Dempster-Shafer Method Research Articles

AUTOIMMUNE DISEASE DETECTION WITH DEMPSTER SHAFER AT TANJUNGBALAI GENERAL HOSPITAL

Autoimmune diseases are diseases that attack the human immune system. Autoimmunity is a disorder of the immune system due to the failure of the body's defenses to stabilize conditions so that the immune system attacks a healthy body which is considered a foreign object that must be destroyed. Helping the public in the early detection of autoimmune diseases by using an expert system that is expected to detect autoimmune diseases early in Tanjungbalai Hospital so that they can provide early information about autoimmune diseases and appropriate and informative subsequent actions to the community. In this study, an expert system was built using the web-based Dempster-Shafer method based on the value of expert trust in the symptoms felt by the patient. There were 7 types of autoimmune diseases studied: ITP, SLE, Type 1 DM, Graves Disease, RA (Rheumatoid Arthritis), Autoimmune Hepatitis, and Hashimoto's Thyroiditis. This study resulted in ITP with an accuracy rate of 98%, SLE with an accuracy rate of 96%, Diabetes Mellitus Type 1 with an accuracy rate of 96%, Graves Disease with an accuracy rate of 93%, RA (Rheumatoid Arthritis) with an accuracy rate of 99%, Autoimmune Hepatitis with a 94% accuracy, and Hashimoto's thyroiditis with 99% accuracy.

Expert System for Identifying Weeds on Oil Palm Plantations Using a Web Based Forward Chaining and Dempster Shafer Method

an expert system for identifying weeds on oil palm plantations using a web-based forward chaining and dempster shafer method. Palm oil is one of the plants that has its own charm in the community because the commodity of palm oil plays an important role in the Indonesian economy, therefore demand for palm oil continues to increase. Along with the increasing demand for palm oil in the world market, oil palm plantations have experienced many disturbances, one of which is weeds which are very detrimental to oil palm farmers, so oil palm farmers are trying to control it. Therefore, the purpose of this research is to build a system application that can be used by farmers to provide information related to weeds that attack oil palm and their control solutions.

Expert System for Diagnosis of Sexual Diseases (Paraphilia) Using Method Dempster Shafer

Sexual behavior disorder (paraphilia) is sexual behavior that is unacceptable in society. The lack of knowledge and the many obstacles to getting information about early and independent detection of the community about sexual deviations and ignorance of the community in responding to deviations are also factors that cannot be handled early The main symptoms of this paraphilia disease are repeated childhood traumatic, coming from a family that is too restrictive or too disruptive to children in sexual behavior, and has a disorder in sexual relations since childhood. There are several types of sexual disorders (paraphilia) including Voyeuristic, Exhibitionistic, Frotteuristic, Sexual Masochim, Sexual Sadism, Pedophilic, Fetishistic and Transvestic. The Dempster Shafer method provides space for the expert in providing the value of trust in his knowledge. Based on the problems above, a Web-based system will be built that functions to help the community conduct independent consultations about disturbances that can be accessed with a wide range and unlimited time. The system built using PHP and MySQL programming as a database. The method used for tracing is the dempster shader. While the results of the diagnosis will inform about the results of the diagnosis containing a list of symptoms entered, information on the results of the rules about the disorder suffered and information about possible treatments that can be done.

Sistem Cerdas Berbasis Android Untuk Diagnosa Penyakit Betta Fish (Ikan Cupang) Menggunakan Metode Dempster Shafer

This study focuses on designing and making intelligent systems to help diagnose diseases found in betta fish or better known as betta fish using the dempster shafer method, the problem that has been happening is the lack of knowledge of novice cultivators related to the diseases suffered by betta fish resulting in their death. Siamese fighting fish. There are several diseases that often attack betta fish, some of which are Stress, White Spot and Pineapple Scales. In building an android-based intelligent system for diagnosing this betta fish disease, the Research & Development research method is used. The use of the dempster shafer is very suitable to be implemented to diagnose diseases in betta fish (betta fish) which basically users of this application do not know for sure the disease that is being suffered by betta fish

Perbandingan Metode CBR dan Dempster-Shafer pada Sistem Pakar Terintegrasi Layanan Kesehatan

Infectious disease is a very dangerous disease with a high mortality rate. Delays in handling the spread of an infectious disease can be minimized using an expert system. This study uses an expert system as a disease consulting service that is integrated with the health care system. Integration with the health care system is used for the knowledge acquisition process. The knowledge base on the expert system uses patient medical record data obtained through the health care system. The expert system can diagnose infectious diseases of sore throat (Pharyngitis), diphtheria, dengue fever, Typhoid fever, tuberculosis, and leprosy. The knowledge acquisition process produces 43 symptoms. These symptoms are used to diagnose new cases using Case-Based Reasoning (CBR) and Dempster-Shafer methods. In the CBR method, the similarity measurement process is determined by comparing the K-Nearest Neighbor, Minkowski Distance, and 3W-Jaccard similarity measurement methods. The expert system obtains accuracy values ​​for the CBR K-Nearest Neighbor, CBR Minkowski Distance, and CBR 3W-Jaccard methods at a threshold of 70%, respectively 65.71%, 80%, and 85.71%. The average length of retrieve time required for each similarity method is 0.083s, 0.107s, and 6.325s, respectively. While the diagnosis of disease with Dempster-Shafer gets an accuracy value of 88.57%.

Application Of Dempster Shafer Method To Diagnose Disease In Sugarcane Plant

Sugarcane is a very important and agrarian plantation product in Indonesia, because sugar cane is a producer of sugar. However, in its cultivation, several problems often occur, such as diseases in sugarcane plants where when sugarcane plants are attacked by disease and are not handled, farmers will experience crop failure. Based on this, a tool is needed in the form of an expert system to diagnose diseases in sugarcane plants. The method used in this study is the Dempster Shafer method which processes data by providing evidence based on the belief funcation and plausible value of each symptom. This study treats 4 types of disease with 13 symptoms. The results showed that the dempster shafer method can be used as a tool in diagnosing sugarcane plant diseases so that it is expected to help farmers in handling diseases that attack sugarcane plants quickly and precisely and can increase profits from sugarcane harvests for farmers.

Penerapan Metode Dempster Shafer Dalam Mendiagnosa Penyakit Tumor Hipofisis

Pituitary tumors are relatively common intracellular neoplasms, and constitute 10-15% of all intracial neoplasms. These types of tumors often have difficulties and not infrequent recurrence, even though surgery has been done. Although there have been many studies on pituitary tumors, the pathogenesis of the occurrence of these tumors is not entirely clear. It is generally considered that pituitary neoplasms are primary pituitary tumors. Biomolecular electrons show that pituitary tumors, both functional and non-functional, originate from the growth of one clone (monoclonal). The Dempster-Shafer method is used to look for inconsistencies in effects and also new facts that will change existing attitudes, using the Dempster-Shafer method that allows a person to be safe in the work of an expert. This study aims to apply the Dempster-Shafer method to expert systems to diagnose the risk level of a person's pituitary tumor based on factors and symptoms of a pituitary tumor. The problem experienced by the community is the difficulty of specialist pituitary tumors due to the limited time of specialists to work, besides that in terms of very expensive costs for them or those whose economies cannot diagnose or consult with tumor specialists.

Ensemble classification for intrusion detection via feature extraction based on deep Learning

An intrusion detection system is a security system that aims to detect sabotage and intrusions on networks to inform experts of the attack and abuse of the network. Different classification methods have been used in the intrusion detection systems such as fuzzy, genetic algorithms, decision trees, artificial neural networks, and support vector machines. Moreover, ensemble classifiers have shown more robust and effective performance for various tasks in the field. In this paper, we adopt ensemble models in order to improve the performance of intrusion detection and, at the same time, decrease the false alarm rate. We use kNN for multi-class classification, as well as SVM to approach the classification problem in normal-based detection. In order to combine multiple outputs, we use the Dempster–Shafer method in which there is the possibility of explicit retrieval of uncertainty. Moreover, we utilize deep learning for extracting features to train the samples, selected by the sample selection algorithm based on ensemble margin. We compare our results with state-of-the-art methods on benchmarking datasets such as UNSW-NB15, CICIDS2017, and NSL-KDD. Our proposed method indicates the superiority in terms of prominent metrics Accuracy, Precision, Recall, and F-measure.

Knowledge-Based Systems Selection of Contraceptive Equipment for The Handling of Uncertainty

Contraceptives is one of the products of the government program for controlling the population. The government has established the Department of Population Control and family planning and empowerment of women and child protection that specifically manages the dissemination and socialization of the apparatus. But to choose the appropriate contraceptives for himself The Community of people still feel trouble. Not only prospective of common people who feel difficulties, sometimes the KB officers also feel uncertain in giving advice of tool contraceptives. That is because, sometimes the condition of the user does not comply with the existing rules, the latest knowledge about the development of contraception has not been owned by the officer, thus resulting in uncertainty in the suggestion of selection of contraceptives. In this study proposed a knowledge-based system to assist the public in providing an overview of the type of contraceptive equipment suitable for theyself and can be used by the KB officers the as interactive media and in the handling of the uncertainty problem that mentioned before. Then for the handling of uncertatinty problems will use dempster shafer method. dempster shafer method is Chosen because this method can provide an estimate of the value of confidence against a result of the diagnosis, by conducting the calculation of the combination of the same symptoms will be obtained the highest confidence value, or the most dominant. In the testing process, there will be 40 cases compared to the results. This research aims to solve the uncertainty problems of the suggestion the selection of contraceptives tools. The results of this research can provide a consulting medium that is able to provide selection of contraceptives that solve the problem of uncertainty and confidence level of the system to the tool. The test showed an accuracy rate of 95%

Sistem Pakar Diagnosa Penyakit Tanaman Kopi Menggunakan Metode Dempster Shafer

At present the total area of Indonesian coffee plantations is ranked second in the world, but for the production and export of Indonesian coffee there is at number four in the world. The low productivity of coffee is caused by inadequate crop cultivation techniques, this encouraging various plant growth disorders. Based on the problems, coffee farmers need a tool that can provide information about coffee plants and diseases that attack coffee plants and provide solutions to handle these diseases. One branch of computer science that can help is the expert system (Expert System). Expert systems use the knowledge of an expert which is intended to increase knowledge into computers. This research used Dempster-Shafer method to diagnose and to knowing the degree of accuracy of the diagnoses results. Information system and expert system of coffee plants diagnose contains information about plants management that begins from planting until harvest. This system built based on the web, with this system is expected to help provide solutions to handle the coffee plants that have disease, that many coffee plants can be saved from disease and can increase the productivity of coffee plants.

Diagnosing MERS-CoV using dempster-shafer method

<span lang="EN-US">MERS-CoV (Middle East Respiratory Syndrome-Coronavirus) is a respiratory syndrome disease caused by the coronavirus which attacks the respiratory tract from mild to severe. MERS-CoV was first reported in Saudi Arabia; total cases of MERS-CoV have continued to increase. In Indonesia, the MERS-CoV enters through the pilgrimage of Hajj and Umrah. But not many people know about this disease and its symptoms. For this reason, an expert system was developed to diagnose MERS-CoV using the Dempster-Shafer method. The system can detect the disease from the symptoms inputted by user. The output of the system is the value of the probability of the disease, the level of the disease and the suggested. The levels of the disease are Stage 1, Stage 2, and Stage 3 where each has a different treatment based on the level of the disease. The system contains information about MERS-CoV in the form of videos and text. The system was built using the PHP programming language. The testing result shows that all system functionalities have been fulfilled. The expert validation testing had obtained at 76.7%, which prove that the system is good enough in diagnosing MERS-CoV and the results obtained from the questionnaire is at an average of 84.13% which is very good in helping users in diagnosing disease. The expert system that has been developed can help people in diagnosing the disease before consulting with a doctor.</span>MERS-CoV (Middle East Respiratory Syndrome-Coronavirus) is arespiratory syndrome disease caused by the coronavirus which attacks therespiratory tract from mild to severe. MERS-CoV was first reported in SaudiArabia; total cases of MERS-CoV have continued to increase. In Indonesia,the MERS-CoV enters through the pilgrimage of Hajj and Umrah. But notmany people know about this disease and its symptoms. For this reason, anexpert system was developed to diagnose MERS-CoV using the DempsterShafer method. The system can detect the disease from the symptomsinputted by user. The output of the system is the value of the probability ofthe disease, the level of the disease and the suggested. The levels of thedisease are Stage 1, Stage 2, and Stage 3 where each has a different treatmentbased on the level of the disease. The system contains information aboutMERS-CoV in the form of videos and text. The system was built using thePHP programming language. The testing result shows that all systemfunctionalities have been fulfilled. The expert validation testing had obtainedat 76.7%, which prove that the system is good enough in diagnosing MERSCoV and the results obtained from the questionnaire is at an average of84.13% which is very good in helping users in diagnosing disease. Theexpert system that has been developed can help people in diagnosing thedisease before consulting with a doctor.<div> </div>

Penerapan Metode Perbandingan Dempster-Shafer dengan Certainty Factor pada Aplikasi Sistem Pakar Deteksi Dini Penyakit Alzheimer pada Lansia Berbasis Web

Alzheimer's is a dementia disease associated with changes in protein in the brain. According to WHO, in 2010 there were 35.6 million people suffering from dementia. People with dementia are usually the elderly. Due to the lack of awareness, information, and knowledge about Alzheimer's disease, an expert system was created using the Dempster-Shafer comparison method with Certainty Factors to assist in early detection of the disease. The results of this study are, there is a final value of the diagnostic results using the Certainty Factor method of 56% while using the Dempster-Shafer method of 54.5%.Keywords:Alzheimer, Expert system, Dempster-Shafer, Certainty Factor, Web-Based.

Predicting Stroke Risk With an Interpretable Classifier

Predicting an individual’s risk of getting a stroke has been a research subject for many authors worldwide since it is a frequent illness and there is strong evidence that early awareness of having that risk can be beneficial for prevention and treatment. Many Governments have been collecting medical data about their own population with the purpose of using artificial intelligence methods for making those predictions. The most accurate ones are based on so called black-box methods which give little or no information about why they make a certain prediction. However, in the medical field the explanations are sometimes more important than the accuracy since they allow specialists to gain insight about the factors that influence the risk level. It is also frequent to find medical information records with some missing data. In this work, we present the development of a prediction method which not only outperforms some other existing ones but it also gives information about the most probable causes of a high stroke risk and can deal with incomplete data records. It is based on the Dempster-Shafer theory of plausibility. For the testing we used data provided by the regional hospital in Okayama, Japan, a country in which people are compelled to undergo annual health checkups by law. This article presents experiments comparing the results of the Dempster-Shafer method with the ones obtained using other well-known machine learning methods like Multilayer perceptron, Support Vector Machines and Naive Bayes. Our approach performed the best in these experiments with some missing data. It also presents an analysis of the interpretation of rules produced by the method for doing the classification. The rules were validated by both medical literature and human specialists.

Classification of Apples Based on the Shelf Life Using ANN and Data Fusion

In this research, two groups of gala variety apples were stored in two conditions of storage for 10 weeks: the first group at 0 °C with 95% relative humidity and the second group at 20 °C with 40% relative humidity, and each week, 10 samples of both groups were selected for experiments. After weighing and measuring the dimensions of the apples, an artificial intelligence and data fusion model was used to classify the apples based on shelf life. The non-destructive acoustic test was performed with a pendulum impactor, which impact signals simultaneously detected by sound and vibration sensors. The acoustic and vibrational signals of the impact were converted from the time domain to the frequency domain using a fast Fourier transform. Then, the pattern recognition technique using the artificial neural network was used to classify the fruits based on the shelf life at 1, 2, and 3week intervals. The dominant acoustic and vibrational frequencies and masses of the samples as three features delivered to the artificial neural network and the shelf life of samples were estimated by individual, binary, and trinary set of features. The binary and trinary set of features are used as mid-level fusion by ANN. The individual features classification results were fused by the conventional and Yager’s modified Dempster-Shafer method as the high-level fusion. In the feature-level fusion, which was done using the artificial neural network, the classification accuracy of the first group increased by an average of 10.84% and the second group by 10.14%. In the decision fusion, the lowest and highest increases of accuracy by the Yager and Dempster-Shafer methods were 1.6 and 19.8% for the first group and 3.7 and 12.5% for the second group, respectively.

Local Mass Addition and Data Fusion for Structural Damage Identification Using Approximate Models

In practical civil engineering, structural damage identification is difficult to implement due to the shortage of measured modal information and the influence of noise. Furthermore, typical damage identification methods generally rely on a precise Finite Element (FE) model of the monitored structure. Pointwise mass alterations of the structure can effectively improve the quantity and sensitivity of the measured data, while the data fusion methods can adequately utilize various kinds of data and identification results. This paper proposes a damage identification method that requires only approximate FE models and combines the advantages of pointwise mass additions and data fusion. First, an additional mass is placed at different positions throughout the structure to collect the dynamic response and obtain the corresponding modal information. The resulting relation between natural frequencies and the position of the added mass is sensitive to local damage, and it is thus utilized to form a new objective function based on the modal assurance criterion (MAC) and [Formula: see text]-based sparsity promotion. The proposed objective function is mostly insensitive to global structural parameters, but remains sensitive to local damage. Several approximate FE models are then established and separately used to identify the damage of the structure, and then the Dempster–Shafer method of data fusion is applied to fuse the results from all the approximate models. Finally, fractional data fusion is proposed to combine the results according to the parametric probability distribution of the approximate FE models, which allows the natural weight of each approximate model to be determined for the fusion process. Such an approach circumvents the need for a precise FE model, which is usually not easy to obtain in real application, and thus enhances the practical applicability of the proposed method, while maintaining the damage identification accuracy. The proposed approach is verified numerically and experimentally. Numerical simulations of a simply supported beam and a long-span bridge confirm that it can be used for damage identification, including a single damage and multiple damages, with a high accuracy. Finally, an experiment of a cantilever beam is successfully performed.

An Expert System of Chicken Disease Diagnosis by Using Dempster Shafer Method

Chicken is an animal that can provide many benefits for human life, meat and eggs can be used as food to fulfill the needs of human food, the excrement can be made fertilizer, and frequently its be used as a farm animal. Although it can provide many benefits, but for chicken farmers, the maintenance of chicken meet some obstacles that must be faced such as disease, poor environmental sanitation, and the production of eggs are declining. From some of the obstacles that have been mentioned, the most frequently encountered are animals infected with the disease. Based on the results of interviews that have been done to some chicken farmers, it can be said that the knowledge of chicken farmers against chicken disease and its handling is still very lacking. But the number of experts who understand and know about the type of chicken disease and the way of handling is limited, then it takes an expert system that can simulate knowledge and understanding of experts to overcome the problem. Based on the study of the libraries, the method suitable for use in the expert system is the Dempster shafer method by processing the value of belief in a disease. Dempster shafer method is a method used to calculate uncertainty due to the addition or reduction of new facts that will change the existing rules. Based on tests in 40 cases using an expert system applying the Dempster Shafer method, obtained the percentage of diagnostic compatibility result given by experts and system is 95%.

Implementasi Dempster Shafer Dalam Diagnosa Penyakit Impetigo Pada Balita

The diagnosis of impetigo by using an expert system that uses the source of expertise aims to obtain accurate information in accordance with the symptoms suffered by the patient, this expert system analysis uses the dempster shafer method with mathematical theory for proof based on belief function and plausible reasoning it makes sense to combine separate pieces of information (evidence) to calculate the likelihood of an event that can produce information from experts about the symptoms and solutions of impetigo symptoms accurately and build an expert application system program that is able to diagnose early impetigo in infants and provide solutions and information quickly

The Implementation Of An Expert System In Diagnosing Skin Diseases Using The Dempster-Shafer Method

In this study we will describe the types of diseases that often attack the skin. The skin is a very sensitive organ of the body and susceptible to disease. This study uses a dempster-shafer method that is used to diagnose symptoms of skin disease. The dempster-shafer method in this study uses rule based obtained from dermatologists. The results of this study were able to produce an accurate diagnosis of 68.8% using trial sample data. The dempster-shafer method can be applied in the diagnosis of skin diseases.

Early Assesment Dyslexia

Early diagnosis of dyslexia disorders symptoms could minimize the disruption of children learning development and also help parents to give proper treatment for the children. The diagnosis process that has been done so far is mostly done manually that depends on the pediatric psychologist which is has limited number and relatively expensive. Therefore, the early diagnosis of dyslexia disorders symptoms is useful to make the diagnosis process easier and shorter. On this research, the researcher using the Dempster-Shafer method to detect three types of dyslexia disorders symptoms they are dyslexia, dysgraphia, and dyscalculia. The method was used to manage the dyslexia disorders symptoms in percentages and in this research the researcher had done a system test by comparing the diagnosis results from the system and the expert using ten sample cases, with 50% accuracy rate diagnose result.

Expert System Diagnosis of Urinary System Diseases using Forward Chaining and Dempster Shafer

Expert system is a computer system that can adopt human knowledge into a computer. Expert system can be used to solve problems commonly performed by experts, one of them is the diagnosis of urinary system diseases. Expert system for the diagnosis of the urinary system disease especially for the inflammation of the bladder and these pyelonephritis using the forward chaining and the dempster shafer method. Forward chaining is used to diagnose disease based on the rules and the dempster shafer is used to determine the value of confidence. The goal is to build an expert system using forward chaining and dempster shafer methods to diagnose early urinary system diseases and to determine the level of accuracy. The data used is the secondary data obtained from the UCI Machine Learning Repository as much as 120 data and 6 attributes. The result of the implementation of the forward chaining and the dempster shafer methods on this expert system of diagnosis of urinary system diseases generates an accuracy value of 87.5%.