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. Generative adversarial networks (GANs) can generate sentences that imitate real language features from discrete spaces, and the implicit expression space learned by training can continuously generate credible sentences. We solve the problem of face image restoration of different genders, different skin colors, and different hair colors through a method of generating face images, in which the face image generated by text description has a better generation effect. We propose a face image generation method (T-GAN) based on generating confrontational text. Due to the particulars of a face, a Word-long short-term memory model is used to extract the text features corresponding to the face. Then combined with the idea of generating a game against “game,” a counter-text network is created, next a third type of input is added to the discriminator, and the real image with unmatched text is composed to strengthen the discriminator’s training effect and force discrimination. The device determines whether the generated image conforms to the text description, so the discriminator can better learn the relationship between the text description and the image content. The experimental results show that, compared with other image generation methods, the face image generated by the proposed method can give high quality generated images with a better effect and less distortion, which can better maintain the original attributes of the image, has the use value, and increases the reduction degree by 3.84%.

In recent years, the researchers have perceived the modifications or transformations motivated by the presence of big data on the definition, complexity, and future direction of the real world optimization problems. Big Data visualization is mainly based on the efficient computer system for ingesting actual data and producing graphical representation for understanding large quantity of data in a fraction of seconds. At the same time, clustering is an effective data mining tool used to analyze big data and computational intelligence (CI) techniques can be employed to solve big data classification process. In this aspect, this study develops a novel Computational Intelligence based Clustering with Classification Model for Big Data Visualization on Map Reduce Environment, named CICC-BDVMR technique. The proposed CICC-BDVMR technique intends to perform effective BDV using the clustering and data classification processes on the Map Reduce environment. For clustering process, a grasshopper optimization algorithm (GOA) with kernelized fuzzy c-means (KFCM) technique is used to cluster the big data and the GOA is mainly utilized to determine the initial cluster centers of the KFCM technique. GOA is a recently proposed metaheuristic algorithm inspired by the swarming behaviour of grasshoppers. This algorithm has been shown to be efficient in tackling global unconstrained and constrained optimization problems. Based on the modified GOA, an effective kernel extreme learning machine model for financial stress prediction was created. Besides, big data classification process takes place using the Ridge Regression (RR) and the parameter optimization of the RR model is carried out via the Red Colobuses Monkey (RCM) algorithm. The design of GOA and RCM algorithms for parameter optimization processes for big data classification shows the novelty of the study. A wide ranging simulation analysis is carried out using benchmark big datasets and the comparative results reported the enhanced outcomes of the CICC-BDVMR technique over the recent state of art approaches. The broad comparison research illustrates the CICC-BDVMR approach’s promising performance against contemporary state-of-the-art techniques. As a result, the CICC-BDVMR technique has been demonstrated to be an effective technique for visualising and classifying large amounts of data.

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Molecularly imprinted composite materials (PM) selective to S-naproxen were prepared in the surface of mesoporous silica sphere (SBA-15) by surface imprinting technique. FT-IR, SEM and surface area analysis were used to study the structural morphology of PM and MIPs particles and probe the incorporation of polymer into the SBA-15 framework. The results revealed that PM showed better binding affinity and selectivity to the template molecule than MIPs and the maximum saturated binding capacity of PM to S-naproxen and R-naproxen was about 10.3332 and 6.0063µmol·g-1. Meanwhile, we achieve a reference strategy for the development of molecularly imprinting polymer for drugs and to handle forms in certain applications such as chromatographic stationary phases for chiral separations.