Miracle-dz/UniFAR-FTUs huggingface dataset:
SVMFILEFS-A NOVEL ENSEMBLE FEATURE SELECTION TECHNIQUE FOR EFFECTIVE BREAST CANCER DIAGNOSIS

This paper describes about a novel ensemble feature selection method SVMFILEFS used for the diagnosis of breast cancer. Firstly, this technique incorporates three filters such as Chi-square, Random Forest and Information Gain and combines their normalized outputs to a quantitative ensemble importance. Based on the threshold value of 50% ensemble importance, the best attributes were selected. Secondly, Support Vector Machine Recursive Feature Elimination (SVMRFE) is then applied to the dataset after eliminating those selected attributes from the first step from the original dataset which leads to another subset selection of attributes. Classification was done using classification models namely random forest (rf), Support Vector Machine-Radial (svm Radial), Linear Discriminate Analysis (LDA), JRip, Recursive Partitioning and Regression Trees (rpart), J48 and Logistic Model Trees (LMT) on Wisconsin Breast Cancer Dataset (WBCD) dataset downloaded from UCI repository. In this experiment classification was performed with attribute subsets obtained by feature selection methods such as SVMRFE, Filter Combo and SVMFILEFS. A comparison study was made with the results obtained from performing classification on this dataset. The findings show that SVMFILEFS, our novel ensemble feature selection technique outperformed the other feature selection methods that were considered for this study and has high classification accuracy
PRIVACY PRESERVING ASSOCIATION RULE MINING

: Introduction of high end technologies used by most of the leading software companies needs a keen focus to be kept on Daas (Data Mining as a Service). The data owner transforms data to protect corporate privacy and ships it to the server, server receives mining queries and true patterns are recovered from the extracted patterns which are received from the server. A study is done on the problem of outsourcing the association rule mining task within a corporate privacy-preserving framework. An attack model based on background knowledge and scheme is devised for privacy preserving outsourced mining. The scheme introduced ensures that from at least k-1 other transformed items each transformed item is indistinguishable with respect to the attacker's background knowledge. Results show that the techniques used provide scalability, effectiveness and privacy protection.
In silico Molecular Docking Studies on Porcine Pancreatic Phospholipase A2 against Plant extracts of Phenolic Inhibitors

Phospholipase A2 (PLA2) catalyzes the hydrolysis of the Sn-2 position of membrane glycerophospholipids to liberate arachidonic acid. The excess stimulation of this pathway leads to the formation of pathophysiological conditions of lipid mediators like prostaglandins, leukotrienes, lysophospholipids and platelet activating factor, which are implicated in the development of inflammatory conditions. In our present study we have focused on structural based drug design for the development of new anti-inflammatory drugs. We have selected thirty five bioactive phenolic compounds which are distributed in the different plant extracts to study for molecular docking. The mode of interaction of compound was studied by autodock software. The hydrogen bond interaction was formed between hydrophobic domain of active site of PLA2 and phenolic compounds are discussed. From the results it has been observed that curcumin enol form, ellagic acid and caffeic acid as formed one hydrogen bond interaction whereas catechin was formed three hydrogen bond interaction with PLA2 when compared with other compounds. The plant derived phenolic compound such as catechin was formed three hydrogen bond interaction with PLA2 and can be developed as an effective anti-inflammatory drug in near future. © 2011 Universal Research Publications. All rights reserved
Wavelet analysis to detect fault in Clutch release bearing

1 ME Scholar, Institute of Engineering & Technology, DAVV, Indore, M. P., India 2 Assistant Professor, Dept. Of Mechanical Engineering, Institute of Engineering & Technology, DAVV, Indore ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Automobile clutch release bearings are important automotive driveline components. An unexpected failure of the bearing may cause catastrophic failure leads to vehicle off road, increased down time and economic losses. For that reason, fault diagnosis in bearing has been the subject of intensive research. Vibration signal analysis has been widely used in the fault detection of bearings. The vibration signal of a bearing carries the signature of the fault in the outer race, inner race, cage and rolling element. Early fault detection of the bearing is possible by analyzing the vibration signal by using wavelet analysis. Wavelet analysis is able to characterize the local features of the signal in different scales. This project addresses fault diagnosis of ball bearing related to clutch release system. Detail analysis using wavelet methodology is done to find out the faults in MATLAB software. Numbers of faults are identified and validated for each fault. Faults are identified on clutch release bearing vibration test rig.
BACTERIAL SYNTHESIS OF SILVER NANOPARTICLES (AGNPS): THE POWERFUL NANOWEAPON

The development of quick and trustworthy processes for the synthesis of nanosized materials is of countless importance in the field of nanotechnology. Biosynthesis of Silver Nanoparticles (AgNPs) using bacteria has arriving weighty interest because of their potential to synthesize nanoparticles. In the current study, synthesis of silver nanoparticles by a bacterial strain (PP-11) isolated from soil is reported. The bacterium was isolated, screened and characterized by morphological, biochemical and 16S rRNA analyses. Molecular identification of the isolate was done which showed a strain is Bacillus thioparans PP-11. When treating the isolated bacteria with 1mM Silver nitrate (AgNO3), it was found to have the proficiency to form silver nanoparticles at 37 0 C within 24 hours of incubation. This was confirmed by the visual observation and FT-IR analysis. Therefore, the current study is a march of an effective synthesis of silver nanoparticle by the present Bacillus strain. Silver nanoparticles were also tested against antibacterial potential of some human pathogens. Kinetic study of growth parameters of silver nanoparticles producing bacteriumwas carried out with respect to pH, temperature and incubation conditions. It was found that the optimum pH and temperature for the maximum growth of the present isolate were 7.0% and 37 0 C respectively.
Handling Anomalies in the System Design: A Unique Methodology and Solution

The primary problem in the software these days is the anomalies occurrence in the design and code of the system. These anomalies or rather these bugs cause a huge impact on the quality and reliability of the software. It is a big issue in the software organizations for the developers or the testers creating or developing the software. In this article, we propose a methodology and a solution for handling these constantly transpiring anomalies in the system design. We take forward our research from the earlier article where we surveyed about the various anomalies and the novel approach using the different metrics for handling the anomalies in the system design.
Objective Measurement of Peripheral Edema as Symptomatic of Congestive Heart Failure

Congestive heart failure is a type of cardiovascular disease that is often accompanied by excess fluid buildup in tissue, causing swelling of the extremities, known as peripheral edema. Assessing the severity level of pitting, or indentation, in peripheral edema can potentially provide insights about one's heart condition. When clinicians use digital manipulation to assess severity, the measurements are subjective. Objectifying measurements requires standardization so the severity score reported is both reliable and consistent across providers. The HeartSMART system was developed to calculate the level of peripheral edema via four factors: the depth (displacement) of the skin, the force exerted on the skin, the surface temperature of the skin, and body weight. The specific aim of this project was to begin the process for developing an algorithm that could provide an overall standardized edema score based on these four parameters obtained from the HeartSMART system. Using the recorded force and displacement measurements from participants with varying levels of edema, we fit the collected data to a derivation of the standard linear sold model in order to establish edematous tissue properties. Additionally through clinical testing, we identified improvement opportunities to the device design to make it more user friendly.
Performance Evaluation of Effluent Treatment Plant of Textile Wet Processing Industry: A Case Study of Narol Textile Cluster, Ahmedabad, Gujarat

The present study has been undertaken to evaluate performance efficiency of an Effluent Treatment Plant (ETP) of a textile industry located in Narol , Ahmedabad (Gujarat). An effluent treatment plant is operating on Conventional Effluent treatment method with an average wastewater inflow of 630 m3/day. has been considered for case study. The wastewater is analyzed for the major water quality parameters, such as pH, Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD) Total Dissolved Solids (TDS),Total Suspended Solids (TSS)and Ammonical Nitrogen (NH3-N) The composite samples were collected on a hourly basis for one day . The raw wastewater pH was highly alkaline it was then bringing down to neutral which was helpful for biological treatment. The BOD, COD, TDS, TSS, NH3-N of the treated effluent reduced significantly, where as very small reduction was observed in dissolved solids (61.25 %). Most of all the parameters were above the permissible limits of Gujarat Pollution Control Board, Gujarat, India.
An Implementation of Support Vector Machine on the Multi-Label Classification of English-Translated Quranic Verses

One of the attempts to understand the meaning and content of the Quran, the central religious text of Islam, is the topic classification of Quranic verses. Verse topic classification aims to help the reader, so he can easily and quickly find information or knowledge contained in the Quran. In this paper, we build a classification model for the topics of English- translated Quranic verses using Support Vector Machine (SVM). The problem of classification of topics of Quranic verses is categorized as a multi-label classification problem. Hence, we design an SVM-based classifier to solve the multi-label classification of topics of Quranic verses. We also implement several techniques such as preprocessing, feature extraction, and dimensionality reduction to solve this problem. Then, we use Hamming Loss as a performance measure to evaluate our proposed classifier model. We find that our proposed model yields outstanding results.
Denoising and Phase–based Binarization of Ancient Document using Adaptive Contrast Map

: Segmentation of text from badly degraded document image is very challenging tasks due to high inter/intra variation between the document background and the foreground text of different document images.Inthis paper,we propose a novel document image binarization technique that addresses these issues by using adaptive image contrast.The adaptive image contrast is a combination of local image contrast and the local image gradient that is tolerant to text and backgroundvariation caused by different types of document degradations.In the proposed technique, an adaptive contrast map is first constructed for an input degraded document image . The contrast map is then binarized and combined with canny’s edge map to identify the text stroke edge pixel. The document text is further segmented by a local threshold that is estimated based on the intensities of detected text stroke edge pixels with in a local window. The proposed method is simple, Robust and involves minimum parameter in tuning.
Face Detection: A Literature Review

: Face perception is currently an active research area in the computer vision literature, not only because of the challenging nature of face as an object, but also due to the countless applications that require the application of face detection as a first step. The goal of this paper is to present a critical survey of existing literatures on human face detection systems. Face detection is a difficult task in image analysis which has each day more and more applications. We can define the face detection problem as a computer vision task which consists in detecting one or several human faces in an image. It is one of the first and the most important steps of Face analysis. Face detection is not Straight forward because it has lots of variations of image appearance, such as pose variation (front, Non-front), occlusion, image orientation, illuminating condition and facial expression . In this paper we presented three methods of face detection, which are commonly used. As the number of proposed techniques increases, survey and evaluation becomes important.
A JOURNEY THROUGH THE EVOLUTION OF THEORIES AND MODELS OF ADOPTION OF INNOVATIONS (YEARS: 1798-1980)

The study furnishes in a nut shell the evolution of diffusion theories and models till 1980. Various steps and factors involved are reviewed and consolidated to give scholars a holistic insight to the process of diffusion. The practical implications of these theories/models are also discussed as to how the adoption/diffusion can be accelerated by manipulating the variables. Narrative Synthesis Approach is used to draft this paper. Narrative Synthesis is all about systematically reviewing diverse literature and concluding findings using words and text. Four data bases – EBSCO, Petro One, JSTOR and Pro Quest were used for Literature search. Primary Search was done using the
Generative Topic Modeling in Taxonomic Structure of Genomic Data using LDA

-Probabilistic topic models have been developed for applications in various domains such as text mining, information retrieval. In this work, we focus on developing probabilistic topic models for LDA and specifically, a probabilistic topic model is proposed for data analysis and function analysis using homogenous approach and composite approach. In this paper, we aim to develop a new method that is able to analyze the genome-level composition of DNA sequences, in order to characterize a set of common genomic features shared by the same species and tell their functional roles. To achieve this end, we firstly apply a We firstly show that generative topic model can be used to model the taxon abundance information obtained by homology based approach and study the microbial core. The model considers each sample as a ‘document’, which has a mixture of functional groups, while each functional group (also known as a ‘latent topic’) is a weight mixture of species. Therefore, estimating the generative topic model for taxon abundance data will uncover the distribution over latent functions (latent topic) in each sample. Secondly composition-based approach to break down DNA sequences into sub-reads called the ‘N-mer’ and represents the sequences by N-mer frequencies. Then, we introduce the Latent DirichletAllocation (LDA) model to study the genome-level statistic patterns (a.k.a. latent topics) of the ‘N-mer’ features. Each estimated latent topic represents a certain component of the whole genome.
A PERFORMANCE ANALYSIS FOR RESOURCE ALLOCATION USING ROUND ROBIN AND MYBROKER POLICY IN CLOUDLET FOR CLOUD COMPUTING

-Now a days, cloud computing is a center of attraction of many researchers, scientist. To provide services online and with pay as you go makes it a strong. Still cloud computing has not reached to its best because of its some issues. Out of this one of the major is resource allocation. At a time so many customers are using the services which are provided by cloud computing provider. In this case the proper scheduling need to be maintain in between the available resources and coming requests. In this paper, we proposed an experimental performance analysis of the round robin and my broker, by this we try to see which one is achieve the optimum resource allocation.
Foreign Direct Investment and Economic Growth in Botswana: A Dynamic Causality Test

Aims: The study examines the relationship between Foreign Direct Investment (FDI) and economic growth using yearly time series data for the period 1980 to 2012. Study Design: Multi-model econometric study. Place and Duration of Study: Botswana, January 1980 to December 2012. Methodology: The study uses Augmented Dickey-Fuller and the Phillips-Perron test to test the stationarity of the variables. Johansen and Juselius cointegration test was used to test for cointegration. Finally, the study uses the Granger causality test to determine whether FDI influences economic growth. Conclusion: Using Johansen cointegration test applied on a dynamic model, we found out that there is a long-term term relationship between FDI and economic growth in Botswana. However, Review Article Chiwira and Kambeu; BJEMT, 11(3): 1-8, 2016; Article no.BJEMT. 21723 2 using the Granger causality tests, we were not able to confirm whether it is the FDI that is spurring economic growth or it is economic growth that is promoting FDI inflows.
Analysis of Hierarchical based MAC in Wireless Sensor Network

: Medium Access Control with Overlapped Schedules for wireless sensor networks has been used to prolong wireless sensor network lifetime. In this technique the sensors are alternate between sleeping and awaking period. During the sleeping period, there is no energy consumption due to communication. However, the MAC algorithm has to ensure the awaking period of sensors sufficiently overlaps in order to allow nodes to communicate with each other. This dissertation proposes an algorithm that arranges the nodes in the form of hierarchical i.e. tree based. The nodes of the same level have overlapped schedules. The nodes of the same level get synchronized. It reduces the energy consumption as well as the delay due to the limited communication between the nodes. The proposed algorithm is implemented using the NS2 and the PDR, E2Edelay and the energy consumption is calculated to analyze the performance of the proposed and existing protocol. The simulation results confirm the better performance of the proposed protocol as compared the existing protocol. The increase in PDR, decrease in the E2Edelay and the decrease in the energy consumption of the proposed protocol as compared to existing protocol show better performance of proposed protocol as compared to exiting protocol. It means the life of the node get increased i.e. enhanced network life time.
Analysis of Software Complexity Using Object Oriented Design Metrics in Java Application

-Ensuring quality of the software without the knowledge of software evaluation metrics is difficult. Some measurement methodologies are required in completion of software quality evaluation. Software Metrics are essential in software engineering formeasuring software complexity, estimating size and evaluating qualityof the software as well as project efforts. This research paper focuses on evaluation of software complexity metrics applied in object oriented systems by evaluatingsource codes developed in Java. To evaluate the complexity the software metrics used in the research are Lines of Code (LOC) and cyclomatic complexity. The complexity of Java classes in a package is determined using software tools. The results show that these complexity metrics can be used to predict the quality of the software.Software system can be evaluated using software metrics thus quality can be improved.
Deduplication Storage optimization for data management

-With increased amount of data, nowadays storage has become a challenge. New technologies of storage like cloud computing whereby data is stored in a cloud is now in place. But still not accurate and does not fully solve the issue of storage. There other challenges like duplication, security of the data and the rest.in our paper therefore; we have proposed a noble deduplication system, which will help in dealing with storage space and enhancing the security of the data especially in the cloud storage.
A Review on Basic of Voice Recognition

— his paper presents the main paradigms for speaker recognition. Biometric systems are mostly used as person identification or verification systems. Speaker recognition is a process where a person is recognized on the basis of his/ her voice samples instead of images. Security is big issue of human life. There is need of reliable and efficient systems which can recognize and authenticate humans by using biometric features like voice face etc. Voice recognition is one of the top security measures in banking systems that are used to provide protection to human’s belongings by his/her voice characteristic . In this article, voice sample is observed with MFCC for extracting features and then used to train Artificial Neural for classification. Speaker recognition is used an automatic recognition, text-dependent and Speaker verification in security systems. It will recognize the speaker by comparing the current voice from the pre-stored database against the password stored in the Database system. It is implemented in Mat lab 7.0 version and showing results as correct acceptance and correct rejections with the error rate in Speaker verification.
Face Image Retrieval Using Pose Specific Set Sparse Feature Representation

— There are largely available consumer photos are available in our life, among those a big percentage of photos are photos with human faces. Thus content based face image retrieval is an emerging technology for many applications. A new method for content based face image retrieval is proposed. Given a query face image, content based face image retrieval find similar faces from the database. There were mainly three steps-preprocessing, feature extraction and face retrieval. Preprocessing included face detection and landmark detection. After preprocessing, extract pose specific uniform LBP features from the face. Novel set sparse feature representation is used to represent extracted pose specific LBP feature as sparse code using a pre-defined set. After 15 attributes from face is used to make face representation more distinguish. Finally set sparse feature representation is used as inverted index in face retrieval to find similarity score of query face image with database face images. It has applications in automatic face annotation, crime investigation etc. Top results related to a query face image with existing method and memory usage were analyzed. Experimental results shows that proposed method have better top results and efficient in memory usage compared to existing method.
A REFINED NEW MODEL FOR EFFICIENT EXPLICIT CONGESTION REDUCTION IN HIGH TRAFFIC HIGH SPEED NETWORKS THROUGH AUTOMATED RATE CONTROLLING

The Transmission Control Protocol (TCP) suffers from poor performance on high bandwidth delay product links meant for supporting data transmission rates of multi Gigabits per seconds (Gbps). During congestion, the TCP’s congestion control algorithm reduces the congestion window cwnd to ½ and enters additive increase mode, which proves to be slow in taking advantage of large amounts of available bandwidth. In this paper a refined new model, to overcome the drawbacks of the TCP protocol, has been presented. We propose to carry out a study of the refined new model based on various parameters viz., Throughput, Fairness, Stability, Performance and Bandwidth Utilization for supporting high data transmission rates across the High Speed Networks.