Physiological sensor signals analysis is common practice in medical domain for diagnosis andclassification of various physiological conditions. Clinicians’ frequently use physiologicalsensor signals to diagnose individual’s psychophysiological parameters i.e., stress tiredness,and fatigue etc. However, parameters obtained from physiological sensors could vary becauseof individual’s age, gender, physical conditions etc. and analyzing data from a single sensorcould mislead the diagnosis result. Today, one proposition is that sensor signal fusion canprovide more reliable and efficient outcome than using data from single sensor and it is alsobecoming significant in numerous diagnosis fields including medical diagnosis andclassification. Case-Based Reasoning (CBR) is another well established and recognizedmethod in health sciences. Here, an entropy based algorithm, “Multivariate MultiscaleEntropy analysis” has been selected to fuse multiple sensor signals. Other physiologicalsensor signals measurements are also taken into consideration for system evaluation. A CBRsystem is proposed to classify ‘healthy’ and ‘stressed’ persons using both fused features andother physiological i.e. Heart Rate Variability (HRV), Respiratory Sinus Arrhythmia (RSA),Finger Temperature (FT) features. The evaluation and performance analysis of the system have been done and the results ofthe classification based on data fusion and physiological measurements are presented in thisthesis work.

Remote healthcare is a demanding as well as emergent research area. The rise of healthcare costs in the developed countries have made the policy makers for trying to find an alternate model of healthcare rather than relying on traditional healthcare system. Although advancement in the sensor technology, forthcomingness of devices like smart phones and improvement in artificial intelligence technology have made the remote healthcare close to reality but still there are plenty of issues to be solved before it becomes a commonly used healthcare model. In this thesis, studies of two vital physiological parameters pulse rate and oxygen saturation were done to unearth some patterns using Case-Based Reasoning technique. A three-tiered application is developed focusing remote healthcare. The results of the thesis could be used as a starting point of further research of two above mentioned physiological parameters in order to detect anomalous condition of health.

Remote healthcare is a demanding as well as emergent research area. The rise of healthcare costs in the developed countries have made the policy makers for trying to find an alternate model of healthcare rather than relying on traditional healthcare system. Although advancement in the sensor technology, forthcomingness of devices like smart phones and improvement in artificial intelligence technology have made the remote healthcare close to reality but still there are plenty of issues to be solved before it becomes a commonly used healthcare model. In this thesis, studies of two vital physiological parameters pulse rate and oxygen saturation were done to unearth some patterns using Case-Based Reasoning technique. A three-tiered application is developed focusing remote healthcare. The results of the thesis could be used as a starting point of further research of two above mentioned physiological parameters in order to detect anomalous condition of health.

Working as a field technician of any sort can many times be a challenging task. Often you find yourself alone, with a machine you have limited knowledge about, and the only support you have are the user manuals. As a result, it is not uncommon for companies to aid the technicians with a knowledge base that often revolves around some share point. But, unfortunately, the share points quickly get cluttered with too much information that leaves the user overwhelmed. Case-based reasoning (CBR), a form of problem-solving technology, uses previous cases to help users solve new problems they encounter, which could benefit the field technician. But for a CBR system to work with a wide variety of machines, the system must have a dynamic nature and handle multiple data types. By developing a prototype focusing on case retrieval, based on .Net core and MySql, this report sets the foundation for a highly dynamic CBR system that uses natural language processing to map case attributes during case retrieval. In addition, using datasets from UCI and Kaggle, the system's accuracy is validated, and by using a dataset created explicitly for this report, the system manifest to be robust.

Many conflicting issues exist between product design and manufacturing department. In the early design stage, designers often do not have enough expertise to successfully address all these issues. This results in a product design with a low level of assemblability and manufacturability. Hence, an intelligent decision support system is needed for early design stages to improve a design. This paper proposed a web-based intelligent decision support system, CBR-DFMA, connecting with a case base, database and knowledge base. Early experimental results indicate that potential design problems can be detected in advance, design expertise can be effectively disseminated and effective training is offered to designer by employing this system.

Robot selection is one of critical decisions in the design of robotic workcells. Over the last ten years, many Case-Based Reasoning (CBR) systems have been developed to solve decision making problems successfully. We propose to develop three sort systems: browsing systems, preference-based selection organizers, and alternative suggestion agents. All four stages of the CBR cycle are designed to assist robotic application designers to go through robot selection and decision-making. A case-based reasoning approach is employed to solve new robot selection decision problems by adapting solutions that were used to solve previous robot selection problems. In this study, CBR has shown that it has several advantages over other techniques. The results of this study will help robot workcell designers to develop a more efficient and effective method to select robots for specific robot applications.

Preconstruction services play a vital role in ensuring timely approval of infrastructure funds and successful execution of construction projects. Most state DOTs use simple methods such as a percentage of estimated construction costs that has proven to be unreliable. Several studies have developed statistical models using historical data to improve current practices. However, such models have performed poorly, and practitioners have not utilized these models. This study develops and evaluates data mining models such as multiple regression and artificial neural networks and concludes that such models do not provide sufficiently accurate estimates of preconstruction service fees and hours. Subsequently, it proposes an alternative approach using a case-based reasoning (CBR) technique that uses similarity scoring to retrieve the most similar projects. The historical preconstruction service fees and hours of similar projects can be used to estimate preconstruction service fees and hours for a new project and make any adjustment necessary. A spreadsheet tool is developed to implement this CBR technique. The tool provides a simple and flexible platform that enables engineers to extract necessary data and help them in making data-driven estimates. Thus, the tool is expected to aid state DOT engineers in negotiating with consultants with higher confidence.

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Developing a head and neck cancer treatment plan for a candidate of Intensity Modulated Radiation Therapy (IMRT) requires extensive domain knowledge and subjective experience. Therefore, it takes a cancer treatment team at least 2 to 3 days to develop such a plan from scratch. Many times the team may not use a reference plan. Sometimes, to reduce the amount of time taken to generate each treatment plan, these experts recall a patient, whose plan they recently prepared, and who had similar symptoms as the candidate. Using this recalled patient's plan as the starting point, the cancer treatment team modifies it based on the differences in the symptoms of the new candidate and those of the reference patient record. The resultant plan after modification is presented as the new treatment plan for the oncologist to evaluate its suitability for treatment of the candidate. This approach is heavily dependent on the team's choice of the reference patient record. Choosing a starting treatment plan where the patient's symptoms are not the closest to the new candidate implies that more time will be spent modifying the plan than is necessary and the resultant treatment plan may not be the best achievable under the same circumstances given a better starting plan. Therefore, the team's bias in choosing the starting plan may affect the quality of treatment plan that is finally produced for the candidate. This thesis proposes a system that behaves like an un-biased radiotherapy expert - following a similar process and standards as the human experts and which searches the entire IMRT patient database and returns the record (with patient symptoms and treatment plan) for a patient whose symptoms are most similar to the candidate's symptoms. It takes in the new candidate's information (from diagnosis, scans of the tumor and interviews with the candidate), searches the database and prints out a patient record showing another patient's treatment plan as the suggested starting point for generating the new plan. The system uses Case-Based Reasoning (CBR) because it mimics the experts' approach since it makes use of previous successes and shuns reasoning that has failed in the past. This occurs by considering only treatment plans that have been implemented successfully on patients in the hospital archive. For this thesis, CBR is applied using fuzzy IF-THEN rules to search the patient database. Fuzzy logic is used because it can handle imprecise expressions commonly used in natural language to determine the appropriate weight of the patient attributes in the search process. Filtering of patient records based on parameter value ranges is also used to reduce the number of records that have to be compared. The system code developed for this thesis was prepared in Java and C Language Integrated Production System (CLIPS) using the Java Expert System Shell (JESS). This system is part of a bigger expert system that is being prepared by the Intelligent Systems Applications Center (ISAC) for Thomas Jefferson University Hospital, expected to generate a radiotherapy plan for a patient designated for IMRT treatment. Initial results from the developed prototype prove the viability of selecting similar patients using CBR. It is important to note that the overall objective of the project is to build a system that effectively aids decision support by the IMRT team when generating a new treatment plan and not to replace them. The team is expected to use the generated plan as a starting point in determining a new treatment plan. If the generated plan is sufficient, the oncologist and their team will have to check this plan (in their various capacities) against expected standards for quality control before passing it on for implementation. This will save them time in planning and allow them to focus more on the patient's needs hence a higher quality of life for the patient after treatment. / Electrical and Computer Engineering

Yes / Case-based reasoning (CBR) is a nature inspired paradigm of machine learning capable to continuously learn from the past experience. Each newly solved problem and its corresponding solution is retained in its central knowledge repository called case-base. Withρ the regular use of the CBR system, the case-base cardinality keeps on growing. It results into performance bottleneck as the number of comparisons of each new problem with the existing problems also increases with the case-base growth. To address this performance bottleneck, different case-base maintenance (CBM) strategies are used so that the growth of the case-base is controlled without compromising on the utility of knowledge maintained in the case-base. This research work presents a hybrid case-base maintenance approach which equally utilizes the benefits of case addition as well as case deletion strategies to maintain the case-base in online and offline modes respectively. The proposed maintenance method has been evaluated using a simulated model of autonomic forest fire application and its performance has been compared with the existing approaches on a large case-base of the simulated case study. / Authors acknowledge the internal funding support received from Namal College Mianwali to complete the research work.