bluuebunny/crossref_metadata_2025_split huggingface dataset:
Performance Evaluation Study of Active Contour Models in Medical Imaging
Authors: Midha Sugandhi 
Published: May 2018

Over the past years image segmentation has played an important role in medical imaging. Segmented images are used for a number of applications such as computer aided surgery, treatment planning, diagnosis, study of anatomical structures and many more. Deformable models provide a general method of performing non-rigid object segmentation. They offer an attractive approach to overcome the limitations of traditional low level segmentation approaches. A snake is a flexible deformable model which can be matched to an image contour by energy minimisation. Snakes are a powerful technique in segmenting formless shapes when little or no prior knowledge about the shape is available. Rather than handcrafting a prior knowledge into the model, the shape variation is extracted from a training set by applying PCA to PDM.Â In this paper some familiar Active Contour models such as Snakes by Kass et al, Balloon model, Greedy snakes method, Gradient Vector Flow Snakes are compared on the basis of qualitative and quantitative measures. The GVF Snakes have the ability to move into boundary concavities whereas the Greedy Snakes, Original Snakes have problems trying to locate the contour of an object which has a boundary concavity.
Studying the Effect of Paracetamol Drug on the Conductivity of 0.5M Hydrochloric Acid Solution at Different Temperatures
Authors: Al-Sawaad Hadi.Z. 
Published: May 2018

In this study paracetamol drug is used to reduce the conductivity of 0.5M hydrochloric acid at different concentrations for each one of them at different temperatures ranged (30-60)Â°C. Generally , increasing of the concentration of Â the inhibitor leads to reducing in conductivity of the acid at constant temperature. On the other hand, at constant concentration ofÂ inhibitor, the conductivity is reduced as temperature increased i.e., paracetamol can adsorbed chemically on the metal or alloy. Furthermore the kinetic study of the molar conductance process reveal that in presence of paracetamol the activation energy and enthalpy of activation are negative compared with their values in absence of paracetamol where they are negative because the reducing the conductivity in presence of paracetamol where the non spontaneous property for the conductance of acid is increased as the paracetamol concentration increased in addition to increasing the negative value of entropy in presence of paracetamol that indicate to restrict for the mobility of hydrogen and chloride ions which correspond to 90.47% as efficiency of reducing the conductance of acid by paracetamol. On the other hand thermodynamic study is achieved which explained the adsorption of paracetamol obey to Freundlisch model.Â
Binary Quantum Communication using Squeezed Light: Theoretical and Experimental Frame Work
Authors: Fyath Raad Sami 
Published: May 2018

The aim of this paper is to develop framework to generate squeezed light for binary quantum communication. Both theoretical and experimental models to generate squeezed state Â using optical parametric amplifier (OPO), which is implemented around He-Ne laser, are described in details.Â The results will be used as a guide line to investigate the performance of squeezed light-based quantum communication over noisy channel and this issue will be presented in accompanying paper [1].Â Â
Performance Evaluation System for Decision Tree Algorithms
Authors: Saini Deepali , Rajavat Prof. Anand 
Published: May 2018

In the machine learning process, classification can be described by supervise learning algorithm. Classification techniques have properties that enable the representation of structures that reflect knowledge of the domain being classified. Industries, education, business and many other domains required knowledge for the growth.Â Some of the common classification algorithms used in data mining and decision support systems is: Neural networks, Logistic regression, Decision trees etc. The decision regarding most suitable data mining algorithm cannot be made spontaneously. Selection of appropriate data mining algorithm for Business domain required comparative analysis of different algorithms based on several input parameters such as accuracy, build time and memory usage.To make analysis and comparative study, implementation of popular algorithm required on the basis of literature survey and frequency of algorithm used in present scenario. The performance of algorithms are enhanced and evaluated after applying boosting on the trees. We selected numerical and nominal types of dataset and apply on algorithms. Comparative analysis is perform on the result obtain by the system. Then we apply the new dataset in order to generate generate prediction outcome.
A Novel LAS-Relief Feature Selection Algorithm for Enhancing Classification Accuracy in Data mining
Authors: Baskar S. S. , Arockiam Dr L. 
Published: May 2018

Feature selection is an important task in data mining and machine learning domain. The main objective of feature selection is to find a relevant feature that predicts the knowledge better than the original set of features. This can be achieved by removing irrelevant or redundant features from original data sets. Feature selection involves a significant task of selecting relevant features from the feature space for data mining and pattern recognition. In this paper, the new approach has been introduced on feature selection on Relief based on Median Variance model. The new approach is named as LAS-Relief algorithm. This algorithm facilitates to stabilise the feature weights estimation compared to mean variance based Relief algorithm and is considered to be a better successful algorithm for feature selection. The random selection of instances in the data sets will lead to the fluctuation of weight estimation. This in turn leads to poor evaluation accuracy. This new approach removes the irrelevant features in the feature space. The novel LAS-Relief algorithm incorporates the median variance in the feature weight estimation. The feature weight is calculated by selection of the instances in random. To overcome this issue, the novel feature selection algorithm called LAS-Relief algorithm is proposed based on median variance. This algorithm takes both the median and the variance of difference between instances. These are considered as the criterion of feature weight estimation in this LAS Relief algorithm.. This algorithm makes the result more stable and more accurate on classification. The relevant features are obtained from the original feature space using LAS-Relief algorithm, which outperforms well than Mean Variance Relief algorithm.
Intuitionistic Fuzzy Modeling to Travelling Salesman Problem
Authors: Garai Arindam , Roy Tapan Kumar 
Published: May 2018

This paper presents solution technique for travelling salesman problem (TSP) under intuitionistic fuzzy environment. Travelling salesman problemÂ is a non-deterministic polynomial-time (NP) hardÂ problem inÂ combinatorial optimization,Â studied inÂ graph theory, operations researchÂ andÂ theoretical computer science. It must be noted that a traveling sales man even face a situation in which he is not able to achieve his objectives completely. There must be a set of alternatives from which he can select one that best meets his aspiration level. For Multi-Objective Symmetric TSP, in fuzzy environment, it is converted into a Linear Program using Fuzzy Multi-Objective Linear Programming technique. A route cannot be simply chosen just as it will most minimize time or it will cover the least possible distance. Examples with requirements to consider the degree of rejection or hesitation (or both) are overflowing in our materialistic world. Here comes the need to consider TSP under intuitionistic fuzzy environment. The degree of rejection as well as the degree of hesitancy must be studied to find the solution in a truly optimum sense! Proposed technique is an extension as well as collaboration of ideas of fuzzy traveling salesperson problem and intuitionistic fuzzy (IF) optimization technique.
Hybrid Model based on unification of Technical Analysis and Sentiment Analysis for Stock Price Prediction
Authors: Wankhade Sunil B , Surana Divyesh , Mansatta Neel J , Shah Karan 
Published: May 2018

Stock price forecasting phenomenon has been majorly made on the basis of quantitative information. Over the time, with the advent of technology, stock forecasting used technical analysis to get more accurate predictions. Until recently, studies have demonstrated that sentiment information hidden in corporate reports can be effectively incorporated to predict short-run stock price returns. Soft computing methods, like neural networks, fuzzy models and support vector regression, have shown great results in the forecasting of stock price due to their ability to model complex non-linear systems.In this paper we propose a hybrid method for stock price predication, which is combinational feature from technical analysis and sentiment analysis (SA). The features of sentiment analysis are based on a Point wise Mutual Information (PMI) and we applyÂ neural network and Îµ-support vector regression models to predict the yearly change in the stock price.
Digital Fingerprinting In Encrypted Domain
Authors: Murala Kranthi Kumar , Kamaraju Dr.M. , Ramanjaneyulu Dr.K. 
Published: May 2018

Digital fingerprinting is a method for protecting multimedia content from illegal redistribution and identified the colluders.In copy protection, a content seller embeds a unique identity as a watermark into the content before it is sold to a buyer. When an illegal copy is found, the seller can identify illegal users by extracting the fingerprint. In thisÂ proposing an anonymous fingerprinting Â based on a homomorphic additive encryption scheme, it present a construction of anti-collision codes created using BIBD(Balanced incomplete block design) codes technique and dither technique which makes use of LFSR (linear feedback shift register) are used for improving the high robustness and Security.
Estimation of Performance Improvement Derived from TCP/IP Offload Engine with Software Emulation
Authors: Nishijima Takamichi , Yokoi Nobuhiro , Nakamoto Yoichi , Ohsaki Hiroyuki 
Published: May 2018

Many TOE (TCP/IP Offload Engine) devices have been developed and installed in high-end systems.Â Although there have been many analysis of the effectiveness of TOE devices, there remain open issues related to them.Â For example, it has not been clarified which part of TCP/IP processing should be performed with hardware and which with software, or how end-to-end TCP/IP performance is affected by the introduction of a TOE device.Â In this paper, we propose VOSE (Virtual Offloading with Software Emulation), which is a technique for measuring TCP/IP performance improvements derived from different type of TOE devices without implementing TOE prototypes really.Â VOSE enables virtual offloading without requiring a hardware TOE device by virtually emulating TOE processing on both source and destination end hosts.Â For demonstrating the effectiveness of VOSE, we apply VOSE to the TCP checksum and IPsec protocol.Â We extensively examine the accuracy of virtual offloading with VOSE, by comparing performance (i.e., end-to-end performance and CPU processing overhead) between VOSE and a dedicated TOE device.Â Moreover, we estimate performance improvement that are derived from several TOE devices of IPsec and combinations of those devices, by applying VOSE to header authenticating and payload encryption in IPsec protocol.Â Consequently, we show that performance improvements which are derived from TOE devices can be estimated correctly.
DIGITAL IMAGE ANALYSIS BASED ON AUTOMATED COUNTING CLUSTERED SOYBEAN SEEDS
Authors: Kaur Jasdeep 
Published: May 2018

- Â This paper investigates the use of digital image analysis techniques for developing for counting clustered soya bean seeds. Images are extracted from source WWW.21food.com). As manual counting have several issues such as low accuracy and higher cost. Automated counting techniques give a fast and low cost of counting soya bean seeds. This paper follows 5 processing steps. First process converting the image into gray scale and thresholding is applied using CLAHE. Second dilation is applied to enhance the image. Third applied masking to enhance the image. Fourth edge detection algorithm is applied. Fifth step beans extracted with respect to bean shape.Â
Scene Oriented Classification of Blurry and Noisy Images Using SVM with Fuzzy C Mean Clustering
Authors: Nema Deepak 
Published: May 2018

Image classification is a challenging task in image processing especially in the case of blurry and noisy images. In this work, we present an extension of scene oriented hierarchical classification of blurry and noisy images using Support Vector Machine (SVM) and Fuzzy C-Mean. Generally, a system for scene-oriented classification of blurry and noisy images attempts to simulate major features of the human visual observation. These approaches areÂ based on three strategies such as Global pathway for extracting essential signature of image, local pathway for extracting local features, and then outcome of both global and local phase are combined and define feature vector and clustered using Monte Carlo approach. Afterwards, these clustered results are fed to a SOTA Algorithm (combination of self organizing map and hierarchical clustering) for final classification. But in these approaches, combination of self organizing map and hierarchical clustering has the problem in terms of accuracy and computation time of classification, especially when used large dataset for classification. To overcome this problem, we propose a combination of Support Vector Machine (SVM) and Fuzzy C-mean. Our proposed approach provides better result in terms of accuracy, especially when used with large dataset. The proposed method is computationally efficient because fuzzy c-mean clustering is faster and less time consuming as compared to hierarchical clustering.
Using Fuzzy Logic in Robot Navigation to Find Animals
Authors: Amiri Mahdi , Abbasi Zeinab , Tafreshi Fakhte Soltani 
Published: May 2018

Â Fuzzy logic is a tool to use human expertise. The simplicity of fuzzy-rule based systems and its power to perform various tasks without accurate measurement and computation makes it very popular between sciences. One of these applications is using fuzzy logic in designing the controller for navigation of autonomous robots to move in various environments. This paper proposes a new method of robot navigation based on fuzzy logic. This method can be drawn upon to design robots which can find and catch different kind of animals, especially endangered species. It works based on a hierarchical set of behavior each of which acts by using a set of fuzzy rules. The proposed method is simulated and tested by MATLAB software.
Skin Color detection Using Stepwise Neural Network and Color Mapping Co-occurrence Matrix
Authors: Osman Ghazali , Hitam Muhammad Suzuri 
Published: May 2018

Skin color has been proven to be a useful and robust cue for face detection, human tracking, image content filtering, pornographic filtering, etc.Â Most of skin classification researches are focused on using pixel-based method to classify skin and non-skin pixels.Â This paper proposed a new technique for region-based skin color detection using texture information.Â The texture information was extracted from the color mapping co-occurrence matrix (CMCM).Â This technique is extension of gray level co-occurrence matrix (GLCM) which is introduced by Haralicket. al to compute second order statistical texture features.Â The new color mapping matrix (CMM) between color bands have been developed for skin and non-skin area for each skin image and then, the CMCM were computed at four direction with distance, d = 1, and angle, Î¸ = 0o, 45o, 90o, and 135o.Â The thirteen Haralickâ€™s textures have been computed and used for formulating a skin color classifiers using stepwise neural network (SNN).Â The performance of each skin color classifier was measured based on true and false positive value.Â Besides that, the benchmark datasets from Universidad de Chile and TDSD were also be employed to test the skin color classifiers ability.Â The results shown that the skin color classifier formulated with [RGB] CMCM at direction (1, 0o) most superior as compared to other direction.Â Its average of true positive and false positive are 98.38 percent and 3.67 percent, respectively.Â Meanwhile, the classifier formulated with [RGB] CMCM at direction (1, 90o) is totally failed to classify skin and non-skin colors.Â Meaning that, the texture features which are computed from [RGB] CMCM at direction (1, 90o) cannot represent skin and non-skin color at all.
A Content Based Approach to Medical X-Ray Image Retrieval using Texture Features
Authors: Ganesan Sumathi , Subashini T.S. 
Published: May 2018

Of late, the amount of digital X-ray images that are produced in hospitals is increasing incredibly fast. Efficient storing, processing and retrieving of X-ray images have thus become an important research topic. With the exponential need that arises in the search for the clinically relevant and visually similar medical images over a vast database, the arena of digital imaging techniques is forced to provide a potential and path-breaking methodology in the midst of technical advancements so as to give the best match in accordance to the userâ€™s query image. CBIR helps doctors to compare X-rays of their current patients with images from similar cases and they could also use these images as queries to find the similar entries in the X-ray database. This paper focuses on six different classes of X-ray images, viz. chest, skull, foot, spine, pelvic and palm for efficient image retrieval. Initially the various X-rays are automatically classified into the six-different classes using BPNN and SVM as classifiers and GLCM co-efficient as features for classification. Indexing is done to make the retrieval fast and retrieval of similar images is based on the city block distance.Â Â
A Novel Chaos-Based Stream Cipher for Image Encryption
Authors: Abd-El-Hafiz Salwa K. , A. Fuad Gamal , Sayed Lina A. 
Published: May 2018

In this paper, we introduce a novel chaos-based image encryption scheme. We perform our scheme using both the binary form and decimal form of chaotic maps. We also use two chaotic maps; one generates the initial values of the other. Thus, we utilize the best two properties of chaos ergodicity and sensitivity to the initial value. We increase the security of the cipher by introducingÂ a reverse order round to resist cryptanalysis attacks.
Theoretical and Computational Perspectives of Arabic Morphological Analyzers and Generators : Theoretical Survey
Authors: Maabid Abdelmawgoud Mohamed , Elghazaly Tarek 
Published: May 2018

Morphology analysis is an essential part of most applications of natural language processing (NLP) which included different applications like Machine Translation (MT) and language rule based Information Retrieval (IR). Many Arabic morphological systems had built for different purposes with different algorithms and approaches; this paper is considered a survey of Arabic Morphological system from researchersâ€™ perspectives and approaches used to build them. Based on this survey; in the first part of this paper; the perspective of Arabic morphological systems had been classified into two major issues; one of them is the theoretical perspective and the second is the computational perspective of Arabic morphology.Â While the second part of this paper deals with approaches used to build the Arabic Morphology systems itself which are Table Lookup Approach, Combinatorial Approach, Linguistic Approach, Traditional Approaches, Finite-state Automata and Two-Level Morphology Approach and Pattern-Based Approach.
EFFECT OF NON-IMAGE FEATURES ON RECOGNITION OF HANDWRITTEN ALPHA-NUMERIC CHARACTERS
Authors: Adeyanju Ibrahim , Fenwa Olusayo , Omidiora Elijah 
Published: May 2018

Handwritten character recognition has applications in several industries such as Banking for reading of cheques and Libraries/ National archives for digital searchable storage of historic texts. The main feature typically used for the recognition task is the character image. However, there are other possible features such as the hand (left or right) used by author, number of strokes and other geometric features that can be captured when writing on digital devices.Â This paper investigates the effect of using some non-image features on the recognition rate of three classifiers: Instance Based Learner (IBk), Support Vector Machines (SVM) and the Multilayer Perceptron (MLP) Neural Network for singly-written alpha-numeric character recognition. Our experiments were conducted using the WEKA machine learning tool on offline and online handwritten acquired locally. A percentage split (66%-34% train-test) evaluation methodology was adopted with the classification accuracy measured. Results indicate that non-image additional features improved the accuracy across the three classifiers for the online and offline character datasets. However, this improvement was not statistically significant. SVM gave the best accuracy for the online dataset while IBk performed better than the other two classifiers for the offline dataset. We intend to investigate the effect of non-image features at other levels of text granularity such as words and sentences
Occlusion Detection in M-FISH Human Chromosome Images
Authors: A Lijiya , Joseph Joshy , Govindan V.K. 
Published: May 2018

Automation of Chromosome Analysis has long been considered a tedious task due to the partial occlusion of chromosomes. This calls for a non-trivial, dedicated procedure to segment chromosomes. In this paper, a new method is proposed which detects and separates occluded chromosomes, by separating out the chromosome cluster from the M-FISH image, followed by detecting the cut-points along which these clusters can be split into multiple regions. These regions are then combined into separate partial chromosomes based on difference matrix. After this stage the invisible regions due to occlusion is reconstructed based on the visibility in the five channels. The performance of the new proposalÂ Â was compared with the existing work and observed better performance in resolving occlusions. With 15 occludedÂ chromosome images tested, 90% accuracy was obtained
Microfinance as Employment Generation Tool (Case Study of Pakistan 2001-02 to 2010-11)
Authors: Saleh Memon Muhammad , Channa Mohammad Asif , Manganhar Izhar Ali 
Published: May 2018

The objective of this study is to know the contributions of Micro finance towards employment development through generating employment opportunities to the alit class of urban and rural community. For this purpose quantitative research techniques were used to collect the data. The data was collected through secondary sources from economic survey from 2011-12 and labor force survey 2010-2011 and from annual plans of Pakistan from 2001-02 toÂ Â 2012-13. Mainly 8 to 10 micro finance banks and micro finance institutes and some NGOs are taken into study. Â The results express the positive contributions of micro finance to the employment that leads to poverty reduction and improved living standard. In contrast micro finance through supporting the employment generates sustainable solutions of other economic problems like poverty, good education, better health etc.Â