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The medical image processing has become indispensable with an increased demand for systematic and efficient detection of brain tumor in a short period of time. There are various techniques for medical image segmentation. Detecting a wide variety of brain images in terms of shape and intensity is a challenging and difficult task to bring out a reliable and authentic data for diagnosing brain tumor diseases. This paper presents an algorithm which combines Region of Interest (ROI), Region Growing and Morphological Operation (Dilation and Erosion). This method initially identifies the approximate Region Growing (RG). Region growing is a procedure that groups pixels into larger regions, which starts from the seed points. Region growing based techniques are better than the edge-based techniques in noisy images where edges are difficult to detect. The Morphological Edge Detection of the input image is done and the input image is reconstructed on the basis of dilation and erosion for the enhancement of the image. The proposed work is divided into preprocessing to reduce the noise, Fuzzy C-Means is used to Region growing, Morphological edge detection is to enhance the image. Then the morphological edge detection can be classified into two categories, one is dilation and another is Erosion. Finally apply Gaussian filter to get output. After that, Fuzzy C-Means clustering (FCM), followed by seeded region growing is applied to detect and segment the tumor from the brain MRI image.

This paper proposes a novel brain tumor segmentation algorithm that uses Active Contour Model and Fuzzy-C-Means optimization. In Active Contour model, the initial Contour selection is a challenging task for MRI brain tumor segmentation because the accuracy of active contour segmentation depends on initial contour. This method uses the two level morphological reconstruction processes such as Dilation and Erosion along with thresholding process for minimizing the non-tumor region. The segmented region thus obtained is not accurate that also contains non-tumor region. Also there is a chance of missing the tumor region along with background while performing two level morphological reconstructions. In order to overcome these issues, active contour model is used to segment the complete tumor part. The initial Contour for Active Contour model is detected by forming a circular region around the tumor region. The radius of the circular region is contracted or expanded based on the shape of the tumor. This proposed Radius Contraction and Expansion (RCE) technique is used to select the initial contour of active Contour model. Further Fuzzy-C-Means algorithm is used to optimize the edge pixels because the boundary of active contour model output also contains the non tumor pixels. The performance of the proposed segmentation algorithm was evaluated using the metrics such as specificity, sensitivity, dice score, Probabilistic Rand Index (PRI) and Hausdorff Distance (HD) on T 1 - weighted contrast enhanced image dataset. The experimental result shows that the proposed segmentation algorithm provides a good performance when compared to the state-of-the-art segmentation methods.

In this paper, the wave propagation of a transversely isotropic thin ring - shaped elliptical plate made up of piezo electric material is considered. The surfaces are assumed to be traction free and coated with electrodes. The trail functions are introduced to uncouple the equations of motion and electric property. A computer program solving the finite element method (FEM) is developed and the numerical calculations are given for an array of six-noded shell elements. The non-dimensionalized natural frequencies of the elliptical plate in different wave numbers under different boundary condition for symmetric and antisymmetric modes are calculated and are plotted as dispersion curves. The proposed model is effective and simple and can be applied to problems with exact solution or a finite element approximation to the governing equations.

This paper proposes an accurate MRI brain tumor segmentation based on a Rotating Triangular Section with Fuzzy C-Means Optimization. Magnetic Resonance Imaging has become so popular due to its capability to differentiate tumors from the non-tumor region. The proposed method initially eliminates most of the background region by two level morphological reconstruction processes followed by thresholding. The two-level morphological reconstruction uses ‘ erosion’ as the first level and ‘ dilation’ as the second level. After eliminating the background, a region for Fuzzy C-Means (FCM) optimization is chosen using the Radius Contraction and Expansion process. The Radius Contraction and Expansion initially, selects the centroid and maximum radius of the region provided by the background elimination. The Radius Contraction and Expansion will give a contour whose shape is approximately the same as the shape of the tumor but larger than the size of the tumor region. The centroid of the new contour which acts as one of the vertices of the triangular region is again estimated. The remaining two vertex pixels are estimated from the contour pixels with a spacing provided by a spacing factor. FCM is then applied to this triangular region to obtain the accurate tumor pixels inside the triangular region. A new triangular region is estimated in the clockwise direction and FCM is again applied to the new triangular region. This process is repeated until the formation of the triangular region based FCM optimization completes one cycle. The performance of the proposed MRI brain tumor segmentation was evaluated using the T 1 - weighted contrast-enhanced image dataset with the metrics such as dice score, sensitivity, specificity, Hausdorff Distance, and Probabilistic Rand Index (PRI). Experimental results reveal that the proposed MRI brain tumor segmentation outperforms the other state-of-the-art MRI brain tumor segmentation method.

A simple and novel sensor of hydrogen peroxide (H 2 O 2 ) was designed successfully. Horseradish peroxidase (HRP) is effectively immobilized on the surface of chitosan (CHI)-stabilized zinc sulphide (ZnS)/graphene (G) nanocomposites modified carbon paper electrode (CPE). The interaction between HRP enzyme and H 2 O 2 was studied from modified CPE. The catalytic performance of the biosensor was examined using cyclic voltammetry (CV) and the obtained results indicated that the prepared ZnS/G/CHI/HRP/NA/CPE nanocomposites material holds excellent catalytic performance for the H 2 O 2 detection. The proposed biosensor showed good analytical performance and long-term storage stability. The repeatability of the sensor is checked up to eight cycles.

The absorption and fluorescence spectra of 1,5-diaminoanthraquinone(1,5-DAAQ) have been investigated in organic solvents-Benzene(BZ), Ethanol (ETOH), Acetonitrile (AN), Dimethylformamide (DMF) and Dimethyl sulfoxide (DMSO). There is an intra molecular hydrogen bond formed between quinoid oxygen and the substituents NH2 [C = O...H-N]. The interaction of the hydrogen atom of - NH2 leads to red shift in both absorption and fluorescence spectra. The dipole moment ratio of 1,5 DAAQ in ground and excited states was calculated from stokes shift obtained from optical absorption and fluorescence spectra. Photo physical properties of 1,5-DAAQ dye was studied using this absorption and fluorescence spectroscopy techniques in binary liquid mixtures(AN + DMF, AN + DMSO, AN + ETOH and BZ + ETOH).

Background: Diabetes is a chronic condition in that Type II diabetes (Formerly called as Non-Insulin Dependent) is the most prevalent, generally affecting adults, diabetes frequently reduces HDL (good) cholesterol levels while raising triglycerides and LDL (bad) cholesterol levels. Both of these factors raise the likelihood of heart disease and stroke. The Indian system of traditional medicine, Ayurveda, also uses curry leaf as a common treatment. Curry leaves can prevent several diseases, including type 2 diabetes and heart disease, because they are rich in antioxidants like beta-carotene,also rich in fibre and vitamin C. Objectives: To determine the effectiveness of curry leaves powder on reducing blood glucose and lipid profile among the clients with Type II diabetes with mild elevated lipid level in the experimental group. To compare the post-test level of blood glucose and lipid profile between the experimental and control group. Methodology: Research approach: quantitative approach, quasi experimental research design was used for 60 samples by Nonprobability purposive sampling technique. Result: on comparing the pre and post-test of blood glucose and lipid profile in relation to administering of curry leaves powder among Type II Diabetic Patients in experimental group, the percentage of reduction for fasting blood glucose is 5.72%, For Postprandial blood sugar is 6.39%, for total Cholesterol is 3.68%, for HDL the increased rate is 7.13%, for LDL the decreased percentage is 6.36%, and for Triglyceride the decrease percentage is 9.04%.

The solvatochromic behavior of the 1,4-bis(((2-methylthio) phenylimino)methyl) benzene [BMTPMB] in single solvents and binary mixtures were investigated. Fluorescence spectra show the dual emission due to twisted intramolecular charge transfer (TICT) state. The preferential solvation parameters: local mole fraction, X2L, solvation index δs2, exchange constant K12 were calculated for the binary mixtures, ACN+MEOH, DMSO+CCl4 and CCl4+1,2 DCE. The dipole moment ratios between ground and excited states were deduced using the solvatochromic shifts of absorption and fluorescence spectra as a function of dielectric constant (ε), refractive index (n) and it was found to be 1.25.

The molecular recognition properties of p-tert-butyl-calix(8)arene with 1,5 -diamino anthraquinone (1,5 DAAQ) were studied by using UV–Visible and Fluorescence spectroscopic techniques. The binding constant was determined by using the Benesi-Hilde brand expression. It was found that the host–guest complex was formed between 1,5 DAAQ and p-tert-butyl- calix(8)arene in the 1:2 Stoichiometry ratio.

The Internet of Things (IoT) affords a new paradigm for the expansion of heterogeneous networks, and it emerge as a ubiquitous computing platform. IoT has specific sorts of applications, which includes smart home, wearable gadgets, clever linked cars, industries, and clever cities. Therefore, IoT based programs turn out to be the critical elements of our day-to-day lifestyles. However, due to the dearth of adequate computing and garage assets dedicated to the processing and garage of big volumes of the IoT statistics, it has a tendency to undertake a cloud based structure to address the problems of useful resource constraints. Hence, a series of tough safety issues have arisen in the cloud-based IoT context. To overcome these issues in this paper we have proposed alight weight one to one authentication (LWO2O) scheme to make certain security in IoT based cloud surroundings. ThisLWO2O scheme specifically uses identity based encryption to make the scheme as light-weight. The security evaluation shows the effectiveness and significance of the LWO2O scheme in comparison to the prevailing schemes