Intelligent Knowledge-based System Research Articles

Machine learning-based decision-making approach for predicting defects detection: a case study

In today’s highly competitive global market, industries must produce faultless products to achieve profitability. Machine learning (ML) algorithms provide a possible method to improve quality standards by enabling the prediction of the outcome of quality control processes. This article presents a real case study based on ML algorithms suggested to develop a knowledge-based intelligent supervisory system to predict defect products in the fashion industry. Defect detection is formulated as a binary classification problem, and several ML algorithms have been compared to determine the most suitable one on the available data. The random forest (RF), LightGBM, and C5.0 algorithms exhibit comparable high-end performances on the pre-processed dataset made available by the company. Nevertheless, since the aim of the analysed industry is to reduce the rate of false negative observations (i.e., the proportion of defected-free products wrongly classified), the best method results is RF, as it minimizes this metric.

Enhancing Human-in-the-Loop Ontology Curation Results through Task Design

The success of artificial intelligence (AI) applications is heavily dependent on the quality of data they rely on. Thus, data curation, dealing with cleaning, organising, and managing data, has become a significant research area to be addressed. Increasingly, semantic data structures such as ontologies and knowledge graphs empower the new generation of AI systems. In this article, we focus on ontologies as a special type of data. Ontologies are conceptual data structures representing a domain of interest and are often used as a backbone to knowledge-based intelligent systems or as an additional input for machine learning algorithms. Low-quality ontologies, containing incorrectly represented information or controversial concepts modelled from a single viewpoint, can lead to invalid application outputs and biased systems. Thus, we focus on the curation of ontologies as a crucial factor for ensuring trust in the enabled AI systems. While some ontology quality aspects can be automatically evaluated, others require a human-in-the-loop evaluation. Yet, despite the importance of the field, several ontology quality aspects have not yet been addressed and there is a lack of guidelines for optimal design of human computation tasks to perform such evaluations. In this article, we advance the state-of-the-art by making two novel contributions. First, we propose a human computation (HC)–based approach for the verification of ontology restrictions —an ontology evaluation aspect that has not yet been addressed with HC techniques. Second, by performing two controlled experiments with a junior expert crowd, we empirically derive task design guidelines for achieving high-quality evaluation results related to (i) the formalism for representing ontology axioms and (ii) crowd qualification testing . We find that the representation format of the ontology does not significantly influence the campaign results. Nevertheless, contributors expressed a preference in working with a graphical ontology representation. Additionally, we show that an objective qualification test is better fitted at assessing contributors’ prior knowledge rather than a subjective self-assessment and that prior modelling knowledge of the contributors had a positive effect on their judgements. We make all artefacts designed and used in the experimental campaign publicly available.

Uncharted Aspects of Human Intelligence in Knowledge-Based “Intelligent” Systems

This paper briefly surveys several prominent modeling approaches to knowledge-based intelligent systems (KBIS) design and, especially, expert systems and the breakthroughs that have most broadened and improved their applications. We argue that the implementation of technology that aims to emulate rudimentary aspects of human intelligence has enhanced KBIS design, but that weaknesses remain that could be addressed with existing research in cognitive science. For example, we propose that systems based on representational plasticity, functional dynamism, domain specificity, creativity, and concept learning, with their theoretical and experimental rigor, can best characterize the problem-solving capabilities of humans and can best overcome five key limitations currently exhibited by knowledge-based intelligent systems. We begin with a brief survey of the relevant work related to KBIS design and then discuss these five shortcomings with new suggestions for how to integrate results from cognitive science to resolve each of them. Our ultimate goal is to increase awareness and direct attention to areas of theoretical and experimental cognitive research that are fundamentally relevant to the goals underlying KBISes.

Knowledge-Based Recommender System Using Artificial Intelligence for Smart Education

Artificial intelligence can open modern opportunities and potentials for smart education. Smart learning purposes at providing holistic learning to learners utilizing modern technologies to fully prepare them for a fast-evolving world where adaptability is vital. With the advancement of technologies and within modern society, smart education will pose several challenges, like educational structures, pedagogical theory, educational ideology, technology leadership, and teachers’ learning leadership. Therefore, in this paper, an Intelligent Knowledge-based recommender system (IKRS) has been proposed using artificial intelligence for smart education. The recommendation is generated by the genetic algorithm and K-nearest neighbor algorithm (KNN) utilizing the optimized weight attributes vectors that signify the learner’s opinions. The experimental results show that the suggested IKRS model enhances student-teacher interaction, student involvement level, learning quality and predicts students’ learning style compared to other existing methods.

Information Retrieval Based on Brazilian Portuguese Texts

Knowledge-based intelligent systems might be used in the banking sector to automate customer service. One of the ways to represent knowledge that is both understandable by humans and readable by machines is by using ontologies. Whenever a customer queries its bank regarding specific products or services, the existing knowledge modeled in an ontology might be used by a customer service chatbot to answer it in an automated way. The existing manual information retrieval process from banking specialists is laborious and time-consuming. Specialists use natural language, visual representations, and common sense, often overlooking details. It is a great challenge to make a specialist's knowledge explicit, formal, precise, and completely scalable, which is the format required by a customer service chatbot. We propose a semi-automatic approach to retrieving banking information in Brazilian Portuguese texts with minimal specialist support. By combining Natural Language Processing techniques (e.g., syntactic analysis to obtain the logical meaning of sentences based on rules and its structure) and an ontology constructor library, it was possible to build a tool that receives texts from the banking domain and constructs an ontology that knowledge-based intelligent systems can use. Specialist support is only needed in intermediate refinement steps, thus optimizing the banking specialist's time. The use cases for investments, opening a banking account, and the comparison of the proposed approach show how we reduced manual labor in the information retrieval process by a factor of 40%. Our approach can identify more information in each sentence compared to a similar method found in the literature. The resulting ontologies can be used in a chatbot that automates customer support for a large Brazilian bank.

A Knowledge-based Expert System for Supporting Security in Software Engineering Projects

Building secure software systems requires the intersection between two engineering disciplines, software engineering and security engineering. There is a lack of a defined security mechanism for each of the software development phases, which affects the quality of the software system intensively. In this paper, the authors are proposing a framework to consider the security aspects in all the phases of the software development process from the requirements until the deployment of the software product, with three additional phases that are important to automatically produce a secure system. The framework is developed after analyzing the existing models for secure system development. The key elements of the framework are the addition of the phases like physical, training, and auditing, where they improve the level of security in software engineering projects. The authors found so a solution for the replacement of the knowledge of the security engineer through the construction of an intelligent knowledge-based system, which provides the software developer with the security rules needed in each phase of the software development lifecycle and it improves the awareness of the software developer about the security-related issues in each phase of the software development lifecycle. The framework and the expert system are tested on a variety of software projects, where a significant improvement of security in each phase of the software development process is achieved.

Knowledge Service Technology for Supporting Intelligent Product Design

In this paper, a knowledge service method that supports the intelligent design of products is investigated. The proposed method provides the solutions to computational problems and reasoning and decision-making problems in the field of intelligent design. The requirement analysis of a knowledge-based intelligent design system integrates design knowledge into case-based reasoning activities through scheme analysis, scheme evaluation, and scheme adjustment, thus achieving knowledge-based intelligent reasoning and decision-making. During the similarity matching, a new hybrid similarity measurement method is proposed to calculate the similarity of crisp and fuzzy sets. This method integrates the fuzzy set similarity theory based on the traditional similarity measurement method. A method of attribute level classification is proposed to assign weight coefficients. The attributes are divided into the primary matching and auxiliary matching levels according to the decisiveness of case matching, and the set of weight coefficients is continuously and dynamically updated through case-based reasoning learning. Then, the weighted global similarity measure is used to obtain the set of similar cases from the case database. Finally, a design example of a computer numerical control tool holder product is studied to present the practicability and effectiveness of the proposed method.

An intelligent knowledge-based chatbot for customer service

• An intelligent knowledge-based conversational agent system architecture for customer services. • A prototype system is built in a real-world setting. • Case analysis in a leading women’s intimate apparel manufacturing firm. • System prototype evaluation are satisfactory and support the contention that the system is effective. This study proposes an intelligent knowledge-based conversational agent system architecture to support customer services in e-commerce sales and marketing. A pilot implementation of a chatbot for customer services is reported in a leading women’s intimate apparel manufacturing firm. The proposed system incorporates various emerging technologies, including web crawling, natural language processing, knowledge bases, and artificial intelligence. In this study, a prototype system is built in a real-world setting. The results of the system prototype evaluation are satisfactory and support the contention that the system is effective. The study also discusses the challenges and lessons learned during system implementation and the theoretical and managerial implications of this study.

Intelligent knowledge-based system to improve injection molding process

The successful implementation of Industry 4.0 requires a comprehensive research, industrial innovation, strategic plans, and integration of engineering concepts. In this study, we described the development of a knowledge-based system to improve the traditional injection molding process using information integration. This system aims to support injection molding industries by applying advanced solutions to optimize production and increase productivity. In this context, based on the current data-driven process, new systems, technologies, and best practices are recommended to users. This work focuses on (a) simulation and generative design for optimization of manufacturing, (b) additive manufacturing, and (c) virtual reality. In this research, after a short overview of the knowledge-driven process of injection molding, the proposed knowledge-based system is presented. Then, we explained the use of the mentioned technologies in the injection molding production line. The current work, indicates that a production process can be significantly changed and revolutionized by intelligent solutions and smart devices. For instance, utilizing 3D printing in fabrication of injection molding clamps leads to 95% and 85% reductions in cost and time, respectively. Further research and experiments can improve and enrich the existing affordable and proper solutions of resulting from the proposed knowledge-based system.

From SLA to vendor‐neutral metrics: An intelligent knowledge‐based approach for multi‐cloud SLA‐based broker

Cloud computing has been consolidated as a support for the vast majority of current and emerging technologies. However, there are some barriers that prevent the exploitation of the full potential of this technology. First, the major cloud providers currently put the onus of implementing the mechanisms that ensure compliance with the desired service levels on cloud consumers. However, consumers do not have the required expertise. Since each cloud provider exports a different set of low-level metrics, the strategies defined to ensure compliance with the established service-level agreement (SLA) are bound to a particular cloud provider. This fosters provider lock-in and prevents consumers from benefiting from the advantages of multi-cloud environments. This paper presents a solution to the problem of automatically translating SLAs into objectives expressed as metrics that can be measured across multiple cloud providers. First, we propose an intelligent knowledge-based system capable of automatically translating high-level SLAs defined by cloud consumers into a set of conditions expressed as vendor-neutral metrics, providing feedback to cloud consumers (intelligent tutoring system). Secondly, we present the set of vendor-neutral metrics and explain how they can be measured for the different cloud providers. Finally, we report a validation based on two use cases (IaaS and PaaS) in a multi-cloud environment formed by leading cloud providers. This evaluation has demonstrated that, thanks to the complementarity of the two solutions, cloud consumers can automatically and transparently exploit the multi-cloud in many application domains, as endorsed by the cloud experts consulted in the course of this study.

Ontology-Based and Architecture-Based Method for the Development of Interoperable Care Systems for Type 2 Diabetes Mellitus.

Processes like the care of type 2 diabetes mellitus patients require support by information systems considering the heterogeneity of the actors from different domains involved, enabling harmonization and integration of their specific methodologies and knowledge representation approaches towards interdisciplinary cooperation. Currently, the development of systems starts from the simplified information world, ignoring the aforementioned heterogeneity and specificity of real-world processes. This paper aims to demonstrate the feasibility of developing an adaptive, interoperable and intelligent system that supports the major aspects of type 2 diabetes mellitus care based on the Generic Component Model as formal methodology for modelling universal systems. The result is a deployable solution based on a formal representation of the diabetes care system, its objectives, and the intended business process. The implemented system enables reasoning over the data, inferring medical diagnosis. The effectiveness of the inference was evaluated, obtaining an F-measure of 0.89. The methods presented in this paper helps to build high quality models based on computation-independent aspects, which enable the construction of knowledge-based adaptive, intelligent and interoperable eHealth systems.

Realization of A Knowledge-based Intelligent System for Power Dispatching Plan Management

With the expanding of power grid scale in Chinese metropolis, the task intensity of power dispatchers increases rapidly in regulation of the power system operation structure and states to deal with everyday scheduled maintenance. In this paper, we propose a knowledge-based intelligent system developed to deal with daily management of the power dispatching plans. The system will analyse all the operation state changing tasks arranged for the next day and group the plans according to their association. It will automatically check the security of each power dispatching plan and generate the corresponding dispatching-order tickets. The proposed system builds up power grid ontology knowledge and first-order logic rules and integrates techniques of knowledge reasoning, natural language understanding and network topology analysis. Application shows that it can effectively realize the day-ahead power dispatching plan management (PDPM) instead of the human dispatchers.

The effect of using negative knowledge based intelligent tutoring system evaluator software to the academic success in English language education

ABSTRACT This research was conducted to determine the effects of the using negative knowledge-based Intelligent Teaching System (ITS) evaluator software in the assessment and evaluation processes of English Teaching as a Foreign language. Experimental design with the pretest-posttest control group was used in this study. The study group consists of 67 students who are studying at the Vocational College of Technical Sciences located in Canakkale/Turkey. In this study, educational evaluator software based on correcting misconceptions developed by the researcher was used within the education process only in the experimental group. At the end of the fifth week following the 4-week experimental procedure, the academic achievement of the students was determined by a posttest. As a result, it was found to be a significant difference [t(33) = −7.13, p < .05] between the pretest-posttest academic achievement of the experimental group. In addition, there was no significant difference [t(65) = −1.15, p > .05] between the pretest scores of the experimental and control groups. There was a significant difference [t(65) = −2.25, p < .05] in posttest scores’ of control and experimental group students. As a result, using knowledge-based ITS evaluator software increased academic achievement much more than traditional education.

IKBT: Solving Symbolic Inverse Kinematics with Behavior Tree

Inverse kinematics solves the problem of how to control robot arm joints to achieve desired end effector positions, which is critical to any robot arm design and implementations of control algorithms. It is a common misunderstanding that closed-form inverse kinematics analysis is solved. Popular software and algorithms, such as gradient descent or any multi-variant equations solving algorithm, claims solving inverse kinematics but only on the numerical level. While the numerical inverse kinematics solutions are relatively straightforward to obtain, these methods often fail, due to dependency on specific numerical values, even when the inverse kinematics solutions exist. Therefore, closed-form inverse kinematics analysis is superior, but there is no generalized automated algorithm. Up till now, the high-level logical reasoning involved in solving closed-form inverse kinematics made it hard to automate, so it's handled by human experts. We developed IKBT, a knowledge-based intelligent system that can mimic human experts' behaviors in solving closed-from inverse kinematics using Behavior Tree. Knowledge and rules used by engineers when solving closed-from inverse kinematics are encoded as actions in Behavior Tree. The order of applying these rules is governed by higher level composite nodes, which resembles the logical reasoning process of engineers. It is also the first time that the dependency of joint variables, an important issue in inverse kinematics analysis, is automatically tracked in graph form. Besides generating closed-form solutions, IKBT also explains its solving strategies in human (engineers) interpretable form. This is a proof-of-concept of using Behavior Trees to solve high-cognitive problems.

Machine learning techniques for quality control in high conformance manufacturing environment

In today’s highly competitive global market, winning requires near-perfect quality. Although most mature organizations operate their processes at very low defects per million opportunities, customers expect completely defect-free products. Therefore, the prompt detection of rare quality events has become an issue of paramount importance and an opportunity for manufacturing companies to move quality standards forward. This article presents the learning process and pattern recognition strategy for a knowledge-based intelligent supervisory system, in which the main goal is the detection of rare quality events. Defect detection is formulated as a binary classification problem. The l1-regularized logistic regression is used as the learning algorithm for the classification task and to select the features that contain the most relevant information about the quality of the process. The proposed strategy is supported by the novelty of a hybrid feature elimination algorithm and optimal classification threshold search algorithm. According to experimental results, 100% of defects can be detected effectively.

5th International Conferences on Geological,Geographical, Aerospaces and Earth Sciences 2017 (5th AeroEarth 2017)

1. Professor Dr Ah-Hwee TanProfessor of Computer Science Associate Chair (Research) School of Computer Science and Engineering Nanyang Technological University - SingaporeTitle: Biologically-inspired Machine Learning Theory for Knowledge Discovery:Machine learning and knowledge discovery are two critical intertwined functions in next generation intelligent information systems. This talk will present a family of self-organizing neural networks, collectively known as fusion Adaptive Resonance Theory (fusion ART), for building intelligent knowledge-based systems with real-time learning capabilities. By extending the Adaptive Resonance Theory (ART) models, consisting of a single input pattern field, into a multi-channel architecture, fusion ART unifies a number of important neural network designs developed over the past decades. Based on a universal set of neural encoding and adaptation principles, fusion AT supports a myriad of machine learning paradigms, notably unsupervised learning, supervised learning, and reinforcement learning. In addition, domain knowledge in the form of symbolic rules can be inserted into fusion ART and subsequently refined as part of the network's dynamics, which maximizes exploitation of the existing knowledge while retaining the plasticity of exploring new solutions. Case studies will be presented, illustrating how such self-adaptive intelligent systems may be used as autonomous Non-Player Characters (NPC) in first-person shooting games, Computer Generated Forces (CGF) in air combat simulation, and human-like avatars in 3D virtual environment.Biography:Dr Ah-Hwee Tan received Ph.D. in Cognitive and Neural Systems from Boston University, Master of Science and Bachelor of Science (First Class Honors) in Computer and Information Science from the National University of Singapore. He is currently a Professor of Computer Science and the Associate Chair (Research) at the School of Computer Science and Engineering (SCE), Nanyang Technological University. Prior to joining NTU, he was a Research Manager at the A*STAR Institute for Infocomm Research (I2R), heading the Text Mining and Intelligent Agents research programmes. His current research interests include cognitive and neural systems, brain-inspired intelligent agents, machine learning, and text mining. Dr. Tan has published ten edited books/proceeding volumes and over 200 technical papers in major international journals and conferences of his fields. He holds two US patents, five Singapore patents, and has spearheaded several A*STAR projects in commercializing a suite of knowledge management and text mining software. He serves as Associate Editor/Editorial Board Member of several journals, including IEEE Access, IEEE Transactions on Neural Networks and Learning, and IEEE Transactions on SMC Systems. He is a Senior Member of IEEE, a Member of Web Intelligence (WI) Technical Committee and Web Intelligence (WI) Conference Steering Committee, and Vice Chair of IEEE CIS Task Force on Towards Human-Like Intelligence.2. Professor Dr. Ida Ayu Dwi GiriantariTitle: The role of Geothermal Energy in Indonesia: Geothermal energy is heat energy generated and stored in the Earth. Thermal energy is the energy that determines the temperature of matter. The geothermal energy of the Earth's crust originates from the original formation of the planet and from radioactive decay of materials (in currently uncertain but possibly roughly equal proportions). The geothermal gradient, which is the difference in temperature between the core of the planet and its surface, drives a continuous conduction of thermal energy in the form of heat from the core to the surface.In the 20th century, demand for electricity led to the consideration of geothermal power as a generating source. Indonesia Power Electricity tested the first geothermal power generator on 4 July 1978, at the same dry steam field where geothermal acid extraction began. It successfully lit four light bulbs. Later, in 1990, the Indonesia's first commercial geothermal power plant was built.Short Bio:Ida Ayu Dwi Giriantari is Professor in Electrical Engineering Udayana University. She hold PhD from The University of New South Wales in 2003. Her research areas are in Power Plant, Renewable Energy and Power Transformers.She is Head of Magister Program of Electrical Engineering, Udayana University.

Software System for Dynamic Athlete Health Monitoring

Workload increase in sport that leads to a greater stress level, both physical and psychoemotional, reflects on health indicators. Therefore, the early multi-component diagnostics and the establishment of prevention and treatment programmes that is based on dynamic analysis of current and previous information on health and abnormalities play a significant role in sport. The development of an intelligent knowledge-based information system is highly relevant. Such a system should include an electronic passport of sportsmen health and the database concerning the health of sportsmen that will provide an increase in efficiency of children, junior, mass, and professional sports in Russian Federation. Based on the authors’ experience of building similar types of systems, core requirements to this system, its structure and functional requirements are formed. A structure of the electronic passport of sportsman health, guidelines for maintenance and usage are suggested.

A knowledge-based intelligent decision system for production planning

This paper presents a Knowledge-Based Intelligent Decision system (KIDs) that takes information from a vision sensor within the manufacturing process and generates automatic planning/path-planning decisions in for collision avoidance in virtual CAM production. In this paper, the integration of KIDs approach is presented in the context of conventional multi-axis machining. The proposed system would be implemented in the conventional machining processes and, in this paper, its future implementation in the context of STEP-NC-based CAM systems is also discussed. This contribution provides secure solutions that can detect and avoid collisions using multiple solutions including tool diversion strategy, but also offers many other opportunities such as process change, sequence change, plan change, etc. A bi-turret machining scenario is used as a test case, where two tools working together possess a synchronization issue. Multi-tool synchronization cycles during preparation should take into account the management of materials left on the work part. The proposed solutions allow multiple opportunities where the machining units (blank groove) can be enriched with new associated data such as uncut material (identified visually) that may cause collisions during production. It is not limited however and therefore could be integrated into the STEP-NC in CAM simulation environment where the system is gaining acceptance from industry.

An integrated knowledge-based and optimization tool for the sustainable selection of wastewater treatment process concepts

The increasing demand on wastewater treatment plants (WWTPs) has involved an interest in improving the alternative treatment selection process. In this study, an integrated framework including an intelligent knowledge-based system and superstructure-based optimization has been developed and applied to a real case study. Hence, a multi-criteria analysis together with mathematical models is applied to generate a ranked short-list of feasible treatments for three different scenarios. Finally, the uncertainty analysis performed allows for increasing the quality and robustness of the decisions considering variation in influent concentrations. For the case study application, the expert system identifies 5 potential process technologies and, using this input, the superstructure identifies membrane bioreactors as the optimal and robust solution under influent uncertainties and tighter effluent limits. A mutual benefit and synergy is achieved when both tools are integrated because expert knowledge and expertise are considered together with mathematical models to select the most appropriate treatment alternative.

Artificial Intelligence Based Selection of Optimal Cutting Tool and Process Parameters for Effective Turning and Milling Operations

With the increased trend in automation of modern manufacturing industry, the human intervention in routine, repetitive and data specific activities of manufacturing is greatly reduced. In this paper, an attempt has been made to reduce the human intervention in selection of optimal cutting tool and process parameters for metal cutting applications, using Artificial Intelligence techniques. Generally, the selection of appropriate cutting tool and parameters in metal cutting is carried out by experienced technician/cutting tool expert based on his knowledge base or extensive search from huge cutting tool database. The present proposed approach replaces the existing practice of physical search for tools from the databooks/tool catalogues with intelligent knowledge-based selection system. This system employs artificial intelligence based techniques such as artificial neural networks, fuzzy logic and genetic algorithm for decision making and optimization. This intelligence based optimal tool selection strategy is developed using Mathworks Matlab Version 7.11.0 and implemented. The cutting tool database was obtained from the tool catalogues of different tool manufacturers. This paper discusses in detail, the methodology and strategies employed for selection of appropriate cutting tool and optimization of process parameters based on multi-objective optimization criteria considering material removal rate, tool life and tool cost.