Global Journal of Computer Science and Technology (GJCST)
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MAGED: Metaheuristic Approach on Gene Expression Data: Predicting the Coronary Artery Disease and the Scope of Unstable Angina and Myocardial Infarction
Get PDFThe Genetic risk prediction strategies found in practice for coronary artery disease are not significant to estimate the scope of adverse cardiovascular events such as unstable angina and myocardial infarction. Hence in regard to this objective, this manuscript contributed a metaheuristic approach to predict coronary artery disease and the scope of unstable angina and myocardial infarction. The proposed metaheuristic is built from the gene expression data of blood samples collected from patients with coronary artery disease diagnosed, unstable angina and Myocardial Infarction. The data also includes gene expression data collected from the blood samples taken from the people clinically proven as salubrious (healthy). The relation between genes and gene expressions are considered as the state of input to devise the metaheuristic. In order to find the confidence of the relation between gene and gene expression a bipartite graph is built between them. The experimental study evincing that the prediction performance of the proposed model is substantial that compared to other benchmarking models
Evaluation of Features Extraction and Classification Techniques for Offline Handwritten Tifinagh Recognition
Get PDFThis paper presents a review on different features extraction and classification methods for off-line handwritten Amazigh characters (called Tifinagh) recognition. The features extraction methods are discussed based on Statistical, Structural, Global transformation and moments.Although a number of techniques are available for feature extraction and classification,but the choice of an excellent technique decides the degree of accuracy of recognition. A series of experimentswere performed on AMHCD databaseallowing to evaluate the effectiveness of different techniques of extraction features based on Hidden Markov models, Neural network and Support vector Machine classifiers. The statistical techniques giveencouraging results
An Extensive Investigation on Coronory Heart Disease using Various Neuro Computational Models
Get PDFThe diagnosis of heart disease at the early time is important to save the life of people as it is absolutely annoying process which requires extent knowledge and rich experience. By and large the expectation of heart infections in conventional method for inspecting reports, for example, Electrocardiogram-ECG, Magnetic Resonance Imaging- MRI, Blood Pressure-BP, Stress tests by medicinal professionals. Presently a-days a huge volume of therapeutic information is accessible in restorative industry in all maladies and these truths goes about as an incredible source in foreseeing the coronary illness by the professionals took after by appropriate ensuing treatment at an early stage can bring about noteworthy life sparing. There are numerous systems in ANN ideas which are likewise contributing themselves in yielding most elevated expectation precision over medical information. As of late, a few programming devices and different techniques have been proposed by analysts for creating powerful decision supportive systems. More over many new tools and algorithms are continued to develop and representing the old ones day by day. This paper aims the study of such different methods by researchers with high accuracy in predicting the heart diseases and more study should go on to improve the accuracy over predictions of heart diseases using Neuro Computing
Automock Automated Mock Backend Generation for JavaScript based Applications
Get PDFModern web development is an intensely collaborative process. Frontend Developers, Backend Developers and Quality Assurance Engineers are integral cogs of a development machine. Frontend developers constantly juggle developing new features, fixing bugs and writing good unit test cases. Achieving this is sometimes difficult as frontend developers are not able to utilize their time completely. They have to wait for the backend to be ready and wait for pages to load during iterations. This paper proposes an approach that enables frontend developers to quickly generate a mock backend that behaves exactly like their actual backend. This generated mock backend minimizes the dependency between frontend developers and backend developers, since both the teams can now utilize the entire sprint duration efficiently. The approach also aids the frontend developer to perform quicker iterations and modifications to his or her code
Recognition of Cursive Arabic Handwritten Text using Embedded Training based on HMMs
Get PDFIn this paper we present a system for offline recognition cursive Arabic handwritten text based on Hidden Markov Models HMMs The system is analytical without explicit segmentation used embedded training to perform and enhance the character models Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution characteristics in the word image These features are modelled using hidden Markov models and trained by embedded training The experiments on images of the benchmark IFN ENIT database show that the proposed system improves recognitio
A New Bayesian Inference Methodology for Modeling Geochemical Elements in Soil with Covariates. Characterization of Lithium in South Iberian Range (Spain)
Get PDFWhen the scientific need to model geochemical elements in soil is using geostatistical methodologies for instance krigings but we can use a new possibility with Bayesian Inference The models for the analysis were specified by the authors and estimated using Bayesian inference for Gaussian Markov Random Field GMRF through the Integrated Nested Laplace Approximation INLA algorithm The results allow us to quantify and assess possible spatial relationships between the distribution of lithium and other possible explanatory elements Are these other elements significant to the study We believe the methods outlined here may help to find elements such as lithium as well as contributing to the prediction and management of new extractions or prospection in a region in order to find each chemical element The application for the modeling is to study the spatial variation in the distribution of lithium and its relationship to other geochemical elements is analyzed in terms of the different possibilities offered by geographical and environmental factors All in all Lithium presents many important and meaningful uses and applications such as ceramics and glass electrical and electronics standing out lithium ion batteries as well as a lubricator for greases in metallurgy pyrotechnics air purification optics organic and polymer chemistry and medicine This study aims to examine the distribution of lithium in sediments from the area of Beceite in the Iberian Range and the Catalan Coastal Range Catal nids within the geological context of the Iberian Plate The Atlas Geoqu mico de Espa a IGME 2012 was used as the main geochemical data bank in order to carry out a statistical analysis stud
A Methodical Study of Content Based Medical Image Retrieval in Current Days
No full textContent-based image retrieval CBIR is standout amongst the most rich research fields in the area of computer vision significant advancement has been made throughout the decade CBIR is an image search methodology that changed the traditional text-based retrieval of images by utilizing various visual features for example color texture shape as criteria of search In the area of medical images particularly digital images are generated in constantly increasing quantities utilized for diagnostics therapy Content based approaches into medical images to support in making clinical decision has been suggested that would simplify the management of clinical data scenarios to incorporate the content-based approaches As the total quantity of data generated in diagnostic centers has increased it leads to the utilization of CBIR in the daily routine of hospitals clinics In this article we recognized and talked about some of the issues exist in the area as numerous proposals for systems are made from the medical domain and research models are made in the department of computer science by utilizing medical datasets Still there are a small number of systems that appear to be utilized as a part of clinical practice There is a needs to be expressed that the objective is not to change the text-based retrieval techniques as they exist right now but to enhance them with visual search tools This article will provide a summary of available literature in the area of content based access to medical image data and on the method used in the are
Comparative Analysis of MapReduce Framework for Efficient Frequent Itemset Mining in Social Network Data
Get PDFSocial networking sites are the virtual community for sharing information among the people It raises its popularity tremendously over the past few years Many social networking sites like Twitter Facebook WhatsApp Instragram LinkedIn generates tremendous amount data Mining such huge amount of data can be very useful Frequent itemset mining plays a significant role to extract knowledge from the dataset Traditional frequent itemsets method is ineffective to process this exponential growth of data almost terabytes on a single computer Map Reduce framework is a programming model that has emerged for mining such huge amount of data in parallel fashion In this paper we have discussed how different MapReduce techniques can be used for mining frequent itemsets and compared each other s to infer greater scalability and speed in order to find out the meaningful information from large dataset
Wildfire Predictions: Determining Reliable Models using Fused Dataset
Get PDFWildfires are a major environmental hazard that causes fatalities greater than structural fire and other disasters Computerized models have increased the possibilities of predictions that enhanced the firefighting capabilities in U S While predictive models are faster and accurate it is still important to identify the right model for the data type analyzed The paper aims at understanding the reliability of three predictive methods using fused dataset Performances of these methods Support Vector Machine K-Nearest Neighbors and decision tree models are evaluated using binary and multiclass classifications that predict wildfire occurrence and its severity Data extracted from meteorological database and U S fire database are utilized to understand the accuracy of these models that enhances the discussion on using right model for dataset based on their size The findings of the paper include SVM as the best optimum models for binary and multiclass classifications on the selected fused datase
Two Degree-Of-Freedom Camera Support System
Get PDFA surveillance camera is used to observer and record the surroundings. There are many types of existing surveillance camera and each of them has their own specifications made to suit their respective purposes. For example, there are fixed, 1-degree-of- freedom (DOF) and2-DOF cameras. As for a moving camera, it is essential for it to be able to move freely so that it can capture the target object in awider range. The camera also should be able to be controlled wirelessly to give a better practicality to the user. Based on the specifications, this project is constructed to overcome these problems. A 2-DOF camera support system is to be created which can be controlled wirelessly via Bluetooth. The support will e made with two motors that can pan and tilt the camera. The user will need to download an application which has o screen control into their gadgets and this can be connected to the Arduino which controls the motors. The Arduino will process the command from the user and will move the right motor to execute the command. This project will help the user to control the surveillance camera from a distance wirelessly and have at least a 360B0; pan view and 90B0; tilt view