006 Spezielle Computerverfahren
Refine
Document Type
Institute
- Fachbereich Ingenieur- und Naturwissenschaften (16) (remove)
Has Fulltext
- yes (16)
Keywords
- machine learning (5)
- Künstliche Intelligenz (3)
- deep learning (3)
- artificial intelligence (2)
- microbiome (2)
- Android (1)
- Automatisierte Fotografie (1)
- BLE (1)
- Bibliotheks-App (1)
- Bibliotheksinformatik (1)
The new coronavirus COVID-19 has been spreading worldwide for almost three years. The global community has developed effective measures to contain and control the pandemic. However, new factors are emerging that are driving the dynamics of COVID-19. One of these factors was the escalation of Russia's war in Ukraine. This study aims to test the hypothesis of the influence of migration flows caused by the Russian war in Ukraine on the dynamics of the epidemic process in Germany. For this, a model of the COVID-19 epidemic process was built based on the polynomial regression method. The model's adequacy was tested 30 days before the start of the escalation of the Russian war in Ukraine. To assess the impact of the war on the dynamics of COVID-19, the model was used to calculate the forecast of cumulative new and fatal cases of COVID-19 in Germany in the first 30 days after the start of the escalation of the Russian war in Ukraine. Modeling showed that migration flows from Ukraine are not a critical factor in the growth of the dynamics of the incidence of COVID-19 in Germany, but they influenced the number of cases. The next stage of the study is the development of more complex models for a detailed analysis of population dynamics, identifying factors influencing the epidemic process in the context of the Russian war in Ukraine, and assessing their information content.
Dieses Paper präsentiert das Forschungsprojekt „Intelligent Camera Unit“ (ICU) der Technischen Hochschule Wildau in Kooperation mit der Beiersdorf AG zur Verbesserung von Portraitaufnahmen in Probandenstudien für Kosmetikprodukte. Das Hauptziel des Projekt besteht darin, Ausrichtungsfehler zu minimieren und die Vergleichbarkeit von Vorher-Nachher-Bildern zu erhöhen. Dies wird durch die Verwendung eines kollaborativen Roboters und KI-gestützter Bildanalyse erreicht, um präzise Ausrichtung und Gesichtspositionen der Probanden zu gewährleisten. Das entwickelte System ermöglicht effiziente und reproduzierbare Aufnahmen aus verschiedenen Winkeln und Entfernungen und bietet eine benutzerfreundliche, web-basierte Bedienoberfläche.
Die Industrie 5.0 fordert neue Lernansätze und zeitgleich auch passende Lernumgebungen. Parallel müssen diese neben den didaktischen Herausforderungen auch den Transfer- und Übertragungsgedanken auf die industriellen Anwendungen gerecht werden. Durch die täglich steigende Anzahl vielfältiger KI-Tools insbesondere textgenerierenden Tools, braucht es Systeme mit einem breiten Anwendungsbereich. Im Rahmen des vorliegenden Beitrags geben die Autoren einen Einblick in die Wildauer Smart Production, welche den transdisziplinären Gedanken von Lern- und Transferumgebungen Rechnung trägt, Möglichkeiten der Gestaltung komplexer Produktionssysteme widerspiegelt, die Integration menschzentrierter Ansätze ermöglicht und als Forschungsumgebung eingesetzt wird.
The aim of our study is to develop a new, simple, and effective method for identification of personality based on the characteristics of the sphenoid sinus structure, using machine learning for subsequent implementation into routine medical practice in Ukraine. The study involved 200 multislice computed tomography (MSCT) scans of individuals of various genders and ages. During the study, we obtained results with an accuracy exceeding 70%.
The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.
Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action
(2023)
The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.
The identification of biomarkers is crucial for cancer diagnosis, understanding the underlying biological mechanisms, and developing targeted therapies. In this study, we propose a machine learning approach to predict ovarian cancer patients’ outcomes and platinum resistance status using publicly available gene expression data. Six classical machine-learning algorithms are compared on their predictive performance. Those with the highest score are analyzed by their feature importance using the SHAP algorithm. We were able to select multiple genes that correlated with the outcome and platinum resistance status of the patients and validated those using Kaplan–Meier plots. In comparison to similar approaches, the performance of the models was higher, and different genes using feature importance analysis were identified. The most promising identified genes that could be used as biomarkers are TMEFF2, ACSM3, SLC4A1, and ALDH4A1.
Microbiomic analysis of human gut samples is a beneficial tool to examine the general well-being and various health conditions. The balance of the intestinal flora is important to prevent chronic gut infections and adiposity, as well as pathological alterations connected to various diseases. The evaluation of microbiome data based on next-generation sequencing (NGS) is complex and their interpretation is often challenging and can be ambiguous. Therefore, we developed an innovative approach for the examination and classification of microbiomic data into healthy and diseased by visualizing the data as a radial heatmap in order to apply deep learning (DL) image classification. The differentiation between 674 healthy and 272 type 2 diabetes mellitus (T2D) samples was chosen as a proof of concept. The residual network with 50 layers (ResNet-50) image classification model was trained and optimized, providing discrimination with 96% accuracy. Samples from healthy persons were detected with a specificity of 97% and those from T2D individuals with a sensitivity of 92%. Image classification using DL of NGS microbiome data enables precise discrimination between healthy and diabetic individuals. In the future, this tool could enable classification of different diseases and imbalances of the gut microbiome and their causative genera.
The share of chronic odontogenic rhinosinusitis is 40% among all chronic rhinosinusitis. Using automated information systems for differential diagnosis will improve the efficiency of decision-making by doctors in diagnosing chronic odontogenic rhinosinusitis. Therefore, this study aimed to develop an intelligent decision support system for the differential diagnosis of chronic odontogenic rhinosinusitis based on computer vision methods. A dataset was collected and processed, including 162 MSCT images. A deep learning model for image segmentation was developed. A 23 convolutional layer U-Net network architecture has been used for the segmentation of multi-spiral computed tomography (MSCT) data with odontogenic maxillary sinusitis. The proposed model is implemented in such a way that each pair of repeated 3 × 3 convolutions layers is followed by an Exponential Linear Unit instead of a Rectified Linear Unit as an activation function. The model showed an accuracy of 90.09%. To develop a decision support system, an intelligent chatbot allows the user to conduct an automated patient survey and collect patient examination data from several doctors of various profiles. The intelligent information system proposed in this study made it possible to combine an image processing model with a patient interview and examination data, improving physician decision-making efficiency in the differential diagnosis of Chronic Odontogenic Rhinosinusitis. The proposed solution is the first comprehensive solution in this area.
Single Pilot Operations is a current topic with the potential to significantly affect the future of commercial aviation. While financially attractive for airlines, Single Pilot Operations bring forth important safety concerns, especially regarding the lack of human redundancy in the flight deck, an increased workload for the single pilot, reduced situational awareness and a higher risk of human error.
It is assumed that potential problems affecting Single Pilot Operations could be addressed by implementing an Augmented Reality (AR) device in the flight deck, by presenting additional information and supporting hints within the pilot’s field of view. Concretely, AR could be used to help reduce the single pilot’s workload, improve situational awareness and reduce the risk of human error.
This paper sets out to demonstrate two use cases for augmented reality in the flight deck. A system, called Pilot Assist, was developed that allows pilots to conduct checklists interactively with a Microsoft HoloLens. The system also provides a holographic Head-up-Display. Pilot Assist was developed and demonstrated with a fixed base Airbus A320 simulator at the Technical University of Wildau.
With the HoloLens’ spatial mapping capabilities – scanning and recognizing the environment around the user – it was possible to create a system that guides the pilot through the conduction of checklists. This is done by prompting the user towards the location of each checklist item in the cockpit, where information regarding necessary actions is projected. Furthermore, Pilot Assist is integrated with the aircraft systems, making it possible to obtain aircraft status data in real time, thus allowing error-checking of the pilot’s actions as well as automating the progress through checklists.
The holographic Head-up-Display allows the user to look at the surrounding environment while presenting critical flight data within the user’s field of view. The holographic Head-up-Display is intended to contribute to the pilot’s situational awareness.
Experts in the aviation field, including pilots, researchers and engineers had the chance to qualitatively assess the Pilot Assist tool. They pointed to limitations of both Pilot Assist and the HoloLens itself, but shared optimism as to how this technology and similar applications could indeed impact the future of flight operations. Concerns regarding the HoloLens’ weight, comfort and narrow field of view were expressed. However, continued development of head mounted devices (e.g. HoloLens 2) is expected in the coming years.
Further research into augmented reality applications in the flight deck is needed to advance this and other use cases. Nonetheless, the experts agreed Pilot Assist provides beneficial support during single pilot operation considering the current prototypical nature of the system.