Design Of Expert System Research Articles

A Multi-Objective Evolutionary Algorithm Model for Product Form Design Based on Improved SPEA2

As a Kansei engineering design expert system, the product form design multi-objective evolutionary algorithm model (PFDMOEAM) contains various methods. Among them, the multi-objective evolutionary algorithm (MOEA) is the key to determine the performance of the model. Due to the deficiency of MOEA, the traditional PFDMOEAM has limited innovation and application value for designers. In this paper, we propose a novel PFDMOEAM with an improved strength Pareto evolutionary algorithm 2 (ISPEA2) as the core and combining the elliptic Fourier analysis (EFA) and the entropy weight and technique for order preference by similarity to ideal solution (entropy-TOPSIS) methods. Based on the improvement of the original operators in SPEA2 and the introduction of a new operator, ISPEA2 outperforms SPEA2 in convergence and diversity simultaneously. The proposed model takes full advantage of this superiority, and further combines the EFA method’s high accuracy and degree of multi-method integration, as well as the entropy-TOPSIS method’s good objectivity and operability, so it has excellent comprehensive performance and innovative application value. The feasibility and effectiveness of the model are verified by a case study of a car form design. The simulation system of the model is developed, and the simulation results demonstrate that the model can provide a universal and effective tool for designers to carry out multi-objective evolutionary design of product form.

Design of Expert System for Early Identification for Suspect Bullying On Vocational Students by Using Dempster Shafer Theory

Bullying is negatif agresif character to cover and hurt someone physically or physicology continoustly to act hard to other people who is lower than him. Some parents ignore this problem because of unknowing about the result of this problem that give negative effect to their children and other people. This system will identificate early the begining bullying character for teenager by knowing the kinds of the bullying character base on rate of presentation of higest quartel who has bullying character. This system designed by using Dempster-hafer teory to know the begining of bullying character by using basic knowledge and forward chaining technic to know more about basic knowledge. Counting of this metodh by combining some symptoms that happen on children by calculating the possibillity disturbing by rating the symptoms from 0 to 1. Implementation of this system use PHP program and My SQL database. The black box trial on the consultation modul is done by using 12 instruments of tria and found error value 0,88 % on the system and result of the trial expert system have suitable accuration 84 % , so we can conclude that this expert system for early identification for suspect bullying on teenager good for use.

ANALISIS DAN PERANCANGAN SISTEM PAKAR DIAGNOSA PENYAKIT TANAMAN SAWIT PADA PT. ANDALAN ALAM SUMATRA MENGGUNAKAN METODE FORWARD CHAINING

Palm oil is one of the plantations that are often attacked by disease. The disease of this planting plants isspread rapidly, so it must be in the culprit. To know the oil palm plant disease or not to be diagnosed tothe symptoms found in the field. At PT Andalan Alam Sumatra there are still many obstacles in perosesdiagnosis of oil palm disease, while the constraint that occurs is the lack of knowledge of employees ofpalm oil disease, still need help from plantation managers in diagnosing disease. Peroses diagnostics willbe hampered when plantation managers are not in place or being out of service, this also greatly burdenthe work of a planter manager. Because of peroses peroses diagnosis of palm oil disease disease perosesdisease prevention also become obstructed. As a result, palm oil disease spreads and disrupts theproductivity of oil palms, causing death in palm oil. Therefore it is necessary media that can helpovercome this as for medianya system of plant disease diagnosis by applying Forward Chaining method.This expert system of plant disease diagnosis using object-based modeling techniques to describe theanalysis and system design, namely in the form of use case diagrams, class diagrams, and activitydiagrams. The output of this research is the design of prototype system Analysis And Design ExpertSystem Diagnosis Disease Palm Plant At PT. Andalan Alam Sumatra With Forward Chaining Method

A new method of diagnosing athlete's anterior cruciate ligament health status using surface electromyography and deep convolutional neural network

Anterior cruciate ligament (ACL) injury is one of the most common injuries in high-demand sports. Due to long-term treatment of this injury, diagnosing recovery of ACL becomes important, particularly six months postoperatively. The purpose of this research is to provide a cost-effective and intelligent method to diagnose ACL's health status. For this purpose, 11 healthy and 27 ACL-injured subjects have been selected. In the proposed method, the athlete performs a single-leg landing protocol and surface electromyographic signals (EMG) are taken from eight lower limb muscles. Then, time–frequency distributions of EMG signals in each landing are calculated as an image, using pseudo Wigner–Ville distribution (PWVD), which are the inputs of a deep convolutional neural network (DCNN). By time–frequency analysis, it has been made clear that any change in ACL's health status causes changes in the extent of energy spread in PWVD, distribution volume, frequency content, damping rate and the peak value of EMG signals. In this research, a new relationship between ACL's health status and lower limb muscles activity is introduced through monitoring of PWVD images. The result indicates that the designed expert system is able to diagnose ACL's health status with 95.8% accuracy. In this non-invasive method, PWVD images of EMG signals are chosen as the inputs of DCNN, instead of MRI images, which, in addition to their high accuracy in diagnosing, are safer and much cheaper. The presented method can play an important role in assessing the recovery process, six months postoperatively and after that.

An expert system for checking the correctness of memory systems using simulation and metamorphic testing

During the last few years, computer performance has reached a turning point where computing power is no longer the only important concern. This way, the emphasis is shifting from an exclusive focus on the optimisation of the computing system to optimising other systems, like the memory system. Broadly speaking, testing memory systems entails two main challenges: the oracle problem and the reliable test set problem. The former consists in deciding if the outputs of a test suite are correct. The latter refers to providing an appropriate test suite for determining the correctness of the system under test.In this paper we propose an expert system for checking the correctness of memory systems. In order to face these challenges, our proposed system combines two orthogonal techniques – simulation and metamorphic testing – enabling the automatic generation of appropriate test cases and deciding if their outputs are correct. In contrast to conventional expert systems, our system includes a factual database containing the results of previous simulations, and a simulation platform for computing the behaviour of memory systems. The knowledge of the expert is represented in the form of metamorphic relations, which are properties of the analysed system involving multiple inputs and their outputs. Thus, the main contribution of this work is two-fold: a method to automatise the testing process of memory systems, and a novel expert system design focusing on increasing the overall performance of the testing process.To show the applicability of our system, we have performed a thorough evaluation using 500 memory configurations and 4 different memory management algorithms, which entailed the execution of more than one million of simulations. The evaluation used mutation testing, injecting faults in the memory management algorithms. The developed expert system was able to detect over 99% of the critical injected faults, hence obtaining very promising results, and outperforming other standard techniques like random testing.

Augmented features to detect image splicing on SWT domain

Nowadays, image forgery is a widespread problem in our lives. Image splicing is one method of forging images, and it has become a common tool for malicious users to modify images. Human eyes cannot perceive forgery operations on forged images; therefore, designing an expert system for detecting image authenticity has become a necessity. The splicing method causes some distortions that can be used to detect forgery on a tampered image. In this work, an expert system is designed that uses the statistical and textural properties of an image in a hybrid manner to detect forgery. The method extracts properties (statistical and textural) from high-level sub-bands of stationary wavelet transform (SWT) domain. SWT is used to obtain image's details in a multi scale manner in frequency domain. The method extracts statistical features from three sub-bands via Markov model, which generates transition probability matrices and then obtains Haralic's textural features based on gray level co-occurrence matrices. Features from each sub-bands are concatenated to form final feature vector. This expert system is the first in the literature to be designed using textural and statistical features together. Finally, the designed expert system classifies images as authentic or forged using SVM. The system's performance was evaluated using three publicly available image databases and tested for different attack types. The experimental results show that using statistical and textural features in a hybrid manner improves detection accuracy when compared to similar works in the literature. The main impact of the proposed system is its high accuracy and robust detection of different attack types.

Expert System Design for Web-Based Lecturer Scheduling

In this study contains an expert system design for web-based lecturer scheduling that can facilitate the Chair of the Study Program in scheduling courses for lecturers. This system was built using Notepad ++ software, XAMPP with HTML, CSS, JavaScript, PHP and database programming languages using MYSQL. Subjects in this study are expert system applications as the media schedule lecturer subjects. Data collection in this study uses the interview method and questionnaire method. This system is built with the Waterfall model namely analysis, design, coding and testing. This application in the inference engine used is the forward chaining method. The results of this study are expert system applications to schedule lecturers’ subjects according to the questionnaire data. Based on the results of the trial it can be concluded that this expert system application can facilitate the Chair of the Study Program in accurately scheduling courses, classes, days, hours, space, and lecturers

Design and implementation of the fuzzy expert system in Monte Carlo methods for fuzzy linear regression

In this study, fuzzy expert system (FES) in Monte Carlo (MC) method, which is used for estimating fuzzy linear regression model (FLRM) parameters, is applied to determine the parameter intervals, for the first time in the literature. MC method in estimating FLRM parameters is a new field of study that is very useful and time saving. However a major problem might occur in determining the parameter intervals from which the regression model parameters are supposed to come. If the intervals are calculated too large, FLRM error will be very large. Accordingly, the actual model parameters will not be obtained if the intervals are calculated too narrow. This drawback has not been addressed in the literature before and only optimization methods have been applied to achieve the best interval values. In this article, the FES is used for the first time in order to solve the problem in parameter estimation process for the FLRM in the field of statistics. For this purpose, the difference between the fuzzy observation value and fuzzy estimation value’s support set (W) is taken into account. The most appropriate intervals calculated for the parameters are those that make W as small as possible. Thus, FES is designed to determine the best intervals for the model parameters. The system knowledge base is composed of 7 fuzzy rules. As a result, it is deduced that the FLRM parameter estimates obtained from the MC method using FES are very close to the real values. The real impact of this paper will be in showing the applicability of FESs in order to solve problems that we encounter in the field of statistics by the help of linguistic expressions. Moreover, these outcomes will be useful for enriching the studies that have already focused on FLRMs and will encourage researchers to use FES to solve problems in statistics. To sum up, this study demonstrates that FESs which is used in technological devices and makes our lives easier can also be used in solving problems that we confront in the field of statistics efficiently with using linguistic expressions like human inference system.

Dynamic Expert System Design for the Prediction of Attention Deficit and Hyperactivity Disorder in Childhood

In this study, for the first time, a Dynamic Expert System was developed to predict attention deficit and hyperactivity impairment in childhood. In this context, the decision-making process, which requires complex and experienced field experts to diagnose the disease, has been transferred to the developed expert system. The subject of the study was determined as prediction of attention deficit and hyperactivity disorder, which is one of the most common psychiatric disorders of childhood. The developed Dynamic Expert System consists of three basic parts, which are the knowledge base, the inference mechanism and the description unit. Data clusters are recorded as attributes and records in the knowledge base. While attributes are determined by field experts, records are composed of clinical patient data received from the Gazi Hospital, Department of Pediatric Mental Health and Diseases. Ensuring the dynamic renewal of the rule base is the most important characteristic of the study using the Naive Bayes Algorithm in the inference mechanism of the developed system. In this way, when the system encounters a new situation that is not previously encountered, it can take advantage of the existing rules and guess which class the rule belongs to. With real data, the system has been trained; and its performance was tested. As a result of this study, accuracy was determined to be 88.62%; precision was determined to be 89.2%, recall was determined to be 88.6%, f-measure was determined to be 88.6% and ROC area value was determined to be 89.8%. It was observed that the performance of the system was quite high compared to the model performance criteria.

Presenting an expert system for early diagnosis of gastrointestinal diseases

The gastrointestinal tract works by taking food, mechanically destroying, digesting, absorbing and disposing of undigested substances. For many gastrointestinal specialists, it is the second-largest human brain because it affects other organs and plays an important role in strengthening the body's immune system. Due to the importance of the gastrointestinal tract and its direct relationship with human daily life, timely diagnosis of this disease has a major role in the treatment and improvement of human beings. Major developments in diagnostic techniques during the past years have led to this update about indications for assortment and performance of currently available tests. So, this article presents an expert system for diagnosing the type of gastrointestinal disease and determining the type of tests needed to definitively diagnose the disease. The outputs of the expert system are designed according to the patient's symptoms. The VP-Expert shell is used to design this expert system. The results show that the designed expert system can be useful in cases where immediate access to the expert is not possible.

Expert System for Milk and Animal Monitoring

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. In the paper is presented 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.

A Neuro-Fuzzy Case Based Reasoning Framework for Detecting Lassa Fever Based on Observed Symptoms

Lassa fever is an acute viral haemorrhagic fever that is awfully infectious through infected rodents in the mastomysnatalensis species that are complex reservoirs capable of excreting the virus through their urine, saliva, excreta and other body fluids to man. The virus is a single stranded RNA virus belonging to the arenaviridae family. It presents no definite signs or symptoms and clinical analysis is often problematic especially at the early onset of the disease. Accurate diagnosis requires highly specialized laboratories, which are expensive and not readily available to the entire populace. Early diagnosis and treatment of Lassa fever is very vital for survival. In this study, we identified that fuzzy logic and rule-based techniques are the only artificial intelligence supported approach that has been used to develop an expert system for diagnosing the dreaded Lassa fever as an alternative to laboratory methodology. It is noted that rule-based is not an efficient technique in the designing expert systems based on its shortcomings such as opaque relations between rules, ineffective search strategy, and its inability to learn; while the fuzzy based technique does not also support the ability to learn but good in areas such as knowledge representation, uncertainty tolerance, imprecision tolerance, and explanation ability. Based on these information gathered, the authors decided to design a hybridized intelligent framework driven by the integration of Neural Network (NN), Fuzzy logic (FL) and Case Based Reasoning (CBR) based on their individual strengths put together in order to proffer a quick and reliable diagnosis for Lassa fever infection using observed clinical symptoms that could aid medical practitioners in decision making.

Ant inspired Monte Carlo algorithm for minimum feedback arc set

It is well known that Minimum Feedback Arc Set is in a general case NP-complete. There are different kinds of exact, heuristic and approximation algorithms for solving this problem, but currently there is only one published Monte Carlo algorithm which solves Minimum Feedback Arc Set in polynomial time with arbitrary probability. To further advance the state of the art we have devised new and improved ant inspired Monte Carlo algorithm which on average has 20% faster empirical running time. Due to a learning mechanism the new algorithm also achieved 511% faster convergence in terms of median and 158% improvement in terms of arithmetic mean. This has been done while at the same time maintaining the ability of the algorithm to find optimal solution with arbitrary probability. In addition, the new and improved ant inspired algorithm has substantially improved convergence consistency. A tighter probability bound has also been calculated for the Monte Carlo algorithm. The aforementioned contributions have their significance in a design and implementation of expert and intelligent system. Considering a wide presence of MFAS in a variety of areas the obtained results are significant in other applications as well.

An expert decision support system for sandstone acidizing design

Based on the fundamentals of expert system (ES) and decision support system (DSS), we developed an integrated system, the expert decision support system (EDSS), to design and optimize sandstone acidizing. The new system combines knowledge of the ES with DSS models to facilitate decision-making for qualitatively and quantitatively acidizing sandstone reservoirs; this approach greatly strengthens the system’s working capability and widens its applicable range. This article introduces the design principle, system structure, functional modules, multi bases, and development process of the EDSS. We illustrate the representation method of the expert’s knowledge, establishing the knowledge decision tree, and creating quantitative mathematic models for decision support and inference process via different reasonings (rule-based vs. case-based). These methods and methodologies worked together to operate different functional modules for inference from both knowledge and calculation. The EDSS connected different design considerations of the acidizing technology. A field test case study proved that the proposal provided by the EDSS was very efficient.

Expert System Design to Diagnos of Virus Infection Disease in Children with Certainty Factor Method

Information technology that continues to grow can be utilized in the service of human life in the world of health. Infectious disease is a disease that is quite dangerous for children. Delay in treating the disease can cause more severe disease. So that a system that has the ability to diagnose diseases is needed. Expert systems are computer applications that try to adopt human knowledge to computer programs like an expert. Many expert systems have been developed including diagnosing stroke and others. In this study, the expert system was used as a tool to diagnose viral infectious diseases in children. In this study we will discuss the making of expert system applications for infectious viral infections in desktop-based children using the certanty factor (CF) method. This expert system was built to diagnose tropical diseases caused by viral infections. This system provides information about diagnosis, treatment and prevention. Methods of tracking the symptoms of each patient, matching them with existing rules, and producing a diagnosis based on the knowledge base. Knowledge based from this expert system was obtained from several pediatricians and reference books. This expert system application is designed based on desktop and MySQL database as data storage. The resulting output is the value of the possibility of child disease based on the symptoms that have been given by the user. The magnitude of the probability value is the result of calculations using the certainty factor method.

Design and Application of a Multi-Variant Expert System Using Apache Hadoop Framework

Movie recommender expert systems are valuable tools to provide recommendation services to users. However, the existing movie recommenders are technically lacking in two areas: first, the available movie recommender systems give general recommendations; secondly, existing recommender systems use either quantitative (likes, ratings, etc.) or qualitative data (polarity score, sentiment score, etc.) for achieving the movie recommendations. A novel approach is presented in this paper that not only provides topic-based (fiction, comedy, horror, etc.) movie recommendation but also uses both quantitative and qualitative data to achieve a true and relevant recommendation of a movie relevant to a topic. The used approach relies on SentiwordNet and tf-idf similarity measures to calculate the polarity score from user reviews, which represent the qualitative aspect of likeness of a movie. Similarly, three quantitative variables (such as likes, ratings, and votes) are used to get final a recommendation score. A fuzzy logic module decides the recommendation category based on this final recommendation score. The proposed approach uses a big data technology, “Hadoop” to handle data diversity and heterogeneity in an efficient manner. An Android application collaborates with a web-bot to use recommendation services and show topic-based recommendation to users.

Design of expert system for owning motorcycle with Naive Bayes classifier

Nowadays, motorcycle is widely used by many people. There are several major categories of motorcycles in Indonesia such as automatic gear (matic), moped, and sport. Each motorcycle has a lot of types; even each type may be divided into several sub types. Generally, a sub-type of motorcycle is distinguished by features and spare parts that determine the sales price of motorcycle. Therefore, customers have trouble to determine the type of motorcycle that suits their needs and wishes. This article proposes a model that helps customer decide the motorcycle in accordance with the following criteria: gender, occupation, income, and age. This model compares and calculates a new customer with many previous customers with Naive Bayes Classifier. In evaluation phase, this model shows that 89.12% of customers feel satisfied. It means that this method valuable implement on this model. In further work, customer’s advance criteria like color, durability, and resale price should increase the customer’s satisfaction.

Maintainable process model driven online legal expert systems

Legal expert systems are computer applications that can mimic the consultation process of a legal expert to provide advice specific to a given scenario. The core of these systems is the experts’ knowledge captured in a sophisticated and often complex logic or rule base. Such complex systems rely on both knowledge engineers or system programmers and domain experts to maintain and update in response to changes in law or circumstances. This paper describes a pragmatic approach using process modelling techniques that enables a complex legal expert system to be maintained and updated dynamically by a domain expert such as a legal practitioner with little computing knowledge. The approach is illustrated using a case study on the design of an online expert system that allows a user to navigate through complex legal options in the domain of International Family Law.

Automated design and optimization of industrial motion control machines: verification and a case-study

Motion control is a form of automation which enables the position- or velocity-control of a machine using an actuator such as an electric motor. The selection of suitable components and actuators for complex mechanical structures requires significant engineering expertise and intelligence. In this work, a design expert system is presented to support the motion control system design process through optimization of mechanical components and accurate selection of an actuator-transmission combination for the system. Verification of the developed system is conducted on an industrial conveyor belt. A case study involving a heavy-weight industrial hoister driven by a DC motor is presented, where the developed automated system is found to consistently outpace human performance on the same design tasks.

Sistem Pakar Penyakit Liver Menggunakan K- Nearest Neighbors Algoritm Berbasis Website

The liver is largest organ for humans, functioning as formation and secretion of bile, and as detoxification of toxins. Liver disease difficult to detect at early stage, but treatment of Liver disease in early stages will greatly help patient's recovery. With increasing number Liver Patients other than caused by unhealthy lifestyle, also caused by delay factor handling early symptoms. So this research aims to design Expert System Liver Disease based Website, that it can facilitate access information society need information about liver disease. Website based system design using K-NN Method because it is considered flexible enough and has tolerance to data that is’not appropriate and based on natural language. Expert system as a tool in determining whether the patient was suffering from Liver disease or not with the concept of K-NN. The results discussion that system design with a simple model so that can be used by all community.