String matching algorithms are the algorithms used to search through different types of text in search of a certain pattern. Many of these algorithms achieve their impressive performance by analysing the pattern and saving that information. That information is then continuously used during the searching phase to know what parts of the text can be skipped. One such algorithm is the Berry-Ravindran. The Berry-Ravindran checks the two characters past the current try for a match and sees if those characters exist in the pattern. This thesis compares the Berry-Ravindran algorithm to new versions of itself that check three and four characters instead of two, along with the Boyer-Moore algorithm. Checking more characters improves the amount of the text that can be skipped by reducing the number of attempts needed but exponentially increases the pre-processing time. The improved performance in attempts does not necessarily mean a faster run-time because of the increased pre-processing time. The variable impacting the pre-processing time the biggest is the size of the alphabet that the text uses. This is researched by testing these algorithms with patterns ranging from 4 to 100 characters long on two different data sets. Protein data which has an alphabet size of 27 and DNA data which has an alphabet size of 4.

Denna rapport analyserar och testar olika metoder som syftar till att särskiljamänskligt genererade lösningar på uppgifter och texter från de som genereras avartificiell intelligens. På senare tid har användningen av artificiell intelligens setten betydande ökning, särskilt bland studenter. Syftet med denna studie är attavgöra om det för närvarande är möjligt att upptäcka fusk från högskolestudenterinom elektroteknik som använder sig av AI. I rapporten testas lösningar påuppgifter och texter genererade av programmet ChatGPT med hjälp av en generellmetod och externa AI-verktyg. Undersökningen omfattar områdena matematik,programmering och skriven text. Resultatet av undersökningen tyder på att detinte är möjligt att upptäcka fusk med hjälp av AI i ämnena matematik ochprogrammering. Dock när det gäller text kan i viss utsträckning fusk vidanvändning av en AI upptäckas. / This report analyzes and tests various methods aimed at distinguishinghuman-generated solutions to tasks and texts from those generated by artificialintelligence. Recently the use of artificial intelligence has seen a significantincrease, especially among students. The purpose of this study is to determinewhether it is currently possible to detect if a college student in electricalengineering is using AI to cheat. In this report, solutions to tasks and textsgenerated by the program ChatGPT are tested using a general methodology andexternal AI-based tools. The research covers the areas of mathematics,programming and written text. The results of the investigation suggest that it is notpossible to detect cheating with the help of an AI in the subjects of mathematicsand programming. In the case of text, cheating by using an AI can be detected tosome extent.