Data Science

Data Science employs techniques and theories to create data products. Data product is merely a data application that acquires its value from the data itself, and creates more data as a result; it's not just an application with data. Data science involves the methodical study of digital data employing techniques of observation, development, analysis, testing and validation. It tackles the real time challenges by adopting a holistic approach. It ‘creates' knowledge about large and dynamic bases, ‘develops' methods to manage data and ‘optimizes' processes to improve its performance. The goal includes vital investigation and innovation in conjunction with functional exploration intended to notify decision-making for individuals, businesses, and governments. This paper discusses the emergence of Data Science and its subsequent developments in the fields of Data Mining and Data Warehousing. The research focuses on need, challenges, impact, ethics and progress of Data Science. Finally the insights of the subsequent phases in research and development of Data Science is provided.

Big Data Deep Analytics for Geosocial Networks

Geosocial network data provides the full information on current trends in human, their behaviors, their living style, the incidents and events, the disasters, current medical infection, and much more with respect to locations. Hence, the current geosocial media can work as a data asset for facilitating the national and the government itself by analyzing the geosocial data at real-time. However, there are millions of geosocial network users, who generates terabytes of heterogeneous data with a variety of information every day with high-speed, termed as Big Data. Analyzing such big amount of data and making real-time decisions is an inspiring task. Therefore, this book chapter discusses the exploration of geosocial networks. A system architecture is discussed and implemented in a real-time environment in order to process the abundant amount of various social network data to monitor the earth events, incidents, medical diseases, user trends and thoughts to make future real-time decisions as well as future planning.

Classifying Images of Drought-Affected Area Using Deep Belief Network, kNN, and Random Forest Learning Techniques

Drought is a condition of land in which the ground water faces a severe shortage. This condition affects the survival of plants and animals. Drought can impact ecosystem and agricultural productivity, severely. Hence, the economy also gets affected by this situation. This paper proposes Deep Belief Network (DBN) learning technique, which is one of the state of the art machine learning algorithms. This proposed work uses DBN, for classification of drought and non-drought images. Also, k nearest neighbour (kNN) and random forest learning methods have been proposed for the classification of the same drought images. The performance of the Deep Belief Network(DBN) has been compared with k nearest neighbour (kNN) and random forest. The data set has been split into 80:20, 70:30 and 60:40 as train and test. Finally, the effectiveness of the three proposed models have been measured by various performance metrics.

Digital Investigation of Cybercrimes Based on Big Data Analytics Using Deep Learning

Big Data Analytics has become an important paradigm that can help digital investigators to investigate cybercrimes as well as provide solutions to malware and threat prediction, detection and prevention at an early stage. Big Data Analytics techniques can use to analysis enormous amount of generated data from new technologies such as Social Networks, Cloud Computing and Internet of Things to understand the committed crimes in addition to predict the new coming severe attacks and crimes in the future. This chapter introduce principles of Digital Forensics and Big Data as well as exploring Big Data Analytics and Deep Learning benefits and advantages that can help the digital investigators to develop and propose new techniques and methods based on Big Data Analytics using Deep Learning techniques that can be adapted to the unique context of Digital Forensics as well as support performing digital investigation process in forensically sound and timely fashion manner.

Big Spectrum Data and Deep Learning Techniques for Cognitive Wireless Networks

The present world is data driven. From social sciences to frontiers of research in science and engineering, one common factor is the continuous data generation. It has started to affect our daily lives. Big data concepts are found to have significant impact in modern wireless communication systems. The analytical tools of big data have been identified as full scale autonomous mode of operation which necessitates a strong role to be played by learning based systems. The chapter has focused on the synergy of big data and deep learning for generating better efficiency in evolving communication frameworks. The chapter has also included discussion on machine learning and cognitive technologies w.r.t. big data and mobile communication. Cyber Physical Systems being indispensable elements of M2M communication, Wireless Sensor Networks and its role in CPS, cognitive radio networking and spectrum sensing have also been discussed. It is expected that spectrum sensing, big data and deep learning will play vital roles in enhancing the capabilities of wireless communication systems.

Cloud-Based Big Data Analytics in Smart Educational System

In this modern Digital era, Technology is a key player in transforming the educational pedagogy for the benefit of students and society at large. Technology in the classroom allows the teacher to deliver more personalized learning to the student with better interaction through the internet. Humongous amount of digital data collected day by day increases has led to the use of big data. It helps to correlate the performance and learning pattern of individual students by analysing large amount of stored activity of the students, offering worthwhile feedback etc. The use of big data analytics in a cloud environment helps in providing an instant infrastructure with low cost, accessibility, usability etc. This paper presents an innovative means towards providing a smarter educational system in schools. It improves individual efficiency by providing a way to monitor the progress of individual student by maintaining a detailed profile. This framework has been established in a cloud environment which is an online learning system where the usage pattern of individual students are collected.

Efficiently Processing Big Data in Real-Time Employing Deep Learning Algorithms

Big Data and deep computation are among the buzzwords in the present sophisticated digital world. Big Data has emerged with the expeditious growth of digital data. This chapter addresses the problem of employing deep learning algorithms in Big Data analytics. Unlike the traditional algorithms, this chapter comes up with various solutions to employ advanced deep learning mechanisms with less complexity and finally present a generic solution. The deep learning algorithms require less time to process the big amount of data based on different contexts. However, collecting the accurate feature and classifying the context into patterns using neural networks algorithms require high time and complexity. Therefore, using deep learning algorithms in integration with neural networks can bring optimize solutions. Consequently, the aim of this chapter is to provide an overview of how the advance deep learning algorithms can be used to solve various existing challenges in Big Data analytics.

After Cloud

It is just now at the top of an aggregation point of globalization's era in terms of things and living creatures. And the communication methods including in many sorts of transfers like commodity, facility, information, system, thought, knowledge, human, etc. may cause many kinds of and many types of interactions among us. And those many kinds of and many types of interactions have been again causing many sorts of problems. Under these situations, Cloud has come out as a smart solution to these problems. However, “Cloud is the final ultimate solution to offer to these problems' solving?” On this chapter, this question is deeply concerned from various aspects. And it is studied on this regard for getting a new paradigm.

Data Science and Computational Biology

Data Science and Computational biology is an interdisciplinary program that brings together the domain specific knowledge of science and engineering with relevant areas of computing and bioinformatics. Data science has the potential to revolutionise healthcare, and respond to the increasing volume and complexity in biomedical and bioinformatics data. From genomics to clinical records, from imaging to mobile health and personalised medicine, the data volume in biomedical research presents urgent challenges for computer science. This chapter elevates the researchers in what way data science play important role in Computational Biology such as Bio-molecular Computation, Computational Photonics, Medical Imaging, Scientific Computing, Structural Biology, Bioinformatics and Bio-Computing etc. Big data analytics of biological data bases, high performance computing in large sequence of genome database and Scientific Visualization are also discussed in this chapter.

Advanced Threat Detection Based on Big Data Technologies

Today constant increase in number of cyber threats apparently shows that current countermeasures are not enough to defend it. With the help of huge generated data, big data brings transformative potential for various sectors. While many are using it for better operations, some of them are noticing that it can also be used for security by providing broader view of vulnerabilities and risks. Meanwhile, deep learning is coming up as a key role by providing predictive analytics solutions. Deep learning and big data analytics are becoming two high-focus of data science. Threat intelligence becoming more and more effective. Since it is based on how much data collected about active threats, this reason has taken many independent vendors into partnerships. In this chapter, we explore big data and big data analytics with its benefits. And we provide a brief overview of deep analytics and finally we present collaborative threat Detection. We also investigate some aspects of standards and key functions of it. We conclude by presenting benefits and challenges of collaborative threat detection.