Titlebook: Big Data Analysis and Deep Learning Applications; Proceedings of the F Thi Thi Zin,Jerry Chun-Wei Lin Conference proceedings 2019 Springer

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A. Marmarou,K. Shapiro,W. Pöll,K. Shulmanproposed for efficient exploitation. The proposed approach has been compared on big database instances, and the results illustrate the acceleration on the summarization process. Further experimentation reveals the superiority of GBSO-RSS compared to Berrado approach in terms of number of satisfied association rules.

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Conference proceedings 2019L 2018), and focuses on novel techniques in the fields of big data analysis, machine learning, system monitoring, image processing, conventional neural networks, communication, industrial information, and their applications. Readers will find insights to help them realize more efficient algorithms a

LASH

Predictive Big Data Analytics Using Multiple Linear Regression Model, we propose MapReduce based Multiple Linear Regression Model which is suitable for parallel and distributed processing with the purpose of predictive analytics on massive datasets. The proposed model will be based on “QR Decomposition” in decomposing big matrix training data to extract model coeffi

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Real Time Semantic Events Detection from Social Media Streamon in real time. To get accurate burst from real time twitter stream, semantic words and phrase extraction from noise polluted text stream is proposed. Our experimental results show that this semantic burst detection in adaptive time windows is efficient and effectiveness for processing in both real

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Multi-label Land Cover Indices Classification of Satellite Images Using Deep Learningfor Ayeyarwaddy Delta is applied and tested with AlexNet. Then the results of land cover classification are compared with Multiclass-SVM using confusion matrices. According to the tested results, 76.6% of building index, 81.5% road index, 91.8% of vegetation index and 93.2% of water index can be cor

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