IEEE 2014 Java Projects
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The document outlines various IEEE 2014 Java projects, including methods for brain tumor detection using x-ray CT imaging, hyperspectral image segmentation, and diabetes and hypertension detection through retinal images. Each project features a unique algorithmic approach, such as region-based iterative reconstruction, fractional-order particle swarm optimization, and automatic graph-based classification. The summaries emphasize advancements in image reconstruction and classification techniques, highlighting their effectiveness, validation, and potential applications in medical diagnostics.
IEEE 2014 Java Projects
- 1. IEEE 2014 Java Projects Web : www.kasanpro.com Email : sales@kasanpro.com List Link : http://kasanpro.com/projects-list/ieee-2014-java-projects Title :Brain Tumor Detection using Region based Iterative Reconstruction and Segmentation Language : Java Project Link : http://kasanpro.com/p/java/brain-tumor-detection-region-based-iterative-reconstruction-segmentation Abstract : X-ray computed tomography (CT) is a powerful tool for noninvasive imaging of time-varying objects. Identifiying tumors from the CT image is a chalanging one. In this paper we proposed a reconstruct method for CT image and tumors are detected then using edge based segmentation algorithm.. In the past, methods have been proposed to reconstruct images from continuously changing objects. For discretely or structurally changing objects, however, such methods fail to reconstruct high quality images, mainly because assumptions about continuity are no longer valid. In this paper, we propose a method to reconstruct structurally changing objects. Starting from the observation that there exist regions within the scanned object that remain unchanged over time, we introduce an iterative optimization routine that can automatically determine these regions and incorporate this knowledge in an algebraic reconstruction method. And tumor detection was made from the reconstructed image. Title :Change Detection of Hyper Spectral Remote Sensing Image by Multilevel Image Segmentation Language : Java Project Link : http://kasanpro.com/p/java/change-detection-hyper-spectral-remote-sensing-image-multilevel-image-segmentation Abstract : Land cover composition and change are important factors that affect ecosystem condition and function. Remote sensing is the most important and effective way to acquire data of land cover. The paper proposes an Improved Change detection with Hyperspectral remote sensing images. Hyperspectral remote sensing images contain hundreds of data channels. Due to the high dimensionality of the hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for such imagery. In this paper, a new multilevel thresholding method is introduced for the seg- mentation of hyperspectral and multispectral images. The new method is based on fractional-order Darwinian particle swarm optimization (FODPSO) which exploits the many swarms of test solutions that may exist at any time. In addition, the concept of fractional derivative is used to control the convergence rate of particles. And finally Post-classification Comparison Change Detection applied which is the most commonly used quantitative method of change detection. Title :Automatic graph based approach for prior detection of diabetes and hypertension in retinal images Language : Java Project Link : http://kasanpro.com/p/java/automatic-graph-based-prior-detection-diabetes-hypertension-retinal-images Abstract : Retinal vessels are affected by several systemic diseases, namely diabetes, hypertension, and vascular disorders. In diabetic retinopathy, the blood vessels often show abnormalities at early stages, as well as vessel diameter alterations . Changes in retinal blood vessels, such as significant dilatation and elongation of main arteries, veins, and their branches are also frequently associated with hypertension and other cardiovascular pathologies. The classification of retinal vessels into artery/vein (A/V) is an important phase for automating the detection of vascular changes, and for the calculation of characteristic signs associated with several systemic diseases such as diabetes, hypertension, and other cardiovascular conditions. This paper presents an automatic approach for A/V classification based on the analysis of a graph extracted from the retinal vasculature. The proposed method classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each vessel segment (graph links). Final classification of a vessel segment as A/V is performed through the combination of the graph-based labeling results with a set of intensity features. The features were extracted, including exudates, bifurcation angle, artery-to-veins diameter ratio, mean artery and veins diameters, form and size of optic disc, and vessel tortuosity. And the identification of diabetes are made by the rule based conditions.
- 2. Title :Ontology-based annotation and retrieval of services in the cloud Language : Java Project Link : http://kasanpro.com/p/java/ontology-based-annotation-retrieval-services-cloud Abstract : Cloud computing is a technological paradigm that permits computing services to be offered over the Internet. This new service model is closely related to previous well-known distributed computing initiatives such as Web services and grid computing. In the current socio-economic climate, the affordability of cloud computing has made it one of the most popular recent innovations. This has led to the availability of more and more cloud services, as a consequence of which it is becoming increasingly difficult for service consumers to find and access those cloud services that fulfil their requirements. In this paper, we present a semantically-enhanced platform that will assist in the process of discovering the cloud services that best match user needs. This fully-fledged system encompasses two basic functions: the creation of a repository with the semantic description of cloud services and the search for services that accomplish the required expectations. The cloud service's semantic repository is generated by means of an automatic tool that first annotates the cloud service descriptions with semantic content and then creates a semantic vector for each service. The comprehensive evaluation of the tool in the ICT domain has led to very promising results that outperform state-of-the-art solutions in similarly broad domains. Title :Comprehensive Explanation of SLA Violations at Runtime Language : Java Project Link : http://kasanpro.com/p/java/comprehensive-explanation-sla-violations-runtime Abstract : Service Level Agreements (SLAs) establish the Quality of Service (QoS) agreed between service-based systems consumers and providers. Since the violation of such SLAs may involve penalties, quality assurance techniques have been developed to supervise the SLAs fulfillment at runtime. However, existing proposals present some drawbacks: 1) the SLAs they support are not expressive enough to model real-world scenarios, 2) they couple the monitoring configuration to a given SLA specification, 3) the explanations of the violations are difficult to understand and even potentially inaccurate, 4) some proposals either do not provide an architecture, or present low cohesion within their elements. In this paper, we propose a comprehensive solution, from a conceptual reference model to its design and implementation, that overcomes these drawbacks. The resulting platform, SALMonADA, receives the SLA agreed between the parties as input and reports timely and comprehensive explanations of SLA violations. SALMonADA performs an automated monitoring configuration and it analyses highly expressive SLAs by means of a constraint satisfaction problems based technique. We have evaluated the impact of SALMonADA over the resulting service consumption time performance. The results are satisfactory enough to consider SALMonADA for SLA supervision because of its low intrusiveness. IEEE 2014 Java Projects Title :Region-Based Iterative Reconstruction of Structurally Changing Objects in CT Language : Java Project Link : http://kasanpro.com/p/java/region-based-iterative-reconstruction-structurally-changing-objects-ct Abstract : X-ray computed tomography (CT) is a powerful tool for noninvasive imaging of time-varying objects. In the past, methods have been proposed to reconstruct images from continuously changing objects. For discretely or structurally changing objects, however, such methods fail to reconstruct high quality images, mainly because assumptions about continuity are no longer valid. In this paper, we propose a method to reconstruct structurally changing objects. Starting from the observation that there exist regions within the scanned object that remain unchanged over time, we introduce an iterative optimization routine that can automatically determine these regions and incorporate this knowledge in an algebraic reconstruction method. The proposed algorithm was validated on simulation data and experimental ?CT data, illustrating its capability to reconstruct structurally changing objects more accurately in comparison to current techniques. Title :Multilevel Image Segmentation Based on Fractional-Order Darwinian Particle Swarm Optimization Language : Java Project Link : http://kasanpro.com/p/java/multilevel-image-segmentation-based-particle-swarm-optimization Abstract : Hyperspectral remote sensing images contain hundreds of data channels. Due to the high dimensionality of the hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for such imagery. In this paper, a new multilevel thresholding method is introduced for the segmentation of hyperspectral and multispectral images. The new method is based on fractional-order Darwinian particle swarm optimization (FODPSO)
- 3. which exploits the many swarms of test solutions that may exist at any time. In addition, the concept of fractional derivative is used to control the convergence rate of particles. In this paper, the so-called Otsu problem is solved for each channel of the multispectral and hyperspectral data. Therefore, the problem of n-level thresholding is reduced to an optimization problem in order to search for the thresholds that maximize the between-class variance. Experimental results are favorable for the FODPSO when compared to other bioinspired methods for multilevel segmentation of multispectral and hyperspectral images. The FODPSO presents a statistically significant improvement in terms of both CPU time and fitness value, i.e., the approach is able to find the optimal set of thresholds with a larger between-class variance in less computational time than the other approaches. In addition, a new classification approach based on support vector machine (SVM) and FODPSO is introduced in this paper. Results confirm that the new segmentation method is able to improve upon results obtained with the standard SVM in terms of classification accuracies. Title :An Automatic Graph-Based Approach for Artery/Vein Classification in Retinal Images Language : Java Project Link : http://kasanpro.com/p/java/an-automatic-graph-based-artery-vein-classification-retinal-images Abstract : The classification of retinal vessels into artery/vein (A/V) is an important phase for automating the detection of vascular changes, and for the calculation of characteristic signs associated with several systemic diseases such as diabetes, hypertension, and other cardiovascular conditions. This paper presents an automatic approach for A/V classification based on the analysis of a graph extracted from the retinal vasculature. The proposed method classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each vessel segment (graph links). Final classification of a vessel segment as A/V is performed through the combination of the graph-based labeling results with a set of intensity features. The results of this proposed method are compared with manual labeling for three public databases. Accuracy values of 88.3%, 87.4%, and 89.8% are obtained for the images of the INSPIREAVR, DRIVE, and VICAVR databases, respectively. These results demonstrate that our method outperforms recent approaches for A/V classification. http://kasanpro.com/ieee/final-year-project-center-nagapattinam-reviews