MLBDA 2018: Machine Learning for Big Data Analytics |
Website | http://teamsb.net/mlbda/ |
Submission link | https://easychair.org/conferences/?conf=mlbda2018 |
Abstract registration deadline | July 31, 2017 |
Submission deadline | September 15, 2017 |
Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. The possible challenges in this direction include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract meaningful value from data without paying any heed to the size of data set. Due to the spurge in data evolution, scientists are encountering limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology and environmental research. Big data analytics is the process of examining large and varied data sets -- i.e., big data -- to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions. Big data analytics applications enable data scientists, predictive modelers, statisticians and other analytics professionals to analyze growing volumes of structured transaction data, plus other forms of data that are often left untapped by conventional business intelligence (BI) and analytics programs which encompasses a mix of semi-structured and unstructured data. On a broad scale, data analytics technologies and techniques provide a means of analyzing data sets and drawing conclusions about them to help organizations make informed business decisions. BI queries answer basic questions about business operations and performance. Big data analytics is a form of advanced analytics, which involves complex applications with elements such as predictive models assisted by statistical algorithms powered by high-performance analytics systems. It may be noted that unstructured and semi-structured data of these types typically don't fit well in traditional data warehouses that are based on relational databases oriented to structured data sets. Furthermore, data warehouses may not be able to handle the processing demands posed by sets of big data that need to be updated frequently -- or even continually, as in the case of real-time data on stock trading, the online activities of website visitors or the performance of mobile applications. As a result, many organizations that collect, process and analyze big data turn to Hadoop and its companion tools, such as YARN, MapReduce, Spark, HBase, Hive, Kafka and Pig, as well as NoSQL databases. In some cases, Hadoop clusters and NoSQL systems are being used primarily as landing pads and staging areas for data before it gets loaded into a data warehouse or analytical database for analysis, usually in a summarized form that is more conducive to relational structures. Of late, scientists and researchers have resorted to machine intelligence for analyzing big data thereby evolving business intelligence. It is a well-known fact that data in any form exhibits varied amount of ambiguity and imprecision. Machine learning tools and strategies are adept in handling these uncertainties thereby extracting relevant and meaningful information from data.
This book is intended to bring together researchers to report the latest progress in Big Data Analytics using Machine Intelligence.
Submission Guidelines
Prospective authors are invited to submit a 3- 4 pages Abstract of the chapter along with title of the chapter and author details. Abstract should highlight the novelty and contribution of the proposed article. Authors need to submit this abstract using the Easy Chair submission link given below. Last date for Abstract submission is 30th June 2017. Once the Abstract (Chapter Proposal) is accepted then the full chapter need to be prepared using the LaTeX template given below, following the guidelines. All submissions should be done through Easy Chair submission link given below
1. Editing Guidelines: http://teamsb.net/mlbda/docs/inst.pdf
2. LaTeX templates: http://teamsb.net/mlbda/docs/dglatex.rar
3. EasyChair link for submission: https://easychair.org/conferences/?conf=mlbda2018
List of Topics
PART 1: Web Content Mining
Multiple feature extraction for large scale text, image, audio and video
Large scale feature/dimensionality reduction for all media types (viz. text, image, audio and video)
Algorithms for feature extraction and transformation for data mining from huge datasets etc.
PART 2: Content-Based Video Retrieval
Multiple feature extraction and hybridization for large scale video segmentation
Video indexing techniques for fast video retrieval from large databases
Techniques for static and dynamic video summarization
High Speed Object Tracking
Action Recognition in videos
Feature point detectors and descriptors for CBVR
Algorithms for Video Stabilization and Denoising
Video Quality Evaluation techniques
PART 3: Video Surveillance
Machine Learning for solutions to problems like complex illumination, background/foreground detection, video matting, pose detection, occlusion and blurring in surveillance videos
Summarization of surveillance videos
Solutions to large distance and low resolution face detection and recognition
Real-time processing and recognition of faces from long surveillance videos
New approaches for biometrics under surveillance conditions
Abnormal activity detection in surveillance videos
Face detection, tracking and registration in CCTV footages
Real-time activity detection in surveillance videos
PART 4: Multiple Feature Hybridization for Image and Video Analysis
Extraction and fusion of multiple features for image and video analysis
Real-time feature extraction for fast CBVR
PART 5: Soft Computing applications for Large Scale Analysis of Audio, Video, Image and Text
Soft computing techniques for large scale clustering of Audio, Video, Image and Text
Soft computing for Medical image processing
Efficient approaches for Image Denoising, analysis, understanding, inpainting, Age estimation based on face recognition, three dimensional image processing, emotion and gesture analysis
Soft computing approaches for audio analysis such as audio matching, speaker recognition, copied audio detection etc.
Text mining applications for Q & A systems, text classification, word sense disambiguation etc. based on soft computing paradigms
PART 6: IoT applications
Mobile crowdsensing and cyber-physical cloud computing
Big data analytics for smart tourism
New approaches for Connected cities, homes, vehicles etc.
Industrial Automation applications
IoT for global smart city initiatives
PART 7: Bioinformatics
Approaches for Data Processing, Management, Infrastructure for Bioinformatics applications
Big data analytics for Identification of novel drug/vaccine targets, structural predictions, tapping into biodiversity, reconstruction of metabolic pathways, systems biology
Data deluging approaches
PART 8: Genomics
Approaches and algorithms for dealing with many sequences
Efficient approaches for Gene finding and genome annotation
Novel approaches for gene expression and proteomics
Editors
Prof. (Dr.) Siddhartha Bhattacharyya
Mr. Hrishikesh Bhaumik
Dr. Anirban Mukherjee
Dr. Sourav De
Publication
The book will be published by DE GRUYTER, Germany.
Contact
All questions about submissions should be emailed to dr.siddhartha.bhattacharyya@gmail.com