Miracle-dz/UniFAR-FTUs huggingface dataset:
Various JPEG Image Steganography Techniques: A Review

— Image Steganography is a technique of providing some hidden data into the cover or host image so that it can be transmitted in a secure manner. There are various Image Steganography techniques implemented with some advantages and limitations. Here in this paper a complete survey of all the Image Steganography technique their advantages and issues are discussed and analyzed, hence on the basis of their issue or limitations a new and efficient technique is implemented in future.
EVALUATION OF ANTIDYSLIPIDEMIC EFFECT OF MANGIFERIN AND AMAROGENTIN FROM SWERTIA CHIRAYITA EXTRACT IN HFD INDUCED CHARLES FOSTER RAT MODEL AND IN VITRO ANTIOXIDANT ACTIVITY AND THEIR DOCKING STUDIES

Swertia chirayita (SC) has been traditionally used for the treatment of several metabolic disorders related health problems. We have investigated antidyslipidemic and antioxidant potential of Swertia chirayita (SC) plant, a potent antidiabetic and its isolated markers. The LC-ESI-MS standardized hot water extracts of different batches from whole plant of Swertia chirayita, were tested in vivo for their antidyslipidemic activity in Triton (WR-1339) and high fat diet (HFD) induced dyslipidemic Charles Foster rats and in vitro for their antioxidant activity. Further solvent fractionation and isolation resulted in identification of antidyslipidemic Mangiferin (MG) and Amarogentin (AM). Treatment of hyperlipidemic rats with MG and AM at 100 and 200mg/kg doses caused reduction in plasma levels of Low density lipoprotein (LDL) 24, 26% and very low density lipoprotein (VLDL) 19, 25% as well as increase in levels of High density lipoprotein (HDL) 18, 22% respectively. In rats with HFD, MG and AM significantly reduced levels of VLDL lipids (26% and 27%) as well as Low density lipoprotein-Total cholesterol (LDL-TC) 27% and 30% in hyperlipidemic rats at 100 and 200 mg/kg doses. This is first report of AM demonstrated as antidyslipidemic agent. MG and AM also showed inhibition of superoxide anion, hydroxyl free radicals and microsomal lipid peroxidation by 24 & 28%, 25 & 31% and 27 & 25%, respectively. In addition, both MG and AM also demonstrated more effective inhibition at 5 μM and 10 μM of HMGR (HMG-CoA reductase) in comparison to Pravastatin, which was further confirmed by docking studies. INTRODUCTION: Cardiovascular disease (CVD) remains the leading cause of death in the developed countries and is major cause of mortality and disease in the Indian subcontinent. QUICK RESPONSE CODE DOI: 10.13040/IJPSR.0975-8232.5(9). 3733-40 Article can be accessed online on: www.ijpsr. com DOI link: http://dx.doi. org/10.13040/IJPSR.0975-8232.5(9). 3733-40 The World Health Organization (WHO) estimates that CVD is today the largest single contributor to global mortality and will continue to dominate mortality trends in the future 1 . Increase in lowdensity lipoprotein (LDL), triglyceride (TG) and total cholesterol (TC) levels with decrease of highdensity lipoprotein (HDL) levels have been established as one of the risk factors for CVD 2 . Clinical and epidemiological studies have consistently shown that low plasma levels of highdensity lipoproteins cholesterol (HDL-C) are
A Review of Microbial Fuel Cells for Bioelectricity Generation

In this review a brief description of microbial fuel cells technology for generation of bioelectricity has been done. The review is mainly intended to focus on standard configurations, electrode materials, membranes, substrates, microorganisms and generation of bioelectricity. Being low in power generation, MFC is a slow pacing technology but has the enormous ability to act as long term sustainable power generation source. The most spectacular feature of MFC is the utilization of organic matter which is generally supposed to be as waste. This innovative expertise has opened the way for alternative renewable energy generation.
Homology Modelling of Lycopene Cleavage

Annatto (Bixa orellana L.) plant contains pigment contains bixin and norbixin, valuable natural colorants (Rodrigues et al.,2007). These pigments are widely used for industrial food and beverages, cosmetics, and as natural dyes for textiles (Chattopadhyay et al., 2008). Bixa orellana seed extracts also lowers blood glucose level by stimulating peripheral utilization of glucose so the seed might be of pharmacological importance (Russell et al., 2008). The natural dye bixin is extracted from the seed of the annatto plant and widely used as food colorant. Many restrictions are on the use of synthetic additives in the food industry as the synthetic dye having adverse effect but the investigation on biochemical properties of Bixa orellana plants and seed extracts has proven that it is having tumour inhibiting capacity (Reddy et al., 2005). Bixin is produced from the sub-pathway of isoprenoid biosynthesis. More specifically from the translycopene the bixin synthetic pathway diverges by the enzyme Lycopene cleavage oxygenase and further the action of intermediate enzymes like Lycopene beta cyclase and Lycopene epsilon cyclase lead to production of the pigment (Bouvier et al., 2003). So due to highly commercial and pharmaceutical importance and also to understand the biochemical and molecular basis of the biosynthesis of the natural compound is an interesting task. The first enzyme i.e. Lycopene cleavage oxygenase of the bixin synthesis pathway has been considered for the present study.The present study aim to model and validate the 3D structure of Lycopene cleavage oxygenase by computational approach whose structure has not been reported yet. Then to check the reliability of the model and validation purposes various tools and molecular dynamics method is used. This work may be further carried out for the biotechnology directed natural food colorant production in industrial level.
Object segmentation using Active Contour Model based on Probability density function

- Active contour are generated by the computer that are the curves that move within an images to find boundaries of the object in an image. They are also used in image analysis and computer vision to recognize and find objects, and to describe their shape. Region based methods are used for locating an object in an image instead of its edges. Region based Active Contour Model that segments one or many image regions that are visually alike to an object of interest said as prior. The Prior contains the object to be segmented in an input image. Shape is the main constraint of finding the exact object in an image, previously color is used to detect the object but that was not accurate. The proposed approach uses the heuristics rules, the probability density function to mapping the images to find the accurate results.
Image Segmentation Using a RBF Approach of Neural Network

Radial Basis function Neural Networks forms a class of neural networks which is much more advantageous then other methods of neural networks such as faster learning, easy networks & structures & better approximations & classifications. The system consist of a multilayer perceptron (MLP)-like network that performs image segmentation by RBF technique of the input image using labels automatically pre-selected by a fuzzy clustering technique. The proposed architecture is feedforward, the learning is unsupervised. The proposed system is capable to perform automatic multilevel segmentation of images, based solely on information contained by the image itself. Keyword — Image segmentation, Multilayer perceptron, unsupervised, Fuzzy clustering.
Draaiboek voor de ontwikkeling, implementatie en evaluatie van een klinisch pad. 30 stappenplan van het Netwerk Klinische Paden

De ontwikkeling van een klinisch pad is tijdrovend en complex, omwille van het feit dat de praktijk complex is. Klinische paden zijn een methode om zorgprogramma’s operationeel te maken, ze op een systematische wijze te plannen en op te volgen in een organisatie. Ze worden zelden opgestart voor een nieuw programma. In hoofdzaak worden bestaande programma’s herwerkt (Vanhaecht et al., 2002, Sermeus & Vanhaecht, 2002).
Review: Web Semantics in Cloud Computing

– This paper shows how cloud computing on the background of Semantic Web is going to important. Cloud computing has become interesting area both in academia and industry. Semantic Web has been an important research area in academic and industrial researchers. Many applications will need to work with large amounts of data, one particular application would certainly not exist without the capability of accessing and processing arbitrary amounts of metadata: search engines that locate the data and services that other applications need. To solve this problem, semantic web services used with cloud computing. In this paper we present the use of well established semantic technologies in Cloud computing. The purpose of this report is to give an overview of semantic web services and semantic web technologies in cloud computing.
A Fingerprint-based Age and Gender Detector System using Fingerprint Pattern Analysis

Humans have distinctive and unique traits which can be used to distinguish them thus, acting as a form of identification. Biometrics identify people by measuring some aspect of individual‟s anatomy or physiology such as hand geometry or fingerprint which consists of a pattern of interleaved ridges and valleys. The year 2015 election in Nigeria was greeted by some petitions including under-aged voters. The need for an age and gender detector system is a major concern for organizations at all levels where integrity of information cannot be compromised. This work developed a system that determines human age-range and gender using fingerprint analysis trained with Back Propagation Neural Network (for gender classification) and DWT+PCA (for age classification). A total of 280 fingerprint samples of people with various age and gender were collected. 140 of these samples were used for training the system‟s Database; 70 males and 70 females respectively. This was done for age groups 1-10, 11-20, 2130, 31-40, 41-50, 51-60 and 61-70 accordingly. In order to determine the gender of an individual, the Ridge Thickness Valley Thickness Ratio (RTVTR) of the person was put into consideration. Result showed 80.00 % classification accuracy for females and 72.86 % for males while 115 subjects out of 140 (82.14%) were correctly classified in age.
Computer aided analysis of 3T dynamic contract-enhanced (DCE) MRI scores correlate with the ultrasound Doppler colour fraction at the wrist joint in patients with rheumatoid arthritis

Poster: "ECR 2012 / C-0645 / Computer aided analysis of 3T dynamic contract-enhanced (DCE) MRI scores correlate with the ultrasound Doppler colour fraction at the wrist joint in patients with rheumatoid arthritis" by: "M. Boesen1, K. Ellegaard1, S. Torp-Pedersen1, H. Gudbergsen1, O. Kubassova2, P. C. Taylor3, M. Ostergaard4, M. A. Cimmino5, H. Bliddal1; 1Frederiksberg/DK, 2Leeds/UK, 3London/UK, 4Glostrup/DK, 5Genoa/IT"
Developing Real World Applications using Augmented Reality

As the hardware capabilities of mobile devices advance, Engineering is coming up with various innovations that would have not been possible to achieve a few years ago. Augmented reality is one of these innovations. Although a relatively newer concept, Augmented Reality is already being widely accepted in fields like medicine, architecture, the automobile industry and many more. There is a plethora of applications available for download which are based on Augmented Reality. This paper discusses the different types of Augmented Reality technologies and various components essential to achieve an Augmented Reality environment. A few examples of the applications made using Augmented Reality are also discussed. While it cannot be implied that Augmented reality is something that has become a must in the industry, it is a growing technology which will have a broad spectrum of applications in the future.
Effects of Dividend Policy on Investment Decisions

Dividend Policy is one of the key factors influence many investment decisions. This paper examine whether the dividend has a any effect on investors decision making? We construct a random sample of 152 firms listed in Tehran Stock Exchange, betweem 2009 and 2014. This correlational research is applied in terms of objective. To test the hypotheses, the combined linear and multiple regression model was used.
Detection of Fibroid Using Image Processing Technique

— Magnetic resonance imaging (MRI) has become a common way to study various tumours. In this paper the magnetic resonance images of uterus is pre-processed to detect the fibroid This paper presents a method that can successfully identify fibroid and thereby help the doctors for analyzing the size and exact location of fibroid. Uterine-cancer is one of the most common malignant gynaecological tumours which cause a serious threat to women health.
PARALLELIZED COMPRISING FOR APRIORI ALGORITHM USING MAPREDUCE FRAMEWORK

: An integrating classification and association rule mining can produce more efficient and accurate classifiers than traditional techniques. The recently introduces MapReduce based association rule mining for extracting strong rules from large datasets. This mining is used later to develop a new large scale classifier. Map Reduce simulator was developed to evaluate the scalability of proposed apriori algorithms on MaReduce. The developed associative rule mining inherits the MapReduce scalability to huge datasets and to thousands of processing nodes. For finding frequent item sets, it uses hybrid approach between miners that uses counting methods. The new miner generates same rules that usually generated using apriori algorithms. Map Reduce classifier that based MapReduce associative rule mining. For the purpose of data mining to big data, parallel comprising this algorithm employs different approaches in rule discovery, rule from frequent itemsets, and rule pruning methods in these research fields. The present Map Reduce was developed to measure the scalability of MapReduce based applications easily and quickly, in this paper comprehensive to evaluate an accurate and effective classification technique, highly competitive and scalable if compared with other traditional and associative classification approaches.
Level Set Method for Image Segmentation and Bias Field Estimation using Coefficient of Variation with Local Statistical Information

— Most of the image processing techniques use image regional information for image segmentation, image registration, etc. Region based methods for image segmentation with bias field estimation based on the statistical information of different region such as intensity mean, intensity distribution etc. These methods rely on the assumption of spatial invariance in the image domain that is not always valid for most images. We present a new variational model in this paper for segmenting important tissues in the image with the estimate of bias field. We use a term like coefficient of variation as a criterion function based on the local statistical information, and define energy functional in the level set framework. Coefficient of variation is a relative measure uses relative intensity information in the neighborhood for achieving the desired result. Minimization of this energy functional gives estimate of bias field and segment important tissue in the image domain. Empirical results reveal that our new proposed model is independent of initialization and accurate in contrast with the existing model.
Design and Implementation of XML Context Diversified Search

--------------------------------------------------------------------- Abstract - An When user searches a keyword he types short and obscure keywords and therefore the ambiguity of keyword queries makes it tough to effectively answer keyword queries. This paper focuses on wide-ranging XML keyword search supported on its completely different contexts within the XML information . If the user enters short and obscure queries it's searched in XML information, and derive keyword search candidates of the queries by a straight forward choice model. Then design a good XML keyword search diversification model by implementing two algorithms.
Design and Implementation of an On-chip Multistage Network Topology for System On-Chip

— This paper presents the silicon-proven design of a novel on-chip network to support guaranteed traffic permutation in multiprocessor system-on-chip applications. The proposed network employs a pipelined circuit-switching approach combined with a dynamic path-setup scheme under a multistage network topology. The dynamic path-setup scheme enables runtime path arrangement for arbitrary trafﬁc permutations. The circuit-switching approach offers a guarantee of permuted data and its compact overhead enables the beneﬁt of stacking multiple networks. It is a challenging task in a network-on-chip to design an on-chip switch/router to dynamically support (hard) guaranteed throughput under very tight on-chip constraints of power, timing, area, and time-to-market, and coded using Xilinx 12.2.
A Novel Architecture of Bilateral Filter for Image Denoising

In this paper, a novel architecture for bilateral filter is described. The bilateral filter is an edge preserving filter that takes both the geometric closeness and photometric similarity into account. The distinctive feature of our design concept is the pipelining of separable modules. The input data is arranged into groups and internal clocks are provided for this kernel-based design. For acceleration of the design concept the separability and symmetry of the geometric filter component is utilized, also reducing the complexity of the design Keywords—Bilateral filtering; image denoising; ; pipelining
Quadtree Algorithm for Improving Fuzzy C-Means Method in Image Segmentation

Image segmentation is an essential processing step for much image application and there are a large number of segmentation techniques. A new algorithm for image segmentation called Quad tree fuzzy c-means (QFCM) is presented I this work. The key idea in our approach is a Quad tree function combined with fuzzy c-means algorithm. In this article we also discuss the advantages and disadvantages of other image segmenting methods like: k-means, c-means, and blocked fuzzy c-means. Different experimental results on several images in this article show that the proposed method significantly increases the accuracy and speed of image segmentation
Finite-element approximations of the nonstationary Navier-Stokes problem. Part IV: error estimates for second-order time discretization

This paper provides an error analysis for the Crank–Nicolson method of time discretization applied to spatially discrete Galerkin approximations of the nonstationary Navier–Stokes equations. Second-order error estimates are proven locally in time under realistic assumptions about the regularity of the solution. For approximations of an exponentially stable solution, these local error estimates are extended uniformly in time as ${\text{t}} \to \infty $.
Performance Analysis of DBA Algorithms for EPON with Differentiated Classes of services

: A network consisting of a number of subscribers connected to a central office by using optical fibers through a passive splitter is called an access network. The access network traffic is growing day by day with the increase in number of applications. Downstream transmission of data happens through broadcast whereas, for upstream transmission, bandwidth scheduling algorithms are required to prevent collision of data during transmission. These scheduling algorithms follow Multi Point Control Protocol as specified by IEEE. A number of bandwidth allocation algorithms exist in the literature. The objective of this paper is to compare the existing algorithms like the Min’s DBA, Russian Doll Model, and our algorithms-the two variants of Modified Russian Doll Model namely MRDM1 and MRDM2 and the User Prioritized Constraint Free Model proposed earlier in terms of the bandwidth utilization factor and average delay of packets. The algorithms are simulated using a Discrete Event Simulator modeled in C++ and designed by us specifically for Ethernet PON Networks having Differentiated classes of services in each Optical Networking Unit.
Personalized and Ranking Travel Recommendation On Predict Of Multi Source Big Data

 Abstract — Recommendation systems and adaptive systems have been introduced in travel application to sustain the travelers in their administrative process. Travel based suggestion and journey planning are demanding tasks because of various importance preferences and trip limitations such as restriction of time, source and destination points for each tourist. Large amount of data can be composed from the Internet and travel guides, but these assets normally suggested personalized Point of Interest (POI) that is considered to be common, but the existing studies do not provide satisfactory information to the substance preference of the users or hold to their constraint. Unlike most existing travel recommendation approaches, the recommendation approach is not only personalized to user’s travel interest but also able to suggest a travel sequence rather than personalized Points of Interest (POIs). Topical package space together with representative tag, the distributions of cost, visiting time and visiting season of each topic, is mined to connection the terms gap between user travel preference and travel routes. The proposed studies take advantage of the balancing of two kinds of social media; travelogue and community-contributed photograph. The proposed system map both user’s and routes’ textual description to the topical package space to get client/user topical package model and route topical package representation (i.e., source, destination, topical interest, cost, time and season). To suggest personalized POI sequence, first, well-known routes are rank according to the resemblance between user package and route package. Then top ranked routes are additional optimized by community similar users’ travel records. In addition, the massive volume of information makes it a challenge for every tourist to pay notice to a potential set of POIs to make a visit in any unknown city. To sort out these problems, this research work expand this method to provide an author topic matrix modeling algorithm (ATMMA) is suggested for personalize tour. Hence, this method is highly explained here for tour recommendation problem based on similar user and similar city forecast, which considers user tags.
Impact of Medicare Part D coverage gap on Medicare beneficiaries with COPD: Adherence, healthcare resource use, and cost

Medicare Part D provides prescription drug coverage for beneficiaries to support their pharmacological treatment; however, the complex deductible structure within benefit plans creates a major coverage gap and unexpected consequences. Some evidence has demonstrated reduced adherence resulting from the coverage gap; however, little research has evaluated the effect on healthcare resource use (HRU) and cost, and no studies have been conducted for beneficiaries with chronic obstructive pulmonary disease (COPD). This study examined the impact of the coverage gap on medication adherence as well as healthcare resource use and medical cost among beneficiaries with COPD. Claims data based on a 5% random sample of Medicare beneficiaries were used in this retrospective cohort study. For each year from 2007 to 2010, beneficiaries diagnosed with COPD were assigned to either an exposure cohort if they were at risk of the coverage gap or a control cohort if they were not. Exposure and control cohorts were matched using a high-dimensional propensity scores. Adherence was defined as no less