Contemporary Approach for Technical Reckoning Code Smells Detection using Textual Analysis

Software Designers should be aware of address design smells that can evident as results of design and decision. In a software project, technical debt needs to be repaid habitually to avoid its accretion. Large technical debt significantly degrades the quality of the software system and affects the productivity of the development team. In tremendous cases, when the accumulated technical reckoning becomes so enormous that it cannot be paid off to any further extent the product has to be abandoned. In this paper, we bridge the gap analyzing to what coverage abstract information, extracted using textual analysis techniques, can be used to identify smells in source code. The proposed textual-based move toward for detecting smells in source code, fabricated as TACO (Textual Analysis for Code smell detection), has been instantiated for detecting the long parameter list smell and has been evaluated on three sampling Java open source projects. The results determined that TACO is able to indentified between 50% and 77% of the smell instances with a exactitude ranging between 63% and 67%. In addition, the results show that TACO identifies smells that are not recognized by approaches based on exclusively structural information

Tagged Grid Matching Based Object Detection in Wavelet Neural Network

Object detection using Wavelet Neural Network (WNN) plays a major contribution in the analysis of image processing. Existing cluster-based algorithm for co-saliency object detection performs the work on the multiple images. The co-saliency detection results are not desirable to handle the multi scale image objects in WNN. Existing Super Resolution (SR) scheme for landmark images identifies the corresponding regions in the images and reduces the mismatching rate. But the Structure-aware matching criterion is not paying attention to detect multiple regions in SR images and fail to enhance the result percentage of object detection. To detect the objects in the high-resolution remote sensing images, Tagged Grid Matching (TGM) technique is proposed in this paper. TGM technique consists of the three main components such as object determination, object searching and object verification in WNN. Initially, object determination in TGM technique specifies the position and size of objects in the current image. The specification of the position and size using the hierarchical grid easily determines the multiple objects. Second component, object searching in TGM technique is carried out using the cross-point searching. The cross out searching point of the objects is selected to faster the searching process and reduces the detection time. Final component performs the object verification process in TGM technique for identifying (i.e.,) detecting the dissimilarity of objects in the current frame. The verification process matches the search result grid points with the stored grid points to easily detect the objects using the Gabor wavelet Transform. The implementation of TGM technique offers a significant improvement on the multi-object detection rate, processing time, precision factor and detection accuracy level

Comparative Analysis and Evaluation of Image inpainting Algorithms

Image inpainting refers to the task of filling in the missing or damaged regions of an image in an undetectable manner. There are a large variety of image inpainting algorithms existing in the literature. They can broadly be grouped into two categories such as Partial Differential Equation (PDE) based algorithms and Exemplar based Texture synthesis algorithms. However no recent study has been undertaken for a comparative evaluation of these algorithms. In this paper, we are comparing two different types of image inpainting algorithms. The algorithms analyzed are Marcelo Bertalmio’s PDE based inpainting algorithm and Zhaolin Lu et al’s exemplar based Image inpainting algorithm.Both theoretical analysis and experiments have made to analyze the results of these image inpainting algorithms on the basis of both qualitative and quantitative way. Keywords:Image inpainting, Exemplar based, Texture synthesis, Partial Differential Equation (PDE)

Combined Structure and Texture Image Inpainting Algorithm for Natural Scene Image Completion

Image inpainting or image completion refers to the task of filling in the missing or damaged regions of an image in a visually plausible way. Many works on this subject have been proposed these recent years. We present a hybrid method for completion of images of natural scenery, where the removal of a foreground object creates a hole in the image. The basic idea is to decompose the original image into a structure and a texture image. Reconstruction of each image is performed separately. The missing information in the structure component is reconstructed using a structure inpainting algorithm, while the texture component is repaired by an improved exemplar based texture synthesis technique. Taking advantage of both the structure inpainting methods and texture synthesis techniques, we designed an effective image reconstruction method. A comparison with some existing methods on different natural images shows the merits of our proposed approach in providing high quality inpainted images. Keywords: Image inpainting, Decomposition method, Structure inpainting, Exemplar based, Texture synthesi

Document Image Binarization Using Post Processing Method

Binarization is the preliminary process of Document Image Analysis and Processing. Image binarization is performed through Local and Global threshold methods. In this paper local thresholding method Nilblack method with post processing was implemented. The Nilblack algorithm was implemented using Matlab and tested with a sample of ground tooth images selected from TOBACCO research database

Substance Based Image Classification using Wavelet Neural Network

Background: Substance based Image Classification (SIC) using a wavelet neural network can use for efficient recognition. The foremost task of SIC is to remove the surrounding regions from an image to reduce the misclassified portion and to effectively reflect a shape of an object. At first, the image to be extracted is performed with SIC system through the segmentation of the image. Next, in order to attain more accurate information, with the extracted set of regions the wavelet transform is applied for extracting the configured set of features. Finally, using the neural network classifier model, misclassification over the given natural images and further background images are removed from the given natural image using the LSEG segmentation. Moreover, to increase the accuracy of object classification, SIC system involves the removal of the regions in the surrounding image.Objective: The main objective is provide better object recognition system for object classification with more accuracy with less error rate using substance based image classification. Results: Experimental results with natural image dataset substance based image classification are complementary to existing region boundary representation model. Performance evaluation reveals that the proposed SIC system reduces the occurrence of misclassification and reflects the exact shape of an object to approximately 10-15%. Conclusion: To reduce the misclassification over the given image data, the background regions are removed from the given image data based by adapting the LSEG segmentation. To obtain the more accurate information from the image object data, the wavelet transform is applied to obtain the configured quality features. Based on the feature set, the information about the image objects data from the region boundary images are obtained. Besides, the object classifier is implemented for classification of image to obtain the exact shape of the object. Experimental evaluation is conducted with the natural image dataset to check the performance of the proposed SIC system. Evaluation results revealed that the proposed SIC system achieved a higher classification rate by removing the surrounding regions of the image. Moreover, the feature extraction process provides the highest classification rate which enhances the performance of substance based image classification system. At the same time, the proposed SIC system revealed that the occurrence of misclassification of image data is less and the acquiring the image object data in the rate of 13% compared to the existing work

Object Classification Using Substance Based Neural Network

Object recognition has shown tremendous increase in the field of image analysis. The required set of image objects is identified and retrieved on the basis of object recognition. In this paper, we propose a novel classification technique called substance based image classification (SIC) using a wavelet neural network. The foremost task of SIC is to remove the surrounding regions from an image to reduce the misclassified portion and to effectively reflect the shape of an object. At first, the image to be extracted is performed with SIC system through the segmentation of the image. Next, in order to attain more accurate information, with the extracted set of regions, the wavelet transform is applied for extracting the configured set of features. Finally, using the neural network classifier model, misclassification over the given natural images and further background images are removed from the given natural image using the LSEG segmentation. Moreover, to increase the accuracy of object classification, SIC system involves the removal of the regions in the surrounding image. Performance evaluation reveals that the proposed SIC system reduces the occurrence of misclassification and reflects the exact shape of an object to approximately 10–15%

What Is New for an Old Molecule? Systematic Review and Recommendations on the Use of Resveratrol

Stilbenes are naturally occurring phytoalexins that generally exist as their more stable E isomers. The most well known natural stilbene is resveratrol (Res), firstly isolated in 1939 from roots of Veratrum grandiflorum (white hellebore) (1) and since then found in various edible plants, notably in Vitis vinifera L. (Vitaceae) (2). The therapeutic potential of Res covers a wide range of diseases, and multiple beneficial effects on human health such as antioxidant, anti-inflammatory and anti-cancer activities have been suggested based on several in vitro and animal studies (3). In particular, Res has been reported to be an inhibitor of carcinogenesis at multiple stages via its ability to inhibit cyclooxygenase, and is an anticancer agent with a role in antiangiogenesis (4). Moreover, both in vitro and in vivo studies showed that Res induces cell cycle arrest and apoptosis in tumor cells (4). However, clinical studies in humans evidenced that Res is rapidly absorbed after oral intake, and that the low level observed in the blood stream is caused by a fast conversion into metabolites that are readily excreted from the body (5). Thus, considerable efforts have gone in the design and synthesis of Res analogues with enhanced metabolic stability. Considering that reduced Res (dihydro- resveratrol, D-Res) conjugates may account for as much as 50% of an oral Res dose (5), and that D-Res has a strong proliferative effect on hormone-sensitive cancer cell lines such as breast cancer cell line MCF7 (6), we recently devoted our synthetic efforts to the preparation of trans-restricted analogues of Res in which the E carbon-carbon double bond is embedded into an imidazole nucleus. To keep the trans geometry, the two aryl rings were linked to the heteroaromatic core in a 1,3 fashion. Based on this design, we successfully prepared a variety of 1,4-, 2,4- and 2,5-diaryl substituted imidazoles including Res analogues 1, 2 and 3, respectively, by procedures that involve transition metal-catalyzed Suzuki-Miyaura cross-coupling reactions and highly selective N-H or C-H direct arylation reactions as key synthetic steps. The anticancer activity of compounds 1–3 was evaluated against the 60 human cancer cell lines panel of the National Cancer Institute (NCI, USA). The obtained results, that will be showed and discussed along with the protocols developed for the preparation of imidazoles 1–3, confirmed that a structural optimization of Res may provide analogues with improved potency in inhibiting the growth of human cancer cell lines in vitro when compared to their natural lead. (1) Takaoka,M.J.Chem.Soc.Jpn.1939,60,1090-1100. (2) Langcake, P.; Pryce, R. J. Physiological. Plant Patology 1976, 9, 77-86. (3) Vang, O.; et al. PLoS ONE 2011, 6, e19881. doi:10.1371/journal.pone.0019881 (4) Kraft, T. E.; et al. Critical Reviews in Food Science and Nutrition 2009, 49, 782-799. (5) Walle, T. Ann. N.Y. Acad. Sci. 2011, 1215, 9-15. doi: 10.1111/j.1749-6632.2010.05842.x (6) Gakh,A.A.;etal.Bioorg.Med.Chem.Lett.2010,20,6149-6151