The aim of the article is to present a novel method for fuzzy medical image retrieval (FMIR) using vector quantization (VQ) with fuzzy signatures in conjunction with fuzzy S-trees. In past times, a task of similar pictures searching was not based on searching for similar content (e.g. shapes, colour) of the pictures but on the picture name. There exist some methods for the same purpose, but there is still some space for development of more efficient methods. The proposed image retrieval system is used for finding similar images, in our case in the medical area – in mammography, in addition to the creation of the list of similar images – cases. The created list is used for assessing the nature of the finding – whether the medical finding is malignant or benign. The suggested method is compared to the method using Normalized Compression Distance (NCD) instead of fuzzy signatures and fuzzy S-tree. The method with NCD is useful for the creation of the list of similar cases for malignancy assessment, but it is not able to capture the area of interest in the image. The proposed method is going to be added to the complex decision support system to help to determine appropriate healthcare according to the experiences of similar, previous cases.

Chatbots are artificial communication systems becoming increasingly popular and not all their security questions are clearly solved. People use chatbots for assistance in shopping, bank communication, meal delivery, healthcare, cars, and many other actions.However, it brings an additional security risk and creates serious security challenges which have to be handled. Understanding the underlying problems requires defining the crucial steps in the techniques used to design chatbots related to security. There are many factors increasing security threats and vulnerabilities. All of them are comprehensively studied, and security practices to decrease security weaknesses are presented. Modern chatbots are no longer rule-based models, but they employ modern natural language and machine learning techniques. Such techniques learn from a conversation, which can contain personal information. The paper discusses circumstances under which such data can be used and how chatbots treat them. Many chatbots operate on a social/messaging platform, which has their terms and conditions about data. The paper aims to present a comprehensive study of security aspects in communication with chatbots. The article could open a discussion and highlight the problems of data storage and usage obtained from the communication user-chatbot and propose some standards to protect the user. K E Y W O R D Schat, chatbots, data protection, GDPR, security, virtual assistants * A bot is sometimes referred to as a chatbot, but to be precise, a bot is a computer program (tool) that automates processes. A chatbot is a sub-genre of the bot environment with a focus on talking or conversation. Some companies instead of Chatbot use the name 'Conversational AI' or ' AI chatbots' to highlight that their chatbot is powered by machine learning and information retrieval techniques. In this article the term 'chatbot' is used for all types of chatbots.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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a r t i c l e i n f o a b s t r a c tArticle history: Available online 15 November 2014 Keywords: Expert system Knowledge base PID controller Ziegler-Nichols step response method Chien Hrones and Reswick design method Fuzzy system Feedback controlAlthough it is long since the first PID controller design method was developed, new methods are still being created and no general applicable method has been found. The paper extends the set of designing methods of PID controllers. Design methods can be divided into classic (analytical tuning methods, optimization methods etc.) and not so common fuzzy knowledge based methods. In this case, a conjunction of both methods is presented. The classic design methods (Ziegler-Nichols step response method and Chien, Hrones and Reswick design method) are used for achieving the knowledge of fuzzy knowledge based design methods. The fuzzy knowledge based design methods are based on Takagi-Sugeno model. It is defined as a class of systems for which -using the new proposed method -the settling time is shorter or the settling time is nearly the same but without overshoot, which could be also very useful. The proof of efficiency of the proposed methods and a numerical experiment is presented including a comparison with the conventional methods (simulated in the software environment Matlab-Simulink).

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