Advisory Expert System Research Articles

Implementation of an Advisory Expert System for Evaluating the Quality of Asphalt Concrete

Implementation of an Advisory Expert System for Evaluating the Quality of Asphalt Concrete

A Rule-Based Expert System for Academic Advising and Guidance Toward Undergraduate Students

The provision of academic guidance to undergraduate students is a crucial and significant duty for the academic faculty in most prestigious universities. Expert systems are often regarded as one of the most successful areas in the field of artificial intelligence. The system utilizes a rule-based decision engine to assist individuals lacking experience in enhancing their skills. Nevertheless, a significant number of students are required to meet with their advisors to arrange their study schedule. This thesis is driven by the belief that the successful development of an academic advisory expert system will result in an expansion of the range and extent of challenges that students, academic staff, and other academic activities can effectively address, leading to a higher level of achievement in the university learning process. Nevertheless, the effectiveness of an advisory expert system is constrained by the caliber of the acquired knowledge, specifically in the context of academic advisory expert systems. The performance of such systems is primarily determined by the quality of the framework employed for academic expert knowledge acquisition. The objective of this study is to suggest a revision of the current knowledge acquisition framework in order to make it appropriate for implementation in higher education institutions. The proposed modification aims to create a prototype rule-based expert system specifically designed for academic advising of undergraduate students. The system's output also offers a precise and conflict-free course proposal for the undergraduate student. It functions as a mentor, providing undergraduate students with guidance and recommendations for suitable subjects based on their completed courses, prerequisites, and project scope, as defined by the student themselves. According to the empirical findings, the utilization of the proposed model for an undergraduate advisory expert system resulted in a noteworthy enhancement in performance.

Mathematical Modeling of the Risks of Stress-Related Diseases: a Review

INTRODUCTION. Stress is one of the risk factors for chronic non-communicable diseases, such as cardiovascular diseases, autoimmune disorders, mental disorders, and neurotic conditions like depression and anxiety. Therefore, it is important to predict and correct stress-related problems early.
 AIM. To assess the impact of stress on the human body, a comprehensive review of both Russian and international sources was conducted across databases like PubMed, eLibrary, and CyberLeninka for the period 2011–2023. The search terms used included “stress effect”, “predictive model”, “mathematical modeling”, “stress”, “mathematical model”, and “stress-related diseases”.
 CONCLUSION. The literature review has revealed that chronic stress exerts a significant negative impact on the human body, verifiably leading to disorders of the digestive, nervous, endocrine, cardiovascular, and immune systems. At the current stage, stress diagnosis is conducted using both questionnaire methods and instrumental techniques, each having its respective advantages and limitations. Several scientific studies emphasize the importance of mathematical modeling as a tool for simulating the effects of stress on the body and analyzing the key mechanisms predisposing to the development of pathologies. The algorithms for constructing predictive models presented in this publication may serve as a foundation for the development of an automated expert advisory system.

A Rule-Based Expert Advisory System for Restaurants Using Machine Learning and Knowledge-Based Systems Techniques

A healthy diet and daily physical activity are a cornerstone in preventing serious diseases and conditions such as heart disease, diabetes, high blood pressure, and hypertension. They also play an important role in the healthy growth and cognitive development for young and old people. Thus, this paper presents a new restaurant advisory system (RAS) using artificial intelligence (AI) techniques such as machine learning, decision tree, and rule-based methods. The proposed system makes a smart decision based on the user's input information to generate a list of appropriate meals that fit his/her health condition. For accuracy and efficiency measurement procedure in the decision-making process, a dataset from 1100 participants suffering from several diseases such as allergy, age, and body has been created and validated. The performance of the RAS was tested using Visual Basic.net Framework and prolog language. The RAS achieves an accuracy of 100% by testing 30 different live cases.

Expert advisory system determining the functional reliability of the human operator based on psychological and psychophysiological indicators

The article is dedicated to the formation of a "generalized portrait" of the condition of the human operator, which is the main component of the ergatic system, based on psychophysiological indicators, and on this basis, providing corrective advice through an expert-advisory system. A formal-constructive model "generalized portrait" is created according to the stratified description of psychological and psychophysiological indicators. Each indicator included in the strats is realized by means of the corresponding software. Each component of the "generalized portrait" is assigned to different scale types (ratio, interval, ordinal, nominative). The theory of fuzzy sets and relations is used to unify different types of scales. The decision and advice functional block of the expert-advisory system makes a decision about the functional reliability based on the fuzzy knowledge based on the current state of the human-operator.

Technology Focus: Production Monitoring (March 2022)

We are seeing an uptrend in the instrumentation of legacy wells across the world, as costs lower and business cases become obvious. Several interesting technology applications have been showcased lately about retrofitting instrumentation and control technology in legacy wells. Advances on the digital front, where data science and engineering analytics are becoming more embedded in regular production monitoring and optimization processes, have been widespread. On the gas lift side, developments in surface controllable gas lift valves, which can be deployed during the completion phase, or retrofitted in existing mandrels through well intervention, have proceeded apace. This technology can bring significant improvement in the monitoring and management of wells because the operating envelope can be significantly expanded. Other technology developments involve the use of alternative data. As the saying goes, somebody’s noise is somebody else’s data. A use case of alternative data for rod pumps uses edge computing to process electric measurements and extract features using signal processing and machine learning. This can be used to create synthetic dynamometer cards used to optimize the wells in real time and predict failures ahead of time. Similar techniques could be applicable for electric submersible pumps. Distributed acoustic sensing (DAS) has many applications, but it is challenging to fully exploit the capability because of the sheer amount of raw data coming from these sensors. The data need to be compressed using feature-extraction algorithms. Each use case may require data over different regions of the frequency spectrum covered by the sensors, so how can feature extraction be set to account for all present and future use cases? Significant advances in inflow profiling have been achieved over the past few years, correlating DAS signals against flow-loop measurements and transient simulations. These technologies are very promising, especially now that wet connect and pumpdown technology for fiber optics is gaining more attention. Digital technology for orchestrating production-optimization and reservoir-management work flows has been increasingly embedding machine-learning functionality. Despite these advances, it remains a challenge to maintain these digital solutions over the long term. The most-successful companies in this area ensure the systems are tightly integrated with business-critical work flows, such as integrated activity planning, loss management, locked-in potential management, reservoir-surveillance planning, well-work-opportunity identification, production forecasting, and production back allocation. This involves a significant management of change process. It takes time and effort, but the rewards are worth it. Recommended additional reading at OnePetro: www.onepetro.org. SPE 207879 - Expert Advisory System for Production Surveillance and Optimization Assisted by Artificial Intelligence by Carlos Mata, ADNOC Upstream, et al. SPE 201313 - Production Rate Measurement Optimization Using Test Separator and In-Well Sound Speed by Ö. Haldun Ünalmis, Weatherford SPE 203119 - Wireless Completion Monitoring and Flow Control: A Hybrid Solution for Extended Capabilities by Marcel Bouman, Emerson Automation Solutions

The Real-Time Approach to Solving the Problems for the Multicriterial Choice of Alternatives in the Knowledge Bases of Onboard Real-Time Advisory Expert Systems

The Real-Time Approach to Solving the Problems for the Multicriterial Choice of Alternatives in the Knowledge Bases of Onboard Real-Time Advisory Expert Systems

Research on Optimizing of the Failure Mode and Effects Analysis Using Advanced Expert Systems

AbstractFailure mode and effects analysis (FMEA) is one of the most efficient and effective methods of preventing defects and other nonconformities, by assessing potential failure of component functions during the fabrication process, their causes and effects, with the purpose of identifying preventive actions which can have the consequence a higher product reliability. Modern FMEA procedures represent a new approach, in seven steps, for documenting technical risks, in order to achieve a more transparent process of design and execution of a new product. By using it in a correct and consistent way, FMEA allows the timely identification and avoidance of quality problems. Beyond its indisputable advantages, FMEA also presents a series of disadvantages due to the rather complicated formalism that it imposes and the direct presence of experts. In order to eliminate the deficiencies presented above, the paper proposes a solution for a new expert system which has the scope to optimize the FMEA. The application of research in the engineering of polymeric and composite products is being considered.

Quantitative Methods to Support Data Acquisition Modernization within Copper Smelters

Sensors and process control systems are essential for process automation and optimization. Many sectors have adapted to the Industry 4.0 paradigm, but copper smelters remain hesitant to implement these technologies without appropriate justification, as many critical functions remain subject to ground operator experience. Recent experiments and industrial trials using radiometric optoelectronic data acquisition, coupled with advanced quantitative methods and expert systems, have successfully distinguished between mineral species in reactive vessels with high classification rates. These experiments demonstrate the increasing potential for the online monitoring of the state of a charge in pyrometallurgical furnaces, allowing data-driven adjustments to critical operational parameters. However, the justification to implement an innovative control system requires a quantitative framework that is conducive to multiphase engineering projects. This paper presents a unified quantitative framework for copper and nickel-copper smelters, which integrates thermochemical modeling into discrete event simulation and is, indeed, able to simulate smelters, with and without a proposed set of sensors, thus quantifying the benefit of these sensors. Sample computations are presented, which are based on the authors’ experiences in smelter reengineering projects.

On the nature of risk in the safety management of structurally complex systems

Aim. In the general case, a risk-oriented approach encompasses probabilistic methods of emergency processes and events simulation as well as deterministic methods. The use of probabilistic and deterministic estimations has been the focus of research aiming to improve safety and operational procedures. However, the experience of using probabilistic analysis only (essentially, one-criterium tool) has shown that this approach does not encompass all the required aspects of safety. The aim of the paper is to introduce (update) the definitions of the very concepts of “analysis” and “synthesis” as regards the risks for the purpose of research of safety of structurally complex systems (SCS) and design of systems for monitoring hazards and threats to their stable development thereof.Method. The paper examines – from the point of view of systems science – the method of analysis and synthesis of risks as a development tool of advanced systems for monitoring SCS safety threats. The paper compares the primary current concepts of risk management in SCS and has shown that they should be developed and improved. A type of risk functionality is proposed that allows defining a safety solution by the value of mathematical expectation of losses, with appropriate corrections taken into account.Result. The concept of “risks synthesis” is introduced as a scientific tool integrated with analysis that takes into consideration the existing connections between the elements of considered SCS in terms of a whole system in its entirety. Principles are formulated for the collection of comprehensive sets of data required for decision-making.Conclusion. The proposed approach paves the way for the development of the method of risks synthesis and suggests the development of advanced expert systems to support decision-making regarding the safety of SCS as multifunctional and multilevel systems intended for both recording and analysis of each individual case (event), and prediction of trends and preparation of prevention measures as necessary.

Artificial Intelligence Agents in the Knowledge Databases of Onboard Real-Time Advisory Expert Systems for the Typical Situations of the Functioning of an Anthropocentric Object

When developing the intelligence support systems designed for the crew of anthropocentric objects that recommend a method of solving the arising tactical-level problem for the crew (real-time targeting + construction of a method for attaining a real-time assigned target of functioning), it is extremely important to adequately understand the subject domain of the database of such systems. One way to accomplish this is to use dynamic fragments of problematic subsituations in a knowledge database with intelligence agents incorporated into them. The general structure of intelligence agents used in the knowledge databases of onboard teal-time advisory expert systems for the typical situations of the functioning of anthropocentric objects is given from the viewpoint of the “Stage” conceptual model of an anthropocentric object. Certain intelligence agents and their use in the knowledge database of a certain onboard real-time advisory expert system for the typical situation of the functioning of anthropocentric objects are considered as an example.

Wireless sensor network based monitoring system for precision agriculture in Uzbekistan

The last decades the WSN technology has been adopted by more and more scientific fields for accurate and effective monitoring of climate phenomena like air pollution, destruction phenomena like landslides, etc. It has been widely used in agriculture for field monitoring. WSN is an emerging technology, which through the research in the labs and the real deployments has been proved to be a significant and valuable tool for scientists to explore another world which is behind the various environmental phenomena using tiny sensor nodes In this article, "Expert Advisory System" was developed to improve the productivity of farmers, save their time and improve the efficiency of the crops. The system monitors real-time crop fields using wireless sensor networks and provides the necessary information to farmers via the Internet. The farmer will be required to undertake the necessary remedial action on the basis of the information received. It’s also provided that the simulation of WSN in Contiki Simulator tool. Moreover, the queing model for WSN to also considered in this work.

A Perfect Triangle with: Artificial Intelligence, Supply Chain Management, and Financial Technology

In recent years, artificial intelligence has seen increasing interest and popularity in the financial services and the other areas like supply chain management. Since the late 1970s, artificial intelligence has been developed to improve human decision-making processes and productivity in businesses, due to the ability to understand patterns and businesses phenomena, to search and analyze information and automating tasks repeatedly by humans. According to Tungsten Network, that valuable time and money are wasted on trivial supply chain related-tasks that are conducted operationally by humans. So, a business could automate some tasks to reduce wasting time with the robotic process, and machine learning algorithms are being integrated into analytics and CRM platforms to uncover information on how to better serve customers. In addition to using artificial intelligence in the supply chain, its presence in financial technology can be noted, including advanced machine learning software and expert systems that are capable of learning and performing intelligent analysis, as well as the automation of some business processes in financial. Meanwhile, given the focus on supply chain finance, which includes solutions for suppliers, manufacturers, vendors, and customers that improve financial processes in the supply chain, the industry is moving toward a revolution and a massive transformation in this approach. This paper gives an overview of artificial intelligence and the application areas of this technology and the current use in financial technology and supply chain management. This paper will also explore the benefits using Artificial Intelligence technologies in the Financial Technology and Supply Chain Management businesses.

ExSIDE: Component Based Object Oriented Expert System’s Integrated Development Environment

This paper describes the design and development of a component-based object oriented Expert System's Integrated Development Environment (ExSIDE). It is integrated with (i) a user-friendly manual and automated knowledge acquisition and management tool (ExSIDE_KAMT);(ii) an independent and customizable runtime module (ExSIDE_RTM); (iii) an object-oriented in-process Component Object Model (COM)-based inference engine (ExSIDE_IE); (iv) an object-oriented out-of-process COM-based inference engine (ExSIDE_IESvr); (v) and a PHP based inference engine (ExSIDE_PHP). ExSIDE_RTM can function independently as an Expert System Shell (ESS) and helps user to develop Expert Systems rapidly. ExSIDE_IE and ExSIDE_IES can be integrated with COM-supporting general purpose and scientific application development tools such as variants of C/C++/C#, BASIC (Visual BASIC®, REALbasic®), Java, MATLAB®, LabVIEW®, and Mathematica® to develop more advanced Expert Systems. Finally, ExSIDE_IE and ExSIDE_PHP can be used with Active Server Pages (ASP) and PHP technologies to generate web based Expert Systems. The unique framework of the ExSIDE enables rapid development of Expert Systems' on PC and web for technical and non-technical users. The overall system was developed successfully, and its usability was demonstrated via five unique Expert Systems case studies discussed in this paper.

Multiple-rank supervised canonical correlation analysis for feature extraction, fusion and recognition

A supervised two-dimensional CCA method is proposed for feature extraction.By further exploration another supervised framework is also presented.Experimental results of our methods superior/comparable to the literature. The traditional CCA and 2D-CCA algorithms are unsupervised multiple feature extraction methods. Hence, introducing the supervised information of samples into these methods should be able to promote the classification performance. In this paper, a novel method is proposed to carry out the multiple feature extraction for classification, called two-dimensional supervised canonical correlation analysis (2D-SCCA), in which the supervised information is added to the criterion function. Then, by analyzing the relationship between GCCA and 2D-SCCA, another feature extraction method called multiple-rank supervised canonical correlation analysis (MSCCA) is also developed. Different from 2D-SCCA, in MSCCA k pairs left transforms and k pairs right transforms are sought to maximize the correlation. The convergence behavior and computational complexity of the algorithms are analyzed. Experimental results on real-world databases demonstrate the viability of the formulation, they also show that the classification results of our methods are higher than the others and the computing time is competitive. In this manner, the proposed methods proved to be the competitive multiple feature extraction and classification methods. As such, the two methods may well help to improve image recognition tasks, which are essential in many advanced expert and intelligent systems.

A Research on Self-Participant Sexual Health Education Among Middle High School Students

The importance of sexual education for adolescents is gradually being promoted in China. However, sexual education isn’t yet mature and has problems such as the lack of teachers, old social consciousness, neglect of family, passive participation and low effectiveness. Middle school students are in the critical period of rapid physical and mental development, which needs a strong demand of sexual knowledge and more safe sexual incidents and information. In the past two years, we have organized medical college student volunteers to drive to four middle high schools so more than 2,000 students can participate in sexual education. These participants were assessed qualitatively and quantitatively to evaluate and improve the current model of sexual health education. All of middle school students took part in training launched by a medical college student volunteer, and those who were interested in sexual education, influential and capable took more in-depth training and provided peer education among the peripheral students. Simultaneously, we built a high school health education base including a gathered youth development micro system, social support system, expert advisory system, and reinforced the connection of college and middle school students to promote self-learning of middle school students by the Internet.

Text classification method based on self-training and LDA topic models

Supervised text classification methods are efficient when they can learn with reasonably sized labeled sets. On the other hand, when only a small set of labeled documents is available, semi-supervised methods become more appropriate. These methods are based on comparing distributions between labeled and unlabeled instances, therefore it is important to focus on the representation and its discrimination abilities. In this paper we present the ST LDA method for text classification in a semi-supervised manner with representations based on topic models. The proposed method comprises a semi-supervised text classification algorithm based on self-training and a model, which determines parameter settings for any new document collection. Self-training is used to enlarge the small initial labeled set with the help of information from unlabeled data. We investigate how topic-based representation affects prediction accuracy by performing NBMN and SVM classification algorithms on an enlarged labeled set and then compare the results with the same method on a typical TF-IDF representation. We also compare ST LDA with supervised classification methods and other well-known semi-supervised methods. Experiments were conducted on 11 very small initial labeled sets sampled from six publicly available document collections. The results show that our ST LDA method, when used in combination with NBMN, performed significantly better in terms of classification accuracy than other comparable methods and variations. In this manner, the ST LDA method proved to be a competitive classification method for different text collections when only a small set of labeled instances is available. As such, the proposed ST LDA method may well help to improve text classification tasks, which are essential in many advanced expert and intelligent systems, especially in the case of a scarcity of labeled texts.

The Development and Acceptance of Autism Advisory Expert System

The Development and Acceptance of Autism Advisory Expert System

An expert knowledge based decommissioning alternative selection system for fixed oil and gas assets in the South China Sea

The decommissioning process is constantly challenged with indeterminate risks associated with deteriorated structures and the practice of late-life decommissioning planning. The issue is further complicated by the absence of a fit-for-purpose decision framework. This work develops a knowledge based advisory expert system to address the late-life structural ambiguity of fixed jacket platforms in the selection of a best practicable decommissioning method. A review on the decommissioning industry and its growing need for data-driven management are presented. Analytic Hierarchy Process (AHP) is utilized to solve the Multi-Criteria Decision Making (MCDM) problem of choosing the best practicable decommissioning alternative. The effects and ranking of identified key structural-operation factors on the decommissioning planning process are numerically computed and discussed. The key system variables are developed with reference to established Asset Integrity Management Systems. Subject matter expert surveys are conducted on leading decommissioning and structural integrity experts in the region which are reflected in the decision matrices. A mathematical standardization technique is employed to remove inconsistencies in the intermediate decision vectors. The model is benchmarked against an actual decommissioning project in Malaysian waters which was based on conventional practices. A comprehensive framework is proposed to establish a practical working philosophy for the developed algorithm.

Upgrade of a current research information system with ontologically supported semantic search engine

Harmonising the metadata format alone does not solve the issue of efficient access to relevant information in heterogeneous environments, when different systems use different content, contextual and semantic concepts for certain entities. One such type of heterogeneous systems are also Current Research Information Systems (CRIS), which store their data primarily in local relational databases, using different formats and various local concepts.In this article, we study the possibilities and propose a new ontologically supported semantic search engine (OSSSE) which, in addition to the harmonisation of the metadata format among local CRIS systems, also ensures that the meaning of data and/or concepts that belong to various metadata entities are also harmonised. A special model of ontological infrastructure was designed, and dedicated test ontology was created alongside with a new simplified algorithm for creating ontology, the basis of which is the distinction between new and already existing classes in terms of content. Finally, we evaluated the proposed OSSSE model using a simulation of the search process on the base of 41,113 real searches within SICRIS. The obtained results show that regardless of the search situation, the proposed OSSSE is always at least as efficient as a search without ontological support in terms of precision, while recall remains the same; the improvement has been shown to be statistically significant with a high confidence interval (p<0.005).The proposed OSSSE model is able to solve the issue of harmonizing the data where different heterogeneous systems use different content, contextual and semantic concepts, which is the case in many advanced expert systems. In this manner, the more the search is carried out based on the properties described by the supporting ontology, the more the infrastructure can help a searcher. The proposed concepts, ontological infrastructure and the designed semantic search engine may well help to improve search precision in several information retrieval systems.