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In the data collection phase, the digital images are captured using sensors that often contaminated by noise (undesired random signal). In digital image processing task, enhancing the image quality and reducing the noise is a central process. Image denoising effectively preserves the image edges to a higher extend in the flat regions. Several adaptive filters (median filter, Gaussian filter, fuzzy filter, etc.) have been utilized to improve the smoothness of digital image, but these filters failed to preserve the image edges while removing noise. In this paper, a modified fuzzy set filter has been proposed to eliminate noise for restoring the digital image. Usually in fuzzy set filter, sixteen fuzzy rules are generated to find the noisy pixels in the digital image. In modified fuzzy set filter, a set of twenty-four fuzzy rules are generated with additional four pixel locations for determining the noisy pixels in the digital image. The additional eight fuzzy rules ease the process of finding the image pixels,whether it required averaging or not. In this scenario, the input digital images were collected from the underwater photography fish dataset. The efficiency of the modified fuzzy set filter was evaluated by varying degrees of Gaussian noise (0.01, 0.03, and 0.1 levels of Gaussian noise). For performance evaluation, Structural Similarity (SSIM), Mean Structural Similarity (MSSIM), Mean Square Error (MSE), Normalized Mean Square Error (NMSE), Universal Image Quality Index (UIQI), Peak Signal to Noise Ratio (PSNR), and Visual Information Fidelity (VIF) were used. The experimental results showed that the modified fuzzy set filter improved PSNR value up to 2-3 dB, MSSIM up to 0.12-0.03, and NMSE value up to 0.38-0.1 compared to the traditional filtering techniques.

In this study an attempt was made to assess the mining associated urbanization impact on rural agricultural lands due to expansion of mining dump yards, pulverizing mills and beneficiation plants in and around Govindampalle village, YSR District, A.P by using Geospatial technologies. Various land use and land cover (LULC) classes, mainly, cropland, plantation, fallow land, mining/industrial, water body, settlements and scrub lands were recognized and their impact on the environment has been discussed. Google earth historical imagery during 2010 and 2017 are used to map and assess these land use and land cover changes in the ArcGIS flat form. LULC change detection analysis data showed that cropland, water bodies and scrublands were decreased by 10, 2, 14 and 20 percent, respectively from 2010 to 2017. On the other hand industrial areas and agricultural plantations showed an increasing trend with 14 and 20 percent growth rate respectively.

In the present investigation, a low-cost fermentation medium was designed to achieve the maximum production of lactase from the Bacillus sp. VUVD101 strain through the screening of different nutritional and physical variables, using the Plackett-Burman design. Fourteen variables of the fermentation process were selected: incubation time, temperature, pH, RPM, DO, inoculum size, inoculum age, MgSO4, l-Cysteine, KH2PO4, CaCl2, K2PHO4, corn steep liquor and lactose. The selected variables were evaluated through statistical analysis, based on their significance, coefficient value and standard effect plot. The results suggested that six variables, namely, corn steep liquor, lactose, MgSO4, temperature, pH and RPM, had influence with high confidence levels, while the remaining eight variables did not show a significant effect on production. The analysis of the variance value R2 (0.96) also showed the model used for prediction to be significant (p less than 0.05). The plot for the standard effect for each component and its traits provided accurate data by which to select well-suited variables for further optimization. In comparison with the basal medium, 68% higher enzyme activity was achieved from the model of the optimized medium, and lactase activity was found to be 18.31 U/ml. Keywords: Lactase, Variables, Nutrient sources, Plackett-Burman design

In infants with cleft lip and palate, failure to thrive (FTT) condition has largely been attributed to early feeding difficulties. Presurgical Nasoalveolar Molding (PNAM) forms an integral part of treatment modality for cleft infants in such conditions, by providing a myriad of benefits. It balances several aspects of treatment such as growth, aesthetics and function in cleft infants and also provides psychological reassurance to the parents. This clinical report describes the presurgical management of an infant with complete unilateral cleft lip and palate who was in failure to thrive condition.

Introduction: The denture base acrylic resins require adjustments for various reasons. During this process there is an alteration in the surface characteristics of the denture base. Rough surfaces promote the bacterial adhesion and plaque accumulation; therefore it is important to know the character of the surface left by instrumentation on denture base materials. This study evaluated the surface characteristics of the machined surfaces of heat-cured acrylic denture base resin, GC supersoft and Permasoft softliners. Materials and Methods: Thirty 15×15×1.5mm acrylic resin specimens were fabricated with each of three acrylic resins: Lucitone 199 denture base resin (Group I), GC supersoft (Group II) and Permasoft (Group III) softliners. They were further divided into three sub Groups A, B and C, in which Sub Group A was control group that is smooth produced against the glass. Sub Group B was produced by machining with the tungsten carbide bur and Sub group C is machined with the stone bur. Each surface was evaluated by a Scanning electron microscope and data were analyzed by analysis of variance followed by Tukey’s HSD test. Results: Stone bur produced smoother surface (Ra 3.6681μm± 0.254) on Lucitone199 than the tungsten carbide bur (Ra 5.3881μm ± 0.3373). Carbide bur produced a smoother surface on the GC super soft (Ra 1.617097μm ± 0.191767) and Permasoft softliners (Ra 2.237419μm ± 0.354259). Whereas stone bur produced rougher surface on GC supersoft(Ra 2.6μm) and Permasoft (Ra 4.184839μm ± 0.409869) softliners. Conclusion: The present study shows each type of rotary instrument produces its own characteristic surface on each type of denture base materials and that care is needed when selecting the most appropriate instrument to adjust denture base materials. These results can have a significant clinical implication. While using Lucitone 199 stone bur can be used for chair side adjustments. Tungsten carbide bur can be used for GC supersoft and Permasoft softliners to achieve smoother surface.

People may quickly obtain information through a variety of channels including social media, blogs, websites, and other online resources. These platforms have made it possible for information to be shared more easily. As a result, the amount of time that individuals spend on various social networking applications has increased. This research predicts the effects of human exposure to social networks in the near future. In this work a competent model for predicting the ill-effects is provided, that is both accurate and efficient. This model represents a combination of the independent models that have been operating independently so far. Thus each of these models makes a forecast and ultimate selection is decided based on whether or not there exists a majority of convergence of results from the operation of various models. By employing the majority voting system, this strategy attempts to take the benefits of the predictions produced by all the models while also to reduce the inaccuracies generated by each model. Theoretically, this model should outperform the use of individual models in terms of performance. Important features are extracted from the datasets using the proposed model, and the extracted features are then classified using an ensemble model that consists of four popular machine learning models: support vector machines (SVMs), logistic regression (logistic regression), random forest (random forest classification), and neural networks (NN). We have analyzed our prediction performance with the existing methods for number of times by changing the train set and test set data. In all the cases our novel method has been predicting with 3% to 4% improved performance in accuracy, precision, F-1 score, and specificity. From the dataset it has been achievable to attain the highest training and testing accuracy from among the existing models. [ABSTRACT FROM AUTHOR]