Development Of Expert System Research Articles

An expert system for circular economy business modelling: advising manufacturing companies in decoupling value creation from resource consumption

Shortcomings in manufacturing companies’ capabilities to execute circular economy business modelling have delayed a broader dissemination of circular business models beyond the stage of pilot projects in niche markets. Circular economy poses additional uncertainties for innovation that are not common for manufacturing companies’ traditional activities and business as usual. To cope with such challenges, they lack systematised practices and proactive advice, which are scant in available literature and approaches. The paper presents the development of the tool Circular Economy Business Modelling Expert System within manufacturing companies, intended to address these limitations. Based on systematised business modelling practices for circular economy and proactive advice on potential circular business model configurations, the expert system enhances strategic thinking for circular economy, supporting companies to come up with varied alternative business models with reasonable and viable value propositions to deploy circular benefits accordingly. The expert system was streamlined based on literature review, development, testing and evaluation with 12 practitioners from 10 companies. The paper discusses the main functionalities of the expert system and the results of its application into varied manufacturing companies. The application of the expert system has demonstrated to benefit companies with: inspiration for best practices on circular business modelling, a structured framework for confirming assumptions and a logic structure that prompts decision-making and reduces uncertainties.

Predicting workers’ unsafe workspace with fuzzy logic-based procedures; exploiting injury count in work hours

Loss due to accident in any workspace is either material or personal and its associated with costs of damages to machines and also injuries, disabilities and death to workers. In this study, a fuzzy logic based expert system called Workspace Injury Sensitive Expert System (WISES) was developed to predict the unsafe level of any workspace. Two input variables, hours-worked” and “Injury-Rate were fired into the system’s Mamdani inference engine to generate the output as “Workspace-unsafe-ratings”. Data were collected from 20 workers in 20 workspaces and were run with WISES. The outcomes were compared with values obtainable using the existing Workplace Hazards Rating Mathematical Expression (WHRME). There existed a strong relationship (correlation coefficient of 0.710) between WISES and WHRME. The WISES had a statistically significantly lower level of rating (0.50 ± 0.02, SEM= 0.007), t(38) = 1.613, p = 0.115 compared to WHRME (0.60 ± 0.30, SEM=0.004) and the prediction capacity of both the systems were not significantly different. The developed expert system can find applications in any work environment that is prone to accident. It can also enhance the planning ability of managers and decision makers in industries when assessing workplaces and/or work methods disposed to accidents.

Development of an AI-based expert system for the part- and process-specific marking of materials

Due to regulations and industry-specific standards, parts of machines, for example in the health care sector, have to be labelled. However, only a few engineering experts possess the knowledge in marking technology within manufacturing companies. Hence, the article introduces a hybrid expert system which reproduces given problem-solving abilities from experts with regard to their technological and process knowledge during the marking of materials. For the implementation, a specific method for expert system development is explained. Through a detailed analysis of the process for part- specific marking of materials at a special machine manufacturer, requirements for the expert system and the knowledge base are created. Based on the results, the architecture and functionality of the classic expert system and extension of the system by machine learning algorithms are explained and evaluated.

Обзор экспертных систем по электромагнитной совместимости технических средств

This paper presents a review of expert systems for electromagnetic compatibility of technical equipment. The history of development and classification of modern expert systems are described. The main elements and features of software architecture development are indicated, as well as examples of expert systems for electromagnetic compatibility of technical equipment for various purposes.

Theoretical-methodological and technological aspects of developing expert systems for monitoring the efficiency of horizontal drainage of irrigated lands

Theoretical-methodological and technological aspects of developing expert systems for monitoring the efficiency of horizontal drainage of irrigated lands

Формирование признакового пространства периодических временных зависимостей для систем диагностики состояния динамических объектов на примере сетчатки глаза

The technology of testing dynamically and structurally similar aircraft models for flutter in subsonic wind tunnels using information and The article addresses techniques for setting up the attribute space of informative features of periodic signals recorded at the output of a dynamic object with an unknown structure in response to rectangular testing signals of different frequencies applied to the object input. The attribute space is used in developing expert systems for diagnosing the current state of an operating dynamic object. With a great variety of possible developing faults, the simplest practical techniques involving the use of characteristic points of change in the observed time dependencies yield a limited number of features with large mutual intersection domains. To expand the attribute space, it is proposed to use the expansion of input and output signals into a Fourier series for setting up a base of additional features. The proposed features characterize, depending on the testing conditions, the object’s transferring properties in the frequency domain from changes in its amplitude and phase characteristics. The test pulse frequency and duration serve as such conditions. For the convenience of comparing the object’s frequency responses variation pattern, two special procedures are used. The first procedure allows the observed time dependencies to be reduced to a single pseudo frequency of the test signals. The second procedure uses specially formed windows for subjecting individual fragments of the observed time dependencies to a spectral analysis. It is shown that, depending on the type of the frequency responses being analyzed, the techniques for their polynomial approximation, as well as integral estimates of frequency response individual domains can be useful. The polynomial approximation makes it possible to use the coefficients of the approximating polynomials as additional features, and the integration of individual characteristic domains of the frequency responses makes it possible to introduce dimensionless relative indicators that characterize the degree of change in the frequency responses depending on the experimental conditions. The considered techniques open the possibility to select additional features that can help distinguish both separate groups of faults and individual faults in operating objects. The study results are illustrated by the examples of analyzing the changes in electroretinograms that record changes in the eye retina biopotential in response to light flashes of different frequencies.

Rancang Bangun Aplikasi Data Mining pada Penjualan Distro Bloods Berbasis Web menggunakan Algoritma Apriori

The design of this data mining application is a computerized system in the field of technology, this proves that technological developments in data processing are increasingly advanced, this can be the basis for the development of data processing systems for sales of bloods based web applications using a priori algorithms, problems in this bloods distribution cannot Minimizing the decline in sales at the Jakarta clothing event in 2019, it is necessary to evaluate the sales data, with market basket analysis or consumer shopping baskets to find out consumer shopping patterns as a reference for the sale strategy of event Jakarta clothing at the end of the year. This analysis uses a priori algorithm with the association rule method, while the SDLC (Software Development Life Cycle) method is used as the basis for developing expert systems. From the results of the study, it was found that sales data for 5 days and 7 items got the highest 100% confidence value from the itemset calculation 1,2,3 which passed the selection so that they became aware of consumer purchasing patterns and rearranged product layouts for promotion and improving the correct sales strategy.Keywords:Applications, Data Mining, Apriori Algorithms, Association Rule Method, SDLC.

Perancangan Expert System Development Life Cycle pada Sistem Pakar Forward Chaining Sebagai Media Pembelajaran

The purpose of this research is to create and develop learning media in the form of applications used for plant identification in The Division of Pinophyta. The approach used is conventional approach with Expert System Development Life Cycle (ESDLC) method from Durkin, the inference mechanism used is Forward Chaining, while the software, used for Data Base Management System (DBMS) is MySQL, Unified Modeling Language (UML), Personal Home Page (PHP). Based on the results of the application assessment test in general obtained results that this expert system application deserves to be used as a learning media with criteria for Quality of end-user interface design (55.5%), Engagement (52.3%), Interactivity (64.3 %) and Tailorability (52.4%). Conclusion, application made by Expert System Development Life Cycle (ESDLC) method, with Forward Chaining inference mechanism worthy to be used as a learning medium of plant identification in Divisio Pinophyta.
 
 Keywords: Application, Plant Identification, Learning Media, Expert System, Forward Chaining

CSGAN: Cyclic-Synthesized Generative Adversarial Networks for image-to-image transformation

The primary motivation of image-to-image transformation is to convert an image of one domain to another domain. The Generative Adversarial Network (GAN) is the recent trend for image-to-image transformation. The existing GAN models suffer due to the lack of utilization of proper synthesization objectives. In this paper, we propose a new Cyclic-Synthesized Generative Adversarial Networks (CSGAN) for the development of expert and intelligent systems for image-to-image transformation. The proposed CSGAN uses a new objective function based on the proposed cyclic-synthesized loss between the synthesized image of one domain and cycled image of another domain. The proposed CSGAN enforces the mapping from one domain to another domain more accurately by limiting the scope of redundant transformation with the help of the cyclic-synthesized loss. The performance of the proposed CSGAN is evaluated on four benchmark image-to-image transformation datasets, including CUHK Face dataset, WHU-IIP Thermal-Visible Face Dataset, CMP Facades dataset, and NYU-Depth Dataset. The results are computed using the widely used evaluation metrics such as MSE, SSIM, PSNR, and LPIPS. The experimental results of the proposed CSGAN approach are compared with the latest state-of-the-art approaches, such as GAN, Pix2Pix, DualGAN, CycleGAN, and PS2GAN. The proposed CSGAN technique outperforms all the methods over CUHK dataset, WHU-IIP dataset, NYU-Depth dataset, and exhibits promising and comparable performance over Facades dataset in terms of both qualitative and quantitative measures. The code is available at https://github.com/KishanKancharagunta/CSGAN.

Development of Expert System for Cataract Self Screening in Android Mobile Platform Based in Pamekasan City: Case Study

Cataract is one of the biggest causes of blindness in Indonesia. More than 3,0% of the population in Indonesia is becoming blind and still increasing. There are so many people who still don’t realize that they are suffering from cataracts because of lack of access to a nearby eye clinic or hospital with an opthamologist. In this research, an application in mobile platform android has been developed to help people to screen by themselves and give a recommendation of opthamologists near them for checkup. An expert had made a decision about weighted in the symptoms and diagnosis in the system using the certainty factor method. The results show the kind of cataract and its weight that was suffered by users and give recommendations to nearby eye clinics

Artificial intelligence in musculoskeletal oncological radiology.

BackgroundDue to the rarity of primary bone tumors, precise radiologic diagnosis often requires an experienced musculoskeletal radiologist. In order to make the diagnosis more precise and to prevent the overlooking of potentially dangerous conditions, artificial intelligence has been continuously incorporated into medical practice in recent decades. This paper reviews some of the most promising systems developed, including those for diagnosis of primary and secondary bone tumors, breast, lung and colon neoplasms.ConclusionsAlthough there is still a shortage of long-term studies confirming its benefits, there is probably a considerable potential for further development of computer-based expert systems aiming at a more efficient diagnosis of bone and soft tissue tumors.

Международный научно-практический журнал «Программные продукты и системы» — более 30-ти лет в цифровом пространстве научных знаний

The international scientific and practical journal “Software products and systems” was organized in 1988 on the initiative of the General Director of the NGO “Centerprogramsystem” prof. V. P. Tikhomirov (Tver) and V. M. Silina (Moscow), head of the Main editorial office of the international journal “Problems of management theory and practice”. In the period from 1988 to 2018, the editor-in-chief of the journal was academician S. V. Yemelyanov. Since 2020, the international editorial Board, which includes well-known scientists in the field of information technologies of the Russian Federation, Belarus, Ukraine, Azerbaijan, Germany, Finland, Mexico, Vietnam and other countries, is headed by academician G. I. Savin. Associate members of the editorial Board are the national research University “MEI” (Moscow), the technological Institute of the southern Federal University (Taganrog), TvSTU and the research Institute “Centerprogramsystem”. The journal is reviewed in the bibliographic databases of RSCI, CrossRef, and is included in the List of leading peerreviewed journals and publications recommended by the higher attestation Commission of the Russian Federation for publication of research results in the defense of candidate and doctoral theses in the fields of computer Science, computer engineering and management (05.13.XX). The journal publishes the results of original research in the field of information technologies, applied artificial intelligence systems, including the development of databases and knowledge bases, expert and training systems, decision support systems and image recognition, multiagent systems, data mining systems, neural networks, robots, and telecommunications systems. However, the subject area of research is not limited. The magazine “Software products and systems” regularly acts as an information sponsor for international and national scientific and technical events in the field of information technology and artificial intelligence.

Automated expert support complex based on a machine learning semantic processor

The article is devoted to the creation of an intelligent software complex of a self-learning automated expert system for registering, accounting and executing incidents based on artificial intelligence. The creation of the software complex includes the development of an advisory support expert system based on a semantic processor for speech recognition and synthesis, which allows implementing the first line advisory support based on artificial intelligence. The obtained expert system consists of the following blocks: an input data flow module–GSM (voice address); a semantic processor–the system core in which, using special designed algorithms, the input data flow is converted into the language understood by the system for subsequent intelligent processing set by the specified skills, based on self-learning algorithms; a semantic processor language module with a protocol of recognition, analysis and extraction of input and output data context; an incident registration module with the accounting system in the Redmine platform; PostgreSQL skills database; a manual learning module and an output data flow module. The developed expert support digital ecosystem will allow to completely automate the receipt, processing, registration, accounting and decision-making of incoming requests. The complex implementation can replace first line support call centers and will have a significant economic effect on retail, banking, and consulting systems.

METHODOLOGICAL SYSTEM OF TRAINING FUTURE INFORMATICS TEACHERS ON THE BASICS OF ARTIFICIAL INTELLIGENCE

The article presents a methodological system for training future Informatics teachers on the basics of artificial intelligence. Currently, artificial intelligence is being used in various fields, from the presentation of knowledge to the development of expert systems, intellectual games and robotics tools. In this case, there is a problem of developing a methodological system for training future Informatics teachers based on elements of artificial intelligence in pedagogical educational institutions. This proposed to solve this problem using the method of problem-based learning and combining theory with practice from the point of view of critical thinking technology. Modern analytical platforms, intelligent training systems, and expert systems are used as training tools. The educational content of the basics of artificial intelligence is built on the basis of systematic, fundamental and interdisciplinary approaches. This made it possible to determine the goals of teaching future computer science teachers the basics of artificial intelligence, reveal the requirements for the formation of concepts in the field of artificial intelligence, identify the basic knowledge system that allows you to teach elements of artificial intelligence in a computer science course. The article describes the results of the implementation of the methodological system for training future computer science teachers on the basics of artificial intelligence in the educational process.

Hybrid clustering system using Nystagmus parameters discrimination for vestibular disorder diagnosis.

The control of clinical manifestation of vestibular system relies on an optimal diagnosis. This study aims to develop and test a new automated diagnostic scheme for vestibular disorder recognition. In this study we stratify the Ellipse-fitting technique using the Video Nysta Gmographic (VNG) sequence to obtain the segmented pupil region. Furthermore, the proposed methodology enabled us to select the most optimum VNG features to effectively conduct quantitative evaluation of nystagmus signal. The proposed scheme using a multilayer neural network classifier (MNN) was tested using a dataset involving 98 patients affected by VD and 41 normal subjects. The new MNN scheme uses only five temporal and frequency parameters selected out of initial thirteen parameters. The scheme generated results reached 94% of classification accuracy. The developed expert system is promising in solving the problem of VNG analysis and achieving accurate results of vestibular disorder recognition or diagnosis comparing to other methods or classifiers.

Medical expert system for low back pain management: design issues and conflict resolution with Bayesian network.

The paper focuses on the development of a reliable medical expert system for diagnosis of low back pain (LBP) by proposing an efficient frame-based knowledge representation scheme and a suitable resolution logic with conflicts in outcomes being resolved using Bayesian network. Considering that LBP is classified into many diseases based on different pain generators, the proposed methodology infers non-conflicting LBP diseases sorted according to their chances of occurrence. A satisfactory clinical efficacy (average relative error - 0.09, recall 74.44%, precision 76.67%, accuracy 71.11%, and F1-score 73.88%) of the proposed methodology has been found after validating the design with empirically selected thirty LBP patient cases. Constraining that an inferred disease having chance of occurrence, prior to pathological investigations, below 0.75 (as set by four pain specialists) is not accepted clinically; the design can correctly identify, on average, 74.44% of actual diagnosis; and 76.67% of inferred diagnosis is included in actual diagnosis. With the predicted chance of occurrence being lower than 0.75 by a fraction of 0.09 on average, the proposed design performs well for 73.88% cases detecting 71.11% inferred outcomes as accurate. The design offers homogeneity to the actual outcomes, with the chi-squared static being calculated as 11.08 having 12 as degree of freedom. Graphical abstract.

Development of rule-based expert system for conceptualisation of poster design

This study discusses development of rule-based expert system (R-ES) as a decision support for poster design conceptualisation. Users of the proposed expert system are novice poster designers while the experts are senior and professional poster designers. Development of R-ES is started by defining type of poster to be designed. Based on professional poster designer’s experiences, 5 types of poster have been proposed, which are advertising, information, movie, event and political poster. A knowledge-based system consists of IF-THEN rules have been developed by the experts to determine poster design concept that are colour combination, poster size, font type and size, expert note, and poster sample. Result of this study is an online expert system for conceptualisation of poster design that could be widely used by novice poster designers without constrained by space and time.

Trends in Expert System Development: A Practicum Content Analysis in Vocational Education for Over Grow Pandemic Learning Problems

The impact of the COVID-19 has emerged as a varied issue, ranging from the economy, society's social order, and edu­cation, especially when social and physical distancing have been introduced in various areas of community activities as one of the prevention. It has also affected the tertiary edu­cation institution where learning activities should be con­ducted from and at home, forcing the higher education communities to shift learning from conventional learning to online learning. Needless to say, learning that does not re­quire practicum is easier compared to learning that needs it. Consequently, it is a new task for lecturers to ensure the meaningful practicum learning implementation is con­ducted as it should be. Therefore, the creation of expert systems for practicum learning in vocational education is urgently required in online learning. The aim of this litera­ture review was to examine the development of an expert system used at practicum learning in tertiary institutions and how the design of learning systems could be integrated into vocational education in facing the COVID-19 pandemic. The method used was the literature study, through research stages starting from the finding and selection of articles and journals with relevant topics, and then analysing the data. The findings of this study was to identify the development of an expert program in an attempt to promote practicum learning using e-learning in the midst of the COVID-19 pan­demic outbreak.

Design and Development of a Methodology Based on Expert Systems, Applied to the Treatment of Pressure Ulcers.

The medical treatment of chronic wounds, pressure ulcers in particular, burdens healthcare systems nowadays with high expenses that result mainly from their monitoring and assessment stages. Decision support systems applied within the ‘remote medicine’ framework may be of help, not only to the process of monitoring the evolution of chronic wounds under treatment, but also to facilitate the prevention and early detection of potential risk conditions in the affected patients. In this paper, the design and definition of a new decision-support methodology to be applied to the monitoring and assessment stages of the medical treatment process for pressure ulcers is proposed. Built upon the use and development of expert systems, the methodology makes it possible to generate alerts derived from the evolution of a patient’s chronic wound, by means of the interpretation and combination of data coming from both an image of the wound, and the considerations of a healthcare professional with expertise in the subject matter. Some positive results are already shown regarding the determination of the ulcer’s status in the tests that have been carried out so far. Therefore, it is considered that the proposed methodology might lead to substantial improvements regarding both the treatment’s efficiency and cost savings.

Develop of Expert System for Early Diagnosis and Prevention of Mastitis

Expert systems (ES) are one of the prominent research domains of artificial intelligence (AI). They are applications developed to solve complex problems in a particular domain, at the level of extra-ordinary human intelligence and expertise. We present design and development of expert system for data collection, analysis and decision making for early mastitis detection. It focuses on both milk quality and animals health.