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Investigation of the fractal footprint in selected EURIBOR panel banks
EURIBOR emerged as a conventional proxy for a risk-free rate for a reasonably long period of time after the creation of the Eurozone. However, the joy was short-lived, as the global credit crisis shook the markets in mid-2008. Significant counterparty risk embedded in a derivative transaction cannot be left out. EURIBOR reflects the credit spread on borrowing. Hence, risk and uncertainty are inextricably linked here. This study investigates five banks out of 19 panel banks that manage EURIBOR in various Eurozone countries. These banks, HSBC, ING, Deutsche Bank, the National Bank of Greece and Barclays, are tested from January 2009 to December 2017 on a daily basis. Bank specific EURIBOR can be predicted in all five cases with different degrees. The trace of a profound herd is observed in the case of the National Bank of Greece, others were relatively mild in nature. The customer base and their risk grade were recognized as the main factor. Their information asymmetry and derived information entropy suggest embedded chaos and uncertainty. Bikramaditya Ghosh, Corlise Le Roux, Anjali Verma, 2020. -
A note on perfect lucky k-colourable graphs
This paper presents the notion of perfect Lucky k-colouring. Basic conditions for a perfect Lucky k-colourable graph are presented. Application thereof is then presented by obtaining the Lucky 4-polynomials for all connected graphs G on six vertices with ten edges. The chromatic number of these connected graphs is ?(G) = 3 or 4. For k = max{?(G): 3 or 4g = 4, it is possible to find Lucky 4-polynomials for all graphs on six vertices and ten edges. The methodology improves substantially on the fundamental methodology such that, vertex partitions begin with Lucky partition forms immediately. Finally, further problems for research related to this study are presented. 2020, International Scientific Research Publications. All rights reserved. -
Border Collie Optimization
In recent times, several metaheuristic algorithms have been proposed for solving real world optimization problems. In this paper, a new metaheuristic algorithm, called the Border Collie Optimization is introduced. The algorithm is developed by mimicking the sheep herding styles of Border Collie dogs. The Border Collie's unique herding style from the front as well as from the sides is adopted successfully in this paper. In this algorithm, the entire population is divided into two parts viz., dogs and sheep. This is done to equally focus on both exploration and exploitation of the search space. The Border Collie utilizes a predatory move called eyeing. This technique of the dogs is utilized to prevent the algorithm from getting stuck into local optima. A sensitivity analysis of the proposed algorithm has been carried out using the Sobol's sensitivity indices with the Sobol g-function for tuning of parameters. The proposed algorithm is applied on thirty-five benchmark functions. The proposed algorithm provides very competitive results, when compared with seven state-of-the-art algorithms like Ant Colony optimization, Differential algorithm, Genetic algorithm, Grey-wolf optimizer, Harris Hawk optimization, Particle Swarm optimization and Whale optimization algorithm. The performance of the proposed algorithm is analytically and visually tested by different methods to judge its supremacy. Finally, the statistical significance of the proposed algorithm is established by comparing it with other algorithms by employing Kruskal-Wallis test and Friedman test. 2013 IEEE. -
Enhancing performance of WSN by utilising secure QoS-based explicit routing
Wireless sensor networks (WSN) are infrastructure less and self-configured a wireless network that allows monitoring the physical conditions of an environment. Many researchers focus on enhancing the performance of WSN in order to provide effective delivery of data on the network, but still results in lower quality of services like energy consumption, delay and routing. We tackle this problem by introducing a new routing algorithm, QoS-based explicit routing algorithm which helps in transmitting the data from source node to destination node on WSN. We also involve clustering process in WSN based on genetic algorithm and particle swarm optimisation (GA and PSO) algorithm. We proposed identity-based digital signature (IBDS) and enhanced identity-based digital signature (EIBDS) that involves reduction of computation overhead and also increasing resilience on the WSN. We also use advanced encryption standard (AES), for ensuring the security between nodes and avoid hacking of data by other intruders. Copyright 2020 Inderscience Enterprises Ltd. -
Development and validation of superstitious beliefs scale
Superstitions though considered as irrational beliefs are widely prevalent in all cultures. Most of the existing work on superstitions are predominantly based on traditional western beliefs. The relevance of established superstition scales which are developed in western societies in collective societies need to explored. Interdependent nature of self which is a characteristic of collectivistic culture also has a role in belief formation. The present study aims at developing a new self-report measure of superstitious beliefs scale. Study 1, focused on exploring the factor structure and establishing reliability over a sample of 338 undergraduate students. The 17-item Superstitious Belief Scale (SBS) developed distinguishes a six-factor structure namely, Popular Beliefs, Belief in Good Luck, Belief in Bad Luck, Personal Superstitions and Social Superstitions. The six-factor structure was evaluated on a new sample (N = 483) using confirmatory factor analysis in Study 2. The internal consistency values of the new SBS over Studies 1 and 2 indicated high reliability. The findings have important implications for existing theory on superstitions. The new framework proposes and demonstrates the need to base the understanding of measurement of superstitious beliefs relevant in India. AesthetixMS 2020. -
Planned fashion obsolescence in the light of supply chain uncertainty
Fast fashion has popularised the phenomenon of perceived obsolescence whereby customers try to stay in line with the current fashion trends in the market even though the apparel they own are in perfect condition. This has ultimately led the fashion industry to become the second largest polluter in the world. The primary objective of this research paper is to comprehend how the media manoeuvres customers to indulge in fast fashion and how that in turn leads to uncertainty in the supply chain. To understand this, a maximum variation sampling method was adopted which consisted of customers, supply chain partners and marketers. In order to draw a parallel between the variables researched in the past and the present day scenario, an interview schedule was employed. Through the variables selected with the help of Dedoose, a model was created to identify the hurdles faced by suppliers as well as the customer in the fast fashion cycle. The results found that the power to break the fast fashion phenomenon lay in the hands of the media as it is through them that customers' perception can be altered. The importance of artificial intelligence in SCM and the modern tools used in industry 4.0 have also been discussed. 2020 Allied Business Academies. -
Pricing of liquidity risk in the indian stock market
Empirical literature from developed stock markets identifies liquidity risk to have impacts on the price of a stock. Given this, using one-minute trade and quote data of fifty stocks constituting the NIFTY 50 Index, this study examines the pricing of liquidity risk in the Indian stock market. The study uses thirteen liquidity measures identified from literature that cover the cost, quantity, time and multidimensional aspects of liquidity. The innovations in the liquidity measures are considered as the proxy for liquidity risk. Employing Generalized Methods of Moments estimation, the study proves that Indian investors expect to have a premium for holding securities that are illiquid when the whole market is illiquid. It proves liquidity risk as a priced factor and thus validates the liquidity-adjusted capital asset pricing model in the Indian stock market. It cautions the investors that the liquidity shocks can have significant inferences on portfolio diversification strategies to be adopted. 2020 GEA College – Faculty of Entrepreneurship. All rights reserved. -
Financial access indicators of financial inclusion: A comparative analysis of SAARC countries
Financial inclusion provides access to formal financial services at reasonable cost to the financially excluded people. Financial inclusion has been one of the most sought after topics in recent times for policy makers, researchers and academicians. Definition of financial inclusion varies from region to region. Financial inclusion is measured using different indicator. The important indicators of financial inclusion measurement include access indicators, usage indicators, quality indicators and financial education indicators. Most of the researchers use access indicators and usage indicators to measure financial inclusion. Access indicators comprise of demographic and geographic branch penetration, demographic and geographic ATM penetration and population per branch. This study focuses on comparative analysis of access indicators of financial inclusion in SAARC countries. The study is based on secondary data available in the central banks of SAARC nations, International Monetary Fund, World Bank and Asian Development Bank. The study has found and analysed about the countries which has performed well in each indicator of financial access. Copyright 2020 Inderscience Enterprises Ltd. -
Performance evaluation of Map-reduce jar pig hive and spark with machine learning using big data
Big data is the biggest challenges as we need huge processing power system and good algorithms to make a decision. We need Hadoop environment with pig hive, machine learning and hadoopecosystem components. The data comes from industries. Many devices around us and sensor, and from social media sites. According to McKinsey There will be a shortage of 15000000 big data professionals by the end of 2020. There are lots of technologies to solve the problem of big data Storage and processing. Such technologies are Apache Hadoop, Apache Spark, Apache Kafka, and many more. Here we analyse the processing speed for the 4GB data on cloudx lab with Hadoop mapreduce with varing mappers and reducers and with pig script and Hive querries and spark environment along with machine learning technology and from the results we can say that machine learning with Hadoop will enhance the processing performance along with with spark, and also we can say that spark is better than Hadoop mapreduce pig and hive, spark with hive and machine learning will be the best performance enhanced compared with pig and hive, Hadoop mapreduce jar. Copyright 2020 Institute of Advanced Engineering and Science. All rights reserved. -
Analysis on techniques used to recognize and identifying the Human emotions
Facial expression is a major area for non-verbal language in day to day life communication. As the statistical analysis shows only 7 percent of the message in communication was covered in verbal communication while 55 percent transmitted by facial expression. Emotional expression has been a research subject of physiology since Darwins work on emotional expression in the 19th century. According to Psychological theory the classification of human emotion is classified majorly into six emotions: happiness, fear, anger, surprise, disgust, and sadness. Facial expressions which involve the emotions and the nature of speech play a foremost role in expressing these emotions. Thereafter, researchers developed a system based on Anatomic of face named Facial Action Coding System (FACS) in 1970. Ever since the development of FACS there is a rapid progress in the domain of emotion recognition. This work is intended to give a thorough comparative analysis of the various techniques and methods that were applied to recognize and identify human emotions. This analysis results will help to identify proper and suitable techniques, algorithms and the methodologies for future research directions. In this paper extensive analysis on various recognition techniques used to identify the complexity in recognizing the facial expression is presented. Copyright 2020 Institute of Advanced Engineering and Science. All rights reserved. -
Effect of treated and untreated domestic sewage water irrigation on tomato plants
Background and Objectives: Agricultural cultivations in the world are suffering from water shortages. Water scarcity poses challenges in the economy and health of people all over the world. The present study aimed the cultivation of tomato plants using groundwater, treated and untreated domestic sewage water and tried to make a comparative study on the heavy metals present in the leaves and fruits of the tomato plants. Materials and Methods: The water samples were analyzed for various physicochemical parameters such as; pH, total hardness, chloride, total alkalinity, dissolved oxygen and heavy metal. Stomatal conductance was measured using porometer. The heavy metal analysis was conducted using Atomic Absorption Spectrometer. Results: All physicochemical parameters were found to be below the permissible level of standard values in the groundwater and treated domestic sewage water, but above the permissible level in untreated domestic sewage water. Stomatal conductance was found to be very low in the plants treated with untreated domestic waste water (296.33/428 in the ventral surface during the morning and noon, respectively) when compared to the leaves of the plants treated with other water samples. Untreated domestic sewage water showed a very high level of lead, i.e., 7.5354 ppm, whereas the treated sewage water contained 0.5650 ppm slightly above the permissible level. Conclusion: The present study has revealed that the treated domestic sewage water would be used for the irrigation of agricultural cultivation. 2020 Jobi Xavier and Akhil K. Varghese. -
Should we judge phcs by only iphs guidelines or probe further?
Background: Indian Public Health Standards (IPHS) evaluates supply side compliance of Primary Health Centers (PHCs). Patient Satisfaction (PS) on the other hand, assesses the demand side. Objective: Examining the supply side compliance and relating it to PS in the domain of Reproductive Health (RH). Methods: Using multistage stratified sampling, six rural and three urban PHCs in sub-districts, Ramanagara and Channapatna, in District Ramanagara, state of Karnataka, India, were chosen. Information collected using IPHS proforma for PHCs was compared with PS questionnaire (PSQ 18) data, collected from 398 patients visiting these facilities. Results: Using descriptive and inferential analysis, sub-optimal compliance levels in ease of access, physical & human infrastructure, patient data and usage of untied funds was found. Existing behavioral compliance was found to be optimal. These findings were in alignment with PS findings. Conclusion: Results call for PHC capacity building, incentivization and a crucial need to look into PS side, before passing judgement about performance standard. 2020, Indian Association of Preventive and Social Medicine. All rights reserved. -
Energy-based features for Kannada handwritten digit recognition
In this paper, Kannada handwritten digit recognition system is proposed based on energy features. Ground truth datasets are not available to test the performance of proposed features. Hence, own dataset of Kannada handwritten digits are collected from schools, colleges, business persons and professionals. The digital images are pre-processed using morphological opening operation for removing the noise and bilinear operation is used for normalisation. The normalised image is divided into 16 blocks, and then wavelet filters were applied for each of the 16 blocks and computed the standard deviation for each of them. In this process, a total of 64 standard deviation of the wavelet coefficients are generated of which 48 coefficients are selected as potential features. The average recognition accuracy of 94.80% is achieved using nearest neighbour classifier. The proposed algorithm is free from skew and thinning and it is novelty of the paper. Copyright 2020 Inderscience Enterprises Ltd. -
Scalar multiplication based matrix public key cryptography
In this work, a matrix key is used as Global Parameter. The work is considered for a large prime number which constitutes the field. A random integer is considered as private key and the Global matrix is exponentiated by the private key to generate public key. The process supports for Security features like authentication and confidentiality which are the necessary services for encryption process. Since the process supports Discrete Lagorithm problem, which is a hard problem it supports sufficient security against crypto analysis. In this work, instead of doing direct exponentiation of matrix key to the power of integer (Private Key), the study focuses on different scalar multiplication techniques that can be performed on matrix key during its exponentiation process to reduce the amount of computing resources for the completion of the process. 2020, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
Effects of supply chain integration on firms performance: a study on micro, small and medium enterprises in India
The cooperation in the supply chain assumes an adequate job for enhancing an organisation's performance and increasing competitive advantage. Supply Chain Integration (SCI) affects organisational performance. This paper studies the impact of the integration of supply chain procedures and practices on organisational performance and explores the effect of SCI on organisational performance at Micro, Small and Medium Enterprises (MSMEs) in Madurai District, Tamilnadu, India. A questionnaire is developed with validated measurement scales from previous studies and empirical data are collected through a survey questionnaire from 250 randomly selected MSMEs. This research provides sound recommendations to MSMEs in Madurai District, Tamilnadu, India, and maybe used for different industries and decision making policies. Finally, the study will contribute to the scientific field by providing some future studies. 2020 by the authors; licensee Growing Science, Canada. -
Stress analysis of the vertical tail skin joint and estimation of fatigue life due to fluctuating side loads
Vertical tail VT is one of the main components of the airframe. VT is attached with a rudder, which is the control surface, which is used for controlling the yawing motion of the aircraft. The deflection of the rudder introduces side load on the VT. Without rudder deflection, the aerodynamic load will not be applied to the VT. The load due to the deflection of the rudder is the major load for the VT. From a design point of view side, gust load is also important in transport aircraft. The present study is on a critical region with a riveted joint in the VT skin. A stiffened panel of the vertical tail with the spliced skin will be considered for the identification of the critical location. FEM will be used for the analysis of the component. In this study, loads of small transport aircraft will be considered. The maximum stress location and distribution of stresses on the stiffened panel are conducted by the FEM method. To obtain the mesh independent magnitude of stress, a refined local analysis is conducted. The tensile stresses on the skin are caused by the side loads of VT on the stiffened panel. Rivet holes are the stress concentration locations. The locations for fatigue crack initiation is the rivet holes. Fatigue damage estimation is calculated by the use of Miners rule. Fluctuating loads due to rudder deflection will be considered for damage calculation. SN data curve of the aluminium alloy material used for the VT skin will be considered for stress-based damage calculation. TJPRC Pvt. Ltd. -
Cobb douglas production function analysis of total factor productivity in Indian textile industry in the post multi-fiber agreement (MFA) period
Internnational tradee in textiles aand clothing wwas governedd by the systemm of bilateral quotas undeer the MMulti Fibre AgreementA (MMFA) since 11973 and thiss agreement has been repeplaced by thhe Agreeement on Texxtiles and Cllothing (ATC)C) from Januaary 1, 2005. The dismanntling of quotta restricctions had brrought about a significant change in thee structure off worldwide trrade in textilees as theere are no quuota barriers for the exporrt of textiles aand apparelss and the hithherto protecteed textilee industry wouldw be exposed to interrnational commpetition. It was widely believed annd highliighted in mosst of the studies that develloping economies, especiaally India andd China woulld be thee major gaineers in the neww policy regiime. In view of the changged trading ennvironment foor textilee trade, it wass considered important to analyze the immpact of quotta removal onn Indian textille industry, especiallyy with respecct to the prodductivity on thhis sector. Thee study has coonsidered fouur sub seectors of Indiian textile inddustry as givven in the Annnual Survey oof Industries, viz, Spinningg, Weavving and Finiishing of Texxtiles, Manuffacture of Other Textiles,, Manufacturre of Wearinng Apparrels and Manufacture off Crocheted aand Knitted Fabrics. Esstimates of CCobb Douglaas produuction function shows that the removal of textile quoota had impaccted all the foour sub sectorrs considdered in the study.s The imppact of quota removal on pproductivity iis positive andd significant iin the caase of spinninng, weaving aand finishingg of textiles ass well as in tthe manufactuure of wearinng apparrels. In the casec of mannufacture of other textiless and the mmanufacture oof knitted annd crochheted fabrics, the quota duummy coefficient is negatiive and signifificant at onee percent leveel. This sshows that thhe removal ofof quota restrrictions have adversely immpacted the ooutput of thesse sectorrs. 2020, North Carolina State University. All rights reserved. -
Self-supervised learning based anomaly detection in online social media
Online Social Media (OSM) produce enormous data related to the human behaviours based on their interactions. One such data is the opinions expressed and posted for any specific issue addressed in the OSM. Majority of the opinions posted would be categorized as positive, negative and neutral. The lighter group's opinions are termed anomalous as it is not conforming the regular opinions posted by other users. Though, lot of conventional classification and clustering based learning algorithms works well under supervised and un-supervised environment, due to the inherent ambiguity in the tweeted data, anomaly detection poses a bigger challenge in text mining. Though the data is un-supervised, for the learning purpose it is treated as Supervised Learning by assigning class labels for the training data. This paper attempts to give an insight into various anomalies of OSM and identify behavioural anomalies for a Twitter Dataset on user's opinions on demonetization policy in India. Through Self-Supervised learning, it is observed that 86% of the user's opinions did agree to the demonetization policy and the remaining have posted negative opinions for the policy implemented. 2020, Intelligent Network and Systems Society. -
Living with Coronavirus outbreak in India
The present paper focuses on living with coronavirus outbreak in India. This piece emphasizes on various policies adopted by the government of India to face the coronavirus crisis. It brings into perspective what financial strides the economy is going through, the mental health of the citizens, and the current situation of health care in the country. The current commentary reflects the learnings from COVID-19, the role of defined governmental policies, and support in surviving such an unforeseen situation. 2020 American Psychological Association. -
Foreground algorithms for detection and extraction of an object in multimedia
Background Subtraction of a foreground object in multimedia is one of the major preprocessing steps involved in many vision-based applications. The main logic for detecting moving objects from the video is difference of the current frame and a reference frame which is called "background image" and this method is known as frame differencing method. Background Subtraction is widely used for real-time motion gesture recognition to be used in gesture enabled items like vehicles or automated gadgets. It is also used in content-based video coding, traffic monitoring, object tracking, digital forensics and human-computer interaction. Now-a-days due to advent in technology it is noticed that most of the conferences, meetings and interviews are done on video calls. It's quite obvious that a conference room like atmosphere is not always readily available at any point of time. To eradicate this issue, an efficient algorithm for foreground extraction in a multimedia on video calls is very much needed. This paper is not to just build Background Subtraction application for Mobile Platform but to optimize the existing OpenCV algorithm to work on limited resources on mobile platform without reducing the performance. In this paper, comparison of various foreground detection, extraction and feature detection algorithms are done on mobile platform using OpenCV. The set of experiments were conducted to appraise the efficiency of each algorithm over the other. The overall performances of these algorithms were compared on the basis of execution time, resolution and resources required. 2020 Institute of Advanced Engineering and Science.