Process automation in the tourism sector using RAG systems and AI agents

Rad istražuje primjenu umjetne inteligencije u automatizaciji procesa u turističkom sektoru s naglaskom na RAG (engl. Retrieval-Augmented Generation) sustave i AI agente. Cilj rada je razviti sustav koji će unaprijediti korisničko iskustvo turista kroz inteligentnu podršku, personalizirane preporuke i poboljšanu obradu podataka. Razmatraju se veliki jezični modeli (LLM), njihova ograničenja i mogućnosti proširenja putem RAG arhitektura. Eksperimentalni dio uključuje razvoj i testiranje različitih RAG pristupa – naivni, napredni i modularni , s ciljem optimizacije pretraživanja i generiranja odgovora. Evaluacija sustava provedena je na turističkim upitima, a rezultati pokazuju značajnu prednost naprednih tehnika poput RAG-Fusion modela. Zaključuje se da kombinacija AI agenata i RAG sustava omogućuje poboljšanje korisničke podrške u turizmu kroz preciznije, relevantnije i personalizirane odgovore.This paper explores the application of artificial intelligence in automating processes in the tourism sector, with a focus on RAG (Retrieval-Augmented Generation) systems and AI agents. The objective is to develop a system that enhances the tourist experience through intelligent support, personalized recommendations, and improved data processing. Large language models (LLMs) are analyzed, along with their limitations and potential extensions through RAG architectures. The experimental part includes the development and testing of different RAG approaches—naïve, advanced, and modular , with the goal of optimizing information retrieval and response generation. The system evaluation was conducted on tourism-related queries, and the results demonstrate a significant advantage of advanced techniques such as the RAG-Fusion model. The study concludes that the combination of AI agents and RAG systems enhances customer support in tourism by providing more precise, relevant, and personalized responses

Application for Practicing Computational Thinking

Tema ovog završnog rada je izrada aplikacije koja pomaže učenicima osnovne škole usvojiti računalno razmišljanje. Promatranjem trenutno korištenog kurikuluma za nastavni predmet Informatika razvijen je mali tečaj kroz čije lekcije učenici usvajaju programske koncepte i vježbaju rad s algoritmima, prepoznavanje uzoraka te primjene koncepata apstrakcije i dekompozicije. Aplikacija učenicima nudi registraciju, prijavu, prolazak kroz tečaj, traženje pomoći od učitelja te preuzimanje diplome po završetku učenja. Učiteljima nudi izradu grupa učenika, prikaz napretka u svakoj grupi te pregled svih zahtjeva za pomoć od strane učenika. Web aplikacija razvijena je uz pomoć biblioteke React, poslužiteljske tehnologije Fastify i PostgreSQL baze podataka.The topic of this final paper is the creation of an application that helps elementary school students to adopt computational thinking. By observing the currently used curriculum for IT-related subjects, a small course was developed. It teaches the students programming concepts and helps them practice working with algorithms, pattern recognition, as well as abstraction and decomposition. The application offers students the options to register, login, study the course, ask for the teacher’s help, as well as downloading their diploma upon completion. The teachers can create groups of students, display progress of each student in each group, and view all requests for help from their students. The web application was developed with the help of React on the frontend, Fastify on the backend and an PostgreSQL database

Computational personality assessment of essay authors based on text and keystroke analysis

Automatsko predviđanje osobnosti temeljeno na tekstu pokazuje se uspješnim. Međutim, izvlačenjem informacija samo iz teksta zanemarujemo ponašanje osobe dok je pisala taj tekst, koje također može biti vrijedan pokazatelj osobnosti. Cilj ovog rada bio je istražiti primjerenost korištenja analize pritisaka tipki za predviđanje osobnosti, pod pretpostavkom da se ponašanje osobe koja tipka odražava u načinu na koji tipka. S obzirom na to, razvijen je alat za prikupljanje podataka o tipkanju, a iz tih podataka ekstrahirane su značajke koje bilježe ponašanje autora teksta, koje su potom združene sa značajkama ekstrahiranim iz samog teksta. Temeljem tih značajki, provedeni su eksperimenti nad modelima strojnog učenja koji predviđaju rezultate testa osobnosti Big Five. Predviđanje neurotičnosti i ekstrovertiranosti bilo je najuspješnije. Sve u svemu, rezultati upućuju na to da su značajke temeljene na pritiscima tipki korisne za predviđanje karakternih crta.Automatic personality prediction based on text has proven to be successful. However, extracting personality information solely from raw text disregards the writer’s behavior during the writing process, which may also contain valuable personality cues. The goal of this thesis was to evaluate the suitability of using keystroke analysis for personality prediction, assuming that a person’s behavior is reflected in the way they type. With that in mind, software for keystroke data collection was developed, and features capturing the writer’s behavior from keystroke data were devised. Those features were combined with features based on raw text, and then employed in machine learning experiments conducted on models for predicting Big Five personality test scores. Predicting Neuroticism and Extraversion scores yielded the best results. Overall, the results suggest that keystroke-based features are valuable predictors of personality traits

Deep models for point cloud registration

Registracija oblaka točaka jedan je od ključnih problema u obradi 3D podataka, a sastoji se od pronalaska rotacije i translacije koje omogućuju preklapanje dva oblaka točaka u jedinstvenu i povezanu cjelinu. Ovaj diplomski rad pruža pregled osnovnih koraka klasičnog postupka registracije 3D oblaka točaka, s posebnim naglaskom na Iterativna najbliža točka (Iterative Closest Point, ICP) algoritam, koji se često koristi za rješavanje ovog problema. Tijekom rada provedeno je istraživanje literature o primjeni dubokog učenja u registraciji 3D podataka. Identificirani su najvažniji modeli dubokog učenja te su podijeljeni u skupine prema njihovim glavnim karakteristikama. Iz svake skupine odabran je po jedan reprezentativni model, a za daljnje istraživanje odabrani su modeli DCP (Deep Closest Point) i PointNetLK. Ovi modeli uspoređeni su međusobno i s klasičnim ICP algoritmom koristeći skup podataka ModelNet40. Eksperimentalni dio rada obuhvaća evaluaciju učinkovitosti odabranih metoda prema kriterijima točnosti registracije. Rezultati istraživanja ističu prednosti i ograničenja klasičnih i dubokih pristupa registraciji oblaka točaka te pružaju smjernice za daljnji razvoj ovog područja. Zaključci rada doprinose boljem razumijevanju problema registracije oblaka točaka i ukazuju na potencijal primjene dubokog učenja za unaprjeđenje ovog procesa.Point cloud registration is one of the key challenges in 3D data processing. It involves determining the rotation and translation that align two point clouds into a single, cohesive representation. This thesis provides an overview of the fundamental steps in the classical 3D point cloud registration process, with a particular focus on the Iterative Closest Point (ICP) algorithm, which is widely used to address this problem. A literature review was conducted to explore the application of deep learning in 3D point cloud registration. The most significant deep learning models were identified and categorized based on their main characteristics. From each category, one representative model was selected, with DCP (Deep Closest Point) and PointNetLK chosen for further investigation. These models were compared against each other and the classical ICP algorithm using the ModelNet40 dataset. The experimental part of the thesis includes an evaluation of the selected methods based on registration accuracy. The results highlight the strengths and limitations of both classical and deep learning-based approaches to point cloud registration, providing insights for further development in this area. The findings of this thesis contribute to a better understanding of the point cloud registration problem and emphasize the potential of deep learning to enhance this process

Optimization of motion trajectories for autonomous racing vehicles

Optimizacija trkaće trajektorije je ključan dio u ostvarivanju najbržeg mogućeg kruga. Ovaj rad je dio cjevovoda autonomnog sustava Formula Student bolida. Korišten je heuristički pristup optimizacije trajektorije gdje su objektivne funkcije minimalna zakrivljenost, najkraći put i njihova kombinacija. Koristeći taj pristup, planiranje putanje je odvojeno od optimizacije trajektorije što uvelike smanjuje vrijeme izvođenja. Kako bi se još ubrzalo izvođenje težina koja određuje kompromis između estimacije korištenja minimalne zakrivljenosti i najkraćeg puta se računa unaprijed. Skup podataka za unaprijedno estimiranje težine se temelji na umjetno generiranim stazama. Već spomenuti bolid je opisan pomoću dinamičkog dvotračnog modela vozila koristeći mapu motora, graf ovisnosti bočnih i uzdužnih koeficijenata trenja o vertikalnim silama i osnovnim parametrima vozila. Generirana trajektorija je nešto sporija već ona direktna, ali smanjuje vremensku složenost.Race trajectory optimization is the essential part to execute the fastest lap possible. It is part of the whole autonomous system pipeline of a Formula Student single-seater. This work is implementing a heuristic approach to trajectory optimization with objective functions being minimal curvature, the shortest path, and the combination of both. Using this approach, path planning is separated from trajectory optimization, which significantly reduces the computation time. To further reduce the computation, the compromise weight between minimal curvature and the shortest path estimation is calculated offline. The dataset for offline weight estimation is based on synthetically generated tracks. The single-seater mentioned is described with a dynamic two-track vehicle model using the engine map, the friction coefficient with respect to the vertical force in both lateral and longitudinal directions, and basic vehicle parameters. The generated trajectory compromises some lap time, regarding direct optimization, to reduce time complexity

Development of web application for connecting users and contractors in households

Cilj završnog rada bio je razviti web-aplikaciju koja omogućava efikasno povezivanje izvođača radova za male popravke u kućanstvima s potencijalnim klijentima. Aplikacija služi kao platforma koja pojednostavljuje suradnju između izvođača radova i korisnika, pružajući transparentne informacije o reputaciji majstora i tržišnim cijenama različitih usluga. S obzirom na smanjenje broja kvalificiranih majstora i nedostatak transparentnosti u industriji, aplikacija adresira te probleme omogućavajući strukturirano umrežavanje i bolje razumijevanje tržišnih vrijednosti. Projekt se sastojao od nekoliko faza, uključujući analizu tržišta, definiranje korisničkih zahtjeva, odabir i primjenu tehnologija te implementaciju i testiranje aplikacije. Razvijen je minimalno održiv proizvod koji omogućava objavu oglasa, pretragu, registraciju, autentikaciju te osnovnu interakciju među korisnicima. Planirana je daljnja nadogradnja aplikacije, uključujući poboljšanje sustava ocjenjivanja, napredniju pretragu pomoću AI te dodatne funkcionalnosti kao što su pomoć pri slaganju ugovora, integracija s računovodstvenim alatima i analitički alati za majstore. Ove nadogradnje će dalje osnažiti funkcionalnost i korisničko zadovoljstvo, čineći aplikaciju nezamjenjivim alatom u ovoj primjeni. Projekt je pružio praktičnu primjenu teoretskih znanja i iskustvo u razvoju web-aplikacija, postavljajući čvrstu osnovu za budući profesionalni razvoj i daljnje poboljšanje aplikacije.The aim of the final thesis was to develop a web application that enables efficient connection between service providers for small household repairs and potential clients. The application serves as a platform that simplifies collaboration between tradespeople and users by providing transparent information about the reputation of service providers and market prices for various services. Given the decline in the number of qualified tradespeople and the lack of transparency in the industry, the application addresses these issues by facilitating structured networking and a better understanding of market values. The project consisted of several phases, including market analysis, defining user requirements, selecting and applying technologies, and implementing and testing the application. A minimum viable product (MVP) was developed that allows for the posting of ads, searching, registration, authentication, and basic user interactions. Further upgrades to the application are planned, including improvements to the rating system, advanced search using AI, and additional features such as contract assistance, integration with accounting tools, and analytics tools for craftsmen. These upgrades will further enhance functionality and user satisfaction, making the application an indispensable tool in this field. The project provided practical application of theoretical knowledge and experience in web application development, laying a solid foundation for future professional growth and further improvement of the application

Recommendation Systems Implementation Using Matrix Factorization Algorithm

Ovaj rad proučava sustave preporučivanja, od njihovih varijanti i metoda do primjene i izazova s kojima se suočavaju. Objašnjeno je filtriranje sadržaja preko implicitnih povratnih informacija korisnika i suradničko filtriranje s pomoću eksplicitnih ocjena korisnika. Poseban je naglasak stavljen na algoritme matrične faktorizacije kao napredniju metodu koja daje kvalitetnije preporuke s većom pouzdanošću. U praktičnom dijelu rada implementiran je sustav preporučivanja koristeći osnovne metode suradničkog filtriranja i sustav koji koristi matričnu faktorizaciju. Sustavi su testirani nad skupom podataka "MovieLens", a rezultati uspoređeni s referentnom "Python" knjižnicom "Surprise". Rezultati su zadovoljavajući i pokazuju superiornost matrične faktorizacije nad običnim metodama filtracije preporuka. Rad potvrđuje važnost matrične faktorizacije u suvremenim sustavima preporučivanja te ukazuje na moguća poboljšanja (naprednije metode matrične faktorizacije).This paper examines recommender systems, covering their variations, methods, applications, and the challenges they face. Content filtering based on users' implicit feedback and collaborative filtering using explicit user ratings are explained. Special emphasis is placed on matrix factorization algorithms as a more advanced method that provides higher-quality recommendations with greater reliability. In the practical part of the paper, a recommender system was implemented using basic collaborative filtering methods, along with a system utilizing matrix factorization. The systems were tested on the "MovieLens" dataset, and the results were compared with the reference "Python" library "Surprise". The results are satisfactory and demonstrate the superiority of matrix factorization over standard recommendation filtering methods. The paper confirms the importance of matrix factorization in modern recommender systems and highlights potential improvements (more advanced matrix factorization methods)

Joint optimal sizing and operation scheduling of a power-to-gas hub based on a linear program

With rising challenges of greening the gas sector and decreasing wasteful curtailment of renewable energy sources, power-to-gas hubs seem to be a suitable solution for mitigation of both. Power-to-gas hubs can act as energy storage and as sector coupling solution between electricity, gas, heat, and water grids, but also agricultural markets when using biomass and/or biochar. However, the economic viability and adequate operation of such hubs is crucial in order to make a difference. The optimization procedure developed tackles the complex problem of finding the optimal structure of a power-to-gas hub and simultaneously optimizing its operation. The unique aspects of the proposed procedure include: i) finding the optimal system structure across a large number of possible technologies, processes and storage components, rather than optimizing a power-to-gas hub with a single mass/energy conversion pathway; ii) collocation of a power-to-gas hub with an existing renewable energy plant and/or an existing industrial plant; and iii) viable computational complexity while short-term intermittence of renewable sources is included. After comprehensive techno-economic modelling of the overall setup, the developed optimization procedure is used on three case studies showcasing the strengths of the procedure. The case studies show how the optimal system configuration and optimal components’ sizes change depending on prices of electricity and natural gas, and on possibility to sell digestate and/or biochar. Also, the case studies show the influence of electricity sources, or rather the intermittence of renewables, on optimal operating schedules

Automated Generation of Multimodal Image-Text Datasets from Synthetic Images Using Vision-Language Models

Vizualno-jezični modeli (VJM) objedinjuju funkcionalnosti računalnog vida i jezičnih modela, obogaćujući vizualne informacije iz slika kontekstualnim informacijama koje pruža tekst. Međutim, modeli opće namjene nisu optimirani za fotografije snimljene iz zraka, zbog čega su za tu svrhu obično potrebni specijalizirani modeli. U takvim se slučajevima često koriste sintetički podatci, no takav pristup ima ograničenja. U ovome je radu razvijen sustav koji koristi VJM opće namjene kako bi proizveo odgovore na pažljivo osmišljene upite uparene s, kako slikama iz zraka iz stvarnog svijeta, tako i sa sintetičkim slikama iz zraka. Sustav je evaluiran na dvama zadatcima: opisivanje slike odgovorom na pitanje i stvaranje metapodataka. Rezultati ukazuju na učinkovitost sustava u generiranju multimodalnih podataka za slike snimljene iz zraka, pokazujući dublje razumijevanje konteksta vizualnog sadržaja, kao i obećavajuće sposobnosti zaključivanja. Ova opažanja naglašavaju potencijal VJM-ova za primjene u stvarnom svijetu, poput nadzora, akcija traganja i spašavanja, kao i autonomne navigacije te robotike.Vision-language models (VLMs) combine the functionalities of computer vision and language models, which enables them to further enrich visual information available in images with contextual information provided through text. However, general-purpose models are not specifically optimized for aerial imagery, a domain where specialized models are often required. Many resort to using synthetic data in such cases, but this approach has its limitations. In this work, a system using a general-purpose VLM was developed and then used to generate responses based on carefully designed prompts paired with both real-world and synthetic aerial images. The system was evaluated on two tasks: image captioning via visual question answering and metadata generation. Results indicate that the system effectively generates multimodal data for aerial imagery, demonstrating a deeper understanding of context of the visual content, and promising reasoning capabilities. These findings highlight the potential of VLMs in real-world applications such as surveillance, search-and-rescue missions, autonomous navigation, and robotics

Web Application for Real-Time Cryptocurrency Investment Risk Assessment

Ovaj rad bavi se tehničkom analizom tržišta kriptovaluta i razvojem web aplikacije za procjenu rizika ulaganja u stvarnom vremenu. Analizirani su ključni tehnički indikatori, uključujući RSI, Stochastic Oscillator, Williams %R, MACD, Bitcoin indeks volatilnosti i Indeks straha i pohlepe, koji omogućuju investitorima objektivnu procjenu tržišnih trendova. Razvijena web aplikacija koristi React.js za frontend i Node.js za backend, omogućujući korisnicima jednostavan pristup tržišnim podacima. Fokus istraživanja bio je na Bitcoinu zbog njegove stabilnosti i dostupnosti pouzdanih API-ja, dok su druge kriptovalute ocijenjene kao manje pouzdane za analizu. Rezultati rada potvrđuju važnost tehničkih indikatora u donošenju informiranih investicijskih odluka te predlažu daljnja poboljšanja aplikacije, uključujući integraciju dodatnih izvora podataka, personalizaciju korisničkog iskustva i implementaciju strojnog učenja.This thesis explores the role of technical analysis in cryptocurrency market investment decisions and presents the development of a real-time risk assessment web application. Key technical indicators such as RSI, Stochastic Oscillator, Williams %R, MACD, Bitcoin Volatility Index, and the Fear & Greed Index were analyzed to provide investors with an objective assessment of market trends. The developed web application, built using React.js for the frontend and Node.js for the backend, allows users to easily access market data. The research primarily focused on Bitcoin due to its stability and reliable API support, while other cryptocurrencies were deemed less suitable for analysis. Findings confirm the significance of technical indicators in investment decision-making and propose further improvements to the application, including the integration of multiple data sources, user customization options, and the implementation of machine learning models for trend prediction