In the era of big data, the rapid development of information technology has made the sharing of data and information more free, bringing convenience to the public, but at the same time, the massive amount of data also brings “information anxiety.” In this paper, we use the knowledge of design discipline, combined with psychological cognitive science, statistics, and communication discipline, and analyze a large number of excellent cases to effectively combine “information visualization-visual representation-design.” The purpose of this study is to enable audiences to efficiently access information through visual symbols in a large amount of data and to increase their interest in reading and satisfaction in accessing information. As a carrier of communication between designers and audiences in the process of visual representation of information visualization, visual symbols can effectively convey information content and emotional concepts to audiences. Finally, based on the theoretical support of the previous paper, combined with our own practical experience, we conduct a systematic study on the application of design thinking and the construction of design methods for the design of visual representations of information visualization. A comparative study on the aesthetics of different categories of APP interface design is conducted, and it is believed that different categories of APP can create distinctive and different categories of APP interface aesthetics through the differentiated design of interface layout, content expression, and visual form, which is considered as the ultimate goal that APP interface design must pursue. Visual representation is a method and means to realize information visualization as a representation practice that expresses the meaning of information in the form of visual symbols. The visual representation of information visualization uses visual symbols as a medium, and the audience interprets the visual symbols based on cognitive experience to obtain information, which helps to maximize the dissemination of information.

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Research on the Teaching of Visual Communication Design Based on Digital Technology

In the current era of technology, artificial intelligence has grown rapidly in such a way that it has established its presence in all fields. The purpose of artificial intelligence is to reduce human intervention and complete tasks with an enhanced result. In this research, we are going to study the application of artificial intelligence technology in art teaching, taking architectural painting as an example. Architectural painting is a type of painting that focuses only on architecture, including indoor and outdoor views of the buildings. In earlier stages, architecture was shown only in the background of paintings that had different objects as the main subject. Later, architecture itself became a mainstream genre in the field of painting. As has been shown by other researchers, the latest technologies such as Internet technology, wireless sensor networks (WSNs), and artificial intelligence like deep learning technologies are deployed in art teaching. Artificial intelligence has made teaching easier. This proposed system makes use of Internet technology, WSNs, artificial intelligence, and lightweight deep learning models in the field of art teaching. The teaching method is enhanced by adapting to this new technology. For performing the analysis of the proposed system, the Limited Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) art algorithm is implemented. This L-BFGS algorithm focuses on finding the local minima in any given application. In this art teaching of architectural painting, the proposed algorithm will aid in explaining the minute works to be noted while doing the artwork. The proposed algorithm is then compared with the traditional Gradient Descent, Adam, and Adadelta algorithms. From the results, it can be observed that the proposed algorithm has achieved accuracy of 97% and 98% in the training and testing phases, respectively.

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The Application of Artificial Intelligence Technology in Art Teaching Taking Architectural Painting as an Example

Nowadays, a demand is increased all over the world in the field of information security and security regulations. Intrusion detection (ID) plays a significant role in providing security to the information, and it is an important technology to identify various threats in network during transmission of information. The proposed system is to develop a two-layer security model: (1) Intrusion Detection, (2) Biometric Multimodal Authentication. In this research, an Improved Recurrent Neural Network with Bi directional Long Short-Term Memory (I-RNN-BiLSTM) is proposed, where the performance of the network is improved by introducing hybrid sigmoid-tanh activation function. The intrusion detection is performed using I-RNN-BiLSTM to classify the NSL-KDD dataset. To develop the biometric multimodal authentication system, three biometric images of face, iris, and fingerprint are considered and combined using Shuffling algorithm. The features are extracted by Gabor, Canny Edge, and Minutiae for face, iris, and fingerprint, respectively. The biometric multimodal authentication is performed by the proposed I-RNN-BiLSTM. The performance of the proposed I-RNN-BiLSTM has been analysed through different metrics like accuracy, f-score, and confusion matrix. The simulation results showed that the proposed system gives better results for intrusion detection. Proposed model attains an accuracy of 98% for the authentication process and accuracy of 98.94% for the intrusion detection process.

A Deep Learning Framework for Intrusion Detection and Multimodal Biometric Image Authentication

DATA protection is becoming increasingly critical as the demand for online services grows, which biometrics authentication can help with. To communicate biometric data authenticating services over the Internet in a secure channel is essential. To ensure the security and the existence of the biometric information over the communication channel Random Visual Cryptography and Multimodal Mask Steganography is used. The biometric features captured are embedded using multimodal mask steganography, considering mask image and cover image. The resultant stego image shares are created using Random visual cryptography technique. The resultant shares are placed in blockchain using blockchain technology to improve security aspects of the cloud application and reliable transmission of data. Combining proposed Random Visual cryptography and multimodal mask steganography methods with blockchain technology ensures high reliable authentication system of server room access or banking application. 

Multifold Secured Bank Application Authentication Service Using Random Visual Cryptography and Multimodal Steganography with Blockchain Technology

Secure two fold transmission of features using multimodal mask steganography and naive based random visual cryptography system

With increase in growth of data and digital threat, demand of securing the data communicated over the internet is an essential play in the digital world. In the vision of digitalizing services with the next generation of security to the sensitive data transmitted over the internet by\n hiding the existence of the data using next generation cryptography by fusing cryptography techniques is one the major technique adopted. With this the aim in traditional Least Significant Bit (LSB) is one of the widely used technique. Where the secret message or image are placed in the cover\n image in the least significant bits of RGB Channels resulting in a stego image. But the drawback is, on suspecting the differences in the pixels of original and stegoimage in the secret data embedded can be guessed and extracted by attacker. The Proposed visual crypto-mask steganography method\n overcomes this drawback and support good payload capacity with multi modal approach of embedding biometrics, resulting in ∞ PSNR. The authenticated person face and fingerprint information is transmitted in a cover image and mask image (magic sheet) using proposed steganography and is\n combined with Random Visual Crypto Technique. Which results in enhanced and advance visual crypto steganography secured model in communicating sensitive (biometric features) information over the internet. Where the complete information cannot be extracted using only cover image. Mask image\n (magic sheet) is used along with cover image that reveals the secret data in the receiving end.

S. Jahnavi

Papers

Novel Multifold Secured System by Combining Multimodal Mask Steganography and Naive Based Random Visual Cryptography System for Digital Communication