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.

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<p dir="ltr">Axial spondyloarthritis (axSpA), a disease that due to its complexity and rarity, presents challenges in diagnosis. With a focus on integrating expert knowledge into an intelligent diagnostic system, the research explores the intricate nature of axSpA, emphasizing the challenges associated with its diverse clinical presentation. By leveraging a comprehensive knowledge base curated by domain experts, encompassing insights into pathophysiology, genetic factors, and evolving diagnostic criteria of axSpA, the expert system strives to provide a nuanced understanding of the disease. The methodology employs a hybrid reasoning approach, combining both forward and backward chaining techniques. Forward chaining iteratively processes clinical data and available evidence, applying logical rules to infer potential diagnoses and refine hypotheses, while backward chaining starts with the desired diagnostic goal and works backward through the knowledge base to validate or refute hypotheses. Additionally, certainty theory is incorporated to manage uncertainty in the diagnostic process, assigning confidence levels to conclusions based on the strength of evidence and expert knowledge. By integrating knowledge base, forward and backward chaining, and certainty theory, the research aims to enhance diagnostic precision for this less common yet impactful inflammatory rheumatic condition, emphasizing the importance of early and accurate identification for effective management and improved patient outcomes. The results indicate a significant improvement in diagnostic accuracy, sensitivity, and specificity compared to traditional methods. The system's potential to enhance early diagnosis and treatment outcomes is discussed, along with suggestions for future research to further refine and expand the system.</p>