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A compression system for Unicode files using an enhanced Lzw method
Data compression plays a vital and pivotal role in the process of computing as it helps in space reduction occupied by a file as well as to reduce the time taken to access the file.This work relates to a method for compressing and decompressing a UTF-8 encoded stream of data pertaining to Lempel-Ziv-welch (LZW) method. It is worth to use an exclusive-purpose LZW compression scheme as many applications are utilizing Unicode text. The system of the present work comprises a compression module, configured to compress the Unicode data by creating the dictionary entries in Unicode format. This is accomplished with adaptive characteristic data compression tables built upon the data to be compressed reflecting the characteristics of the most recent input data. The decompression module is configured to decompress the compressed file with the help of unique Unicode character table obtained from the compression module and the encoded output. We can have remarkable gain in compression, wherein the knowledge that we gather from the source is used to explore the decompression process. Universiti Putra Malaysia Press. -
Linear and non-linear analyses of double diffusive chandrasekhar convection with heat and concentration source in micropolar fluid with saturated porous media under gravity modulation
In this paper, linear and non-linear analysis of Double-Diffusive convection in the presence of magnetic field and gravity modulation with heat and concentration source in a micropolar fluid is studied by assuming the strength of heat and concentration source same. The expression for Rayleigh number and correction Rayleigh number are obtained using regular perturbation method. The effects of parameters on heat and mass transport is investigated using non-linear analysis by deriving eighth order Lorenz equation. It is found that coupling parameter and Chandrasekhar number stabilizes the system. Whereas internal Rayleigh number and Darcy number destabilizes the system. 2020 International Association of Engineers. -
Two-phase Sakiadis flow of a nanoliquid with nonlinear Boussinesq approximation and Brownian motion past a vertical plate: Koo-Kleinstreuer-Li model
This paper investigates the Sakiadis flow of a Al2O3-H2O nanoliquid with consistently scattered dust particles over a vertical plate. To account for the effect of the Brownian movement, the Koo-Kleinstreuer-Li model is considered. In some thermal systems such as reactor safety areas, and solar collectors, combustion works from moderate to high temperature, making the relationship between the temperature and density nonlinear. To consider this temperature-dependent density, the nonlinear Boussinesq estimation is utilized. The present physical structure, which includes energy and momentum equations, is converted into a system of ordinary, coupled, and nonlinear differential conditions through the help of similarity transformations. By using the finite difference code, the subsequent equations have been numerically solved. The impact on the velocity and the thermal profiles of the nondimensional parameters is visualized through graphs. Both the Nusselt number and friction factor strengthen with ahigher nonlinear thermal parameter in the case of nonlinear Boussinesq approximation compared to the linear Boussinesq case. Growing estimations of nonlinear thermal parameter deteriorate the thermal profile but it boosts the velocity profile of both liquid and dust phases. 2020 Wiley Periodicals LLC -
Evaluating forces associated with sentient drivers over the purchase intention of organic food products
The study proposes to find out the factors which influence awareness among the consumers towards purchasing organic food product. The study is based on primary data by using tools Chi-square test, Cronbach alpha, KMO, and Bartlett's test, ANOVA, regression, correlation, and cross-tabulation. The study found that awareness driver's nutritional information, price, certification, brand name, and logos have an essential influence on the purchase intention of the product of organic food. However, labeling and food standards do not show a noteworthy rapport between labeling and organic food products' purchase plans. The core commitment and flow to explore are to analyze purchasers with respect to organic guarantee systems (accreditation, guidelines, logo, imprints, and confirmation) so we can distinguish the genuine organic products. The independent factors of awareness like organic buying preference and buying frequency, have a significant influence on the purchase intention of organic food. The research provided evidence of consumer awareness and purchase intention of organic food that would help the organic food industry to promote their products according to the attribute of customers. 2020 Asian Economic and Social Society. All rights reserved. -
The concept of entrepreneurial ability-evidence from women in MSMEs of Karnataka state
The Indian women entrepreneurs have come a long way today from the traditional deep-rooted view of the Indian society and are predominantly found in the MSME sectors of India. To understand their growth and advancements, a proper understanding of their entrepreneurial ability with respect to their performance is of paramount importance. The objective of this study is to explore the factors of women entrepreneurial ability which impacts the successful performance of the women entrepreneurs in MSMEs of Karnataka state in India. A theoretical framework model of entrepreneurial ability developed for the study is tested with a primary data collected through a survey-questionnaire method from a sample size of 427 women entrepreneurs using a random sampling method, factor analysis and Pearson correlations. Overall the results of this study support the contention: the perceived business performances of women entrepreneurs have a significant influence on their entrepreneurial ability. Copyright 2020 Inderscience Enterprises Ltd. -
Skin lesion classification using decision trees and random forest algorithms
Any superficial skin growth that does not resemble the surrounding area is referred to as skin lesion. It can occur in the form of mole, bump, cyst, rash or other changes that can be classified either as primary or secondary lesion. While primary skin lesions correspond to those changes in color or texture, secondary lesions occur as a primary lesion progression. Skin lesion image segmentation and classification at the early stages can help the patients recover through proper medication and treatment. Many algorithms for segmentation and classification are available in the literature but they all fail to extract lesion boundaries perfectly and classify them with more accuracy. To improve the reliability of the skin image segmentation and classification, we propose to use decision trees and random forest algorithms in this works and compare them with different data sets. The proposed method can generate high-resolution feature maps that can help to preserve the spatial details of the image. While tested against the ISIC 2017 and HAM10000 dataset, we found that the proposed method is more accurate as compared to the existing algorithms in this domain and is also very robust to artifacts or hair fibers present in the skin images. 2020, Springer-Verlag GmbH Germany, part of Springer Nature. -
Framework for proactive visualization of text based narrative using NLP
Language is an essential mode, not only for human communicationbut also for thinking. A story is conveyed or a report of an incident is being told, humans perceive the conveyed information in the form of visual insights. The increasing advancements in the field of artificial intelligence can help with the same in machines. This paper reflects on the internalization of stories from a cognitive perspective and outlines a scalable framework for supporting the visualization of narrative text data. This paper leverages natural language processing (NLP), probabilistic modelling of discourse knowledge, information extraction of narrative components (who, where, when, what) and the narrative visualization. The graphics knowledge base storage structure has been redesigned to obviate the necessity of having a larger database for all graphics entity. With the developed framework, any user can input unrestricted natural language for the dynamic generation of animated scenes. This provides users with direct visual output in response to their natural language input. This tool can potentially impact the way humans interact with computers and expand a completely new way of understanding conversations. 2020 IJSTR. -
Injective coloring of complementary prism and generalized complementary prism graphs
The complementary prism Gof a graph G is the graph obtained by drawing edges between the corresponding vertices of a graph G and its complement. In this paper, we generalize the concept of complementary prisms of graphs and determine the injective chromatic number of generalized complementary prisms of graphs. We prove that for any simple graph G of order n, ?i(G ? n and if G is a graph with a universal vertex, then ?i(G = n. 2020 World Scientific Publishing Company. -
A study on prediction of health care data using machine learning
Every clinical-decision relies on the doctors experience and knowledge. Perhaps this conventional practice may look appropriate, but it may lead to unpredictable errors, biases, and maximized costs that may affect QoS (Quality-of-Service) given to patients. To help the doctor to save time, the conventional practice to analyze the data for clinical-decision support has to be updated. Machine Learning (ML) and Data Mining (DM) algorithms have applied to have greater and higher predictions. This paper studies a set of ML algorithms by which clinical-predictions are going to be more appropriate and cost-effective. IJSTR 2020. -
MnO2-Pi on biomass derived porous carbon for electro-catalytic oxidation of pyridyl carbinol
A facile electrochemical oxidation of pyridyl carbinol based on Manganese dioxide-Phosphate (MnO2-Pi) was fabricated by electro-deposition of MnO2-Pi on Porous carbon nanospheres (PCN) modified carbon fiber paper (CFP) electrode. A simple working electrode was developed initially by coating Monkey Pod (MP) derived PCN on carbon fiber paper (CFP) electrode. Voltammetric deposition of MnO2-Pi on PCN/CFP electrode was carried out in an electrolyte containing phosphate buffer and KMnO4. The modified electrodes (PCN/CFP and MnO2-Pi-PCN/CFP) were characterized by different physicochemical methods and electroanalytical techniques like cyclic voltammetry and AC impedance spectroscopy. Inorganic phosphate (Pi) and MnO2 centers present on PCN/CFP electrode plays a major role towards oxidation of pyridyl carbinol electrochemically. The proposed MnO2-Pi-PCN/CFP electrode was effectively applied for the electrochemical oxidation of pyridyl carbinol in TEMPO medium. 2020 The Author(s). -
Influence of employees' perception on the use of flexible work arrangements
The study aims to explore the factors that influence the perception of employees on the usability of flexible work arrangements and to predict whether those factors induce them to opt for such flexible practices. The data was collected from 239 Indian employees working across different sectors of the country. The study employed a quantitative approach for data collection by using a structured questionnaire consisting of close-ended questions. The data was analyzed using factor analysis, binomial logistic regression and Analysis of Variance on SPSS Statistics 25. The study identified five major factors that influenced the employees perception about using flexible work options. Among them two factors namely, FWA perquisites and FWA anxiety were found significant in predicting the employees use of flexible work options. Further, it was found that married employees recognized strong benefits from using flexible options. This study contributes to the existing literature by unveiling the mindset of Indian employees towards flexible work arrangement and suggests that the employers, society and the government should create favorable environment for deploying flexible work practices. 2020 IJSTR. -
Novel quantum inspired approaches for automatic clustering of gray level images using Particle Swarm Optimization, Spider Monkey Optimization and Ageist Spider Monkey Optimization algorithms
This paper is intended to identify the optimal number of clusters automatically from an image dataset using some quantum behaved nature inspired meta-heuristic algorithms. Due to the lack of sufficient information, it is difficult to identify the appropriate number of clusters from a dataset, which has enthused the researchers to solve the problem of automatic clustering and to open up a new era of cluster analysis with the help of several natures inspired meta-heuristic algorithms. In this paper, three quantum inspired meta-heuristic techniques, viz., Quantum Inspired Particle Swarm Optimization (QIPSO), Quantum Inspired Spider Monkey Optimization (QISMO) and Quantum Inspired Ageist Spider Monkey Optimization (QIASMO), have been proposed. A comparison has been outlined between the quantum inspired algorithms with their corresponding classical counterparts. The efficiency of the quantum inspired algorithms has been established over their corresponding classical counterparts with regards to fitness, mean, standard deviation, standard errors of fitness, convergence curves (for benchmarked mathematical functions) and computational time. Finally, the results of two statistical superiority tests, viz., t- test and Friedman test have been provided to prove the superiority of the proposed methods. The superiority of the proposed methods has been established on five publicly available real life image datasets, five Berkeley image datasets of different dimensions and four benchmark mathematical functions both visually and quantitatively. 2019 Elsevier B.V. -
Nexus Between The Carbon Dioxide Emission And Economic Growth: Evidence From India
Increase in economic activities contributes to the economic growth of a country. It is evident that emerging economies have recorded higher economic growth and significant increase in coal consumption, energy consumption and electricity consumption. On the other hand, the emission of greenhouse gases (GHG) generating consequences in the atmosphere. In this context, this study tries to analyse the association between GDP per capita, FDI, population, trade openness and CO2 emissions per capita in India. The study is based on secondary data, which has been collected from the World Bank database. The time period under consideration is from 1960 to 2017. Augmented Dickey Fuller test has been used to test the unit root. VAR lag order criteria have been used for lag selection of the model. Since the variables are integrated at I (1) and I (0), the ARDL model has been used for the purpose of analysis. Furthermore, for checking the stability of the model, the CUSUM test has been used. The results show that in the long run, GDP per capita and FDI has a positive impact on CO2 emission whereas, in the short run coal consumption, FDI, GDP per capita and trade openness appears to have a significant and positive impact towards CO2 emission. 2020 - Kalpana Corporation -
Secure Through Development: Evaluation of Indias Border Area Development Programme
The Border Area Development Programme was initiated in the year 198687, to strengthen Indias security by ensuring developed and secure borders. Initially, the programme was implemented in the western border states to facilitate deployment of the Border Security Force. Later, the geographical and functional scope of the programme was widened to include eastern and northern sectors of Indias borders and as well as socio-economic aspects such as education, health, agriculture and other allied sectors. But, it is difficult to say that the implementation has been uniform in all the sectors. While the programme on the Western front along the IndiaPakistan border has been fairly successful, it is yet to achieve its stated objectives on the Eastern front. Using an analytical framework, the article examines three aspects of BADP: Its context and concept; identifying challenges that hinder the Programmes effectiveness; and certain policy prescriptions. 2019, 2019 Institute for Defence Studies and Analyses. -
Iot based real time potholes detection system using image processing techniques
Accidents owing to potholes has become an alarming problem in todays life. The first step to solve this problem requires, designing a device embedded on the vehicle which can continuously scan the road surface for identifying potholes, alerting the driver in time and enable the driver to avoid the pothole. The second step is to introduce a technique to enable the device to locate the position of the pothole via GPS (Global Positioning System). The GPS data can be uploaded via a GPRS (General Packet Radio Service) module or Bluetooth module onto a data base which is stored locally. This database can then be transferred to the cloud using WiFi or 4G technology by connecting the system. The third aspect is to link the database to a network system incorporating mapping software such as Google Maps or Open-Street Map. The data in the system can be made available to the general public as well as municipalities and road maintenance agencies. Awareness of the location of potholes will help drivers to avoid those roads and being more careful while driving on the same roads. This paper focuses on the pothole detection task based on image processing algorithms and the data captured from ultrasonic sensor placed on the vehicle. The later steps were implemented through Bluetooth interface available in smartphones. IJSTR 2020. -
Factors influencing purchase decision and brand switching in the passenger car segment in Bengaluru
This study identifies and analyses the Product Attributes of Passenger Cars and the demographic factors that influence consumer Purchase Decision and Brand Switching in the Indian context, specific to the city of Bengaluru. It discusses the existing knowledge pertaining to Passenger Cars and a conceptual framework is developed based on the review of literature. The research identifies what drives the Purchase Decision and Brand Switching for the Indian consumers and analyses how it differs based on demographic variables such as age, gender and income. Based on the model thus created, the research seeks to segment the Indian Passenger Car consumers according to the significant demographic variables thus identified. A questionnaire was administered to 200 respondents of different age, income and gender groups within the city of Bangalore. The data was then analyzed using Factor Analysis, One-way ANOVA and frequency analysis in SPSS.It was found that Quality, Aftersales Service, Safety and Price are the major value factors effecting purchase decision of Indian Passenger Car consumer. Age and income also has a significant influence of Purchase Decision and Brand Switching. It was also found that purchase intention varies between different age and income groups. The research was conducted within the city of Bangalore alone which may not be generalized to the entire country. 2020 SERSC. -
Designing a Dynamic Topology (DHT) for Cluster Head Selection in Mobile Adhoc Network
The mobile ad hoc networks (MANETs) are a collection of dynamic nodes facilitating communication from source to destination either using single or multi hop forwarding mechanism. The nodes within the network possess energy constraints for which an effective clustering mechanism is used for facilitating communication between the nodes within and outside the clusters by designing a dynamic hybrid topology (DHT). The paper concentrates on clustering mechanism (EBCH) for reducing the energy consumption during communication from source to destination and number of parameters where analyzed in order to determine the selection of cluster head based on the energy consumption because this is directly related to the lifetime of the network. The implementation was carried out using MATLAB which offered an environment for performing simulation. The obtained results on comparison with conventional ENB and CPN algorithm improved the operations of cluster computation in ad hoc environments effectively in relation to the cluster head selection and reduced energy consumption. 2019, Springer Science+Business Media, LLC, part of Springer Nature. -
Well-being of North Eastern Migrant Workers in Bangalore
This paper explores the quality of life and subjective well-being of north-east migrant workers engaged in various formal and informal jobs in Bangalore. The composite well-being index reveals moderate well-being for the majority of workers. The disaggregated analysis, however, shows poor material conditions of life. Using the Day Reconstruction Method, we also find positive emotions associated with activities such as socialising but negative emotions for work and commuting. With respect to interacting partners, the negative emotions were highest while dealing with clients and customers. We also found positive correlations between life satisfaction and quality of life indicators, most strongly, with job quality. Lower quality of jobs, reported by women in comparison to men, suggests that organisations should aim to create more equal and enabling work spaces for all genders. 2020 Institute for Human Development. -
Early prediction of lungs cancer by deep learning algorithms from the CT images with LBP features
The early prediction of the any type of cancer can save the lives of many especially if it is lung cancer which is one of the deadly diseases in the world. Thus the early prediction is implemented we can increase life expectancy and bring the mortality level low. Although there are various methods to detect the lung cancer cells by X-ray and CT scans, however the CT images are more preferred. The 2D images like CT scans are used to get medical results more accurate. The proposed method here will discuss how the LBP features are used to analyze the CT images with the support of Deep Learning methods. In this research work we will discuss how the image manipulation can be done to achieve better results from the CT images through various image processing methods. LBP features helps in estimating the distribution of local binary pattern of an image. A final result with 93% is achieved after the training of the processed images by LBP features. 2020 SERSC. -
A comparative study on decision tree and random forest using konstanz information miner (KNIME)
With vast amounts of data floating around everywhere, it is imperative to comprehend and draw meaningful insights from the same. With the proliferation of Internet and Information Technology, data has been increasing exponentially. The 5 Vs of data i.e. Value, volume, Velocity, variety and veracity will only make sense if we are able to examine the data and uncover the hidden, yet meaningful insights. With large data becoming a norm, a lot of data mining algorithms are available that help in data mining. We have tried to compare two classification algorithms, primarily Decision trees and Random forest. A total of 10 datasets have been taken from UCI Repository and Kaggle and with the help of Konstanz Information Miner (KNIME) workflows, a comparative performance has been made pertaining to the accuracy statistics of Random Forest and decision Tree. The results show that Random Forest gives better and accurate results for a dataset as compared to decision trees. 2020 SERSC.