This paper presents results of research in development of parallel implementations of genetic algorithms with focus on Map-Reduce programming paradigm. It tries to classify and fit this particular implementation in sp...
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ISBN:
(纸本)9781467348751
This paper presents results of research in development of parallel implementations of genetic algorithms with focus on Map-Reduce programming paradigm. It tries to classify and fit this particular implementation in special model of algorithm having in mind all specific features of programming paradigm used. Besides that, we analyze details of existing proposals for implementation and scaling GA with MapReduce, and show the results of different approach which turned out to be anti-pattern for most general cases.
Throughput maximization is one of the main challenges in cognitive radio ad hoc networks, where the availability of local spectrum resources may change from time to time and hop by hop. Technologies based on 802.16e w...
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Throughput maximization is one of the main challenges in cognitive radio ad hoc networks, where the availability of local spectrum resources may change from time to time and hop by hop. Technologies based on 802.16e which called Mobile WiMAX (Worldwide Interoperability Microwave Access) promises to deliver high data rates over large distances and deliver multimedia services and are expected to play a major role in high speed broadband delivery. The maximum allowed number of hops must be carefully considered because higher number of hops will increase the transmission time and degrades the throughput and end to end delay. Multi-hop based network may also improve the system performance by using cooperative relay technique. There are various challenges for the routing in WiMAX mesh such as delay, long transmission scheduling, and increasingly stringent Quality of Service (QoS) support and load balance and fairness limitations. In this paper we use cognitive radio network composed of wireless devices able to opportunistically access the shred radio resource. The core of such networking paradigm is the capability of cognitive radio to monitor spectrum occupation to exploit spectrums holes for transition. Extensive simulations are conducted under MATLAB and compare the performance of our routing protocol with AODV for Mobile WiMAX environment. The propose algorithm shows high throughput, reduce end to end delay, and increase packet delivery ratio.
Recent studies have shown that people with mild cognitive impairment (MCI) may convert to Alzheimer's disease (AD) over time although not all MCI cases progress to dementia. The diagnosis of MCI is important to al...
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Recent studies have shown that people with mild cognitive impairment (MCI) may convert to Alzheimer's disease (AD) over time although not all MCI cases progress to dementia. The diagnosis of MCI is important to allow prompt treatment and disease management before the neurons degenerate to a stage beyond repair. Hence, the ability to obtain a method of identifying MCI is of great importance. VREAD is a quick, easy and friendly tool that was developed with an aim to investigate cognitive functioning in a group of healthy elderly and those with MCI. It focuses on the task of following a route, since Topographical Disorientation (TD) is common in AD. The results shows that this novel simulation was able to predict with about 90% overall accuracy using weighting function proposed to discriminate between MCI and healthy elderly.
We initiate the study of a new parameterization of graph problems. In a multiple interval representation of a graph, each vertex is associated to at least one interval of the real line, with an edge between two vertic...
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Hybrid intelligent systems play an important role in the survival prediction of breast cancer. The life-expectancy prediction of a patient is highly significant in decision making for treatments, medications and thera...
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Hybrid intelligent systems play an important role in the survival prediction of breast cancer. The life-expectancy prediction of a patient is highly significant in decision making for treatments, medications and therapies. This paper addresses the motivation behind the need of hybrid model approach to survival prediction for breast cancer. The conventional approach of survival prediction faces difficulties in handling complex non-linear correlation between the prognostic factors and tumor progression, the censoring issue in medical data and the need to process the growing number of macro-scale and molecular-scale prognostic factors. The issues in breast cancer survivability are discussed with some examples of prominent works from machine learning approaches. Current trends and advancements of hybrid intelligent system are also presented.
To extend a complete workflow of automatic acquisition of morphological rules for morphological analyser, we propose a semi-automatic workflow for under-resourced language, which is Iban language. The workflow focuses...
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To extend a complete workflow of automatic acquisition of morphological rules for morphological analyser, we propose a semi-automatic workflow for under-resourced language, which is Iban language. The workflow focuses in determining the rules to be used for building Iban morphological analyser without prior knowledge of language-specific morphological rules. This work introduces three main steps in acquiring the rules from the under-resourced language, which are morphological rules extraction, validation of the extracted rules and evaluation of the generated rules. From the proposed workflow, 25 rules were generated from 744 rules candidate. This work has achieved 76% of precision and 99% of recall. We believe the workflow will assist other researchers to build morphological analyser with the validated morphological rules for the under-resourced languages.
In this paper, we propose an Artificial Bee Colony (ABC) algorithm, a swarm-based artificial intelligence algorithm, for computing a connected dominating set (CDS) in wireless networks. ABC Algorithm is an optimizatio...
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In this paper, we propose an Artificial Bee Colony (ABC) algorithm, a swarm-based artificial intelligence algorithm, for computing a connected dominating set (CDS) in wireless networks. ABC Algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. Wireless ad hoc networks appear in a wide variety of applications. In this work ABC algorithm is used for optimizing heuristic algorithm for the connected dominating set problem in wireless environment. This approach guarantees properties of correctness, locality, and also has better throughput than cyclic iterative local solution (ILS). Extensive simulations are conducted to evaluate the effectiveness of the proposed approach in static environments.
In this work, we present intrinsic shape context (ISC) descriptors for 3D shapes. We generalize to surfaces the polar sampling of the image domain used in shape contexts: for this purpose, we chart the surface by shoo...
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In this work, we present intrinsic shape context (ISC) descriptors for 3D shapes. We generalize to surfaces the polar sampling of the image domain used in shape contexts: for this purpose, we chart the surface by shooting geodesic outwards from the point being analyzed; `angle' is treated as tantamount to geodesic shooting direction, and radius as geodesic distance. To deal with orientation ambiguity, we exploit properties of the Fourier transform. Our charting method is intrinsic, i.e., invariant to isometric shape transformations. The resulting descriptor is a meta-descriptor that can be applied to any photometric or geometric property field defined on the shape, in particular, we can leverage recent developments in intrinsic shape analysis and construct ISC based on state-of-the-art dense shape descriptors such as heat kernel signatures. Our experiments demonstrate a notable improvement in shape matching on standard benchmarks.
Data system analysis methods for designing of collective neural network classifiers are considered. It is suggested to use methods of sign graph local balancing and algorithms of system behavior stereotype selection f...
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