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  • 1
    In: PLoS ONE, 2014, Vol.9(6)
    Description: Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use that information in the functional annotation of proteins within the network. We propose several graph representations for the protein interaction network, each having different level of complexity and inclusion of the annotation information within the graph. We aim to explore what the benefits and the drawbacks of these proposed graphs are, when they are used in the function prediction process via clustering methods. For making this cluster based prediction, we adopt well established approaches for cluster detection in complex networks using most recent representative algorithms that have been proven as efficient in the task at hand. The experiments are performed using a purified and reliable Saccharomyces cerevisiae protein interaction network, which is then used to generate the different graph representations. Each of the graph representations is later analysed in combination with each of the clustering algorithms, which have been possibly modified and implemented to fit the specific graph. We evaluate results in regards of biological validity and function prediction performance. Our results indicate that the novel ways of presenting the complex graph improve the prediction process, although the computational complexity should be taken into account when deciding on a particular approach.
    Keywords: Research Article ; Biology And Life Sciences ; Computer And Information Sciences ; Physical Sciences
    E-ISSN: 1932-6203
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  • 2
    Language: English
    In: Journal of Cleaner Production, 01 January 2017, Vol.140, pp.1454-1464
    Description: Although Internet of Things (IoT) brings significant advantages over traditional communication technologies for smart grid and smart home applications, these implementations are still very rare. Relying on a comprehensive literature review, this paper aims to contribute towards narrowing the gap between the existing state-of-the-art smart home applications and the prospect of their integration into an IoT enabled environment. We propose a holistic framework which incorporates different components from IoT architectures/frameworks proposed in the literature, in order to efficiently integrate smart home objects in a cloud-centric IoT based solution. We identify a smart home management model for the proposed framework and the main tasks that should be performed at each level. We additionally discuss practical design challenges with emphasis on data processing, as well as smart home communication protocols and their interoperability. We believe that the holistic framework ascertained in this paper can be used as a solid base for the future developers of Internet of Things based smart home solutions.
    Keywords: Internet of Things ; Smart Home ; Holistic Framework ; Smart Grid ; Engineering
    ISSN: 0959-6526
    E-ISSN: 1879-1786
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  • 3
    In: International Journal of Machine Learning and Computing, 2/2014, pp.52-56
    Description:   The proposed protein function prediction methods are mostly based on sequence or structure protein similarity and do not take into account the semantic similarity extracted from protein knowledge databases such as Gene Ontology. Many studies have shown that identification of protein complexes or functional modules can be effectively done by clustering protein interaction network (PIN). A significant number of proteins in such PIN remain uncharacterized and predicting their function remains a major challenge in system biology. In this paper we present a "semantic driven" clustering approach for protein function prediction by using both semantic similarity metrics and the whole network topology of a PIN. We apply k-medoids clustering combined with several semantic similarity metrics as a weight factor in the distance-clustering matrix. Protein functions are assigned based on cluster information. Results reveal improvement over standard non-semantic similarity metric.
    Keywords: Protein Clustering ; Gene Ontology ; Semantic Similarity;
    ISSN: International Journal of Machine Learning and Computing
    E-ISSN: 20103700
    Source: CrossRef
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  • 4
    In: Wireless Communications and Mobile Computing, 2017, Vol.2017, 10 pages
    Description: The increasing average age of the population in most industrialized countries imposes a necessity for developing advanced and practical services using state-of-the-art technologies, dedicated to personal living spaces. In this paper, we introduce a hierarchical distributed approach for home care systems based on a new paradigm known as Internet of Things (IoT). The proposed generic framework is supported by a three-level data management model composed of dew computing, fog computing, and cloud computing for efficient data flow in IoT based home care systems. We examine the proposed model through a real case scenario of an early fire detection system using a distributed fuzzy logic approach. The obtained results prove that such implementation of dew and fog computing provides high accuracy in fire detection IoT systems, while achieving minimum data latency.
    Keywords: Engineering;
    ISSN: 1530-8669
    E-ISSN: 1530-8677
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  • 5
    Language: English
    In: Advances in protein chemistry and structural biology, 2015, Vol.101, pp.323-49
    Description: Protein interaction networks (PINs) are argued to be the richest source of hidden knowledge of the intrinsic physical and/or functional meanings of the involved proteins. We propose a novel method for computational protein function prediction based on semantic homogeneity optimization in PIN (SHOPIN). The SHOPIN method creates graph representations of the PIN augmented by inclusion of the semantics of the proteins and their interacting contexts. Network wide semantic relationships, modeled using random walks, are used to map the augmented PIN graphs in a new semantic metric space. The method produces a hierarchical partitioning of the PIN optimal in terms of semantic homogeneity by iterative optimization of the ratio of between clusters dissimilarities and within clusters similarities in the new semantic metric space. Function prediction is done using cluster wide-hierarchy high function enrichment. Results validate the rationale of the SHOPIN method placing it right next to state-of-the-art approaches performance wise.
    Keywords: Clustering ; Protein Interaction Networks ; Semantic Homogeneity ; Semantic Similarity ; Computational Biology ; Protein Interaction Maps ; Proteins -- Chemistry
    ISSN: 1876-1623
    Source: MEDLINE/PubMed (U.S. National Library of Medicine)
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  • 6
    Description: Traditional teaching, usually based on lectures and tutorials fosters the idea of instruction-driven learning model where students are passive listeners. Besides this approach, Project Based Learning (PBL) as a different learning paradigm is standing behind constructivism learning theory, where learning from real-world situations is put on the first place. The purpose of this paper is to present our approach in learning embedded systems at our University. It is based on combination of traditional (face-to-face) learning and PBL. Our PBL represents an interdisciplinary project based on wireless sensor monitoring of real-world environment (greenhouse). The students use UML that was shown as an excellent tool for developing such a projects. From the student perspective, we found that this high level of interdisciplinary is very valuable from the point of view of facing the students with real-life problems.
    Keywords: Computer Science - Other Computer Science
    ISSN: 1790-5117, ISBN: 978-960-6766-34-3, pp.120-125
    Source: Cornell University
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  • 7
    Language: English
    In: 2017 25th Telecommunication Forum (TELFOR), November 2017, pp.1-4
    Description: Smart grid is the process of applying ICT in order to optimize energy consumption and decrease energy loses. This paper presents a three tier Internet of Thing based hierarchical framework for the smart home, as a reflection to the present lack of intelligent solutions that do not fully use the advantages of Internet of Thing technologies. Our framework aims to extend the smart home to microgrid level, in order to integrate all renewable distributed energy sources from the microgrid and to achieve better energy optimization. As an extension to the traditional data processing, we define fog computing approach for smart home. Through simulation on real smart meter dataset, we showed that fog computing based on predictive filters can reduce the number of transmissions and minimize smart home network traffic.
    Keywords: Smart Homes ; Edge Computing ; Smart Grids ; Smart Meters ; Microgrids ; Internet of Things ; Home Appliances ; Data Reduction ; Energy Management ; Internet of Things ; Smart Home ; Smart Grid ; Smart Metering ; Engineering
    Source: IEEE Conference Publications
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines 
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  • 8
    Language: English
    In: IEEE EUROCON 2017 -17th International Conference on Smart Technologies, July 2017, pp.120-125
    Description: Nowadays, finding the Unmanned Aerial Vehicle (UAV) position in the absence of GPS is attractive and challenging problem in the research community. In this paper, we present a novel algorithm for mini UAV indoor localization based on distance measurements between the UAV and the existing infrastructure consisting of WiFi Access Points. Our algorithm uses two well-known techniques from the literature: Multi-dimensional Scaling (MDS) and Weighted Centroid Localization (WCL). Through extensive simulations we have shown that our algorithm is very suitable for indoor localization of mini UAVs. For small radio-range error, our algorithm exhibits a small localization error of less than 5% of the radio range.
    Keywords: Wireless Fidelity ; Trajectory ; Three-Dimensional Displays ; Distance Measurement ; Drones ; Fingerprint Recognition ; Indoor Localization ; Positioning ; Unmanned Aerial Vehicle ; Multidimensional Scaling ; Weighted Centroid
    Source: IEEE Conference Publications
    Source: IEEE Xplore
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  • 9
    Language: English
    In: Advances in Intelligent and Soft Computing, ICT Innovations 2011, pp.39-49
    Description: The recent advent of high throughput methods has generated large amounts of protein-protein interaction network (PPIN) data. When studying the workings of a biological cell, it is useful to be able to detect known and predict still undiscovered protein complexes within the cell’s PPINs. Such predictions may be used as an inexpensive tool to direct biological experiments. Because of its importance in the studies of protein interaction network, there are different models and algorithms in identifying functional modules in PPINs. In this paper, we present two representative methods, focusing on the comparison of their clustering properties in PPIN and their contribution towards function prediction. The work is done with PPIN data from the bakers’ yeast (Saccaromyces cerevisiae) and since the network is noisy and still incomplete, we use pre-processing and purifying. As a conclusion new progress and future research directions are discussed.
    Keywords: Engineering ; Computational Intelligence ; Artificial Intelligence (Incl. Robotics) ; Engineering ; Computer Science
    ISBN: 9783642286636
    ISBN: 3642286631
    Source: SpringerLink Books
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  • 10
    Language: English
    In: 2018 26th Telecommunications Forum (TELFOR), November 2018, pp.1-4
    Description: This paper addresses the routes planning problem in a scenario where an UAV (Unmanned Aerial Vehicle) and a land-based transportation vehicle are used to deliver parcels to customer locations. We developed and implemented a solution based on the well-known Bellman-Held-Karp dynamic programming algorithm for the Travelling Salesman Problem that finds the least-cost routes for both the aircraft and the land-based transportation vehicle. We applied this algorithm to different, randomly selected commercial drones, with different maximum velocity and flight range, in order to select the best performing drone for the lowest price.
    Keywords: Drones ; Planning ; Dynamic Programming ; Heuristic Algorithms ; Computer Science ; Drones ; Delivery ; Planning ; Parcels ; Transportation ; Uavs ; Computer Science
    Source: IEEE Conference Publications
    Source: IEEE Xplore
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