MapReduce Based Text Detection in Big Data Natural Scene Videos

Ayed, Abdelkarim Ben; Halima, Mohamed Ben; Alimi, Adel M.

doi:10.1016/j.procs.2015.07.297

Cited by 29 publications

(11 citation statements)

References 14 publications

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“…A new scheme for texture based text detection in videos that makes use of Apache Hadoop big data analysis has been suggested by Ayed et al, [3]. Video frames are decomposed by the suggested system into several blocks of fixed sizes.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An Selection System For Automotive Sentiment Classification In Hadoop Using KNN Classifier

K¹,

Mathivanan²,

Al-Alawi³

et al. 2019

EJBMR

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With the growing popularity of big data analytics in the area of online product review, the biggest issue is voluminous data. Sentiment analysis and opinion mining are useful for solving text and web based issues. For sentiment analysis, this work makes use of the Hadoop framework. The Hadoop is not only reliable but also a fault immune model for processing huge amounts of data. There is a critical role that is played by sentiment analysis in text mining purposes such as in consumer attitude recognition, trade name and product spotting, customer relationship management, and market research. Data is labelled either as subjective or objective based on the subjectivity classification. This subjective classification is further divided as positive, negative or neutral by sentiment classification. The sentiment is classified based on the features which are taken from the data. As feature selection contributes in conserving the classification expense with regard to time and computation load, feature selection has gained a lot of prominence. This work uses the Term Frequency (TF) feature extraction. The objective here is using feature selection based on information Gain (IG) and Particle Swarm Optimization (PSO) for feature selection in sentiment classification. These schemes can decrease the features in the original set as they eliminate redundant features for text sentiment categorization and thus improvise the accuracy of classification. Also, the running time of the learning algorithms is decreased. K-nearest neighbour (KNN) classifier is used for evaluating the suggested scheme. It has been shown by empirical outcomes that compared to the IG based feature selection; the PSO based feature selection scheme attains better and more robust performance.

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Section: Related Workmentioning

confidence: 99%

“…There are two purposes to be printed by the user in the MapReduce: The problems are divided into smaller ones by "Map" function. The results are combined by the "Reduce function" in order that all the features of the distributed and parallel computations are abstracted [3].…”

Section: Introductionmentioning

confidence: 99%

An Selection System For Automotive Sentiment Classification In Hadoop Using KNN Classifier

K¹,

Mathivanan²,

Al-Alawi³

et al. 2019

EJBMR

View full text Add to dashboard Cite

show abstract

“…The outcome of the study was compared with existing mechanism to find approximately 90% of recognition rate. Most recent, the actual study towards video analytics was seen in the work of Ayed et al [39]. The author have used MapReduce model in order to perform mining of video dataset.…”

Section: Existing Techniques Of Vamentioning

confidence: 99%

“…Not tested for analytics Aryanfar et al [38] support vector machine and naïve Bayes -Good Recognition rate Not tested for analytics Ayed et al [39] Mining with MapReduce, Wavelets -Reduce processing time -No comparative analysis -No complexity Analysis Cai et al [40] Statistical Analysis Good precision -No comparative analysis -No complexity Analysis Chen et al [41] PeakVizor, Glyph visualization -Interactive detection of user's online behaviour -Doesn't work online -cannot perform classification -No complexity Analysis Kim et al [42] Morphological study of facial expression -ideal for online games -No comparative analysis -No complexity Analysis Mao et al [43] Video traceability Accurate target extraction -Doesn't include high level of data extraction or mining.…”

Section: Existing Techniques Of Vamentioning

confidence: 99%

Insights to Video Analytic Modelling Approach with Future Line of Research

Chandra¹,

Reddy²

2016

IJCA

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The area of Video analytics has made some significant improvement due to advancement in image processing and datamining techniques. However, the inclination is still towards image contents and less to mined contents owing to many unsolved issues. Although concept of mining is more than 2 decade old, but mining approaches are yet to be standardized in the area of video surveillance system. With evolution of newer set of challenges in video capturing, existing mining models finds itself less applicable due to unstructured format of dynamic frames. Hence, this paper discusses about video analytics and presents a brief discussion of frequently used mining approaches in video as well as discussed some recent studies in this direction in order to scale the degree of effectiveness in existing system. The paper also presents research gap and provided solution as future line of research as a possible way to overcome the research gap.

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“…1 ) consists of two parts: the Hadoop Distributed File System (HDFS) that consists of a storage part, and a data processing and management (MapReduce) part. The master node has two processes, a Job Tracker that manages the processing tasks and a Name Node that manages the storage tasks [50] .…”

Section: Introductionmentioning

confidence: 99%

Big data analytics for wireless and wired network design: A survey

et al. 2018

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a b s t r a c tCurrently, the world is witnessing a mounting avalanche of data due to the increasing number of mobile network subscribers, Internet websites, and online services. This trend is continuing to develop in a quick and diverse manner in the form of big data. Big data analytics can process large amounts of raw data and extract useful, smaller-sized information, which can be used by different parties to make reliable decisions.In this paper, we conduct a survey on the role that big data analytics can play in the design of data communication networks. Integrating the latest advances that employ big data analytics with the networks' control/traffic layers might be the best way to build robust data communication networks with refined performance and intelligent features. First, the survey starts with the introduction of the big data basic concepts, framework, and characteristics. Second, we illustrate the main network design cycle employing big data analytics. This cycle represents the umbrella concept that unifies the surveyed topics. Third, there is a detailed review of the current academic and industrial effort s toward network design using big data analytics. Forth, we identify the challenges confronting the utilization of big data analytics in network design. Finally, we highlight several future research directions. To the best of our knowledge, this is the first survey that addresses the use of big data analytics techniques for the design of a broad range of networks.

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MapReduce Based Text Detection in Big Data Natural Scene Videos

Cited by 29 publications

References 14 publications

An Selection System For Automotive Sentiment Classification In Hadoop Using KNN Classifier

An Selection System For Automotive Sentiment Classification In Hadoop Using KNN Classifier

Insights to Video Analytic Modelling Approach with Future Line of Research

Big data analytics for wireless and wired network design: A survey

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