Self-learning Knowledge Research Articles

Robotic Process Automation to Increase Teaching Efficiency in Higher Education

Digitalization is advancing and changing our world, and with it the whole economy. Universities must meet this changing demand with new training offers. The AUFLADEN (Charging) project, funded by the Stiftung Innovation in der Hochschullehre, is developing new training formats for students involved in construction planning, predominantly students of architecture and civil engineering. One of the questions of the research project and the subject of this article is whether and how Robotic Process Automation (RPA) can be integrated into teaching as a learning tool to support students specifically and individually in learning software applications through automation. For this purpose, so-called self-learning knowledge Nuggets were developed. RPA makes it possible to have operating steps carried out computer-aided, a technology that is becoming popular to simplify recurring work processes. By slowing down this automation process, the user can study and imitate necessary steps. In a case study it is determined whether better learning results can be achieved by this training method. The results are that the learning speed increases and the teaching effort decreases. This encourages the use of automation technology to free teaching from method teaching and to use the precious teaching time for higher learning targets.

Hierarchical Self-Learning Knowledge Inference Based on Markov Random Field for Semantic Segmentation of Remote Sensing Images

Hierarchical Self-Learning Knowledge Inference Based on Markov Random Field for Semantic Segmentation of Remote Sensing Images

A Relation-Oriented Model With Global Context Information for Joint Extraction of Overlapping Relations and Entities.

The entity relation extraction in the form of triples from unstructured text is a key step for self-learning knowledge graph construction. Two main methods have been proposed to extract relation triples, namely, the pipeline method and the joint learning approach. However, these models do not deal with the overlapping relation problem well. To overcome this challenge, we present a relation-oriented model with global context information for joint entity relation extraction, namely, ROMGCJE, which is an encoder–decoder model. The encoder layer aims to build long-term dependencies among words and capture rich global context representation. Besides, the relation-aware attention mechanism is applied to make use of the relation information to guide the entity detection. The decoder part consists of a multi-relation classifier for the relation classification task, and an improved long short-term memory for the entity recognition task. Finally, the minimum risk training mechanism is introduced to jointly train the model to generate final relation triples. Comprehensive experiments conducted on two public datasets, NYT and WebNLG, show that our model can effectively extract overlapping relation triples and outperforms the current state-of-the-art methods.

A Self-Learning Knowledge Based System to Mitigate the Impact of Drug Interactions in Type Two Diabetes Mellitus

The topic of drug interactions has received great attention worldwide recently. If a drug is not administered in appropriate quantity with an appropriate combination of the drug with drug or other substances, the result will be a high risk of dangerous interactions which lead to potentially harmful side effects which are ranging from treatment failure, economic degradation, and death due to lack of drug information and maladministration of drugs among health professionals. Thus, this study was initiated with the main aim to develop a self-learning knowledge-based system to mitigate the impact of drug interactions. To develop this system, design science methodology with the integration of knowledge engineering method was used; and semi-structured interview, document analysis to acquire knowledge; questionnaire to grasp users' feedback, and purposive sampling technique to select domain experts were used. The acquired knowledge was then represented using a production rule and modelled using a decision tree. The system was implemented using PROLOG on SWI-PROLOG editor and evaluated using system performance and user acceptance testing. The developed system was evaluated and 96% of the users were satisfied. As well as the performance of the system was evaluated and recorded 80% accuracy, thus it can be concluded that the system achieves good performance. However, in order to make the system fully applicable in the domain area, further research work to incorporate adequate knowledge, develop online knowledge-based systems and mobile applications were recommended to enhance the accessibility of the developed system.

Rail Multi Vehicle Scheduling Method for Intermediate Depot of Steel Plant Based on Improved Genetic Algorithm

Appropriate transportation network plays a significant role in the economic development of country. The rising transport demand increases the congestion in railway networks, and thus, they become more interdependent and more complex to operate. A ground railway system is established in the reservoir area of steel plants to ensure smooth running of multi vehicle logistics transportation. In recent years, the development of the ground rail multi vehicle logistics system in the reservoir area of steel plants has been increased. However, such logistic systems need an intelligent dispatching control system to avoid the possibility of safety hazards, to follow optimal tracks and to effectively manage logistics operation. In this paper, an improved hybrid genetic algorithm is proposed to realize the decision making and control of multi vehicle scheduling. Task assignment to multiple vehicles and multi-stage subparent vehicle scheduling is performed based on the information obtained from the concerned subsection. The self-learning hybrid algorithm works on the data extracted from an improved population and suggests an optimized solution. The individual behavior and optimization process are updated by self-learning, which ensures the effectiveness of iterative evolution. The selection, cross mutation, and self-learning expert base operation methods of the hybrid genetic algorithm are optimized. The proposed system is evaluated by taking the multi vehicle logistics system of cold rolling intermediate steel depot as a case study. The improved algorithm is implemented in MATLAB and is compared with the traditional genetic and particle swarm optimization algorithms. Results of the analysis prove that the hybrid genetic algorithm of self-learning knowledge expert base is effective in solving the multi vehicle logistics scheduling optimization problem. With the inclusion of the self-learning knowledge expert base genetic algorithm, the convergence trend of the proposed algorithm is enhanced; the traditional genetic algorithm converges after 140 iterations, while in the proposed algorithm, it is reduced to 100 iterations. The evaluation reveals that the proposed algorithm is speedier than the traditional genetic algorithm (GA) and particle swam optimization (PSO); the average solution time of the proposed algorithm is 119.1 while that of GA, PSO is 137.4, 131.4, respectively. The proposed algorithm is applicable to improve the operation and efficiency of the logistics transportation system. The approach is beneficial in intelligent control of metallurgical production. The proposed algorithm has practical significance to be followed in the development of intelligent metallurgical production and logistics transportation.

A Study of Analysis and Trends for Embedded Business Intelligence Market

Abstract: Self-service Business Intelligence (SSBI) is an emerging topic for many companies. Casual users should be enabled to independently build their own analyses and reports. This accelerates and simplifies the decision-making processes. Although recent studies began to discuss parts of a self-service environment, none of these present a comprehensive architecture. Following a design science research approach, this study proposes a new self-service oriented BI architecture in order to address this gap. Starting from an in-depth literature review, an initial model was developed and improved by qualitative data analysis from interviews with 18 BI and IT specialists form companies across different industries. The proposed architecture model demonstrates the interaction between introduced self-service elements with each other and with traditional BI components. For example, we look at the integration of collaboration rooms and a self-learning knowledge database that aims to be a source for a report recommender. Keywords: Business Intelligence, Big Data, Architecture, Self-Service, Analytics

A analytics study Mapping Study of Collaborative business intelligence and self-service

Abstract: Self-service Business Intelligence (SSBI) is an emerging topic for many companies. Casual users should be enabled to independently build their own analyses and reports. This accelerates and simplifies the decision-making processes. Although recent studies began to discuss parts of a self-service environment, none of these present a comprehensive architecture. Following a design science research approach, this study proposes a new self-service oriented BI architecture in order to address this gap. Starting from an in-depth literature review, an initial model was developed and improved by qualitative data analysis from interviews with 18 BI and IT specialists form companies across different industries. The proposed architecture model demonstrates the interaction between introduced self-service elements with each other and with traditional BI components. For example, we look at the integration of collaboration rooms and a self-learning knowledge database that aims to be a source for a report recommender. Keywords: Business Intelligence, Big Data, Architecture, Self-Service, Analytics

Pedagogical conditions for forming and developing the competence of self-education and self-improvement of teachers

The article reveals and substantiates the pedagogical conditions for the formation of the competence of self-education and self-improvement of teachers in the educational space of postgraduate education institutions. The main research methods are theoretical and systemic analysis; systematization of information on the problem of the formation and development of the competence of self-education and self-improvement in the system of postgraduate education. The essence of the concept of "competence of self-education and self-improvement" of a teacher of higher education is revealed. It is proposed to consider the designated concept as the readiness and ability of an individual to carry out effective independently cognitive creative activity within a certain professional sphere due to the formation of self-learning knowledge and skills of self-educational activity. The article analyzes the experience of its formation among teachers during their training in the system of postgraduate education. An important pedagogical condition for the implementation of the process of formation and development of the competence of self-education and self-improvement of teachers is the willingness of scientific and pedagogical workers of the institution of postgraduate education to stimulate the applicant for education to self-education and self-improvement, which contributes to the development of his personality, the ability for independent personal and professional development in the future. It is stated that the second pedagogical condition is the provision of self-education and self-improvement of the teacher in the process of mastering the content of the advanced training course on the basis of the institution of postgraduate education. It was established by means of a survey of students of advanced training courses and interviews with scientific and pedagogical workers of IATPS NUPh that the specified pedagogical conditions for the formation and development of the competence of self-education and self-improvement of teachers provide for the creation of a stimulating educational environment, contributes to the inclusion of both teachers and applicants for the institution of postgraduate education in the process of creative activity and stimulates the development of their professional and personal qualities.

A Self-Learning Knowledge Based System for Credit Evaluation of Loan Application: The Case of Commercial Bank of Ethiopia

This study on prototype self-learning knowledge based system (KBS) is focused on evaluation of loan application used to overcome the challenges that resulted from lack of domain experts and poor loan evaluations. We attempted to design and develop a prototype self-learning KBS that provide advisory services for any credit customers and assists the domain experts in evaluation of customer’s requests for the loan. To develop this prototype system, knowledge was acquired using semi-structured interview from domain experts which are selected using purposive sampling technique from Commercial Bank of Ethiopia (CBE) and critique the acquired knowledge. Explicit knowledge is acquired by analyzing the secondary source of knowledge by method of document analysis. Then, the acquired knowledge is modeled using decision tree that represents concepts and procedures involved in credit evaluation and production rules are used to represent the domain knowledge. The prototype system is implemented using SWI Prolog editor tool. To determine the applicability of the prototype system in the domain area, the system has been evaluated and tested by the domain experts. Eighteen (18) test cases were selected purposively. Test cases are equally selected from both ineligible and eligible cases. The overall total performance of the prototype system is 77.71%. The performance of the prototype system is hopeful and meets the objective of the study. The study concludes that the major credit production type that advanced to customer is import letter of credit facility, export credit facility, pre-shipment credit facility and merchandise. The eligibility of application is focused on general and specific criteria. Credit customer is classified as business, corporate and commercial based on the score sheet they achieved. Generally, in this study, the applicability of knowledge of prototype self-learning KBS is proved as hopeful approach in banking industry for credit evaluation. Keywords : KBS, self-learning and credit (loan). DOI: 10.7176/IKM/10-6-03 Publication date: September 30 th 2020

A Self-Learning Knowledge Based System for Credit Evaluation of Loan Application: The Case of Commercial Bank of Ethiopia

This study on prototype self-learning knowledge based system (KBS) is focused on evaluation of loan application used to overcome the challenges that resulted from lack of domain experts and poor loan evaluations. We attempted to design and develop a prototype self-learning KBS that provide advisory services for any credit customers and assists the domain experts in evaluation of customer’s requests for the loan. To develop this prototype system, knowledge was acquired using semi-structured interview from domain experts which are selected using purposive sampling technique from Commercial Bank of Ethiopia (CBE) and critique the acquired knowledge. Explicit knowledge is acquired by analyzing the secondary source of knowledge by method of document analysis. Then, the acquired knowledge is modeled using decision tree that represents concepts and procedures involved in credit evaluation and production rules are used to represent the domain knowledge. The prototype system is implemented using SWI Prolog editor tool. To determine the applicability of the prototype system in the domain area, the system has been evaluated and tested by the domain experts. Eighteen (18) test cases were selected purposively. Test cases are equally selected from both ineligible and eligible cases. The overall total performance of the prototype system is 77.71%. The performance of the prototype system is hopeful and meets the objective of the study. The study concludes that the major credit production type that advanced to customer is import letter of credit facility, export credit facility, pre-shipment credit facility and merchandise. The eligibility of application is focused on general and specific criteria. Credit customer is classified as business, corporate and commercial based on the score sheet they achieved. Generally, in this study, the applicability of knowledge of prototype self-learning KBS is proved as hopeful approach in banking industry for credit evaluation. Keywords : KBS, self-learning and credit (loan). DOI: 10.7176/IKM/10-5-02 Publication date: August 31 st 2020

A Self-learning Knowledge based System for Diagnosis and Treatment of Chronic Kidney Disease

A Self-learning Knowledge based System for Diagnosis and Treatment of Chronic Kidney Disease

Enterprise Interoperability for Global Collaborative Networks through Cloud Knowledge-Oriented Collaboration System

New opportunities for global enterprise collaboration are leading into an Information and Communications Technology (ICT) era of network ubiquity through the development of new cooperative cyber-processes, which will provide the ability to deliver real-time integration capabilities. This move is supported by interconnected clouds computing with the idea of extending interoperable software and hardware environments from a physical to a virtual infrastructure. Whilst the Internet and cloud technology are indispensable for the operation and collaboration of modern enterprises, they remain nevertheless, at the level of cloud-enabled databases and IT software integration. They fall far short of the knowledge that is relevant for collaborative creation and operation. This paper aims to introduce and develop the concept of a Cloud Knowledge-Oriented Collaboration Systems (cKOCS), which combines cloud computing, the knowledge community, self-learning knowledge and rule-based moderator technology for global collaborative networks. The cloud knowledgeoriented collaboration system must facilitate the effective and efficient transmission of knowledge with the right expertise and those that enable connections with the right information, enable ad-hoc collaboration, provide a platform to establish real-time notification and awareness in collaborative situations and support collaborative enterprises decision making among participations.

Cloud-Based Knowledge Service for Global Enterprise Collaboration

With the growing popularity of cloud computing, enterprises are turning their collaborative platforms towards Software as a Service (SaaS) via the Internet. Cloud technology paradigms are being considered for digital data intensive science, business transactions, open source data, discussion forums, social networks, wikis, tweets and other unstructured data to connect people and share knowledge on a global level. This paper aim to introduce and develop the concept of a knowledge service, which is a combination of cloud computing, knowledge community, self-learning knowledge for Global Enterprise Collaboration (GEC). The cloud-based infrastructure must facilitate the effective and efficiency transmission of knowledge with the right expertise and those that enable connections with the right information for GEC and share knowledge at the international level.

GEMINI

Healthcare systems around the world are facing the challenge of information overload in caring for patients in an affordable, safe and high-quality manner in a system with limited healthcare resources and increasing costs. To alleviate this problem, we develop an integrative healthcare analytics system called GEMINI which allows point of care analytics for doctors where real-time usable and relevant information of their patients are required through the questions they asked about the patients they are caring for. GEMINI extracts data of each patient from various data sources and stores them as information in a patient profile graph. The data sources are complex and varied consisting of both structured data (such as, patients' demographic data, laboratory results and medications) and unstructured data (such as, doctors' notes). Hence, the patient profile graph provides a holistic and comprehensive information of patients' healthcare profile, from which GEMINI can infer implicit information useful for administrative and clinical purposes, and extract relevant information for performing predictive analytics. At the core, GEMINI keeps interacting with the healthcare professionals as part of a feedback loop to gather, infer, ascertain and enhance the self-learning knowledge base. We present a case study on using GEMINI to predict the risk of unplanned patient readmissions.