A graphical model shows our aim of research attained with exemplary efficient implementation. The primary purpose of the research is to reduce the rising size of data stored and sent, improve data security, and create organized data using machine learning techniques. They are many types of classification algorithms that are there to predict the outcome of unstructured data. In this research, we involved big data analytics in machine learning technique (BDML) to reduce the challenges mentioned above using a classification algorithm. Big data are working more number of data to process, and nowadays, the stored data are increased exponentially in terabytes to zeta bytes, so we will not handle those increases by filtering each data to stored classified data value, removing the replicated data and the unnecessary data stored in big data. Here, big data analytics processing develops many applications but also leads to some challenges: data security, data structure, data standardization, and data storage and transfer. Further, the results also are established manually with chemical experts, which proves the exhaustiveness of the proposed method.īig data play an enormous role in the real-world industry, such as financial, banking, government sector, and healthcare, to store the enormous amount of data stored and processed. Hence, based on the experimentation, the proposed study suggested that the PNN classification with texture features is the best classifier used to classify the boron, iron, and silver nanoparticle images as compared to the K-NN classifier. The K-NN classifier has an accuracy of 80.00% for boron, 86.67% for iron, and 93.33% for the silver nanoparticle images, and the PNN classifier has an accuracy of 86.67% for boron, 93.33% for iron, and 93.33% for silver nanoparticle images.
The classification is done by using PNN and K-NN classifiers. The proposed technique extracts various textural features such as kurtosis, skewness, and entropy from boron, iron, and silver nanoparticle images. The advantages of digital image processing techniques increase the precision of object recognition in computer vision and pattern recognition. Digital image processing techniques play a vigorous part in identifying the size and structure, and classifying them precisely helps scientists and investigators to use them in numerous applications. Identifying the nanomaterial from FESEM and TEM images with bare eyes is an exceedingly impossible task. Synthesis techniques and conditions greatly affect the properties of synthesized nanomaterials. Nanomaterials are used in almost every field of engineering. Toward the end of the paper, we discuss the vast future scope of this study. The proposed model also fights issues like the vanishing gradient.
This enhancement can potentially be reflected in every deep learning architecture that uses a dense layer and will be remarkably higher in larger architectures that incorporate a very high number of parameters and output classes. Initial convergence to higher accuracies is always much faster in the proposed model, and the computational time of the model is reduced to half (or even less). The initial accuracy itself is much higher when the new neurons are used.
There is a significant improvement in the performance of a simple dense neural network. With the aim of achieving a higher accuracy, we test the proposed process on a number of well-known data sets (MNIST, CIFAR). In this study, we propose a new methodology for the computation of a neuron’s output, by adding a quadratic term to the conventional linear operation. Here, we are referring to the function used to process the input from the neurons of previous layers, also called the transformation function. Despite the intense improvements in existing architectures and the development of new deep-learning models, the core of the dense layers remains the same.
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