Mohd Naved

V. Ajantha Devi

Aditya Kumar Gupta

FINTECH and CRYPTOCURRENCY Dive into the world of fintech and cryptocurrency through the engaging perspectives of this diverse group of authors and uncover the intricate connections between technology, finance, and cryptocurrency that make this a must-have for anyone intrigued by the future of digital society. Digital currencies, decentralization of money, and the growth of new technologies like blockchain, the Internet of Things, and machine learning have produced new opportunities and difficulties for banking and finance, as well as users of these services in electronic commerce. New banking and finance technologies may improve operational efficiency, risk management, compliance, and client pleasure, but they can decrease barriers and introduce new concerns, such as cybersecurity risk. Cryptocurrencies with smart contracts for payments and trading, as well as AI systems with adaptive algorithms that allow picture and speech recognition, expert judgement, group categorization, and forecasting in a variety of fields, are instances of increased automation. Simultaneously, the potentials pose risks and raise regulatory concerns. The rise of blockchain technology and its widespread use have had a significant impact on the operation and management of digital systems. At the same time, researchers and practitioners have paid close attention to digital finance. Blockchain’s first applications were limited to the production of digital currency, but it has now been expanded to include financial and commercial applications. Innovative digital finance has had a huge impact on business and society since it has been extensively adopted by businesses and consumers. As a result, the goal of this edited book is to expand and deepen our knowledge of the business possibilities of novel blockchain and digital financial applications.

Fintech and Cryptocurrency

Anand Nayyar

Joanna Rosak-Szyrocka

Justyna Żywiołek

This book is devoted to the issues faced by universities in the field of distance learning during and after COVID, as well as in digitalization times. The book devotes a lot of space to the issues of Web 3.0 in university e-learning, Industry 4.0, artificial intelligence and digital equity. The aim and scope of this book is to draw a holistic picture of education before and after COVID, the psychological effects of COVID in education, and using modern technologies application in education, taking into consideration aspects of sustainability development, Industry 4.0 and Society 5.0. The authors also raise the issue of artificial intelligence investigation in learner-instructor interaction. Features: • To elaborate the functions of online education and numerous pedagogical strategies based on electronic learning to aid teachers and students with the tools required to succeed in the 21st century via engaging virtual experiences • To analyze tools provided by Ed-Tech firms and the effect of digital tools on maintaining the educational process in times of crisis and after pandemic • To create a roadmap for higher education institutions and provide tips regarding how to improve the effectiveness of the hybrid learning system • To understand e-learning characteristic in the era of Industry 4.0 and Society 5.0 and characteristics of the different web generations • To use AI applications to improve connections and relationships between students and teachers and in education in the future The book is both scientific and educational. It can be used at the university level and by anyone interested in the topics it covers.

The Role of Sustainability and Artificial Intelligence in Education Improvement

Arun Solanki

Generative Adversarial Networks (GAN) have started a revolution in Deep Learning, and today GAN is one of the most researched topics in Artificial Intelligence. Generative Adversarial Networks for Image-to-Image Translation provides a comprehensive overview of the GAN (Generative Adversarial Network) concept starting from the original GAN network to various GAN-based systems such as Deep Convolutional GANs (DCGANs), Conditional GANs (cGANs), StackGAN, Wasserstein GANs (WGAN), cyclical GANs, and many more. The book also provides readers with detailed real-world applications and common projects built using the GAN system with respective Python code. A typical GAN system consists of two neural networks, i.e., generator and discriminator. Both of these networks contest with each other, similar to game theory. The generator is responsible for generating quality images that should resemble ground truth, and the discriminator is accountable for identifying whether the generated image is a real image or a fake image generated by the generator. Being one of the unsupervised learning-based architectures, GAN is a preferred method in cases where labeled data is not available. GAN can generate high-quality images, images of human faces developed from several sketches, convert images from one domain to another, enhance images, combine an image with the style of another image, change the appearance of a human face image to show the effects in the progression of aging, generate images from text, and many more applications. GAN is helpful in generating output very close to the output generated by humans in a fraction of second, and it can efficiently produce high-quality music, speech, and images. Introduces the concept of Generative Adversarial Networks (GAN), including the basics of Generative Modelling, Deep Learning, Autoencoders, and advanced topics in GAN Demonstrates GANs for a wide variety of applications, including image generation, Big Data and data analytics, cloud computing, digital transformation, E-Commerce, and Artistic Neural Networks Includes a wide variety of biomedical and scientific applications, including unsupervised learning, natural language processing, pattern recognition, image and video processing, and disease diagnosis Provides a robust set of methods that will help readers to appropriately and judiciously use the suitable GANs for their applications

Mohd Naved

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