3,603 research outputs found
Wind Energy in Egypt: Economic Feasibility for Cairo
Motivated by the rise of the electricity tariffs applied on industrial customer and the frequent electricity cut offs recently experienced in Egypt, this paper assesses the economic feasibility of installing a stand alone wind energy technology by an industrial customer who seeks to reduce his dependency on the national grid. For this purpose, the wind energy potential at the wind regime of Cairo was chosen to be assessed using half an hour wind speed data for a full one-year period (2009). The Weibull parameters of the wind speed distribution function were estimated by employing the maximum likelihood approach. The estimation revealed that Cairo has poor wind resources. Despite the poor resources, the financial analysis has shown that under certain parameters the wind project can prove to be financially viable. Thus harnessing wind energy through stand alone systems can help in meeting the industries electric power needs.Renewable energy, wind resources, Weibull distribution, electricity
Next Generation M2M Cellular Networks: Challenges and Practical Considerations
In this article, we present the major challenges of future machine-to-machine
(M2M) cellular networks such as spectrum scarcity problem, support for
low-power, low-cost, and numerous number of devices. As being an integral part
of the future Internet-of-Things (IoT), the true vision of M2M communications
cannot be reached with conventional solutions that are typically cost
inefficient. Cognitive radio concept has emerged to significantly tackle the
spectrum under-utilization or scarcity problem. Heterogeneous network model is
another alternative to relax the number of covered users. To this extent, we
present a complete fundamental understanding and engineering knowledge of
cognitive radios, heterogeneous network model, and power and cost challenges in
the context of future M2M cellular networks
New techniques for Arabic document classification
Text classification (TC) concerns automatically assigning a class (category) label to
a text document, and has increasingly many applications, particularly in the domain
of organizing, for browsing in large document collections. It is typically achieved
via machine learning, where a model is built on the basis of a typically large collection
of document features. Feature selection is critical in this process, since there
are typically several thousand potential features (distinct words or terms). In text
classification, feature selection aims to improve the computational e ciency and
classification accuracy by removing irrelevant and redundant terms (features), while
retaining features (words) that contain su cient information that help with the
classification task.
This thesis proposes binary particle swarm optimization (BPSO) hybridized with
either K Nearest Neighbour (KNN) or Support Vector Machines (SVM) for feature
selection in Arabic text classi cation tasks. Comparison between feature selection
approaches is done on the basis of using the selected features in conjunction with
SVM, Decision Trees (C4.5), and Naive Bayes (NB), to classify a hold out test
set. Using publically available Arabic datasets, results show that BPSO/KNN and
BPSO/SVM techniques are promising in this domain. The sets of selected features
(words) are also analyzed to consider the di erences between the types of features
that BPSO/KNN and BPSO/SVM tend to choose. This leads to speculation concerning
the appropriate feature selection strategy, based on the relationship between
the classes in the document categorization task at hand.
The thesis also investigates the use of statistically extracted phrases of length
two as terms in Arabic text classi cation. In comparison with Bag of Words text
representation, results show that using phrases alone as terms in Arabic TC task
decreases the classification accuracy of Arabic TC classifiers significantly while combining
bag of words and phrase based representations may increase the classification
accuracy of the SVM classifier slightly
Overlapping Community Structure in Co-authorship Networks: a Case Study
Community structure is one of the key properties of real-world complex
networks. It plays a crucial role in their behaviors and topology. While an
important work has been done on the issue of community detection, very little
attention has been devoted to the analysis of the community structure. In this
paper, we present an extensive investigation of the overlapping community
network deduced from a large-scale co-authorship network. The nodes of the
overlapping community network represent the functional communities of the
co-authorship network, and the links account for the fact that communities
share some nodes in the co-authorship network. The comparative evaluation of
the topological properties of these two networks shows that they share similar
topological properties. These results are very interesting. Indeed, the network
of communities seems to be a good representative of the original co-authorship
network. With its smaller size, it may be more practical in order to realize
various analyses that cannot be performed easily in large-scale real-world
networks.Comment: 2014 7th International Conference on u- and e- Service, Science and
Technolog
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