## 3 Related Work  
A variety of intelligent systems have been recently developed that deal generally with animal disease diagnosis and specifically with fish disease diagnosis. As a matter of fact, most of them use some kind of rule-based reasoning. The authors in [19] use rules with certainty factors (CFs) in their conclusions for the diagnosis of horse diseases. Conditions of the rules, which represent symptoms of the diseases, are assigned a weight factor given by the experts. A CF is also assigned to each condition by the users during system running. Additionally, there is a threshold for the conclusion CF, over which the conclusion is acceptable. The system in [20] is an expert system for fish disease diagnosis that uses case-based reasoning combined with an expert symptom scoring method. Both methods are used after the input of symptoms, and their results are compared. If the results are different, the system shows the symptoms of both diseases to the user and asks for re-entering symptoms until common results are produced. If the results are the same, then the case similarity metric is checked; its value should be between 0.65 and 0.95. If it is not, the user is asked either to accept it, despite the low similarity, or re-enter symptoms. The system in [21] uses rule-based representation to relate symptoms to diseases, elicited from experts in the field. A forward-chaining strategy for dog disease diagnosis is proposed. The Expert System in [15] is based on the CFs method for diagnosing Catfish Diseases. It does not use rules but works directly with CFs. Symptoms are associated with diseases of Catfish and a weight is associated with each symptom, representing its influence on the associated diseases. The user is asked to specify the symptoms, and the system calculates the CFs of all affected diseases. The disease with the largest CF is the most certain. The MYCIN expert system's calculation formulas are used. However, there are two problems with this paper. First, the provided CFs (weights) have only two different values (0.7 and 0.8). Second, the same weight for each symptom is considered for all diseases, which may not be reasonable. The system in [22] concerns the diagnosis of cattle diseases, where a hybrid approach is employed. Case-based reasoning (CBR) is primarily used, and if it fails, rule-based reasoning (RBR) takes over. Domain knowledge is represented via an ontology and a relational database. Discrete weight values are assigned to the specified symptoms. The nearest neighbor approach is used for assessing case similarity. The System of Diagnosing Koi's Fish Disease [23] uses rules with CFs for knowledge representation and reasoning. A Koi fish disease-symptoms association table is used for extracting rules. The weights of the symptoms were specified by numerical interpretation of the uncertain terms (e.g., not probably, maybe not, probably, almost certainly) used by the experts and considered as CFs of corresponding rule antecedents. Also, rule CFs were assigned in the same way. The users can complete an input form by choosing the symptoms of the koi fish and answering some questions regarding the symptoms. After the validation is over, a solution is presented, along with a level of confidence. The system in [24] includes an ontology that represents knowledge elicited from experts and concerns concepts such as disease, symptom, body system, treatment, etc., implemented in OWL. The system uses rule-based reasoning for the diagnosis of cattle diseases based on the symptoms provided by the user. Symptoms are assigned weights, representing their significance in the diagnosis. Rules are implemented in SWRL. An expert system for the diagnosis of chicken diseases is presented in [25]. The system uses the Bayes theorem for making diagnoses in a forward-chaining way. The required probabilities are given either by experts or taken from statistical reports. A pure CBR model is used in [26] for fish disease diagnosis.