Advances in intelligent systems and computing 

About: Advances in intelligent systems and computing is an academic journal. The journal publishes majorly in the area(s): Computer science & Medicine. It has an ISSN identifier of 2194-5365. Over the lifetime, 1656 publications have been published receiving 6894 citations.

Towards a Novel Privacy-Preserving Access Control Model Based on Blockchain Technology in IoT

Abstract: Access control face big challenges in IoT. Unfortunately, it is hard to implement current access control standards on smart object due to its constrained nature while the introduction of powerful and trusted third party to handle access control logic could harm user privacy. In this work we show how blockchain, the promising technology behind Bitcoin, can be very attractive to face those arising challenges. We therefore propose FairAccess as a new decentralized pseudonymous and privacy preserving authorization management framework that leverages the consistency of blockchain technology to manage access control on behalf of constrained devices.

ICT for Sustainability: An Emerging Research Field

Abstract: This introductory chapter provides definitions of sustainability, sustainable development, decoupling, and related terms; gives an overview of existing interdisciplinary research fields related to ICT for Sustainability, including Environmental Informatics, Computational Sustainability, Sustainable HCI, and Green ICT; introduces a conceptual framework to structure the effects of ICT on sustainability; and provides an overview of this book.

ICT Innovations for Sustainability

Abstract: ICT Innovations for Sustainability is an investigation of how information and communication technology can contribute to sustainable development. It presents clear definitions of sustainability, suggesting conceptual frameworks for the positive and negative effects of ICT on sustainable development. It reviews methods of assessing the direct and indirect impact of ICT systems on energy and materials demand, and examines the results of such assessments. In addition, it investigates ICT-based approaches to supporting sustainable patterns of production and consumption, analyzing them at various levels of abstraction from end-user devices, Internet infrastructure, user behavior, and social practices to macro-economic indicators.Combining approaches from Computer Science, Information Systems, Human-Computer Interaction, Economics, and Environmental Sciences, the book presents a new, holistic perspective on ICT for Sustainability (ICT4S). It is an indispensable resource for anyone working in the area of ICT for Energy Efficiency, Life Cycle Assessment of ICT, Green IT, Green Information Systems, Environmental Informatics, Energy Informatics, Sustainable HCI, or Computational Sustainability.

Emotion Recognition and Its Applications

Abstract: This paper aims at illustrating diversity of possible emotion recognition applications. It provides concise review of affect recognition methods based on different inputs such as biometrics, video channel or behavioral data. It proposes a set of research scenarios of emotion recognition applications in the following domains: software engineering, website customization, education, and gaming. The scenarios show complexity and problems of applying affective computing in different domains. Analysis of the scenarios allows drawing some conclusions on challenges of automatic recognition that have to be addressed by further research.

Short-Term Load Forecasting Using Random Forests

Abstract: This study proposes using a random forest model for short-term electricity load forecasting. This is an ensemble learning method that generates many regression trees (CART) and aggregates their results. The model operates on patterns of the time series seasonal cycles which simplifies the forecasting problem especially when a time series exhibits nonstationarity, heteroscedasticity, trend and multiple seasonal cycles. The main advantages of the model are its ability to generalization, built-in cross-validation and low sensitivity to parameter values. As an illustration, the proposed forecasting model is applied to historical load data in Poland and its performance is compared with some alternative models such as CART, ARIMA, exponential smoothing and neural networks. Application examples confirm good properties of the model and its high accuracy.