A Neural Network Approach to Identify Hyperspectral Image Content

A Hyperspectral is the imaging technique that contains very large dimension data with the hundreds of channels. Meanwhile, the Hyperspectral Images (HISs) delivers the complete knowledge of imaging; therefore applying a classification algorithm is very important tool for practical uses. The HSIs are always having a large number of correlated and redundant feature, which causes the decrement in the classification accuracy; moreover, the features redundancy come up with some extra burden of computation that without adding any beneficial information to the classification accuracy. In this study, an unsupervised based Band Selection Algorithm (BSA) is considered with the Linear Projection (LP) that depends upon the metric-band similarities. Afterwards Monogenetic Binary Feature (MBF) has consider to perform the ‘texture analysis’ of the HSI, where three operational component represents the monogenetic signal such as; phase, amplitude and orientation. In post processing classification stage, feature-mapping function can provide important information, which help to adopt the Kernel based Neural Network (KNN) to optimize the generalization ability. However, an alternative method of multiclass application can be adopt through KNN, if we consider the multi-output nodes instead of taking single-output node

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

Climate Change and Geographic Information in Real Estate Research

Der Kampf gegen den Klimawandel und die Sicherstellung einer nachhaltigen Wirtschaftsweise sind wohl zwei der größten Herausforderungen unserer Epoche. Die Vermeidung von Treibhausgasemissionen, die Anpassung des Immobilienbestands an zunehmende Naturgefahren, Maßnahmen gegen den Mangel an bezahlbarem Wohnraum oder auch das Management eines Einkaufszentrum vor dem Hintergrund neuer Wettbewerber und verändertem Kundenverhalten - all diese unterschiedlichen Herausforderungen verlangen auch nach ökonomischen Entscheidungen und setzen, um Erfolgreich bewältigt zu werden, in tiefgreifendes Verständnis der zu Grunde liegenden Strukturen und Prozesse voraus. Ziel dieser Dissertation war es daher, die genannten Fragestellungen aus einer wissenschaftlichen Perspektive heraus zu beleuchten, um eine verlässliche und handlungsweisende Informationsbasis zu schaffen. Als Geograph hat sich der Autor dabei schwerpunktmäßig mit der Analyse raumbezogener Daten beschäftigt. Daneben kommen eine Reihe aktueller statistischer Analysemethoden sowie insbesondere Geographische Informationssysteme (GIS) zum Einsatz. Die fünf enthaltenen Aufsätze demonstrieren, wie räumliche Informationen und geostatistische Methoden auf sehr unterschiedliche Fragestellungen und räumliche Maßstäbe angewandt werden können - von Strukturen und Kundenströmen innerhalb von Einkaufszentren über Wohnungspreise auf städtischer Ebene bis zur Erfassung von Naturrisiken in ganz Deutschland. Wenn neben Forschern auch die Akteure der Immobilienwirtschaft eine Anregung durch die vorgestellten Ergebnisse und Methoden erfahren, können wirtschaftliche Aktivitäten und Planungsentscheidungen hoffentlich in Zukunft etwas mehr an den Zielen eines ökonomisch, ökologisch und sozial nachhaltigen Wirtschaftens ausgerichtet werden

Joint Session-Item Encoding for Session-Based Recommendation: A Metric- Learning Approach with Temporal Smoothing

In recommendation systems, a system is in charge of providing relevant recommendations towards users with either a clear target in mind or a mere vague mental representation. Session-based recommendation targets a specific scenario in recommendation systems, where users are anonymous. Thus the recommendation system must work under more challenging conditions, having only the current session to extract any user preferences to provide recommendations. This setting requires a model capable of understanding and relating different inter- actions across different sessions involving different items. This dissertation reflects such relationships on a commonly learned space for sessions and items. Such space is built using metric-learning, which can capture such relationships and build such space, where the distances between the elements (session and item embeddings) reflect how they relate to each other. We then use this learned space as the intermediary to provide relevant rec- ommendations. This work continues and extends on top of other relevant work showing the potential of metric-learning addressed to the session-based recommendation field. This dissertation proposes three significant contributions: (i) propose a novel joint session-item encoding model with temporal smoothing, with fewer parameters and the inclusion of temporal characteristics in learning (temporal proximity and temporal re- cency); (ii) enhanced recommendation performance surpassing other state-of-the-art metric-learning models for session-based recommendation; (iii) a thorough critical analy- sis, addressing and raising awareness to common problems in the field of session-based recommendation, discussing the reasons behind them and their impact on model perfor- mance.Em sistemas de recomendação, um sistema fica encarregue de fornecer recomendações relevantes aos seus utilizadores que podem ter, ou uma ideia concreta daquilo que pre- tendem ou apenas uma vaga representação mental. Recomendação com base na sessão dirige-se principalmente a um cenário específico de sistemas de recomendação, onde os utilizadores são anónimos. Ou seja, estes sistemas têm de ser capazes de funcionar em condições mais desfavoráveis, tendo apenas a sessão atual disponível como input do utilizador para efetuar recomendações. Este contexto requer um modelo capaz de perceber e relacionar diferentes interações ao longo de várias outras sessões envolvendo diferentes itens. Esta dissertação reflete tais interações por via de um espaço comum, que é aprendido, para representar sessões e itens. Este espaço é construído usando metric-learning, técnica que consegue capturar tais relações e construir o espaço em questão, no qual a distância entre os vários elementos (embeddings de sessões e itens) reflete como estes se relacionam entre si. Usamos este espaço, que foi aprendido, como intermediário no fornecimento de recomendações rele- vantes. Este trabalho continua e extende para além de outros trabalhos relevantes na área que mostraram o potencial de aplicar metric-learning para o domínio de recomendação com base na sessão. Esta dissertação propõe as seguintes três principais e significativas contribuições: (i) propõe um novo modelo de codificação sessão-item conjunto com suavização temporal, com menos parâmetros e com a inclusão de características temporais no processo de aprendizagem (proximidade temporal e recência); (ii) um desempenho de recomenda- ção melhorado que ultrapassa outros métodos do estado-da-arte que utilizam técnicas de metric-learning para sistemas de recomendação com base na sessão; (iii) uma análise cuidada, que foca e tenta destacar alguns erros comuns neste campo de sistemas de re- comendação com base na sessão, discutindo as razões por detrás de tais erros e o seu impacto no desempenho dos modelos

Reconstructing networks

Complex networks datasets often come with the problem of missing information: interactions data that have not been measured or discovered, may be affected by errors, or are simply hidden because of privacy issues. This Element provides an overview of the ideas, methods and techniques to deal with this problem and that together define the field of network reconstruction. Given the extent of the subject, we shall focus on the inference methods rooted in statistical physics and information theory. The discussion will be organized according to the different scales of the reconstruction task, that is, whether the goal is to reconstruct the macroscopic structure of the network, to infer its mesoscale properties, or to predict the individual microscopic connections.Comment: 107 pages, 25 figure

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p

Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications

This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference