## 1 Introduction  
Fish farming is an extensive business activity all over the world. Fish farming management is a complicated task. One of the main problems to tackle is the diagnosis of fish disease. The occurrence of diseases in fish farms restricts the quality of production and has an economic impact on fish farm operations [1; 2]. On the other hand, the problem itself requires special skills and expertise to be solved [3], which most farmers lack. Therefore, it is necessary to develop systems that can automatically or semi-automatically diagnose or help in diagnosing fish diseases. Given that for solving the problem of fish disease diagnosis, human expertise is necessary, artificial intelligence (AI) techniques should be employed [4]. There are two general AI approaches that could be used in such a system: the knowledge representation (KR) approach and the machine learning (ML) approach [4]. The KR approach consists of representing knowledge and the way an expert (or many experts) uses it in making diagnoses of fish diseases. Its most practical representative is the expert system (ES) approach. An ES represents expert knowledge, usually in the form of if-then rules, and employs an inference engine to produce conclusions (diagnoses). To build an expert system, corresponding knowledge should be acquired from experts and other knowledge sources and represented in the knowledge base of the system [5]. There are also other representatives, like case-based reasoning (CBR), where new cases are compared with (validated) old cases, stored in a case base, to find a solution for the new case; here, similarity metrics play a crucial role [6]. Given that the problem of making fish disease diagnoses can be considered a classification problem, machine learning (ML) approaches can be used to solve it. ML approaches seek out models, called classifiers, that can identify fish diseases based on suitable data. There are various \"traditional\" ML models, like decision trees (DTs), neural networks (NNs), or statistical models, like support vector machines (SVM), k nearest neighbors (kNN), etc. To be able to build such a model, an adequate dataset consisting of real cases is required for its training [7]. Recently, a modern approach called deep learning (DL) has become very popular due to its very successful results. Deep learning neural networks (DLNNs) are complex neural networks that require very large datasets to be trained [8]. The Convolutional Neural Network (CNN) is the basic deep learning architecture used as the basis for more complex DLNNs. Its main domain of success is image classification [9; 10]. A full diagnosis of fish disease should take into account a variety of parameters: environmental, clinical, microbiological, microscopic, and molecular [3]. The difficulty in using ML approaches for fish disease diagnosis is the lack of real datasets that include all necessary features. It is very difficult to find an adequate number of records of diseased fish cases, as you can find for human patients in hospitals. Also, it is difficult to find the required number of images of diseased fish to use DL methods. On the other hand, even if the required number of images can be found, an image-based diagnosis is not wholly accurate. On the other hand, ML and DL methods cannot give any explanation about their outputs (decisions), while explanations are very desirable in such systems [11]. So, although the expert system approach is an old one, it is still necessary as a primary framework for full fish disease diagnosis [12]. Therefore, in this paper, we propose and present a web-based intelligent system for farmed European sea bass disease diagnosis that uses a combination of the ES approach with other KR approaches and a level-based diagnostic process. It can be regarded as an implementation of the general architecture proposed in [12]. Our main focus is on the hybrid reasoning scheme introduced here. The contributions of this paper are the following:  
* A novel knowledge acquisition and representation method.