## 7 Conclusions  
In this paper, we present the design, implementation, and evaluation of a web-based intelligent system for the diagnosis of farmed fish diseases. The system performs diagnosis in two stages: In the first stage, a general process based on all environmental, external, and internal parameters/symptoms is performed, resulting in an ordered list of the three most probable diseases. In the second stage, which is optional, the specialized expert systems of the resulted diseases can be used for more valid results, where additional parameters related to laboratory tests are considered. The system uses a novel hybrid approach that combines several methods for diagnosis, like rule-based, case-based, weight-based, and voting-based reasoning. The system was evaluated for the general process via an artificial dataset created from the decision trees provided by the experts. The results are very promising. Of course, our hybrid approach can be easily used in any intelligent system that deals with the diagnosis of any animal disease (including humans) and generally in any system that deals with a classification problem and has similar requirements to farmed fish diagnosis. A future research direction concerns further tuning of the system and evaluation based on real data to improve its performance, especially in diagnosing the first disease on the list. Another one is related to using deep learning techniques for the image recognition part of the system. Both directions, however, demand the gathering of adequate real datasets, which is not an easy task. A final direction concerns the use of ontologies for representing the domain knowledge of farmed fish diseases and ontological reasoning for making decisions on diseases. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Disease** & **Accuracy** & **Top-3 Accuracy** & **Average Rank** \\ \hline pasteridiasi & **0.322** & **0.762** & **2.399** \\ \hline mivoxukitriadis & 0.673 & 0.940 & 1.547 \\ \hline metavolika & 0.800 & 0.960 & 1.327 \\ \hline don-vibrio-anguil & 0.707 & 0.967 & 1.473 \\ \hline don-vibrio-harv & 0.660 & 0.947 & 1.580 \\ \hline egkefalopatheia & 0.700 & 0.973 & 1.407 \\ \hline ceratotho & 0.720 & 1.000 & 1.333 \\ \hline galicus & 0.520 & 0.933 & 1.773 \\ \hline diplectanum & 0.520 & 0.840 & 2.007 \\ \hline lernantropus & 0.627 & 0.973 & 1.493 \\ \hline mycobacteriosis & 0.773 & 0.987 & 1.353 \\ \hline aeromonad & 0.860 & 0.993 & 1.180 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: AGM results per disease. **Author Contributions:** Conceptualization, I.H. and K.K. ; methodology, K.K. and I.H. ; software, K.K., K.D. and T.A. ; validation, K.K. and K.D. ; formal analysis, I.H. and K.K. ; investigation, K.K., G.S., K.P., G.K. and A.I. ; resources, G.S., K.P., G.K., E.A. and J.A.T. ; data curation, K.K. and K.D. ; writing--original draft preparation, I.H. and K.K. ; writing--review and editing, K.K. and J.A.T. ; visualization, I.H. and A.I. ; supervision, I.H. ; project administration, I.H. and J.A.T. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the action \"Improving Competitiveness of the Greek Fish Farming Through the Development of Intelligent Systems for Disease Diagnosis and Treatment Proposal and Relevant Risk Management Supporting Actions (MIS 5067321).\" EU-Greece Operational Program of Fisheries (EPAL) 2014-2020. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The dataset used for the system evaluation was artificially created by authors of this paper and is available here: [[http://aigroup.ceid.upatras.gr/fish_dataset.xlsx](http://aigroup.ceid.upatras.gr/fish_dataset.xlsx)]([http://aigroup.ceid.upatras.gr/fish_dataset.xlsx](http://aigroup.ceid.upatras.gr/fish_dataset.xlsx)) (accessed on 17 October 2023). **Conflicts of Interest:** Authors [PERSON] and [PERSON] were employed by the company Kefalonia Fisheries S.A.