Case-based Reasoning Method Research Articles

Expert System For Diagnosis of Mental Health Disorders in Students Using Case-Based Reasoning Method With a Web-Based Positive Psychology Approach

Mental health issues among students have become a significant concern affecting their quality of life and academic performance. An effective expert system is needed to diagnose and provide appropriate interventions. This research develops a web-based expert system that utilizes the Case-Based Reasoning (CBR) method combined with a positive psychology approach to diagnose mental health disorders in students. The CBR method identifies similarities between new and previous cases, while the positive psychology approach focuses on individual strengths and potential for growth. The system integrates a database of student mental health cases and CBR algorithms to produce relevant diagnoses. This study investigates four types of mental health disorders: panic, anxiety, stress, and depression. The method used for data analysis is Case-Based Reasoning. The diagnosis results are based on calculations from symptom choices within the system, where each symptom has a weight. The highest similarity calculation obtained from past cases is used as a solution to address the problem. System testing, based on expert knowledge with 15 test data samples categorized by mental health disorders and 38 symptoms, achieved an accuracy rate of 85%.

Diagnosa Penyakit Tuber Culosis (TBC) menggunakan Metode Case Based Reasoning (CBR)

Tuberculosis (TB) is one of the infectious diseases caused by Mycobacterium tuberculosis bacteria infection in the human lungs. Tuberculosis is a disease that can be transmitted from people with TB through coughing, sneezing, talking, laughing or singing. Lack of public knowledge about TB and lack of funds for health checks make many people late to be treated. Expert systems are technologies developed based on programs, in accordance with human methods and mindsets. This aims to help people who want to check their health, but are hampered by costs, besides saving time if the examination place is far from the residential environment of the community concerned. Expert systems require a method that can help solve existing problems. In this study, the method used is the Case-Based Reasoning (CBR) method, because the main function of this method is to diagnose the disease. The calculation process of the Case-Based Reasoning (CBR) method which looks for the similarity value or proximity of old cases to new cases of a patient.

Diagnosa Penyakit Malaria Menggunakan Metode Case Base Reasoning (CBR) (Studi Kasus: RSUD Djoelham Kota Binjai)

The application of information technology has been widely used in medicine. This application provides convenience and smoothness in the medical world to detect symptoms of various diseases, especially malaria. Malaria is still included in the endemic diseases suffered by the community in Binjai City, the more malaria patients, of course the more doctors are needed/work to diagnose patients. Artificial intelligence is one solution and helps doctors in supporting decision making for certain diseases. Building a system to diagnose malaria using the Case-Based Reasoning (CBR) method offers various significant advantages. CBR utilizes experience and knowledge from previous cases, allowing the system to provide a more accurate diagnosis based on patterns and symptoms that have occurred in the past.

A Spatial Case-Based Reasoning Method for Healthy City Assessment: A Case Study of Middle Layer Super Output Areas (MSOAs) in Birmingham, England

Assessing healthy cities is a crucial strategy for realizing the concept of “health in all policies”. However, most current quantitative assessment methods for healthy cities are predominantly city-level and often overlook intra-urban evaluations. Building on the concept of geographic spatial case-based reasoning (CBR), we present an innovative healthy city spatial case-based reasoning (HCSCBR) model. This model comprehensively integrates spatial relationships and attribute characteristics that impact urban health. We conducted experiments using a detailed multi-source dataset of health environment determinants for middle-layer super output areas (MSOAs) in Birmingham, England. The results demonstrate that our method surpasses traditional data mining techniques in classification performance, offering greater accuracy and efficiency than conventional CBR models. The flexibility of this method permits its application not only in intra-city health evaluations but also in extending to inter-city assessments. Our research concludes that the HCSCBR model significantly improves the precision and reliability of healthy city assessments by incorporating spatial relationships. Additionally, the model’s adaptability and efficiency render it a valuable tool for urban planners and public health researchers. Future research will focus on integrating the temporal dimension to further enhance and refine the healthy city evaluation model, thereby increasing its dynamism and predictive accuracy.

Per-Instance Algorithm Configuration in Homogeneous Instance Spaces: A Use Case in Reconfigurable Assembly Systems

The physical capabilities of a reconfigurable assembly system (RAS) increase the agility and responsiveness of the system in highly volatile market conditions. However, achieving optimal RAS utilization entails solving complex optimization problems effectively and efficiently. These optimizations often define homogenous sets of problem instances. While algorithm configuration in such homogeneous contexts traditionally adopts a “one-size-fits-all” approach, recent studies have shown the potential of per-instance algorithm configuration (PIAC) methods in these settings. In this work, we evaluate and compare the performance of different PIAC methods in this context, namely Hydra—a state-of-the-art PIAC method—and a simpler case-based reasoning (CBR) approach. We evaluate the impact of the tuning time budget and/or the number of unique problem instances used for training on each of the method’s performance and robustness. Our experiments show that whilst Hydra fails to improve upon the default algorithm configuration, the CBR method can lead to 16% performance increase using as few as 100 training instances. Following these findings, we evaluate Hydra’s methodology when applied to homogenous instance spaces. This analysis shows the limitations of Hydra’s inference mechanisms in these settings and showcases the advantages of distance-based approaches used in CBR.

Rancang Bangun Aplikasi Sistem Pakar Diagnosis Penyakit Jantung: Studi Kasus pada Klinik Siaga Menggunakan Metode Case-Based Reasoning (CBR) Berbasis Web

In the era of modernization, technological advancements are developing rapidly and play an essential role in various aspects of life, including healthcare. Technology enables people to easily access information in fields such as business, education, and health. In the medical field, computer-based expert systems assist professionals in diagnosing diseases and solving specific problems. This study focuses on developing an expert system application for diagnosing heart diseases using the Case-Based Reasoning (CBR) method, implemented as a web-based system with PHP and MySQL. The heart, a vital organ responsible for pumping blood, is slightly larger than a fist (around 200–425 grams). According to Dr. Helmanu Kurniadi and Ulfa Nurrahmani, S.Kep., smoking is identified as one of the contributing factors to heart disease. The developed application consists of five primary elements, with several menus such as the homepage, user data, disease information, disease list, and login page. Based on black box testing, the application has been thoroughly tested to ensure it functions correctly and is free from coding errors. Additionally, acceptance testing was conducted eight times, resulting in a 100% accuracy rate, aligning with the intended design. Thus, the application is ready to be deployed and is expected to assist both the public and healthcare professionals in diagnosing heart diseases effectively.

Online optimization of petrochemical process via case-based reasoning and conditional mutual information

The strict mechanism model of petrochemical processes is often complex, resulting in long modeling times, low computational efficiency, and poor accuracy, which limits the application of mechanistic models in process optimization and advanced control. The advent of big data technology provides new solutions for this problem. Herein, a new method based on historical Case-Based Reasoning (CBR) is proposed to process online optimization and calculate variable attribute weights for case retrieval using conditional mutual information. Considering the process time-lag characteristics, a piecewise sequential CBR method for optimization is further proposed, which directly recognizes the pattern based on the industrial past data and amply utilized the process information. For case study and method validation, the approach is applied to an actual fluid catalytic cracking (FCC) process, converting low-quality heavy oils into valuable light transportation fuels and chemicals such as gasoline and propylene. As demonstrated by the case study results, the proposed method increases the average yield of gasoline and total liquid respectively by 2.5 % and 4.0 % while decreasing the average yield of coke by 1.3 %. The results further indicate that the overall optimization performance is comparable to many advanced intellectual optimization algorithms, allowing favorable operability and ensuring good robustness for different optimization targets. It could provide a solution with high accuracy and good adaptability for online process optimization in complex chemical processes.

Identifikasi Kerusakan Pada Landing Gear Pesawat Cessna C208B

One type of aircraft commonly used for flight training, patrol, and transportation in remote areas is the Cessna. It should be understood that each tool and component in the aircraft has its own level of importance and can experience failures in carrying out its functions. These failures often become a problem for users due to a limited understanding of the aircraft engine field. Issues arising from the aircraft's landing gear sometimes involve minor problems that do Does not require a high level of expertise. To solve this, it may be possible for someone with knowledge of landing gear to address the issue. By using the Case-Based Reasoning (CBR) method for fault identification, a technician's expertise in aircraft landing gear can be applied and integrated. The search for solutions or fault identification Can be obtained promptly In the testing results of the Fault System for Cessna C208B Aircraft Landing Gear using the CBR method, the system's calculations were consistent with the manual. However, the application still has some shortcomings in the system, such as the preprocessing stage, which has not been able to search for the root words in a question sentence.

Implementasi Metode Case Base Reasioning dan SUPR-Q Untuk Sistem Diagnosis Kerusakan Lokomotif CC 201

The rapid growth of information and communication technology has also reached the transportation sector, especially in Indonesia. Railway locomotives have experienced significant advancements by transitioning from conventional systems to the use of microcontroller-based sensors. However, to expedite repairs, swift identification of faults is necessary. Railway workers need to continuously improve the quality of human resources and develop application systems to facilitate technicians in identifying issues with CC 201 locomotives. The aim of this research is to build a system that can identify faults and facilitate technicians in diagnosing and providing repair solutions for CC 201 locomotives, using the Case-Based Reasoning method. Research results indicate that this system can be used to diagnose CC 201 locomotive issues based on observed symptoms. Usability testing for 20 respondents using the SUPR-Q method shows an excellent level of system ease and satisfaction, with a score of 90.86%.

Social Impact Assessment in Urban Security Management Projects: A Case Study from Pakistan

Public safety and security management projects are devised to reduce crime, fear and calamities by prevailing law and order to reduce the harm in society. In a certain context, social impact assessment is a novel way to reveal the extent of effectiveness for these projects. This case study presents an Expert System based methodology regarding social impact assessment for two urban security management projects in Pakistan. An Expert System, DoctuS is employed as a tool to build a rule-based model tool using social impact attributes (as variables) from literature and expert knowledge (Author’s own insight as being involved in projects). Case-based reasoning (CBR) method is employed for the measurement of the impact of urban security management KPIs (Key Performance Indicators) in the two case projects. The empirical findings in this case study approaches the impact assessment by quantitative analysis of crime rates in JMP statistical package and Qualitative Expert knowledge-based model in DoctuS. Thereby, the study evaluates the pre and post ante project situations in order to assess the project’s effectiveness and impact on improved urban safety and security. In the first step, the projects are briefly described, followed by the crime rate analysis under the project jurisdictions and rules between attributes are defined. The second step defines the qualitative rule-based model along with the two cases described, and validated in DoctuS tool. Some attributes and rules have changed, and a new concise model is presented. The Expert System compares the project cases and evaluates the social impact assessment based on the defined KPIs. The Expert system validation presents a novel model for social impact assessment of subject urban security management projects. We propose this methodology and model as useful in investigating social impact assessment of projects specifically dealing with urban safety and security management. However, the generalizability of the findings of this case-study based articles need more sophisticated tests as recommendation for the extension to this study.
 
 Received: 31 May 2023 / Accepted: 19 November 2023 / Published: 5 January 2024

Development of Dynamic Balanced Scorecard Using Case-Based Reasoning Method and Adaptive Neuro-Fuzzy Inference System

In recent years, selecting the strategies has been recognized as one of the most challenging issues facing senior and strategy managers of companies. In this regard, this article introduced an integrated framework that helps strategy managers to determine the organization's strategy by analyzing the long-term objectives and visions. The proposed methodology is based on developing and enhancing the performance of the balanced scorecard (BSC) by covering its limitations through being combined with system dynamics (SD) simulation, case-based reasoning (CBR) method, and adaptive neuro-fuzzy inference system (ANFIS) model. The SD model is built based on the company's strategy map to predict the future status of the company based on its selected strategies. The CBR method and ANFIS model are also to develop the sensitivity analysis and policy-making stage utilizing learning and human memory application. An Iranian food and beverage company is considered as a real-world case study to demonstrate how the proposed method could work and validate the research methodology's applicability. As the main finding, the model yields appropriate strategies with minor errors to reach the targets defined by managers. According to the proposed dynamic BSC, managers can select their strategies and observe their financial variables’ outcomes on the planning horizon. Moreover, they can set future targets for their financial variable to receive strategies for their achievement.

Research on Case-Based Reasoning for Energy-Efficient Rail Transit Emergency Timetables

The continuous expansion of rail transit lines has led to a significant increase in energy consumption. Choosing an appropriate timetable based on external conditions of train operation can reduce energy consumption by more than 5%. When emergencies occur in rail transit, external conditions change, rendering the original timetable unable to meet energy-saving requirements. This paper primarily explores how to quickly determine an energy-efficient timetable based on historical energy-saving timetable data and current external conditions using case-based reasoning methods. The paper represents emergency event timetable cases using a framework approach, and then employs a retrieval algorithm based on the grey correlation degree method to search for cases. The energy-efficient timetable for the target case is selected from the retrieved source cases, considering the one with the lowest energy consumption.

Analisa Perbandingan Metode Teorema Bayes Dan Case Based Reasoning Diagnosa Penyakit Pada Tanaman Tomat

Diseases in tomato plants have a significant impact on the agriculture industry as they can reduce crop yields and tomato quality. Therefore, this research aims to compare the Bayesian Theorem and Case-Based Reasoning (CBR) methods in diagnosing tomato plant diseases. The Bayesian Theorem is a statistical approach based on probability, while CBR uses knowledge from previous cases. This study includes an analysis of the performance of both methods in terms of diagnostic accuracy, result delivery speed, and resource efficiency. The research results have the potential to assist farmers and agricultural experts in choosing the most suitable method for diagnosing tomato plant diseases. Furthermore, the implementation of expert systems in agriculture can have a positive impact on tomato cultivation productivity and sustainability. This research aims to provide practical guidance for stakeholders in the agricultural field and contribute to sustainable agriculture improvement, with a specific focus on disease identification and management in tomato plants. The percentage values of the application of the Bayesian Theorem and Case-Based Reasoning methods show that Case-Based Reasoning has a lower success rate in diagnosing Fusarium Wilt and Bacterial Wilt compared to the Bayesian Theorem. However, Case-Based Reasoning excels in diagnosing Tomato Yellow Leaf Curl Virus (TYLCV), achieving a success rate of 100%, while the Bayesian Theorem reaches 63%.

Application of the Minkowski Distance Similarity Method in Case-Based Reasoning for Stroke Diagnosis

A Stroke is a cerebrovascular disease characterized by impaired brain function due to damage or death of brain tissue caused by reduced or blocked blood and oxygen flow to the brain. Expert systems can be used as learning aids for medical students to diagnose stroke. Medical records of stroke cases can be reused as a reference for diagnosing stroke when there are new cases, known as the case-based reasoning (CBR) method. This study implements the Minkowski distance similarity method in CBR to calculate the similarity value between cases, where each similar case has the same solution. This study uses the Minkowski distance similarity method in CBR to obtain the most optimal value of r and the most appropriate threshold value in the expert system for stroke diagnosis. The diagnosis process is carried out by inputting the patient's condition, symptoms, and risk factors. Then the system will calculate the similarity value and take the case with the highest similarity value as the solution, providing that the similarity value must be greater than or equal to the threshold value. Based on system testing, the best accuracy value was achieved by applying a threshold value of 75 with an r value of 3 or 4, with an accuracy rate of 88,89%, a recall value of 88%, and a precision of 100%.

Development of SCOR Database for Digitalisation of Supply Chain Customer Feedback Analysis

High-level customer service, with improved quality at a lower cost, is imperative in today's global supply chain, where customers have myriad options across borders. This is in line with the primary objective of supply chain management, which is to enable and deliver high-level customer service with lower costs, reduce lead time, and improve the quality of products/services. The Supply Chain Operations Reference (SCOR) model based on KPI metrics enables an increase in the quality of products/services by monitoring and digitalising involved processes. The current paper suggests the structure of the SCOR database for Supply Chain process improvement by applying the best practices of the SCOR for Business Processes improvement. We recommend the Bayesian Belief Network (BBN) and Case-Based Reasoning (CBR) methods to estimate the influence of these improvements on Supply Chain efficiency through key performance indicators before implementation. Integrating the methods proposed in this article focuses on an approach that minimises supply chain failures, decreases failure elimination time, understands consumer needs, and offers more accurate price proposals and lead times to improve customer satisfaction. Accordingly, the integrated SCOR-based Business Process Modelling (BPM) was not previously combined with BBN to identify the most efficient ways to improve the reliability of a Supply Chain by applying best practices, which impacts the entire supply chain. Our research is limited to SME companies in electronics manufacturing, but our ambition is to develop a universal framework suitable for broader areas. The study aims to analyse how the customers see the company by combining the CDA (Customer Delivery Accuracy) and Customer Complaints measures and find the optimal way to improve business processes by applying best practices. Still, it is limited to SCOR reliability performance metrics.

Evaluation of case-based reasoning to estimate liquefaction manifestation

This article develops a framework for and explores the use of case-based reasoning (CBR) to predict seismically induced liquefaction manifestation. CBR is an artificial intelligence process that solves new problems using the known answers to similar past problems. CBR sorts a database of case histories based on their similarity to a design case and predicts the outcome of the design case as the observed outcome of the most similar case history or majority outcome of the most similar case histories. Two databases of liquefaction case histories are used to develop and validate numerous CBR models. Different input parameters and aspects of the CBR method and their influence on the predictive capability of the models are evaluated. Some of the developed CBR models were shown to have a better predictive power than currently existing models. However, more research is needed to refine these models before they can be used in practice. Nevertheless, this study shows the potential of CBR as a method to estimate liquefaction manifestation and suggests several avenues of future research.

Application of Fuzzy Case-Based Reasoning and Fuzzy Analytic Hierarchy Process for Machining Cutter Planning and Control

Cutter planning and control are the crucial problems in machining processes. The current literature indicates that the issue of cutter planning and control problem was not adequately researched in the past in a metal-cutting process. Usually, cutter planning and control problems were addressed using different optimization, simulation, and computer-aided planning (CAP) methods. To bridge this knowledge gap, this study proposed a decision support system (DSS) that can integrate fuzzy case-based reasoning (F-CBR) and fuzzy analytic hierarchy process (F-AHP) methods. This integration was applied to determine hybrid similarity measures between new and prior cases. The study provides new insights into the integration of fuzzy set theory (FST), CBR, and AHP for solving machining cutter planning and control problems. Our proposed system retrieves the best similar prior cases to reuse and adapt them to new order arrivals. A numerical example was illustrated to validate the soundness of the researched DSS.

A Case-Based Reasoning Method for Generalizing Depth Contours considering Both Navigational Safety Assurance and Line Shape Preservation

To address the problem that the parameters are relatively fixed in the existing automatic methods of depth contour generalization, a case-based reasoning method for generalizing depth contours is proposed considering navigational safety and line shape. First, the structured description of depth contours before and after cartography generalization is made to form case samples. Then, driven by the training samples, the machine learning of BP neural network model is constructed, to obtain the simplification degree taking into consideration the preservation of contour shapes. Finally, the generalization parameters are flexibly adjusted based on the simplification degree obtained through the case-based reasoning, so that depth contours can be adaptively generalized for various complex situations. The experimental results demonstrate that: (1) The case-based reasoning method can make the generalization of depth contours comply with the principle of navigational safety; (2) The case-based reasoning method has a stronger applicability maintaining the shape of depth contour, and is more suitable for the automatic generalization of depth contours, compared with the rolling circle method and the triangulation method. Generally, the case-based reasoning method has the potential to improve cartographic quality meeting the requirements of IHO specification, supporting the automatic production of ENC and nautical chart product.

Perbandingan Metode Dempster Shafer, Case-Based Reasoning dan Certainty Factor dalam Diagnosis Penyakit Craniopharyngioma

Craniopharyngioma is a rare benign brain tumor that originates from embryonic tissue of the pituitary gland. It is a type of brain tumor that develops near the pituitary gland at the base of the skull. Its prevalence is approximately 2-4% of all brain tumor cases. Collaboration among the medical team is crucial in providing optimal perioperative care. In diagnosing craniopharyngioma, an expert system can be utilized. The Expert System aids in identifying problems and providing appropriate solutions. Its existence can expedite the process of problem identification and system repair. Additionally, the Expert System can also help reduce consultation costs with experts, as it can provide solutions comparable to those given by experts. This study aims to compare the results of the Dempster-Shafer, Case-Based Reasoning, and Certainty Factor methods in diagnosing craniopharyngioma. The study will determine the percentage results of the Dempster-Shafer, Case-Based Reasoning, and Certainty Factor methods in diagnosing craniopharyngioma to ascertain which method is most accurate in diagnosing the disease. The percentage results from the application of the DS and CF methods yielded the same result of 99.8%, whereas the CBR method only achieved a diagnosis percentage of 51.4% for craniopharyngioma. Hence, it can be observed that the CBR method yields the lowest percentage diagnosis for craniopharyngioma.

CBR-RBR fusion based parametric rapid construction method of bridge BIM model

The development of BIM (Building Information Modeling) parametric modeling technology has improved the reusability and modeling efficiency of building information models. Aiming at the problems of too many parameters and long time in the rapid parametric construction of the bridge BIM model, we propose a novel parametric rapid construction method of bridge BIM model based on the integration of CBR (Case Based Reasoning) and RBR (Rule Based Reasoning), which is used to realize the rapid construction of bridge BIM model. Firstly, the modeling characteristics of CBR and RBR are analyzed, and a parametric modeling framework of CBR-RBR fusion is proposed. Secondly, a MHPO (Modified Hunter-Prey Optimization) algorithm is proposed to assign the weight of feature parameters to improve the accuracy of the prediction model. Then, modeling parameter reasoning rules are designed to realize the construction of parameter reasoning model. Finally, the prototype system is developed to verify the proposed method based on Revit and C#. Compared with manual modeling and traditional parametric modeling, the fusion method of CBR and RBR greatly shortens the modeling time and improves modeling efficiency and flexibility.