Diagnosis Expert System Research Articles

Application Research of Artificial Intelligence Technology in Power System Alarm Processing Method

The paper first introduces the principles of artificial intelligence and expert systems, and makes a detailed analysis of the application of expert systems in power systems. Secondly, the paper uses VC++ language to establish an expert system for judging power system fault areas and fault types. The system can identify power system bus faults, line faults, circuit breakers and relay malfunctions and refusal faults. Finally, the system simulation test shows that the fault diagnosis expert system established in this paper has certain theoretical and practical significance.

Expert system for corn plant disease diagnosis with the breadth-first search method

Corn is one of the most important carbohydrate-producing food plants in the world, besides wheat and rice. But farmers often experience crop failures caused by disease attacks on corn plants. Some diseases that interfere with the productivity of maize plants are downy mildew, leaf spot disease, leaf rust disease, dwarf virus disease, midrib rot, stem rot and swollen burns. If the disease in corn plants is not known quickly, then the handling will be too late so that the plant will not develop, stop producing even the corn plants can die. This can lead to reduced yields and also cause crop failure. To overcome this problem, an expert system application for diagnosing maize diseases was made by adopting expert knowledge into the system using the breadth first search method that provides information about plant diseases and can diagnose symptoms of plant diseases, especially corn plants, while providing a solution to overcome them, which can later be used to reduce or reduce the risk of damage plant.

Online Expert System for Diagnosis Psychological Disorders Using Case-Based Reasoning Method

Expert system of diagnosis of psychological disorders is a system designed to diagnose early symptoms of psychological disorders that may be suffered by individuals, online-based expert systems by applying the method of Case-Based Reasoning allows users to check the psychological condition directly with the results that can be used as a basis for more examination continue. The use of Case-Based Reasoning method facilitates the examination process for each user consultation because the result of past examination data can be used to compare with consultation data, psychological disorder examination with Case-Based Reasoning method can be done well.

Sistem Pakar Diagnosa Kerusakan Mesin Sepeda Motor Transmission Automatic dengan Metode Forward Chaining Studi Kasus: AHASS 00955 Mitra Perdana

Implementation of Expert System Diagnosis of motorcycle engine damage is a solution of motorcycle service at AHASS 00955 Mitra Perdana, Expert System is a system that is related to the knowledge possessed by experts and technology users to solve a problem. This article, discusses the design of decision-making applications that have an important role in the process of diagnosing motor engine damage. Because the system is currently running the user only hands over the motorcycle to the technician. The use of computer-based artificial intelligence can be a solution to problems in structured decision making. The design of this expert system is designed using the forward tracking inference method and is designed in the form of a website to facilitate users in using it.

Applied artificial intelligence as expert system for diagnosis of Alzheimer's disease using fuzzy sukamoto

Alzheimer's disease is indeed difficult to detect at the beginning of an attack because it resembles ordinary senility, but gradually the ability to think and the motoric abilities of sufferers decreases. The peak, sufferers can no longer think normally. In the end the sufferer can experience death after several years because his motoric ability is not functioning. As a solution, technology can help in solving existing problems as early as possible. By using technology information and computers, doctors and paramedics are expected to be able to detect the presence of Alzheimer's disease earlier quickly and easily. One technology that can be used is the Expert System. This expert system will collect the necessary information, then the existing information is processed in a predetermined rule and then draw conclusions from the data obtained. An expert system prototype for the early diagnosis of Alzheimer's disease, which the author developed is a model to support diagnosis decision making. By using Fuzzy Tsukamoto calculations the results of accuracy are high so that it can help doctors make decisions for patients. The author has tested 25 patients diagnosed samples obtained an accuracy of 0.92. This shows that Fuzzy Tsukamoto has a high level of accuracy to diagnose this disease. As conclution, Fuzzy Tsukamoto's calculation technique is proven to be able to solve the problem of diagnosing Alzheimer's disease quickly and accurately.

Designing and Implementing Fuzzy Expert System for Diagnosis of Psoriasis

The differential diagnosis of Erythemato-Squamous disease is a very important issue in dermatology. These diseases include dermatitis seborrhea, lichen planus, Pityriasis rosea, Chronic Dermatiticand Pityriasis rubra pilaris. The differential diagnosis of psoriasis need to spend a lot of time and costs and incorrect or delayed diagnosis will lead to risky consequences for the patient. In this regard, a fuzzy expert system was created to diagnose psoriasis at any time and in any place.Methods: In this study, an expert system is created to diagnose psoriasis based on fuzzy logic and rule-based database using MATLAB software. Also to test system the data from 190 patients with psoriasis was used among which 126 people with psoriasis were selected.Results: The accuracy, sensitivity and accuracy of the system, was obtained 96%, 97% and 95%, respectively. The area under the ROC curve was obtained 0.97 and that of Kappa test was obtained 0.92 (P < 0.001).Conclusion: Given the importance of early diagnosis of psoriasis, the use of expert systems can play an important role in preventing disease progression, reducing severe pain and costs.

Application of Fault Diagnosis Expert System for Unmanned Vehicle Safety

In order to ensure the safety of the underwater unmanned vehicle (UUV) and reduce the risk of damage or loss during operation, the safety system will be equipped on the UUV. It can accurately locate the fault when the submersible encounters dangerous situations, such as equipment failure and cabin flooding, assess the dangerous situation and make emergency measures, so as to help the underwater unmanned submersible realize self-rescue. Fault diagnosis expert system is a knowledge-based fault diagnosis method, which is suitable for complex systems with incomplete knowledge. However, because the working environment of the underwater unmanned underwater vehicle is very complex and the fault types are diverse and change in real time, the traditional fault diagnosis expert system may not meet the response time requirements of the safety system due to the long response time required. Therefore, the finite state machine technology is applied to the diagnostic expert system to improve the response speed of the diagnostic expert system, and at the same time make the system more flexible and convenient to expand its functions. In addition, the model-based design method was applied to establish the model of the security system in stateflow, and the model verification was carried out. With the help of PLC Coder tool, the code of the target controller was generated and the algorithm was deployed.

A Medical Expert System for Tropical Disease Diagnosis

A Medical Expert System for Tropical Disease Diagnosis

Adaptive windows multiple deep residual networks for speech recognition

The hybrid convolutional neural network and hidden Markov model (CNN-HMM) has recently achieved considerable performance in speech recognition because deep neural networks, model complex correlations between features. Automatic speech recognition (ASR) as an input to many intelligent and expert systems has impacts in various fields such as evolving search engines (inclusion of speech recognition in search engines), healthcare industry (medical reporting by medical personnel, and disease diagnosis expert systems), service delivery, communication in service providers (to establish the callers demands and then direct them to the appropriate operator for assistance), etc. This paper introduces a method, which further reduces the recognition error rate. In this paper, we first propose adaptive windows convolutional neural network (AWCNN) to analyze joint temporal-spectral features variation. AWCNN makes the model more robust against both intra- and inter-speaker variations. We further propose a new residual learning, which leads to better utilization of information in deep layers and provides a better control on transferring input information. The proposed speech recognition system can be used as the vocal input for many artificial and expert systems. We evaluated the proposed method on TIMIT, FARSDAT, Switchboard, and CallHome datasets and one image database i.e. MNIST. The experimental results show that the proposed method reduces the absolute error rate by 7% compared with the state-of-the-art methods in some speech recognition tasks.

Expert System Diagnosis Corn Pests And Diseases Using Certainty Factor Method

The development of technology has been very significant, not only in the fields of information, industry, education, but in agriculture. Therefore technological sophistication is also utilized by corn farmers to obtain information about corn crops. Corn is included in plantation commodities in Indonesia. However, in corn cultivation there are various problems associated with the disease that is likely to attack the plant. Therefore, the authors designed the expert system of corn disease diagnosis using certainty method. This system will analyze based on symptoms - symptoms that are in the input by the user so that in obtaining a result of disease identification on corn in the name of the disease, the cause of the disease, the percentage value and how to handle it. This expert system is developed by using the visual basic application and Microsoft Access database with Certainty Factor method to measure the value of certainty of a hypothesis against a fact.

Dokuma Kumaş Hatalarının Sistematik Sınıflandırılması Üzerine Bir Çalışma

In this study, a systematic classification of defects of woven fabrics which are one of the most common textile materials is presented. Discrepancies between various classifications of woven fabric defects and the fabric defects included in the relevant TSE standard are evaluated. A systematic defect database, which may be used as an Expert System for woven fabric defect diagnosis, is developed.

An Improved Rule-Base Expert System for Diagnosis of Obesity

An Improved Rule-Base Expert System for Diagnosis of Obesity

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

Bayesian network approach to fault diagnosis of a hydroelectric generation system

AbstractThis study focuses on the fault diagnosis of a hydroelectric generation system with hydraulic‐mechanical‐electric structures. To achieve this analysis, a methodology combining Bayesian network approach and fault diagnosis expert system is presented, which enables the time‐based maintenance to transform to the condition‐based maintenance. First, fault types and the associated fault characteristics of the generation system are extensively analyzed to establish a precise Bayesian network. Then, the Noisy‐Or modeling approach is used to implement the fault diagnosis expert system, which not only reduces node computations without severe information loss but also eliminates the data dependency. Some typical applications are proposed to fully show the methodology capability of the fault diagnosis of the hydroelectric generation system.

Expert System for Early Diagnosis of Heart Disease Using the Random Forest Method

In Indonesia, coronary heart disease continues to grow. However, the efforts to prevention it can still be done by diagnosing the initial symptoms caused by using an expert system. This study was designed to build an expert system application to diagnose early coronary disease by random forest methods. The application interface was built using the PHP programming language using framework bootstrap, and uses the python programming language to build a random forest. To make an early diagnosis of coronary heart disease, a decision tree was built by training data from the UCI Dataset Machine Learning Repository using the random forest method. Followed by patient classification data that has been collected through 13 questions to get the diagnosis. The diagnosis results were normal, stadium 1, stadium 2, stadium 3 and stadium 4. Based on the tests that had been carried out, the application was able to provide results in accordance with the sample data collected using a confusion matrix resulting in an accuracy of 92.25% +/- 0.62 with 70% precision, remember 46%, which obtained a score of f0,5 72%.

“Virtual Doctor” Management Technique in the Diagnosis of ENT Diseases

This research project is about the managing of an ENT ( Ear Nose Throat) diagnosis expert system through the virtual doctor that can assist physicians in diagnosing ENT related diseases. ENT problems can distress hearing, speaking, learning and many other significant behaviors and untreated ENT diseases can be serious. Therefore, early diagnose of ENT diseases is fundamental. This study is qualitative in nature where we have used the concept of Virtual Doctor under artificial intelligence (AI) based expert system, already designed to assist physicians in the diagnosis of ENT related disease in the absence of ENT experts. This system can reduce the excess created due to the busy schedules of ENT experts and enhance the effectiveness and efficiency of healthcare system. Virtual Doctor for ENT diagnosis uses rule based system for knowledge representation and has sub-systems which can enhance the physician’s ability in reaching a diagnosis decision with assurance. In this paper, we described the management system for application of Virtual Doctor for the diagnosis of ENT related diseases which can be used by physicians in their daily practice.

Presenting a Medical Expert System for Diagnosis and Treatment of Nephrolithiasis

Expert systems aim to employ expert skills for non-expert person. These designs simulate intellectual and practical performance of human making the performance of expert systems close to that of an expert human. Various expert systems have been proposed in medicine, thus this area is attracting attention. Most problems in modern medicine are very complicated and there is no logic reason for accurate decision making. To this end, doctors decide arbitrarily and variably. On the other hand, large volume of medical information makes decision making more difficult while modern technologies add to volume of information and make problems more difficult. Considering these problems, there is a great challenge in medical diagnosis which requires decision-making support systems. In this paper, an expert system is presented for diagnosis and treatment of nephrolithiasis in which knowledge required for diagnosis and treatment is stored as rules in the system knowledge base. If experts are absent, diagnosis and treatment can be done reliably. Expert systems can be used as decision support by the users. However, currently, they cannot replace experts.

Hybrid Expert System for Wheat Diseases Diagnosis Using Fuzzy Logic, Neural Network and Bayesian Method

Hybrid Expert System for Wheat Diseases Diagnosis Using Fuzzy Logic, Neural Network and Bayesian Method

Perancangan Aplikasi Berbasis Android Untuk Diagnosis Penyakit Ginjal Menggunakan Metode Forward Chaining

The pattern of life that bad makes modern society must give up if it is faced with a kidney disease. Kidney disease can get worse because society did not have the time to do health checks, so kidney disease cannot be treated as early as possible. Therefore, the peniliti want to design the application of expert system for diagnosis of early kidney disease. Expert system on this research method using forward chaining rules where it is made into a form of if-then rules. Expert system knowledge base consists of 53 symptom data, disease data, 75 6 data rules and 5 data treatment. System modeling method used is the Unified Modeling Language (UML). Draft form questions to ask the doctor who made such direct symptoms that occur to users. The question is not shown all but will be pursed the possibility of kidney disease. Android-based application designed so that these applications can be run anywhere and anytime as long as the application is installed in a smartphone with Android OS.

Designing a general type-2 fuzzy expert system for diagnosis of depression

Depression is a common and important mental disorder that affects the quality of human life. Since people with depression are not aware of their disorder and sometimes suffer from physical symptoms such as chronic pain, refer to a physician instead of a psychologist. Hence, physician’s diagnosis is not always correct in all patients. In the other words, misdiagnosis may occur by mislabeling their mental disorder as physical diseases. Delay in depression diagnosis may have irrecoverable outcomes such as suicide. Therefore, the most challenging aspect of depression diagnosis is to limit time loss and preserve accuracy. In this paper, a novel general type-2 fuzzy expert system for depression diagnosis, considering two main objectives, was developed. These objectives include accuracy of the system and diagnosis time. The proposed system might be a helpful guideline for the physician to lead patients toward psychologist by asking 15 questions from patients. The proposed general Type-2 expert system has five steps. In the first step, we generate general type-2 membership function by using zSlices method and interval agreement approach (IAA). Then fuzzy rules are extracted out of data gathered from hospital and we extend Mendel method briefly in the second step. Approximate reasoning is applied in the third step. In the fourth step, we solve a multi-objective problem to minimize time and maximize accuracy by using MOEA/D method. Accordingly, in order to minimize time, feature selection is applied. In this process, we use MIFS (Mutual Information Feature Selection) method and briefly, we extend it. In the final step, we choose an appropriate solution from achieved Pareto Front (PF). The proposed general type-2 expert system has been tested and evaluated to show its performance. This Intelligent system is able to diagnose depression accurately at a suitable time.