Acta Informatica Pragensia, 2023 (vol. 12), issue 2
Article
AnnoJOB: Semantic Annotation-Based System for Job Recommendation
Assia Brek, Zizette Boufaida
Acta Informatica Pragensia 2023, 12(2), 200-224 | DOI: 10.18267/j.aip.2043901 
With the vast success of e-recruitment, online job offers have increased. Therefore, there is a number of job portals and recommendation systems trying to help users filter this massive amount of offers when searching for the right job. Until today, most of these systems' searching techniques are confined to using keywords such as job titles or skills, which also returns many results. This paper proposes a job recommender system that exploits the candidate's resume to select the appropriate job. Our system, AnnoJob, adopts a semantic annotation approach to: (1) intelligently extract contextual entities from resumes/offers, and (2) semantically structure...
Emotion-Based Sentiment Analysis Using Conv-BiLSTM with Frog Leap Algorithms
Sandeep Yelisetti, Nellore Geethanjali
Acta Informatica Pragensia 2023, 12(2), 225-242 | DOI: 10.18267/j.aip.2064930
Social media, blogs, review sites and forums can produce large volumes of data in the form of users’ emotions, views, arguments and opinions about various political events, brands, products and social problems. The user's sentiment expressed on the web influences readers, politicians and product vendors. These unstructured social media data are analysed to form structured data, and for this reason sentiment analysis has recently received the most important research attention. Sentiment analysis is a process of classifying the user’s feelings in different manners such as positive, negative or both. The major issue of sentiment analysis is...
Multi-Class Text Classification on Khmer News Using Ensemble Method in Machine Learning Algorithms
Raksmey Phann, Chitsutha Soomlek, Pusadee Seresangtakul
Acta Informatica Pragensia 2023, 12(2), 243-259 | DOI: 10.18267/j.aip.2104157
The research herein applies text classification with which to categorize Khmer news articles. News articles were collected from three online websites through web scraping and grouped into nine categories. After text preprocessing, the dataset was split into training and testing sets. We then evaluated the performance of the ensemble learning method via machine learning classifiers with k-fold validation. Various machine learning classifiers were employed, namely logistic regression, Complement Naive Bayes, Bernoulli Naive Bayes, k-nearest neighbours, perceptron, support vector machines, stochastic gradient descent, AdaBoost, decision tree, and random...
Diagnostic Performance Evaluation of Deep Learning-Based Medical Text Modelling to Predict Pulmonary Diseases from Unstructured Radiology Free-Text Reports
Shashank Shetty, Ananthanarayana V S, Ajit Mahale
Acta Informatica Pragensia 2023, 12(2), 260-274 | DOI: 10.18267/j.aip.2143337
The third most common cause of death worldwide is attributed to pulmonary diseases, making it imperative to diagnose them promptly. Radiology is a medical discipline that utilizes medical imaging to guide treatment. Radiologists prepare reports interpreting details and findings analysed from medical images. Radiology free-text reports are a rich source of textual information that can be exploited to enhance the efficacy of medical prognosis, treatment and research. Radiology reports exist in an unstructured format as are not suitable by themselves for mathematical computation or machine learning operations. Therefore, natural language processing (NLP)...
Use of Data Mining for Analysis of Czech Real Estate Market
Ilya Tsakunov, David Chudán
Acta Informatica Pragensia 2023, 12(2), 275-295 | DOI: 10.18267/j.aip.2153195
This paper analyses data from the real estate market domain. The data were scraped from the bezrealitky.cz portal. The analysis looks at both sales and rental data. A total of 3546 records and 54 attributes were obtained. A basic overview of the data was performed using exploratory data analysis where some basic characteristics of the data were identified, such as the average price of sold and rented flats. More specific results were obtained by applying data mining methods such as regression (linear regression, lasso regression and ridge regression) for predicting the flat prices and payments for utilities, classification (support vector machines,...
Factors Influencing Cloud Computing Adoption by SMEs in the Czech Republic: An Empirical Analysis Using Technology-Organization-Environment Framework
Jiří Homan, Ladislav Beránek
Acta Informatica Pragensia 2023, 12(2), 296-310 | DOI: 10.18267/j.aip.2172702
Cloud computing technologies have come a long way and are available to virtually any company today. However, which factors will cause the company to decide to implement these services? Based on existing research abroad, we compiled a Technology-Organization-Environment (TOE) framework and proposed questions that support individual factors in our model to address this problem. Small and medium-sized enterprises (SMEs) in the Czech Republic actively participated in the research, from which we received 99 valid responses. Our results show a significant influence of four factors. The first factor is relative advantage, and the second is competitive pressure....
Impact of Women Driving Rights on Adoption and Usage of E-hailing Applications in Saudi Arabia
Muhammad Ahsan Qureshi, Azra Shamim
Acta Informatica Pragensia 2023, 12(2), 311-326 | DOI: 10.18267/j.aip.2163207
E-hailing applications are becoming popular around the globe. However, the motivations and barriers to use these applications may differ in different countries. Therefore, the aim of current work is to explore noteworthy factors affecting the acceptance of the e-hailing application ‘Careem’ in the context of Saudi Arabia. Due to recent driving permission given to women in Saudi Arabia and the lesser acceptance of public transport in Saudi Arabia, this study is of fundamental importance. To achieve the purpose of this study, the technology acceptance model is extended by adding external variables: perceived convenience, perceived accessibility...
Digital Archives as Research Infrastructure of the Future
Michal Lorenz, Michal Konečný
Acta Informatica Pragensia 2023, 12(2), 327-341 | DOI: 10.18267/j.aip.2193372
While a new paradigm of scientific research based on data centres and research infrastructures is gaining ground in science, and convergence between infrastructures and scientific domains is growing in cyberspace, epistemic cultures, particularly conservative in some fields, play a significant role in the dynamics of knowledge production in general and the adoption of data-intensive scientific practices in particular. In the present study, we focus on the transformations of scholarly communication through the perspective of digital curation of research data in the humanities, which certainly belong to these conservative epistemic cultures. The aim...
Automated Medical Document Verification on Cloud Computing Platform: Blockchain-Based Soulbound Tokens
Ashish Khanna, Yogesh Sharma, Devansh Singh, Ria Monga, Tarun Kumar
Acta Informatica Pragensia 2023, 12(2), 342-356 | DOI: 10.18267/j.aip.2184151
Medical document verification is a critical and expensive process that often relies on centralized databases. However, manual verification of such documents is time-consuming and lacks credibility. Deep learning and blockchain technology can be employed to address this issue by reducing fraud and increasing efficiency. The use of non-transferable soulbound tokens (SBTs) can provide a secure and tamper-proof system for verifying medical records. The authors have proposed an algorithm for automated document verification and authenticity using blockchain-based SBTs. The system uses cloud computing to access the decentralized database, reducing the time...
Segmenting Customers with Data Analytics Tools: Understanding and Engaging Target Audiences
Tomáš Pitka, Jozef Bucko
Acta Informatica Pragensia 2023, 12(2), 357-378 | DOI: 10.18267/j.aip.2203739
This paper presents a decision support system for identifying customer typology using cluster analysis to segment relevant customers. The approach is demonstrated using data from a company selling nutritional supplements, consisting of approximately 130,000 records from six Central European countries. The analysis results in distinct groups of customers, which are proposed for more effective management of customer relationships. The findings have implications for retailers, helping them focus on the most profitable customer segments to increase sales and profits and build lasting relationships. Furthermore, cluster analysis proves to be an appropriate...
ck-means and fck-means: Two Deterministic Initialization Procedures for k-means Algorithm Using a Modified Crowding Distance
Abdesslem Layeb
Acta Informatica Pragensia 2023, 12(2), 379-399 | DOI: 10.18267/j.aip.2233224
This paper presents two novel deterministic initialization procedures for k-means clustering based on a modified crowding distance. The procedures, named ck-means and fck-means, use more crowded points as initial centroids. Experimental studies on multiple datasets demonstrate that the proposed approach outperforms k-means and k-means++ in terms of clustering accuracy. The effectiveness of ck-means and fck-means is attributed to their ability to select better initial centroids based on the modified crowding distance. Overall, the proposed approach provides a promising alternative for improving k-means...
Safe Haven for Asian Equity Markets During Financial Distress: Bitcoin Versus Gold
Pham Thi Ngoc Dung, Luong Kim Long, Le Ngoc Thuy Trang, Do Thi Thanh Nhan
Acta Informatica Pragensia 2023, 12(2), 400-418 | DOI: 10.18267/j.aip.2242614
This study aims to analyse the role of bitcoin and gold as safe haven assets for Asian equity markets during periods of high market uncertainty related to the global COVID-19 pandemic, high volatility and extreme stock market conditions. Our empirical analysis employs the DCC-GARCH methodology to estimate the time-varying relationship between bitcoin/gold and the Asian stock market from 2016 to 2023. Our findings reveal that bitcoin serves as a strong hedge for Taiwan and Pakistan, whereas gold can be considered a strong hedge for Japan, Singapore, India, Thailand and Vietnam. Interestingly, we observe that bitcoin does not exhibit safe haven properties...
Review
Visualisation of User Stories in UML Models: A Systematic Literature Review
Mohammad Nazrul Mornie, Nurfauza Jali, Syahrul Nizam Junaini, Edwin Mit, Cheah Wai Shiang, Suhaila Saee
Acta Informatica Pragensia 2023, 12(2), 419-438 | DOI: 10.18267/j.aip.2123595
The use of agile methodology in software development projects is growing rapidly among industry professionals and academia. The Unified Modelling Language (UML) conventionally accompanies agile software development to model the software requirements. The user story is fundamental and should be identified to communicate the basic requirements between the development team and the stakeholders before the UML model such as the use case diagram, class diagrams and many others can be designed. However, there are several challenges associated with this process such as poorly organised user stories, natural language complexity and high time consumption to...
Consumer Behaviour in Gamified Environment: A Bibliometric and Systematic Literature Review in Business and Management Area
Deeksha Singh, Sambashiva Rao Kunja
Acta Informatica Pragensia 2023, 12(2), 439-467 | DOI: 10.18267/j.aip.2214965
Marketers utilize gamification as it provides an efficacious platform to communicate and reach a large consumer base. Previous literature has explored the impact of various aspects of gamification on consumer behaviour. This review synthesizes such studies and systematically examines 68 publications from Scopus from 2012 to 2022, employing bibliometric and systematic analysis. Performance analysis and science mapping are evaluated in bibliometric analysis to ascertain the most influential authors, documents, countries and journals. Thereafter, through cluster analysis, five major themes are identified, namely customer engagement, consumer experience,...
Consumer Behaviour and Acceptance in Fintech Adoption: A Systematic Literature Review
Muhardi Saputra, Paulus Insap Santosa, Adhistya Erna Permanasari
Acta Informatica Pragensia 2023, 12(2), 468-489 | DOI: 10.18267/j.aip.2227735
The literature review was conducted systematically, following a rigorous process to address specific research questions. The review procedure was designed to provide guidance and minimize researcher bias. It outlined the study selection process, including inclusion and exclusion criteria, research questions, search methods, quality evaluation, and data extraction and synthesis. The Scopus database was utilized for this systematic literature review, and a comprehensive search was conducted to identify relevant studies. We used the Kitchenham systematic literature review (SLR) method required to process metadata at the time of processing this SLR, and...