Searching for New Technology Acceptance Model under Social Context: Analyzing the Determinants of Acceptance of Intelligent Information Technology in Digital Transformation and Implications for the Requisites of Digital Sustainability

Intelligent information technology (IIT) based on AI and intelligent network communication technology is rapidly changing the social structure and the personal lives. However, IIT acceptancefrom various perspectives still requires extensive research. The research question in this paper examines how five factors—psychological, technological, resource, risk perception, and value factors—influence IIT acceptance. Based on an analysis of survey data, it was first found that the acceptance rate of IIT itself was generally very high. Second, in terms of IIT acceptance, among twenty-five predictors, voluntariness (+), positive image of technology (+), performance expectancy (+), relative advantage (+), radical innovation (+), and experience of use (+) were found to have significant effects on the IIT acceptance. Third, in addition to technological factors, psychological factors and risk perception factors also played an important role in individuals’ decisions regarding IIT acceptance.

Intelligent information retrieval for reducing missed cancer and improving the healthcare system

This study presents an intelligent information retrieval system that will effectively extract useful information from breast cancer datasets and utilized that information to build a classification model. The proposed model will reduce the missed cancer rate by providing a comprehensive decision support to the radiologist. The model is built on two datasets, Wisconsin Breast Cancer Dataset (WBCD) and 365 free text mammography reports from a hospital. Effective pre-processing techniques including filling missing values with regression, an effective Natural Language Processing (NLP) Parser is developed to handle free text mammography reports, balancing the dataset with Synthetic Minority Oversampling (SMOTE) was applied to prepare the dataset for learning. Most relevant features were selected with the help of filter method and tf-idf scores. K-NN and SGD classifiers are optimized with optimum value of k for K-NN and hyper tuning the SGD parameters with grid search technique.

Intelligent Information System for Academic Institutions

Research and publication is considered an authenticated certificate of innovative work done by researchers in various fields. In research, new scientific results may be assessed, corrected, and further built up by the scientific neighborhood only if they are available in published form. Guidelines on accountable research and publication are currently set to encourage and promote high ethical standards in the conduct of research and in biomedical publications. They address various aspects of the research and publishing including duties of editors and authorship determination. The chapter presents research and publication system using big data analytics and research data management techniques with a background of information systems and need of information in research data management.

Data transfer in a decentralized network of robots using a local voting protocol

The construction of an effective intelligent information transmission system in a group of cyber-physical systems is one of the important problems in both practical and theoretical contexts. Such a system for transmitting information for a group is being built for a network consisting of separate robotic complexes. Increasingly, decentralized solutions are used to build effective interaction between group members. As a rule, in networks, decentralization is present in computing software modules, and the data transmission system between nodes is centralized. One of the aspects of such centralization is the need to send data to a specific destination directly or by relaying through other nodes - routing data in the network. In this work, a method of data transmission in a decentralized network between robotic complexes without reference to routing is proposed. The method consists of the exchange of data on the state of the entire network as a whole between the nodes.

COUNTERFACTUAL TEMPORAL MODEL OF CAUSAL RELATIONSHIPS FOR CONSTRUCTING EXPLANATIONS IN INTELLIGENT SYSTEMS

The subject of the research is the processes of constructing explanations based on causal relationships between states or actions of an intellectualsystem. An explanation is knowledge about the sequence of causes and effects that determine the process and result of an intelligent informationsystem. The aim of the work is to develop a counterfactual temporal model of cause-and-effect relationships as part of an explanation of the process offunctioning of an intelligent system in order to ensure the identification of causal dependencies based on the analysis of the logs of the behavior ofsuch a system. To achieve the stated goals, the following tasks are solved: determination of the temporal properties of the counterfactual description ofcause-and-effect relationships between actions or states of an intelligent information system; development of a temporal model of causal connections,taking into account both the facts of occurrence of events in the intellectual system, and the possibility of occurrence of events that do not affect theformation of the current decision. Conclusions. The structuring of the temporal properties of causal links for pairs of events that occur sequentially intime or have intermediate events is performed. Such relationships are represented by alternative causal relationships using the temporal operators"Next" and "Future", which allows realizing a counterfactual approach to the representation of causality. A counterfactual temporal model of causalrelationships is proposed, which determines deterministic causal relationships for pairs of consecutive events and pairs of events between which thereare other events, which determines the transitivity property of such dependencies and, accordingly, creates conditions for describing the sequence ofcauses and effects as part of the explanation in intelligent system with a given degree of detail The model provides the ability to determine cause-andeffect relationships, between which there are intermediate events that do not affect the final result of the intelligent information system.

HIERARCHICAL REPRESENTATION OF CAUSAL RELATIONSHIPS TO DETAIL EXPLANATIONS IN INTELLIGENT SYSTEMS

The subject of research in the article is the processes of constructing explanations in intelligent systems based on the use of causal dependencies. The aim is to develop a hierarchical representation of causal relationships between the actions of an intelligent system to form an explanation of the process of the system's operation with a given degree of generalization or detailing. Representation of the hierarchy of cause-and-effect relationships allows you to form an explanation at a given level of detail using the input data in the form of a temporally ordered sequence of events reflecting the known actions of an intelligent system. Tasks: structuring the hierarchy of cause-and-effect relationships for known variants of the decision-making process in an intelligent information system, considering the temporal ordering of the corresponding actions; development of a model of a multi-level representation of causal dependencies for description for explanations in an intelligent system. The approaches used are: counterfactual analysis of causality, used to describe alternative dependencies for possible decision-making options; linear temporal logic to reflect the temporal aspect of causation. The following results were obtained. A generalized hierarchy of cause-and-effect relationships is highlighted for the known variants of the process of obtaining recommendations in an intelligent information system based on the temporal ordering of the corresponding decision-making actions. A model of hierarchical representation of causal dependencies has been developed to describe explanations in an intellectual system with a given degree of detail. Conclusions. The scientific novelty of the results obtained is as follows. A model of hierarchical representation of time-ordered causal relationships is proposed to describe the explanations of the operation of an intelligent system with a given degree of detail. At the top level of the hierarchy, the model defines a generalized causal relationship between the event of using the input data and the event of the result of the system's operation. This connection describes the current task that the intelligent information system solves. At the lower level, cause-and-effect relationships are set between events sequential in time, between which there are no other events. At intermediate levels of the hierarchical representation, the causal dependencies of pairs of events are determined, between which there are other events. The developed model creates conditions for constructing explanations with a given degree of detailing of the actions of the decision-making process in an intelligent system. The model also provides the ability to describe early and late anticipation of alternative sequences of the decision-making process by describing causal dependencies for events between which there are other events.

Concept of convergence of augmented reality technologies and artificial intelligence for spe-cial purpose vehicles

The use of artificial intelligence is a modern important trend in the creation of promising information and control systems of vehicles, including special purpose. The high demands on augmented reality software simply cannot rely solely on human programming to display virtual objects against the real world. Neural networks and machine learning can perform these tasks with much greater efficiency and can greatly improve the augmented reality experience. Goal. The purpose of the article is to develop the concept of convergence of augmented reality and artificial intelligence technologies for special purpose vehicles. Methodology. Artificial intelligence technologies contribute to the transformation of the economy, labor market, government institutions and society as a whole. The use of artificial intelligence technologies helps to reduce costs, increase production efficiency, quality of goods and services. Augmented reality technology offers more innovative methods of visualization by expanding the boundaries of reality, controlling the perspective of the object and visualization in a real context. Results. The concept of convergence of augmented reality and artificial intelligence technologies for special purpose vehicles based on a synergetic approach has been developed. An integrated intelligent information and control system for special purpose vehicles with augmented reality technology has been developed. Originality. Using the convergence of augmented reality and artificial intelligence technologies for special purpose vehicles. Practical value. Convergence is the foundation for achieving numerous positive social and economic effects. The use of augmented reality technology and artificial intelligence can significantly increase the efficiency of special purpose vehicles.