Miracle-dz/UniFAR-FTUs huggingface dataset:
Hibiscus Sabdariffa Calyx Extract Alleviate Hepatotoxicity Induced by Carbon Tetrachloride on Male Albino Rats

Background: The liver disorders are one of the world problems. Unfortunately, conventional or synthetic drugs used in the pretreatment of liver diseases are inadequate and sometimes can have serious side effects. Roselle (Hibiscus sabdariffa L., family Malvaceae) is an annual shrub, it exhibits many biological activities, such as antiatherosclerosis, anti-carcinogenic, and anti-oxidative properties. Aim: The present study aims to determine the hepatoprotective effects of Hibiscus sabdariffa extract (HPEEE) against carbon tetrachloride (CCl4) induced liver injury in male rats. Material and Methods: Male albino rats (n=30) were intraperitoneally (i.p.) injected with CCl4 (1 mL/kg b.wt., 1:1 v/v mixture of CCl4 and liquid paraffin) every 72 h for 14 days to induce hepatotoxicity. Pretreatment with HPEEE was administered orally at three dosage levels (200, 400 or 600 mg/kg b. wt) daily started two weeks prior to CCl4 injection and continued until the end of the experiment. Serum alanine transaminase (ALT), aspartate transaminase (AST) and alkaline phosphatase (ALP) activities, lipid profile and hepatic malondialdehyde (MDA) and reduced glutathione (GSH) were measured. As well as the liver tissues of different groups were examined. Results: The oral administration of HPEEE displayed a strong hepatoprotective effect as it significantly dose-dependent reduced CCl4-induced hepatotoxicity in rats, as judged from the serum activities of ALT, AST, and ALP. As well as it showed a significant decrease in CCl4induced MDA formation in the liver and increase in hepatic GSH activity. Also HPEEE pretreatment, showed a significant increase in HDL-C concentration and a decrease in the levels of TC, TG, LDL-C, and VLDL-C as compared to untreated CCl4-group. Histopathological examination of the liver tissues of CCl4 group represented the presence of focal hepatic necrosis associated with leucocytic cell infiltration, and apoptosis of hepatocytes, multiple focal areas of hepatic necrosis, and marked congestion of central vein, while the pretreatment with HPEEE overcome most of these changes, the majority of the cells tend to be normal in group received 600 mg/kg b.wt. Conclusion: These results suggest that the extract of dried flowers of Hibiscus sabdariffa L. Possesses strong antioxidant, hepatoprotective and hypolipidemic effects on CCl4-induced oxidative stress in rats. Therefore, HPEEE could be of potential help as a medicament or food supplement for alleviation of liver toxicity. [Jamilah M Hashemi. Hibiscus Sabdariffa Calyx Extract Alleviate Hepatotoxicity Induced by Carbon Tetrachloride on Male Albino Rats. Nat Sci 2014;12(6):111-120]. (ISSN: 1545-0740). http://www.sciencepub. net/nature. 16
DEEP WEB CONTENT EXTRACTION USING VISUAL APPROACH

- The Contents of World Wide Web has tremendous amount of data stored in web databases which can be searched through web query interfaces. The web pages searched by search engines are called surface web which need not be accessed through web databases while deep web can be accessed only by websites interfaces. Deep web pages are complicated in structure therefore extracting data from these web pages is a major problem. Solutions to this problem usually depend on web-page-programming language. Web pages are designed using HTML and it is frequently updating. For better presentation of web pages, more and more presentation techniques are used by web page designers. This makes structure of web page more complicated. Previous systems for deep web data extraction have many limitations. First, they are HTML dependent as they analyze HTML source code of web pages. Second, they are not able to handle increasingly complex structure of HTML source code. This leads to seek a different and efficient approach for deep web data extraction and to overcome limitations of previous works by using visual features. Visual features of web pages can be used for deep web data extraction. The visual system obtains visual representation of a given deep web page and converts it into Visual Block Tree. This Visual Block Tree helps to identify data region which contains the useful information to be extracted. After removing noise blocks a filtered data region is further processed to extract data records. Finally Visual wrapper gets generated for web database to which a given deep web page belongs. This improves efficiency of deep web data extraction as compared to previous method.
Mel Frequency Cepstral Coefficients Based Pattern Recognition for Limb Motor Action

: This paper proposes a Mel Frequency Cepstral Coefficient (MFCC) based hybrid algorithm for motor imagery classification of Electroencephalogram (EEG) signal for Brain Computer Interface (BCI). The proposed hybrid algorithm contains MFCC with Hjorth Parameter. Regression coefficient method was used for eye artifacts cancellation. The feature extraction method based on the difference of the different hjorth parameters taken from the cepstral coefficients. The extracted features from the cepstral coefficients were classified using two linear classifiers.
Credit Risk of Bank Customers can be Predicted from Customer's Attribute using Neural Network

The aim of this paper is to present a model based on Multi-layer perceptron neural networks to recognize bad or good credit customers. Credit risk is one of the major problems in banking sector. Banks are faced with credit Risk while doing their tasks. Credit risk is the probability of non-repayment of bank loan granted to lenders. Decreasing Credit Risk, banks may perform better duties and responsibilities successfully for the economic growth of the country. This study will help for a banker to select a right borrower for investing bank fund and hereby may reduce non-performing loan. Artificial neural network is used for loan applicants' credit risk measurement and the calculations have been done by using SPSS and WEKA software. Number of samples was 101 and 12 variables were used to identify good customers from bad customers. The results showed that, History of borrower (Defaulter or non-defaulter), amount of loan, type of collateral security (physical assets or financial assets) and Value of collateral security had most important effect in identifying classification criteria of good and bad borrowers. The main contribution of this paper is specifying for credit rating of bank customers in Bangladesh’s banking sector.
ENERGY EFFICIENT DATA TRANSMISSION USING TREE-CLUSTER BASED DATA AGGREGATION IN WIRELESS SENSOR NETWORK

: In WSN sensor nodes have batteries of limited capacity which will cause node failure after energy depletion. Cluster-based data aggregation consumes more energy as all nodes in the network sends the packet to the cluster head. This clustering method leads to overhead at cluster head. So to overcome these issues we proposed genetically derived tree-cluster based data aggregation technique based upon tree-cluster based data aggregation approach. Initially, nodes are deployed in the network in the form of tree cluster model. Then CH is selected on the basis of node connectivity and fitness function of every individual node. Once all the factors are computed then data is transmitted in the network. By simulation results, we show that proposed technique reduce energy consumption.
Deep Learning Based-Phishing Attack Detection

Due to the rapid development of the communication technologies and global networking, lots of daily human life activities such as electronic banking, social networks, ecommerce, etc are transferred to the cyberspace. The anonymous, open and uncontrolled infrastructure of the internet enables an excellent platform for cyber attacks. Phishing is one of the cyber attacks in which attackers open some fraudulent websites similar to the popular and legal websites to steal the user’s sensitive information. Machine learning techniques such as J48, Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB) and Artificial Neural Network (ANN) were widely to detect the phishing attacks. But, getting goodquality training data is one of the biggest problems in machine learning. So, a deep learning method called Deep Neural Network (DNN) is introduced to detect the phishing Uniform Resource Locators (URLs). Initially, a feature extractor is used to construct a 30-dimension feature vector based on URL-based features, HTML-based features and domain-based features. These features are given as input to the DNN classifier for phishing attack detection. It consists of one input layer, multiple hidden layers and one output layer. The multiple hidden layers in DNN try to learn high-level features in an incremental manner. Finally, the DNN returns a probability value which represent the phishing URLs and legitimate URLs. By using DNN the accuracy, precision and recall of phishing attack detection is improved.
THE MEDIATING ROLE OF SUPPLY CHAIN MANAGEMENT PRACTICES IN THE RELATIONSHIP BETWEEN MANUFACTURING FLEXIBILITY AND MANUFACTURING PERFORMANCE

Purpose: The current study is interested in exploring the nexus between manufacturing flexibility supply chain practices and manufacturing performance of Indonesian manufacturing firms. Multidimensionality of manufacturing flexibility within the function of manufacturing is generally accepted by past researchers and its importance honoured. Methodology: Employing the survey-based methodology, the SEM-PLS technique is used to test the hypothesized relationships. So, the current study has used SEM-PLS as a statistical tool to answer the research questions raised in this study and research objectives envisaged in the current study. Results: The complexity of manufacturing flexibility has made this concept difficult to comprehend yet delimit. To date, agreement on how to practice this concept has not yet been resolved. The findings of the study have provided support to the theoretical foundation and proposed hypothesis of the current study. Current study will be helpful for policymakers and practitioners in understanding the issues related to supply chain risk, supply chain integration and supply chain agility. In the author's knowledge this is among very few pioneering studies on this issue.
A Review Report on Approaches to Software Metrics and Process Improvement

— Software metrics are useful in different phase of the Object-Oriented Software Development Life Cycle (SDLC). Metrics are used by the software industry to itemize the development, operation and maintenance of software. Application of proper metrics reduces mainternnace costs and efforts. The practice of applying software metrics to a software process and to a software product is a complex task that requires study and restraint, which brings knowledge of the status of the process and / or product of software in regards to the goals to achieve. In this paper, we have presented metrics for Object Oriented Software Systems. They provide a basis for measuring all characteristics. This demand has spurred the provision of a number of new and/or improved approaches to software development, with perhaps the most prominent being object-orientation (OO). In addition, the focus on process improvement has increased the demand for software measures, or metrics with which to manage the process. The needs for such metrics are important while an organization is adopting a new technology for which established practices have yet to be developed. This research addresses these needs through the development and implementation of a suite of metrics for OO design.
RDFSpark: a new solution for querying massive RDF data using spark

The invasion of semantic data, the rapid growth of RDF data has brought significant new challenges in the querying of RDF data. On the other hand, Apache Spark is an open source distributed computing framework, characterized by its speed as MapReduce, Big Data processing has never been easier. In last years MapReduce solves problems at scales unimaginable a few years ago. But like any other tool, it remains limited. Several research works propose the querying of large volumes of RDF data using MapReduce as H2RDF, We find Spark which is more powerful and robust than MapReduce, and it is 100 times faster than MapReduce. In this paper, we present RDFSpark: a new distributed RDF query management based on Spark to ensure scalability, fault tolerance, and performance to solve low-efficiency problems for RDF data query. n this approach, a SPARQL query is analyzed first by the Parser, and compiled before being passed through a series of optimization techniques, then it will be translated to a Spark program. The results of experiments conducted on huge volumes of RDF data show that RDFSpark has a high querying performance.
Design of Fir Filter Architecture Using Manifold Steady Method

: The power consumption and speed are the two main challenging factors in Very Large Scale Integrated Circuit (VLSI) design techniques. The computation saving is one of the way to obtain the optimized power consumption and speed. The design of finite-impulse response (FIR) filter using transpose form structure is naturally pipelined and upholds Manifold Steady Multiplication (MSM) technique. This MSM technique results in reduction of large complex computation. But, the transpose form configurations are not supporting the block processing. In the existing method, the possibility of realization of FIR filter in transpose form configuration to achieve efficient area and delay for large order FIR filters were explored. In the FIR filter structure the ripple carry adder is used to add the partial inner products. The ripple carry adder provides efficient area utilization but its operating speed is slow. In this proposed method, the carry look ahead adder is used to increase the speed and also to reduce the area and power consumption. The proposed structure significantly reduces the area delay product (ADP) and energy per sample (EPS) than the existing FIR structure. Application-specific integrated circuit synthesis result shows that the proposed structure for block size 4 and filter length 64 involves 42% less ADP and 40% less EPS than the best available FIR filter structure proposed for reconfigurable applications. For the same filter length and the same block size, the proposed structure involves 13% less ADP and 12.8% less EPS than that of the existing direct-form blocks FIR structure.
AN ENHANCED BRAIN TUMOUR MRI SEGMENTATION AND CLASSIFICATION USING MORPHOLOGICAL AND SUPPORT VECTOR MACHINE

In this development a dynamic segmentation tool for the detection of brain tumor is used to help clinician and researchers in radio surgery applications. Segmentation is a vital role in medical image processing, where clustering technique widely used in medical application particularly for brain tumor detection in magnetic resonance imaging (MRI). We use MRI because of its provide accurate visualize of anatomical structure of tissues. Usually tumor occupies the locations of normal tissues and their intensity characteristics differ from the surrounding normal tissues. Though the intensity characteristics of tumor region and surroundings normal tissues differs but in T2 scan the tumor intensity characteristics are very close to CSF.This project describe segmentation method consists of two phases. In the first phase, the MRI brain image is acquired from patient database. In that film artifact, noise is removed then converted images RGB to Gray conversion also used median filter. Log method and sobel method used for edge detection . In this enhancement image involved feature extraction using by morphological operator. Next the second phase (MR) image segmentation support vector machine is used to accurately identify the principal tissue structures in these image volumes. Finally the numbers of affected cells are counted using the row and column wise scanning method. In this paper clustering method that have been used for segmentation in MRI are reviewed.
An Efficient Fingerprint Compression Algorithm Using Sparse Coding

Recognition of person by means of their biometric characteristics is very popular among the society. Among this, for personal identification fingerprint recognition is an important technology due to its unique structure. Human fingerprints are rich in details called minutiae. This can be used for identification marks for fingerprint verification. Large volume of fingerprint images are collected and stored day by day from a wider range of applications. Compression of data is commanding of under certain circumstances due to large amount of data transmission and efficient memory utilization. A new and efficient fingerprint compression algorithm using sparse representation is introduced. Obtaining an over complete dictionary from a set of fingerprint patches allows us to represent them as a sparse linear combination of dictionary atoms. In the algorithm, first construct a dictionary for predefined fingerprint image patches. For a new given fingerprint images, represent its patches according to the dictionary by computing l0-minimization and then quantize and encode the coefficients and other related information using lossless coding methods. A fast Fourier filtering transformation for image post processing helped to improve the contrast ratio of the regenerated image Here, considered the effect of various factors on compression results. In Automatic Fingerprint identification System, the main feature used to match two fingerprint images are minutiae. Therefore, the difference of the minutiae between pre-and post-compression is considered in the project. The experiments illustrate that the proposed algorithm is robust to extract minutiae. From the results, we can say that the proposed system provides better PSNR and verbosity for reconstructed images. Compression using sparse provides a better compression ratio also.
PI Control of Current-Fed Dual Active Bridge Converter for DC Microgrid

A detailed modelling of soft-switching isolated current-fed dual active bridge (CFDAB) DC/DC converter is presented. This converter is suitable for an interface between two DC buses in a microgrid but is not controlled. The model of the converter is derived with the help of state space averaging method. Based on this modelling, the PI controller designed to improve the operation of the CFDAB converter. Simulation results using MATLAB/Simulink with controller design to verify the stability of the converter and validated performance of the system used for DC microgrid applications of between two buses. The effectiveness of the controller has justified in terms of line, load variation and tracking the reference values.
Model for Intrusion Detection System with Data Mining

— Today internet has become very popular medium to communicate between users publicly, due to this, lots of intruder has spread across the internet that perform malicious activity and attack to destroy useful information. There are many techniques to detect cyber-attacks and malicious activities in computer systems in which networks Intrusion detection systems (IDSs) is one of them. It is well-known and widely-used security tool. This paper is presenting general study of the existing techniques of intrusion detection using data mining methods. Furthermore there is proposed model of IDS which is the combination of clustering and classification technique to. At the time of experimental analysis we will compare performance of the proposed IDS with existing IDS in terms of execution time and memory utilization.
Development and thermal modeling of an induction machine

In induction machines, the major concern is the temperature rise since it determines the maximum loading, in an attempt to avoid insula-tion deterioration and eventual loss of motor life. The effect of excessive heat in the motor stator and rotor windings and the stator mag-netic circuit can degrade the developed performance of the machine and also affect the motor loading and life span if not dispensed properly. This research work examines the thermal model for estimating the stator and rotor temperatures in cage induction motor. A state-variable model of the induction is used. The twin-axis stator reference frame is used to model the motor’s electrical behavior, because physical measurements are made in this reference frame. The thermal model is derived by considering the power dissipation, heat transfer and rate of temperature rise in the stator and rotor. The non-linear equations for electrical behavior of the motor and the thermal state equations for the stator and the rotor are solved using the MATLAB/Simulink blocks. This is to give room for the determination of the temperature of the stator and rotor windings inside the induction machine so as to evaluate the thermal stability of the induction motor and to check whether the insulation of the copper windings is sufficient at different operating conditions. It was found out from the thermal model analysis that the temperature of the stator and rotor windings increases due to stator and rotor copper losses which depend on the stator current. As the stator current is increased by increasing the torque, the temperature of each element is consequentially made to increase.
Decentralized erasure code assisted cloud with smartcard implementation

— Cloud Computing is becoming next stage platform in the evolution of the internet. It provides the customer an enhanced and efficient way to store data in the cloud with different range of capabilities and applications. The data in the cloud is stored by the service provider. To ensure security and to prevent the data from discloser by unauthorized users, encryption of the data at the local device, e.g. desktop, tablet, or smartphone, prior to the data transfer to the remote cloud-based storage is the only possible solution. But if a cloud system is performing a task of storage of data and encryption and decryption of data on the same cloud then there are much more chances of getting access to the confidential data without authorization. This increases the risk factor in terms of security and privacy. This paper describes an enhancement for the already existing data security model in cloud environment. The proposed data security model provides user authentication and data protection using digital signature. It also uses locking protocol in cloud data storage with the help of CSP for data update. The main technical contribution is that the proxy re-encryption scheme supports encoding operations over encrypted messages as well as forwarding operations over encoded and encrypted messages. Our method fully integrates encrypting, encoding, and forwarding. We analyze and suggest suitable parameters for the number of copies of a message dispatched to storage servers and the number of storage servers queried by a key server. These parameters allow more flexible adjustment between the number of storage servers and robustness.