Abstract: For a variety of reasons, artificial intelligent devices are becoming increasingly vital for enterprises. One of the major characteristics that makes AI enhanced services ideal for use in organisations is their ability to complete jobs faster and more accurately than humans. Almost all firms are utilising AI-based Chatbots on social media and messaging applications such as Whats App, Facebook, and others to engage with their enormous consumer base and provide real-time help. But AI is capable of much more. For many individuals, having a discussion with a bot that sounds like a human is a new disruption, and it serves as a tool to engage and entice customers to the point where they leave the website. But AI do more than just that. Having a conversation with a bot which feels like a human talking is a new disruption for a lot of people and it works as a tool to engage these customers and attract them in such a way that they leave the website after making the purchase. And as far as disruption is concerned from guiding the customer how to order pizza to describing through complex sales processes, AI Chatbots have been able to help both B2C and B2B interactions. This chapter provides an insight at how the AI Chatbots influences user interactions, how brands are using Chatbots for marketing and customer service, and why customers are attracted to interact with augmented agents such as Chatbots.

Abstract: Today’s supply chains are becoming more data-driven with the impact of big data, but there are many challenges that need to be overcome in big data for better service operations management in supply chains. Blockchain has the great potential to improve big data services and applications with its decentralisation and security nature. However, blockchain interoperability is critical to realising more value creation in blockchain networks and achieving promising results for global supply chains that intersect with multiple business ecosystems and blockchain platforms. In addition, it is unclear how to address interoperability issues for mass adoption. In this chapter, a three-step approach is applied to analyse blockchain interoperability in supply chains for mass adoption. First, a literature review is conducted to explain blockchain technology and the widely used methodologies for blockchain interoperability in supply chains. Then, four real-case blockchain use cases in supply chains from different industry segments are analysed in terms of their technical capabilities addressing interoperability concerns. Finally, we discuss results of use case analysis based on the comments of interviewees. The analysis reveals that REST-APIs with a common interface and GS1 standards are very useful to integrate with blockchain applications in supply chains for mass adoption.

Abstract: AbstractIn recent years, the term ‘fake news’ has become popular as a paradigm of mis- and disinformation. The term tends to put this phenomenon within the framework of media organizations. However, the problem is more complex, since it also involves other entities dedicated to deliberately producing and spreading falsehoods, as well as social networks and large internet platforms that work as global carriers of such misleading content. This chapter analyzes the evolution of disinformation in this expanded framework, contextualizing the production, dissemination and consumption of deceptive content beyond the media. Drawing upon a historical review of the mis- and disinformation phenomena over the last few centuries, it examines the more recent transformation experienced by purposefully false content on the internet and big data ecosystem.KeywordsDisinformationFake newsJournalismBig data

Abstract: AbstractA comparatively novel advance field of deep learning is the Generative Adversarial Network called GAN. These different types of networks if they start working in line with each other and work not to get the better of each other but start working keeping arm to arm connected to each other's world will be different. GAN is a category of machine learning frameworks intended for generation of images in which neural networks challenge each other in format of playing game. One network generates metaphors also called the generator and an additional network attempt to differentiate between the fake and real from the data set called as the discriminator. If suppose a training set is presented this procedure guides to manufacture a innovative information comparable to training set. Pictures created from GAN are same metaphors giving the notion of genuine to any observer having real features. GAN networks work on supervised, unsupervised and for reinforcement learning but earlier it applies or used only unsupervised learning only. This generative network produces candidate and on the other hand, the discriminative network evaluate them. Here producer is a complex neural network and differentiator is a convolution neural network. The GAN network divided into three categories to produce generative model learns and generation of data by probabilistic ideas. Next training of model can be completed in contradictory state. Lastly for training using neural networks, deep learning, and artificial intelligence methods. If generative networks used deep leaning methods then deep learning models can employ a very large amount of dataset, heavily dependent on high-end Machines, tries to solves problem from end to end machines, takes longer time to train means the results are better after getting trained on the other hand takes lesser time to test the data. Applications of GAN networks are increasing day by day as it is touching every sphere of our day today life. Some of the benefits of the deep learning are to creating artificial Intelligence function which mimics the mechanism of human brain in handing out data for decision making. Deep learning if combined with artificial intelligence can be capable of learning data which is considered unlabeled and unstructured. The chapter will relate different models of deep learning and their efficiency can be measured by studying different methods, models and simulation techniques.KeywordsGenerative adversarial networkGANGenerative adversarial network in 3D imagesData analyticsDeep learning modelsDeep learningArtificial intelligence

Abstract: Quantum technology is an asset for digitisation and the cyberrealm. It aims at designing faster and more advanced solutions to present-day problem statements. Blockchain is a decentralised structure and thus lacks a supervisory authority to monitor it. Hence, it is important to imbibe security in blockchain when we are specifically moving towards quantum development. While dealing with blockchain, quantum technology enables faster transactions and quantum cryptosystems and devices can safeguard security into the blockchain systems as well. Thus, the paper focuses on discussing approaches to implement blockchain within quantum cryptosystems along with quantum cryptography. The paper discusses the attacks compromising the classical or quantum security and mechanisms proposed to counter the attacks. The prime focus of the paper is to discuss security implementations and methods in the quantum domain and contribute to quantum cryptography in the blockchain.

Abstract: AbstractThis chapter looks at the intricate relationship between journalism and mathematics (big data, algorithms, data mining) as a tool to verify information and fight disinformation. The first section focuses on the relationship current students have with techniques such as big data or artificial intelligence and their ideas on applying them to their profession. The second section maps which universities and researchers in the world are looking into that relationship, how they approach it and where they publish their results. A relevant result is the presence of engineers in those studies, as well as Asian-origin researchers. Finally, we present results that show the increasingly close relationship between different disciplines such as computational linguistics, artificial intelligence and big data to solve the challenge of fake news and disinformation.KeywordsDisinformationAlgorithmsArtificial intelligenceAutomatic fact-checking

Abstract: In recent times, discussion about mental health is getting importance alongside physical health. Due to the pandemic, people got stuck inside their home resulting reduction in physical activity. This has worked as a catalyst in boosting the mental health issues. Due to the pandemic, social media has been used as a medium of communication to a greater extend. Social media posts can tell a lot about the personality and states of mind of people. A section of people with mental health issues provide some hint through their social media handles. In this study, we collect a dataset containing Facebook confessions and apply Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for predicting mental health status. We find a training accuracy of 71.67% and test accuracy of 73.94% on CNN. We implement SimpleRNN that yields results with 71.50% accuracy on training data and 70.10% accuracy on test data. We also implement Long Short-Term Memory (LSTM) network and get exactly the same accuracy as that of SimpleRNN for both training and test data. These results show that the implemented models predict the mental health status with comparably good accuracy.

Abstract: Blockchain is a computational data structure that provides open and distributed and decentralized public ledger technology that has many promising applications. Blockchain applies block structure linked with each other to store and verify data and provides trustworthy consensus mechanism for the synchronization of changes in data which results in a tamperproof digital platform. Blockchain has many approaches for security services which includes integrity assurance, confidentiality, resource provenance and access control list. Blockchain is an emerging technology and believed to be employed in diverse interactive system of the Internet. The goal of this paper is to give a detailed overview on the blockchain technology such as its background, architecture and properties. Further, it describes the quantum-level vulnerabilities of different popular blockchains in-use and the different cryptographic concepts that are used in blockchain, and then it highlights the concept of quantum computing along with blockchain technology. In the end, it gives an insight of pre-quantum to post-quantum blockchain.

Abstract: Blockchain is a prominent technology having a wide range of applications in the domain of cybersecurity, Artificial Intelligence, Internet of Things and many more. Blockchains have the benefits of being immutable, data not being damaged by the host machine, and strong confidentiality. The drawbacks are that they need a lot more energy to store the same amount of data and are prone to 51 percent attacks. Blockchains have the potential to greatly improve cybersecurity. Blockchain is an unchangeable distributed ledger that allows data to be stored without the involvement of a third party. The implementation of blockchain technology in artificial intelligence for cybersecurity has piqued researchers' curiosity. The emphasis of this chapter is on blockchain's applicability in cybersecurity. This chapter envisages the role of blockchain and AI in Cybersecurity. Moreover, the open challenges for blockchain enabled AI solutions for Cybersecurity systems are also discussed in this chapter.

Abstract: Computer vision has a great potential to deal with agriculture problems. It is crucial to utilize novel tools and techniques in the agriculture food industry. The focus of current studies is to automate the fruit harvesting, grading of fruits, fruit recognition, and identification of diseases in the agriculture domain using deep learning and computer vision. Integrating deep learning with computer vision facilitates the consistent, speedy and trustworthy classification of fruit and vegetables compared to the traditional machine learning algorithm. However, there are still some challenges, such as the need for expert farmers to develop large-scale datasets to recognize and identify the problems of agriculture production. This survey includes eighty papers relevant to deep learning and computer vision techniques in the agriculture field.