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Abstract: Because of the prevalence of depression, its often-chronic course, relapse and associated disability, early detection and non-intrusive monitoring is a crucial tool for timely diagnosis and treatment, remission of depression and prevention of relapse. In this way, its impact on quality of life and well-being can be limited. Current attempts to use artificial intelligence for the early classification of depression are mostly data-driven and thus non-transparent and lack effective means to deal with uncertainties. Therefore, in this paper, we propose an end-to-end framework for extracting observable depression cues from diary recordings. Furthermore, we also explore its feasibility for automatic detection of depression symptoms using observable behavioural cues. The proposed end-to-end framework for extracting depression was used to evaluate 28 video recordings from the Symptom Media dataset and 27 recordings from the DAIC-WOZ dataset. We compared the presence of the extracted features between recordings of individuals with and without a depressive disorder. We identified several cues consistent with previous studies in terms of their differentiation between individuals with and without depressive disorder across both datasets among language (i.e., use of negatively valanced words, use of first-person singular pronouns, some features of language complexity, explicit mentions of treatment for depression), speech (i.e., monotonous speech, voiced speech and pauses, speaking rate, low articulation rate), and facial cues (i.e., rotational energy of head movements). The nature/context of the discourse, the impact of other disorders and physical/psychological stress, and the quality and resolution of the recordings all play an important role in matching the digital features to the relevant background. In this way, the work presented in this paper provides a novel approach to extracting a wide range of cues relevant to the classification of depression and opens up new opportunities for further research.
Keywords: digital biomarkers of depression, facial cues, speech cues, language cues, deep learning, end-to-end pipeline, artificial intelligence
Published in DKUM: 17.01.2025; Views: 0; Downloads: 0
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Abstract: The master’s degree thesis is composed of theoretical and practical parts. The theoretical part describes the basics of predictive data analytics and machine learning algorithms for classification such as Logistic Regression, Decision Tree, Random Forest, SVM, and KNN. We also describe different evaluation metrics such as Recall, Precision, Accuracy, F1 Score, Cohen’s Kappa, Hamming Loss, and Jaccard Index that are used to measure the performance of these algorithms. Additionally, we record the time taken for the training and prediction processes to provide insights into algorithm scalability. The key part master’s thesis is the practical part that compares these algorithms with a self-implemented tool that shows results for different evaluation metrics on seven datasets. First, we describe the implementation of an application for testing where we measure evaluation metrics scores. We tested these algorithms on all seven datasets using Python libraries such as scikit-learn. Finally, w
Keywords: data analytics, machine learning, classification, evaluation metrics
Published in DKUM: 15.01.2025; Views: 0; Downloads: 23
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Abstract: The paper deals with the field of developing communicationand digital literacy with the help of the lifelong learning model "DigInGreen" for all layers of modern society, including people with special needs. The aim of the authors is a targeted search for new solutions in the field of using digital learning and communication tools for the development of digital competences in the light of"Society 5.0". We focus on a unique way of connecting paradigms,such as: inclusive society, competences, digital, special needs, literacy and lifelong learning. We want to connect the thinking about the communicative ability, an indispensable part of which is an individual's digital literacy and point out,that the circumstances of living in a modern, but still partially exclusive society, limit the development of an individual's communicativeand digital literacy,due to a demonstrated special need or disability. We believe,that the activities we carry out,are a unique example of good practice, because they connect many scientific disciplines and fields into a modern concept of competence development without excluding or conditioning the end user. We combine the paradigms of logistics, informatics, statistics, social studies, linguistics and didactics. All the above and more represent the concept of "Society 5.0", which cannot exist as a scientific construct,if it does not consider the key aspect of one's own identification -social equality and the provision of equal opportunities.
Keywords: communication ability, digital literacy, competences, inclusion, special needs, lifelong learning
Published in DKUM: 06.01.2025; Views: 0; Downloads: 6
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Keywords: brain-computer interfaces, electroencephalogram, feature and channel investigation, feature selection, machine learning, motor imagery task classification
Published in DKUM: 19.12.2024; Views: 0; Downloads: 2
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Abstract: Artificial intelligence (AI) has found applications in enterprises′ production planning processes. However, a critical question remains: could AI replace human planners? We conducted a comparative analysis to evaluate the main task of planners in an intermittent process: planning the duration of production orders. Specifically, we analysed the results of a human planner using master data and those of an AI algorithm compared to the actual realisation. The case study was conducted in a large production company using a sample of production products and machines. We were able to confirm two of the three research questions (RQ1 and RQ3), while the results of the third question (RQ2) did not meet our expectations. The AI algorithms demonstrated significant improvement with each iteration. Despite this progress, it is still difficult to determine the exact threshold at which AI outperforms human planners due to the unpredictability of unexpected events. Even though AI significantly improves prediction accuracy, the inherent variability and incomplete input data pose a major challenge. As progress is made, robust data collection and management strategies need to be integrated to bridge the gap between the potential of AI and its practical application, fostering the symbiosis between human expertise and AI capabilities in production planning.
Keywords: artificial intelligence, machine learning, production processes, production planning, production scheduling
Published in DKUM: 04.12.2024; Views: 0; Downloads: 2
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