Development Of Expert System Research Articles

Costless Expert Systems Development and Re-engineering

Costless Expert Systems Development and Re-engineering

A Comprehensive Survey of Fuzzy Logic Utilization in Different Agricultural Sectors

Fuzzy logic (FL) has emerged as a pivotal component within the realm of Expert Systems, demonstrating its efficacy in addressing real-life challenges that had previously eluded resolution. Its versatile applications span a multitude of domains, with this paper specifically delving into the successful utilization of fuzzy logic methods to tackle various agricultural issues. This comprehensive review explores instances where fuzzy logic has been seamlessly integrated into expert systems to provide innovative solutions within the field of agricultural sciences. The examined applications encompass a spectrum of challenges encountered in agriculture, showcasing the adaptability and effectiveness of fuzzy logic in addressing complex issues. This paper serves not only as an insightful examination of existing applications but also as a valuable contribution to the literature survey, laying the groundwork for future research endeavours. Particularly, it provides a foundational reference for those undertaking research aimed at developing expert systems tailored for specific crops in designated regions of our country. As a part of the broader landscape, this study acts as a cornerstone, offering a starting point for further investigations and advancements in the intersection of fuzzy logic and agricultural sciences.

Proposal for an Inspection Tool for Damaged Structures after Disasters

This study focuses on the development of a multifunctional Expert System (ES) called post-seismic damage inspection tool (PSDIT), a powerful tool which allows the evaluation, the processing and the archiving of the collected data stock after earthquakes. PSDIT can be operated by two user types; an ordinary user (engineer, expert or architect) for the damage visual inspection and an administrative user for updating the knowledge and / or for adding or removing the ordinary user. The knowledge acquisition is driven by a hierarchical knowledge model, the Information from investigation reports, and those acquired through feedback from expert / engineer questionnaires are part.

Analisis Metode Certainty Factor Pada Sistem Pakar Diagnosa Kerusakan Sepeda Motor

Motorcycles are a predominant mode of transportation in Indonesian society, comprising 84.5% of the total transportation vehicles in 2021 according to national BPS data. Despite providing convenience in mobility, motorcycles are susceptible to disturbances or damages that can hinder normal usage and potentially lead to accidents. Many motorcycle riders lack knowledge or awareness regarding potential issues with their motorcycles. This research aims to analyze the implementation of the certainty factor method in an expert system for identifying motorcycle malfunctions, with a focus on Giska Servis workshop. The certainty factor method serves as a reasoning tool to determine identification outcomes based on identified symptoms. The results of this study are expected to contribute to facilitating motorcycle riders in diagnosing symptoms of malfunctions in their vehicles. The certainty factor method offers a systematic and structured approach to identifying motorcycle issues. Through the implementation of this method, the research attempts to measure the success rate of the expert system in diagnosing malfunctions. Data from the identification results at Giska Servis workshop will be comprehensively analyzed to evaluate the accuracy and effectiveness of the certainty factor method in this context.By highlighting the success of this method, this research is expected to provide valuable insights for the development of expert systems for motorcycle issue identification. The findings of this study can serve as a guide for workshops and motorcycle users to enhance understanding and management of vehicle issues, thereby minimizing the potential for accidents and extending the lifespan of motorcycles.

Inovatyvių analitinių ir statistinių technologijų, kaip kovos su korupcija priemonės, įgyvendinimo kryptys

Relevance of the topic. The article sets out to explore the potential of innovative analytical and statistical technologies in monitoring, detecting, and counteracting corruption within state systems. The central objective is to clarify key concepts, determine the nature and content of these technologies, and analyse their applications in anti-corruption efforts. The research methodology combines the scrutiny of official documents, legislations, public authority websites, and software providers with a focus on their anti-corruption capabilities. The study reveals that analytical and statistical technologies constitute a specific form of information technology, embodied in functional information systems that fulfil organisational objectives and assist in decision-making processes. They assist primarily in the identification and prediction of probable cases of corruption or fraudulent activities, utilising mathematical methods and advanced analytical algorithms. Key areas where these technologies can significantly contribute include data collection and processing, communication and office processes, general management processes, decision-making processes, and expert systems development. The problem. Corruption remains one of the largest socio-economic issues affecting a country’s development and undermining citizens’ trust in governing structures. Innovative analytical and statistical technologies such as artificial intelligence, machine learning, big data analysis, and others, hold the potential to provide effective monitoring and detection of corrupt schemes. The purpose of this article is to determine the directions for implementing innovative analytical and statistical technologies as a tool for monitoring, detecting and counteracting corruption in the state. Research object: Application of innovative analytical and statistical technologies in the fight against corruption. The results. Innovative analytical and statistical technology is a type of information technology, which is embodied in the form of a functional information system aimed to fulfil organisational objectives and assist in decision-making processes. These technologies are considered essential tools for countering corruption at the state level, primarily by identifying and predicting probable cases. The application of mathematical methods and advanced analytical and statistical algorithms can reveal inconsistencies and potential corruption or fraudulent activities. Counteracting corruption has also benefitted from advancements in information technologies. Currently, efforts to counter corruption focus on digital government services, crowdsourcing platforms, transparency portals, big data, and artificial intelligence. In this context, there are five key areas where analytical and statistical technologies can contribute: data collection and processing, communication and office processes, general management processes, decision-making processes, and the development of expert systems. Keywords: corruption counteraction, artificial intelligence, machine learning, information

Design and development of a fuzzy explainable expert system for a diagnostic robot of COVID-19

<p><span lang="EN-US">Expert systems have been widely used in medicine to diagnose different diseases. However, these rule-based systems only explain why and how their outcomes are reached. The rules leading to those outcomes are also expressed in a machine language and confronted with the familiar problems of coverage and specificity. This fact prevents procuring expert systems with fully human-understandable explanations. Furthermore, early diagnosis involves a high degree of uncertainty and vagueness which constitutes another challenge to overcome in this study. This paper aims to design and develop a fuzzy explainable expert system for coronavirus disease-2019 (COVID-19) diagnosis that could be incorporated into medical robots. The proposed medical robotic application deduces the likelihood level of contracting COVID-19 from the entered symptoms, the personal information, and the patient's activities. The proposal integrates fuzzy logic to deal with uncertainty and vagueness in diagnosis. Besides, it adopts a hybrid explainable artificial intelligence (XAI) technique to provide different explanation forms. In particular, the textual explanations are generated as rules expressed in a natural language while avoiding coverage and specificity problems. Therefore, the proposal could help overwhelmed hospitals during the epidemic propagation and avoid contamination using a solution with a high level of explicability.</span></p>

Topological Data Analysis for Software Test Cases Generation

The escalating development of digital technologies over the last several decades has given rise to an accompanying surge in data analysis.The software based smart applications increase the importance of behaviour analysis. Software testing of expert systems such as electronic health records, health information system and software as a medical device (SaMD) refers to detect the difference between expected behaviour and actual outcome during healthcare expert systems development. Test cases are the core source of effective software testing. Test cases generation for expert systems are discover challenges of identifying the expected behaviour of the system, where the decision logic is obtained via a data –driven paradigm. In the traditional system software object oriented expected behaviour, provide the clear test cases does not change the flow such as in the healthcare system. Intelligent software test case generation approach required for smart health systems. In this research contributes key performance indicators from massive data sets, node –link diagrams from decision trees, test cases from adjacency matrix are elaborated. The experimental results of healthcare expert systems provide empirically evidence that topological data analysis are compact contribution for software requirement validation.

Perkembangan Expert System Dalam Sistem Informasi Manajemen

The application of an expert system (expert system) which is very useful for the wider community is certainly very much needed by society today. Apart from that, this development also supports various human activities. Expert systems are also able to make recommendations for a series of user actions. The aim of this research is to determine the development of expert systems in management information systems. The research method used in this research is a descriptive research method with theoretical studies, namely a method that discusses several possibilities for solving actual problems by collecting data, compiling or classifying them, analyzing and interpreting them with existing theories and comparing them from literature sources. The research results show the development of expert systems in management information systems, namely knowledge bases, inference engines, and user interfaces. It can be concluded that expert systems help work and technological progress more quickly and the decision-making process is based on existing information and facts. Expert systems have also been used in various fields, from medical, legal, to financial. In developing an expert system, it is necessary to increase the level of knowledge related to the latest technology and information. So you can utilize modern technology to complete work more effectively and efficiently.

MENDETEKSI KERUSAKAN JARINGAN INTERNET PADA CELEBES MEDIA JARINGAN (BNET) MENGGUNAKAN ALGORITMA FUZZY LOGIC DAN NAÏVE BAYES

The use of cable internet service at home is often disrupted by problems or damage to the network. Not all cable internet service users understand how to solve problems on their internet network. While there are many ways to solve this problem, it often takes administrators and technicians a long time to find and fix the problem. Therefore, the purpose of this research is to develop an expert system that can detect and solve problems on the Celebes Media Network (Bnet) internet network. An expert system is a system that can store knowledge from an expert and use that knowledge to solve problems with the help of a computer. This expert system was created using the Fuzzy Logic method with Naive Bayes, where Fuzzy Logic can be used to change and transform the uncertainty of the value given by the user into a definite value, which is then processed and processed by Naive Bayes to calculate the weight of the user's answer. This method is particularly suitable for expert systems that require reliable safeguards to ensure the accuracy of defined values. The Expert System Development Life Cycle (ESDLC) method was used to develop this system. The benefit of this system is the ability to quickly and easily detect and solve problems on the internet network, which can assist administrators and technicians in dealing with problems on the network. The test results are then analyzed and optimized by administrators and technicians, resulting in an accuracy rate of 85% between the system and the original expert.

Web-Based Expert System for Early Diagnosis of Skin Diseases in Cats Using the Naïve Bayes Method

This journal discusses the development of a web-based expert system for early diagnosis of skin diseases in cats using the Naïve Bayes method. Skin disease in cats is a health problem that often occurs and requires fast and accurate diagnosis. This expert system is designed to assist cat owners and veterinarians in identifying potential causes of skin symptoms in cats. The Naïve Bayes method is used in this system because of its ability to process symptom data and produce predictions based on probability. Symptom data is collected from various sources and used to train a Naïve Bayes model. Next, the system allows users to enter symptoms observed in their cat, and the system will provide an initial diagnosis based on the information provided. The experimental results show that this expert system is able to provide an initial diagnosis of skin diseases in cats with a sufficient level of accuracy. This provides a great benefit to cat owners in taking early action and further veterinary consultation. Apart from that, this expert system can also be used as a supporting tool for veterinarians in the process of diagnosing skin diseases in cats. Thus, this research provides an important contribution to the development of expert systems in the field of animal health, especially in the early diagnosis of skin diseases in cats.

Development of a New Approach to Assessing the Technical Condition of Equipment for Expert Systems Development Purposes

Development of a New Approach to Assessing the Technical Condition of Equipment for Expert Systems Development Purposes

Expert System for Diagnosis Pregnancy Disorders using Forward Chaining Method Based on Android

Technology's rapid evolution has extended its impact into the healthcare field, including the development of artificial intelligence-based expert systems designed to streamline the work processes of nurses and obstetricians. In this research, we use the forward chaining method to build an android-based expert system for diagnosing fetal disorders in pregnant women. This system is made for ease of use on mobile devices by targeting pregnant women where this application provides a self-detection mechanism for pregnancy abnormalities. The test results show a high level of respondent satisfaction with this expert system application, with an average score of 90.16%, indicating a strong acceptance of the quality and functionality of the application. It can be concluded that our proposed expert system application shows a positive response from respondents and is considered successful in providing pregnancy diagnosis services independently.

PENERAPAN METODE CERTAINTY FACTOR UNTUK MENGIDENTIFIKASI PENYEBAB CACAT PADA PRODUK PLASTIC INJECTION MOLDING DI PT TENMA CIKARANG INDONESIA

This study aims to develop and implement an expert system with the certainty factor method to identify the causes of defects in products produced by plastic injection molding machines at PT Tenma Cikarang Indonesia. This research begins by analyzing data on product defects recorded over a certain period to identify patterns of defects that frequently occur. Furthermore, an analysis of the factors that influence the occurrence of defects in plastic injection molding products is carried out. The certainty factor method was chosen as the basis for developing this expert system because this method is able to combine expert knowledge in the form of rules with the level of certainty for each of these parties. The process of developing this expert system involves the stages of modeling rules based on expert knowledge related to the causes of defects in plastic injection molding products. Furthermore, the implementation of these rules is carried out to calculate the level of certainty or certainty factor. The results of this study indicate that the developed expert system is successful in identifying the causes of defects in plastic injection molding products with a high degree of accuracy. This system can provide recommendations for appropriate corrective actions to address the type of identified defects. It is hoped that the application of this expert system at PT Tenma Cikarang Indonesia can increase production efficiency, reduce the number of product defects, and improve the quality of the products produced.
 Keyword: expert system, certainty factor, product defects, plastic injection molding

“DIA SMART” Android-Based Intelligent Application Expert System for Diagnosis and Mapping of 10 Diseases at Teaching Factory Nutrition Care Center

The Basic Health Research in 2018 showed that the prevalence of non-communicable diseases including cancer, stroke, chronic kidney disease, diabetes mellitus, and hypertension increased in Indonesia. The main risk factors are metabolic factors, behavioral factors, and environmental factors. Based on the existing problems, an Android-based intelligent application for the diagnosis and mapping of 10 diseases was proposed at the Teaching Factory Nutrition Care Center. This application development used the Expert System Development Life Cycle model and Dart Language. The framework used is Flutter. This article only discusses the implementation and testing of applications made based on Android. This application was developed to perform a diagnosis based on some patients’ data. This application is used as a reference for early detection of a disease, allowing patients to take preventive measures. It can map the number of patients who had health consultations in each department. The users of the application were a doctor, nutritionist or health consultant at the Teaching Factory Nutrition Care Center. System functionality tested using Blackbox-testing showed that the application ran well.

INNOVATIVE ANALYTICAL AND STATISTICAL TECHNOLOGIES AS A TOOL FOR MONITORING AND COUNTERACTING CORRUPTION

The article focuses on exploring the directions for implementing innovative analytical-statistical technologies as a tool for monitoring and detecting corruption in the state. To achieve this goal, the author clarifies the content of key concepts, defines the essence of innovative analytical-statistical technologies, and analyzes the applications of these technologies as elements of the state’s anti-corruption policy. It is determined that modern analytical-statistical technologies are integral to information technologies, which have emerged as a separate branch of production known as the information industry. Information technologies, including analytical-statistical ones, are realized through information systems, which combine technical tools (computers, communication devices and channels, peripherals, input/output devices, etc.) with software applications that facilitate the functioning of the technical components and analytical aspects, including the mathematical component (statistics). The use of analytical-statistical technologies as a tool to counter corruption at the state level is driven by the specificity of the corruption identification process. The application of relevant technologies is possible when perceiving corruption as a form of fraud. Therefore, the search for corruption in modern society requires a comprehensive approach, utilizing mathematical, analytical, and psychophysical research methods. This approach enables not only the identification but also the prediction of probable instances of abuse. Five directions for implementing innovative analytical-statistical technologies in anti-corruption activities are identified: 1) automation of data collection and processing; 2) automation of communication and office processes; 3) automation of direct management processes; 4) automation of decision-making processes and development; 5) development of expert systems. It is noted that analytical-statistical technologies find the most implementation in four of the identified directions: data collection/processing, direct management, decision support, and expert support.

Artificial intelligence governance: Ethical considerations and implications for social responsibility

AbstractA number of articles are increasingly raising awareness on the different uses of artificial intelligence (AI) technologies for customers and businesses. Many authors discuss about their benefits and possible challenges. However, for the time being, there is still limited research focused on AI principles and regulatory guidelines for the developers of expert systems like machine learning (ML) and/or deep learning (DL) technologies. This research addresses this knowledge gap in the academic literature. The objectives of this contribution are threefold: (i) It describes AI governance frameworks that were put forward by technology conglomerates, policy makers and by intergovernmental organizations, (ii) It sheds light on the extant literature on ‘AI governance’ as well as on the intersection of ‘AI’ and ‘corporate social responsibility’ (CSR), (iii) It identifies key dimensions of AI governance, and elaborates about the promotion of accountability and transparency; explainability, interpretability and reproducibility; fairness and inclusiveness; privacy and safety of end users, as well as on the prevention of risks and of cyber security issues from AI systems. This research implies that all those who are involved in the research, development and maintenance of AI systems, have social and ethical responsibilities to bear toward their consumers as well as to other stakeholders in society.

Development of An Expert System for The Diagnosis of Kidney Disease Using the Certainty Factor Method

Kidney disease is a prevalent health issue affecting millions of people globally. Early and accurate diagnosis of kidney diseases can help in the timely and effective management of the condition. Expert systems, such as those using the Certainty Factor (CF) method, can provide doctors with valuable assistance in diagnosing kidney diseases more efficiently and accurately. This study aims to develop a kidney disease diagnosis expert system using the CF method. The developed system consists of data collection, data storage, and data processing components, with the CF method used to calculate diagnostic confidence levels and decision-making based on predetermined rules. The knowledge acquisition process was carried out by interviewing three nephrologists to obtain rules for diagnosing kidney diseases. The expert system's evaluation is conducted by comparing the system's diagnostic accuracy with a specialist doctors. The results show that the developed expert system has an accuracy rate of 85.7% in diagnosing kidney diseases. The system also has a user-friendly interface, which allows doctors to input symptoms and obtain a diagnosis quickly and accurately. The developed system has several advantages over traditional diagnosis methods. It can diagnose multiple kidney diseases simultaneously and provide a differential diagnosis, allowing doctors to choose the most appropriate treatment plan for their patients. The system also has the potential to reduce diagnostic errors and improve patient outcomes.

Sistem Pakar Diagnosa Penyakit Pencernaan Pada Manusia Dengan Menggunakan Metode Certainty Factor Berbasis Web

ABSTRACT
 
 A study was conducted to develop a traditional system at Syhifa Medika Public Clinic to overcome the lack of information and time-consuming queues at the clinic, so that people can check their digestive system conditions more easily and efficiently. The developed expert system for diagnosing digestive diseases in humans has succeeded in helping many local people in finding information on digestive diseases and providing time efficiency that is useful for people who live far from the clinic. Therefore, the expert system for diagnosing digestive diseases in humans can be a very useful alternative to improve the current health system.
 
 Keywords: Expert System, Digestion, Digestive Disease, Certainty Factor, Web

Analisis Perbandingan Metode Dempster Shafer Dan Certainty Factor Dalam Mendeteksi Penyakit Anemia


 
 
 
 Anemia is one of the nutritional disorders that are still often found in Indonesia. The Household Health Survey found that around 57% of Indonesian adolescent girls aged 10-14 years and 39.5% of women aged 15 years and over still suffer from anemia. From the high cases of anemia sufferers, researchers aim to help the community in preventing and treating patients by providing information related to the symptoms of anemia, so that researchers will create an expert system that can diagnose anemia. An expert system is a computer-based system that uses knowledge, facts, and reasoning techniques to solve problems that can usually only be solved by an expert in a particular field. The purpose of developing expert systems is actually not to replace the role of humans, but to transfer human knowledge into the form of systems, so that they can be used by many people and are not limited by time. In this study, researchers want to build an expert system that can detect anemia, where in this study the researcher will compare the Dempster-Shafer method and Certainty Factor, where the system is built using PHP programming language and MySQL database that can be accessed anytime and anywhere so as to make it easier for patients to diagnose anemia without the need to meet directly with experts.
 
 
 

Методика разработки экспертных систем с использованием графовой СУБД и онтологического подхода.

Expert systems are a complex software system that accumulates the knowledge of specialists in specific subject areas and disseminates this empirical experience for consultations with less qualified users, which reduces the number of medical errors caused, in particular, by a lack of specialist doctors in local areas and the time required for a single consultation. P u r p o s e of the study is to present a modern methodology for developing expert systems based on knowledge engineering using an ontological approach and graph databases. M a t e r i a l s a n d m e t h o d s . System analysis methods were applied during the creation of the methodology for developing expert systems, including analytical and descriptive methods, knowledge engineering methods such as structuring methods, knowledge representation, and fuzzy logic. The methodology assumes the use of a graph database as a storage for the knowledge base. R e s u l t s . The methodology describes the process of developing expert systems based on a graph database using an ontological approach. It includes the following components: a knowledge base, an intelligent editor of the knowledge base, a problem solver, and explanation subsystem. The methodology assumes the participation of a group of experts in the development of the expert system, with whom the development of two artifacts is carried out: a terminological database of the subject area and intermediate information objects describing the logic of solving the problem. Both artifacts should be formed automatically using the intelligent solver of the knowledge base, as well as filling the knowledge base, which will facilitate the subsequent maintenance of the knowledge base and keep it up-todate. According to the methodology, the solver and explanation subsystem work with a graph database, which is a means of storing the knowledge base, in order to find the optimal path in which nodes are used to solve the problem of the expert system, for example, supporting a specialist doctor in establishing a preliminary/final diagnosis, and the path is used by the explanation subsystem to argue the proposed solution of the expert system. F i n d i n g s . A methodology for creating expert systems using a graph database and an ontological approach has been developed and presented. The methodology describes the development of two components: a knowledge base and a combined component consisting of a solver and an explanation subsystem.