Abstract Background: Social networks allow real-time interaction that enhances a bank’s ability to respond to customers in a timely, intuitive and personalized manner. By using social networks, banks can improve the understanding of their clients and bank’s products they need. Also, banks can enhance relations with clients and strengthen their brand through raising client loyalty. Objectives: The paper explores and analyses the current presence of banks in Bosnia and Herzegovina on social networks. Methods/Approach: The paper studies the presence of 24 banks in Bosnia and Herzegovina on social networks and analyses the basic characteristics of profiles/pages of the banks on the most popular social networks. Results: A half of the banks have their profiles/pages on different social networks (mostly on Facebook and YouTube). They use the profiles/pages mainly for content marketing, i.e. for presenting their business operations. Unfortunately, banks do not encourage interaction with clients, except through likes. Conclusions: The analysis does not show that banks have a systematized and planned appearance on social networks. There is a plenty of room for improvement, and it is necessary primarily to address the interaction between clients and banks through social networks.

Experimental methods used for characterizing epitopes that play a vital role in the development of peptide vaccines, in diagnosis of diseases, and also for allergy research are time consuming and need There are many online epitope prediction tools that can help experimenters in short listing the candidate peptides. To predict B epitopes in an antigenic sequence, Jordan recurrent neural network (JRNN) are found to be more successful. To train and test neural networks, 262.583 B epitopes are retrieved from IEDB database. 99.9% of these epitopes have lengths in the interval 6-25 amino acids. For each of these lengths, committees of 11 expert recurrent neural networks are trained. To train these experts alongside epitopes, non needed. Non-epitopes are created as random sequences of amino acids of the same length followed by a filtering process. To distinguish epitopes and non-epitopes, the votes of eleven experts are aggrega vote. An overall accuracy of 97.23% is achieved. Then these experts are used to predict the linear b epitopes of antigen, ESAT6 (Tuberculosis).