Big Data Analytics for Human-Computer Interactions: A New Era of Computation

Big Data is a new social and economic development engine worldwide. The accumulation of data globally is approaching a critical threshold due to recent innovations in health, education, and other sectors. Data complexity depends on data volumes, diversity, speed, and truthfulness. These also affect the capacity to find big data analytics and associated tools. Big Data Analytics is a significant challenge in developing highly scalable data and data integration algorithms. New algorithms, methods, systems, and applications in Big Data Analytics are potential discoveries that will effectively identify valuable and hidden information in Big Data. This chapter discusses big data, and its history; Big Data drives the world's modern organizations. There is a need to convert Big Data into Business Intelligence that enterprises can readily deploy. Better data leads to better decision-making and improved strategies for organizations.

HCI is creating and developing interactive computer systems in which users can communicate with each other. It covers both laptops and embedded systems in various devices. The success of technology comes simply from the user's ease of interacting with it. The customer will automatically disregard the product or technology when the interface is wrong or difficult to use. A convenient and easy way of using a device does not mean that behind such a system is simple technology; a very sophisticated technology is required to construct it. Functionality and accessibility are the main principles of HCI. Systems services are customarily called functions. Functions are commonly referred to as services delivered by a device. Usefulness is where users simply, correctly, and explicitly use the device's features. Features and usability could differ between systems. This chapter, “Human-Computer Interface (HCI)”, deals with man-machine studies or man-machine interaction design, execution and assessment of computer systems and related phenomena for human use.

A cognitive framework is suggested in this article to monitor learning processes based on the combination of human-computer interaction. The observation is founded on the interaction of elements between humans and the computer. The adaptive architecture of cognitive learning is introduced for interaction between humans and machines. The authors have also chosen a topology tree as the hierarchical model of a low-dimensional educational space to perform online observations. In addition, the methodology for the BSM (coupling-manifold brain human cognitive scenario) is provided for the coupling morphism. It proposes that things be observed in a mental or learning diverse way. Finally, this chapter suggests developing new tools and implementing different functionalities integrating intelligent data analysis techniques. An area that still needs further work is the cognitive area, particularly towards helping build more accurate mental model. 

This chapter provides methods to systematically and efficiently examine massive complex systems to describe the conduct of a realistic CRT exercise in larger companies. Situations are explored to capture complicated probable futures, and suggestions for creating CRT exercises are provided. Complexity is organizations. As a result, a part discusses the intricacy of the interactions and interconnection between the four fields: physical, technological, cognitive, and social. This debate introduces two approaches to managing this degree of complexity. One of the offered approaches has been intended to separate this complexity into chunks in which huge organizations can generate impacts. This chapter presents the different operations that can be conducted on networks in which it is possible to capture a complex system such as a social system in a network form.

This chapter deals with CRT calculations. The idea of experimenting, optimization, simulation, data mining, and large data is explained in plain language before introducing the intelligent architectures, which turn data into CRT system judgments. Most of the architecture may be employed in any circumstance outside the CRT. However, increasing this architecture to the ability of CRT provides unparalleled computing skills for both offline and real-time decision-making. In this chapter, augmenting these architectures with CRT capabilities offers unprecedented computational capabilities for offline and real-time decision-making situations equally.

 The capacity to get insights and operations from data has not significantly altered with tremendous technological advances over the previous 30 years. Applications are generally built to fulfill default responsibilities or automate tasks; thus, the designer must prepare and write the logic for every situation. Computers are quicker and less expensive but not significantly more intelligent. Naturally, people are not more brilliant than they were 30 years before. For people and robots, this is going to change. A new generation of information technology emerges, starting with the automation technology from the previous computer model to offer a collaborative discovery platform. These technologies' initial wave has already increased human knowledge in several disciplines. These computers may draw meaning from volumes of natural language text as collaborators or collaborators for their human users and create and assess hypotheses in minutes based on analysing more significant facts than a person would absorb in a lifetime. That's the potentil of artificial intelligence. This chapter discusses a relationship between big data, NLP, and Cognitive Systems, voice in NLP component and performing the related tasks. This chapter contrasts unstructured data in written material, video, and images, designed for human consumption and interpretation and also explains big data's role in creating cognitive computing systems.

Automation is not a new phenomenon, and it automates the mechanization that helps us learn, develop, decide and act in a context, like a human being in the new paradigm change as a result of emerging technologies. Recent improvements in processing capacity, historical data availability, low-cost sensors, and systems for transmitting high-speed data give rise to the possibility of imitating and automating a mature human brain function. Businesses increasingly rely on software robot systems that mimic how people perform a repetitive activity and eliminate the need for human involvement. However, these systems cannot judge or learn from past actions and consequences, thus confining the automation process to basic repeating process automation. Integrating cognitive characteristics in robotic software systems, which makes the business digital, can address this challenge. This chapter discusses the developments and the problems faced in using cognitive systems, architecture, applications, and cognitive models for electronics manufacturing. This chapter also discusses the cognitive computing principle to improve the knowledge base and the dependencies between these components.

This chapter focuses on how ontologies and semantic web technology can be used in artificial intelligence or systems engineering. Technological trends imply that future digital technology approaches and tools will use AI and ML technology. Logicbased reasoning and semantic modeling assist in classification, customization, and relationship detection but struggle to describe how decisions are made. Knowledge acquisition plays a vital role in using this form of AI. Ontologies are methods for mode modeling of reasoning domains required for digital fields instantiated in Digital System Models (DSM). They grow as digital twins and evolve with the physical instantiations of a DSM over time. Semantic innovations and ontologies codify knowledge of systems engineering as a prerequisite for reasoning using interoperable ontologies. This chapter explores the technologies behind advanced analytics and how they can be leveraged in a knowledge-driven cognitive environment. Advanced analytics help gaining deeper insights and predict outcomes more accurately and insightfully. 

Design thinking has a significant influence on innovation in business, education, health, and other vital fields. This involves human-centered approaches lijke fast prototyping, and abductive reasoning. There are many parallels and contrasts between design visualized and a path to the innovative design of Human-Computer Interaction (HCI). In this chapter, we will discuss the method of Hasse diagrams for structured learning domains visualizing the progress of a learner through this domain and reducing attrition through early risk identification, improving learning performance and achievement levels, enabling more effective use of teaching time, and enhancing performance learning design/instructional design.

Society has evolved quickly, and individuals are continually forced to acquire new abilities viatraining. This means that education/training resources are substantially restricted; therefore, methods must be developed to tackle this problem. Intelligent Tutoring Systems (ITS) deployment is being proposed as a solution to address this problem. In addition, ITS makes it possible for users to learn and improve their abilities in a particular area. ITS adopts user actions and requirements in a non-intrusive and transparent manner to achieve this aim. The tastes and habits of the users must be known to deliver a tailored and adaptable solution. Therefore, the capacity to learn behavioural patterns becomes a crucial component for an ITS to succeed. In this article, we offer an ITS student model, which monitors the biometric conduct and style of the user throughout e-learning activities. A classification model supervises the student’s work throughout this session. This chapter also emphasises the principles of intelligent learning differences for each activity. Information extraction techniques can automatically extract knowledge from the text by converting unstructured text into relational structures. To achieve this aim, traditional information extraction systems must rely on significant human involvement.

This chapter uses Light Detection and Ranging (LIDAR) technology field data to assess efficient terrain visualization. There are two algorithms for detailed computer rendering. The test results for the productivity of the two these algorithms or techniques are presented in the subsequent sections. In visual-spatial perception, the assessment of the results is ultimately examined. In this chapter, the model then uses the information to select the optimal level of details (LOD) to prevent visible changes in representation. The relationship between computer processing power and mental representation is critical for understanding these cognitive processes.

In healthcare, education, large companies, and scientific research, extensive data have played a critical role. Big data analytics demand modern tools and technologies to store, process, and analyse large data volumes. Big data comprise extensive unstructured data, which needs to be examined in real-time before making use of it. Many academics are interested in advanced technologies and methods to tackle the problems in comprehensive data management. The business enterprises, public sector, and academic institutions have received considerable attention due to their Big Data. This chapter summarises the latest algorithms involved in Big Data processing and the associated features, applications, possibilities, and problems. This chapter also provides an overview of the state-of-the-art algorithms for processing big data and challenges in human-computer interaction with big data analytics.