Artificial Intelligence and Multimedia Data Engineering

 Present manufacturing machines have few methods to investigate machine health. To minimize issues and enhance the correctness of machine decisions and automation, machine health conditions require to be investigated. Therefore, the evolution of a fresh investigating and diagnostics approach for additive manufacturing machines is needed for better productivity in Industry 4.0. In the current chapter, an intelligent technique for the condition monitoring of additive manufacturing (AM) is described, where an accelerometer fitted on the extruder assembly is used to receive vibration signals. The process errors with the printer were the worn-out timing belts driving the extruder assembly. Quantum-based Support Vector Machine was simulated to identify the 3D-printer status. The simulation outcomes presented here show that this approach has better correctness as compared to the previous Support Vector Machine techniques.

Evaluating mental workload is crucial to preserve health and prevent mishaps. The reliability and mental states of individuals in any human-computer interaction scenario are assessed utilizing features of the electroencephalogram (EEG) by means of many approaches in machine learning and deep learning This study reviews and identifies the multiple Machine Learning and Deep Learning algorithms used for workload assessment, as well as the various datasets, characteristics, and features that contribute to workload assessment. When ML and DL approaches were compared, it was found that deep learning techniques and ensemble techniques work best when EEG's Power Spectral Density Features are used. We have also used optimization techniques like GWO and taken into account numerous features from various domains and assessed the workload. This study discovered that when measuring cognitive load, features like PSD were employed and deep learning algorithms were applied if algorithm performance was crucial. However, when accuracy was valued more highly, all features were taken into account and only a small subset of them was chosen using optimization techniques. The latter method was found to be more accurate and reliable than the methods currently in use.

Cloud computing is basically a new phenomenon for providing services over the internet. The biggest plus point of cloud computing is that it uses third-party hardware and software applications for providing services. It is very much costeffective and easy to maintain. This type of emerging technology is being adopted by the 21st century libraries and information centres. Cloud computing can be used in libraries to provide better services. Cloud computing allows users of the library to access information from any geographic location. Cloud computing is helpful for libraries and information centres in automating and managing their services. 

Several studies have empirically explored biometric voting using the IoT to transfer votes to the central system. There aren't many bibliometric studies that categorize the output in this area, though. By keeping an eye on the papers posted on the Scopus platform, this study’s goal is to present a research bibliometric analysis of biometric voting utilizing IoT to transfer votes to a central system, classifying trends, the state of the art, and other indications. 267 different materials made up the sample. Using the VOS viewer program, the data was processed and the outcomes graphically represented. According to a study, that examined publications’ simultaneous occurrence by year, trends of keyword, co-citations, coupling bibliographic, and coauthorship analysis, institutions, and countries, the body of knowledge on biometric voting that uses the Internet of Things to transfer votes to a central system is expanding quickly. More than 530 citations were found in just eight works. However, there are other industrious writers. The most significant of the 267 sources used in the review were published in 26.066 percent of the papers. China is the world's leader in this field. This study offers knowledge about the current state of the art and indicates research opportunities and gaps in IoT-based biometric voting.

Biometric applications have massive demand in today’s era. The areas of applications are mostly linked with the security of the system. Biometric features are regarded as the primary resource for security purposes due to their own distinctiveness and non-volatile essence. System authentication using biometrics is considered to be a sophisticated technology. Noise effect inducts variation in the biometric subject that causes an adverse impact on establishing the recognition. The proposed model supported the development of an effective method for performing facial biometric feature recognition. The model's goal is to reduce the number of false approvals and refusals. The proposed algorithm has been applied over a video dataset containing surveillance video frames that capture facial subjects dynamically. The first step is the pre-processing of the video frames that have been carried out in the proposed model. Then, the Viola-Jones algorithm was applied to detect the facial subjects in the video frames. Feature extraction from the facial subject has been accomplished by applying a deep reinforcement learning algorithm. Further, the proposed model applied a convolutional neural network (CNN) algorithm to perform feature recognition of facial identity accurately. The proposed technique aims to maintain a huge recognition rate of dynamic facial subjects under various unprecedented noise variations. In the classification algorithm, the recognition accuracy is found to be 98.85%

The security of Digital media has been varying continuously due to advanced malware attacks. Multimedia security has become one of the major concerns since new technologies are introduced. The proposed paper applied the watermarking technique in digital audio signals in which unique data is inserted in one-dimensional data in such a way that it must not affect the major information of the audio signal. The hybrid decomposition scheme has been applied to the audio data in order to extract features in terms of energy bands. The data is kept hidden in a low significant energy band that contains less information. This watermarking technique ensures the ownership of the multimedia data. Only authorized authors can be able to claim ownership of the audio data. The correct authorization of audio data can be proven by the extraction method in which the hidden watermark data has been extracted back to its original form without leaving any distortion in audio data. The proposed work introduces a hybrid approach to watermarking 2D data into an audio file. A hybrid audio decomposition technique was introduced by the proposed scheme in which a dual form of audio decomposition method has been applied containing Fast Fourier transform (FFT) and Cordic QR scheme. The correct location from the energy band has been found to embed the watermark data. Before the embedding procedure, the watermarking data has been selected. The proposed method selects an image containing information as a watermark that is first encrypted before initiating the embedding process. Watermark Encryption has been done using a cyclic coding algorithm and Arnold’s cat map. The disintegration of the audio file will finally result in Q and R matrices. Both such matrices are of orthogonal type. Then, the encrypted watermark data has been implanted in a random fashion in the R component of decomposed audio data during the embedding process. The inverse procedure has been applied for the watermark extraction and decryption process.

The term “multimedia learning” refers to education that combines words and images. Reading a physics textbook, seeing a recorded lecture, or watching a PowerPoint presentation are all examples of multimedia learning. Also with the advent of artificial intelligence, the format of the learning procedure has now become more advanced, personalized, and relevant as students can get their answers more random with full specification as compared to earlier processes. The 21st century, known colloquially as the era of information and technology (IT), is currently in effect. Nowadays, the educational sector makes extensive use of information and technology to make teaching and learning successful and enjoyable for both teachers and students. Teachers are the cornerstone of any society that is able to function. The use of technology is crucial in teacher training programmers. Students can learn and gain information through varied sources like the Internet, digital media, cable networks, and social media sites like Whatsapp, Linkedin, Igo, Line, Facebook, Twitter, and Wechat. Thus, multimedia, Information, and Communication Technologies (ICT) play a significant role in training purposes and enhancing skills of teaching abilities. In the ushering era of technology, namely multimedia, it is now utilized as a teaching tool. Multimedia applications can be designed in effective ways to produce successful educational results, according to several researchers and educators. Not only that, but we'll also talk about the definition of multimedia, how it relates to learning tools, the idea of multimedia applications, how they're made using various media, the kinds of educational components that encourage students to learn in their natural environments, and real-world problems. This article explains the concepts and traits of multimedia and educational components. In light of the many altering needs of our society, attention is now paid to various educational conceptions and practices. Changes are being made in teacher education as well, as per these beliefs and practices. The interdisciplinary approach, correspondence courses, orientation courses, and other modern trends in teacher education are included below. Other methods utilized in teacher education include team teaching, programmed instruction, micro-teaching, and simulations. Action research is now used in teacher education as well. 

Intelligent and interactive multimedia systems are in a constant state of evolution, with new technologies and developments being introduced daily. AI is a fundamental enabler of these technologies, providing intelligence and interactivity required to make them more useful and user-friendly. This article examines the current state of AI-based intelligent and interactive multimedia systems, highlighting the most promising trends and obstacles. Then, we explore emerging AI trends that are anticipated to play a significant role in overcoming these obstacles and enabling the development of new and more complex intelligent and interactive multimedia systems.