Intelligent Knowledge Research Articles

Modeling of Business Intelligence Systems Using the Potential Determinants and Theories with the Lens of Individual, Technological, Organizational, and Environmental Contexts-A Systematic Literature Review

Race towards industry 4.0 increases the hyper competition and puts pressure on organizations to integrate the advanced technologies. Business intelligence system (BIS) is one of the top prioritized technologies that attracted the significant attention of policy-makers and industry experts due to its ability to provide more informed and intelligent knowledge for decision-making processes. It is evident by literature that organizations and industries are prone to integrate the BIS at large scale, but more than 70% BIS projects fail to give the expected results. Hence, it is pertinent to explore the significant determinants that influence the BIS adoption and acceptance in organizations. Although previous literature did not have any comprehensive review relevant to the individual, technological, organizational, and environmental determinants. Therefore, the current study tries to narrow this gap by a systematic literature review (SLR) of 84 studies that were published during the period of 2011–2020. A total of 93 determinants are identified based on content analysis by using text mining techniques of Yoshikoder and human coding skills. The identified determinants are ranked according to their frequency of use. A theoretical framework has been developed with potential determinants and theories. The study results will enrich the recent BIS literature and improve the understanding of practitioners’ decision-making processes to leverage maximum value from the adoption of BIS.

The Realization of Intelligent Knowledge Adaptive Learning Method in the Field of Substation Operation and Maintenance

In view of the lack of targeted and personalized knowledge learning in existing substation operators, this paper proposes an intelligent adaptive knowledge learning method in the field of substation operation and maintenance. First, an adaptive knowledge learning system is established including domain model, user model, teaching model and adaptive engine. In the user model, the item response theory (IRT) is used to estimate the learner’s master degree of knowledge points contained in the domain model, and then their learning needs are obtained. Meanwhile, the Bayesian network method is used to speculate the cognitive style of learners. On the basis of a comprehensive consideration of learning needs and cognitive style, the particle swarm optimization algorithm (PSO) in the teaching model is used to recommend the optimal learning resource for learners to realize the high-efficiency personalized knowledge learning in substation operation and maintenance.

An intelligent knowledge system for designing, modeling, and recognizing the behavior of elderly people in smart space

In this paper, a context-sensitive descriptive language is proposed to design and model the daily living activities of elderly people. The objective is to simplify and represent correctly the knowledge collected by sensors (low level) and to have a relevant recognition of the person’s knowledge (high level). The proposed language is based on several rules and constraints through intelligent meaning. It is dedicated to a better understanding and semantic design and description of the behavior of elderly people. Subsequently, in order to provide a powerful knowledge recognition system, a hybrid Markov model is proposed to recognize and predict the activities designed by the proposed language. The proposed model is adapted to the reasoning of the new language. This allows providing a hierarchical and temporal relationship within the knowledge. It is responsible to recognize and predict the behavior of the elderly people efficiently. The flexibility and the intelligibility of the proposed language is proven and the accuracy of the recognition model is demonstrated which ensures the efficiency of the proposed knowledge recognition system.

Pengaruh Pengetahuan Awal Akuntansi, Kecerdasan Emosional Dan Efikasi Diri Terhadap Tingkat Pemahaman Akuntansi

This study was conducted to examine and analyze the effect of prior knowledge of accounting, emotional intelligence and self-efficacy on the level of understanding of accounting in accounting education students, Faculty of Economics, State University of Surabaya. This research is a type of quantitative method. The research sample used is a saturated sampling technique with a total of 96 PAK undergraduate students departing 2017. Questionnaires (questionnaires) and documentation are techniques in collecting data. The results showed that initial accounting knowledge had a partial effect, emotional intelligence had no partial effect and self-efficacy had no partial effect. The results of the F test show that there is a simultaneous influence of initial accounting knowledge, emotional intelligence and self-efficacy on the level of accounting understanding in accounting education students, Faculty of Economics, Universitas Negeri Surabaya.

Research and Application of Key Technologies for Medical Image Intelligence Knowledge Discovery and Data Processing

Hospitals have accumulated a large amount of medical image data which need to be analyzed and integrated so as to be able to find the needed medical image in time, which is the basis of key technologies such as intelligent diagnosis of diseases. Meanwhile, through the analysis and integrated processing of medical images, the potential value of existing medical image data can be fully explored. In this paper, the key technologies in the intelligent image knowledge discovery system and the characteristics of medical image data are studied and improved. In this paper, the characteristics of knowledge discovery and medical image data are comprehensively considered, and RDM texture features are selected as the feature representation of medical images. An improved RDM operator is proposed and proved by experimental results. Experimental results show that the improved RDM coding method can improve the stability of medical image data expression.

Ambient Intelligence and e-Learning

The use of ambient intelligence knowledge inevitably leads to a new education concept particularly in creating an environment towards the implementation of teaching as well as the process of education. The process of teaching and education, besides conventional and physical elements of the environment, will be enriched with elements regarding modern information technology. Ambient intelligence will be presented in this paper as a result of the artificial algorithm neural networks, through the following contexts: e-learning environment, identification, and security. The key role in raising students’ achievements as well as competency levels belongs to modern information technology which works towards creating ambient intelligence. It is also executed through the concept of e-learning onto one of the convenient learning management platforms. Survey results indicate that with the use of ambient intelligence, better results are achieved, especially in mathematics taught at the elementary school level. Furthermore, learned lessons are memorized by students for a long period, which is proved by higher levels of students’ knowledge and skill acquisition in terms of general progress.

A Knowledge-Intensive Decision Support System for Industrial Machines Maintenance

-In industrial plants, the profitability of the plant is significantly affected by the quality of machines maintenance. To ensure continuous production, the high valued machines should be kept in good working conditions. This brings plants to search for means to control and reduce equipment failures. When faults emerge in plants, appropriate actions for fault diagnosis and troubleshooting must be executed promptly and effectively to prevent large costs due to breakdowns. To provide reliable and effective maintenance support, the aid of advanced decision support technology utilizing previous repair experience is of crucial importance for the expert operators as it provides them valuable troubleshooting clues for new faults. Artificial intelligence (AI) technology, particularly, knowledge-based approach is promising for this domain. It captures efficiency of problem solving expertise from the domain experts; guides the expert operators in rapid fault detection and troubleshooting. This paper focuses on the design and development of a Knowledge-Intensive Decision Support System (KI-DSS) for Maintenance, Repair and Service in industrial plants to support better maintenance decision and improve maintenance efficiency. With integration of case-based Reasoning and ontology, the Ki- DSS not only carries out data matching retrieval, but also performs semantic associated data access which is important for intelligent knowledge retrieval in decision support system. A case is executed to illustrate the use of the proposed KI-DSS to show the feasibility of our approach and the benefit of the ontology support.

RETRACTED ARTICLE: Research on intelligent knowledge representation method and algorithm based on basic-element theory

At present, there are many ways to express knowledge, all of which play an important role in information system. However, these knowledge representation methods cannot reduce the contradiction between knowledge, nor can they effectively extend more new knowledge from existing knowledge. Therefore, this paper puts forward the basic-elements theory and studies the intelligent knowledge representation method of the basic-elements theory. This method studies things, features of things and their corresponding feature quantities as a whole, and USES basic elements to formally describe things, behaviors and relationships, and establishes extended models to express knowledge. The knowledge representation method based on the theory of basic elements can not only express knowledge more accurately and reduce the contradiction between knowledge, but also extend more knowledge from existing knowledge and systematically describe the development of things according to the development nature of basic elements. Experiments show that this method is superior to the common knowledge representation method in terms of expansion rate, clarity and simplification rate.

Enhanced humanoid assisted human interaction model based on linear structural modeling for knowledge representation

Certain rules and regulations are adapted to the linguistic production model to support the decision of human computer interaction using linguistic protection rule method. This paper proposes linear linguistic advanced structural modeling (LLASM) approach to develop weighted linguistic reasoning (WLR) algorithm for the purpose of reasoning and knowledge representation. In this article human computer interaction faces optimal flow and job Characteristics experience using individual computers in the workplace which helps to overcome the issues using LLASM in a variety of organizations with Robotic assistance for verbal and non-verbal sequence using humanoid robot. This model introduces global weight and local knowledge fuzzy rules to determine the optimal flow and job characterization problem faced in linguistic reasoning method. Here, The Weighted linguistic reasoning algorithm allows fuzzy rule-based expert systems with the help of the LLASM method to execute the intelligent and flexible knowledge reasoning model with robotic assistance. Finally, case studies are presented to show the effectiveness of the re-scheduling process which benefits the proposed method using the Linguistic reasoning model.

ARTIFICIAL INTELLIGENCE TO MASTER ENGLISH LISTENING SKILLS FOR NON-ENGLISH MAJOR STUDENTS

Technology plays an important role in people’s day-to-day life and various aspects of life including education also use technology. Artificial Intelligence is regarded as an example of the advancement of technology. The purpose of this study is to investigate students’ perceptions of using Artificial Intelligence mobile applications to improve English listening skills. It was under phenomenological qualitative research characterized by an interdisciplinary approach, mainly education and technology. The online interview was used to gain information from students’ on the specific theme of artificial intelligence mobile applications in improving English listening skills. The survey consists of seven questions which become category such as 1. The Knowledge of Artificial Intelligence; 2. The Knowledge of Tune In, Joox Music, VOA Learning English Listening every day, and Netflix; 3. The Use of the Applications; 4. The Reason to Use the Applications; 5. The Use of Applications in Improving English listening Skills; 6. The Influence of the Applications toward The Users; 7. The Most Effective and Efficient Applications to Improve English Listening Skills. This survey was conducted at the Faculty of Psychology in Universitas Sarjanawiyata took part in this study. Participants were three females and two males. The results showed that the most effective and efficient Artificial Intelligence mobile application to improve English listening skills is Netflix.

New technology foresight method based on intelligent knowledge management

The increasing importance of technology foresight has simultaneously raised the significance of methods that determine crucial areas and technologies. However, qualitative and quantitative methods have shortcomings. The former involve high costs and many limitations, while the latter lack expert experience. Intelligent knowledge management emphasizes human–machine integration, which combines the advantages of expert experience and data mining. Thus, we proposed a new technology foresight method based on intelligent knowledge management. This method constructs a technological online platform to increase the number of participating experts. A secondary mining is performed on the results of patent analysis and bibliometrics. Thus, forward-looking, innovative, and disruptive areas and relevant experts must be discovered through the following comprehensive process: Topic acquisition → topic delivery → topic monitoring → topic guidance → topic reclamation → topic sorting → topic evolution → topic conforming → expert recommendation.

A Study to Evaluate the effectiveness of Structured Teaching Programme on knowledge regarding of Polycystic Ovarian Syndrome among School teachers of selected school of Ghaziabad

Polycystic ovarian syndrome is the condition which can affect woman menstrual cycle fertility hormone and aspect of their appearance. PCOS produce symptoms in approximately five to ten percent of women of reproductive age group 15 to 44 years old the prevalence of PCOS depends upon choice of diagnostic criteria. About 18% of women had PCOS and that 70% of them were previously diagnosed. A study to evaluate the effectiveness of structured teaching programme on knowledge regarding polycystic ovarian syndrome among school teachers of selected School of Ghaziabad. The study was aimed to assess the knowledge regarding awareness of polycystic ovarian syndrome among school teachers and to evaluate the effectiveness of structured teaching programme on knowledge regarding awareness of polycystic ovarian syndrome among school teachers. A pre experimental one group pre test post test design was adopted and Non probability convenient sampling was used to select 30 school teachers working in J.D Public school, Ghaziabad. Structured knowledge questionnaire consists of 30 items were administered to school teachers. Results showed that knowledge score of school teachers in pre test 29 (96.67%) average knowledge and 1 (3.33%) poor knowledge. The knowledge score of school teachers in post test revealed that 23(73.67%) school teachers have good knowledge, 6(20%) have intelligent knowledge and 1(3.33%) have average knowledge. The mean post-test knowledge score of School teachers was 20.63 with standard deviation 3.22. The mean pre test knowledge score was 11.56 with standard deviation 2.37. The obtained mean difference was found to be 9.07. The obtained mean difference was found to be statistically significant as evident from the “t” value of 14.87 which was greater than table value (2.05) for df (29) at 0.05 level of significance. These findings revealed that the structured teaching programme was effective in improving the knowledge of school teachers on polycystic ovarian syndrome. Findings also showed that there was no significant association between post test knowledge score with the selected demographic variables at 0.05 level of significance. Study concluded that the structured teaching programme was effective in improving the knowledge of school teachers regard POCS. Based on the findings the recommendation for similar study can be conducted with large sample for better generalization

Fuzzy knowledge graph system for artificial intelligence-based smart education

Fuzzy knowledge graph system is a semantic network that reveals the relationships between entities, and a tool or methodology that can formally describe things in the real world and their relationships. Smart education is an educational concept or model that uses advanced information technology to build a smart environment, integrates theory and practice to build an educational framework for information age, and provides paths to practice it. Artificial intelligence (AI) is a comprehensive discipline developed by the interpenetration of computer science, cybernetics, information theory, linguistics, neurophysiology and other disciplines, which is a direction for the development of information technology in the future. On the basis of summarizing and analyzing of previous research works, this paper expounded the research status and significance of AI technology, elaborated the development background, current status and future challenges of the construction and application of fuzzy knowledge graph system for smart education, introduced the methods and principles of data acquisition methods and digitalized apprenticeship, realized the process design, information extraction, entity recognition and relationship mining of smart education, constructed a systematic framework for fuzzy knowledge graph, and analyzed the high-quality resources sharing and personalized service of AI-assisted smart education, discussed automatic knowledge acquisition and fusion of fuzzy knowledge graph, performed co-occurrence relationship analysis, and finally conducted application case analysis. The results show that the smart education knowledge graph for AI-assisted smart education can integrate teaching experience and domain knowledge of discipline experts, enhance explainable and robust machine intelligence for AI-assisted smart education, and provide data-driven and knowledge-driven information processing methods; it can also discover the analysis hotspots and main content of research objects through clustering of high-frequency topic words, reveal the corresponding research structure in depth, and then systematically explore its research dimensions, subject background and theoretical basis.

Construction of first-class university course based on artificial intelligence and neural network algorithm

Curriculum is the basis of vocational training, its development level and teaching efficiency determine the realization of vocational training objectives, as well as the quality and level of major vocational academic training. Therefore, the development of curriculum is an important issue. And affect the school’s teaching capacity building. The analysis of the latest developments in the main courses shows that there are some deviations or irrationalities in the curriculum in some colleges and universities, and the general problems of understanding the latest courses, such as lack of solid foundation in curriculum setting, unclear direction of objectives, unclear reform ideas, inadequate and systematic construction measures, lack of attention to the quality of education. This paper explains the rules for the establishment of first-level courses, clarifies the ideas and priorities of architecture, and explores strategies for building university-level courses using knowledge of artificial intelligence and neural network algorithms in order to gain experience from them.

Intelligent system for English translation using automated knowledge base

In the process of globalization, machine translation has undergone a long period of evolution and development. Although the development level of machine translation has been greatly improved, the quality of machine translation is still not very high, and it is difficult to meet the needs of users. Artificial intelligence is the science that studies the laws of human intelligent activity. The application of artificial intelligence technology in the English depression and depression, combined with the Internet and intelligent knowledge base, can develop English translation systems to solve the problem of English translation to a certain extent. Based on the above background, the research content of this article is a neural network-based artificial intelligence technology English translation system based on the intelligent knowledge base. This article is mainly based on the existing English-Chinese machine translation to find a more favorable method for English long sentence translation. By improving part-of-speech tagging and rules, the rules can match more sentence patterns to improve the quality of existing machine translations. This paper proposes an improved hybrid recommendation algorithm, and through experimental simulation, the results show that the accuracy of the algorithm is not very high. The highest is 35.64%. The possible reason may be that the k value is selected during k-means text clustering, or the N value recommended by TopN is not selected properly, but the hybrid recommendation is still better than ordinary collaborative filtering.

Інформаційна технологія перевірки відповідей в інтелектуальній автоматизованій системі контролю знань

The process of verification the answers to the open type questions (short text answer, essay) in the testing systems was researched. The analysis of automated systems in which this process is implemented has been carried out. The limitations of their application for control the level of knowledge on technical disciplines were determined. There has been justified the relevance of research on the fuzzy string comparison methods in the problems of verification of answers, submitted in an arbitrary text form. The functional structure of the module for verification such answers in intelligent automated system for control the level of knowledge in the form of a structural step-by-step diagram of the process of testing the input answer was proposed. The computational algorithms of each stage of processing are given; their implementation in C# was executed. The first step is to normalize the words of the answer and the etalon (the correct answer to the question stored in the data-base). The result of the first stage is a string arrays: the first is a set of response words, the second is a set of etalon words (words less than four characters in length are not included. The second step involves the cyclic calling the function of finding the length of the longest common subsequence (LCS) of the words of arrays of the response and the etalon. A block diagram of the LCS calculation procedure based on the recursive algorithm proposed by Hirschberg was presented. Comparison of each word of the input answer with all the words of the etalon, including synonyms, makes it possible to find the length of the LCS, even if the order of the words in the answer and the etalon does not match, which is an advantage of the proposed approach. In the third step, the total indicator of similarity of response and etalon is calculated by finding the sum of LCS of individual words. The last step is to formulate the result of validation of the answer based on the value of the similarity indicator (set depending on requirements: high level of coincidence — 50 %, sufficient — 30 %, low — 10 %). The proposed information technology has been tested for the processing of the answers in an arbitrary text form. A comparison of the obtained results with Levenshtein distance and latent-semantic analysis was performed. The proposed IT gives the best result when checking the answers in which synonymous words are used. The number of false results for the 50 variants of responses of different lengths (from 10 to 200 characters) was 4 %. As a result of the test, the recommended length of the answer and the etalon (maximum 200 characters) was set. Such length provides the higher accuracy. The directions of future research are formed: increasing the efficiency of the algorithm by introducing an additional stage of processing, — determining the general degree of similarity of the answer and the etalon based on the Jacquard coefficient; implementation of an automated intelligent knowledge control system based on client-server technology; forming the result of the test to the relative scale of assessment of the level of knowledge.

IT Governance Knowledge: From Repositories to Artificial Intelligence Solutions

Engineering sciences are nowadays interested in companies’ knowledge, either individual or collective one, in order to implement processing and management systems allowing better decisions as well as replacing humans with intelligent agents for technical and business reasons. Knowledge management (KM) is an important pillar to achieve organization best performance by deploying technics and know-how of brilliant and competent profile. It crosses several components namely: strategy processes, information systems and decision systems. IT Governance is the knowledge allowing the strategic alignment of IT with business so that the maximum value of the company is achieved by the development and effective control of information, responsibility, performance and risk management. This knowledge is mainly capitalized in good practice guidelines written by experts in the field. They make them available to companies and auditors for certification or performance improvement. However, the use of this knowledge in the right way is both expensive and complicated. This article addresses the issues related to the design and implementation of an intelligent knowledge management system for IT governance within the company normative and technical expectations.

Defining the term "intelligence" - insight into existing intelligence knowledge

For more than half a century of Intelligence Studies, this field has been characterized by the problem of lack of uniform definition of the term intelligence, a contentious place in the corpus of existing knowledge. The determinant of this is the existence of different types of intelligence, that is, the term is related to the intelligence product or information, the process/cycle in which information is collected, processed, analyzed and disseminated, and to the intelligence producing organization. Furthermore, it is a broad concept that initially developed and presented itself throughout history as exclusive state property, only later to become an equally represented term in other fields, more specifically business, science, sports, etc. Defining the term "intelligence" is important not only for the sake of development of intelligence theory and scientific discipline, but also because of the practical part of "intelligence" which is an essential feature of every state as it provides support for state decision-making process and defining policies in the national security spectrum. The paper analyzes 35 scientific, expert and institutional definitions of the term intelligence using quantitative and qualitative content analysis. Qualitative content analysis identified 15 key elements. The quantitative analysis found that the most represented element was "information", followed by "end user/decision maker", followed by "actionable character", "foreign countries" and "knowledge". Based on the elements extracted, a new definition is presented. Intelligence is characterized by actionable knowledge of foreign/other countries that is disseminated towards end users, i.e. decision makers, in the form of information.

Prognostication of discharge coefficient of labyrinth weir using outlier robust extreme learning machine

In this paper, for the first time, a robust artificial intelligence (AI) technique called “outlier robust extreme learning machine (ORELM)” is used to estimate the discharge coefficient of labyrinth weirs. At the beginning, the number of the hidden layer neurons initials from 5 and continues to 45 and the most optimal number of the hidden layer neurons are taken into account equal to 5. Then, the numerical model is verified by the k-fold cross-validation approach in which k is considered equal to 5. After that, different activation functions are evaluated to detect the most accurate one for the numerical model. Subsequently, six different ORELM models are developed using the parameters affecting the discharge coefficient of labyrinth weirs. Also, the superior model and the most effective input parameters are identified through a sensitivity analysis. For example, the values of R2, RMSRE and NSC for the superior model are calculated 0.943, 5.224 and 0.940, respectively. Furthermore, the ratio of the head above the weir to the weir height (HT/P) and the ratio of the width of a single cycle to the weir height (w/P) are introduced as the most important input parameters. Also, the results of the ORELM superior model are compared with the artificial intelligence models including the extreme learning machine, artificial neural network and the support vector machine and it is concluded that ORELM has a better performance. Then, an uncertainty analysis is conducted for the ORELM, ELM, ANN and SVM models and it is proved that the ORELM has an overestimated performance. Eventually, a practical formula is presented to engineers without knowledge of artificial intelligence for approximating the discharge coefficient of labyrinth weirs. Additionally, a partial derivative sensitivity analysis is carried out for this equation.

Decisional DNA based intelligent knowledge model for flexible manufacturing system

Modeling an effective mechanism for design and control strategies for the implementation of a flexible manufacturing system (FMS) has been a challenge. Consequently, to overcome this issue various techniques have applied in the past but most of these models are effective only for some specific situation or an element of FMS. In this study, the knowledge representation technique of Decisional DNA (DDNA) is applied to FMS to develop a generic model to achieve effective scheduling and manufacturing flexibility. Decisional DNA based Virtual Engineering Objects (VEO) are used as communicating media between machines, equipment and works pieces. The concept of Virtual Engineering Process (VEP) is applied for modeling routing flexibility. VEOs combined with VEPs form FMS-DDNA model, which facilitates in enhancing the performance of FMS, by inducing intelligence based on its own previous experience thus making it practical and smart.