Rule Based (Forward Chaining/Data Driven) Expert System for Node Level Congestion Handling in Opportunistic Network

2018 ◽  
Vol 23 (3) ◽  
pp. 446-455 ◽  
Author(s):  
Ahthasham Sajid ◽  
Khalid Hussain
Keyword(s):  
Expert System ◽  
Data Driven ◽  
Opportunistic Network ◽  
Rule Based ◽  
Forward Chaining ◽  
Node Level

HISTORICAL RETE NETWORKS TO SUPPORT THE DEBUGGING OF FORWARD-CHAINING RULE-BASED PROGRAMS

1993 ◽  
Vol 02 (01) ◽  
pp. 47-70
Author(s):  
SHARON M. TUTTLE ◽  
CHRISTOPH F. EICK
Keyword(s):  
Working Memory ◽  
Data Driven ◽  
Rule Based ◽  
Historical Information ◽  
Forward Chaining ◽  
Changing Environments ◽  
Time Performance ◽  
Inference Network ◽  
One Step ◽  
Explanation System

Forward-chaining rule-based programs, being data-driven, can function in changing environments in which backward-chaining rule-based programs would have problems. But, degugging forward-chaining programs can be tedious; to debug a forward-chaining rule-based program, certain ‘historical’ information about the program run is needed. Programmers should be able to directly request such information, instead of having to rerun the program one step at a time or search a trace of run details. As a first step in designing an explanation system for answering such questions, this paper discusses how a forward-chaining program run’s ‘historical’ details can be stored in its Rete inference network, used to match rule conditions to working memory. This can be done without seriously affecting the network’s run-time performance. We call this generalization of the Rete network a historical Rete network. Various algorithms for maintaining this network are discussed, along with how it can be used during debugging, and a debugging tool, MIRO, that incorporates these techniques is also discussed.

Integrating Rule-Based Systems and Data Analytics Tools Using Open Standard PMML

2015 ◽  
Author(s):  
Yunpeng Li ◽  
Utpal Roy ◽  
Y. Tina Lee ◽  
Sudarsan Rachuri
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Data Analytics ◽  
Domain Knowledge ◽  
Prediction Models ◽  
Data Driven ◽  
Sensor Data ◽  
Formal Representation ◽  
Rule Based ◽  
Open Standard

Rule-based expert systems such as CLIPS (C Language Integrated Production System) are 1) based on inductive (if-then) rules to elicit domain knowledge and 2) designed to reason new knowledge based on existing knowledge and given inputs. Recently, data mining techniques have been advocated for discovering knowledge from massive historical or real-time sensor data. Combining top-down expert-driven rule models with bottom-up data-driven prediction models facilitates enrichment and improvement of the predefined knowledge in an expert system with data-driven insights. However, combining is possible only if there is a common and formal representation of these models so that they are capable of being exchanged, reused, and orchestrated among different authoring tools. This paper investigates the open standard PMML (Predictive Model Mockup Language) in integrating rule-based expert systems with data analytics tools, so that a decision maker would have access to powerful tools in dealing with both reasoning-intensive tasks and data-intensive tasks. We present a process planning use case in the manufacturing domain, which is originally implemented as a CLIPS-based expert system. Different paradigms in interpreting expert system facts and rules as PMML models (and vice versa), as well as challenges in representing and composing these models, have been explored. They will be discussed in detail.

scholarly journals Sistem Pakar Diagnosa Penyakit THT Menggunakan Metode Dempster Shafer

Repositor ◽  
2020 ◽  
Vol 2 (9) ◽  
Author(s):  
Doni Yulianto ◽  
Yufiz Azhar ◽  
Nur Hayatin
Keyword(s):  
Expert System ◽  
Search Method ◽  
Rule Based ◽  
Forward Chaining ◽  
Lack Of Information ◽  
The Subject ◽  
The Right ◽  
Uncertainty Method ◽  
Development Step ◽  
Nose And Throat

AbstrakBerbagai penyakit pada manusia dapat menimbulkan masalah serius jika tidak cepat ditangani, seperti halnya penyakit THT (Telinga, Hidung, dan Tenggorokan). Penderita penyakit THT di Indonesia cukup tinggi, karena masyarakat sering menganggap remeh penyakit THT dan kurangnya informasi mengenai penyakit tersebut. Perlu adanya sistem yang memberikan informasi mengenai gejala pada penyakit THT dan jenis penyakit apa saja yang diderita, serta solusi apa yang tepat untuk menangani penyakit THT. Subjek dalam penelitian ini adalah sistem pakar untuk mendiagnosa penyakit THT. Pada penelitian ini menggunakan dua metode, yaitu metode ketidakpastian menggunakan Dempster Shafer dan metode penelusuran yaitu Forward Chaining. Langkah pengembangan diawali dari pengumpulan data, lalu pembuatan Rule Based, mengimplementasikan metode, dan melakukan pengujian akurasi pakar. Hasil penelitan ini adalah sistem pakar mendiagnosa penyakit THT sebanyak 7 jenis penyakit dengan gejala sebanyak 24 jenis. Penelitian ini juga menggunakan metode Dempster Shafer untuk mendapatkan nilai kepastian berupa persentase nilai kepastian pada hasil diagnosa penyakitnya. Berdasarkan hasil pengujian pakar, dapat disimpulkan bahwa sistem pakar memiliki tingkat kesamaan dengan pakar sebesar 85% yang berarti bahwa sistem pakar ini layak untuk digunakan.AbstractVarious diseases in humans can cause serious problems if not quickly handled, such as ENT diseases (ear, nose, and throat). People with ENT disease in Indonesia is quite high, because people often consider the condition of ENT disease and lack of information about the disease. There is a system that provides information about the symptoms in ENT diseases and what types of diseases suffered, as well as what is the right solution to handle ENT diseases. The subject in this study is an expert system for diagnosing ENT diseases. The study used two methods, namely the uncertainty method using Putty Shafer and the search method that is Forward Chaining. The development step starts from collecting data, then creating a Rule Based, implementing methods, and conducting expert accuracy testing. The results of this research is a system of experts diagnose ENT diseases as many as 7 types of diseases with the symptoms as much as 24 types. This research also uses the method of putty Shafer to get certainty of the percentage value of certainty in the diagnosis of diseases. Based on expert testing results, it can be concluded that an expert system has a level of similarity with experts at 85% which means that the expert system is worthy of use.

Rancang Bangun Sistem Pakar Pendiagnosa Penyakit Demam Typhoid dan Demam Berdarah Dengue dengan Metode Forward Chaining

2012 ◽  
Vol 4 (1) ◽  
pp. 17-23
Author(s):  
Benny Wijaya ◽  
Maria Irmina Prasetiyowati
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Rule Based ◽  
Forward Chaining

Penyakit demam typhoid dan demam berdarah dengue merupakan penyakit yang umum di Indonesia. Kedua penyakit ini memiliki gejala yang hampir sama. Apabila pada saat menangani pasien, dokter salah mengetahui jenis penyakit yang diderita, hal ini dapat menyebabkan kematian. Oleh karena itu, dibuatlah Sistem pakar pendiagnosa penyakit demam typhoid dan demam berdarah dengue. Sistem pakar ini dibangun menggunakan metode inferensi forward chaining. Metode inferensi forward chaining ini diimplementasikan dengan menggunakan bahasa pemrograman C#. Sistem pakar yang dirancang dalam skripsi ini merupakan rule-based expert system. Dari hasil uji coba sistem dapat disimpulkan bahwa tingkat keakuratan sistem adalah 93,33%, rata – rata waktu yang dibutuhkan untuk mendiagnosa penyakit menggunakan sistem ini adalah 3,16 menit. Tingkat keakuratan sistem bergantung pada knowledge base yang disimpan dalam database

A TOOL THAT SUPPORTS CONSTRUCTING CHINESE EXPERT SYSTEMS

1987 ◽  
Vol 01 (03n04) ◽  
pp. 393-404
Author(s):  
LI-MIN FU
Keyword(s):  
Expert System ◽  
Knowledge Representation ◽  
General Purpose ◽  
Data Driven ◽  
Input Output ◽  
Design Decisions ◽  
Rule Based ◽  
System Building ◽  
Control Schemes ◽  
And Control

This paper describes EJAUNDICE, which is designed to be a general-purpose expert system building tool. Considerations behind a number of design decisions for purposes of generality are examined. EJAUNDICE provides several control schemes, including biphasical control with goal-directed reasoning, data-driven processing, and control blocks, and integrates rule-based, frame-based, and logic-based reasoning paradigms in its framework. The issues of knowledge representation and input/output in developing a Chinese expert system are also investigated.

scholarly journals Rancang Bangun Sistem Pakar dengan Metode Forward Chaining untuk Rekomendasi Pariwisata di Labuan Bajo Menggunakan iOS Platform

2020 ◽  
Vol 3 (3) ◽  
pp. 125
Author(s):  
Leonardo Jeffry Sutedjo ◽  
Rinabi Tanamal
Keyword(s):  
Expert System ◽  
Online Media ◽  
Process Data ◽  
Rule Based ◽  
Tourist Attractions ◽  
Forward Chaining ◽  
Tourist Destinations

Nowadays many tourists like to travel. When on vacation the user is confused about where to go and whether it's good or not. When tourists want to take a vacation and the user must choose to do a tour and it turns out that it is not good in terms of good or bad attractions. So there is encouragement to help tourists to provide recommendations for good tourist places to visit. But now with so many online media to buy tickets, but the local guide is still rampant in maintaining tourism. This location with the presence of local guides who do not understand the price and when they are in Labuan Bajo the price of the user is still not right then it makes tourists confused. The existence of a problem raises the urge to make an application of an expert system that recommends tourist attractions in Labuan Bajo by using the Forward Chaining method. In making this application uses a rule based to process data on applications on iOS. And using mcgoo is used to create and process data taken from experts. This application can help tourists to find excellent places to travel. Because the purpose of the user to travel is to have fun if the user is confused to determine the existing location then this application will help the user determine the desired tourist attractions. This application also provides several tourist options such as tours on the sea, cities, or hills/mountains. The target of visitors is always less because tourists lack the desire to travel in Labuan Bajo because of the lack of recommendations on Labuan Bajo tourism. The results of research conducted by more foreign tourists who prefer to visit Labuan Bajo because local tourists prefer tourist destinations that are still crowded, such as Bali and Lombok. So that makes tourism in Labuan Bajo not yet an attraction for local tourists.

scholarly journals An Expert System for the Prognostication of the Brain and Nerve Diseases in Children with Convulsion Signs Based on Certainty Factors (Preprint)

2018 ◽  
Author(s):  
Narges Roozitalab ◽  
Hamid Nemati
Keyword(s):  
Expert System ◽  
Naive Bayes ◽  
Neurological Diseases ◽  
Naïve Bayes ◽  
Rule Based ◽  
Forward Chaining ◽  
Two Systems ◽  
The One ◽  
Time Required ◽  
Expert Physician

BACKGROUND Nowadays, the importance of the treatment of neurological diseases in children is well known to everybody. Currently, there are only 60 neurological specialists in Iran. Due to the lack of necessary equipment, we need to send lab. Samples to the countries which have advanced laboratories for diagnosis. Therefore, a diagnostic expert system can significantly prevent deaths of children. In this paper, we design and implement a rule-based expert system in CLIPS language using certainty factors and forward chaining inference in order to help physicians. Naïve Bayes and Logistic algorithm and the experiences of the expert person have been considered to estimate the certainty factors. The accuracy of this system is 94% for detecting diseases. Performances of these two systems are compared and the results confirm that the certainty factors that are specified by the expert physician have 6.2 percent prediction improvement over the one with Naïve Bayes. It is worth noting that an automatic rule generating program has been developed to reduce the time required to produce rules manually and minimize errors. OBJECTIVE Nowadays, the importance of the treatment of neurological diseases in children is well known to everybody. Currently, there are only 60 neurological specialists in Iran. Due to the lack of necessary equipment, we need to send lab. Samples to the countries which have advanced laboratories for diagnosis. Therefore, a diagnostic expert system can significantly prevent deaths of children. In this paper, we design and implement a rule-based expert system in CLIPS language using certainty factors and forward chaining inference in order to help physicians. Naïve Bayes and Logistic algorithm and the experiences of the expert person have been considered to estimate the certainty factors. The accuracy of this system is 94% for detecting diseases. Performances of these two systems are compared and the results confirm that the certainty factors that are specified by the expert physician have 6.2 percent prediction improvement over the one with Naïve Bayes. It is worth noting that an automatic rule generating program has been developed to reduce the time required to produce rules manually and minimize errors. METHODS Nowadays, the importance of the treatment of neurological diseases in children is well known to everybody. Currently, there are only 60 neurological specialists in Iran. Due to the lack of necessary equipment, we need to send lab. Samples to the countries which have advanced laboratories for diagnosis. Therefore, a diagnostic expert system can significantly prevent deaths of children. In this paper, we design and implement a rule-based expert system in CLIPS language using certainty factors and forward chaining inference in order to help physicians. Naïve Bayes and Logistic algorithm and the experiences of the expert person have been considered to estimate the certainty factors. The accuracy of this system is 94% for detecting diseases. Performances of these two systems are compared and the results confirm that the certainty factors that are specified by the expert physician have 6.2 percent prediction improvement over the one with Naïve Bayes. It is worth noting that an automatic rule generating program has been developed to reduce the time required to produce rules manually and minimize errors. RESULTS Nowadays, the importance of the treatment of neurological diseases in children is well known to everybody. Currently, there are only 60 neurological specialists in Iran. Due to the lack of necessary equipment, we need to send lab. Samples to the countries which have advanced laboratories for diagnosis. Therefore, a diagnostic expert system can significantly prevent deaths of children. In this paper, we design and implement a rule-based expert system in CLIPS language using certainty factors and forward chaining inference in order to help physicians. Naïve Bayes and Logistic algorithm and the experiences of the expert person have been considered to estimate the certainty factors. The accuracy of this system is 94% for detecting diseases. Performances of these two systems are compared and the results confirm that the certainty factors that are specified by the expert physician have 6.2 percent prediction improvement over the one with Naïve Bayes. It is worth noting that an automatic rule generating program has been developed to reduce the time required to produce rules manually and minimize errors. CONCLUSIONS Nowadays, the importance of the treatment of neurological diseases in children is well known to everybody. Currently, there are only 60 neurological specialists in Iran. Due to the lack of necessary equipment, we need to send lab. Samples to the countries which have advanced laboratories for diagnosis. Therefore, a diagnostic expert system can significantly prevent deaths of children. In this paper, we design and implement a rule-based expert system in CLIPS language using certainty factors and forward chaining inference in order to help physicians. Naïve Bayes and Logistic algorithm and the experiences of the expert person have been considered to estimate the certainty factors. The accuracy of this system is 94% for detecting diseases. Performances of these two systems are compared and the results confirm that the certainty factors that are specified by the expert physician have 6.2 percent prediction improvement over the one with Naïve Bayes. It is worth noting that an automatic rule generating program has been developed to reduce the time required to produce rules manually and minimize errors.

scholarly journals DIAGNOSIS DETECTION OF ACUTE RESPIRATOR INFECTION WITH FORWARD CHAINING METHOD

2021 ◽  
Vol 18 (1) ◽  
pp. 73-80
Author(s):  
Tri Wisnu Pamungkas ◽  
Resi Taufan ◽  
Petrus Damianus Batlayeri ◽  
Gabriel Vangeran Saragih ◽  
Tri Retnasari
Keyword(s):  
Expert System ◽  
Input Data ◽  
Respiratory Infections ◽  
The Body ◽  
Data Driven ◽  
Acute Respiratory Infections ◽  
Early Step ◽  
Forward Chaining ◽  
Data Driven Approach ◽  
Respiratory Inflammation

Many acute respiratory infections or ARI are caused by viruses that attack the nose, trachea (breathing tube), or the lungs. It can be said that ARI is caused by inflammation that disrupts a person's breathing process. If not treated quickly, ARI can spread to all respiratory systems and prevent the body from getting proper oxygen, moreover it can cause the loss of a person's life. This research aims to diagnose ARI as an early step in practicing artificial intelligence in medicine, designing and apply an expert system that can diagnose ARI. The procedure used in this study uses forward chaining with tracking that begins with input data, and then creates a diagnosis or solution. The expert system used to diagnose acute respiratory inflammation uses the Forward chaining procedure with a data-driven approach, in this approach tracking starts from input data, and then seeks to draw conclusions, so that it can be used. diagnose the type of disease associated with the ARD disease experienced by showing the existing signs.

Sign in / Sign up

Export Citation Format

Share Document