Data Management, Analytics and Innovation

Table of Contents

1. Frontmatter

2. Data Management and Smart Informatics

   1. Frontmatter

   2. Empirical Study of Soft Clustering Technique for Determining Click Through Rate in Online Advertising

      Akshi Kumar, Anand Nayyar, Shubhangi Upasani, Arushi Arora

      Abstract

      Online advertising is an industry with the potential for maximum revenue extraction. Displaying the ad which is more likely to be clicked plays a crucial role in generating maximum revenue. A high click through rate (CTR) is an indication that the user finds the ad useful and relevant. For suitable placement of ads online and rich user experience, determining CTR has become imperative. Accurate estimation of CTR helps in placement of advertisements in relevant locations which would result in more profits and return of investment for the advertisers and publishers. This paper presents the application of a soft clustering method namely fuzzy c-means (FCM) clustering for determining if a particular ad would be clicked by the user or not. This is done by classifying the ads in the dataset into broad clusters depending on whether they were actually clicked or not. This way the kind of advertisements that the user is interested in can be found out and subsequently more advertisements of the same kind can be recommended to him, thereby increasing the CTR of the displayed ads. Experimental results show that FCM outperforms k-means clustering (KMC) in determining CTR.

   3. ASK Approach: A Pre-migration Approach for Legacy Application Migration to Cloud

      Sanjeev Kumar Yadav, Akhil Khare, Choudhary Kavita

      Abstract

      Legacy application migration is a mammoth task, if migration approach is not well thought at the very start, i.e. pre-migration, and supported by robust planning especially at pre-migration process area. This paper proposes a mathematical pre-migration approach, which will help the enterprise to analyse existing/legacy application based on the application’s available information and parameters an enterprise would like to consider for analysis. Proposed pre-migration assessment will help in understanding the legacy application’s current state and will help in un-earthing the information with respect candidate application. Proposed pre-migration approach will help to take appropriate well-informed decision, whether to migrate or not to migrate the legacy application. As it is said that application migration is a journey, if kick-started once, needs to reach its destination else it can result into a disaster hence pre-migration is one of the important areas of migration journey.

   4. A Fuzzy Logic Based Cardiovascular Disease Risk Level Prediction System in Correlation to Diabetes and Smoking

      Kanak Saxena, Umesh Banodha

      Abstract

      The cardiovascular disease (CVD) is one of the major causes of death among the people having diabetes in addition to smoking habits. It will create tribulations for every organ of the human body. Smoking becomes fashion among the youth from their childhood which results in premature death. The intention of this paper is to explain the impact of diabetes and smoking along with high BP, high pulse rate, angina affect, and family history on the CVD risk level. The concept used is based on the knowledge-based system. We have proposed a fuzzy-logic-based prediction system to evaluate the CVD risk among the people having diabetes with smoking habits. The aim is to facilitate the experts to provide the medication as well as counsel the smokers well in advance. This will not merely save the individual but also an immense relief to concern. The data set is used from UCI (Machine Learning Repository). Most of the researchers worked on diabetes or smoking impact on CVD separately, but the proposed system demonstrates how drastically it will affect ones’ health condition.

   5. An Integrated Fault Classification Approach for Microgrid System

      Ruchita Nale, Ruchi Chandrakar, Monalisa Biswal

      Abstract

      In this paper, a moving windowing approach-based integrated fault classification algorithm is proposed for microgrid system. In a microgrid system, the nonlinear operation of control devices connected to distributed generation (DG) imposes problem for identifying the exact faulty class. In order to mitigate this issue, an integrated moving window averaging technique (IMWAT) is proposed. The method utilizes current signal at the line end. In this technique, first, the decision of the fault detection unit (FDU) is analyzed and based on that fault class is detected. The FDU uses the conventional moving window averaging technique. Different logics are framed to identify the symmetrical and unsymmetrical faults. The method is tested on a standard microgrid network and obtained results for different fault cases prove the efficacy of the proposed method.

   6. Role of Data Analytics in Human Resource Management for Prediction of Attrition Using Job Satisfaction

      Neerja Aswale, Kavya Mukul

      Abstract

      The reputed management publications like Harvard Business Review (HBR) have started stressing upon the emergence of data-driven management decisions. The enhancing investments in data and analytics are underlining the aforementioned emergence. According to International Data Corporation, this investment is expected to grow up to $200 billion by 2020. In such a data lead management world collecting, managing, and analysing the human resources-related data becomes a key for any rather every organization. Human resource analytics is changing into necessary as strategic personnel designing is the need of the hour and helps organizations to investigate each side of HR metrics. HR analytics could be a holist approach. According to KPMG—India’s Annual Compensation Trends Survey 2018–19 the average annual voluntary attrition across sectors is 13.1%. This is a considerably high percentage. Hence, antecedents leading to attrition are needed to be explored in order to propose appropriate HR policies, strategies, and practices. In relevance to these facts, this study focused on proposing a data-driven predictive approach that examines the relationship between the attrition (dependent variable) and other demographic and psychographic independent variables (Antecedents). The present study found that there is a strong relationship between job satisfaction and attrition. Further, there is a higher probability that the employees having work experience between 0–5 years may leave the organizations. Such data-based outcomes may offer help to HR managers in addressing the problems like attrition which intern may increase ROI. Thus, this paper underlines the emergence and relevance of analytics with special reference to human resource management domain.

   7. A Study of Business Performance Management in Special Reference to Automobile Industry

      Gurinder Singh, Smiti Kashyap, Kanika Singh Tomar, Vikas Garg

      Abstract

      In contemporary times, the automobile is one of the well-paid industries in the Indian market with an annual growth rate of 7.64% in the passenger-car market. The increasing disposable income of the people along with the ever-growing financial sector has led to this expendable growth. In accordance, there has been an increase in sales of passenger cars from 13.35% in –July 2018. This oligopoly market has fierce competition due to new entrants into the Indian markets. Thus, there emerges a need to grasp the knowledge to understand the ever-growing needs of the customers and dynamism in the technology-driven market. Every organization seeks to study consumer buying behavior and measure its business performance by analyzing customer perception toward the product. The understanding of customer’s perception is an ongoing process to survive the cut throat competition. Taking this into consideration, the study provides insights about several attributes which drive a consumer behavior toward buying a product of a brand. The study has used theories and exploratory research design along with analytical tools to identify the major attributes of consumer buying behavior toward sedan cars within Delhi/NCR among high-end consumers. This knowledge helps the car manufacturers in market segmentation which enables the organization to plan the market strategies toward consumer retention and product upgradation.

   8. Secure Online Voting System Using Biometric and Blockchain

      Dipti Pawade, Avani Sakhapara, Aishwarya Badgujar, Divya Adepu, Melvita Andrade

      Abstract

      Elections play an important role is democracy. If the election process is not transparent, secure and tamper proof then the reliability and authenticity of whole process is at stake. In this paper, we have discussed online voting system which fulfills all the above system requirements. We have addressed the issue of user authentication through iris recognition. We have used One Time Password (OTP) to have additional security check. We have also taken care that one valid user should not cast multiple votes. Use of Blockchain is the another security measure implemented in order to provide decentralized, tamper proof storage of data related to users biometric, personal details and votes casted by them. Thus we are not only focusing on user authenticity but also data security is also taken into consideration. The performance of the system has been tested for users from different age group and different background and its inference is presented.

   9. An Approach: Applicability of Existing Heterogeneous Multicore Real-Time Task Scheduling in Commercially Available Heterogeneous Multicore Systems

      Kalyan Baital, Amlan Chakrabarti

      Abstract

      Interest in design and use of heterogeneous multicore architectures has been increased in recent years due to the fact that the energy optimization and parallelization in heterogeneous multicore architecture are better than that of homogeneous multicore architecture. In heterogeneous multicore architectures, cores have similar Instruction Set Architecture (ISA) but the characteristics of the cores are different with respect to power and performance. Hence, heterogeneous architecture provides new prospects for energy-efficient computation and parallelization. Heterogeneous systems, furnished with different types of cores provide the mechanism to take actions with respect to irregular communication patterns, energy efficiency, high parallelism, load balancing, and unexpected behaviors. However, designing such heterogeneous systems for the different platforms like cloud, Internet of Things (IoT), Smart Devices, and Embedded Systems is still challenging. This paper studies the commercially available heterogeneous multicore architectures and finds out an approach or method to apply the existing work on heterogeneous multicore real-time task scheduling model to commercially available heterogeneous multicore architecture to achieve the parallelism, load balancing, and maximum throughput. The paper shows that the approach can be applied very efficiently to some of the commercially available heterogeneous systems to establish a generic heterogeneous model for the platforms like cloud, Internet of Things (IoT), Smart Devices, Embedded Systems, and other application areas.

   10. Analyzing the Detectability of Harmful Postures for Patient with Hip Prosthesis Based on a Single Accelerometer in Mobile Phone

       Kitti Naonueng, Opas Chutatape, Rong Phoophuangpairoj

       Abstract

       This research studies the use of a single accelerometer inside a smartphone as a sensor to detect those postures that may be risks for patients with hip surgery to dislocate their joints. Various postures were analyzed using Euclidean distances to determine the feasibility to detect eight postures that were harmful. With the mobile phone attached to the affected upper leg, it was found that there was one harmful posture that could not be detected due to its close similarity with a normal posture. Meanwhile, the other two harmful postures, although indistinguishable based on their measured data, were still detectable with the suitably selected threshold. The distance measure analysis is useful as an indicator as to which posture will be near to missing out in the detection process. This will form a guideline for further design of a practical and more robust detecting system.

   11. Software Development Process Evolution in Malaysian Companies

       Rehan Akbar, Asif Riaz Khan, Kiran Adnan

       Abstract

       GSD is a phenomenon mainly associated with the outsourcing of software development projects to some offshore company. Reduction in software development cost increased productivity and advantage of multisite development with respect to time are the main benefits that software development companies (SDCs) get from GSD. Besides benefits, a number of challenges associated with GSD are also observed. Consequently, the traditional processes to develop software are evolving and being replaced with a new set of processes which are lightweight and outcome-based. The process evolution has been deeply investigated in the context of companies mostly in Europe, Australia, USA and mainly other countries in those regions. In this regard, limited research has been carried out on Malaysian companies. The present research investigates the process evolution phenomenon in Malaysian companies. The current software development processes and the reasons for the evolution of software processes in Malaysian software companies have been identified. A qualitative approach using structured interviews has been followed for the collection of data collection and its analysis. The findings explain that software processes in most of the Malaysia companies are increasingly evolving or have been evolved. The companies are overwhelmingly adopting agile methods because of their support to GSD. Some of the companies are using ad hoc approaches for software development. The size of the company and project has been found as one of the main factors behind using ad hoc approaches. Mainly the small and medium-size companies and projects are involved in this practice.

   12. Automated Scheduling of Hostel Room Allocation Using Genetic Algorithm

       Rayner Alfred, Hin Fuk Yu

       Abstract

       Due to the rapid growth of the student population in tertiary institutions in many developing countries, hostel space has become one of the most important resources in university. Therefore, the decision of student selection and hostel room allocation is indeed a critical issue for university administration. This paper proposes a hierarchical heuristics approach to cope with hostel room allocation problem. The proposed approach involves selecting eligible students using rank based selection method and allocating selected students to the most suitable hostel room possible via the implementation of a genetic algorithm (GA). We also have examined the effects of using different weight associated with constraints on the performance of the GA. Results obtained from the experiments illustrate the feasibility of the suggested approach in solving the hostel room allocation problem.

   13. Evaluation of ASTER TIR Data-Based Lithological Indices in Parts of Madhya Pradesh and Chhattisgarh State, India

       Himanshu Govil, Subhanil Guha, Prabhat Diwan, Neetu Gill, Anindita Dey

       Abstract

       The present study was performed in some parts of Madhya Pradesh and Chhattisgarh State, India to compare the different quartz indices, feldspar indices and mafic indices according to Ninomiya (2005) and Guha (2016) using thermal infrared (TIR) bands (band 10, band 11, band 12, band 13, and band 14) of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for detecting quartz, feldspar and mafic minerals. Results showed that these indices are equally useful for delineating quartz, feldspar or mafic minerals. It was noticed from the correlation coefficients that Guha’s mafic index (GMI) and Ninomiya’s mafic index (NMI) presented almost the same result. Guha’s quartz index (GQI) was more powerful than Ninomiya’s quartz index (NQI) in identifying quartz content in alkali granites and this GQI was also comparable with the Rockwall and Hofstra’s quartz index (RHQI) in identifying quartz content in alkali granite.

   14. Analyzing Linear Relationships of LST with NDVI and MNDISI Using Various Resolution Levels of Landsat 8 OLI and TIRS Data

       Himanshu Govil, Subhanil Guha, Prabhat Diwan, Neetu Gill, Anindita Dey

       Abstract

       The present study used the Normalized Difference Vegetation Index (NDVI) and the Modified Normalized Difference Impervious Surface Index (MNDISI) to determine the linear relationship between Land Surface Temperature (LST) distribution and these remote sensing indices under various spatial resolutions. Four multi-date Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) images of parts of Chhattisgarh State of India were used from four different seasons (spring, summer, autumn and winter). The results indicate that LST established moderate to strong negative correlations with NDVI and weak negative to moderate positive correlations with MNDISI at various spatial resolutions (30–960 m). Generally, the coarser resolutions (840–960 m) possess stronger correlation coefficient values due to more homogeneity. The autumn or post-monsoon image represents the strongest correlation for LST–NDVI and LST–MNDISI at any resolution levels. The image of winter season reveals the best predictability of LST distribution with the known NDVI and MNDISI values.

   15. Automatic Robot Processing Using Speech Recognition System

       S. Elavarasi, G. Suseendran

       Abstract

       Nowadays, speech recognition is becoming a more useful technology in computer applications. Many interactive speech-aware applications exist in the field. In order to use this kind of easy way of communication technique into the computer field, speech recognition technique has to be evolved. The computer has to be programmed to accept the voice input and then process it to provide the required output, using various speech recognition software. Speech recognition is the process of converting speech signal to a sequence of words using appropriate algorithm. This provides an alternative and efficient way for the people who are not well educated or not having sufficient computer knowledge to access the systems and where typing becomes difficult. This speech recognition technique also reduces the manpower to accept and process the commands. In our research work, we have to implement this speech recognition technique in customer care center, where many queries have to be processed every day. Some of the queries are repeated often and the responses also seem to be the same. In such cases, we have to propose a methodology to automate the query-processing activities using this speech recognition technique. The ways of how to automate the system and how to process the queries automatically are explained in our methodology with suitable algorithm.

   16. Banking and FinTech (Financial Technology) Embraced with IoT Device

       G. Suseendran, E. Chandrasekaran, D. Akila, A. Sasi Kumar

       Abstract

       In recent years the traditional financial industries have motivated for a new technology of financial technology (FinTech) clinch embraced with internet of things (IoT). The requirements of FinTech and IoT need to be integrated into new business environment. Several companies are affected because of the financial-level investments. So, there is a need to improve the next level of the business. FinTech can introduce a new service of tools and products for the emergent businesses through the internet of services which provide ideas linked in internet. Nowadays, increasing number of companies uses the IoT and creates new added values. The administrators of existing money-related organization in the direct society are dreadful by means of budgetary innovation. The social innovation is accomplished by new innovation. To make a powerful business plan and action, the FinTech and IoT are combined in order to create new innovative ideas based on the requirements.