Dempster-Shafer Method Research Articles

Dempster Shafer Analysis in Mental and Emotional Health Monitoring

Monitoring and diagnosing mental and emotional health is a significant challenge in the healthcare field due to its complex and subjective nature. This research aims to develop an expert system using the Dempster-Shafer method in monitoring and diagnosing mental and emotional health conditions. The Dempster-Shafer method was chosen because of its ability to handle uncertainty and combine various evidence from different information sources. This analysis is designed to identify seven types of mental and emotional illness by considering twenty-four related symptoms. The results of the analysis show that this expert system can provide a more accurate and comprehensive assessment compared to conventional methods. It is hoped that the implementation of this expert system can be an effective tool for medical personnel in making diagnoses and determining appropriate treatment steps for patients with mental and emotional health conditions. This study also highlights the potential of the Dempster-Shafer method in other applications that require evidence-based analysis under uncertainty.

Application of Dempster Shafer to Diagnose Cholecystitis Based on Web

Cholecystitis is an inflammation of the gallbladder. The gallbladder is an organ where bile is stored, which is a fluid that plays an important role in the digestion of fat in the body. Data processing at Bangkatan Hospitals is currently still done by means of patients coming to the hospital and heading to the receptionist to carry out the process before meeting an expert (doctor), it makes patients have to queue and wait their turn to meet the doctor. The current lack of knowledge and information from the community/patients causes delays in handling the early symptoms of cholecystitis. This makes this research carried out by building an expert system that is able to diagnose cholecystitis. With the dempster shafer method, it is expected to be able to provide a high level of accuracy. From the calculation process using the Dempster Shafer method, it can be concluded that the disease suffered by the user is Acute Cholecystitis with a density value of 0.571 or 57.1%.

Diagnosis Penyakit Dispepsia menggunakan Metode Dempster-Shafer

Dyspepsia is a gastroduodenal disorder that is often characterized by symptoms such as epigastric pain, burning, bloating, and a feeling of fullness after eating. Treatment of dyspepsia often requires examination by a specialist doctor, which may not always be easily accessible due to distance, cost, or time constraints. Therefore, this study aims to diagnose dyspepsia using the Dempster-Shafer method to identify possible dyspeptic diseases such as GERD, gastritis, dyspepsia, and gastric ulcers based on 16 detected symptoms and 4 different treatments. to make it easier for patients to consult and get an initial diagnosis without having to see a specialist doctor directly. From this study, it is expected to help patients get information on the initial diagnosis of the patient.

Predictive Analytics of Dental and Oral Health Using Dempster Shafer Parameters at Pelita Bunda Clinic

This study aims to provide a precise and analytical approach to predicting dental and oral health. This condition will be achieved by utilizing the Dempster-Shafer parameters at the Pelita Bunda Clinic. Maintaining dental and oral health is crucial for overall well-being. Nevertheless, making treatment decisions is frequently challenging due to aspects that need clarification and cannot be measured. The Dempster-Shafer method is a highly efficient approach for handling situations with uncertainty. This strategy can employ data in the form of hypotheses and leverage probability theory to amalgamate and analyze unclear information. The study demonstrates the effectiveness of the analytical prediction approach for dental and oral health utilizing the Dempster-Shafer parameters at Pelita Bunda Clinic in forecasting patients' dental and oral health state. Employing this approach can assist dentists in making treatment decisions with greater accuracy and efficiency.

Distributed multi‐station target tracking based on unscented particle filter and Dempster‐Shafer theory

AbstractIn a distributed multi‐station system, the observations received by local radar nodes for a single target will have a large signal‐to‐noise ratio (SNR) bias due to inconsistent radar cross‐sections from distinct angles, different distances from the target, various local interference such as harsh weather, and dissimilar background noise. Integrating heterogeneous information in dynamic and uncertain environments can be challenging for the fusion centre. Moreover, the particles in the basic particle filter (PF) may degrade after many iterations, making it difficult to achieve accurate target state estimation in the local tracking process. To address these issues, the authors propose a novel method named DS‐UPF based on the Dempster–Shafer (DS) theory and the unscented particle filter (UPF). By updating the important density function, the UPF efficiently suppresses particle degradation. The weighted Basic Probability Assignments (BPAs) are proposed and integrated under the new synthesis formula. The weight‐modified DS method restrains the impact of significant local estimation errors on weighted BPAs fusion result, improving robustness without local interference prior knowledge. The experimental results demonstrate that the DS‐UPF outperforms the unscented Kalman filter, PF, and UPF in tracking tasks under various local interference. This indicates that the proposed algorithm can improve estimation precision in dynamic and uncertain environments.

Improvement of Smart Grid Stability Based on Artificial Intelligence with Fusion Methods

It is crucial to evaluate and anticipate stability under various conditions, as the ability to stabilize a smart grid (SG) is one of its key features for assessing the effectiveness of its design. Intelligent approaches to stability forecasting are necessary to mitigate inadvertent instability in SG design. This is particularly crucial with the expansion of residential and commercial infrastructures, along with the growing integration of renewable energies into these grids. Predicting the stability of SGs is currently a major challenge. The concept of an SG encompasses a broad range of emerging technologies in which artificial intelligence (AI) plays a crucial role and is increasingly being utilized in light of the limitations of conventional methods. It empowers informed decision-making and adaptable responses to fluctuations in customer energy needs, unexpected power outages, rapid changes in renewable energy generation, or any unforeseen crises within an SG system. In this paper, we propose a symmetric approach to enhance SG stability by integrating various machine learning (ML) and deep learning (DL) algorithms, where symmetry is observed in the balanced application of these diverse computational techniques to predict and ensure the grid’s stability. These algorithms utilized a dataset containing the simulation results of the SG stability. The learning phase of these algorithms is based on imprecise and unreliable data. To overcome this limitation, the fusion of classifiers can be a powerful approach to modeling inaccurate and uncertain data, providing more robust and reliable predictions than individual classifiers. Voting and Dempster–Shafer (DS) methods, two commonly used techniques in ensemble learning, were employed and compared. The results show that the use of the fusion of distinct classifiers with voting theory achieves an accuracy of 99.8% and outperforms several other methods including the DS method.

Penerapan Metode Dempster Shafer Sistem Pakar Pada Penyakit Tuberkolosis

The Dempster-Shafer (DS) method is a method in trust theory that is used to overcome uncertainty in decision making. An expert system that utilizes the Dempster-Shafer method can be used to diagnose tuberculosis (TB). Application of the Dempster-Shafer method in an expert system for TB requires a good understanding of the medical domain and the ability to correctly combine evidence from multiple sources. In addition, collaboration with medical experts in developing this system will increase the accuracy and usefulness of the expert system. At the Wonokromo Community Health Center, the use of websites to diagnose tuberculosis is still lacking, therefore the aim of using this website is to help the community know more about tuberculosis. In the design process, this system uses a user interface, and is equipped with HTML, CSS, Boostrap, and Java Script. The result of this research is a website-based expert system application using the website-based Dempster Shafer method, and is able to overcome the problems that exist at the Wonokromo Community Health Center. The black box testing results show that the module configuration functions well and meets user needs.

Exploration of the Dempster-Shafer Theory for Diagnosing Hardware Damage: Comparative Study with the Certainty Factor Method

Comparative research is a research method that compares two or more research objects to expand understanding of them. In this context, we compare the Certainty Factor method and the Dempster-Shafer method in diagnosing computer hardware damage. The Certainty Factor method measures an expert's confidence in the solution that will be provided. On the other hand, the Dempster-Shafer method is a useful theory in situations where uncertainty dominates. This research focuses on comparing the effectiveness of the two methods in detecting computer hardware damage. An experimental approach is used to look for causal relationships between variables controlled by the researcher. The research results show that the Certainty Factor method has an average success rate of 14.51%, while the Dempster-Shafer method reaches 84.27% in terms of error difference. From these results, it can be concluded that the Certainty Factor method is more effective in diagnosing computer hardware damage. Keywords: INISTA, journal, MS.Word template

Identification of Student Learning Styles based on Multiple Intelligences Aspects using the Dempster-Shafer Method

Identification of Student Learning Styles based on Multiple Intelligences Aspects using the Dempster-Shafer Method

Implementasi Metode Dempster-Shafer Untuk Deteksi Kesehatan Mental Pada Mahasiswa Berbasis Web

Mental health is a person's soul condition to budaptasi in its environment to feel happy or get the comfort of life, so as not to experience mental disorders. Often mental health is ignored by most people because it is different from physical health that can be seen directly with the eyes and can be identified easily. Lack of awareness of mental health in the life of the people of Indonesia and the assumption that a person who goes to psychologists is a person inseasonable, often the individual who actually undergoes mental health problems reluctant to get help from experts or deny that he does not have mental health problems. Limitations of time and costs are also one of the constraints of a student reluctant to get help from experts like psychologists. Therefore, a web-based expert system is built with a dempster-shafer method to use as detection on the student and allows the user to know whether the user has a tendency of the problem on its mental health or not before the official consultation is required from the expert. Testing Accuracy Comparison System between the results of the system and experts by using 100 correspondents from students at Muhammadiyah Magelang University (UNIMMA) 89% know mental health and 65% have experienced mental disorders. The results of the SRQ29 data used and were spread among campus students, this study has used 20 sample data and produces 70% expert suit compliance. From the results of expert suitability obtained from the calculation of the system by selecting symptoms and automatically the system will calculate the accuracy of the existing Belief Valident in every symptom. Then the system will take decisions based on the results of the largest calculation value.

SISTEM PAKAR IDENTIFIKASI PENYAKIT KULIT ACNE VULGARIS MENGGUNAKAN METODE DEMPSTER SHAFERER

Acne vulgaris is a common skin disease caused by clogged pores due to blockage from excessive oil glands and piles of dead skin cells, causing the initial appearance of acne, namely blackheads, which can then develop into more severe festering. These types of acne are categorized into mild, moderate, and severe acne. The low level of awareness for acne sufferers is caused by a lack of knowledge about acne and limited costs for consulting a skin expert or beautician to get the right facial treatment. For this reason, an expert system is presented to help ordinary people deal with skin diseases, especially acne vulgaris, quickly and precisely. The expert system was built using the Dempster-Shafer method in carrying out the reasoning process to find a conclusion from the facts that exist. The accuracy results obtained by comparing the results of the system diagnosis with the results of diagnoses made by experts on 115 acne vulgaris disease data obtained an accuracy value of 94%.

The Expert System of Determining the Type of Malaria by using Dempster-Shafer Method

Malaria is the most dominant disease in Asia and Africa and may become a life-threatening disease for it suffers. The types of malaria such as Plasmodium Vivax, Plasmodium Ovale, Plasmodium Malariae, and Plasmodium Falciparum are mostly infected people around the world. These types of malaria have certain symptoms that drives difficulties for some patients to confirm which malaria that their infected. A clinical testing and medical diagnostic assessments may be performed to determine the types of malaria, but utilizing a system also brings some benefits for rural areas which lack of medical facilities. The study develops a system by implementing the Dempster Shafer method to determine types of malaria. We collected the knowledge from the experts including 18 possible symptoms along with the density value. This paper present 5 cases of sufferers and provide the system result with the possibilities of malaria types. The result pointed out a various percentage of malaria types that may infected to the patients.

Analyzing Customers in E-Commerce Using Dempster-Shafer Method

This research explores the analysis of consumer sentiment in the context of e-commerce by applying the sophisticated Dempster-Shafer method. We started with the collection of more than 20,000 consumer reviews from various leading e-commerce platforms and continued with a detailed data pre-processing stage to obtain a clean and structured dataset. Next, we leverage the Dempster-Shafer method to represent and combine information from multiple sources, addressing uncertainty in diverse consumer opinions. The results of the sentiment analysis show that the Dempster-Shafer method achieves an accuracy of around 85%, with good evaluation metrics. Additionally, this research provides insight into the factors that influence consumers' views of products or services in the growing e-commerce context. The literature review also reveals the potential application of the Dempster-Shafer method in other aspects of e-commerce business, such as risk management and consumer trust. This research highlights the contribution of the Dempster-Shafer method in addressing uncertainty and complexity in consumer sentiment analysis, yielding a deep understanding of consumer perceptions, and enabling more accurate decision making in a dynamic e-commerce context. This research also provides a foundation for further development in consumer sentiment analysis and the application of the Dempster-Shafer method in e-commerce.

Android Application for Initial Screening of Atrial Fibrillation Using The Dempster Shafer Method

Atrial Fibrillation (FA) is one of the most common types of heart rhythm abnormalities found in clinical practice in Indonesia. Atrial fibrillation can cause a stroke risk 5 times. Meanwhile, stroke cases themselves tend to rise and become one of the main causes of death every year. The cause of the high number of FA cases is the lack of public knowledge and awareness/sensitivity to the early symptoms of the disease. The purpose of this study was to design an android device for early screening of suspected FA through examination of pulse, complaints/symptoms and disease risk factors. The dempster shafer method is used as a decision making tool for suspected FA or not FA. The results of the detection system performance test, obtained a sensitivity of 93.5%, a specificity of 89.7%, and an accuracy of 91.7%. The test results of the android application design, namely Visual Design and User Interaction, Functionality, Stability and Performance and Overall Satisfaction, show a "good" response in all aspects.

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


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

Implementasi Dempster Shafer Untuk Deteksi Dini Gizi Buruk Pada Balita

Malnutrition that occurs in toddlers is often caused by an unbalanced intake of nutritious food or caused by certain diseases. Malnutrition is also a cause of digestive disorders in toddlers. Parents who do not understand the problem of malnutrition, make parents not aware of the problems that occur in their children. So, to get accurate and fast information, we need a system that can solve this problem. The dempster shafer method was used in this study to perform early detection of malnutrition in children under five. In this expert system, 3 types of malnutrition can be detected with 23 related symptoms. From the expert accuracy tests that have been carried out, it can be concluded, based on 20 data on early detection of malnutrition in toddlers who have been tested, gives a match rate of 85%. The value of 85% is obtained from the calculation of 17 matching data compared to 20 test data.

Diagnosing Learning Disorders in Children: A Comparison of Certainty Factor and Dempster-Shafer Methods

Even though educational technology is very advanced, some children experience learning disorders. Learning disorders in children include Dyslexia, Dysgraphia, Dyscalculia, and Dyspraxia. Ignorance about learning disorders in children will result in the child not getting help to reach his potential and have an impact on problematic behavior and destructive mental disorders in children. That is why it is necessary to make an early diagnosis to determine the presence of learning disorders in children. Therefore, for this reason, this study aims to develop an expert system for the early diagnosis of learning disorders in children using the Certainty Factor and Dempster-Shafer methods. The results show that the Certainty Factor method is more accurate than the Dempster-Shafer method in diagnosing children with disorders. The accuracy of the test results by diagnosing children’s learning disorders using the Certainty Factor method is 90%, and by the Dempster-Shafer method, it is 87%. The novelty of this research is to build a system for diagnosing the types of learning disorders in children using the Certainty Factor and Dempster-Shafer methods which have never been done by previous researchers.

Metode Dempster Shafer pada Sistem Pakar Penentuan Penyakit Bayi

The baby’s immune system is still weak so it needs special attention. Parents are advised to check their baby to the health service regularly. There are along queues due to limited hours of service or doctor’s practice in serving patients. Parents need expert systems to facilitate detection and early prevention. Systems that try to adapt human knowledge to computers to solve problems in the way that experts do are called expert systems. The dempster shafer method will be used in this study as an expert system for infant disease. The way the dempster shafer works is by defining each sysptom with a density value. This study uses 23 type pf symptoms with 3 types of disease. Dengue fever can be identified based on 11 symptoms, 13 sysmptoms of malaria, and 14 symptoms of diarrhea. The results of calculation on a case study found that the Malaria (TOD02) was the strongest with a value of 0,83 or 83% obtained from the symptoms of the body shivering but sweating (SOD02), vomiting (SOD03), blood in the stool (SOD06), fever appears and disappears simultaneously (SOD16), and red eyes (SOD17) so it is believed that the baby’s disease is Malaria

Artificial Intelligence-Based Diabetes Diagnosis with Belief Functions Theory

We compared various machine learning (ML) methods, such as the K-nearest neighbor (KNN), support vector machine (SVM), and decision tree and deep learning (DL) methods, like the recurrent neural network, convolutional neural network, long short-term memory (LSTM), and gated recurrent unit (GRU), to determine the ones with the highest precision. These algorithms learn from data and are subject to different imprecisions and uncertainties. The uncertainty arises from the bad reading of data and/or inaccurate sensor acquisition. We studied how these methods may be combined in a fusion classifier to improve their performance. The Dempster–Shafer method, which uses the formalism of belief functions characterized by asymmetry to model nonprecise and uncertain data, is used for classifier fusion. Diagnosis in the medical field is an important step for the early detection of diseases. In this study, the fusion classifiers were used to diagnose diabetes with the required accuracy. The results demonstrated that the fusion classifiers outperformed the individual classifiers as well as those obtained in the literature. The combined LSTM and GRU fusion classifiers achieved the highest accuracy rate of 98%.

An integrated framework for bridge infrastructure resilience analysis against seismic hazard

ABSTRACT Resilient bridge infrastructure is a fundamental component of an uninterrupted transportation system. Thus, assessing the resilience of bridge infrastructure against natural hazards is crucial for transportation agencies. Therefore, the main objective of this study is to develop an integrated framework for analyzing the bridge infrastructure resilience against seismic hazards. The Dempster-Shafer method has been incorporated with the Best Worst Method to achieve this objective and accommodate uncertainty. At first, various resilience criteria have been identified based on an extensive literature review. The weights of the resilience criteria have been determined using the Best Worth Method based on the response provided by the experts. After that, the Dempster-Shafer rule of combination has been used to assess the seismic resilience of a highway bridge by proposing a Bridge Resilience Index. This proposed resilience framework can support transportation agencies in taking effective strategies against seismic hazards.