Conceptual estimation techniques play an important role in determining the approximate costs of construction projects especially during feasibility stages. Moreover, pre-design estimates are also crucial for the contractors. With the help of the conceptual predictions companies can determine approximate project costs and can gain several advantages before tendering phase. The main objective of this thesis is to focus on modeling of quantities instead of costs and to develop quantity take-off models for pre-design cost estimation of bridge projects. Majority of the existing studies focus on modeling of costs for conceptual cost estimation. This study includes modeling of the quantity take off items in a specific single span highway bridge using three different techniques namely, linear regression, neural network and case based reasoning. During this study 40 single span highway bridge projects whose owner is Republic of Turkey General Directorate of Railways, Ports and Airports Constructions were investigated and models for each work item were developed. Then by integrating the quantity take off estimations with unit costs, total project costs were calculated. As a result by evaluating the prediction performance of the models, comparison of the methods was achieved. Results are discussed along with the advantages of the proposed method for conceptual cost estimation of bridge projects.

This study adopts large access graph algorithm and case-base reasoning approach to generalize user access patterns and diagnose network events respectively for facilitating the network management. Large access graph (LAG) algorithm discovers the frequently inter-connections among hosts to provide an overview of network access relation. The case-based reasoning (CBR) system diagnoses the instant network events with the past experience. NetFlow log data collected from the router of the dormitory network of National Sun Yat-Sen University is used for demonstrating these two methods. The evaluation results measured by recall, precision, and accuracy show that these two mechanisms are useful to support the network administer to keep track of network access relations and diagnose the network events.

On one hand, academic and industrial researchers have been developing and deploying robots that are used as educational tutors, mediators, and motivational tools. On the other hand, an increasing amount of interest has been placed on non-expert users being able to program robots intuitively, which has led to promising research efforts in the fields of machine learning and human-robot interaction. This dissertation focuses on bridging the gap between the two subfields of robotics to provide personalized experience for the users during educational, entertainment, and therapeutic sessions with social robots. In order to make the interaction continuously engaging, the workspace shared between the user and the robot should provide personalized contexts for interaction while the robot learns to participate in new tasks that arise. This dissertation aims to solve the task-learning problem using an instance-based framework that stores human demonstrations as task instances. These instances are retrieved when confronted with a similar task in which the system generates predictions of task behaviors based on prior solutions. The main issues associated with the instance-based approach, i.e., knowledge encoding and acquisition, are addressed in this dissertation research using interactive methods of machine learning. This approach, further referred to as interactive instance-based learning (IIBL), utilizes the keywords people use to convey task knowledge to others to formulate task instances. The key features suggested by the human teacher are extracted during the demonstrations of the task. Regression approaches have been developed in this dissertation to model similarities between cases for instance retrieval including multivariate linear regression and sensitivity analysis using neural networks. The learning performance of the IIBL methods were then evaluated while participants engaged in various block stacking and inserting scenarios and tasks on a touchscreen tablet with a humanoid robot Darwin. In regard to end-users programming robots, the main benefit of the IIBL framework is that the approach fully utilizes the explanatory behavior of the instance-based method which makes the learning process transparent to the human teacher. Such an environment not only encourages the user to produce better demonstrations, but also prompts the user to intervene at the moment a new instance is needed. It was shown through user studies that participants naturally adapt their teaching behavior to the robot learner's progress and adjust the timing and the number of demonstrations. It was also observed that the human-robot teaching and learning scenarios facilitate the emergence of various social behaviors from participants. Encouraging social interaction is often an objective of the task especially with children with cognitive disabilities, and a pilot study with children with autism spectrum disorder revealed promising results comparable to the typically developing group. Finally, this dissertation investigated the necessity of renewable context for prolonged interaction with robot companions. Providing personalized tasks that match each individual's preferences and developmental stages enhances the quality of the user experience with robot learners. Confronted with the limitations of the physical workspace, this research proposes utilizing commercially available touchscreen smart devices as a shared platform for engaging the user in educational, entertainment, and therapeutic tasks with the robot learners. To summarize, this dissertation attempts to defend the thesis statement that a robot learner that utilizes an IIBL approach improves the performance and efficiency of general task learning, and when combined with the state-of-the-art mobile technology that provides personalized context for interaction, enhances the user's experience for prolonged engagement of the task.

This work has investigated the possibility of classifying Java source code into cases for a case-based reasoning system. A Case-Based Reasoning system is a problem solving method in Artificial Intelligence that uses knowledge of previously solved problems to solve new problems. A case in case-based reasoning consists of two parts: the problem part and solution part. The problem part describes a problem that needs to be solved and the solution part describes how this problem was solved. In this work, the problem is described as a Java source file using words that describes the content in the source file and the solution is a classification of the source file along with the source code. To classify Java source code, a classification system was developed. It consists of four analyzers: type filter, documentation analyzer, syntactic analyzer and semantic analyzer. The type filter determines if a Java source file contains a class or interface. The documentation analyzer determines the level of documentation in asource file to see the usefulness of a file. The syntactic analyzer extracts statistics from the source code to be used for similarity, and the semantic analyzer extracts semantics from the source code. The finished classification system is formed as a kd-tree, where the leaf nodes contains the classified source files i.e. the cases. Furthermore, a vocabulary was developed to contain the domain knowledge about the Java language. The resulting kd-tree was found to be imbalanced when tested, as the majority of source files analyzed were placed inthe left-most leaf nodes. The conclusion from this was that using documentation as a part of the classification made the tree imbalanced and thus another way has to be found. This is due to the fact that source code is not documented to such an extent that it would be useful for this purpose.

A Stroke can affect different parts of the human body depending on the area of brain effected; our research focuses on upper limb motor dysfunction for stroke patients. In current practice, ordinal scale systems are used for conducting physical assessment of upper limb impairment. The reliability of these assessments is questionable, since their coarse ratings cannot reliably distinguish between the different levels of performance. This thesis describes the design, implementation and evaluation of a novel system to facilitate stroke diagnosis which relies on data collected with an innovative KINARM robotic tool. This robotic tool allows for an objective quantification of motor function and performance assessment for stroke patients. The main methodology for the research is Case Based Reasoning (CBR) - an effective paradigm of artificial intelligence that relies on the principle that a new problem is solved by observing similar, previously encountered problems and adapting their known solutions. A CBR system was designed and implemented for a repository of stroke subjects who had an explicit diagnosis and prognosis. For a new stroke patient, whose diagnosis was yet to be confirmed and who had an indefinite prognosis, the CBR model was effectively used to retrieve analogous cases of previous stroke patients. These similar cases provide useful information to the clinicians, facilitating them in reaching a potential solution for stroke diagnosis and also a means to validate other imaging tests and clinical assessments to confirm the diagnosis and prognosis. / Thesis (Master, Computing) -- Queen's University, 2008-09-27 11:14:04.85

Faculty members in many schools of nursing have been urged to include critical thinking in all aspects of the nursing curriculum. The faculty at the Institutes of Nursing in the United Arab Emirates, have adopted in the academic year I998 a case-based curriculum that teaches nursing courses by using case studies, which represent a terrific and non-threatening method to use to teach and learn either critical thinking skills or clinical decision-making (Robinson, 1998; Glendon and Ulrich, 1992, 1997). The development of critical thinking skills in a case-based curriculum was investigated. A randomly selected, cross-sectional sample of nursing students at the Abu Dhabi Institute of Nursing (N= 88) was studied. Three groups (n=30) from each level of a three-level-diploma nursing program were measured for development of critical thinking skills using the Test of Everyday Reasoning (TER). Relationships were investigated between TER scores, the level of the program and other socio-demographic and academic achievement determinants. Critical thinking ability did not change significantly among the three levels during the educational experience in a case-based curriculum; however, the participants in the highest level of the program were able to get a higher mean TER scores from the other two levels. One of the conclusions that could be drawn from this study was that critical thinking might not change as an associated factor with a case-based curriculum at this premature phase of its implementation until some time after the graduates of this program become practicing nurses where clinical decision-making would be in action. The other conclusions focused on the necessity of unfolding the utilized cases in the curriculum and on determining whether the construct of critical thinking has been incorporated in them. / Thesis (M.Cur.)-University of Natal, Durban, 2001.

The Institutes of Nursing in the United Arab Emirates adopted a new approach for educating and training the Diploma Nursing students in 1997. This approach emphasized the use of case-based learning, which was characterized by self-directed and cooperative learning. As the students were experiencing changes in the educational setting and teaching practices it was important to determine the impact of the teaching and learning approaches on students' learning, and to describe suggestions needed for improvement. The purposes of this study were to determine the learning styles and stages of self-direction in learning for students at Abu Dhabi Institute of Nursing, and to investigate whether there was a relationship between learning style, stage of self-direction and academic performance in courses taught by the case-based method. This study was guided by Kolb's theory of Experiential Learning, and Grow's theory of the Staged Self-Directed Learning Model. Kolb's learning style inventory and a self designed tool to measure stages of self-direction were administered to 186 students, who agreed to participate in the study. The design was a descriptive correlational one, and data was analyzed by descriptive, correlation, and inferential statistics methods. The assimilator learning style was the most predominant learning style (35.5%) followed by the converger (29.6%). Accommodators and divergers had equal percentage (17%) for each. As for the stages in self-direction, most of the students rated themselves in the moderate stages of self-direction (67.2%), however, Diploma III had the higest percentage of high self-directed learners (57%). Significant relationship was found between learning styles and academic performance, where convergers and divergers scored higher than assimilators and accommodators. Also a significant relationship was found between the stage of self-direction in learning and academic performance, where students in higher stages of self-direction had higher mean scores compared to students in low and moderate stages of self-direction. / Thesis (M.Cur.)-University of Natal, Durban, 2001.

Increased stress is a continuing problem in our present world. Especiallynegative stress could cause serious health problems if it remainsundiagnosed/misdiagnosed and untreated. In the stress medicine, clinicians’measure blood pressure, ECG, finger temperature and breathing rate during anumber of exercises to diagnose stress-related disorders. One of the physiologicalparameters for quantifying stress levels is the finger temperature that helps theclinicians in diagnosis and treatment of stress. However, in practice, it is difficultand tedious for a clinician to understand, interpret and analyze complex, lengthysequential sensor signals. There are only few experts who are able to diagnose andpredict stress-related problems. A system that can help the clinician in diagnosingstress is important, but the large individual variations make it difficult to build sucha system.This research work has attempted to investigate several artificial Intelligencetechniques to develop an intelligent, integrated sensor system for diagnosis andtreatment plan in the Psychophysiological domain. To diagnose individualsensitivity to stress, case-based reasoning is applied as a core technique to facilitateexperience reuse by retrieving previous similar cases. Further, fuzzy techniques arealso employed and incorporated into the case-based reasoning system to handlevagueness, uncertainty inherently existing in clinicians reasoning process. Thevalidation of the approach is based on close collaboration with experts andmeasurements from twenty four persons used as reference.Thirty nine time series from these 24 persons have been used to evaluate theapproach (in terms of the matching algorithms) and an expert has ranked andestimated similarity which shows a level of performance close to an expert. Theproposed system could be used as an expert for a less experienced clinician or as asecond option for an experienced clinician to their decision making process. / Integrated Personal Health Optimizing System (IPOS)

In this study, the inability to in a future meet the electricity demand and the urge to change the consumption behavior considered. In a smart grid context there are several possible ways to do this. Means include ways to increase the consumer’s awareness, add energy storages or build smarter homes which can control the appliances. To be able to implement these, indications on how the future consumption will be could be useful. Therefore we look further into how a framework for short-term consumption predictions can be created using electricity consumption data in relation to external factors. To do this a literature study is made to see what kind of methods that are relevant and which qualities is interesting to look at in order to choose a good prediction method. Case Based Reasoning seemed to be able to be suitable method. This method was examined further and built using relational databases. After this the method was tested and evaluated using datasets and evaluation methods CV, MBE and MAPE, which have previously been used in the domain of consumption prediction. The result was compared to the results of the winning methods in the ASHRAE competition. The CBR method was expected to perform better than what it did, and still not as good as the winning methods from the ASHRAE competition. The result showed that the CBR method can be used as a predictor and has potential to make good energy consumption predictions. and there is room for improvement in future studies.

Subsequent to preparation of a valid base estimate for a construction project tender, it is required to add a bid markup on top of the base estimate. While an exaggerated bid markup weakens the competitiveness of the contractor, an underestimated one makes the contractor susceptible to financial losses. Therefore, an effective and reliable bid markup estimation method is indispensable to the success of a contractor both at the tender and the performance stages. The prevalent practice among contractors is to identify a certain percentage to add on the base estimate relying on their judgment without substantial ex! plicit support. In this thesis, a case based decision support system, which will count on the experience of the top experts of Turkish international contractors, will be constructed. Meanwhile, factors which are essential to bid markup estimation are to be identified.