1,721,513 research outputs found
“The problem with politics in India today is we’re creating polarity where we should be creating unity” – Suhel Seth
No full textTomorrow Suhel Seth will participate in the LSESU India Forum. Ahead of the conference, he spoke to Saanya Gulati about entrepreneurship, political branding, media polarisation and why he is looking forward to taking part in the conference
Dataset for influence of CNG and HCNG on engine performance and emission parameters at diverse injection pressure
No full textPerformance and emission data of diesel engine operated with dual fuel under different loads at different injection pressure (200 bar, 220 bar, and 240 bar) are presented in the uploaded file. Only for experimental repeat two and three, data and associated figure mentioned in the file
Dataset for influence of CNG and HCNG on engine performance and emission parameters at diverse injection pressure Journal
No full textPerformance and emission data of diesel engine operated with dual fuel under different loads at different injection pressure (200 bar, 220 bar, and 240 bar) are presented in the uploaded file. Only for experimental repeat two and three, data and associated figure mentioned in the file
Patterns of species participation across multiple mixed-species flock types in a tropical forest in northeastern India
No full textSrinivasan, Umesh, Raza, Rashid Hasnain, Quader, Suhel (2012): Patterns of species participation across multiple mixed-species flock types in a tropical forest in northeastern India. Journal of Natural History 46 (43-44): 2749-2762, DOI: 10.1080/00222933.2012.717644, URL: http://dx.doi.org/10.1080/00222933.2012.71764
MapReduce network enabled algorithms for classification based on association rules
No full textThis thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.There is growing evidence that integrating classification and association rule mining can produce more efficient and accurate classifiers than traditional techniques. This thesis introduces a new MapReduce based association rule miner for extracting strong rules from large datasets. This miner is used later to develop a new large scale classifier. Also new MapReduce simulator was developed to evaluate the scalability of proposed algorithms on MapReduce clusters.
The developed associative rule miner inherits the MapReduce scalability to huge datasets and to thousands of processing nodes. For finding frequent itemsets, it uses hybrid approach between miners that uses counting methods on horizontal datasets, and miners that use set intersections on datasets of vertical formats. The new miner generates same rules that usually generated using apriori-like algorithms because it uses the same confidence and support thresholds definitions.
In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. This thesis also introduces a new MapReduce classifier that based MapReduce associative rule mining. This algorithm employs different approaches in rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. The new classifier works on multi-class datasets and is able to produce multi-label predications with probabilities for each predicted label. To evaluate the classifier 20 different datasets from the UCI data collection were used. Results show that the proposed approach is an accurate and effective classification technique, highly competitive and scalable if compared with other traditional and associative classification approaches.
Also a MapReduce simulator was developed to measure the scalability of MapReduce based applications easily and quickly, and to captures the behaviour of algorithms on cluster environments. This also allows optimizing the configurations of MapReduce clusters to get better execution times and hardware utilization
Venetan to English machine translation: issues and possible solutions
Get PDFIn this paper we describe a prototype of a Venetan to English
translation system developed under the STILVEN project financed by the Regional
Authorities of Veneto Region in Italy. The general approach is a
statistical one with some preprocessing operations both at training and
translation time (ortographic normalization and POS tagging to make
use of factored models) which are needed especially to overcome two
main problems: the scarcity of Venetan resources (our Venetan-English
corpus is made up of only 13,000 sentences, amounting to 128,000 Venetan
tokens excluding punctuation) and the diasystemic nature of Venetan,
which really represents an ensemble of varieties rather than a single
dialect. We will present in detail the problems related to Venetan, our
ideas to solve them, their implementation and the results obtained so
far
Arabic Morphology Parsing Revisited
No full textIn this paper we propose a new approach to the description of Arabic
morphology using 2-tape finite state transducers, based on a particular and
systematic use of the operation of composition in a way that allows for
incremental substitutions of concatenated lexical morpheme specifications with
their surface realization for non-concatenative processes (the case of Arabic
templatic interdigitation and non-templatic circumfixation)
Sarrif – The Elegant Arabic Morphology Parser
No full textIn this paper we present Sarrif, our Arabic Morphology Parser, featuring a novel approach to the description of Arabic morphology with 2-tape finite state transducers, based on a particular and systematic use of the operation of composition in a way that allows for incremental substitutions of concatenated lexical morpheme specifications with their surface realization for non-concatenative
processes (the case of Arabic templatic interdigitation and non-templatic circumfixation). We argue that:
1. the method of incremental substitutions through compositions allows for an elegant description of all main morphological processes present in natural languages including non-concatenative ones in strict finite-state terms, without the need to resort to extensions of any sort;
2. our approach allows for the most logical encoding of every kind of dependency, including traditional long-distance ones (mutual exclusiveness), circumfixations and idiosyncratic root and pattern combinations;
3. a smart usage of composition such as ours allows for the creation of a same system that can be easily accomodated to fulfil the duties of both a stemmer (or lexicon development tool) and a full-fledged lexical transducer
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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