With the efforts to understand the protein structure, many computational approaches have been made recently. Among them, the Support Vector Machine (SVM) methods have been recently applied and showed successful performance compared with other machine learning schemes. However, despite the high performance, the SVM approaches suffer from the problem of understandability since it is a black-box model; the predictions made by SVM cannot be interpreted as biologically meaningful way. To overcome this limitation, a new association rule based classifier PCPAR was devised based on the existing classifier, CPAR to handle the sequential data. The performance of the PCPAR was improved more by designing the following two hybrid schemes. The PCPAR/SVM method is a parallel combination of the PCPAR and the SVM and the PCPAR_SVM method is a sequential combination of the PCPAR and the SVM. To understand the SVM prediction, the SVM_PCPAR scheme was developed. The experimental result presents that the PCPAR scheme shows better performance with respect to the accuracy and the number of generated patterns than CPAR method. The PCPAR/SVM scheme presents better performance than the PCPAR, PCPAR_SVM or the SVM_PCPAR and almost equal performance to the SVM. The generated patterns are easily understandable and biologically meaningful. The system sturdiness evaluation and the ROC curve analysis proved that this new scheme is robust and competent.

Spatial decision making is a process of providing an effective solution for a problem that encompasses semi-structured spatial data. It is a challenging task which involves various factors to consider. For example, in order to build a new industry, an appropriate site must be selected for which several factors have to be taken into consideration. Some of the factors, which can affect the decision in this particular case, are air pollution, noise pollution, and distance from living areas, which makes the decision difficult. The geographic information systems (GIS) and the expert systems (ES) have many advantages in solving problems in their prospective areas. Integrating these two systems will benefit in solving spatial decision making problems. In the past, many researchers have proposed integrating systems which extracts the data from the GIS and saves it in the database for decision making. Most of the frameworks which have been developed were system dependent and are not properly structured. So it is difficult to search the data. This thesis proposes a framework which extracts the GIS data and processes it with the help of ES decision making capabilities to solve the spatial decision making problem. This framework is named GeoFilter. This research classifies various types of mechanisms that can be used to integrate these two systems.

Over the past decade, fuzzy systems have gained remarkable acceptance in many fields including control and automation, pattern recognition, medical diagnosis and forecasting. The fuzzy system application has also been accepted as a promising approach to dealing with uncertainty in real estate valuation analysis. This is mainly due to the necessity of coping with a large number of qualitative and quantitative variables that affect the value of a real property. The appraisers use a great deal of judgment to identify both the characteristics that contribute to property values and the relationships among these characteristics in order to derive estimates of market values. This thesis uses the two widely-used fuzzy rule-based systems / namely the Mamdani and Takagi- Sugeno-Kang (TSK) type fuzzy models in an attempt to examine the main determinants of office rents in Istanbul Central Business District (CBD). The input variables of the fuzzy rule-based systems (FRBS) comprise: i) physical attributes of office spaces and office buildings, ii) lease contract terms, and iii) tenants&rsquo / perception of the office rent determinants, tenants&rsquo / location of residence, tenants&rsquo / transportation modes, etc and as the output the system proposes the office property&rsquo / s rental price. Obtaining office rent determinants is a significant issue for both practitioners and academics. While,practitioners use them directly in demand and sensitivity analyses, academics are more interested in the relative significance of these variables and their effect on the variation in office rent to forecast market behavior. Our data set includes a detailed survey of 500 office spaces located in Istanbul CBD. We have carried out two Mamdani-type FRBS and two TSK-type FRBS for the office space and office building data sets. In these FRBS analyses, firstly the so-called representative office spaces are determined, then the average office space rents are estimated. Finally, the spatial variation in the average office rents across the CBD sub-districts, along with the Office space rent variations with respect to different clusters, like number of workers, number of floors and so on, have been analyzed. We believe that presenting the spatial variation in office rents will make a noteworthy contribution both to the real estate investors and appraisers interested in Istanbul office market.

Radiology departments utilize various visualization techniques of patients&rsquo / bodies, and narrative free text reports describing the findings in these visualizations are written by medical doctors. The information within these narrative reports is required to be extracted for medical information systems. Turkish is an highly agglutinative language and this poses problems in information retrieval and extraction from Turkish free texts. In this thesis one rule-based and one data-driven alternate methods for information retrieval and structured information extraction from Turkish radiology reports are presented. Contrary to previous studies in medical NLP systems, both of these methods do not utilize any medical lexicon or ontology. Information extraction is performed on the level of extracting medically related phrases from the sentence. The aim is to measure baseline performance Turkish language can provide for medical information extraction and retrieval, in isolation of other factors.

In this thesis, we implement a rule-based machine ranslation (MT) system for Personal Digital Assistants (PDAs). Rule-based MT system has three modules in general: analysis, transfer and generation. Grammars used in our system are lexicalized tree automata-based grammar (LTA) and synchronous lexicalized tree adjoining grammar (SLTAG). LTA is used for analysis, and SLTAG is used for transfer and generation. We adjust developed parser to PDAs as a parser in the analysis module. The SLTAG parser in the transfer module would search possible source side of SLTAG in source parse tree. Then, growing target parse tree and scoring each hypothesis is based on language model and rule probability. To avoid too much estimation, generation step would prune some hypotheses under threshold. Compared with other rule-based MT systems, we can build rules automatically and design a flexible rule type. SLTAG parser is coded specially for the rule type. In experiments, Chinese-English BTEC is our training and test data. We can get 17% BLEU score for the test data.

The objective of this thesis is to develop a method for the reconfiguration of three-dimensional modular robots. A modular robot is composed of simple individual building blocks or modules. Each of these modules needs to be controlled and actuated individually in order to make the robot perform useful tasks. The presented method allows us to reconfigure arbitrary initial configurations of modules into any pre-specified target configuration by using graph grammar rules that rely on local information only. Local in a sense that each module needs just information from neighboring modules in order to decide its next reconfiguration step. The advantage of this approach is that the modules do not need global knowledge about the whole configuration. We propose a two stage reconfiguration process composed of a centralized planning stage and a decentralized, rule-based reconfiguration stage. In the first stage, paths are planned for each module and then rewritten into a ruleset, also called a graph grammar. Global knowledge about the configuration is available to the planner. In stage two, these rules are applied in a decentralized fashion by each node individually and with local knowledge only. Each module can check the ruleset for applicable rules in parallel. This approach has been implemented in Matlab and currently, we are able to generate rulesets for arbitrary homogeneous input configurations.

在追求組織目標當中的管理活動中，管理者都需要資訊。而管理會計資訊系統可以提供資訊給管理人員以評估作業和人員的績效，規劃與控制一個組織的營運，作為制定決策的基礎。本研究應用規則式推論系統的分析設計方法讓組織內部的非資訊技術人員完成商業系統的模型，接著利用規則與事實的邏輯化表達將商業邏輯從績效歸屬作業中萃取出來並形成一個結構化的形式。可將知識儲存在商業系統中，達到知識的可再用性與延伸性。而規則式系統的彈性可改善當商業環境快速變化時，系統可應付快速的商業需求的改變。 本研究的貢獻在於探討管理會計資訊系統的績效歸屬功能，建立分析的步驟，並提供一個商業規則系統的開發環境。利用規則式系統將商業邏輯與推論引擎分開，讓知識工程師只需專注在知識工程上，而程式設計師則專注在使用者介面及資料轉換存取。當商業邏輯改變，只需更改知識庫的內容，抽換規則與事實，不需更改程式碼。分析績效歸屬作業的目標與政策，使用模組化規則與三層式設計(目標、政策、規則)，提供更抽象的商業規則，減少例外的發生。使用JESS來實作規則，驗證規則的邏輯可行性。 / Managers need information in the activities pursuing the goals of the organization. Accounting Information System can provide information as making decision to administrators to evaluate the task and employees’ performance and to plan and control the operation in the organization. In this research we utilize the design and analysis approach of rule-based inference to carry out the business model, and then extract the business logic from the activities of performance attribution to form a template structure by representing rules and facts logically. In order to achieve the reuse and extensibility of knowledge, we physically store it in the business system. The rule-based system in this research also provides flexibility and modifiability to meet the requirement of the rapid business environment change in the future. The contribution of the research is to confer the performance attribution in Accounting Information System, to establish the analytical steps and to provide the development circumstance. Separating the business logic and inference engine with rule-based system can make the knowledge engineers to concentrate their attention on knowledge engineering and programmers are absorbed in the user interface and data transformation. It updates only the content of knowledge base, changes the rules and facts and not to modify the code when business logic is changed. Analyzing the goal and the policy of the performance attribution operation and making use of rule module and three-tier design to put up the more abstract business rules and less exception. Implement the rules with JESS to verify the feasibility of rules.

The growing development and use of pervasive systems, equipped with increasingly sophisticated functionalities and communication means, offer fantastic potentialities of services, particularly in the eHealth and Telemedicine domains, for the benifit of each citizen, patient or healthcare professional. One of the current societal challenges is to enable a better exploitation of the available services for all actors involved in a given domain. Nevertheless, the multiplicity of the offered services, the systems functional variety, and the heterogeneity of the needs require the development of knowledge models of these services, systems functions, and needs. In addition, the distributed computing environments heterogeneity, the availability and potential capabilities of various human and material resources (devices, services, data sources, etc.) required by the different tasks and processes, the variety of services providing users with data, the interoperability conflicts between schemas and data sources are all issues that we have to consider in our research works. Our contribution aims to empower the intelligent exploitation of ubiquitous resources and to optimize the quality of service in ambient environment. For this, we propose a knowledge meta-model of the main concepts of a pervasive environment, such as Actor, Task, Resource, Object, Service, Location, Organization, etc. This knowledge meta-model is based on ontologies describing the different aforementioned entities from a given domain and their interrelationships. We have then formalized it by using a standard language for knowledge description. After that, we have designed an architectural framework called ONOF-PAS (ONtology Oriented Framework for Pervasive Applications and Services) mainly based on ontological models, a set of rules, an inference engine, and object oriented components for tasks management and resources processing. Being generic, extensible, and applicable in different domains, ONOF-PAS has the ability to perform rule-based reasoning to handle various contexts of use and enable decision making in dynamic and heterogeneous environments while taking into account the availability and capabilities of the human and material resources required by the multiples tasks and processes executed by pervasive systems. Finally, we have instantiated ONOF-PAS in the telemedicine domain to handle the scenario of the transfer of persons victim of health problems during their presence in hostile environments such as high mountains resorts or geographically isolated areas. A prototype implementing this scenario, called T-TROIE (Telemedicine Tasks and Resources Ontologies for Inimical Environments), has been developed to validate our approach and the proposed ONOF-PAS framework.

Text -to-speech systems currently are of great importance in the community. One core technology in this human language technology resource is stress assignment which plays an important role in any text-to-speech system. At present no automatic stress assigner for Afrikaans exists. For these reasons, the two most important aims of this project will be: a) to develop a complete and accurate set of stress rules for Afrikaans that can be implemented in an automatic stress assigner, and b) to develop an effective and highly accurate stress assigner in order to assign Afrikaans stress to words quickly and effectively. A set of stress rules for Afrikaans was developed in order to reach the first goal. It consists of 18 rules that are divided into groups for words that contain a schwa, derivations, and disyllabic, tri-syllabic and polysyllabic simplex words. Next, different approaches that can be used to develop a stress assigner were examined, and the rule-based approach was used to implement the developed stress rules within the stress assigner. The programming language, Perl, was chosen for the implementation of the rules. The chosen algorithm was used to generate a stress assigner for Afrikaans by implementing the stress rules developed. The hyphenator, Calomo and the compound analyser, CKarma was used to hyphenate all the test data and detect word boundaries within compounds. A dataset of 10 000 correctly annotated tokens was developed during the testing process. The evaluation of the stress assigner consists of four phases. During the first phase, the stress assigner was evaluated with the 10 000 tokens and achieved an accuracy of 92.09%. The grapheme - to-phoneme converter was evaluated with the same data and scored 91.9%. The influence of various factors on stress assignment was determined, and it was established that stress assignment is an essential component of rule-based grapheme-to-phoneme conversion. In conclusion, it can be said that the stress assigner achieved satisfactory results, and that the stress assigner can be successfully utilized in future projects to develop training data for further experiments with stress assignment and grapheme-to-phoneme conversion for Afrikaans. Experiments can be conducted in future with data-driven approaches that possibly may lead to better results in Afrikaans stress assignment and grapheme-to-phoneme conversion. / Thesis (M.A. (Applied Language and Literary Studies))--North-West University, Potchefstroom Campus, 2010.

Text -to-speech systems currently are of great importance in the community. One core technology in this human language technology resource is stress assignment which plays an important role in any text-to-speech system. At present no automatic stress assigner for Afrikaans exists. For these reasons, the two most important aims of this project will be: a) to develop a complete and accurate set of stress rules for Afrikaans that can be implemented in an automatic stress assigner, and b) to develop an effective and highly accurate stress assigner in order to assign Afrikaans stress to words quickly and effectively. A set of stress rules for Afrikaans was developed in order to reach the first goal. It consists of 18 rules that are divided into groups for words that contain a schwa, derivations, and disyllabic, tri-syllabic and polysyllabic simplex words. Next, different approaches that can be used to develop a stress assigner were examined, and the rule-based approach was used to implement the developed stress rules within the stress assigner. The programming language, Perl, was chosen for the implementation of the rules. The chosen algorithm was used to generate a stress assigner for Afrikaans by implementing the stress rules developed. The hyphenator, Calomo and the compound analyser, CKarma was used to hyphenate all the test data and detect word boundaries within compounds. A dataset of 10 000 correctly annotated tokens was developed during the testing process. The evaluation of the stress assigner consists of four phases. During the first phase, the stress assigner was evaluated with the 10 000 tokens and achieved an accuracy of 92.09%. The grapheme - to-phoneme converter was evaluated with the same data and scored 91.9%. The influence of various factors on stress assignment was determined, and it was established that stress assignment is an essential component of rule-based grapheme-to-phoneme conversion. In conclusion, it can be said that the stress assigner achieved satisfactory results, and that the stress assigner can be successfully utilized in future projects to develop training data for further experiments with stress assignment and grapheme-to-phoneme conversion for Afrikaans. Experiments can be conducted in future with data-driven approaches that possibly may lead to better results in Afrikaans stress assignment and grapheme-to-phoneme conversion. / Thesis (M.A. (Applied Language and Literary Studies))--North-West University, Potchefstroom Campus, 2010.