Computational microRNA (miRNA) target identification in animal genomes is a challenging problem due to the imperfect pairing of the miRNA with the target site. Techniques based on sequence alone are prone to produce many false positive interactions. Therefore, integrative techniques have been developed to utilize additional genomic, structural features, and evolu- tionary conservation information for reducing the high false positive rate. We propose that the context of a putative miRNA target in a protein-protein interaction (PPI) network can be used as an additional filter in a computational miRNA target pr ediction algorithm. We compute several graph theoretic measures on human PPI network as indicators of network context. We assess the performance of individual and combined contextual measures in increasing the precision of a popular miRNA target prediction tool, TargetScan, using low throughput and high throughput datasets of experimentally verified human miRNA targets. We used clas- sification algorithms for that assessment. Since there exists only miRNA targets as training samples, this problem becomes a One Class Classification (OCC) problem. We devised a novel OCC method, DiVo, based on simple distance metrics and voting. Comparative analysis with the state of the art methods show that, DiVo attains better classification performance. Our eventual results indicate that topological properties of target gene products in PPI networks are valuable sources of information for filtering out false positive miRNA target genes. We show that, for targets of a number of miRNAs, netwo rk context correlates better with being a target compared to a sequence based score provided by the prediction tool.

Computational microRNA (miRNA) target identification in animal genomes is a challenging problem due to the imperfect pairing of the miRNA with the target site. Techniques based on sequence alone are prone to produce many false positive interactions. Therefore, integrative techniques have been developed to utilize additional genomic, structural features, and evolu- tionary conservation information for reducing the high false positive rate. We propose that the context of a putative miRNA target in a protein-protein interaction (PPI) network can be used as an additional filter in a computational miRNA target prediction algorithm. We compute several graph theoretic measures on human PPI network as indicators of network context. We assess the performance of individual and combined contextual measures in increasing the precision of a popular miRNA target prediction tool, TargetScan, using low throughput and high throughput datasets of experimentally verified human miRNA targets. We used clas- sification algorithms for that assessment. Since there exists only miRNA targets as training samples, this problem becomes a One Class Classification (OCC) problem. We devised a novel OCC method, DiVo, based on simple distance metrics and voting. Comparative analysis with the state of the art methods show that, DiVo attains better classification performance. Our eventual results indicate that topological properties of target gene products in PPI networks are valuable sources of information for filtering out false positive miRNA target genes. We show that, for targets of a number of miRNAs, network context correlates better with being a target compared to a sequence based score provided by the prediction tool.

Interactive classi&amp / #64257 / cation is an attractive alternative and complementary for automatic classi&amp / #64257 / cation of satellite image content, since the subject is visual and there are not yet powerful computational features corresponding to the sought visual features. In this study, we improve our previous attempt by building a more stable software system with better capabilities for interactive classi&amp / #64257 / cation of the content of satellite images. The system allows user to indicate a few number of image regions that contain a speci&amp / #64257 / c geographical object, for example, a bridge, and to retrieve similar objects on the same satellite images. Retrieval process is iterative in the sense that user guides the classi&amp / #64257 / cation procedure by interaction and visual observation of the results. The classi&amp / #64257 / cation procedure is based on one-class classi&amp / #64257 / cation.

Video database applications call for flexible and powerful modeling and querying facilities, which require an integration or interaction between database and knowledge base technologies. It is also necessary for many real life video database applications to incorporate uncertainty, which naturally occurs due to the complex and subjective semantic content of video data. In this thesis study, firstly, a fuzzy conceptual data model is introduced to represent the semantic content of video data. UML (Unified Modeling Language) is utilized and extended to represent uncertain information along with video specific properties at the conceptual level. Secondly, an intelligent fuzzy object-oriented database framework is presented for video database applications. The introduced fuzzy conceptual model is mapped to the presented framework, which is an adaptation of the previously proposed IFOOD architecture. The framework provides modeling and querying of complex and rich semantic content and knowledge of video data including uncertainty. Moreover, it allows (fuzzy) semantic, temporal, (fuzzy) spatial, hierarchical, regional and trajectory queries, based on the video data model. We think that the presented conceptual data model and framework can be adapted to any application domain related to video databases.

The tremendous growth of Web has made information overload problem increasingly serious. Users are often confused by huge amount of information available on the internet and they are faced with the problem of finding the most relevant information that meets their needs. Recommender systems have proven to be an important solution approach to this problem. This thesis will present OPENMORE, a movie recommendation system, which is primarily based on content-based filtering technique. The distinctive point of this study lies in the methodology used to construct and update user and item profiles and the optimizations used to fine-tune the constructed user models. The proposed system arranges movie content data as features of a set of dimension slots, where each feature is assigned a stable feature weight regardless of individual movies. These feature weights and the explicit feedbacks provided by the user are then used to construct the user profile, which is fine-tuned through a set of optimization mechanisms. Users are enabled to view their profile, update them and create multiple contexts where they can provide negative and positive feedback for the movies on the feature level.

In order to manage the content of multimedia data, the content must be annotated. Although any user-defined annotation is acceptable, it is preferable if systems agree on the same annotation format. MPEG-7 is a widely accepted standard for multimedia content annotation. However, in MPEG-7, semantically identical metadata can be represented in multiple ways due to lack of precise semantics in its XML-based syntax. Unfortunately this prevents metadata interoperability. To overcome this problem, MPEG-7 standard is translated into an ontology. In this thesis, MPEG-7 ontology is used on top and the given user-defined ontologies are attached to the MPEG-7 ontology via a user friendly interface, thus building MPEG-7 based ontologies automatically. Our proposed system is an ontology-based multimedia information management framework due to its modular architecture, ease of integrating with domain specific ontologies naturally and automatic harmonization of MPEG-7 ontology and domain-specific ontologies. Integration with domain specific ontologies is carried out by enabling import of domain ontologies via a user-friendly interface which makes the system independent of application domains.

In this thesis, detection of traffic signs is studied. Since, both color and shape properties of traffic signs are distinctive / these two properties have been employed for detection. Detection using color properties is studied in two different color domains in order to examine and compare the advantages and the disadvantages of these domains for detection purposes. In addition to their color information, shape information is also employed for detection purpose. Edge information (obtained by using the Sobel Operator) of the images/frames is considered as search domain to find triangular, rectangular, octagonal and circular traffic signs. In order to improve the performance of detection process a joint implementation of shape and color based algorithms is utilized. Two different methods have been used v in order to combine these two features. Both of the algorithms help reducing the number of pixels to check whether they belong to a sign or not. This, of course, reduces the processing time of detection process. Each utilized algorithm is tested and compared with the others by using both static images from different sources and video streams. Images having adverse properties are used in order to state algorithms response for some specific conditions such as bad illumination and shadow. After implementation, results show that joint implementation of the color and shape based detection algorithms produces more accurate results. Moreover, joint implementation reduces the processing time of the detection process when compared to application of algorithms individually since it diminishes the search domain.

Some mechanisms to enable an effective transition from domain models to web service descriptions are developed. The introduced domain modeling support provides verification and correction on the customization part. An automated mapping mechanism from the domain model to web service ontologies is also developed. The proposed approach is based on Feature-Oriented Domain Analysis (FODA), Semantic Web technologies and ebXML Business Process Specification Schema (ebBP). Major contributions of this work are the conceptualizations of a feature model for web services and a novel approach for knowledge-based elicitation of domain-specific outcomes in order to allow designing and deploying services better aligned with dynamically changing business goals, stakeholders&#039 / concerns and end-users&#039 / viewpoints. The main idea behind enabling a knowledge-based approach is to pursue automation and intelligence on reflecting business requirements into service descriptions via model transformations and automated reasoning. The proposed reference variability model encloses the domain-specific knowledge and is formalized by using Web Ontology Language (OWL). Adding formal semantics to feature models allows us to perform automated analysis over them such as the verification of model customizations through exploiting rule-based automated reasoners. This research was motivated due to the needs for achieving productivity gains, maintainability and better alignment of business requirements with technical capabilities in engineering service-oriented applications and systems.

In design and detailing of a reinforced concrete frame project, there are many engineers who contribute a single project. Wide variety of information is exchanged between these engineers in design and detailing stages. If the coordination between engineers is not performed sufficiently, data exchange may result in loss of important information that may cause inadequate design and detailing of a structure. Thus, a data model developed for different stages of design and detailing of reinforced concrete structure can facilitate the data exchange among engineers and help improving the quality of structural design. In this study, an object oriented data model was developed for the design and detailing of reinforced concrete columns and beam column joints. The geometry of the structure, amount, shape and placement of reinforcement were defined in this data model. In addition to these, classes that facilitate the design and detailing of reinforced concrete columns and beam column joints according to a building codes were also developed. Another focus of this study is to develop a web based, platform independent data management and multi-user framework for structural design and detailing of reinforced concrete frames. The framework allows simultaneous design of a structure by multiple engineers. XML Web Services technology was utilized for the web based environment in such a way that the design related data was stored and managed centrally by the server in XML files. As a final step, CAD drawings of column reinforcement details in DXF format are prepared.

In this dissertation we lay the groundwork of automated analysis of extended feature models with constraint programming. Among different proposals, feature modeling has proven to be very effective for modeling and managing variability in Software Product Lines. However, industrial experiences showed that feature models often grow too large with hundreds of features and complex cross-tree relationships, which necessitates automated analysis support. To address this issue we present a mapping from extended feature models, which may include complex feature-feature, feature-attribute and attribute-attribute cross-tree relationships as well as global constraints, to constraint logic programming over finite domains. Then, we discuss the effects of including complex feature attribute relationships on various analysis operations defined on the feature models. As new types of variability emerge due to the inclusion of feature attributes in cross-tree relationships, we discuss the necessity of reformulation of some of the analysis operations and suggest a revised understanding for some other. We also propose new analysis operations arising due to the nature of the new variability introduced. Then we propose a transformation from extended feature models to basic/cardinality-based feature models that may be applied under certain circumstances and enables using SAT or BDD solvers in automated analysis of extended feature models. Finally, we discuss the role of the context information in feature modeling, and propose to use context information in staged configuration of feature-models.