 1/2 the time and with 1/4 the memory footprint for scaffold building. In this study, we have laid the basis for multiple sequence alignment of large-scale datasets on a massively parallel, distributed memory supercomputer, thus enabling comparison of hundreds instead of a few genome sequences within reasonable time.