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Degumming and bleaching are critical steps in the palm oil refining process, as they are the precursors to the qualities of refined, bleached, and deodorized palm oil. In practice, plant operators often face oil rejections in these processes and solve the problem by trial and error. Hence, a fuzzy expert system is developed to troubleshoot the degumming and bleaching process, for identifying failures and suggesting actions. However, developing the knowledge base and inference engine in the fuzzy expert system for troubleshooting the degumming and bleaching process is challenging because the data in the actual palm oil refining process are poorly documented and must be obtained from various sources, including field observation, document analysis, and interviews, and need to be analyzed using thematic analysis. The results from the thematic analysis were represented as input and output variables of the fuzzy expert system. The developed fuzzy expert system is tested and validated against different data sets and industrial data to identify faults and suggest necessary actions. To evaluate the robustness of the troubleshooting system, the membership functions of the fuzzy expert system are adjusted based on the distributed control system (DCS). The results show that the troubleshooting system can effectively diagnose potential faults and provide necessary actions and can serve as a useful guidance for failures in the degumming and bleaching process. [ABSTRACT FROM AUTHOR]

This paper introduces an expert system to decision-making. The expert system is linguistic summarization combined with prioritized operators. In the practical decision-making problems, the information of attributes is linguistic type and needs to be converted into numerical type. The validity of the linguistic summarization is recorded as the attribute value. We discuss how to calculate the validity of the linguistic summarization, and present three prioritized operators. Then the three prioritized operators are used to aggregate the attribute values. Finally, a practical example is given. In addition, we conduct a comparative analysis between the expert system method and another multi-attribute decision-making method by using a measure of specificity, and conclude that the expert system method is better. [ABSTRACT FROM AUTHOR]

The emergence of the highly contagious coronavirus disease has led to multiple pandemic waves, resulting in a significant number of hospitalizations and fatalities. Even outside of hospitals, general practitioners have faced serious challenges, stretching their resources and putting themselves at risk of infection. Telemonitoring systems based on Internet of things technology have emerged as valuable tools for remotely monitoring disease progression, facilitating rapid intervention, and reducing the risk of hospitalization and mortality. They allow for personalized monitoring strategies and tailored treatment plans, which are crucial for improving health outcomes. However, determining the appropriate monitoring intensity remains the responsibility of physicians, which poses challenges and impacts their workload, and thus, can hinder timely responses. To address these challenges, this paper proposes an expert system designed to recommend and adjust the monitoring intensity for COVID-19 patients receiving home treatment based on their medical history, vital signs, and reported symptoms. The system underwent initial validation using real-world cases, demonstrating a favorable performance (F1-score of 0.85). Subsequently, once integrated with an Internet of Things telemonitoring system, a clinical trial will assess the system's reliability in creating telemonitoring plans comparable with those of medics, evaluate its effectiveness in reducing medic–patient interactions or hospitalizations, and gauge patient satisfaction and safety. [ABSTRACT FROM AUTHOR]

Stunting is a chronic nutritional problem that occurs in toddler due to lack of nutritional intake which results in impaired growth toddler. Usually, toddler who experience stunting are characterized by not increasing weight over a long period of time. Application utilization health which makes it easier for users to access information, one of which can be used to identify toddler who are stunted by selecting symptoms. The symptoms experienced by toddlers go through a system known as the system expert. In this research an expert system will be developed that is capable of early detection developmental disorders in toddlers using the Mamdani fuzzy method. The results obtained from this research are an expert system design for early detection of stunting using the Mamdani fuzzy method. The Mamdani fuzzy method was implemented to group the criteria for toddlers who fall into the stunting category or not from the initial data which is still gray because they are still unsure whether to categorize the toddler as having stunting or not. The detection accuracy rate using the Mamdani fuzzy method is 80.87% compared to expert diagnosis. [ABSTRACT FROM AUTHOR]

In this work, we enhance the sustainability of an enterprise network (EN) by complementing it with an expert system that apprehends the explore‐exploit behavioural intelligence of Physarum to survive against the attractive‐adversarial nutritional environment. EN sustainability is dynamic since it depends on how well EN can react to an adversarial environment. We capture a reverse analogy to characterize EN's workload‐environment with Physarum's nutritive‐environment, where the high volume of workloads at the backbone network corresponds to a poor‐nutrient environment. The expert system explores EN to find out how to manage the workloads as Physarum handles its survivability, and exploits the users' workload patterns by grouping the highly communicating users together to redesign the network structure as Physarum's intelligence to exploit energy from rich‐ and poor‐nutrient food sources through redesigned tubular structures. We define two factors, such as nutrient‐intensity and chemo‐attractant to aid the redesign process. EN evolves through a set of redesigned clusters with an objective function to maximize its sustainability for a given set of explored workloads by minimizing the workloads through the backbone. EN evolution terminates when there is no change in the backbone utilization, resembling the organism's stay in a dormant state until it experiences a favourable environment. Our experimental results on an EN with a higher volume of workloads at the backbone producing 14.26 kWh energy consumption demonstrated that the developed expert system reduced the energy consumption to 11.27 kWh, thus enhanced the sustainability from 21% to 61%. [ABSTRACT FROM AUTHOR]

Typical state-of-the-art flow cytometry data samples typically consist of measures of 10 to 30 features of more than 100,000 cell "events". Artificial intelligence (AI) systems are able to diagnose such data with almost the same accuracy as human experts. However, such systems face one central challenge: their decisions have far-reaching consequences for the health and lives of people. Therefore, the decisions of AI systems need to be understandable and justifiable by humans. In this work, we present a novel explainable AI (XAI) method called algorithmic population descriptions (ALPODS), which is able to classify (diagnose) cases based on subpopulations in high-dimensional data. ALPODS is able to explain its decisions in a form that is understandable to human experts. For the identified subpopulations, fuzzy reasoning rules expressed in the typical language of domain experts are generated. A visualization method based on these rules allows human experts to understand the reasoning used by the AI system. A comparison with a selection of state-of-the-art XAI systems shows that ALPODS operates efficiently on known benchmark data and on everyday routine case data. [ABSTRACT FROM AUTHOR]

This study aims to measure the quality of distance university higher education in the Peruvian department of Junín, as well as to seek strategies of improvement. To meet this objective, a survey was carried out on students from the 4 universities located in this department, all with an institutional license. An intentional non-probabilistic sampling was used. The quality variable in university higher education is described in nine dimensions derived from the educational policies imposed by the National Superintendency of Higher University Education (SUNEDU in Spanish), which are: academic objectives, educational offer, infrastructure and equipment, research lines, qualified teacher, complementary services, job bank, transparency, and satisfaction. Among the results, it stands out that 59% of the students on average show satisfaction with the education received, which allows us to conclude that quality should be directed by a unit with its own name and established transversally in all administrative, academic, and academic procedures, investigative, social projection, and university extension. Transparency avoids any act of corruption, therefore it is important to associate it with the ethics of the authorities and workers of the university entity, strengthening timely communication with the student body. On the other hand, Kosko's Hidden Patterns Method was used to process a fuzzy cognitive map designed by education quality experts' opinions and in this way conclusions were reached about the strategies to follow for improving the quality of Peruvian non-face-to-face education in the department. [ABSTRACT FROM AUTHOR]

Since organic rice storage silos were faced with an insect problem, an owner solved this problem using the expert system (ES) in the controlled atmosphere process (CAP) under the required standard, fumigating insects with an N2, reducing O2 concentration to less than 2% for 21 days. This article presents the computational fluid dynamics (CFD) assisted ES successfully solved this problem. First, CFD was employed to determine the gas flow pattern, O2 concentration, proper operating conditions, and a correction factor (K) of silos. As expected, CFD results were consistent with the experimental results and theory, assuring the CFD's credibility. Significantly, CFD results revealed that the ES controlled N2 distribution throughout the silos and effectively reduced O2 concentration to meet the requirement. Next, the ES was developed based on the inference engine assisted by CFD results and the sweep-through purging principle, and it was implemented in the CAP. Last, the experiments evaluated CAP's efficacy in controlling O2 concentration and insect extermination in the actual silos. The experimental results and owner's feedback confirmed the excellent efficacy of ES implementation; therefore, the CAP is effective and practical. The novel aspect of this research is a CFD methodology to create the inference engine and the ES. [ABSTRACT FROM AUTHOR]

Train operation monitoring device (LKJ) is one of the most widely used on-board equipment in Chinese current ordinary speed railway. With the continuous improvement of railway transportation demand, as the key equipment to ensure traffic safety and improve operation efficiency, LKJ equipment is becoming more and more important. In order to solve the problems such as heavy workload, low efficiency and difficulty in finding the fault in the manual test process of LKJ equipment, an LKJ automatic test platform is designed and key technologies are studied. The automatic test platform fulfills optimization based on the traditional manual test process, generates test cases in a graphical manner, designs signal adaptation units to simulate the operating conditions of the equipment, adds humancomputer interaction units to simplify the test operation, automatically executes the test based on the Robot Framework automatic test framework, calls on fault analysis expert system to give the cause of the fault, so as to realize the unattended automatic test. Through the verification of LKJ-15 equipment and laboratory simulation environment, the verification results show that the automatic test platform can replace some manual operations in the traditional simulation test, provide guidance for the analysis of fault phenomena, and effectively improve the test efficiency. [ABSTRACT FROM AUTHOR]

Cats are the most common pet in Indonesia with a cat ownership rate of 47% (Rakuten Insight, 2021). Cat owners need to know and recognize the signs and symptoms of the diseases that often occur in cats, especially respiratory problems and diseases. Although vaccines in cats can significantly reduce the incidence of respiratory diseases, they do not eliminate infectious disease pathogens. During the COVID-19 pandemic, it was necessary to adjust health consultations that could reduce the transmission of COVID-19, which is the contactless method. In animal health, there is an online consultation through the WhatsApp platform between veterinarians and cat owners. Cat owners manually type every symptom experienced by the cat. However, there are several shortcomings in the online consultation, including that the symptoms described by the cat owner are unclear, so the diagnosis data is lacking and the consultation fee is quite expensive. Based on the problems that have been mentioned, the purpose of this study is to create an expert system for diagnosing respiratory diseases in cats using the Certainty Factor method. The result of this study is the availability of an expert system that can be used to diagnose respiratory diseases in cats.

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Typical state-of-the-art flow cytometry data samples typically consist of measures of 10 to 30 features of more than 100,000 cell “events”. Artificial intelligence (AI) systems are able to diagnose such data with almost the same accuracy as human experts. However, such systems face one central challenge: their decisions have far-reaching consequences for the health and lives of people. Therefore, the decisions of AI systems need to be understandable and justifiable by humans. In this work, we present a novel explainable AI (XAI) method called algorithmic population descriptions (ALPODS), which is able to classify (diagnose) cases based on subpopulations in high-dimensional data. ALPODS is able to explain its decisions in a form that is understandable to human experts. For the identified subpopulations, fuzzy reasoning rules expressed in the typical language of domain experts are generated. A visualization method based on these rules allows human experts to understand the reasoning used by the AI system. A comparison with a selection of state-of-the-art XAI systems shows that ALPODS operates efficiently on known benchmark data and on everyday routine case data.

The manufacturing of hard disk drives involves the intricate assembly of numerous components, making the testing process time-consuming and resource intensive. To optimize the manufacturing process and increase testing efficiency, the development of a rule-based expert system is proposed. This system leverages predictive models constructed from assembly process data to identify potentially defective hard drives before undergoing extensive testing. By preemptively identifying defects, this approach substantially reduces testing time and enhances tester capacity. Given the categorical and imbalanced nature of assembly data, Decision Trees are employed as the prediction model. Specifically, three Decision Tree algorithms are explored: ID3, C4.5, and CART. In addition, four feature selection techniques, namely Information Gain, Gain Ratio, Chi-Square, and Symmetrical Uncertainty, are utilized to identify high-impact features. Our experimental findings reveal that Information Gain coupled with the C4.5 algorithm yields the most favorable results in terms of prediction accuracy, modeling efficiency, and rule generation. Moreover, our study establishes that setting the failure probability threshold between 0.15 and 0.70 provides the shortest total test time for the proposed process, as supported by a 95% confidence level. This achievement represents a statistically significant enhancement compared with the existing manufacturing process.

The paper involved conducting preliminary research to explore the identification and control of a multi-dimensional, non-linear, and non-stationary cement grinding process using artificial neural networks and various other non-linear models. The primary objective was to establish a precise model that accurately characterizes the functioning of the grinding system. Several model structures were employed, including NARX models based on feed-forward network, Elman, Jordan, and Layer-Recurrent Network (LRN) recurrent networks, as well as MTL (Multi-Task Learning) and traditional NARX non-linear models. It was observed that, in contrast to the linear models, the non-linear models exhibited significantly superior performance in the modeling of the system. Another notable outcome of this research is the proposal of a neurocontroller, functioning as an expert system, which can provide control signals to operators. The development and implementation of such a neurocontroller have the potential to enhance the quality, simplicity, and efficiency of cement grinding process control.

ObjectiveTo fully utilize the experience of nuclear power plant operation and management to assist nuclear power operators in fault diagnosis, a marine nuclear power plant fault diagnosis expert system is designed. MethodFirst, based on the logical consistency between fault trees and production rules, a method is proposed to transform fault tree knowledge into production rules. The knowledge base and inference machine of the expert system are then optimized by using a mixed forward and backward inference method, and a forward inference strategy is designed to simplify the inference process based on the minimum cut set and importance analysis results of the fault tree. Finally, based on the idea of manually judging the fault status, a status monitoring module is designed to collect key equipment parameters in real time and convert them into equipment information that can be recognized by expert systems. ResultsThe results show that the proposed method solves the problem of difficult knowledge acquisition in expert systems and improves inference efficiency while ensuring inference accuracy, thereby achieving the online fault diagnosis function of expert systems. ConclusionUsing the proposed method can enhance the knowledge acquisition ability and inference efficiency of expert systems, which is of great significance for ensuring the operational management safety of nuclear power plants.

Abstract Background Menopause is a period of women’s life that has the especial physical, psychological and social challenges. So provision of an effective, practical and affordable way for meeting women’s related needs is important. In addition, women should be able to incorporate such programs into their daily work. Considering the dearth of suitable services in this regard, this study will be conducted with the aim of designing, validating and evaluating the “Healthy Menopause” expert system on the management of menopausal symptoms. Methods/design A mixed methods exploratory design will be used to conduct this study in 3 phases. The first phase is a qualitative conventional content analysis study with purposes of exploring the women’s experience of menopausal symptoms and extracting their needs, and collecting data about their expectations from a healthy menopause expert system.. The purposive sampling (In his phase data will be gathered through interviewing menopaused women aged 40 to 60 years old and other persons that have rich information in this regard and will be continued until data saturation. The second phase includes designing a healthy menopause expert system in this stage, the needs will be extracted from the qualitative findings along with a comprehensive literature review. The extracted needs will be again confirmed by the participants. Then, through a participatory approach (Participatory Design) using nominal group or Delphi technique the experts’ opinion about the priority needs of menopaused women and related solutions will be explored based on the categories of identified needs. Such findings will be used to design a healthy menopause expert system at this stage. The third phase of study is a quantitative research in which the evaluation of the healthy menopause expert system will be done through a randomized controlled clinical trial with the aim of determining the effect of the healthy menopause expert system on the management of menopause symptoms by menopausal women themselves. Discussion This is the first study that uses a mixed method approach for designing, validating and evaluating of the expert system “Healthy Menopause”. This study will fill the research gap in the field of improving menopausal symptoms and designing a healthy menopause expert system based on the needs of the large group of menopause women. We hope that by applying this expert system, the menopausal women be empowered to management and improving their health with an easy and affordable manner.

The control of the pH neutralization process has a decisive role in many industrial branches, as: wastewater treatment, biotechnology, pharmaceutical industry, chemical processing, etc. However, the pH neutralization process is a difficult one to control, due to time-varying process parameters, its high non-linearity and to the sudden variation of the pH around the equivalence point. The paper presents a device and an integrated application that uses artificial intelligence (IA) developed for dosage of the necessary reactants in wastewater pH control. The AI techniques were integrated into the application by developing an expert system (ES). For this purpose, were used the necessary if-then rules (rules that make up the inference engine (IE) of the ES) related to the flowrates of the reactants necessary for wastewater pH neutralization. The ES knowledge base (KB) was built based on the recorded pH values. The hardware component (the device) was created using the Arduino equipment.

Identification of inflammatory bowel disease quickly and accurately is motivated by the large number of patients who come with pain in the abdomen and receive minimal treatment because they are considered to be just ordinary abdominal pain. This study aims to identify inflammatory bowel disease which is still considered by some people as a common stomach ache quickly, and precisely and to recommend therapy that can be done as an initial treatment before getting medical action by medical personnel. The method used in this expert system research is a combination of forward chaining and certainty factors. The forward chaining method traces the disease forward starting from a set of facts adjusted to a hypothesis that leads to conclusions, while the certainty factor method is used to confirm a hypothesis by measuring the amount of trust in concluding the process of detecting inflammatory bowel disease. The results of this study are a conclusion from the process of identifying inflammatory bowel disease which begins with selecting the symptoms experienced by the patient so that the diagnosis results appear using forward chaining and certainty factor in the form of a percentage along with therapy that can be given to the patient to reduce pain in the abdomen. A comparison of the diagnosis results using the system and diagnosis by experts, in this case, specialist doctors, shows an accuracy rate of 82,18%, which means that the expert system diagnosis results can be accounted for and follow the expert diagnosis.

Background Nowadays, financial institutions face and solve challenges to optimise Information Management (IM), so the use of new technologies such as Expert Systems (ES) is indispensable. Therefore, the objective of this research is to implement an ES to improve IM in insurance companies. Method In terms of approach, the agile methodology SCRUM was chosen, which consists of five phases: initiation, planning and estimation, implementation, review and retrospective, launch, and thus decision making and optimisation of the IM process. In addition, the following technologies were chosen: ASP.NET as programming language, HTML as markup language, SQL Server as database management and CSS for design and visual styling. Results The results also showed a significant increase of 35% in user service, accompanied by a significant improvement of 44% in report delivery. Finally, a significant improvement of 24% was observed following the implementation of the expert system. This streamlines processes, reduces waiting times, improves the user experience in real time and optimises the management of large volumes of data in the insurance company. Conclusions The system demonstrated that this tool improves decision making, reduces errors in the issuing area and provides a user-friendly interface for information management.

Background The vocational guidance process in educational institutions faces important challenges in managing trials and errors in diagnoses. Technological tools are identified as an effective solution to address these problems. This research seeks to improve career guidance in educational institutions through the implementation of an expert system. The main objective is to reduce test processing time and achieve greater efficiency in students’ self-knowledge regarding their interests, based on the personalities of the Holland Test. Methods The development of the expert system followed a six-model approach. First, an organisational model was created to assess the scope and feasibility of the project. Next, a task and agent model was developed to investigate the impact and look for improvements. A knowledge model was then developed to analyse the relevant knowledge bases. A communication model was also developed to evaluate the communication interface of the system. Next, a design model was created to provide guidelines for the implementation of the system. Finally, the implementation of the knowledge system was carried out to ensure its correct functioning. Results The implementation of the expert system has shown significant improvements in the vocational guidance process. It was possible to reduce the time needed to apply the test, thus optimising the psychologist’s time and allowing a greater capacity for analysis. In addition, an improvement in the effectiveness of the students’ self-knowledge in relation to their vocational interests based on the personalities of the Holland Test was observed. Conclusions This study contributes to career guidance in educational institutions by introducing an innovative expert system. This technological solution optimizes the career guidance process, benefiting psychologists administering tests and students seeking self-knowledge about their career interests.

This paper presents an intelligent speed adaption system for vehicles on conventional roads. The fuzzy logic based expert system outputs a recommended speed to ensure both safety and passenger comfort. This intelligent system includes geometrical features of the road, as well as subjective perceptions of the drivers. It has been developed and checked with real data that were measured with an instrumental system incorporated in a vehicle, on several two‐lane roads located in the Madrid Region, Spain. Along with the road geometrical characteristics, other input variables to the system are external factors, such as weather conditions, distance to the preceding vehicle, tire pressure, and other subjective criteria, such as the desired comfort level, selected by the driver. The expert system output is the most suitable speed for the specific road type, considering real factors that may modify the category of the road and thus, the appropriate speed. This information could be added to the adaptive cruise control of the vehicle. The recommended speed can be a very useful input for both, drivers and the autonomous vehicles, to improve safety on the road system. [ABSTRACT FROM AUTHOR]

Violent attacks have been one of the hot issues in recent years. In the presence of closed-circuit televisions (CCTVs) in smart cities, there is an emerging challenge in apprehending criminals, leading to a need for innovative solutions. In this paper, the propose a model aimed at enhancing real-time emergency response capabilities and swiftly identifying criminals. This initiative aims to foster a safer environment and better manage criminal activity within smart cities. The proposed architecture combines an image-to-image stable diffusion model with violence detection and pose estimation approaches. The diffusion model generates synthetic data while the object detection approach uses YOLO v7 to identify violent objects like baseball bats, knives, and pistols, complemented by MediaPipe for action detection. Further, a long short-term memory (LSTM) network classifies the action attacks involving violent objects. Subsequently, an ensemble consisting of an edge device and the entire proposed model is deployed onto the edge device for real-time data testing using a dash camera. Thus, this study can handle violent attacks and send alerts in emergencies. As a result, our proposed YOLO model achieves a mean average precision (MAP) of 89.5% for violent attack detection, and the LSTM classifier model achieves an accuracy of 88.33% for violent action classification. The results highlight the model's enhanced capability to accurately detect violent objects, particularly in effectively identifying violence through the implemented artificial intelligence system. [ABSTRACT FROM AUTHOR]

Background: Menopause is a period of women's life that has the especial physical, psychological and social challenges. So provision of an effective, practical and affordable way for meeting women's related needs is important. In addition, women should be able to incorporate such programs into their daily work. Considering the dearth of suitable services in this regard, this study will be conducted with the aim of designing, validating and evaluating the "Healthy Menopause" expert system on the management of menopausal symptoms. Methods/design: A mixed methods exploratory design will be used to conduct this study in 3 phases. The first phase is a qualitative conventional content analysis study with purposes of exploring the women's experience of menopausal symptoms and extracting their needs, and collecting data about their expectations from a healthy menopause expert system.. The purposive sampling (In his phase data will be gathered through interviewing menopaused women aged 40 to 60 years old and other persons that have rich information in this regard and will be continued until data saturation. The second phase includes designing a healthy menopause expert system in this stage, the needs will be extracted from the qualitative findings along with a comprehensive literature review. The extracted needs will be again confirmed by the participants. Then, through a participatory approach (Participatory Design) using nominal group or Delphi technique the experts' opinion about the priority needs of menopaused women and related solutions will be explored based on the categories of identified needs. Such findings will be used to design a healthy menopause expert system at this stage. The third phase of study is a quantitative research in which the evaluation of the healthy menopause expert system will be done through a randomized controlled clinical trial with the aim of determining the effect of the healthy menopause expert system on the management of menopause symptoms by menopausal women themselves. Discussion: This is the first study that uses a mixed method approach for designing, validating and evaluating of the expert system "Healthy Menopause". This study will fill the research gap in the field of improving menopausal symptoms and designing a healthy menopause expert system based on the needs of the large group of menopause women. We hope that by applying this expert system, the menopausal women be empowered to management and improving their health with an easy and affordable manner. Plain English summary: Menopause is a period of women's life that has the especial physical, psychological and social challenges. So provision of an effective, easy for use and affordable way for managing related problems and meeting related needs is important. Menopause is a period of women's life that has physical, psychological and social consequences. It is important to identify methods that are effective, practical and affordable. New technologies can increase women's ability to access educational information. This is the first study for designing, validating and evaluating of the expert system "Healthy Menopause". A mixed methods exploratory design will be used to conduct this study in 3 phases. The first phase (qualitative): The conventional content analysis method will be used. The second phase: Designing a healthy menopause expert system: It is based on the codes of women's challenges from the first phase, along with conducting interviews and literature review. The participatory approach (Participatory Design) through nominal group or if needed, Delphi method based on the categories of needs and solutions by considering the opinions of the participants, available experts related to this issue will be listed. It should be used to design a healthy menopause expert system at this stage. The third phase (quantitative): The evaluation of the healthy menopause expert system will be a randomized clinical trial that determine the effect of the healthy menopause expert system on the management of menopause symptoms. In the present study an expert system (ES) will be designed that can be installed on mobile phones and computers. This tool is not only educational but also interactively helps to adapt to continuous changes, so by asking questions about menopause the system will respond as if an expert (midwife or gynecologist) is giving advice. [ABSTRACT FROM AUTHOR]

Addressing the challenges in diagnosing and classifying self-care difficulties in exceptional children's healthcare systems is crucial. The conventional diagnostic process, reliant on professional healthcare personnel, is time-consuming and costly. This study introduces an intelligent approach employing expert systems built on artificial intelligence technologies, specifically random forest, decision tree, support vector machine, and bagging classifier. The focus is on binary and multi-label SCADI datasets. To enhance model performance, we implemented resampling and data shuffling methods to tackle data imbalance and generalization issues, respectively. Additionally, a hyper framework feature selection strategy was applied, using mutual-information statistics and random forest recursive feature elimination (RF-RFE) based on a forward elimination method. Prediction performance and feature significance experiments, employing Shapley value explanation (SHAP), demonstrated the effectiveness of the proposed model. The framework achieved a remarkable overall accuracy of 99% for both datasets used with the fewest number of unique features reported in contemporary literature. The use of hyperparameter tuning for RF modeling further contributed to this significant improvement, suggesting its potential utility in diagnosing self-care issues within the medical industry. [ABSTRACT FROM AUTHOR]

This research is mostly about Guillain-Barre syndrome (GBS), a complicated neurological condition with many subtypes that make diagnosis and treatment hard, even though medical care is always getting better. The main goal of this study is to build and test an expert system that can correctly diagnose these subtypes, with a focus on early detection and personalized treatments. The evaluation of the system was carried out using a dataset composed of 20 cases (12 positive and 8 negative). A confusion matrix was used to evaluate key metrics such as precision, sensitivity, and specificity. The findings demonstrate precision and sensitivity of 83% and specificity of 75%. These findings unambiguously demonstrate the efficacy of the system in correctly identifying positive Guillain-Barre cases while substantially reducing false negatives. In conclusion, this expert system offers a potentially useful tool to improve the accuracy of the diagnosis and treatment of Guillain-Barre patients. However, to take advantage of its full potential in clinical practice, it should be used as diagnostic support and not replace the medical staff, and it should be updated periodically to reflect new findings in medicine. [ABSTRACT FROM AUTHOR]

Farmed fish disease diagnosis is an important problem in the fish farming industry, affecting quality of production and financial losses. In this paper, we present a web-based intelligent system that tackles the problem of fish disease diagnosis. To this end, it uses multiple knowledge representation and reasoning methods: rule-based, case-based, weight-based, and voting. Knowledge, which concerns the diagnosis of sea bass diseases, was acquired from experts in the field and represented in the form of decision trees. The diagnostic process is performed in two stages: a general one and a specialized one. In the general stage, a level-based diagnosis is performed, where environmental parameters, external signs, and internal signs are successively examined, and the three most probable diseases are identified. In the specialized stage, which is optional, a specialized expert system is used for each of the resulting diseases, where additional parameters concerning laboratory tests (microbiological, microscopic, molecular, and chemical) are considered. The general stage is the most useful, given that it can be performed on-site in real-time, whereas the specialized one requires time-consuming lab tests. The system also provides explanations for its decisions. Evaluation of the general-stage diagnostic process showed a top-3 accuracy of 78.79% on expert test cases and 94% on an artificial dataset. [ABSTRACT FROM AUTHOR]

AbstractWhen assessing the ablation status of circuit breaker contacts, the average dynamic contact resistance of the arc contacts of high-voltage SF6 circuit breakers is crucial. The average dynamic contact resistance of arc contacts on circuit breakers under various current levels may be accurately predicted using a method based on a differential evolution algorithm and extreme learning machine (DE-ELM), which is suggested in this research. Combining optimization algorithms yields the ideal input weight and hidden layer bias of the ELM method. The DE-ELM approach has outstanding anticipation performance for anticipation data under various current levels when compared to other anticipation methods. Finally, an expert system for assessing the ablation state of contacts based on the DE-ELM algorithm is created using the average dynamic contact resistance data of arc contacts predicted by DE-ELM. As a guide for the upkeep of high-voltage SF6 circuit breakers, the ablation status of contacts is categorized into four grades: A-level ablation, B-level ablation, C-level ablation, and D-level ablation. [ABSTRACT FROM AUTHOR]

In response to the difficulty in accurately describing the fault diagnosis of exhaust temperature exceeding the limit of a piston type hydrogen compressor using traditional two-state fault diagnosis at each stage, and the lack of application of a fault diagnosis system that could finish self-diagnose for diagnosing the exhaust temperature exceeding the limit, the DW-(5.6-13)/(0.2-0.5)-20 oil-free variable frequency piston type hydrogen compressor was taken as the research object, the importance of relevant components were analyzed, and a fault self-diagnosis system was designed. Firstly, the corresponding fuzzy Takagi-Sugeno(T-S) fault tree analysis model was built for the fault, and the T-S fault tree analysis method based on Bayesian network was used to analyze the importance of the bottom event to determine the weak links and the diagnosis sequence of faulty parts. Then, the fuzzy number was used to represent the fault status of the parts, and it was brought into the T-S fault tree analysis algorithm based on Bayesian-network(BN) to predict the occurrence probability of the primary exhaust temperature overrun fault. Finally, a rule-based fault diagnosis expert system was designed to infer and troubleshoot the fault causes of the instance. The research results indicate that after analyzing existing fault data, the rule-based fault diagnosis expert system’s diagnosis results are consistent with the facts, indicating that the system can accurately diagnose the cause of the fault and achieve self-diagnosis. At the same time, combining with the T-S fault tree analysis method based on BN, the system can also realize the prediction of the probability of top event failure, which provides a feasible scheme for the fault diagnosis of the piston hydrogen compressor. [ABSTRACT FROM AUTHOR]

Among the agricultural products, mushroom is one of the best candidates for robotic harvesting methods. One of the main problems of mushroom growers is a critical need for labor within the specified time for harvesting mushrooms, so mushroom growing is labor intensive. In this research, we attempted to develop and apply a harvesting robot for this crop to reduce the problems that growers face in terms of harvesting labor. For ease of end-effector movement between shelves, the robot was developed by Cartesian Mechanism. This robot used image processing and a computer expert system to detect the position of mushrooms on the substrate on the shelf. The end-effector acts by using a suction cup and a non-shear mechanism to harvest mushrooms from the substrate. By testing this robot on the substrate, we could harvest perfect whole mushrooms on average of 81.5% of mushrooms on the prepared substrates. Harvesting time per mushroom was obtained 12.45s, which is the amount of respective time of 8.45s more than harvesting time by labor hands, but in robotic harvesting, robots unlike humans can work 24 hours a day, continuously during growing time. An expert system also could be a valuable asset to change the grower’s strategy in terms of harvested mushrooms size and quality based on customer needs in the market. [ABSTRACT FROM AUTHOR]

In recent days the manufacturing process have become more precise and cost efficient due to advancement in the field of computer technology. Information technology has been integrated with manufacturing practice and has resulted in time reduction from concept of a product to marketing of the product. Cutting force generated is the main manufacturing issue raised among industries as it clearly affects quality and cost of the final product. Hence using extensive literature and data base knowledge optimum cutting parameters are selected. Therefore, this paper focuses on a response surface methodology (RSM) based expert system that has been developed using JAVA programming with the help of response surface second order model to automatically generate values of cutting force during machining of Ti–6Al–4V alloy under minimum quantity lubrication (MQL) for different process input parameters. From RSM it has been observed that calculated value of F (20.36) was greater than the F-table value (3.02) and hence the model developed can be effectively used for machining of Ti–6Al–4V alloy. Further the developed RSM based expert system model can be successfully used to predict the force generated during cutting process while machining Ti–6Al–4V alloy under MQL conditions. [ABSTRACT FROM AUTHOR]

Expert systems have been widely used in medicine to diagnose different diseases. However, these rule-based systems only explain why and how their outcomes are reached. The rules leading to those outcomes are also expressed in a machine language and confronted with the familiar problems of coverage and specificity. This fact prevents procuring expert systems with fully human-understandable explanations. Furthermore, early diagnosis involves a high degree of uncertainty and vagueness which constitutes another challenge to overcome in this study. This paper aims to design and develop a fuzzy explainable expert system for coronavirus disease-2019 (COVID-19) diagnosis that could be incorporated into medical robots. The proposed medical robotic application deduces the likelihood level of contracting COVID-19 from the entered symptoms, the personal information, and the patient's activities. The proposal integrates fuzzy logic to deal with uncertainty and vagueness in diagnosis. Besides, it adopts a hybrid explainable artificial intelligence (XAI) technique to provide different explanation forms. In particular, the textual explanations are generated as rules expressed in a natural language while avoiding coverage and specificity problems. Therefore, the proposal could help overwhelmed hospitals during the epidemic propagation and avoid contamination using a solution with a high level of explicability. [ABSTRACT FROM AUTHOR]

The importance of Big Data and the analysis of this data in recent years is indisputable, and this boom has spread to all areas of life, including professional sports and, within this, soccer. The significant amounts of money involved in this sport have led to the need for the top clubs to employ these techniques to gain a competitive advantage over their competitors. Despite this, there is very little information on how these tools are used or what parameters they consider. Similarly, there are a multitude of amateur analyses that offer very few conclusions. They simply focus on collecting and presenting the data in the form of a comparison without any analysis or pre-processing. This work describes the implementation of an expert system based on fuzzy logic used to evaluate the talent of a soccer player at all levels, his/her aptitude and attitude, to face his/her individual and collective professional development. For this purpose, the above aspects will be evaluated specifically in the different aspects of the game, which will allow us to evaluate the performance of a soccer team and thus determine the probability of victory, draw, and defeat in a confrontation. [ABSTRACT FROM AUTHOR]