Graph based gene/protein prediction and clustering over uncertain medical databases.

Clustering over protein or gene data is now a popular issue in biomedical databases. In general, large sets of gene tags are clustered using high computation techniques over gene or protein distributed data. Most of the traditional clustering techniques are based on subspace, hierarchical and partitioning feature extraction. Various clustering techniques have been proposed in the literature with different cluster measures, but their performance is limited due to spatial noise and uncertainty. In this paper, an improved graph-based clustering technique is proposed for the generation of efficient gene or protein clusters over uncertain and noisy data. The proposed graph-based visualization can effectively identify different types of genes or proteins along with relational attributes. Experimental results show that the proposed graph model more effectively clusters complex gene or protein data when compared with conventional clustering approaches

Servicio Grid para la clasificación no supervisada de imágenes satelitales utilizando autómatas celulares

This paper describes the research process to explore and implement a Grid technology as a promoter of unsupervised, LANDSAT satellite image classification process. In fact, a Grid service has been implemented using cellular automata as an artificial intelligence technique. Those tasks have been achieved by establishing a Grid infrastructure and an implementation model supporting the Grid service. The service model displays the service container and the portlet container, which are integrated to form both client and server. The cellular automaton used is defined by two-dimension neighborhoods, and also a digital level projection of each three bands of the image and location on a defined neighborhood is established. The algorithm is based on transition rules generating changes to neighborhoods to get the desired categories. As a result of the classification process a new image is generated in which categories are expressed in values from 0 to 255 but providing false color to display the results.Este artículo describe el proceso investigativo por el cual se exploró y puso en práctica la tecnología Grid como elemento promotor del proceso de clasificación no supervisada de imágenes satelitales LANDSAT; se ha implementado un servicio Grid que aplica autómatas celulares como técnica de inteligencia artificial. Las anteriores tareas se han logrado estableciendo una infraestructura Grid y un modelo de implementación que da soporte al servicio Grid. El modelo del servicio visualiza el contenedor de servicios y el contenedor de "portlet", los cuales se integran para formar tanto el cliente como el servidor. El autómata celular utilizado, esta definido en vecindarios de dos dimensiones y se establece la proyección de los niveles digitales de tres de las bandas de la imagen y la ubicación de cada una de ellas sobre el vecindario definido. El algoritmo se basa en reglas de transición que generan modificaciones a los vecindarios hasta obtener las categorías deseadas. Como resultado del proceso de clasificación se genera una nueva imagen en la cual se expresan las categorías en valores de 0 a 255 y se establece falso color para visualizar los resultados obtenidos

Simulation und Optimierung von Flugzeug-Groundverkehr mit Hilfe von Zellularautomatenmodellen am Beispiel des Flughafens Düsseldorf

In der heutigen Zeit ist ein Leben ohne Flugzeuge nicht mehr vorstellbar. In den Urlaub oder zu einem dienstlichen Termin zu fliegen, ist weit verbreitet. Sogar das Fliegen als Hobby in kleinen Privatflugzeugen ist nicht mehr außergewöhnlich und bereichert unser Leben. Die Kehrseite dieser Entwicklung ist der wachsende Flugverkehr, der mittlerweile die am stärksten wachsende Personentransportart geworden ist. In Deutschland liegt die Wachstumsrate für Flugverkehr über 2,3 % pro Jahr. Die Wachstumsrate für motorisierten Individualverkehr liegt hingegen bei 0,2 %, für Eisenbahnverkehr bei 0,3 % und für den öffentlichen Personenverkehr bei -0,1 %. Dieser starke Zuwachs im Luftverkehr verursacht Probleme hinsichtlich der vorhandenen Kapazitäten an allen internationalen Flughäfen in Deutschland. Der Flughafen Düsseldorf ist bezüglich der Flugbewegungen der drittgrößte Flughafen in Deutschland. Er wurde 1927 eröffnet und besteht aus 2 parallelen Pisten, einer zu den Pisten parallel verlaufenden Rollbahn, einem Passagierterminal und drei Vorfeldern (ein Vorfeld grenzt direkt an das Terminal). Am Flughafen Düsseldorf gab es im Jahr 2016 217.500 Flugbewegungen mit 23,5 Millionen Passagieren. Die Kapazitätsgrenze des Flughafens liegt bei 24 Millionen Passagieren und ist 2017 erstmals überschritten worden. Aufgrund des beschränkten Platzes im Umfeld des Flughafens ist eine weitere räumliche Ausdehnung nicht möglich. Optimierungen an der Infrastruktur des Flughafens selbst sind aus politischen Gründen sehr schwer zu realisieren. 1965 wurde ein Vergleich zwischen dem Flughafen und den umliegenden Städten geschlossen, um die Lärmbelastung durch Limitierung der Flugbewegungen nicht weiter zu erhöhen. Über 50 Jahre später ist diese Grenze nun erreicht und ein Ausbau des Flughafens wäre vonnöten. Der Vergleich ist aber noch immer gültig und verhindert notwendige Erweiterungen. Alternativ wird versucht, Verbesserungen der Situation durch eine effizientere Nutzung der vorhandenen Infrastruktur, z.B. durch Optimierung der Rollwege, zu erzielen. Hierbei können Simulationen helfen, um eventuelle Fehlplanungen schon während der Konzeptionsphase zu erkennen und zu verhindern. In dieser Arbeit wird ein neues Simulationsmodell, das CAMAT-Modell (Cellular Automaton Model for Airport Traffic) vorgestellt. Es kann die Dynamik aller Flugzeuge und die Interaktionen der Flugzeuge untereinander simulieren. Das Modell wird durch Realdaten aus verschiedenen Quellen kalibriert. So werden Daten genutzt, die durch Beobachtungen am Flughafen Düsseldorf entstanden sind. Ferner werden Daten der Flugsicherung, vor allem Daten hinsichtlich der Gate- und Rollwegezuweisungen, und viele undokumentierte Informationen, die auf der Erfahrung der Fluglotsen beruhen, genutzt. Zuletzt werden diese Daten durch Daten von Flightradar24, wie die tatsächlichen Ankunfts- und Abflugzeiten ergänzt. Ein Vergleich zwischen Realdaten und den Ergebnissen der Simulation zeigt die Genauigkeit des entwickelten Modells. Im zweiten Teil dieser Arbeit wird das entwickelte Modell verwendet, um die Folgen für die Rollzeiten bei verschiedenen Szenarien, wie neuen Rollwegen, das Ergänzen von Rollbahnen oder Bauarbeiten auf Rollbahnen, am Flughafen Düsseldorf zu simulieren. Für jedes Szenario werden die Änderungen hinsichtlich der Rollzeiten der Flugzeuge berechnet und deren Auswirkungen auf den Kerosinverbrauch erläutert. Diese Arbeit schließt mit einem Ausblick auf mögliche Erweiterungen des Simulationsmodells, welche die Idee der Optimierung des Flugbetriebs durch den Flughafen Düsseldorf, aber auch durch die rollenden Flugzeuge selbst, weiterverfolgen.Nowadays a world without people flying in airplanes is hard to imagine. Going on vacation or on a business trip by plane has become quite common and even flying just for fun in small private planes is no longer unusual and enriches our lives. This development’s downside is the increasing growth of airplane traffic, being the most increasing mode of transportation. In Germany the expected growth rate for airplane traffic is over 2.3 % per year in contrast to 0.2 % for motorized private transport, 0.3 % for railway transport and -0.1 % for public transport. This increase creates problems on all international airports in Germany because of their limited capacity. The airport of Duesseldorf is the third largest airport in Germany with regard to airplane movement. It was opened in 1927 and consists of two parallel runways, one parallel taxiway, one passenger terminal and three aprons (one close to the terminal). The airport handled 217,500 airplane movements in 2016 with 23.5 million passengers. The capacity limit of 24 million passengers was reached in 2017 for the first time. Due to the limited space around the airport further expansion is not possible. Furthermore optimizations on the airport’s infrastructure are hard to realize due to political reasons. The airport reached a settlement with all nearby towns in 1965 for reducing noise pollution by limiting airplane movement. Over 50 years later this limit is reached; however the settlement is still valid and impedes necessary expansions. In addition to airport expansion, improvements of airport surface operations are necessary to handle the increasing number of airplanes. Therefore simulations are helpful to evaluate suggestions and to avoid poor planning in advance. In this work a new simulation model, the CAMAT-Model (Cellular Automaton Model for Airport Traffic), is developed. It simulates all airplanes in a microscopic way and considers the interactions between them. The model is adjusted by real-world data from a range of sources. First, real-world data is used, collected through visual observation at the airport of Duesseldorf. Second, data of Air-Traffic Control (ATC) is put on use, meaning data about gates used by the airplanes and undocumented tower agents’ experience about taxiing routes. Third, the usage of additional data collected by flightradar24, such as actual departure and arrival times, is helpful. A comparison between real-word data and simulated data is presented to prove the accuracy of the model. Some examples for the utilization of the simulation model are given in the second part of this work, including simulations of new taxiing routes for airplane traffic at the airport of Duesseldorf in case of construction work as well as in case of possible future extensions. For each scenario changes in taxiing time are calculated to evaluate the effects on taxiing times in general and on fuel consumption. This work concludes with some outlooks on future work pursuing the main ideas for optimization of airplane taxiing, containing ideas for improvement by the airport as well as improvements for airplanes to reduce their taxiing times themselves

Common metrics for cellular automata models of complex systems

The creation and use of models is critical not only to the scientific process, but also to life in general. Selected features of a system are abstracted into a model that can then be used to gain knowledge of the workings of the observed system and even anticipate its future behaviour. A key feature of the modelling process is the identification of commonality. This allows previous experience of one model to be used in a new or unfamiliar situation. This recognition of commonality between models allows standards to be formed, especially in areas such as measurement. How everyday physical objects are measured is built on an ingrained acceptance of their underlying commonality. Complex systems, often with their layers of interwoven interactions, are harder to model and, therefore, to measure and predict. Indeed, the inability to compute and model a complex system, except at a localised and temporal level, can be seen as one of its defining attributes. The establishing of commonality between complex systems provides the opportunity to find common metrics. This work looks at two dimensional cellular automata, which are widely used as a simple modelling tool for a variety of systems. This has led to a very diverse range of systems using a common modelling environment based on a lattice of cells. This provides a possible common link between systems using cellular automata that could be exploited to find a common metric that provided information on a diverse range of systems. An enhancement of a categorisation of cellular automata model types used for biological studies is proposed and expanded to include other disciplines. The thesis outlines a new metric, the C-Value, created by the author. This metric, based on the connectedness of the active elements on the cellular automata grid, is then tested with three models built to represent three of the four categories of cellular automata model types. The results show that the new C-Value provides a good indicator of the gathering of active cells on a grid into a single, compact cluster and of indicating, when correlated with the mean density of active cells on the lattice, that their distribution is random. This provides a range to define the disordered and ordered state of a grid. The use of the C-Value in a localised context shows potential for identifying patterns of clusters on the grid

Projection-Based Clustering through Self-Organization and Swarm Intelligence

It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm (DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures. The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining

Projection-Based Clustering through Self-Organization and Swarm Intelligence: Combining Cluster Analysis with the Visualization of High-Dimensional Data

Cluster Analysis; Dimensionality Reduction; Swarm Intelligence; Visualization; Unsupervised Machine Learning; Data Science; Knowledge Discovery; 3D Printing; Self-Organization; Emergence; Game Theory; Advanced Analytics; High-Dimensional Data; Multivariate Data; Analysis of Structured Dat