TY - CHAP A1 - Hubert, Andreas A1 - Guo, C. A1 - Doll, Konrad A1 - Kuhn, F. A1 - Kröhn, M. T1 - Multimodales hochautomatisiert lernendes Assistenzsystem für manuelle Montageprozesse T2 - Automation 2025 KW - Maschinelles Lernen KW - Künstliche Intelligenz KW - Montage Y1 - 2025 SN - 9783181024577 U6 - https://doi.org/https://doi.org/10.51202/9783181024577-575 SP - 575 EP - 590 PB - VDI Verlag ER - TY - CHAP A1 - Hubert, Andreas A1 - Guo, Chenxi A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Improving Fast 6D Pose Estimation with Adaptive Model Fusion T2 - 2025 5th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) KW - Maschinelles Lernen KW - Deep Learning KW - Montagezelle Y1 - 2025 U6 - https://doi.org/https://doi.org/10.1109/MLISE66443.2025.11100239 SP - 58 EP - 63 PB - IEEE ER - TY - CHAP A1 - Talluri, Kranthi Kumar A1 - Weidl, Galia A1 - Kasuluru, Vaishnavi T1 - Accident-Driven Congestion Prediction and Simulation: An Explainable Framework Using Advanced Clustering and Bayesian Networks T2 - IEEE 102nd Vehicular Technology Conference N2 - Traffic congestion due to uncertainties, such as accidents, is a significant issue in urban areas, as the ripple effect of accidents causes longer delays, increased emissions, and safety concerns. To address this issue, we propose a robust framework for predicting the impact of accidents on congestion. We implement Automated Machine Learning (AutoML)-enhanced Deep Embedding Clustering (DEC) to assign congestion labels to accident data and predict congestion probability using a Bayesian Network (BN). The Simulation of Urban Mobility (SUMO) simulation is utilized to evaluate the correctness of BN predictions using evidence-based scenarios. Results demonstrate that the AutoML-enhanced DEC has outperformed traditional clustering approaches. The performance of the proposed BN model achieved an overall accuracy of 95.6%, indicating its ability to understand the complex relationship of accidents causing congestion. Validation in SUMO with evidence-based scenarios demonstrated that the BN model's prediction of congestion states closely matches those of SUMO, indicating the high reliability of the proposed BN model in ensuring smooth urban mobility. KW - Verkehrsstau KW - Verkehrsunfall KW - Maschinelles Lernen Y1 - 2025 UR - https://www.researchgate.net/publication/394121957_Accident-Driven_Congestion_Prediction_and_Simulation_An_Explainable_Framework_Using_Advanced_Clustering_and_Bayesian_Networks U6 - https://doi.org/10.48550/arXiv.2507.22529 ER - TY - INPR A1 - Alves, Caroline A1 - Paulo, Artur José Marques A1 - de Faria, Danilo Donizete A1 - Sato, João Ricardo A1 - Borges, Vanderci A1 - Silva, Sonia de Azevedo A1 - Ferraz, Henrique Ballalai A1 - Rodrigues, Francisco A. A1 - Thielemann, Christiane A1 - Moeckel, Michael A1 - Aguiar, Patricia de Carvalho T1 - Decoding Dystonia: unveiling neural patterns with interpretable EEG-Based Machine Learning N2 - Dystonia has a multifaceted and complex pathogenesis. Current diagnostic proce-dures, which focus primarily on clinical signs, may lack accuracy due to the variable presentationsof different dystonia types. There is a need for objective, interpretable, and non-invasive diagnostictools. This study aims to develop an interpretable electroencephalography (EEG)-basedmachine learning (ML) and deep learning (DL) approach to distinguish between focal upper limbdystonia (ULD), cervical dystonia (CD), and healthy controls (HC). EEG data were recorded during resting-state, writing-from-memory, and finger-tapping tasks. The EEG signals were segmented into windows to generate connectivity matricesusing various pairwise correlation metrics. Machine learning models were trained to classify thegroups, with performance evaluated using accuracy and area under the curve (AUC) metrics. Our approach achieved accuracy and AUC scores close to 100%. Transfer entropyemerged as the most effective connectivity metric, revealing altered brain connections in dystonia.Complex network measures outperformed traditional EEG features, highlighting the relevance offunctional connectivity. Resting-state EEG showed the highest classification performance for ULD,suggesting strong diagnostic potential. Conclusions: This study provides the first machine learning-based comparison between differenttypes of dystonia, introduces novel cervical dystonia EEG data, and yields medically interpretableinsights into altered brain connectivity. The findings enhance our understanding of dystonia and support using EEG as alow-cost, interpretable tool for diagnosing and developing brain-machine interfaces. KW - Dystonie KW - Elektroencephalographie KW - Maschinelles Lernen Y1 - 2025 U6 - https://doi.org/https://doi.org/10.21203/rs.3.rs-7483388/v1 PB - Springer Science and Business Media LLC ER - TY - CHAP A1 - Jung, Janis A1 - Hubert, Andreas A1 - Doll, Konrad A1 - Kröhn, Michael A1 - Stadler, Jochen T1 - Prozessinnovation T2 - Wissenstransfer im Spannungsfeld von Autonomisierung und Fachkräftemangel, Tagungsband, 18. AALE-Konferenz, Pforzheim, 09.03.-11.03.2022 N2 - Manuelle Montageprozesse sind nach wie vor unverzichtbar in vielen Bereichen der produzierenden Industrie. Vor allem die Qualitätskontrolle, sowie das Einlernen neuer Mitarbeitenden stellen Betriebe durch die voranschreitende Digitalisierung vor neue Herausforderungen. Assistenzsysteme können hier helfen, die Lücke zwischen Anforderungen und Qualifikation zu überbrücken. Wir stellen einen Ansatz zur intelligenten Assistenz vor, welcher auf einer kamerabasierten Erkennung von Arbeitsabläufen mit Hilfe von Methoden des maschinellen Lernens beruht. Das Assistenzsystem erzeugt automatisiert Hilfsmaterial zur Unterstützung der Werkenden. Zusätzlich zur Darstellung der technischen Aspekte, werden psychologische Aspekte, wie Akzeptanz und Motivation untersucht. KW - Assistenzsystem KW - Montage KW - Maschinelles Lernen Y1 - 2022 U6 - https://doi.org/10.33968/2022.20 PB - Hochschule für Technik, Wirtschaft und Kultur Leipzig ER - TY - CHAP A1 - Weeger, Nicolas A1 - Stiehl, Annika A1 - von Kistowski, Jóakim A1 - Geißelsöder, Stefan A1 - Uhl, Christian T1 - Towards Practicable Machine Learning Development Using AI Engineering Blueprints T2 - 2025 IEEE 22nd International Conference on Software Architecture Companion (ICSA-C) N2 - The implementation of artificial intelligence (AI) in business applications holds considerable promise for significant improvements. The development of AI systems is becoming increasingly complex, thereby underscoring the growing importance of AI engineering and MLOps techniques. Small and medium-sized enterprises (SMEs) face considerable challenges when implementing AI in their products or processes. These enterprises often lack the necessary resources and expertise to develop, deploy, and operate AI systems that are tailored to address their specific problems. Given the lack of studies on the application of AI engineering practices, particularly in the context of SMEs, this paper proposes a research plan designed to develop blueprints for the creation of proprietary machine learning (ML) models using AI engineering and MLOps practices. These blueprints enable SMEs to develop, deploy, and operate AI systems by providing reference architectures and suitable automation approaches for different types of ML. The efficacy of the blueprints is assessed through their application to a series of field projects. This process gives rise to further requirements and additional development loops for the purpose of generalization. The benefits of using the blueprints for organizations are demonstrated by observing the process of developing ML models and by conducting interviews with the developers. KW - Machine Learning KW - AI Engineering KW - Blueprints KW - Reference Architecture KW - MLOps KW - Maschinelles Lernen KW - Künstliche Intelligenz KW - Klein- und Mittelbetrieb KW - Prozessautomation Y1 - 2025 U6 - https://doi.org/10.1109/ICSA-C65153.2025.00078 SP - 525 EP - 528 PB - IEEE ER - TY - CHAP A1 - Hubert, Andreas T1 - Scene Understanding at Manual Assembly Cells BT - Doctoral Dissertation Colloquium 2023 T2 - Organic Computing KW - Maschinelles Lernen KW - Montage KW - Simulation Y1 - 2024 VL - 2023 SP - 109 EP - 120 PB - Kassel University Press CY - Kassel ER - TY - CHAP A1 - Hubert, Andreas A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Influence of Background Color on 6D Pose Tracking Accuracy T2 - 2024 International Conference on Engineering and Emerging Technologies (ICEET), 27-28 December 2024 KW - Maschinelles Lernen KW - Deep Learning Y1 - 2024 U6 - https://doi.org/10.1109/ICEET65156.2024.10913824 SP - 1 EP - 6 PB - IEEE ER - TY - JOUR A1 - Ciba, Manuel A1 - Petzold, Marc A1 - Alves, Caroline L. A1 - Rodrigues, Francisco A. A1 - Jimbo, Yasuhiko A1 - Thielemann, Christiane T1 - Machine learning and complex network analysis of drug effects on neuronal microelectrode biosensor data JF - Scientific Reports N2 - Biosensors, such as microelectrode arrays that record in vitro neuronal activity, provide powerful platforms for studying neuroactive substances. This study presents a machine learning workflow to analyze drug-induced changes in neuronal biosensor data using complex network measures from graph theory. Microelectrode array recordings of neuronal networks exposed to bicuculline, a GABA $$_A$$ receptor antagonist known to induce hypersynchrony, demonstrated the workflow’s ability to detect and characterize pharmacological effects. The workflow integrates network-based features with synchrony, optimizing preprocessing parameters, including spike train bin sizes, segmentation window sizes, and correlation methods. It achieved high classification accuracy (AUC up to 90%) and used Shapley Additive Explanations to interpret feature importance rankings. Significant reductions in network complexity and segregation, hallmarks of epileptiform activity induced by bicuculline, were revealed. While bicuculline’s effects are well established, this framework is designed to be broadly applicable for detecting both strong and subtle network alterations induced by neuroactive compounds. The results demonstrate the potential of this methodology for advancing biosensor applications in neuropharmacology and drug discovery. KW - Maschinelles Lernen KW - Biosensor KW - Mikroelektrode Y1 - 2025 U6 - https://doi.org/https://doi.org/10.1038/s41598-025-99479-7 SN - 2045-2322 VL - 15 IS - 1 PB - Springer Science and Business Media LLC ER - TY - JOUR A1 - Sallum, Loriz Francisco A1 - Alves, Caroline L. A1 - de O Toutain, Thaise Graziele L A1 - Porto, Joel Augusto Moura A1 - Thielemann, Christiane A1 - Rodrigues, Francisco A. T1 - Revealing patterns in major depressive disorder with machine learning and networks JF - Chaos, Solitons & Fractals N2 - Major depressive disorder (MDD) is a multifaceted condition that affects millions of people worldwide and is a leading cause of disability. There is an urgent need for an automated and objective method to detect MDD due to the limitations of traditional diagnostic approaches. In this paper, we propose a methodology based on machine and deep learning to classify patients with MDD and identify altered functional connectivity patterns from EEG data. We compare several connectivity metrics and machine learning algorithms. Complex network measures are used to identify structural brain abnormalities in MDD. Using Spearman correlation for network construction and the SVM classifier, we verify that it is possible to identify MDD patients with high accuracy, exceeding literature results. The SHAP (SHAPley Additive Explanations) summary plot highlights the importance of C4-F8 connections and also reveals dysfunction in certain brain areas and hyperconnectivity in others. Despite the lower performance of the complex network measures for the classification problem, assortativity was found to be a promising biomarker. Our findings suggest that understanding and diagnosing MDD may be aided by the use of machine learning methods and complex networks. KW - Depression KW - Maschinelles Lernen KW - Elektroencephalographie Y1 - 2025 U6 - https://doi.org/https://doi.org/10.1016/j.chaos.2025.116163 SN - 0960-0779 VL - 194 PB - Elsevier BV ER - TY - CHAP A1 - Xu, Xukuan A1 - Möckel, Michael T1 - Machine Learning Based Early Rejection of Low Performance Cells in Li Ion Battery Production T2 - CACML '24: Proceedings of the 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning N2 - Lithium-ion battery cell production is conducted through a multistep production process which suffers from a notable scrap rate. Machine learning (ML) based process monitoring provides solutions to mitigate the impact of substantial scrap rates by repeated multifactorial quality predictions (virtual quality gates) along the process line. This enables an early rejection of battery cells which are unlikely to reach required specifications, avoids further waste of resources at later process steps and simplifies recycling of rejected cells. A hierarchical architecture is used to apply ML algorithms first for process-adapted feature extraction which is guided by a priori knowledge on typical production anomalies. In a second step, these features are correlated with end-of-line quality control data using explainable ML methods. The resulting predictions may lead to pass or fail of a battery cell, or -in the context of flexible production- may also trigger adjustments of later process steps to compensate for detected deficiencies. An example ML based quality control concept is illustrated for a pilot battery cell production line. KW - Maschinelles Lernen Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1145/3654823.3654870 SP - 251 EP - 256 ER - TY - JOUR A1 - Xu, Xukuan A1 - Donghui, Li A1 - Bi, Jinghou A1 - Möckel, Michael T1 - AutoML based workflow for design of experiments (DOE) selection and benchmarking data acquisition strategies with simulation models JF - Scientific Reports N2 - Design of experiments (DOE) is an established method to allocate resources for efficient parameter space exploration. Model based active learning (AL) data sampling strategies have shown potential for further optimization. This paper introduces a workflow for conducting DOE comparative studies using automated machine learning. Based on a practical definition of model complexity in the context of machine learning, the interplay of systematic data generation and model performance is examined considering various sources of uncertainty: this includes uncertainties caused by stochastic sampling strategies, imprecise data, suboptimal modeling, and model evaluation. Results obtained from electrical circuit models with varying complexity show that not all AL sampling strategies outperform conventional DOE strategies, depending on the available data volume, the complexity of the dataset, and data uncertainties. Trade-offs in resource allocation strategies, in particular between identical replication of data points for statistical noise reduction and broad sampling for maximum parameter space exploration, and their impact on subsequent machine learning analysis are systematically investigated. Results indicate that replication oriented strategies should not be dismissed but may prove advantageous for cases with non-negligible noise impact and intermediate resource availability. The provided workflow can be used to simulate practical experimental conditions for DOE testing and DOE selection. KW - Maschinelles Lernen Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1038/s41598-024-83581-3 ER - TY - CHAP A1 - Talluri, Kranthi Kumar A1 - Weidl, Galia T1 - Impact of Accidents on Traffic Congestions: A Bayesian Network Approach Using Real City Data T2 - Advances in Transdisciplinary Engineering N2 - Traffic congestion has been a major concern in urban areas due to its strong impact on various social, economic, and human safety sectors. Understanding the relationship and analyzing the trends and patterns between congestion and accidents can strengthen the strategy for reducing traffic congestion. Research on causes of accidents and their impact on congestion has recently been explored on a greater scale, but there is still a lot of scope for vast areas of improvement. To tackle this issue, we built a Bayesian Network (BN) model for analyzing and predicting congestion probability that can occur due to accidents. In this work, the complexity of handling real data obtained from Darmstadt city is described in detail. The accidents and congestion are correlated by introducing a novel threshold-based approach, which identifies congestion based on the change in vehicle density immediately following an accident. Different thresholds are explored to determine the most reliable measure of congestion, with the T4 threshold emerging as the optimal choice. Moreover, the proposed BN model is evaluated against several machine learning models, demonstrating competitive performance and its ability to understand the root cause of traffic congestion. KW - Verkehrsstau KW - Verkehrsunfall KW - Bayes-Netz KW - Maschinelles Lernen Y1 - 2024 SN - 9781643685601 U6 - https://doi.org/10.3233/ATDE241181 SN - 2352-751X IS - 63 SP - 64 EP - 78 PB - IOS Press ER - TY - JOUR A1 - Sallum, Loriz Francisco A1 - Alves, Caroline L. A1 - Thielemann, Christiane A1 - Rodrigues, Francisco A. T1 - Revealing patterns in major depressive disorder with machine learning and networks JF - medrxiv N2 - Major depressive disorder (MDD) is a multifaceted condition that affects millions of people worldwide and is a leading cause of disability. There is an urgent need for an automated and objective method to detect MDD due to the limitations of traditional diagnostic approaches. In this paper, we propose a methodology based on machine and deep learning to classify patients with MDD and identify altered functional connectivity patterns from EEG data. We compare several connectivity metrics and machine learning algorithms. Complex network measures are used to identify structural brain abnormalities in MDD. Using Spearman correlation for network construction and the SVM classifier, we verify that it is possible to identify MDD patients with high accuracy, exceeding literature results. The SHAP (SHAPley Additive Explanations) summary plot highlights the importance of C4-F8 connections and also reveals dysfunction in certain brain areas and hyperconnectivity in others. Despite the lower performance of the complex network measures for the classification problem, assortativity was found to be a promising biomarker. Our findings suggest that understanding and diagnosing MDD may be aided by the use of machine learning methods and complex networks. KW - Depression KW - Elektroencephalographie KW - Maschinelles Lernen Y1 - 2024 U6 - https://doi.org/doi.org/10.1101/2024.06.07.24308619 VL - 2024 IS - 1 SP - 1 EP - 17 ER - TY - CHAP A1 - Akcatepe, Osman A1 - Möckel, Michael T1 - Hybrid Physics-Inspired Machine Learning Framework for Predictive Maintenance of Forklift Chains: Leveraging Sensor Data Characteristics N2 - Bridging the gap between physics-based modeling and data-driven machine learning promises to reduce the amount of training data required and to improve explainability in predictive maintenance applications. For a small fleet of industrial forklift trucks, we develop a physically inspired framework for predicting remaining useful life (RUL) for selected components by integrating physically motivated feature extraction, degradation modelling and machine learning. The discussed approach is promising for situations of limited data availability or large data heterogeneity, which often occurs in fleets of customized vehicles optimized for particular tasks. KW - Maschinelles Lernen KW - Gabelstapler Y1 - 2023 UR - https://ieeexplore.ieee.org/abstract/document/10375057/authors#authors U6 - https://doi.org/10.1109/ICCMA59762.2023.10375057 ER - TY - JOUR A1 - Voigt, Jorrit A1 - Möckel, Michael T1 - Benchmarking a multi-layer approach and neural network architectures for defect detection in PBF-LB/M JF - Materials Today Communications N2 - The substitution of expensive non-destructive material testing by data-based process monitoring is intensively explored in quality assurance for additive manufactured components. Machine learning show promising results for defect detection but require conceptual adaption to layer wise manufacturing and line scanning patterns in laser powder bed fusion. A multi-layer approach to co-register µ-computer tomography measurements with process monitoring data is developed and a workflow for automatic data set generation is implemented. The objective of this research is to benchmark the volumetric multi-layer approach and specifically selected deep learning methods for defect detection. The volumetric approach shows superior results compared to single slice monitoring. All investigated structured neural network topologies deliver similar performance. KW - Neuronales Netz KW - Maschinelles Lernen KW - Fehlererkennung Y1 - 2022 UR - https://www.sciencedirect.com/science/article/pii/S2352492822017196?via%3Dihub U6 - https://doi.org/https://doi.org/10.1016/j.mtcomm.2022.104878 VL - 2022 IS - 33/104878 SP - * EP - * ER - TY - JOUR A1 - Voigt, Jorrit A1 - Bock, Thomas A1 - Hilpert, Uwe A1 - Hellmann, Ralf A1 - Möckel, Michael T1 - Increased relative density and characteristic melt pool Signals at the edge in PBF-LB/M JF - Additive Manufacturing N2 - Limited process control can cause metallurgical defect formation and inhomogeneous relative density in laser powder bed fusion manufactured parts. This study shows that process monitoring, based on optical melt-pool signal analysis is capable of tracing relative density variations: Unsupervised machine learning, applied to cluster multiple-slice monitoring data, reveals characteristic patterns in this noisy time-series signal, which can be co-registered with geometrical positions in the build part. For cylindrical 15–5 PH stainless steel specimens, manufactured under constant process parameters and post-analyzed by µ-computer tomography, correlations between such patterns and an increased local relative density at the edge have been observed. Finite element method (FEM) modeling of thermal histories at exemplary positions close to the edge suggest pre-heating effects caused by neighboring laser scan trajectories as possible reasons for the increased melt pool intensity at the edge. KW - Maschinelles Lernen KW - Schmelze Y1 - 2022 UR - https://www.sciencedirect.com/science/article/pii/S2214860422001993?via%3Dihub U6 - https://doi.org/https://doi.org/10.1016/j.addma.2022.102798 VL - 2022 IS - 57/102798 SP - * EP - * ER - TY - JOUR A1 - Pineda, Aruane M. A1 - Alves, Caroline L. A1 - Möckel, Michael A1 - de O Toutain, Thaise Graziele L A1 - Moura Porto, Joel Augusto A1 - Rodrigues, Francisco A. T1 - Analysis of quantile graphs in EGC data from elderly and young individuals using machine learning and deep learning JF - Journal of Complex Networks N2 - Heart disease, also known as cardiovascular disease, encompasses a variety of heart conditions that can result in sudden death for many people. Examples include high blood pressure, ischaemia, irregular heartbeats and pericardial effusion. Electrocardiogram (ECG) signal analysis is frequently used to diagnose heart diseases, providing crucial information on how the heart functions. To analyse ECG signals, quantile graphs (QGs) is a method that maps a time series into a network based on the time-series fluctuation proprieties. Here, we demonstrate that the QG methodology can differentiate younger and older patients. Furthermore, we construct networks from the QG method and use machine-learning algorithms to perform the automatic diagnosis, obtaining high accuracy. Indeed, we verify that this method can automatically detect changes in the ECG of elderly and young subjects, with the highest classification performance for the adjacency matrix with a mean area under the receiver operating characteristic curve close to one. The findings reported here confirm the QG method’s utility in deciphering intricate, nonlinear signals like those produced by patient ECGs. Furthermore, we find a more significant, more connected and lower distribution of information networks associated with the networks from ECG data of the elderly compared with younger subjects. Finally, this methodology can be applied to other ECG data related to other diseases, such as ischaemia. KW - Maschinelles Lernen KW - Elektrokardiogramm KW - Älterer Mensch KW - Heranwachsender Y1 - 2023 UR - https://academic.oup.com/comnet/article-abstract/11/5/cnad030/7260365?redirectedFrom=fulltext U6 - https://doi.org/https://doi.org/10.1093/comnet/cnad030 VL - 2023 IS - 11/5 SP - * EP - * ER - TY - JOUR A1 - Alves, Caroline L. A1 - Toutain, Thaise A1 - Porto, Joel A1 - Aguiar, Patricia A1 - de Sena, Eduardo Pondé A1 - Rodrigues, Francisco A. A1 - Pineda, Aruane M. A1 - Thielemann, Christiane T1 - Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia JF - Journal of Neural Engineering N2 - Objective. Schizophrenia (SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization. Several studies encompass machine learning (ML) and deep learning algorithms to automate the diagnosis of this mental disorder. Others study SCZ brain networks to get new insights into the dynamics of information processing in individuals suffering from the condition. In this paper, we offer a rigorous approach with ML and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in SCZ individuals. Approach. For this purpose, we employed an functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) dataset. In addition, we combined EEG measures, i.e. Hjorth mobility and complexity, with complex network measurements to be analyzed in our model for the first time in the literature. Main results. When comparing the SCZ group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. Regarding complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most crucial measure in different data modalities. Furthermore, the SCZ brain networks exhibit less segregation and a lower distribution of information. As a result, EEG measures outperformed complex networks in capturing the brain alterations associated with SCZ. Significance. Our model achieved an area under receiver operating characteristic curve (AUC) of 100% and an accuracy of 98.5% for the fMRI, an AUC of 95%, and an accuracy of 95.4% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures on these results, which helped us better describe changes in the diseased brain. KW - Maschinelles Lernen KW - Schizophrenie KW - Deep Learning Y1 - 2023 U6 - https://doi.org/10.1088/1741-2552/acf734 VL - 2023 IS - 20/5 SP - 0 EP - 0 ER - TY - JOUR A1 - Alves, Caroline L. A1 - Toutain, Thaise A1 - Aguiar, Patricia A1 - Pineda, Aruane M. A1 - Roster, Kirstin A1 - Thielemann, Christiane A1 - Porto, Joel A1 - Rodrigues, Francisco A. T1 - Diagnosis of autism spectrum disorder based on functional brain networks and machine learning JF - Scientific Reports N2 - Autism is a multifaceted neurodevelopmental condition whose accurate diagnosis may be challenging because the associated symptoms and severity vary considerably. The wrong diagnosis can affect families and the educational system, raising the risk of depression, eating disorders, and self-harm. Recently, many works have proposed new methods for the diagnosis of autism based on machine learning and brain data. However, these works focus on only one pairwise statistical metric, ignoring the brain network organization. In this paper, we propose a method for the automatic diagnosis of autism based on functional brain imaging data recorded from 500 subjects, where 242 present autism spectrum disorder considering the regions of interest throughout Bootstrap Analysis of Stable Cluster map. Our method can distinguish the control group from autism spectrum disorder patients with high accuracy. Indeed the best performance provides an AUC near 1.0, which is higher than that found in the literature. We verify that the left ventral posterior cingulate cortex region is less connected to an area in the cerebellum of patients with this neurodevelopment disorder, which agrees with previous studies. The functional brain networks of autism spectrum disorder patients show more segregation, less distribution of information across the network, and less connectivity compared to the control cases. Our workflow provides medical interpretability and can be used on other fMRI and EEG data, including small data sets. KW - Maschinelles Lernen KW - Gehirn KW - Neuronales Netz KW - Autismus Y1 - 2023 UR - https://rdcu.be/dcwIP U6 - https://doi.org/https://doi.org/10.1038/s41598-023-34650-6 VL - 2023 IS - 13/8072 SP - 1 EP - 20 ER -