Raghvendra Kumar

Machine Learning on Big Data: A Developmental Approach on Societal Applications

Machine Learning (ML) is a potential tool that can be used to make predictions on the future based on the past history data. It constructs a model from input examples to make data-driven predictions or decisions. The growing concept “Big Data” need to be brought a great deal accomplishment in the field from claiming data science. It gives data quantifiability in a variety of ways that endow into data science. ML techniques have made huge societal effects in extensive varieties of applications. Effective and interactive ML relies on the design of novel interactive and collaborative techniques based on an understanding of end-user capabilities, behaviors, and necessities. ML could additionally make utilized within conjunction for enormous information to build effective predictive frameworks or to solve complex data analytic societal problems. In this chapter, we concentrate on the most recent progress over researches with respect to machine learning for big data analytic and different techniques in the context of modern computing environments for various societal applications. Specifically, our aim is to investigate opportunities and challenges of ML on big data and how it affects the society. The chapter covers discussion on ML in Big Data in specific societal areas.

Privacy preservation in data intensive environment

Abstract: Healthcare data frameworks have enormously expanded accessibility of medicinal reports and profited human services administration and research work. In many cases, there are developing worries about protection in sharing restorative files. Protection procedures for unstructured restorative content spotlight on recognition and expulsion of patient identifiers from the content, which might be lacking for safeguarding privacy and information utility. For medicinal services, maybe related exploration thinks about the therapeutic records of patients ought to be recovered from various destinations with various regulations on the divulgence of healthcare data.

Spatial data analysis using association rule mining in distributed environments: a privacy prospect

In this paper, we investigate the privacy issue that is remained in the spatial association rule mining (SARM). The main aim of SARM is to calculate the relationship among attributes according to their geographic locations. However, the major problem of the distributed data mining is the privacy and security issues, which require executing global results without disclosing private information by third parties. The problem of privacy preservation for SARM in distributed environments is controlled by the proposed algorithm, which is able to extract association among different numbers of attributes in geo-graphical distributed database with high privacy. The proposed algorithm is validated in term of data utility rate, efficiency and privacy preservation against the existing algorithms. It has been revealed that this algorithm decreases the execution time, memory requirements, and privacy failure rate when the size of database increases within the geographically distributed database environment.

Cloud Computing and Virtualization

Cloud computing and Virtualization are listed as top strategic technology trends. Analysts say the economics of cloud are compelling with expected savings for business applications of 3-5 times. In this certificate, students study virtualization environments and cloud-base virtualization architecture, developing knowledge and proficiency with cloud and virtualization technologies, mechanisms, platforms, and practices. Holders of this cloud computing certificate will demonstrate an understanding of the architectural concepts of cloud computing platform and procedures for deploying, operating and managing applications in the cloud, as well as knowledge in implementing and managing virtualization technology, one of the essential components to developing effective cloud environments.

Validation Lamina for Maintaining Confidentiality within the Hadoop

In the white paper we strive to cogitate vulnerabilities of one of the most popular big data technology tool Hadoop. The elephant technology is not a bundled one rather by product of the last five decades of technological evolution. The astronomical data today looks like a potential gold mine, but like a gold mine, we only have a little of gold and more of everything else. We can say Big Data is a trending technology but not a fancy one. It is needed for survival for system to exist & persist. Critical Analysis of historic data thus becomes very crucial to play in market with the competitors. Such a state of global organizations where data is going more and more important, illegal attempts are obvious and needed to be checked. Hadoop provides data local processing computation style in which we try to go towards data rather than moving data towards us. Thus, confidentiality of data should be monitored by authorities while sharing it within organization or with third parties so that it does not get leaked out by mistake by naïve employees having access to it. We are proposing a technique of introducing Validation Lamina in Hadoop system that will review electronic signatures from an access control list of concerned authorities while sending & receiving confidential data in organization. If Validation gets failed, concerned authorities would be urgently intimated by the system and the request shall be automatically put on halt till required action is not taken for privacy governance by the authorities.

Real-time water quality monitoring using Internet of Things in SCADA

Water pollution is the root cause for many diseases in the world. It is necessary to measure water quality using sensors for prevention of water pollution. However, the related works remain the problems of communication, mobility, scalability, and accuracy. In this paper, we propose a new Supervisory Control and Data Acquisition (SCADA) system that integrates with the Internet of Things (IoT) technology for real-time water quality monitoring. It aims to determine the contamination of water, leakage in pipeline, and also automatic measure of parameters (such as temperature sensor, flow sensor, color sensor) in real time using Arduino Atmega 368 using Global System for Mobile Communication (GSM) module. The system is applied in the Tirunelveli Corporation (Metro city of Tamilnadu state, India) for automatic capturing of sensor data (pressure, pH, level, and energy sensors). SCADA system is fine-tuned with additional sensors and reduced cost. The results show that the proposed system outperforms the existing ones and produces better results. SCADA captures the real-time accurate sensor values of flow, temperature, and color and turbidity through the GSM communication.

Recognition of surgically altered face images: an empirical analysis on recent advances

Biometric recognition plays a vital role in our daily lives. Face recognition is a subset of biometric recognition. Face verification and identification processes are prone to plastic surgery challenges which are commonly used nowadays to alter facial features for good looking demonstration. With increasing trend in technology and intellect robust biometric recognition systems are developed for human recognition after plastic surgery. However, these systems have some limitations because recognition after plastic surgery is affected by lightning, aging, pose, expressions, disguise and occlusion effects. In this survey, we aim to highlight the mitigating effects of cutting edge plastic surgical operations. These procedures lead to medical identity thefts, which is a serious offense for human community as an individual’s identity is forged. Thus, this makes one’s safety a critical issue and human recognition after plastic surgery a crucial challenge. Since the existing methods for human recognition after plastic surgical operations are not promising, in the current scenario plastic surgical operations secure above facial recognition. A number of existing biometric recognition algorithms for face images have been opted such as principal component analysis, Fisher/linear discriminant analysis, local feature analysis, local/circular binary patterns, speeded up robust features, granular system, correlation based approach, evolutionary granular/genetic approach, grouping recognition by parts and sparse demonstration approach, geometrical face recognition after plastic surgery, feature/texture based fusion scheme and deep convolutional neural networks (DCNN). The validation metrics used for the evaluation of recognition techniques are expected error rate, recognition rate, half total error rate and F-score. All algorithms are tested on an open plastic surgery facial dataset containing 1800 before and after surgery image samples pertaining to 900 humans. For a particular human being, two front facing image samples with appropriate luminance and unbiased gesture are taken: the former is taken pre cosmetic procedure and the latter is taken post cosmetic procedure. It has been deduced that feature and texture based fusion approach gives best results till date. It is predicted that DCNN has full potential of giving consistent results on surgical databases as it is already validated on non surgical databases. The need of a novel human identification system which is steady to the anomalies posed by plastic surgical operations is highlighted in this survey.