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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
COGNITIVE BEHAVIOR AND HUMAN COMPUTER INTERACTION BASED ON MACHINE LEARNING ALGORITHMS
The objective of this book is to provide the most relevant information on Human-Computer Interaction to academics, researchers, and students and for those from industry who wish to know more about the real-time application of user interface design.
Human-computer interaction (HCI) is the academic discipline, which most of us think of as UI design, that focuses on how human beings and computers interact at ever-increasing levels of both complexity and simplicity. Because of the importance of the subject, this book aims to provide more relevant information that will be useful to students, academics, and researchers in the industry who wish to know more about its real-time application. In addition to providing content on theory, cognition, design, evaluation, and user diversity, this book also explains the underlying causes of the cognitive, social and organizational problems typically devoted to descriptions of rehabilitation methods for specific cognitive processes. Also described are the new modeling algorithms accessible to cognitive scientists from a variety of different areas.
This book is inherently interdisciplinary and contains original research in computing, engineering, artificial intelligence, psychology, linguistics, and social and system organization as applied to the design, implementation, application, analysis, and evaluation of interactive systems. Since machine learning research has already been carried out for a decade in various applications, the new learning approach is mainly used in machine learning-based cognitive applications. Since this will direct the future research of scientists and researchers working in neuroscience, neuroimaging, machine learning-based brain mapping, and modeling, etc., this book highlights the framework of a novel robust method for advanced cross-industry HCI technologies. These implementation strategies and future research directions will meet the design and application requirements of several modern and real-time applications for a long time to come.
Audience: A wide range of researchers, industry practitioners, and students will be interested in this book including those in artificial intelligence, machine learning, cognition, computer programming and engineering, as well as social sciences such as psychology and linguistics.
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Veröffentlichungsjahr: 2021
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Table of Contents
Cover
Title page
Copyright
Preface
1 Cognitive Behavior: Different Human-Computer Interaction Types
1.1 Introduction: Cognitive Models and Human-Computer User Interface Management Systems
1.2 Cognitive Modeling: Decision Processing User Interacting Device System (DPUIDS)
1.3 Cognitive Modeling: Decision Support User Interactive Device Systems (DSUIDS)
1.4 Cognitive Modeling: Management Information User Interactive Device System (MIUIDS)
1.5 Cognitive Modeling: Environment Role With User Interactive Device Systems
1.6 Conclusion and Scope
References
2 Classification of HCI and Issues and Challenges in Smart Home HCI Implementation
2.1 Introduction
2.2 Literature Review of Human-Computer Interfaces
2.3 Programming: Convenience and Gadget Explicit Substance
2.4 Equipment: BCI and Proxemic Associations
2.5 CHI for Current Smart Homes
2.6 Four Approaches to Improve HCI and UX
2.7 Conclusion and Discussion
References
3 Teaching-Learning Process and Brain-Computer Interaction Using ICT Tools
3.1 The Concept of Teaching
3.2 The Concept of Learning
3.3 The Concept of Teaching-Learning Process
3.4 Use of ICT Tools in Teaching-Learning Process
3.5 Conclusion
References
4 Denoising of Digital Images Using Wavelet-Based Thresholding Techniques: A Comparison
4.1 Introduction
4.2 Literature Survey
4.3 Theoretical Analysis
4.4 Methodology
4.5 Results and Discussion
4.6 Conclusions
References
5 Smart Virtual Reality–Based Gaze-Perceptive Common Communication System for Children With Autism Spectrum Disorder
5.1 Need for Focus on Advancement of ASD Intervention Systems
5.2 Computer and Virtual Reality–Based Intervention Systems
5.3 Why Eye Physiology and Viewing Pattern Pose Advantage for Affect Recognition of Children With ASD
5.4 Potential Advantages of Applying the Proposed Adaptive Response Technology to Autism Intervention
5.5 Issue
5.6 Global Status
5.7 VR and Adaptive Skills
5.8 VR for Empowering Play Skills
5.9 VR for Encouraging Social Skills
5.10 Public Status
5.11 Importance
5.12 Achievability of VR-Based Social Interaction to Cause Variation in Viewing Pattern of Youngsters With ASD
5.13 Achievability of VR-Based Social Interaction to Cause Variety in Eye Physiological Indices for Kids With ASD
5.14 Possibility of VR-Based Social Interaction to Cause Variations in the Anxiety Level for Youngsters With ASD
References
6 Construction and Reconstruction of 3D Facial and Wireframe Model Using Syntactic Pattern Recognition
6.1 Introduction
6.2 Literature Survey
6.3 Proposed Methodology
6.4 Datasets and Experiment Setup
6.5 Results
6.6 Conclusion
References
7 Attack Detection Using Deep Learning–Based Multimodal Biometric Authentication System
7.1 Introduction
7.2 Proposed Methodology
7.3 Experimental Analysis
7.4 Conclusion and Future Scope
References
8 Feature Optimized Machine Learning Framework for Unbalanced Bioassays
8.1 Introduction
8.2 Related Work
8.3 Proposed Work
8.4 Experimental
8.5 Result and Discussion
8.6 Conclusion
References
9 Predictive Model and Theory of Interaction
9.1 Introduction
9.2 Related Work
9.3 Predictive Analytics Process
9.4 Predictive Analytics Opportunities
9.5 Classes of Predictive Analytics Models
9.6 Predictive Analytics Techniques
9.7 Dataset Used in Our Research
9.8 Methodology
9.9 Results
9.10 Discussion
9.11 Use of Predictive Analytics
9.12 Conclusion and Future Work
References
10 Advancement in Augmented and Virtual Reality
10.1 Introduction
10.2 Proposed Methodology
10.3 Results
10.4 Conclusion
References
11 Computer Vision and Image Processing for Precision Agriculture
11.1 Introduction
11.2 Computer Vision
11.3 Machine Learning
11.4 Computer Vision and Image Processing in Agriculture
11.5 Conclusion
References
12 A Novel Approach for Low-Quality Fingerprint Image Enhancement Using Spatial and Frequency Domain Filtering Techniques
12.1 Introduction
12.2 Existing Works for the Fingerprint Ehancement
12.3 Design and Implementation of the Proposed Algorithm
12.4 Results and Discussion
12.5 Conclusion and Future Scope
References
13 Elevate Primary Tumor Detection Using Machine Learning
13.1 Introduction
13.2 Related Works
13.3 Proposed Work
13.4 Experimental Investigation
13.5 Result and Discussion
13.6 Conclusion
13.7 Future Work
References
14 Comparative Sentiment Analysis Through Traditional and Machine Learning-Based Approach
14.1 Introduction to Sentiment Analysis
14.2 Four Types of Sentiment Analyses
14.3 Working of SA System
14.4 Challenges Associated With SA System
14.5 Real-Life Applications of SA
14.6 Machine Learning Methods Used for SA
14.7 A Proposed Method
14.8 Results and Discussions
14.9 Conclusion
References
15 Application of Artificial Intelligence and Computer Vision to Identify Edible Bird’s Nest
15.1 Introduction
15.2 Prior Work
15.3 Auto Grading of Edible Birds Nest
15.4 Experimental Results
15.5 Conclusion
Acknowledgments
References
16 Enhancement of Satellite and Underwater Image Utilizing Luminance Model by Color Correction Method
16.1 Introduction
16.2 Related Work
16.3 Proposed Methodology
16.4 Investigational Findings and Evaluation
16.5 Conclusion
References
Index
End User License Agreement
List of Table
Chapter 1
Table 1.1
The core artifacts provided at the cognitive modeling of user interact...
Table 1.2
Representational uses of cognitive modeling for decision support user ...
Chapter 4
Table 4.1
PSNR values for grayscale images (512×512) for different values of AWG...
Table 4.2
SSIM values for grayscale images (512×512) for different values of AWG...
Chapter 6
Table 6.1
Study of existing methodology.
Table 6.2
Sample of possible convex polyhedrons.
Table 6.3
Comparative analysis of mean and standard deviation of point to point ...
Chapter 7
Table 7.1
LivDet 2015 dataset details.
Table 7.2
LivDet 2015 dataset details.
Chapter 8
Table 8.1
Exhibition correlation of enhanced multilayer perception by different ...
Chapter 9
Table 9.1
The solid ability sets as controlled by area specialists.
Table 9.2
The after-effects of the PCA examination. All highlights aside from Z-...
Table 9.3
The coefficients and noteworthiness estimations of the summed up segme...
Table 9.4
The models developed from highlights in the critical summed up parts. ...
Chapter 10
Table 10.1
Search measure synopsis.
Table 10.2
Evolution of publications houses.
Table 10.3
Outline of EU and USA publications by topics.
Chapter 11
Table 11.1
Cameras used in precision agriculture application.
Table 11.2
Plant and fruit detection techniques.
Table 11.3
Fruit grading and ripeness detection approaches.
Table 11.4
Fruit counting and yield prediction.
Table 11.5
Weed and disease detection.
Chapter 12
Table 12.1
Texture descriptor results for FVC2004DB1 107_2.tif.
Table 12.2
Texture descriptor results for FVC2004DB2 101_2.tif.
Table 12.3
Texture descriptor results for FVC2004DB3 107_7.tif.
Table 12.4
Texture descriptor results for FVC2004DB4 110_8.tif.
Table 12.5
Minutiae ratio results for the thinning technique.
Table 12.6
Minutiae ratio results for mindset technique.
Table 12.7
Minutiae ratios obtained for the proposed algorithm using the thinnin...
Table 12.8
Minutiae ratios obtained for the proposed algorithm using the mindset...
Chapter 13
Table 13.1
Comparison of performance of applied classifiers using certain specif...
Table 13.2
Analytical estimation of selected attributes.
Chapter 14
Table 14.1
Dataset statistics.
Table 14.2
Performance comparison of different classifiers for the IMDB dataset ...
Table 14.3
Performance comparison of different classifiers for Amazon product re...
Table 14.4
Performance comparison of different classifiers for news headlines da...
Table 14.5
Performance comparison of different classifiers for online blogs data...
Table 14.6
Performance comparison of different classifiers for Wikipedia dataset...
Table 14.7
Accuracy comparison of different classifiers for different datasets.
Chapter 15
Table 15.1
Features extracted for various grades.
Table 15.2
Classification accuracies for various radii of subtractive clustering...
Table 15.3
Accuracies for FCM with different clusters.
Table 15.4
Sensitivity of the neural net with different number of hidden neurons...
Table 15.5
Auto-grading accuracies (%).
Table 15.6
Maximum and minimum classification accuracies (%).
Table 15.7
Best classification accuracies.
Chapter 16
Table 16.1
Comparative analysis of submerged images.
Table 16.2
Proposed method time and entropy measured value.
Guide
Cover
Table of Contents
Title page
Copyright
Preface
Begin Reading
Index
End User License Agreement
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithm
Edited by
Sandeep Kumar
Rohit Raja
Shrikant Tiwari
Shilpa Rani
This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA
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10 9 8 7 6 5 4 3 2 1
Preface
Human-computer interaction (HCI) is the academic discipline, which most of us think of as UI design, that focuses on how human beings and computers interact at ever-increasing levels of both complexity and simplicity. Because of the importance of the subject, this book aims to provide more relevant information that will be useful to students, academics, and researchers in the industry who wish to know more about its realtime application. In addition to providing content on theory, cognition, design, evaluation, and user diversity, this book also explains the underlying causes of the cognitive, social and organizational problems typically devoted to descriptions of rehabilitation methods for specific cognitive processes. Also described are the new modeling algorithms accessible to cognitive scientists from a variety of different areas. Advances in HCI involve interdisciplinary research, the results of which are published in theoretical and applied articles covering a broad spectrum of interactive systems. Therefore, this book is inherently interdisciplinary and publishes original research in computing, engineering, artificial intelligence, psychology, linguistics, and social and system organization as applied to the design, implementation, application, analysis, and evaluation of interactive systems. Since machine learning research has already been carried out for a decade at the international level in various applications, the new learning approach is mainly used in machine learning-based cognitive applications. Since this will direct the future research of scientists and researchers working in neuroscience, neuroimaging, machine learning-based brain mapping and modeling, etc., this book highlights the framework of a novel robust method for advanced cross-industry HCI technologies. These implementation strategies and future research directions will meet the design and application requirements of several modern and real-time applications for a long time to come. Therefore, this book will be a better choice than most available books that were published a long time ago, and hence seldom elaborate on the current advancements necessary for cognitive behavior and HCI algorithms. Included in the book are:
A review of the state-of-the-art in cognitive behavior and HCI processing models, methods, techniques, etc.
A review and description of the learning methods in HCI.
The new techniques and applications in cognitive behavior along with their practical implementation.
The existing and emerging image challenges and opportunities in the cognitive behavior and HCI field.
How to promote mutual understanding and networking among researchers in different disciplines.
The facilitation of future research development and collaborations.
Real-time applications.
To conclude, we would like to express our appreciation to all of the contributing authors who helped us tremendously with their contributions, time, critical thoughts, and suggestions to put together this peer-reviewed edited volume. The editors are also thankful to Scrivener Publishing and its team members for the opportunity to publish this volume. Lastly, we thank our family members for their love, support, encouragement, and patience during the entire period of this work.
Sandeep KumarRohit RajaShrikant TiwariShilpa RaniOctober 2021
1Cognitive Behavior: Different Human-Computer Interaction Types
S. Venkata Achyuth Rao1*, Sandeep Kumar2 and GVRK Acharyulu3
1CSE, SIET, Hyderabad, Telangana, India
2Computer Science and Engineering Department, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andra Pradesh, India
3Operations & Supply Chain, MBA (Healthcare & Hospital Management), School of Management Studies, University of Hyderabad, Telangana, India
*Corresponding author: [email protected]
Abstract
Cognitive behavior plays a significant and strategic role in human-computer interaction devices that are deployed nowadays, with artificial intelligence, deep learning, and machine learning computing techniques. User experience is the crucial factor of any successful interacting device between machine and human. The idea of providing a HCUIMS is to create interfaces in terms of the bottom level of any organization as Decision Processing User Interacting Device System (DPUIDS), next at middle level management, Decision Support User Interacting Device Systems (DSUIDS), lastly at executive level, Management Information User Interacting Device System (MIUIDS), where decisions can take at uncertainty at various catastrophic situations. Here are specific gaps demonstrated in the various user’s processes in communicating with computers and that cognitive modeling is useful in the inception phase to evolve the design and provide training.
This is provided with the fulfillment of various interactive devices like Individual Intelligences Interactions (I3), Artificial and Individual Intelligences Interaction (AI3), Brain-Computer Interaction (BCI), and Individual Interactions through Computers (I2C) in a playful manner to meet the corporate challenges in all stakeholders of various domains with better user experience.
Keywords: Cognitive behavior, user experience, interacting devices, modeling, intelligence
1.1 Introduction: Cognitive Models and Human-Computer User Interface Management Systems
Cognitive models are useful in assessing to make predictions ease at top-level management systems in several aspects or many variables to interact and provide the approximate behavioral aspects observed in various experimental empirical studies. In a real-world lifetime situation, many factors are influenced to produce outcome reports as a behavioral analysis report. This is done neural processing data with the representation of patterns. These models outcome in terms of processes and products interact with various people which are shown in the empirical experiments. These below are necessary tools for psychologists to interact with various designers who care about cognitive models. These models for HCI have an adequate different goal to use necessary interfaces better for users. In general, there are at least three cognitive models in service as a general goal [1].
Interactive user behavioral predicting systems
Adaptive interaction observatory changing systems
Group interaction model building systems
1.1.1 Interactive User Behavior Predicting Systems
Human behavior predicting system interface is designed and deployed as the interaction and communication between users and a machine, an automatic dynamic, versatile system, through a user-machine interface [2]. There are strongly related real-world assumptions, and aspects are there to distinguish the domain of user-machine automatic dynamic, versatile systems, and user-computer interaction. For 50 years onward, the investigations on research in this domain are going on with different interactive human predicting systems that are evolved with the necessary propagated embedded events via a hardware and software interaction built-in displays. The best and emerging ambient designs of user interaction automatic predicting system applications have a right market place and gain values vertically in all the verticals for many products and services in various sectors like medical, transportation, education, games, and entertainment, which are the needs of the industry [3].
1.1.2 Adaptive Interaction Observatory Changing Systems
An adaptive interactive observatory system acquires its psychological aspects to the independent user based on inferences of the user prototype acquisition and reports involving activity in learning, training, inference, or necessary constraints of the decision process. The primary and needful goal of adaptive interaction observatory changing system interfacing adaptation is to consider unique perceptual or physical impairments of individual users; it allowed them to use a dynamic system more flexibly, efficiently, with minimal errors and with less frustration. An adaptive interaction observatory system interface is an embedded software artifact that improves its functionality to interact with an individual user by prototype model, thereby constructing a user model based on partial psychological considerable experience with that user [4].
As there are widespread of www, internet, and gopher services among the population day by day, more sophisticated variety of softwares, emerging technologies involve hardware events, gadgets, widgets, and events that are more and more highly interactive and responsive. Only limited early individual novice people are doing programs on punch cards and submitting late nights and overnight jobs, and subsequently time-sharing systems and debug monitors, text editors have become slower and slower and depend on multiple cores and moving forward to parallel processing. The latest emerging operating systems and real-time operating systems support various interactive software like what you see and what you get. The editor system software is too high for interactive computer games, most efficient and eminent embedded systems, automotive responsive, interactive, and adaptive conservative systems in layered interactive graphical user interfaces, and such subscribers and listeners are the key roles of adaptive interaction observatory changing systems. Such systems have been treated as an essential part of any business and academic lives with a trillion people depend on them to move toward their daily lives. Most academic work on machine learning still focuses on refining techniques and humiliating the steps that may happen at foreseen and after their invocation. Indeed, most investigations, conferences, workshops, and research interests, especially media and entertainment, virtual reality, simulation, modeling, and design, still emphasize differences between broader areas of learning methods. Eventually, evidenced by the decision-tree induction, the design analysis of algorithms, case-based reasoning methods, and statistical and probabilistic schemes often produce very similar results [5].
1.1.3 Group Interaction Model Building Systems
This chapter’s main objective is to describe the existing cognitive framework activities on group modeling information systems using synergy responsive dynamics. Such information systems are very few and necessary to be applied in hybrid organizations in order to support to increase in a wide range of business expansion and to take their strategic decisions. In this cognitive group interaction model building theory, the vital methodological dynamics were first located under the individual user interactions and then classified to allow an intensive idea to be given as a requirement analysis report for group activity prototype being a building system consideration [6]. The outcome of this brainstorming dynamics indicates the existing methods to propose a global view of interaction model systems are very rare. Also, three complex issues are needed to discuss: the inception of knowing the users’ knowledge, the interaction establishment of a consensus among users, and the main aspects of providing necessary facilitation.
A group interaction model building system is a dynamic system that is characterized by the following:
The responsive nature and strong interactions among the actors of the group;
An integration exists with necessary interactions, interrelations, and a strong dependency together;
An internal abstractive complex cohesiveness is subjected to their feedback; and
Fuzziness of the delayed behavioral reactions among the groups to assess or predict.
An organized framework is described here as a generalization of any organized approach, providing inference process and cohesive interactions in the detailed guidelines related to any aspect of group interaction model building. This analysis aims to obtain a broad view of a global vision of investigating the research that applied group interaction modeling systems. Using system dynamics allows drawing keenness to the lack of advanced interactive device management aspects to support the relating behavior aspects.
The group modeling system approach’s dynamic behavior is characterized below, emphasizing group interaction model systems.
The modeling process using two types of information systems [7]:
Modeling information systems versus group interaction model information systems.
Expert modeling systems versus team expert modeling information systems.
1.1.4 Human-Computer User Interface Management Systems
Human-Computer User Interface (HCUI) design mainly emphasizes foreseeing what computer interaction users need to do and approve that the human-computer interface has several elements that are flexible and easy to know, view, navigate, update, manage and modify, and use to provide facilitation in the form of events and widgets. HCUI accomplishes the related features from interpreting, layout design, interaction design, visual design, and information architecture.
A HCUIMS (HCUI Management System) is treated as not as a system but rather an interactive software architecture (an HCUIMS is also called a HCUI Architecture) “in which the design, deployment of various applications’ user interface is precise and clearly distinguished from that of other applications’ underlying its functionality.” Such an eminent division’s cohesive objective is to enhance the maintenance ease and adaptability with other softwares. Most of the Modern HCUIMS Architectures are designed with integrated development environments. With the help of abstraction of a user interface from the applications logic, syntax, and semantics, the code generation is better supported for customization. Even these architectures have been proven and useful with a high degree of interaction and had semantic feedback at manipulating interfacing boundaries between applications and HCUIs are difficult or impossible to maintain [8].
1.1.5 Different Types of Human-Computer User Interfaces
Interface for Command Line
Interface for Menu Driven
Interface for Touch-Screen Driven
Interface for Graphical User Purpose
Interface for Event-Driven Purpose
Interface for Sensor-Based Users
Interface for Voice-Based Users
Interface for Natural Language Users
Interface for Form-Based Users
Interface for Gesture Driven Users
Interface for Mobile Users
Interface for Data Base Users
Interface for VR Design
1.1.6 The Role of User Interface Management Systems
User interface management system architecture’s role is broader than a narrow concern concerning hardware, embedded system software applications, design analysis and algorithms, software procedures, packages, distributed servers, and other programs. The majority of domains with respective disciplines are contributed widely to the discipline of management informative systems, including the following:
Traditional ancestor science and technology related disciplines such as functional forms, lambda expressions, predicative calculus, systems theory, operation research, and econometrics;
Technology such as electronics, information technology, bioinformatics, nano technology, and computer science;
Emerging technologies like security management studies that include cognitive networking, link taping, a man-in-the-middle attack, brute force, cross-site request forgery, and doom-based attack; and
Social engineering and behavioral theory of ergonomics, linguistics, etc.
User interface management systems development is exceptionally different because the Information Systems are to be continued adequate modeling and working staff need to serve an efficient role in the enterprise management system organizations [9]. The roles and responsibilities needed to be performed efficiently as given below. Some of them are discussed below.
Information system programmers and system analysts need to spare longer to interact with stakeholders individually or group-wise to elicit more useful information to design and evolve the system interaction meaningful and rapid responsive purpose.
Determine what information is useful to take decision-making in uncertain times is a challenging task. For this, information system staff forcing to spare longer time and a great deal to interact with system users.
Development and deployment approaches likely building prototyping models are based on either rapid application development model feedback or iterative, incremental feedback from connected users on interaction efficiency concerning their needs.
The resultant outcome in the form of Information is visualized as an essential asset by executive information system management people at the top-level directors.
The visualized information systems are displayed, not only at the given organizations but also use or deployed in many organizations, as it follows strategically rather than just had an operational role of the given organization.
If an uncertain condition, catastrophic, or pandemic environment propagates in uncertainty to take decisions at top-level management, these systems allowed you to give an optional decision-making to be implemented to interactive among connected users.
1.1.7 Basic Cognitive Behavioral Elements of Human-Computer User Interface Management Systems
HCUIMS is more than just the user interface management system interface. There are a gap and significant difference between the user interface and a computer interaction system. As the above discussed, finally, what we consider the HCUIMS to be broad includes any interfaces among users (developers and users) that may require the systems till the life. Hence, operational research scientists, investigators of system development, implementation, acceptance, use, and impact lot in management personnel’s decision-making, capture broad HCI issues and concerns. In a nutshell, the broad view of human interaction activities has five components among them: human (users), technology (H/W, S/W, and other related), interaction (communication), task (to accomplish), and context (domain-based).
Finally, from an organizational point of view, there are four essential contexts identified; these are seen in Figure 1.1 [10]:
Figure 1.1 Cognitive behavioral elements of broad view of human-computer interface in management [10].
Organizational context,
Technology context,
Social context,
Global context.
When designing your interface in any one of the above contexts, it should be consistent and predictable in the user choice of interacting elements. Initially, the experts need to train them to use the functionality, operation of various events; if whether they are aware of it or not, users must have to be trained; once they become familiar with elements; if they act in a certain way, they need to adopt those elements when appropriate will help to accomplish with efficiently in utmost satisfaction.
Interface elements include but are not limited to the following:
Technology and advanced technology: input, output, information, etc.
Task/job: task goals and task characters
Human: Demographics, physical/motor, cognitional level, and emotional level
Context: Organizational context, technology context, social context, and global context.
However, design usability is rapidly increasing day by day and refer primarily to the ease with connecting users accomplish their intended tasks and relatively closely associated with the use of evaluation impact calculated with the usability testing. Therefore, many perceive usability as a rather tactical aspect of any human-computer interface management system product design: the global context, social context, technology context, and organizational context. However, usability may not complete with the encompassment of all UI elements relating to ease of use. User interface elements’ outcome gives out things like flexibility, adaptability, compatibility and can ease to learn and recognize information in a possible manner and economic affordability also comes into this category [10].
1.2 Cognitive Modeling: Decision Processing User Interacting Device System (DPUIDS)
Cognitive modeling is helpful in the decision processing systems through user interface device systems. Data science and behavioral sciences are viewed as significant parts of any decision making. It gives us a powerful new tool and these are suppressing tedious tasks to make it as simple by analytic indication through behavior changes and represent their consequences day by day and presented to their visuals. Machine learning and data science studies help predict future outcomes by using analytics from widespread large data sets to assess the desired outcomes to accomplish personalized behavioral interventions. This may not be a concern for most businesses’ aspects; some of the programs are adequate and applicable to everyday issues. Through cognitive behavior assessment, the investigators and researchers are designed new algorithms to recognize the circumstances around their environments and subsequently change the negative energy to positive energy to bring out more outcomes to meet the predicted outcome. It allows us to quickly do basic arithmetic and read emotional intelligence, body language, postures and gestures, and complete sentences.
1.2.1 Cognitive Modeling Automation of Decision Process Interactive Device Example
For a typical discussion, if anyone of the person, energy is low from any number of tasks or processes or over successive or meetings or engaging intensive concentration, his or her mental energy will be going to be decreased to the point that point the automatic system needs to take over the carry out next task. Where cognitive decision processing user interface device systems are designed and developed with algorithmic prediction, there can begin to identify policymakers’ characteristics, factors, and like benefit and appropriately target the interacting people.
The paramedical structure describes the business intelligence user community decision processing system. Data analytics is a process of monitoring, the inception of inspection, cleaning of data like imbalances, identifying skewness, external noise, transforming the data and information through online analytical process and online transaction process, and modeling data to extract useful information through supporting decision-making. Data analysis process has multiple facets and strategic approaches, encompassing diverse techniques under a variety of cubes, names, under a different business, science, and social science domains. Suppose a typical user does not have the expertise or the resources to employ dedicated information technology resources to develop reports, tools, or customization applications. He or she can take the help of software tools, and the visualization of events will help make decisions. In this respect, automatic interactive visualizations are helped on behalf of users.
One aspect of decision processing user interaction device systems is a collection of integrated embodiments of events. Those who respond to a system and collect interactive visualizations methods include receiving a selection of required data through the report processing generation system. Integrated data consists of database storage systems and their active listening interfaces are given between the source senders to the received listener. Those storage databases and respective interfacing devices invoke the necessary methods, automatically generated functional activity then accomplish the user tasks. They are easy to determine an associated visualization for the selected data based on heuristics; it is said that a set of rules is used to determine the associated visualization most appropriately for interacting decision process systems [12].
Decision processing and interacting device systems identify complex data as more accessible, understandable, and usable. These systems are used in the domains like business, organizations, and various endeavors, and massive amounts of data are being collected, processed, and stored. This trend is growing exponentially with the adoption of the internet, intranet, advancing networking technologies, powerful mobile devices, wearable devices, and the like many vast device’s interconnectivity. The world makes it into a Global village and most of the devices are connected in the Internet of Things (IoT) and through Sensor Networks. The applications of the interactive device systems are one of the sets of cognitive-behavioral and neural network-related machine learning, deep learning and type of convolutional networks, and recurrent neural networks that are running an enterprise, such as without limitation, payrolls, inventory, marketing, sales and distribution, vendor management, accounting, supply chain management, and resource planning applications (Figure 1.2) [13].
Figure 1.2 Decision processing system user interface device management as external customer [13].
1.2.2 Cognitive Modeling Process in the Visualization Decision Processing User Interactive Device System
Cognitive models are useful artifacts used to understand a better way to accomplish a real-world object task in our world. In the context of knowledge representation and automated reasoning. The use of visualization tools is used to create useful patterns in the extraction of knowledge.
The important modeling visualization tools are described below with their functionality and objective role of decision processing interaction role in various devices that are shown in the below diagram (Figure 1.3) [15].
The flow chart description is step by step.
Views to mental model, thereby computing sensory input devices to visualize data to discover useful information. Understand and justify.
A collection of methods, procedures, algorithms, and learning methods on the data preprocessing, interpretation, visualization, storage, analysis, and transformation as compared to desired outcomes.
System (DPUIMS). The following are models Visualize model outputs to understand and communicate the necessary computational models. Finally, the visualization model has been resultant as information.
Figure 1.3 Cognitive modeling process in the visualization decision processing user interacting device system [15].
1.3 Cognitive Modeling: Decision Support User Interactive Device Systems (DSUIDS)
Cognitive models are used to support user interactive device systems in the form of computer programs, applications, algorithms, events, and sensors or devices or components or controls or tools that simulate human performance based on cognitive skills. They are useful through human-computer interaction to assist users in predicting tasks and finding meaningful and useful patterns. If these models are evolved through emerging design methodologies compared with historical interfaces, excellent and strange results are produced with high interactive graphical visualization tools. This strategic approach is abstracted and encapsulated as a yield of the cognitive model decision supported interface device, analogous to and based on a Cognitive Model Decision Support User Interface Management System (DPUIMS). The following are models and structural representation of interactive management interactive device system. The systems will help exploit the synergy between the branches, and interdisciplinary domain areas have interactions among the users [15].
1.3.1 The Core Artifacts of the Cognitive Modeling of User Interaction
There are various artifacts helped as tools to provide the development of interaction among user interfaces. Some tools can be designed and deployed through a task simulation mechanism in the development of cognitive models. There is no other linkage mechanism that may support and interpret cognitive models to the wide range of interfaces in a large organization’s decision support systems (Table 1.1).
For an initial consideration for an Integration purpose, the following is featured process [16]:
Creation of computer user interface tools.
Task simulation involvement mechanism in a model eye during run-time is necessary interaction as per the model.
Need communication mechanism to be passed with information in the cognitive model and simulation of the task.
1.3.2 Supporting Cognitive Model for Interaction Decision Supportive Mechanism
Cognitive decision supportive mechanism implementation is based on essential elements; they are composed with the cognitive architecture via a cognitive modeling tool, and then communication mechanism combined with the hand and eye is implemented, thereby find the respective HCUI to interact with users. This environment model with task simulation tools effectively runs on heterogeneous and homogeneous environments (operating systems, real-time operating systems, various servers and clients, multiple computers, databases, etc.). It is finally integrated with the user interaction management system interface and computer-based management interaction management systems.
Table 1.1 The core artifacts provided at the cognitive modeling of user interaction [16].
Artifact
Purpose
Cognitive model
It provides the simulation of the cognitive performance and user’s behavior to perform the task.
Task simulation
It provides the task for the cognitive model. Also, the user interface will be used in the model.
Linkage mechanism
It provides the pathway between the model and simulation to communicate for human perception and action. It simulates human perception and action.
Figure 1.4 Supporting cognitive model for the interaction of decision supportive mechanism [16].
Supporting cognitive model (Figure 1.4) for interaction of decision supportive architecture is embodied with the following three necessary steps.
The initial step to provide the model with supporting decision-making capabilities for perception and action among human-computer interaction with the task simulation is to extend the necessary cognitive tools as architecture to become a complete model by adding an eye with a simulated hand.
In the second step of the cognitive model to the simulation, the simulated eye and hand observations are to be recorded, and that information is to pass into the cognitive model for necessary actions.
The model’s final step is categorized into two specific parts as simulated eye and hand implemented in that environment as the simulation by using necessary simulation tools, whereas the cognitive model can be separated. Here, there occurs a communication mechanism between two such separated specific parts as in the form of interaction done simulated eye and hand with cognitive modeling [16].
1.3.3 Representational Uses of Cognitive Modeling for Decision Support User Interactive Device Systems
Some of the representations in cognitive modeling topics are described with descriptions in the following diagram [17].
Table 1.2 Representational uses of cognitive modeling for decision support user interactive device systems [17].
Topic
Representational expectations
Comments
Model understand the context
Objectives in the form of sentential statements, to verify the relationship, data discovery, and investigation of data. To high-level requirements for visualization model or architecture. The dominant type of visualization is based on data analysis and exploration.
The ambiguity possible with sentential representations can be an advantage without ambiguity.
Model structure definition
The relationship provided in model supportability through data analysis, visualization of the model, decomposition of the problem, and variable specifications.
The dominant type of visualization model may be computed probably with the help of given fullest resources utilization.
The activity usually received total resources what we thought was the significant portion.
Visual tools range from “Balloons and Strings representation of relatedness” to tables of storage format, spreadsheets, and visual framework of activities.
Realization of the model
Identification of solution with the help of a more concrete model as adequate parameter estimation.
The dominant visualization type is to be built by continuing the suitable model at various levels of hierarchy.
Supports for the hierarchical problem decomposition into chunks at various levels visualization.
Assessment of the model
Provided correctness, feasibility, and acceptability in validation of the model.
The stakeholder target is justified through context given by the right modeler with colleagues, customers, and users.
Implementation of the model
The suitable model is implemented and managed its transmission into active usage.
Completeness of visualization to assist marketing and training. Good speed and benefits concerning turnover in personals the number of new users of the model.
Understand model context
Define structural model
Realization of the model
Assessment of the model
Model implementation
1.4 Cognitive Modeling: Management Information User Interactive Device System (MIUIDS)
Today, all industry stakeholders consider the different interfaces since it provides feedback on a new product’s effectiveness in real life. However, one must not forget the adoption of interface communication from character user interface data to voice user interface information. The information is a key to the process and storage of any organization. The stakeholder, mainly customer experience, is immediate valuable feedback and product safety and low maintenance are complemented strategically designed with the necessary management user interactive device system. The essential elements of the management user interactive device system are described with the necessary diagram (Figure 1.5).
Memory
Encoding
Storage
Retrieval
In comparisons of actual with predicted performance, bars for actual performance are always wider. Comments are added to the displays to explain abnormal conditions, explain graphic depictions, reference related displays, and inform pending changes. For example, a display may show that signups may be less than three as forecasted. However, the staff member responsible for the display knows that a down payment from Peru for three aircraft is an end route and adds this information as a comment on the display. Without added comments, situations can arise, referred to as “paper tigers”, because they appear to require managerial attention though they do not. The MIDS staff believes that “transmitting data is not the same as conveying information” [8]. The displays have been created with the executives’ critical success factors in mind. Some of the measures, such as profits and aircrafts sold, are obvious. Other measures, such as employee participation in company-sponsored programs, are less obvious and reflect the MIDS staff’s efforts to understand and accommodate the executives’ information needs fully.
Figure 1.5 Basic elements of management information user interactive device system.
Figure 1.6 Model of memory, information passes through distinct stages in order for it to be stored in long-term memory.
Keys to the success of MIDS descriptions of successful systems are useful to people responsible for conceptualizing, approving, and developing similar systems. Perhaps even more critical are insights about what makes a system a success. A committed senior executive sponsor wanted a system like MIDS, committed the necessary resources, participated in its creation, and encouraged its use by others. It carefully defined system requirements. Several considerations governed the design of the system. It had to be custom-tailored to meet the information needs of its users. Ease of use, an essential item to executives who were wary of computers, was critical. Response time had to be fast. The displays had to be updated quickly and efficiently as conditions changed. They have carefully defined information requirements. There has been a continuing effort to understand management’s information requirements. Displays have been added, modified, and deleted over time. Providing information relevant to management has been of paramount importance (Figure 1.6). The staff that developed the operated and evolved MIDS combines information systems skills and functional area knowledge. The computer analysts are responsible for the system’s technical aspects, while the information analysts are responsible for providing the information needed by management. This latter responsibility demands that the information analysts know the business and maintain close contact with information sources and users [18].
The initial version of MIDS successfully addressed the company president’s most critical information needs and strengthened his support for the system. There is little doubt that developing a fully integrated system for a full complement of users would have substantial delays and less enthusiasm for the system.
Careful computer hardware and software selection is essential in this model. The decision to proceed with MIDS development was made when the right color terminals at reasonable prices became available. At that time, graphics software was very limited, and it was necessary to develop the software for MIDS in-house. MIDS development could have been postponed until hardware and software with improved performance at reduced cost appeared, but this decision would have delayed providing management with the information needed. Also affecting the hardware selection was the organization’s existing hardware and the need to integrate MIDS into the overall computing architecture. While it is believed that excellent hardware and software decisions have been made for MIDS, different circumstances at other firms may lead to different hardware and software configurations. Future plans for MIDS continues to evolve along the lines mentioned previously. Improvements in display graphics are also planned through the use of a video camera with screen digitizing capabilities. Several other enhancements are also projected. A future version of MIDS may automatically present variance reports when actual conditions deviate by more than user-defined levels. Audio output may supplement what is presented by the displays. The system may contain artificial intelligence components. There may be a large screen projection of MIDS displays with better resolution than is currently available. The overriding objective is to provide Lockheed Georgia management with the information they need to effectively and efficiently carry out their job responsibilities.
1.5 Cognitive Modeling: Environment Role With User Interactive Device Systems
Environment plays a crucial role in interacting with various kinds of interactive device systems. Behind this, there are four “E’s” that motivate the theories and assumptions of cognition modeling [19]; these are mainly the following:
Embodied,
Embedded,
Extended, and
Enactive.
So, various interactive devices like Individual Intelligences Interactions (I3), Artificial and Individual Intelligences Interaction (AI3), Brain-Computer Interaction (BCI), and Individual Interactions through Computers (I2C) in a playful manner are provided to meet the corporate challenges in all stakeholders of various domains with better user experience.
1.6 Conclusion and Scope
Cognitive modeling plays a significant and strategic role in human-computer interaction devices deployed these days and in the future, with artificial intelligence, deep learning, and machine learning computing techniques. Data science and data analytics provided an accurate visualization analysis with customer feedback experiences to know the expeditions of the users with their interactions of the above interactive devices. User experience is the crucial factor of any successful interacting device between machine and human because decisions can be uncertain due to various situations. One of the key strengths of the cognitive model interactive device system is its many practical applications. It is used in the field experiment to investigate the effects of cognitive interviewing techniques training on detectives’ performance in eyewitness interviews. This means that studies taking the cognitive approach are somewhat scientific and have good internal validity in the long future deterministic decision-making in all the levels of management decisions.
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