Intelligent Information Systems Research Articles

The problem of loss of solutions in the task of searching similar documents: Applying terminology in the construction of a corpus vector model

This article considers the problem of finding text documents similar in meaning in the corpus. We investigate a problem arising when developing applied intelligent information systems that is non-detection of a part of solutions by the TF-IDF algorithm: one can lose some document pairs that are similar according to human assessment, but receive a low similarity assessment from the program. A modification of the algorithm, with the replacement of the complete vocabulary with a vocabulary of specific terms is proposed. The addition of thesauri when building a corpus vector model based on a ranking function has not been previously investigated; the use of thesauri has so far been studied only to improve topic models. The purpose of this work is to improve the quality of the solution by minimizing the loss of its significant part and not adding “false similar” pairs of documents. The improvement is provided by the use of a vocabulary of specific terms extracted from the text of the analyzed documents when calculating the TF-IDF values for corpus vector representation. The experiment was carried out on two corpora of structured normative and technical documents united by a subject: state standards related to information technology and to the field of railways. The glossary of specific terms was compiled by automatic analysis of the text of the documents under consideration, and rule-based NER methods were used. It was demonstrated that the calculation of TF-IDF based on the terminology vocabulary gives more relevant results for the problem under study, which confirmed the hypothesis put forward. The proposed method is less dependent on the shortcomings of the text layer (such as recognition errors) than the calculation of the documents’ proximity using the complete vocabulary of the corpus. We determined the factors that can affect the quality of the decision: the way of compiling a terminology vocabulary, the choice of the range of n-grams for the vocabulary, the correctness of the wording of specific terms and the validity of their inclusion in the glossary of the document. The findings can be used to solve applied problems related to the search for documents that are close in meaning, such as semantic search, taking into account the subject area, corporate search in multi-user mode, detection of hidden plagiarism, identification of contradictions in a collection of documents, determination of novelty in documents when building a knowledge base.

Group key agreement protocol among terminals of the intelligent information system for mobile edge computing

Security communication and information sharing among mobile devices are important application technologies of the intelligent information system (IIS). Because IIS is vulnerable to attacks, so the security of information sharing among mobile devices is seriously threatened. Thence, it is necessary to establish a secure channel for communication among mobile devices of IIS over an opening network. Group key agreement (GKA) can establish a secure channel among mobile devices of IIS by encryption technology. Due to the resource-constraints of mobile devices, such as weak computing power, small storage capacity, and limited communication range. To address these issues, an asymmetric GKA protocol among terminals of IIS for mobile edge computing (GKA–IIS–MEC) network is proposed in this paper. Adopted asymmetric GKA to achieve the group secure communication mechanisms that message sender unfettered in this protocol; the protocol also uses edge computing environment to migrate the computation and communication loads of mobile devices of IIS to edge nodes, thereby ensuring that mobile devices have lightweight computation and communication loads; and the members participating in the GKA can verify whether the group session keys they calculated are correctness. Under the hardness assumption of bilinear inverse Diffie–Hellman problem, the proposed protocol is proven that it can resist negative attacks. After evaluating the performance of the protocol, GKA–IIS–MEC has higher efficiency than the referred works in terms of time cost, communication consumption, and computation consumption.

Towards Semantic Integration of Machine Vision Systems to Aid Manufacturing Event Understanding

A manufacturing paradigm shift from conventional control pyramids to decentralized, service-oriented, and cyber-physical systems (CPSs) is taking place in today’s 4th industrial revolution. Generally accepted roles and implementation recipes of cyber systems are expected to be standardized in the future of manufacturing industry. The authors intend to develop a novel CPS-enabled control architecture that accommodates: (1) intelligent information systems involving domain knowledge, empirical model, and simulation; (2) fast and secured industrial communication networks; (3) cognitive automation by rapid signal analytics and machine learning (ML) based feature extraction; (4) interoperability between machine and human. Semantic integration of process indicators is fundamental to the success of such implementation. This work proposes an automated semantic integration of data-intensive process signals that is deployable to industrial signal-based control loops. The proposed system rapidly infers manufacturing events from image-based data feeds, and hence triggers process control signals. Two image inference approaches are implemented: cloud-based ML model query and edge-end object shape detection. Depending on use cases and task requirements, these two approaches can be designated with different event detection tasks to provide a comprehensive system self-awareness. Coupled with conventional industrial sensor signals, machine vision system can rapidly understand manufacturing scenes, and feed extracted semantic information to a manufacturing ontology developed by either expert or ML-enabled cyber systems. Moreover, extracted signals are interpreted by Programmable Logical Controllers (PLCs) and field devices for cognitive automation towards fully autonomous industrial systems.

Artificial Intelligence based Intelligent Tourism System-Go My Way

At the moment, we find that tourists usually spend more time planning their trip because they need to spend every minute. In this context, this application aims to identify the main computer needs to support the improvement of the tourist promotion point, using the mobile application proposal. Currently, for regular tourists and travelers they spend a lot of time planning and deciding on their trip to achieve maximum satisfaction. In this case, the app aims to identify the main computer needs to support the development of the tourist promotion point. This paper suggests a model for use in an intelligent visitor information system. It uses the concept of knowledge-base. The model will be based on a study of human behavior as a tourism guide. It builds a relationship between an information-based system and a guide, to provide a service to any visitor who meets their needs and the purpose of obtaining location information. There are different modules, different methods of acquisition methods and a shorter way to acquire the ingenuity of the artificial intelligence in this thesis. The proposed system should be designed in such a way that it works on most devices namely palmtop and mobiles. So it can be helpful when visiting new places. This application will find the route using user terms. The short-term method of finding an algorithm should work well and in the right way in most cases. The system must find a method that fulfills the user's terms, indicating the name of the item, images related to a brief description of the location. It should also be able to find the distance, time and travel costs to your destination and over time the user can also make bookings using the app interface only.

Analysis of Metrics for Intelligent Information Systems

The problem of constructing metrics is crucial for solving the problem of quantitative evaluation of both systems of objects of arbitrary nature as a whole and the relationships that describe the connections between the components of these systems. Modern information systems simulate subject areas that contain objects and systems of complex structure. The network model is most appropriate for describing the world around it: it reflects objects and systems of objects of arbitrary nature that interact with each other. In fact, any system can be described using a network model. Hierarchical models should be singled out as a kind of network models of complex systems. Hierarchical models are very widespread and are used in various fields — in biology, sociology, economics, technology, management, etc. — each industry has a set of its own hierarchical models. The paper analyzes metrics suitable for evaluating intelligent information systems, in particular — systems that are based on ontologies, non-relational (hierarchical) databases, non-normalized (nested) relationships.

Ontology Learning from Relational Database: Opportunities for Semantic Information Integration

Along with the rapidly growing scale of relational database (RDB), how to construct domain-related ontologies from various databases effectively and efficiently has been a bottleneck of the ontology-based integration. The traditional methods for constructing ontology from RDB are mainly based on the manual mapping and transformation, which not only requires a lot of human experience but also easily leads to the semantic loss during the transformation. Ontology learning from RDB is a new paradigm to (semi-)automatically construct ontologies from RDB by borrowing the techniques of machine learning, it provides potential opportunities for integrating heterogeneous data from various data sources efficiently. This paper surveys the recent methods and tools of the ontology learning from RDB, and highlights the potential opportunities and challenges of using ontology learning in semantic information integration. Initially, the previous surveys on the topic of the ontology-based integration and ontology learning were summarized, and then the limitations of previous surveys were identified and analyzed. Furthermore, the methods and techniques of ontology learning from RDB were investigated by classifying into three categories: reverse engineering, mapping, and machine learning. Accordingly, the opportunities and possibility of using ontology learning from RDB in semantic information integration were discussed based on the mapping results between the bottlenecks of ontology-based integration and the features of ontology learning. a a This paper is an extension of our prior work [C. Ma and B. Molnár, Use of ontology learning in information system integration: A literature survey, in Intelligent Information and Database Systems, eds. P. Sitek, M. Pietranik, M. Krótkiewicz and C. Srinilta, Communications in Computer and Information Science, Vol. 1178 (Springer, Singapore, 2020), pp. 342–353]

STRATEGY AND DEVELOPMENT OF MAS AGENTS IN MULTIFACTORIAL CLASSES

The paper considers the issues of behavioral control of reactive MAS agents for multifactorial classes. The applicability of MAS technologies for a large data flow of different nature characteristic of classes of complex problems is substantiated. The conceptual MAS model and the architecture of the COVID-19 intelligent information system on its basis are proposed. The structure, behavior, choice of strategy, and functionality of the reactive agent are investigated.

Development of domain thesaurus as a set of ontology concepts with use of semantic similarity and elements of combinatorial optimization

We consider use of ontological background knowledge in intelligent information systems and analyze directions of their reduction in compliance with specifics of particular user task. Such reduction is aimed at simplification of knowledge processing without loss of significant information. We propose methods of generation of task thesauri based on domain ontology that contain such subset of ontological concepts and relations that can be used in task solving. Combinatorial optimization is used for minimization of task thesaurus. In this approach, semantic similarity estimates are used for determination of concept significance for user task. Some practical examples of optimized thesauri application for semantic retrieval and competence analysis demonstrate efficiency of proposed approach.

Research on the Application and Influence of Artificial Intelligence Technology

At present, with the steady progress of industrialization and information technology in China, lots of high-tech, including artificial intelligence, such as financial robot and face recognition system, are gradually coming into the public view. This paper points out the constituent elements and design logic of artificial intelligence, starting from the background of the development and application of related technologies. Meanwhile, based on this artificial logic, assisted by voice recognition, image recognition, data analysis and other processes, it is applied to neural network construction, data mining, intelligent information system and other fields. Finally, taking one of the fields as an example, this article deeply analyzes the possible effect and impact of the application of these technologies, which provides a theoretical basis for the large-scale promotion of artificial intelligence technology.

Artificial intelligence and database technology work together

Ai - database integration is an effective application for many artificial intelligence technologies as well as major technologies for database development, and for the next generation of computing It supports intelligent information systems based on distributed cooperative tasks. The relationship between AI and large data and the potential values of Bayes in AI are described.

Parametric optimization the automated plan of extracurricular activities of students by means of information technologies

Abstract This article explains problems of individualization of education and extracurricular activities’ programs’ development. Main problems that need to be solved at various stages of the organization of extracurricular activities, from its methodological study to organizational and technical planning are analyzed. An overview of the directions of extracurricular activities, the forms in which it can be organized as well as the specifics of its implementation at various levels of education is offered. In order to reduce the labor intensity of time costs taking into account the time allocated for its implementation as well as the ability to generate plans taking into account individual preferences an intelligent information system was developed. Multi-criteria mathematical models from the class of optimization methods for solving the posed problem of decision support, functional and software-architectural developed model of a web service and examples of its work on the stages of planning general activities in the optimization formulation are presented. The proposed approach can be widely used in educational institutions of various levels interested in reducing the cost of organizing extracurricular work, automatic checking for compliance with requirements, as well as generating a work plan taking into account the opinions of various categories of experts.

Does Emotional Intelligence Contribute to Quality of Strategic Decisions? The Mediating Role of Open Innovation

Purpose—The purpose of this research is to explore the direct relationship between the emotional intelligence of top management and the quality of strategic decisions they take for their companies. This relationship is further examined by the mediating role of open innovation in the context of intelligent information systems that can impact the way top managers take decisions. This research adopted a survey design as cross-sectional data were taken through questionnaires from top management of the UAE national banks. A final sample size of 213 questionnaires completed by managers was obtained and analyzed. As predicted, there was a strong, positive relationship between managers’ emotional intelligence and the quality of their strategic decisions. Open innovation has revolutionized the way top managers of banks take decisions that are later transformed into policies. Decision-makers are required to possess the skill of decision-making by being vigilant of their surroundings. Hence, they have emotional intelligence and intelligent information systems (IIS) only enhances the trait. IIS is the glorified version of open innovation that further contributes to the decision-making process and the quality of decisions. This research is one of a kind as no one has explored these dimensions of emotional intelligence in the UAE.

Forecasting the biomass-based energy potential using artificial intelligence and geographic information systems: A case study

To meet the energy demand in a sustainable way, fossil fuels must be substituted with alternative resources and technologies. This transformation is encouraged to reduce greenhouse gases using environmental-friendly practices. Although our country is rich in biomass resources due to climate, land conditions, agriculture and animal husbandry activities, the installed power is quite below its potential. Focusing on this point, the aim of this study is to propose a forecasting method that determines the quantities, distributions, production amounts, waste amounts and energy potential of various biomass resources consistently. The integrated method used in the solution utilizes statistical data and consists of artificial intelligence and geographic information systems. First of all, various bioenergy sources that can be used as energy resources have been determined, and the amount, yield, and energy potential of animal and agricultural wastes expected to occur in the following years have been estimated using an artificial intelligence-based method, support vector regression. Then, spatial analysis has been carried out using geographic information systems, and the distribution of existing and possible agricultural lands has been determined. Finally, the amount of energy that can be obtained using wastes from different biomass sources under various scenarios has been calculated and solutions have been compared. To the best of our knowledge, this study is the first proposing an integrated method consisting of support vector regression and geographic information systems to forecast the biomass-based energy potential in Turkey. The integrated method was applied to Acıpayam district in Denizli. Among the various scenario approaches, the cultivation of rapeseed (canola) plants on non-utilized arable land and the use of its wastes in bioenergy production have been found to yield the highest energy potential. The results showed that approximately 29,2%, 27,8%, and 27,6% energy increase could be obtained from agricultural residues of rapeseed in the next three years if it was planted on the quarter of the idle land. Besides, under this scenario, the total annual electricity demand of 6972, 6663 and 6545 houses could be met from agricultural residues in a sustainable and clean manner. The proposed method can be applied to different regions, various biomass resources and used to make strategic decisions in this field.

Modeling measurement situation in intelligent information and measurement system to determine thermo-physical properties of materials

Definition of thermo-physical properties in different thermal conductivity ranges of solid materials causes the need to consider numerous affecting factors, to create an information environment and model the measurement situation in the operation of intelligent information and measurement system. The purpose of the study is to reduce the measurement error of thermo-physical properties in materials. This problem is relevant and important in the process of controlling the reference parameters of products at the manufacturers. A theoretical framework for modeling the measurement situation is developed. This includes a conceptual model for the formation of the measurement situation, and a model of the research object domain. A mathematical model of the measurement situation and the methodology of its development is created. The problem of selecting the type of measurement situation in the range of investigated materials in terms of thermal conductivity is solved. The risk calculation algorithm of forming a measurement situation in an intelligent information and measurement system for determining the thermo-physical properties of materials is developed.

A conceptual framework for establishing trust in real world intelligent systems

Intelligent information systems that contain emergent elements often encounter trust problems because results do not get sufficiently explained and the procedure itself can not be fully retraced. This is caused by a control flow depending either on stochastic elements or on the structure and relevance of the input data. Trust in such algorithms can be established by letting users interact with the system so that they can explore results and find patterns that can be compared with their expected solution. Reflecting features and patterns of human understanding of a domain against algorithmic results can create awareness of such patterns and may increase the trust that a user has in the solution. If expectations are not met, close inspection can be used to decide whether a solution conforms to the expectations or whether it goes beyond the expected. By either accepting or rejecting a solution, the user’s set of expectations evolves and a learning process for the users is established. In this paper we present a conceptual framework that reflects and supports this process. The framework is the result of an analysis of two exemplary case studies from two different disciplines with information systems that assist experts in their complex tasks.

A conceptual approach to assessing the cluster potential of regions based on the use of intelligent information systems

Subject. This article discusses the role of intelligent information systems in assessing the cluster potential of regions. Objectives. The article aims to develop methodological tools to assess the cluster potential of regions applying intelligent information systems, and test them using the Siberian Federal District regions as a case study. Methods. For the study, we used econometric and expert assessment methods. Results. The article proposes a stepwise algorithm for assessing the cluster potential of regions, taking into account the appropriate methodological and mathematical apparatus. It presents a cumulative indicator of the development potential of cluster groups. Conclusions. The proposed methodological, and information and analysis tools can serve as a basis for decision-making on cluster policy development at the regional level.

Chat communication in library user services

The chat communication is a new type of user distance service. The authors examine the technology applications in different organizations and libraries, in particular. Intelligent information systems are used for designing virtual assistants and chatbots. The authors emphasize that, as for today, the scientific classification for chatbots is lacking. They attempt to categorize virtual talkbots. They discuss iPavlov artificial intellect library. The most interesting Russian developments are Telegram chatbots of Russian State Library for Young Adults, Directorate of Cultural Centers of Moscow, and that of St. Petersburg State Library for the Blind and Visually Impaired. RNPLS&T has implemented jivo system of chat communication. The reasons for choosing this system and operation results are discussed. The authors conclude that chat communication meets the principles and improves efficiency of user services.

Tourism Management System Based on mobile Internet

With the development of cloud computing, Internet of things, mobile communication, mobile Internet and other technologies, the development of intelligent, multi-dimensional and all-round tourism management information system has become the development trend of tourism informatization. This paper studies and realizes a mobile, information, intelligent and personalized intelligent travel service system for tourism industry by comprehensively utilizing the tracking and registration technology based on location and multi-sensor, tile map technology, 3D landscape map technology and virtual reality Superposition Technology of mobile augmented reality system. The system can provide a new perspective for tourists to understand the surrounding landscape and get a new interactive experience. The system includes three-dimensional landscape map, virtual roaming, route navigation, environment identification, historical building query, thermal map display, friend footprints, personal trajectory, Wechat sharing and other functional modules. Tourists can enjoy more convenient travel services at the same time. The model proposed in this paper has a certain reference value for the study of tourism management system.

Intelligent Urban Rail Transit Based on Big Data Analysis and Application

It is an intelligent information system developed in recent years to manage and control the traffic system with modern and intelligent technology. It plays an important role in alleviating traffic congestion, reducing traffic accidents and reducing energy consumption. This paper mainly studies the smart urban rail transit based on big data analysis and application. According to the characteristics of complex spatial correlation and time dependence of passenger flow, a deep learning model-temporal attention network is proposed, and multi-input multi-output multi-step forecasting strategy is used to solve the problem of error accumulation in long-term forecasting of passenger flow. Experimental results show that the prediction performance of our method in each time step is better than the traditional linear model, machine learning model and deep learning model, and the error is reduced by 7%-9% compared with the benchmark model. Moreover, the multi-input multi-output multi-step prediction strategy can effectively reduce the error accumulation of the traditional iteration strategy.

A Systematic Mapping with Bibliometric Analysis on Information Systems Using Ontology and Fuzzy Logic

The ontology-based information systems (IS) development is beneficial for analyzing, conceptual modeling, designing, and re-engineering complex IS to be semantically enriched and suitable for sophisticated reasoning on the IS content. On the other hand, fuzzy theory employment to handle uncertainty and fuzziness in IS becomes a hot topic in different practical domains, such as engineering, IS, computer sciences, etc. As such, ontology- and fuzzy-based IS are being developed. Consequently, there is a need to provide a comprehensive systematic mapping study (SMS) to build a structure on the ontology- and fuzzy-based IS field of interest and to grasp the main ideas. This paper presents findings of SMS, based on the papers extracted from Web of Science and Scopus and employing a bibliometric analysis tool to automate keyword mapping. We conclude this paper by summarizing the previous work and identifying possible research trends, which future investigations can extend. The main finding indicates that ontology and fuzzy logic contribute to ISs by expanding traditional IS to be intelligent IS, which is applicable for solving complex, fuzzy, and semantically rich (ontological) information collection, saving, processing, sharing, and reasoning in different application domains according to users’ needs in various countries.