The Intersection of Data Analytics and Data-Driven Innovation

This chapter discusses businesses, key technology implementations, case studies, limitations, and trends. It also presents recommendations to improve data analysis, data-driven innovation, and big data project implementation. Small-to-large-scale project inefficiencies present unique challenges to both public and private sector institutions and their management. Data analytics management, data-driven innovation, and related project initiatives have grown in scope, scale, and frequency. This evolution is due to continued technological advances in analytical methods and computing technologies. Most public and private sector organizations do not deliver on project benefits and results. Many organizational and managerial practices emphasize these technical limitations. Specialized human and technical resources are essential for an organization's effective project completion. Functional and practical areas affecting analytics domain and ability requirements, stakeholder expectations, solution infrastructure choices, legal and ethical concerns will also be discussed in this chapter.

Applications of Big Data and AI in Electric Power Systems Engineering

The production, transmission, and distribution of energy can only be made stable and continuous by detailed analysis of the data. The energy demand needs to be met by a number of optimization algorithms during the distribution of the generated energy. The pricing of the energy supplied to the users and the change for investments according to the demand hours led to the formation of energy exchanges. This use costs varies for active or reactive powers. All of these supply-demand and pricing plans can only be achieved by collecting and analyzing data at each stage. In the study, an electrical power line with real parameters was modeled and fault scenarios were created, and faults were determined by artificial intelligence methods. In this study, both the power flow of electrical power systems and the methods of meeting the demands were investigated with big data, machine learning, and artificial neural network approaches.

Blockchain as a Disruptive Technology

Blockchain is a digital, distributed, and decentralized network to store information in a tamper-proof way with an automated way to enforce trust among different participants. An open distributed ledger can record all transactions between different parties efficiently in a verifiable and permanent way. It captures and builds consensus among participants in the network. Each block is uniquely connected to the previous blocks via a digital signature which means that making a change to a record without disturbing the previous records in the chain is not possible, thus rendering the information tamper-proof. Blockchain holds the potential to disrupt any form of transaction that requires information to be trusted. This means that all intermediaries of trust, as they exist today, exposed to disruption in some form with the initiation of Blockchain technology. Blockchain works by validating transactions through a distributed network in order to create a permanent, verified, and unalterable ledger of information.

Artificial Intelligence for Extended Software Robots, Applications, Algorithms, and Simulators

Robotics has become a rapidly emerging branch of science, addressing the needs of humankind by way of advanced technique, like artificial intelligence (AI). This chapter gives detailed explanation about the background knowledge required in implementing the software robots. This chapter has an in-depth explanation about different types of software robots with respect to different applications. This chapter would also highlight some of the important contributions made in this field. Path planning algorithms are required for performing robot navigation efficiently. This chapter discusses several robot path planning algorithms which help in utilizing the domain knowledge, avoiding the possible obstacles, and successfully accomplishing the tasks in lesser computational time. This chapter would also provide a case study on robot navigation data and explain the significant of machine learning algorithms in decision making. This chapter would also discuss some of the potential simulators used in implementing software robots.

Automated Grading of Tomatoes Using Artificial Intelligence

The rate of production of horticultural produce had been seen increasing from the past century owing to the increase of population. Manual sorting and grading of tomatoes had become a challenge in market places and fruit processing firms since the demand of the fruit had increased. Considering grading of tomatoes, color is of major importance when it comes to the maturity of the tomatoes. Hence, there is a need to accurately classify them according to color. This process is very complicated, tiresome, and laborious when it is done manually by a human being. Apart from being labor-demanding, human sorting, and grading results in inaccuracy in classifying of tomatoes which is a loss to both the farmer and customer. This chapter had been prepared focusing on the automatic and effective tomato fruit grading system using artificial intelligence particularly using artificial neural network in Matlab. The system makes use of the image processing toolbox and the ANN toolbox to process and classify the tomatoes images according to color and size.

Disrupting Agriculture

The United Nations (UN) Food and Agriculture (FAO) estimates that farmers will need to produce about 70% more food by 2050. To accommodate the growing demand, the agricultural industry has grown from labor-intensive to smart agriculture, or Agriculture 4.0, which includes farm equipment that are enhanced using autonomous unmanned decision systems (robotics), big data, and artificial intelligence. In this chapter, the authors conduct a systematic review focusing on big data and artificial intelligence in agriculture. To further guide the literature review process and organize the findings, they devise a framework based on extant literature. The framework is aimed to capture key aspects of agricultural processes, supporting supply chain, key stakeholders with a particular emphasis on the potential, drivers, and challenges of big data and artificial intelligence. They discuss how this new paradigm may be shaped differently depending on context, namely developed and developing countries.

Cyber Secure Man-in-the-Middle Attack Intrusion Detection Using Machine Learning Algorithms

The main objective of this chapter is to enhance security system in network communication by using machine learning algorithm. Cyber security network attack issues and possible machine learning solutions are also elaborated. The basic network communication component and working principle are also addressed. Cyber security and data analytics are two major pillars in modern technology. Data attackers try to attack network data in the name of man-in-the-middle attack. Machine learning algorithm is providing numerous solutions for this cyber-attack. Application of machine learning algorithm is also discussed in this chapter. The proposed method is to solve man-in-the-middle attack problem by using reinforcement machine learning algorithm. The reinforcement learning is to create virtual agent that should predict cyber-attack based on previous history. This proposed solution is to avoid future cyber middle man attack in network transmission.

Blockchain and Its Integration as a Disruptive Technology

Blockchain is the process of development in bitcoin. It's a digitized, decentralized, distributed ledger of cryptocurrency transactions. The central authorities secure that transaction with other users to validate transactions and record data, data is encrypted and immutable format with secured manner. The cryptography systems make use for securing the process of recording transactions in private and public key pair with ensuring secrecy and authenticity. Ensuring bitcoin transaction, to be processed in network, and ensuring transaction used for elliptic curve digital signature algorithm, all transactions are valid and in chronological order. The blockchain systems potential to transform financial and model of governance. In Blockchain, databases hold their information in an encrypted state, that only the private keys must be kept, so these AI algorithms are expected to increasingly be used, whether financial transactions are fraudulent, and should be blocked or investigated.

Big Data Intelligence and Perspectives in Darwinian Disruption

The convergence of big data and artificial intelligence, namely big data intelligence, seems inevitable at an epoch just as the automation of smart decision making becomes the future digital disruptor. Every industry will be confronted with the same Darwinian pressure of excellence and adaptation, and must conjointly be supported by the major stakeholder, the ultimate client. Authenticated by the hypothesis that big data intelligence has the potential of Darwinian disruption, the objective of this chapter was to identify the most recent worldwide research trends in the field of big data intelligence and its most relevant research areas. A social network analysis tool was employed to interpret the interrelationship between generated keywords and key phrases. The resulting taxonomy of published peer-reviewed scientific papers was bibliographically analyzed. This investigation permitted all manner of social and business interests underpinned by this technology to understand what to embrace, what to ignore, and how to adapt.

Wearables, Artificial intelligence, and the Future of Healthcare

Common underlying risk factors for chronic diseases include physical inactivity accompanying modern sedentary lifestyle, unhealthy eating habits, and tobacco use. Interestingly, these prominent risk factors fall under what is referred to as modifiable behavioral risk factors, emphasizing the importance of self-care to improve wellness and prevent the onset of many debilitating conditions. In that regard, advances in wearable devices capable of pervasively collecting data about oneself coupled with the analytic capability provided by artificial intelligence and machine learning can potentially upend how we care for ourselves. This chapter aims to assess the current state and future implications of using big data and artificial intelligence in wearables for health and wellbeing. The results of the systematic review capture key developments and emphasize the potential for leveraging AI and wearables for inducing a paradigm shift in improving health and wellbeing.