Deep Learning Classification of Building Types in Northern Cyprus

Among the areas where AI studies centered on developing models that provide real-time solutions for the real estate industry are real estate price forecasting, building age, and types and design of the building (villa, apartment, floor number). Nevertheless, within the ML sector, DL is an emerging region with an Interest increases every year. As a result, a growing number of DL research are in conferences and papers, models for real estate have begun to emerge. In this study, we present a deep learning method for classification of houses in Northern Cyprus using Convolutional neural network. This work proposes the use of Convolutional neural networks in the classification of houses images. The classification will be based on the house age, house price, number of floors in the house, house type i.e. Villa and Apartment. The first category is Villa versus Apartments class; based on the training dataset of 362 images the class result shows the overall accuracy of 96.40%. The second category is split into two classes according to age of the buildings, namely 0 to 5 years Apartments 6 to 10 years Apartments. This class is to classify the building based on their age and the result shows the accuracy of 87.42%. The third category is villa with roof versus Villa without roof apartments class which also shows the overall accuracy of 87.60%. The fourth category is Villa Price from 10,000 euro to 200,000 Versus Villa Price from 200,000 Euro to above and the result shows the accuracy of 81.84%. The last category consists of three classes namely 2 floor Apartment versus 3 floor Apartment, 2 floor Apartment versus 4 floor Apartment and 2 floor Apartment versus 5 floor Apartment which all shows the accuracy of 83.54%, 82.48% and 84.77% respectively. From the experiments carried out in this thesis and the results obtained we conclude that the main aims and objectives of this thesis which is to used Deep learning in Classification and detection of houses in Northern Cyprus and to test the performance of AlexNet for houses classification was successful. This study will be very significant in creation of smart cities and digitization of real estate sector as the world embrace the used of the vast power of Artificial Intelligence, machine learning and machine vision

Semantic multimedia modelling & interpretation for annotation

The emergence of multimedia enabled devices, particularly the incorporation of cameras in mobile phones, and the accelerated revolutions in the low cost storage devices, boosts the multimedia data production rate drastically. Witnessing such an iniquitousness of digital images and videos, the research community has been projecting the issue of its significant utilization and management. Stored in monumental multimedia corpora, digital data need to be retrieved and organized in an intelligent way, leaning on the rich semantics involved. The utilization of these image and video collections demands proficient image and video annotation and retrieval techniques. Recently, the multimedia research community is progressively veering its emphasis to the personalization of these media. The main impediment in the image and video analysis is the semantic gap, which is the discrepancy among a user’s high-level interpretation of an image and the video and the low level computational interpretation of it. Content-based image and video annotation systems are remarkably susceptible to the semantic gap due to their reliance on low-level visual features for delineating semantically rich image and video contents. However, the fact is that the visual similarity is not semantic similarity, so there is a demand to break through this dilemma through an alternative way. The semantic gap can be narrowed by counting high-level and user-generated information in the annotation. High-level descriptions of images and or videos are more proficient of capturing the semantic meaning of multimedia content, but it is not always applicable to collect this information. It is commonly agreed that the problem of high level semantic annotation of multimedia is still far from being answered. This dissertation puts forward approaches for intelligent multimedia semantic extraction for high level annotation. This dissertation intends to bridge the gap between the visual features and semantics. It proposes a framework for annotation enhancement and refinement for the object/concept annotated images and videos datasets. The entire theme is to first purify the datasets from noisy keyword and then expand the concepts lexically and commonsensical to fill the vocabulary and lexical gap to achieve high level semantics for the corpus. This dissertation also explored a novel approach for high level semantic (HLS) propagation through the images corpora. The HLS propagation takes the advantages of the semantic intensity (SI), which is the concept dominancy factor in the image and annotation based semantic similarity of the images. As we are aware of the fact that the image is the combination of various concepts and among the list of concepts some of them are more dominant then the other, while semantic similarity of the images are based on the SI and concept semantic similarity among the pair of images. Moreover, the HLS exploits the clustering techniques to group similar images, where a single effort of the human experts to assign high level semantic to a randomly selected image and propagate to other images through clustering. The investigation has been made on the LabelMe image and LabelMe video dataset. Experiments exhibit that the proposed approaches perform a noticeable improvement towards bridging the semantic gap and reveal that our proposed system outperforms the traditional systems

Integrating openstreetmap data and sentinel-2 Imagery for classifying and monitoring informal settlements

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesThe identification and monitoring of informal settlements in urban areas is an important step in developing and implementing pro-poor urban policies. Understanding when, where and who lives inside informal settlements is critical to efforts to improve their resilience. This study aims at integrating OSM data and sentinel-2 imagery for classifying and monitoring the growth of informal settlements methods to map informal areas in Kampala (Uganda) and Dar es Salaam (Tanzania) and to monitor their growth in Kampala. Three building feature characteristics of size, shape and Distance to nearest Neighbour were derived and used to cluster and classify informal areas using Hotspot Cluster analysis and ML approach on OSM buildings data. The resultant informal regions in Kampala were used with Sentinel-2 image tiles to investigate the spatiotemporal changes in informal areas using Convolutional Neural Networks (CNNs). Results from Optimized Hot Spot Analysis and Random Forest Classification show that Informal regions can be mapped based on building outline characteristics. An accuracy of 90.3% was achieved when an optimally trained CNN was executed on a test set of 2019 satellite image tiles. Predictions of informality from new datasets for the years 2016 and 2017 provided promising results on combining different open source geospatial datasets to identify, classify and monitor informal settlements

Fine Art Pattern Extraction and Recognition

This is a reprint of articles from the Special Issue published online in the open access journal Journal of Imaging (ISSN 2313-433X) (available at: https://www.mdpi.com/journal/jimaging/special issues/faper2020)

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Automated Semantic Understanding of Human Emotions in Writing and Speech

Affective Human Computer Interaction (A-HCI) will be critical for the success of new technologies that will prevalent in the 21st century. If cell phones and the internet are any indication, there will be continued rapid development of automated assistive systems that help humans to live better, more productive lives. These will not be just passive systems such as cell phones, but active assistive systems like robot aides in use in hospitals, homes, entertainment room, office, and other work environments. Such systems will need to be able to properly deduce human emotional state before they determine how to best interact with people. This dissertation explores and extends the body of knowledge related to Affective HCI. New semantic methodologies are developed and studied for reliable and accurate detection of human emotional states and magnitudes in written and spoken speech; and for mapping emotional states and magnitudes to 3-D facial expression outputs. The automatic detection of affect in language is based on natural language processing and machine learning approaches. Two affect corpora were developed to perform this analysis. Emotion classification is performed at the sentence level using a step-wise approach which incorporates sentiment flow and sentiment composition features. For emotion magnitude estimation, a regression model was developed to predict evolving emotional magnitude of actors. Emotional magnitudes at any point during a story or conversation are determined by 1) previous emotional state magnitude; 2) new text and speech inputs that might act upon that state; and 3) information about the context the actors are in. Acoustic features are also used to capture additional information from the speech signal. Evaluation of the automatic understanding of affect is performed by testing the model on a testing subset of the newly extended corpus. To visualize actor emotions as perceived by the system, a methodology was also developed to map predicted emotion class magnitudes to 3-D facial parameters using vertex-level mesh morphing. The developed sentence level emotion state detection approach achieved classification accuracies as high as 71% for the neutral vs. emotion classification task in a test corpus of children’s stories. After class re-sampling, the results of the step-wise classification methodology on a test sub-set of a medical drama corpus achieved accuracies in the 56% to 84% range for each emotion class and polarity. For emotion magnitude prediction, the developed recurrent (prior-state feedback) regression model using both text-based and acoustic based features achieved correlation coefficients in the range of 0.69 to 0.80. This prediction function was modeled using a non-linear approach based on Support Vector Regression (SVR) and performed better than other approaches based on Linear Regression or Artificial Neural Networks

Persönliche Wege der Interaktion mit multimedialen Inhalten

Today the world of multimedia is almost completely device- and content-centered. It focuses it’s energy nearly exclusively on technical issues such as computing power, network specifics or content and device characteristics and capabilities. In most multimedia systems, the presentation of multimedia content and the basic controls for playback are main issues. Because of this, a very passive user experience, comparable to that of traditional TV, is most often provided. In the face of recent developments and changes in the realm of multimedia and mass media, this ”traditional” focus seems outdated. The increasing use of multimedia content on mobile devices, along with the continuous growth in the amount and variety of content available, make necessary an urgent re-orientation of this domain. In order to highlight the depth of the increasingly difficult situation faced by users of such systems, it is only logical that these individuals be brought to the center of attention. In this thesis we consider these trends and developments by applying concepts and mechanisms to multimedia systems that were first introduced in the domain of usercentrism. Central to the concept of user-centrism is that devices should provide users with an easy way to access services and applications. Thus, the current challenge is to combine mobility, additional services and easy access in a single and user-centric approach. This thesis presents a framework for introducing and supporting several of the key concepts of user-centrism in multimedia systems. Additionally, a new definition of a user-centric multimedia framework has been developed and implemented. To satisfy the user’s need for mobility and flexibility, our framework makes possible seamless media and service consumption. The main aim of session mobility is to help people cope with the increasing number of different devices in use. Using a mobile agent system, multimedia sessions can be transferred between different devices in a context-sensitive way. The use of the international standard MPEG-21 guarantees extensibility and the integration of content adaptation mechanisms. Furthermore, a concept is presented that will allow for individualized and personalized selection and face the need for finding appropriate content. All of which can be done, using this approach, in an easy and intuitive way. Especially in the realm of television, the demand that such systems cater to the need of the audience is constantly growing. Our approach combines content-filtering methods, state-of-the-art classification techniques and mechanisms well known from the area of information retrieval and text mining. These are all utilized for the generation of recommendations in a promising new way. Additionally, concepts from the area of collaborative tagging systems are also used. An extensive experimental evaluation resulted in several interesting findings and proves the applicability of our approach. In contrast to the ”lean-back” experience of traditional media consumption, interactive media services offer a solution to make possible the active participation of the audience. Thus, we present a concept which enables the use of interactive media services on mobile devices in a personalized way. Finally, a use case for enriching TV with additional content and services demonstrates the feasibility of this concept.Die heutige Welt der Medien und der multimedialen Inhalte ist nahezu ausschließlich inhalts- und geräteorientiert. Im Fokus verschiedener Systeme und Entwicklungen stehen oft primär die Art und Weise der Inhaltspräsentation und technische Spezifika, die meist geräteabhängig sind. Die zunehmende Menge und Vielfalt an multimedialen Inhalten und der verstärkte Einsatz von mobilen Geräten machen ein Umdenken bei der Konzeption von Multimedia Systemen und Frameworks dringend notwendig. Statt an eher starren und passiven Konzepten, wie sie aus dem TV Umfeld bekannt sind, festzuhalten, sollte der Nutzer in den Fokus der multimedialen Konzepte rücken. Um dem Nutzer im Umgang mit dieser immer komplexeren und schwierigen Situation zu helfen, ist ein Umdenken im grundlegenden Paradigma des Medienkonsums notwendig. Durch eine Fokussierung auf den Nutzer kann der beschriebenen Situation entgegengewirkt werden. In der folgenden Arbeit wird auf Konzepte aus dem Bereich Nutzerzentrierung zurückgegriffen, um diese auf den Medienbereich zu übertragen und sie im Sinne einer stärker nutzerspezifischen und nutzerorientierten Ausrichtung einzusetzen. Im Fokus steht hierbei der TV-Bereich, wobei die meisten Konzepte auch auf die allgemeine Mediennutzung übertragbar sind. Im Folgenden wird ein Framework für die Unterstützung der wichtigsten Konzepte der Nutzerzentrierung im Multimedia Bereich vorgestellt. Um dem Trend zur mobilen Mediennutzung Sorge zu tragen, ermöglicht das vorgestellte Framework die Nutzung von multimedialen Diensten und Inhalten auf und über die Grenzen verschiedener Geräte und Netzwerke hinweg (Session mobility). Durch die Nutzung einer mobilen Agentenplattform in Kombination mit dem MPEG-21 Standard konnte ein neuer und flexibel erweiterbarer Ansatz zur Mobilität von Benutzungssitzungen realisiert werden. Im Zusammenhang mit der stetig wachsenden Menge an Inhalten und Diensten stellt diese Arbeit ein Konzept zur einfachen und individualisierten Selektion und dem Auffinden von interessanten Inhalten und Diensten in einer kontextspezifischen Weise vor. Hierbei werden Konzepte und Methoden des inhaltsbasierten Filterns, aktuelle Klassifikationsmechanismen und Methoden aus dem Bereich des ”Textminings” in neuer Art und Weise in einem Multimedia Empfehlungssystem eingesetzt. Zusätzlich sind Methoden des Web 2.0 in eine als Tag-basierte kollaborative Komponente integriert. In einer umfassenden Evaluation wurde sowohl die Umsetzbarkeit als auch der Mehrwert dieser Komponente demonstriert. Eine aktivere Beteiligung im Medienkonsum ermöglicht unsere iTV Komponente. Sie unterstützt das Anbieten und die Nutzung von interaktiven Diensten, begleitend zum Medienkonsum, auf mobilen Geräten. Basierend auf einem Szenario zur Anreicherung von TV Sendungen um interaktive Dienste konnte die Umsetzbarkeit dieses Konzepts demonstriert werden

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems