Miracle-dz/UniFAR-FTUs huggingface dataset:
Production of Flavonoids from Terminalia arjuna (ROXB.) in vivo and in vitro Tissue cultures

In the present investigation extraction, isolation, purification and identification of flavonoids were carried out in vivo (stem, bark, leaves, flower and fruits) and i vitro (callus) tissues of selected plant species Terminalia arjuna (family-Combretaceae) . Unorganized cultures were raised and maintained by frequent subculturings on MS medium supplemented with 2,4-D, using seedling as explant . TLC, Spectrophotometry and infra-red spectral studies were used for qualit ative and quantitative estimation of flavonoids. Three flavonoids namely Luteolin, Kaempferol, Quercetin h ave been identified.
Performance Evaluation of feature extraction model to identify student appraisals

: Educational Data mining techniques plays an important role in educational institution. It can be used to understand the difficulties arising in the teaching-learning professions. In machine learning, feature selection or Attribute analysis usually emerges as a pre-processing step. Feature selection is the problem of choosing a small subset of feature that ideally is necessary and sufficient for predictive / decision-making type of learning tasks. This study proposes a framework for identifying the most significant attributes towards academia, for the performance of second year students of computer science and application course. The authors realize that the some features are non-changeable and so do not contribute in upgraded academic performances of the students as they do not reveal any added academic effort. In this study, authors decided to work upon only external attributes of students by assigning weights that reflect their residual efforts put in for those attributes. The model is able to extract the fitness precedence relations of external efforts put up by student belonging to both „above-risk‟ and „at-risk‟ categories in their on-going course. The end-user can make use of these precedence relations to identify and resolve the most unfit governing factor for upgrading students‟ appraisals. The accuracy of these precedence relations is computed upon the most popular feature extraction (FE) algorithm „RELIEF „The model accuracy of 75% indicates the encouraging results in the direction of identifying graded precedence of the participating model attributes.
Comparative Analysis of ZF and MMSE Receiver for Multicode MC-CDMA Downlink

A new wireless communication system denoted as Multi-Code Multi-Carrier CDMA (MC-MC CDMA), which is the combination of Multi-Code CDMA and Multi-Carrier CDMA, is analyzed in this paper. This system can satisfy multi-rate services using multi-code schemes and multi-carrier services used for high rate transmission. The equalization techniques used in this work are Zero Forcing (ZF) and Minimum mean square error (MMSE) and their performances are compared on the basis of bit error rate (BER). The BER is analyzed with respect to Signal to Noise ratio (SNR). QPSK modulation scheme is implemented for the analysis. The comparative analysis is done through software simulation.
PHYTOCHEMICAL CONSTITUENTS AND ANTIOXIDANT POTENTIAL IN FLORAL EXTRACTS OF CALENDULA OFFICINALIS LINN

Calendula officinalis Linn. (Compositae) commonly known as pot marigold is distributed throughout world as an ornamental plant. It also has long history of usage by folk systems because of rich ethanomedicinal values. Present study was aimed to determine phytochemical constituents endowed with antioxidant potential in floral extracts of C. officinalis. Ethanolic extract has significantly high (P<0.05) total phenolic, flavonoids, β-carotene, lycopene, tannin, chlorophylls contents as compared to aqueous extract of C. officinalis. Phytochemical ingredients present in ethanolic extract have high total antioxidant activity, superoxide, nitric oxide, hydroxyl, free radicals scavenging activity and reducing power. A dose dependent relationship has been observed between different concentrations of extract and radicals scavenging activity. Median effective concentrations of ethanolic extract for total antioxidant activity, free radical, superoxide, nitric oxide and hydroxyl radicals scavenging activities are 0.88, 1.20, 0.53, 0.68 and 0.79 mg ml respectively. Ethanolic extract of C. officinalis has high phenolic, flavonoids, β-carotene, lycopene, tannin, chlorophylls contents endowed with high antioxidant potential as compared to aqueous extract. Thus, the dietary supplementation or addition of ethanolic extract of C. officinalis may provide protection against free radicals induced cellular damage besides improving the food quality by retarding oxidative degeneration of food lipids.
Weighted Approach for 3-D/2-D Localization Method in WSN using Fuzzy Modeling with Parameter Analysis

: Wireless sensor network consists of a large number of sensor nodes. Determining the actual position of each sensor node is thus difficult. Finding the exact position of each sensor forms the basis of localization. Localization in wireless sensor network refers to finding position of unknown nodes based on position of known nodes known as anchors or reference nodes. This paper propose centroid localization method using edge weights of adjacent nodes based on fuzzy modeling for 3D environment. In proposed method neighboring adjacent anchor nodes that are near to unknown sensor node are detected first and then fuzzy membership functions for edge weights based on received signal strength is developed. After calculating edge weights, weighted centroid method is used to localize unknown node position in 3D environment. Results are then compared with weighted centroid approach for unknown sensor nodes in 2D environment for various combinations of reference nodes , unknown sensor nodes and received signal strength range.
Dual Layer Randomization in Audio Steganography Using Random Byte Position Encoding

- The approach used in this paper is to design a steganography scheme which is used to hide data into an audio file in such a way that the data hiding points or so called the sample point is randomly selected so that with every new embedding process the position of the sample point changes. That signifies that unlike (Least Significant Bit) LSB where the hiding points remains similar for every embedding process the propose algorithm makes sure that the position of sample points changes with every time data is embedded into an audio file along with this the bit position at which the embedding is done is also varied from 1LSB to 7 LSB.
A Scheduling Approach with Processor and Network Heterogeneity for Grid Environment

— Processor heterogeneity is an important issue in grid environment. In this paper, a list based task scheduling algorithm, called “critical path scheduling with t-level” (CPST) for grid computing system is proposed. There are no. of scheduling algorithms such as HEFT [1] use mean execution time based b-level for task priority and SHCP [2] use task priority based on simple critical path. In CPST, a critical path based task sequence is generated with t-level value of tasks, where variance based computation and communication cost is used. The experimental results show that CPST algorithm performs better than HEFT, SHCP and HHS algorithm in grid environment for task graphs.
567-572 Goon-Anthropomet

Anecdotal evidence concerning the prevalence of abdominal obesity (AO) among nurses in South Africa is alarming; however, no scientific study has confirmed this notion. The purpose of this study was to determine AO, using proxy measures of waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) among nurses in Vhembe and Capricorn districts, Limpopo, South Africa. The study involved a purposive sample of 153 practising nurses aged 19 to 50+ years. Height, weight, waist and hip circumferences were measured and participation in physical activity was assessed by report. Cut-off points for abdominal obesity were: WC= ≥102 cm (men) and ≥88 cm (women); WHR = ≥0.9 (men) and ≥0.8 (women) and WHtR= ≥0.5. The prevalence of AO was 68.2%, 80.5%, 56.5% according to WC, WHR and WHtR, respectively. Among the males nurses, AO were: WC (18.8%), WHR (37.0%), and WHtR (%) while corresponding figures for females nurses were WC (49.4%), WHR (43.5%), WHtR (0.0%). Female nurses had significantly ( 2 X =2.2 03, p = 0.003) higher prevalence of AO compared to the male nurses. The prevalence of AO (WHR, WHtR) increased with age, peaking at ages 40-49 for both sexes, while WC measured obesity showed a similar pattern but peaks at age 50+ among the male nurses. Majority of the nurses decline participating in physical activity. There is high prevalence of AO among nurses in this region, a rate comparable to the general population in South Africa. Future studies identifying risk factors for prevalence of AO in nurses are warranted.
The Performance of Nigerian Mutual Funds in the Period 2011 - 2014

This paper aims to contribute to the financial economics literature on the performance of mutual funds in frontier markets. I utilise various measures; the risk adjusted measure for portfolio performance, the “Jensen Alpha” measure, the Treynor ratio and Sharpe ratio, to assess the performance of 31 equity and mixed mutual funds formed on instruments listed on the Nigerian stock exchange and run by fund managers in the period December 2011 to November 2014. In addition to a review of the performance of mutual funds, I also the review the performance of a portfolio between the periods, December 2011 to November 2014 formed using the Minimum Conditional VAR (“Minimum CVAR”) portfolio approach. The evidence from this study indicates that the 31 mutual funds reviewed were on average not able to predict stock prices well enough to outperform a buy-the-market and-hold policy, but also that there is very little evidence that any individual fund was able to do significantly better than that which we expected from mere random chance. It is therefore difficult to see the justification for the fees charged. The limitation of this study is also discussed. The Minimum CVAR approach outperforms the mutual funds and NSE All Share Index in the review period December 2012 – November 2014.
Breast Cancer Detection in Mammogram Using FuzzyC-Means And Random Forest Classifier

Breast Cancer is one of the important reasons for death among ladies. Many research has been done on the diagnosis and detection of breast cancer using various image processing techniques. The proposed work deals with a technique for extracting the malignant masses in the mammography image for the earlier detection of breast cancer. The mammography images are complex, and also because of the noisy, inconsistent and incomplete data, several pre-processing techniques are used to enhance and make clear the targeted areas in the mammogram images. After segmenting the images into speciﬁc regions, based on its homogeneous characteristics, features are extracted which helps the classiﬁcation more accurate. In this work, Fuzzy C-Means method is combined with Random Forest classiﬁer to improve the accuracy.
Data Mining Techniques to Find Out Heart Diseases: An Overview

: Heart disease is a major cause of morbidity and mortality in modern society. Medical diagnosis is extremely important but complicated task that should be performed accurately and efficiently. Although significant progress has been made in the diagnosis and treatment of heart disease, further investigation is still needed. The availability of huge amounts of medical data leads to the need for powerful data analysis tools to extract useful knowledge. There is a huge data available within the healthcare systems. However, there is a task of effective analysis tools to discover hidden relationships and trends in data. Knowledge discovery and data mining have found numerous application in business and scientific domain. Researchers have long been concerned with applying statistical and data mining tools to improve data analysis on large data sets. Disease diagnosis is one of the applications where data mining tools are proving successful results. This research paper proposed to find out the heart diseases through data mining, Support Vector Machine (SVM), Genetic Algorithm, rough set theory, association rules and Neural Networks. In this study, we briefly examined that out of the above techniques Decision tree and SVM is most effective for the heart disease. So it is observed that, the data mining could help in the identification or the prediction of high or low risk heart diseases.
Energy Aware Architecture Using Spectrum Sensing Technique in Cognitive Radio Network

— Cognitive radio (CR) is a novel concept that allows wireless systems to sense the environment, adapt, and learn from previous experience to improve the communication quality. The signal is detected by comparing the output of the energy detector with a threshold which depends on the noise floor. Spectrum sensing on reallocation is a concept of detecting the usage of carrier signal by different nodes and allowing the free spectrum to be used by nodes other than the principle of spectrum holder node. In this paper we Defines overall spectrum (range of frequency for the communication). Then space is divided into spectrum of equal span for example, if the communication basic spectrum is between 1Hz to 100Hz and there is a provision for 20 users than the spectrum ranges will be 1-5, 1-6, and so on. Each node transmits at its allocated spectrum using frequency modulation. Sensor takes the FFTs of channel and determines the pick. Each pick represent used spectrums then subtract used spectrum from total spectrum to get the free spectrum. After getting free spectrum we allocate one of the free spectrum to existing communicating node so that node can completes communication fast. At the end again free the spectrum so the reallocation problem is determined by the energy consumption at the node. If the noise level is uncertain, energy detection will eventually become difficult below a certain SNR level.
A Framework for Processing XML data Using Eclat Algorithm

— since a huge amount of XML documents are available in digital form which represents a semi structured data. And it is one of difficult problems for users to obtain really necessary information efficiently from these XML files. In order to deal with such problems, many researchers are engaged in studies on Information Retrieval (IR) and Text Clustering. Association rules provide a better result and they can predict the mining patterns too. There are several mining algorithms of association rules. One of the most popular algorithms is Apriori that is used to extract frequent itemsets from large database and getting the association rule for discovering the knowledge. But Apriori rules are using horizontal frequent patterns, due to this many times vertical features may lost in mining. So our research proposed an idea of improving mining pattern by using Eclat algorithm for the XML data in vertical pattern and then cluster them efficiently.
Raindrop Detection: Performance analysis of recent approaches for the use in electronic driver assistance systems

- Advanced driver assistive system (ADAS) forms one of the most important as well as a common feature in today’s automotive industry. ADAS depends upon various sensors that aid the driver in visual or auditory feedback. Video based driver assistive systems are designed to work in clear weather and often fail to be of any help in rainy climatic conditions. This affects the overall performance of ADAS. Various research studies make use of algorithms and photometric models of raindrop for its detection and provide a reliable feedback signal. This paper reviews various techniques and photometric models defined for raindrop detection in various climatic conditions.
Performance Analysis of Acoustic Signal in OFDM Using FEC Codes

– This paper addresses the analysis of speech communication over an orthogonal frequency division multiplexing (OFDM) based system and also the impact of Forward Error Correction (FEC) codes namely Reed Solomon encoder Code and Convolution Code on the performance of OFDM wireless communication system for speech signal transmission over both AWGN and fading (AWGN, Rayleigh and Nakagami) channels in term of Bit Error Probability. The simulation has been done under different modulation techniques i.e. BPSK, QPSK and QAM with different code rates.
Implementation and Analysis of a Hybrid approach for Clustering Weblog.

 Abstract — Web mining for usage pattern is the key to discover marketing intelligence in e-commerce. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Various web usage mining techniques are used to analyze the user’s navigational patterns. Clustering is unsupervised classification of data items into groups called clusters. Recently, algorithms inspired by nature are used for clustering. The study of ant colonies behavior and their self-organizing capabilities is of interest to knowledge retrieval. In this paper, an Ant based clustering algorithm is proposed to discover Web usage patterns (data clusters). This paper includes evaluation and the implementation of Ant based Clustering algorithm. The clustered data is then used to analyze the trends like URL visit Analysis, Traffic, maximum hit and session killing pages with the help of Fuzzy Inference rule.
Virol Immunol J 2018, 2(4): 000157

Cancer is a major health problem worldwide. In recent years a lot of studies are focused on preliminary screening of plant extracts for their cytotoxic effect on human cancer cells. This brief review summarizes some of them [1-10]. Chosen studies share similar distinctiveness:  Medicinal plants wеre selected on the basis of their ethnomedicinal uses;  Native and endemic species from geographical regions which possess a great diversity of species have been elected;  Whole plant extracts have been tested;  Anticancer potential of plant extracts was investigated in vitro;  The results illustrate the value of preference based on the traditional knowledge.
Bank Lending and its Impact on Small Scale Enterprises in Nigeria

The research investigates bank lending and its impact on small-scale enterprises in Nigeria. The main issues in this paper relate to understanding the effects and impact of bank lending on small-scale enterprises. In order to analyse the data, econometric methods was used. The econometric regression model of ordinary least square was applied in evaluating the impact of bank lending on small-scale enterprises. The model revealed a positive impact of bank lending on small-scale enterprises. Some recommendations were made therein that measures be taken to create a pool of long-term funds to enable long tenor lending. In addition, measures should be taken to influence the regime of interest rate downward, ease loan documentation process and requirements, create additional inducement to boost lending to SMEs and augment monitoring of funds disbursed under the CBN intervention schemes, etc.
To Study the Informational Efficiency of Indian Stock Market Ms

The current study sheds light on the informational efficiency of Indian stock market. This paper examines the relationship between return, return volatility, and volume in a contemporaneous and dynamic context in Indian stock market and contributes to the literature in several respects. For this purpose, a daily closing price is collected from S&P CNX NIFTY index for a period of 10 years from April, 2007 till March, 2017(i.e. 2890 observations). Granger causality test is applied to investigate information flow between the variables. In addition, it uses the GARCH models in the study of return-volume investigation. This study further checks the information asymmetry with EGARCH (1, 1) model. The study provides evidence of positive impact of volume on return using GARCH (1, 1) model. It also shows the negative impact of volume on conditional volatility because of asymmetry that is observed in significant Jarque-Bera test. The study has also used the EGARCH (1, 1) model, which allows for asymmetric shocks to volatility. It indicates the presence of leverage effect and positive impact of volume on volatility. The differential cost of taking long and short positions is the main reason for information asymmetry (leverage effect). In addition, linear Granger causality results support the sequential arrival of information hypothesis, which implies that new information is not simultaneously available to all traders and it takes time to absorb, hampering the price discovery efficiency of the market.
International Journal of Applied Sciences and Biotechnology

Protease is the largest selling enzyme in the world due to its various applications in the various industries such as detergent, food and leather, meat tenderisation and pharmaceutical industries. The aim of the study is to isolate and identify thermophilic Bacillus licheniformis U1 strains for thermostable protease production. The partial purified enzyme was characterized under different conditions using Anson-Hagihara’s method. Casein as a substrate in the concentration of 0.6 % w/v optimum for enzyme activity and tolerant up to 2.0% casein concentration. An optimum enzyme activity was reported at pH 7 and decreased with increase in pH, while temperature optima was found to be 50 °C. The enzyme was stable at 40 °C to 50 °C for half an hour and nearly 50% residual activity was indicated at 60 °C. NaCl was not required for catalysis. Stability of enzymes in the presence of various organic solvents and different detergents was remarkable. The enzyme was stable up to 3 days into various solvents and slowly denatured with prolonged incubation. The result of the washing performance with detergent was clearly indicated. Moreover the removal of blood stains and dehairing in goat skin suggests the crucial application in the commercial production at large scale.
An optimized technique for test suite refinement in regression test

Quality is the prime goal of any software engineered product. Rigorous product development and testing is required to assure the quality of product. Regression testing is essential to ensure software quality. The software testers apply regression test suites to ensure that new or modified features do not regress the existing features. As new features are added, new test cases are generated to test the new functionality and add up to the existing test case pool, thus increasing the testing cost and the time of regression testing and this directly impacts the delivery and schedule of the product. Thus, there should be technique to deliver a quality product with decreased regression testing time and cost and when rigorous regression test is performed with reduced test suites it should produce the same quality product as the original test suites would produce. In order to address the above problem, two phase test suite refinement approach is proposed. The two phases of the proposed approach are Test Suite Mini-mization and Test Suite Prioritization. In Test Suite Minimization specification based minimization technique is used, whereas in Test Suite Prioritization risk based prioritization technique is used. This approach will enable us to achieve quality product with decreased regression testing time and cost, as it will generate the reduced test suites which will be equivalent to the original test suited for the product.