https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/2407 2026-10-27T16:29:59Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/3155 Title: Species Speci c Primer Design for Unique Identi cation of Closely Related Species Authors: Tharanga, W.D.R. Abstract: Unique identi cation of closely related species is important when it comes to biomedi- cal and molecular biology research. For example Dengue virus has four serotypes(DEN-1, DEN-2, DEN-3 and DEN-4) and identifying the speci c serotype is important for its med- ical treatment. Conventional identi cation methods based on physiochemical properties could not uniquely distinguish each subtype due to the high degree of similarities in their interactions. DNA analysis can be employed for the purpose of unique identi cation, because of its capability to identify genetic information which makes one species di erent from another. Polymerase Chain Reaction(PCR) is a technique which has been proven to be e cient in DNA analysis of closely-related species. The primary requirement of PCR is a pair of special DNA fragments called "primers" which are used to mark the DNA fragment of interest. However, to use PCR for this purpose, we need to design primers which mark unique DNA fragment(s) of a species which can distinguish it from the rest of the closely- related species. This research focuses on nding a computational method to generate species-speci c unique primers. PCR experiments could possibly fail due to several reasons such as self-dimer and hair- pin formation. Recent research from other classes of primer design (eg. general primer design, conserved primer design) have addressed this issue by using computational primer validation methods. We have extended this concept in our research to introduce a compu- tational primer validation method based on Fuzzy-Adaptive Particle Swarm Optimization (FAPSO) for the class of "species-speci c" primer design. Considering the requirements of unique primer design as well as experimental needs of PCR, we propose an overall design of a possible tool that can be implemented to gen- erate unique primers. Moreover, to validate our design, we have built a prototype by manipulating existing tools and algorithms according to our overall design. Considering the requirement of using resultant primers of a possible tool directly to a "wet-lab" ex- periment, we introduce a thorough evaluation scheme to evaluate a primer design tool of this kind. Based on the evaluation results of the primers generated by our prototype for serotypes of Dengue, we have identi ed that primers generated by our method can be directly used for a wet-lab experiment as the quality is higher than the primers generated by pre- existing methods. 2015-06-12T00:00:00Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/3153 Title: Automated Drinking Action Recognition in videos Authors: Kariyapperuma, K.M.M. Abstract: Today, human action recognition has been becoming one of the famous research areas in Computer Vision. This interest is motivated by a wide set of applications including security applications, video surveillance, video indexing, robot learning, etc. In terms of action recognition, recognizing actions that involve objects is a di cult task in current action recognition approaches. This research is to recognize such actions in another way to improve the accuracy by combining object detection and human action de- tection. In order to recognize the drinking action, a robust descriptor has been created to represent the body action by combining motion descriptor which has been calculated from optical ow and appearance descriptor which has been calculated from still video frames. This descriptor has been used in the action classi cation process. After detecting the action, object detection is conducted to verify the drinking action. Video clips containing drinking actions which have been extracted from movies, internet and some data sets were used in the training, testing and evaluation phases. In the evaluation, datasets were tested separately with action detection and object detection to identify the e ect of the veri cation process. 2015-06-08T00:00:00Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/2485 Title: Secure Routing for Mobile Ad Hoc Networks Authors: Chathuranga, T.N. Abstract: Mobile Ad hoc Network (MANET) is a kind of wireless network which is formed by mobile nodes communicating with each other through wireless link without having xed infrastructure. Each device in MANET is free to move independently in any direction and change its links to other devices frequency. Securing wireless ad hoc networks is a highly challenging issue. Absence of any central co-ordination mechanism and shared wireless medium makes MANET more vulnerable to digitalcyber-attacks than wired network there are a number of attacks that a ect MANET. Proposed security mechanism more suitable for a less resource MANET such as Wireless Sensor Networks (WSN).It prevents routing attacks by au- thenticating and validating integrity of routing messages such as route re- quest and route reply with having an asymmetric security mechanism. Shar- ing a secret master key among all node in the network, it is impossible to achieve an asymmetric security. Therefore hash based message authenti- cation code (HMAC) M with a random number n which used to generate HMAC, are used with each routing messages by attaching them with the message. HAMC generation function works on cipher feedback mode and it will loop the process up to n times for a given routing message and given random number n, and return M. This mechanism is used in both route request and reply message generation and veri cation of those routing mes- sages. Proposed security mechanism courses to a route discovery latency but saves the average power consumption during the route discovery. It is important to select a better range for random number since it courses to the cost of computation. 2014-05-20T00:00:00Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/2482 Title: PageRank Based Core-Attachment Model to Detect Protein Complexes by Analysing Protein Networks Authors: Shazan, M.J.M. Abstract: Virtually all major biological processes in cells are performed by assemblies of physically interacting proteins which are commonly known as protein complexes. Hence, the detection of protein complexes has become a fundamental task in understanding the higher order biological functions in living organisms. Recent advances in highthroughput techniques to infer protein-protein interactions (PPI) data have facilitated the need of computational approaches to analyse them e ciently, and to reconstruct protein complexes accurately. Analysing protein interaction networks (PPI networks) to detect community structures is a popular computational approach to detect protein complexes. Most of the existing complex detection algorithms based on this approach, are not robust against the sparse and noisy nature of the PPI networks, thus failing to achieve the desired level of performance. Furthermore, most of these techniques are primarily relied on the topological properties of the network and have incorporated a minimal number of biological insights with their algorithms, which further contribute to the limitations in their performance. In this thesis, we address some of these issues by proposing WMCORERanker, a novel protein complex detection algorithm which incorporates multiple forms of biological insights to accurately reconstruct protein complexes. In particular, we designed the core mechanism of our algorithm, based on the experimentally observed \Core-Attachment" organizational structure of protein complexes. Core-Attachment model essentially requires the identi cation of core proteins which are central and critical to the functioning of a particular protein complex. A common approach to identify central proteins is to use network centrality measures. PageRank technique, which initially emerged from the web science, is one such successful centrality measure, extensively used across various disciplines. Based on this background we hypothesise, that with su cient biological prior knowledge, PageRank algorithm can accurately capture these core proteins, leading to the accurate reconstruction of protein complexes. Speci cally, we have incorporated evolutionary relationship based information of proteins with the PageRank algorithm, and successfully demonstrated that it can accurately capture core proteins. WMCORERanker is the rst algorithm iii ever to use PageRank to detect core proteins and reconstruct the protein complexes based on the Core-Attachment model. WMCORERanker outperforms all the existing methods in recall, while remaining competitive at the precision rate. The best recall rate we achieved can accurately quantify the capability of our algorithm to retrieve the largest number of benchmark complexes than any other method, indicating that the other predicted, yet unmatched complexes also hold a high potential to be actual protein complexes. 2014-05-20T00:00:00Z