Handling default data under a case-based reasoning approach

The knowledge acquired through past experiences is of the most importance when humans or machines try to find solutions for new problems based on past ones, which makes the core of any Case-based Reasoning approach to problem solving. On the other hand, existent CBR systems are neither complete nor adaptable to specific domains. Indeed, the effort to adapt either the reasoning process or the knowledge representation mechanism to a new problem is too high, i.e., it is extremely difficult to adapt the input to the computational framework in order to get a solution to a particular problem. This is the drawback that is addressed in this work.This work is funded by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within projects PEst-OE/EEI/UI0752/2014 and PEst-OE/QUI/UI0619/2012

Penerapan Algoritma Nearest Neighbor Retrieval Untuk Mendiagnosa Penyakit Hepatitis

Hepatitis merupakan penyakit peradangan hati karena berbagai sebab. Penyebab tersebut adalah beberapa jenis virus yang menyerang dan menyebabkan peradangan dan kerusakan pada sel-sel dan fungsi organ hati. Untuk mengetahui jenis hepatitis yang diderita pasien sistem pakar  telah dibangun menggunakan analisis pendekatan Cased-based Reasoning(CBR) dalam membangun sistem ini menggunakan pemodelan UML, metode perhitungan menggunakan kemiripan kasus lama dengan kasus baru atau Similirity. Untuk pengujian kelayakan software menggunakan Alfa Testing, dengan indicator mudah digunakan, informasi cepat, biaya murah dimana dalam pengujian menggunakan 10 sample user dengan hasil software diagnosa hepatitis layak dan bisa digunakan

Case-Based Decision Support for Disaster Management

Disasters are characterized by severe disruptions of the society’s functionality and adverse impacts on humans, the environment, and economy that cannot be coped with by society using its own resources. This work presents a decision support method that identifies appropriate measures for protecting the public in the course of a nuclear accident. The method particularly considers the issue of uncertainty in decision-making as well as the structured integration of experience and expert knowledge

Optimizing Similarity Assessment in Case-Based Reasoning

The definition of accurate similarity measures is a key issue of every Case-Based Reasoning application. Although some approaches to optimize similarity measures automatically have already been applied, these approaches are not suited for all CBR application domains. On the one hand, they are restricted to classification tasks, on the other hand, they only allow optimization of feature weights. We propose a novel learning approach which addresses both problems, i.e. it is suited for most CBR application domains beyond simple classification and it enables learning of more sophisticated similarity measures