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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]

An invariable mix of Biometric recognition as well as authentication process is used in almost every application for prevention of unauthorized access to computing resources. Though several research initiatives have been undertaken in recent years for making biometric algorithms more accurate for various kinds of features, they have largely ignored the critical aspects like reliability and robustness. Typically, information from multiple modalities is fused in a Multimodal biometric system to counter the shortcomings an individual classifier. This work primarily seeks to make optimum integration of biometric traits viz., iris, fingerprint and finger vein that have great complementary relation among them. It is basically done through optimization of performance in every single classifier through a novel Search Optimization Algorithm with backtracking function. Moreover, it tries to resolve conflicting beliefs affecting individual classifiers through use of proportional conflict redistribution rules, before obtaining a concurrent solution. Optimal behavior under a dynamic environment is shown in this system, either by boosting or, even by suppressing concurrent classifiers, thereby ensuring that classifiers do not have any more issues between them. The suggested multimodal system is developed by applying multimodal datasets generated using benchmarked digital imagery. Our system performs better averaging precision at 98.43% having 1.57% EER, thereby outperforming the existing techniques and promising to develop an advanced multimodal biometric system in subsequent efforts. [ABSTRACT FROM AUTHOR]