Miracle-dz/UniFAR-FTUs huggingface dataset:
TOTAL PHENOLIC CONTENT AND IN - VITRO ANTIOXIDANT PROPERTY OF THE STEM EXTRACTS OF CALLICARPA MACROPHY LA

Several plants traditionally used in treatment of a variety of infections in South ern A sia are reported in ethnobotanical s urveys. Many of these plants including ca l licarpa macrophyla lack scientific reports to support their medicinal importance. The antioxidant activities and phenolic contents of the acetone, ethanol and aqueous extracts of the stems of Callicarpa macrophyla were evaluated using in vitro standard methods. The total phenol, total flavonoids and proanthocyanidin content were determined spectrophotometrically. Quercetin, Tannic acid and catechin equivalents were used fo r these parameters. The antioxidant activit ies of the stem extracts of this plant were determined by ABTS, DPPH, and ferrous reducing antioxidant property (FRAP) method s. The quantity of the phenolic compounds, flavonoids and proanthocyanidins detected differ significantly in the various extracts. The phenolics were significantly higher than the flavonoids and proanthocyanidin contents in all the extracts investigated. The ferric reducing ability and th e radical scavenging activities of the extracts were very high and dose - dependent. The ethanol ext ract had the highest antioxidant activity, followed by the acetone extract while the aqueous extract was the least active. Reacting with ABTS, the 50% inhibitory concentrations (IC 50 ) were (0.0429 ± 0.04 mg/ml) for aqueous, (0.0317 ± 0.04 mg/ml) for aceton e and (0.0306 ± 0.04 mg/ml) for ethanol extracts while they inhibited DPPH radical with 50% inhibitory concentration (IC 50 ) values of 0.0646 ± 0.02 mg/ml (aqueous), 0.0482 ± 0.02 mg/ml (acetone) and 0.0422 ± 0.03 mg/ml (ethanol). A correlation between the antioxidant activity and the total phenolic contents of the extracts indicated that phenolic compounds were the dominant contributors to the antioxidant activity of the plant. This study, therefore,
A GENERIC STUDY ON DM TECHNIQUES AND APPLICATIONS FOR KNOWLEDGE DISCOVERY

- Data mining (DM) [1] is identifying significanttrends and correlations in large amounts of stored data. A main task in DM is exploration of data for analysis. The need for automated extraction of beneficial knowledge is widely recognized. DM techniques identify similarity in data for necessary inferences. DM can be used in medicinal, marketing, business, social media and any other field having voluminous data. DM or Knowledge discovery in data is a growing field driven by research interests and needs. This paper details on the DM techniques and applications prevalently found for knowledge discovery.
Image Encryption Using Scrambling and Diffusion Operation Using Chaotic Map

— Security of image data has become increasingly important for many applications like video conferencing; secure facsimile, medical, military applications etc. In this paper we are proposed image encryption technique which includes scrambling and diffusion stages. In scrambling stage, Input image undergo row scrambling and column scrambling with the help of chaotic map. In diffusion stage manipulating the pixels value based on parity function. The result shows that proposed method achieves good security in terms of entropy and NPCR. Decryption is the reverse process of encryption.
Assertiveness, Locus of Control, Affiliation and Vulnerability ofPersonality to Manipulation: Connection and Properties

In the article for Russian-speaking residents of Belarus positive connection between assertiveness and internal locus of control both for men and women is established, while in English speaking society it is takes place only for men. A high statistically significant negative correlation between assertiveness and striving to acceptance, assertiveness and the fear of rejection is found, which indicates the self-sufficiency of the assertive personality regarding the process of communication. The fact received by the author earlier that women are generally better protected against manipulative influences than men is confirmed. Mental states experienced by the assertive as well as vulnerable to manipulations objects and subjects of manipulation are revealed. The results of this experimental study are compared with the results obtained by other researchers.
Quantum Image Storage, Retrieval and Teleportation

-Image storage, retrieval and compression are the basic tasks of an imaging system. Many classical approaches are there to do this task but the results of this paper showed that the use of quantum mechanical approaches give better results. For image storage a quantum machine was used and the quantum states were prepared as the function of 𝜽 which itself was a function of the frequency. For image retrieval measurements on the spin of the maximally entangled pairs was done for every pixel. The probabilistic result gives the probability of the presence of a color in an image. Classical approach gave 0.5 probability of retrieving the color whereas quantum mechanical approach gave 0.846 probability of retrieving the two colors. Quantum teleportation was used for image transmission. The teleportation was performed using bell channel for which 100% probabilistic success was obtained.
Spectral Optimization of Two-Phosphor-Coated White Light-Emitting Diodes

The optimal spectra of two-phosphor-coated white light-emitting diodes with correlated color temperatures (CCTs) of 2700 K to 6500 K have been obtained by nonlinear program for maximizing luminous efficacy of radiation (LER) under different conditions of color rendering indices (CRIs), color quality scale (CQS), and special CRIs of R9 for strong red, The simulation results show that the two-phosphor-coated LEDs consisting of the InGaN blue chip, traditional yellow/green phosphors and red-emitting QDs could realize white lights with LER  337 lm/W for CRI  90 and CQS  80, LER  315 lm/W for CRI  97, CQS  94 and R9  94 at CCTs of 2700 K to 6500 K. The changes of LER, CRI, CCT, and chromaticity difference (uv) have been observed to be associated with the shifts of peak wavelength (p) and full width at half-maximum (FWHM) affected by LED operating temperatures. Furthermore, a solution of warm-white LED, which is consists of the InGaN blue chip (B = 459 nm and FWHMB = 30 nm), traditional yellow/green phosphors (Y/G = 552 nm and FWHMY/G = 50 nm) and red-emitting QDs (R = 621 nm and FWHMR = 19 nm) with LER = 379 lm/W, CRI = 90, CQS = 86 and R9 = 88 at CCT = 2700 K, is recommended in order to achieve warm-white light with high photometric and colorimetric performance.
AQUEOUS EXTRACT OF CYCLEA PELTATA LEAVES AS CORROSION INHIBITOR FOR MILD STEEL IN 1.0N H2SO4 BY EMPLOYING ISOTHERMS

Inhibition efficiency of corrosion of mild steel in acid medium by using aqueous extract of Cyclea peltata plant leaves has been examined by applying weight loss method. The conclusion of this study exposed that various concentrations of the leaves extract of Cyclea peltata plant inhibited the corrosion of mild steel in 1.0N H2SO4 acid solution. The inhibition suitability of the leaves extract is consigned to the presence of the phytochemical constituents in that. The weight loss study reports exhibited that inhibition efficiency of corrosion on mild steel is increased with increasing the concentration of inhibitor. Adsorption study also exposed that adsorption of the aqueous extract of Cyclea peltata leaves on mild steel surface occurred by agreeing to Langmuir,Tempkin, FlorryHuggins and El-awady adsorption isotherm.
Performance Analysis of Huffman Coding Algorithm

— Huffman coding is such a widespread method for creating prefix-free codes. Huffman code is used as a synonym for prefix-free code even when such a code is not produced by Huffman's algorithm. Huffman coding is an efficient source coding algorithm for source symbols that are not equally probable. It yields the smallest possible number of code symbols per source symbol [7]. In this paper, the Lossless method of compression and decompression is proposed using a simple coding technique called Huffman coding which is used on text string as well as on different image file formats for performance analysis. The results reveals that the original image used for coding is almost close to the decoded output image.
A Genetic Algorithm for Clustering in Data Mining

Clustering techniques have obtained adequate results when are applied to data mining problems. Clustering is the process of subdividing an input data set into a desired number of subgroups so that members of the same subgroup are similar and members of different subgroups have diverse properties. Many heuristic algorithms have been applied to the clustering problem, which is known to be NP Hard. Genetic algorithms have been used in a wide variety of fields to perform clustering, however, the technique normally has a long running time in terms of input set size. In this paper we investigate the use of Genetic Algorithms to determine the best initialization of clusters, as well as the optimization of the initial parameters. The genetic algorithm uses the most time efficient techniques along with preprocessing of the input data set. The experimental results show the great potential of the Genetic Algorithms for the improvement of the clusters. The techniques of clustering are most used in the analysis of information or Data Mining, this method was applied to Data Set at mining
STRUCTURAL EQUATION MODELING FOR IDENTIFYING ANTECEDENTS IN STRATEGIC TALENT MANAGEMENT PRACTICES AND JOB DESIGN FOR INNOVATION IN INDIAN MANUFACTURING FIRMS

In this article, a search for antecedents’ latent variables of innovation from Strategic Talent Management Practices and Job Design for Indian Manufacturing industries was made. Structural Equation Modeling (SEM) was used to assess the fit of the model that identified Job Characteristics, Job Identity and Overall Job Design of employees as statistically significant antecedents of Innovation. Our models provided a good fit. Direct and Indirect effects of exogenous variables on endogenous variables estimated and resultant percent of variance accounted into Innovation reported. Limitation of the study, managerial implications and direction for future research discussed.
A Channel independent precoding with different equalization techniques in a MIMO based system

MIMO systems are used in wireless communication where the capacity of the radio link can be increased by using multiple transceivers to support the multipath propagation. The interferences occurring between the different antenna elements in MIMO OFDM can be mitigated by using the precoding technique, where the data is coded and transmitted to reduce the bit error rate. In this paper, a channel independent precoding technique with interference alignment concept is considered to mitigate the interference in the system. To recover this precoded data, different equalizers like Zero Forcing equalizers, MMSE equalizers etc. are used in the receiver section. The simulation results shown that the precoding with MMSE equalizers at the receivers can outperform precoding with the Zero forcing detectors.
Accommodate Negative Requirement Model for Security Testing in Cloud Computing

Providing security for a software system has always been a difficult task. The success of a software system always depends on how good the system is tested and the level of assurance for error prone system. All the positive requirements are considered for the testing which will not may enough for providing assurance. Hence negative requirements are also incorporated using risk analysis. Considering the implementation in Cloud computing environment where a huge number of test cases are also required due to its openness.
A Neural Network Approach to Personality Prediction based on the Big-Five Model

— The use of social networking websites has increased since last few decades. The social networking sites such as Twitter, Facebook, LinkedIn and YouTube are sources of human interaction, where the users are allowed to create and share their activities, thoughts and opinions pertaining to different subjects. These sites have not only connected large user populations but have also captured massive information associated with their daily interactions in the form of Big Data. This data provides unprecedented information about human behavior and social interactions. It makes it possible to understand who the users are, what their interests are and what they need. This information is vital for businesses to target potential consumers or seek customer opinions in the event of diversification as a business strategy. Thus, this work analyzes social media data to predict significant personality traits, i
Blockchain Based Secrete Key And Transaction Storage System For Documents Stored On Cloud

: Cloud document storage is becoming very popular, cost efficient and secure nowadays. Though cloud is very cost-efficient storage, the documents are accessible to service providers. Many literatures are available for document security in cloud. In most of the literatures, document encryption technique is described using different algorithms. Encrypting documents before storage is a good idea, but if encrypted documents as well as the secrete keys both are maintained on cloud server only, there is a possibility of leakage as the cloud service provider is an honest but curious entity. So, it can be a big risk if we maintain documents as well as their keys on cloud. On the other hand, if we apply unbreakable encryption on documents before storing on cloud then the searching over documents will be critical. Therefore, in this paper we focus on cloud document security as well as searching over cloud documents using AES algorithm, Caesar algorithm and blockchain technology.
Roll Attitude Control of a Space Vehicle

Guided missiles are space-vehicles carrying war-heads. Three attitudesthe roll, pitch and yaw have to be continuously controlled in a space flight. This is done by using automatic control systems. The design procedure for roll attitude control of a guided missile is the subject of the paper. This is extremely important for following proper trajectory and hitting the target. Two designs have been advanced. In the first design, there is no constraint on velocity error. The design has been optimized by simultaneous variation of forward path gain and the rate feedback. In the second design, the velocity error has been specified. It has been optimized by simultaneous variation of the rate feedback and the parameters of a lead compensator.
Financial Theories with a Focus on Corporate Cash Holding Behavior: A Comprehensive Review

This study reviews the role of different financial theories such as, trade off theory, pecking order theory and free cash flow theory in the decision making of corporate cash management practices. The review also shed a light on the ground breaking study of Modigliani and Miller (1958) in the field of corporate financial behavior. Majority of the literature depicts the prominence of trade off theory and pecking order theory in the cash management practices of firms. However, some empirical and theoretical studies also described the significance of free cash flow theory. Because, the managers desires to hold the distinctive power in the firm’s financial and investment policies through holding higher cash level. Most importantly, based on the previous empirical findings, this study warrants the need to empirically extent the significance of financial theories in the future at sector level.
A Radiomics Model in Predicting Recurrence Time of Small Hepatocellular Carcinoma After Hepatectomy Base on MR by ANN

Objective: The recurrence time of small hepatocellular carcinoma (sHCC) after resection are heterogeneous. Prediction the recurrence time of sHCC after resection is propitious to the fine management and individualized treatment of patients with sHCC, especially preoperative noninvasive. Methods: Collected the patients who with SHCC resection, and performed MR before operation one month long, cases with complete follow-up data in the Fifth Medical Center of the Chinese PLA General Hospital during January 2010 to January 2017. Abstract radiographic features of MR LAVA sequence Mask images by pyradiomics and input ANN. Results: A total of 179 cases were enroll into the study, of which 89 were early recurrence (≤24 months) cases and 90 were non-early recurrence (>24 months) cases. Abstract 121 radiographic features of MR LAVA sequence Mask images. Input ANN model into training group (150 cases) and test group (29 cases), the AUC value is 0. 64. The correlation factors of AFP and tumor size were 0.03 and 0.06 respectively. Conclusion: the ANN model of Mask features of MR T1 LAVA sequence can be used to predict the recurrence time of sHCC after resection before the operation, to establish the operative strategy and determine the Image examination frequency of postoperative follow-up.
Spatio-Temporal Video Denoising by Block-Based Motion detection

– This paper proposes a new video denoising technique where spatially adaptive noise filtering in wavelet (transform) domain is combined with temporal filtering in signal domain. AWGN is being considered which behaves as Gaussian random variable. In this paper, spatial filtering of individual frames is done in the wavelet domain, and the filtering between the frames is done by recursive temporal filter. Spatial filtering is done by taking wavelet transform of individual frames and then modifying the wavelet coefficients by spatially adaptive bayesian wavelet shrinkage method. The denoising artifacts and residual noise differ from frame to frame which produces unpleasant visual effect. Hence filtering in time domain is essential. Temporal filtering is based on a simple block based motion detector and on selective recursive time averaging of frames. This technique outperforms sequential spatio-temporal filters, 2-D spatial filters and 3-D (spatio-temporal) in terms of visual quality as well as quantitative (PSNR) performance measures .
Refined Clustering technique based on boosting and outlier detection

- Boosting is the repetitive process to perk up the accuracy in functions for prediction that supervised learning (SL) system learn using training data. In this prediction process, boosting considers multiple function rather that considering only single function from the same supervised learning system. Boosting process then predicts the label for new data instances using a weighted vote over all the functions. By considering and merging multiple functions together , boosting manage to get fine grained decision boundary on training data than using single function. Boosting for supervised learning having certain limitations like e.g. because of problematic data difficulty arises to analyze the data , over-fitting of training data , wrong label prediction by initial function etc. Previous worked reflected that boosting is resistant to over fitting problem. Also in case of wrong label prediction from function, boosting achieves higher accuracy when multiple functions are used to decide the labels for clusters. Previous work have some difficulties like A] Wrong data i.e. label noise in training data which causes wrong output instances and B] Another problem is that when feature of label instances are different and not relevant with respective rest of training data then its proper cluster cannot be defined properly. Hence there must be proposed system that work on these problems. Also clustering can be achieved on problematic dataset also. For this cluster based boosting (CBB) approach should be adopted to achieve this. Also along with CBB, the outlier detection should be achieved so that data will be easy to analyze and cluster can be formed smartly.
Sociability and stock market participation: a sociological study of investors from Bangalore

The purpose of this study is to shed more light to limited stock market participation which has been found to have many implications on both household level and public at large. To reach this goal, I analyze a comprehensive list of stock market participation drivers and compare their explanatory power. Even though limited stock market participation is widely studied in the field of economics and finance, there is a dearth of studies from the sociological perspective which takes social interaction into account for explaining stock market participation, controlling for all relevant demographic variables.abstract should summarize the content of the paper.
A Bilingual (Gurmukhi-Roman) Online Handwriting Identification and Recognition System

: Advances in recent technologies and the power of Artificial Intelligence (AI) always motivate the researchers to work for Human-Computer Interaction (HCI). So that, HCI behaves similar to HHI (Human-Human Interaction). Various applications which come under HCI are automatic emotion detection from facial images and videos, sentiment analysis from textual data or abbreviation slangs, handwriting recognition etc. Automatic recognition of handwriting is one of the most complex task for computer systems. The factors like the existence of variety of languages with their unique scripts, difference among the writing styles of different writers or even same writer over time, delayed strokes, noise, requirement of huge amount of training etc. make this task most difficult. In this paper, the problem of Online Handwriting Recognition (OHR) is addressed for the text containing bilingual data. The input data is written by using English and Punjabi languages, so both Roman and Gurmukhi scripts are used for the purpose of writing. During feature extraction process, the stress is given to the local features by considering direction of strokes, position or order of strokes, slope and area covered by one complete stroke. Global features include the information about Headline stroke, straight lines and dots. Statistical features consider Zone identification and Direction code histogram. The final recognition is done by using Multi Layered-Perceptron (MLP) neural network as a classifier in the presence of two simultaneous recognition engines for Roman and Gurmukhi scripts respectively. Experimental observations show good results for the bilingual text.
Deployment Techniques in Wireless Sensor Networks

 Abstract— In this paper, we study coverage with connectivity properties in large wireless sensor networks (WSN). Coverage is one of the main research interest in wireless sensor network, it is use to determine the quality of service of the networks. Therefore this paper aims to review the common strategies use in solving coverage problem in WSN. The strategies reviewed are categorised in to three groups based on the approaches used; force based, grid based or computational geometry based approach.