| 1. |
- Ilić, Mihailo, et al.
(author)
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Towards optimal learning : Investigating the impact of different model updating strategies in federated learning
- 2024
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In: Expert systems with applications. - : Elsevier. - 0957-4174 .- 1873-6793. ; 249:Part A
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Journal article (peer-reviewed)abstract
- With rising data security concerns, privacy preserving machine learning (ML) methods have become a key research topic. Federated learning (FL) is one such approach which has gained a lot of attention recently as it offers greater data security in ML tasks. Substantial research has already been done on different aggregation methods, personalized FL algorithms etc. However, insufficient work has been done to identify the effects different model update strategies (concurrent FL, incremental FL, etc.) have on federated model performance. This paper presents results of extensive FL simulations run on multiple datasets with different conditions in order to determine the efficiency of 4 different FL model update strategies: concurrent, semi -concurrent, incremental, and cyclic -incremental. We have found that incremental updating methods offer more reliable FL models in cases where data is distributed both evenly and unevenly between edge nodes, especially when the number of data samples across all edge nodes is small.
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| 2. |
- Klašnja-Milićević, Aleksandra, 1977, et al.
(author)
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E-Learning Systems: Intelligent Techniques for Personalization : Intelligent Techniques for Personalization
- 2017
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Book (other academic/artistic)abstract
- This monograph provides a comprehensive research review of intelligent techniques for personalisation of e-learning systems. Special emphasis is given to intelligent tutoring systems as a particular class of e-learning systems, which support and improve the learning and teaching of domain-specific knowledge. A new approach to perform effective personalization based on Semantic web technologies achieved in a tutoring system is presented. This approach incorporates a recommender system based on collaborative tagging techniques that adapts to the interests and level of students' knowledge. These innovations are important contributions of this monograph. Theoretical models and techniques are illustrated on a real personalised tutoring system for teaching Java programming language. The monograph is directed to, students and researchers interested in the e-learning and personalization techniques.
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| 3. |
- Kurbalija, Vladimir, et al.
(author)
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Analysis of neuropsychological and neuroradiological features for diagnosis of Alzheimer's disease and mild cognitive impairment
- 2023
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In: International Journal of Medical Informatics. - : Elsevier BV. - 1386-5056 .- 1872-8243. ; 178
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Journal article (peer-reviewed)abstract
- Background: Age-related neurodegenerative diseases are constantly increasing with prediction that in 2050 over 60 % of population will suffer from some level of cognitive impairment. A cure for the Alzheimer's disease (AD) does not exist, so early diagnosis is of a great importance. Machine learning techniques can help in early diagnosis with deep medical data processing, disease understanding, intervention analysis and knowledge dis-covery for achieving better medical decision making.Methods: In this paper, we analyze the dataset consisting of 90 individuals and 482 input features. We investigate the achieved AD prediction performances using seven classifiers and five feature selection algorithms. We pay special focus on analyzing performance by utilizing only a subset of best ranked attributes to establish the minimum amount of input features that ensure acceptable performance. We also investigate the significance of neuropsychological (NP) and neuroradiological (NR) attributes for the AD diagnosis.Results: The accuracy for the whole set of attributes ranged between 66.22 % and 81.00 %, and the weighted average AUROC was between 76.3 % and 95.0 %. The best results were achieved by the naive Bayes classifier and the Relief feature selection algorithm. Additionally, Support Vector Machines classifier shows the most stable results since it depends the least on the feature selection algorithm which is used. As the main result of this paper, we compare the performance of models trained with automatically selected features to models trained with hand-selected features performed by medical experts (NP and NR features).Conclusions: The results reveal that unlike the NR attributes, the NP attributes achieve a good performance that is comparable to the full set of attributes, which suggests that they possess a high predictive power for AD diagnosis.
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| 4. |
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New Trends in Database and Information Systems - ADBIS 2021 Short Papers, Doctoral Consortium and Workshops: DOING, SIMPDA, MADEISD, MegaData, CAoNS, Tartu, Estonia, August 24-26, 2021, Proceedings
- 2021
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Editorial proceedings (peer-reviewed)
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| 5. |
- Savic, Milos, et al.
(author)
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The Application of Machine Learning Techniques in Prediction of Quality of Life Features for Cancer Patients
- 2023
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In: Computer Science and Information Systems. - : ComSIS Consortium. - 1820-0214. ; 20:1, s. 381-404
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Journal article (peer-reviewed)abstract
- Quality of life (QoL) is one of the major issues for cancer patients. With the advent of medical databases containing large amounts of relevant QoL infor-mation it becomes possible to train predictive QoL models by machine learning (ML) techniques. However, the training of predictive QoL models poses several challenges mostly due to data privacy concerns and missing values in patient data. In this paper, we analyze several classification and regression ML models predicting QoL indicators for breast and prostate cancer patients. Three different approaches are employed for imputing missing values, and several settings for data privacy pre-serving are tested. The examined ML models are trained on datasets formed from two databases containing a large number of anonymized medical records of can-cer patients from Sweden. Two learning scenarios are considered: centralized and federated learning. In the centralized learning scenario all patient data coming from different data sources is collected at a central location prior to model training. On the other hand, federated learning enables collective training of machine learning models without data sharing. The results of our experimental evaluation show that the predictive power of federated models is comparable to that of centrally trained models for short-term QoL predictions, whereas for long-term periods centralized models provide more accurate QoL predictions. Furthermore, we provide insights into the quality of data preprocessing tasks (missing value imputation and differen-tial privacy).
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| 6. |
- Tzelves, Lazaros, et al.
(author)
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Artificial intelligence supporting cancer patients across Europe - The ASCAPE project
- 2022
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In: PLOS ONE. - : Public Library of Science. - 1932-6203. ; 17:4
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Journal article (peer-reviewed)abstract
- INTRODUCTION: Breast and prostate cancer survivors can experience impaired quality of life (QoL) in several QoL domains. The current strategy to support cancer survivors with impaired QoL is suboptimal, leading to unmet patient needs. ASCAPE aims to provide personalized- and artificial intelligence (AI)-based predictions for QoL issues in breast- and prostate cancer patients as well as to suggest potential interventions to their physicians to offer a more modern and holistic approach on cancer rehabilitation.METHODS AND ANALYSES: An AI-based platform aiming to predict QoL issues and suggest appropriate interventions to clinicians will be built based on patient data gathered through medical records, questionnaires, apps, and wearables. This platform will be prospectively evaluated through a longitudinal study where breast and prostate cancer survivors from four different study sites across the Europe will be enrolled. The evaluation of the AI-based follow-up strategy through the ASCAPE platform will be based on patients' experience, engagement, and potential improvement in QoL during the study as well as on clinicians' view on how ASCAPE platform impacts their clinical practice and doctor-patient relationship, and their experience in using the platform.ETHICS AND DISSEMINATION: ASCAPE is the first research project that will prospectively investigate an AI-based approach for an individualized follow-up strategy for patients with breast- or prostate cancer focusing on patients' QoL issues. ASCAPE represents a paradigm shift both in terms of a more individualized approach for follow-up based on QoL issues, which is an unmet need for cancer survivors, and in terms of how to use Big Data in cancer care through democratizing the knowledge and the access to AI and Big Data related innovations.TRIAL REGISTRATION: Trial Registration on clinicaltrials.gov: NCT04879563.
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| 7. |
- Vesin, Boban, 1978, et al.
(author)
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Protus 2.1: Applying collaborative tagging for providing recommendation in programming tutoring system
- 2016
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In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 0302-9743 .- 1611-3349.
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Conference paper (peer-reviewed)abstract
- © Springer International Publishing AG 2016.The success of intelligent tutoring system depends on the retrieval of relevant learning material according to the learner’s requirements. Therefore, the ultimate goal is development of the intelligent tutoring system that provides learning materials considering the requirements and understanding capability of the specific learner. In our previous research, we implemented a tutoring system named Protus 2.1 (PROgramming TUtoring System) that is used for learning basic concepts of Java programming language. It directs the learner’s activities and recommends relevant actions during the learning process based on the personal profile of each learner. This paper presents the implementation of collaborative tagging technique for content recommendation in Protus 2.1. This technique can be applied in intelligent tutoring systems for providing tag-enabled recommendations of concepts and resources. We investigated and identified tagging practices of students and their evolution over time.
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| 8. |
- Vesin, Boban, et al.
(author)
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Web-based educational ecosystem for automatization of teaching process and assessment of students
- 2018
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In: ACM International Conference Proceeding Series. - New York, NY, USA : ACM.
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Conference paper (peer-reviewed)abstract
- The complexity of the teaching process at universities creates many challenges. It becomes much harder for teachers to observe, control and adjust the learning process. Teaching process can be enhanced with use of different educational systems that not only help students construct their knowledge, but also make this process the most effective and efficient. One of the processes that could be automated and supported is the assessment of students’ assignments. Three e-learning systems are currently used at different universities for teaching software design basics. The goal of this paper is to propose new integrated tool that can be used in university courses to support different stages of learning and evaluation of students’ assignments. Such integrated system will be used to simplify the correction process of software design assignments.
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