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Prenatal care refers to medical attention that a woman receives to maintain her health and the health of her unborn child. The traditional methods of medical experts in prenatal care advice can sometimes be inadequate and less efficient due to a number of factors. One major issue is the lack of knowledge in the area of prenatal care for most medical experts, which hinders quality advice to pregnant women. In order to overcome some of these challenges, computer-based methods for any health issue have been proposed, which have shown to be more effective and are being utilized more frequently to raise the standard of medical care. This research work proposes the use of an expert system approach based on a forward-chain inference engine. Java programming language was used in the implementation of the proposed expert system. The expert system for prenatal care recommendations had a mean satisfaction level score of 3.87 out of a possible 5.0, according to the assessment results based on survey opinions. This suggests a 77.5% level of user satisfaction. The performance level of the expert system in providing quality advice to pregnant women indicates that it is faster, more sensitive, and more reliable than the traditional method of using medical experts to provide prenatal care advice.

Help desks play a crucial function in the information technology department by acting as the main point of solution to customers’ issues. The speed and efficiency of agent-centric help desks are diminished by the inability of a help desk agents to share their knowledge when they leave and the inability of agents applying past cases to solve present problem. In this study, a knowledge-based expert system for campus helpdesk request processing was presented. The presented system consists of a fusion of expert system and fuzzy inference system for campus helpdesk request processing. The fuzzy inference system is responsible for the rules while the expert system provided the interface for the user interactions. The presented system takes input queries from students through the interface of the expert system. The input queries are converted into fuzzy variables and the inference engine is then used to compare the fuzzy variables with the fuzzy rules in the knowledge base of the expert system. The database base of the expert system contains the solutions to past queries while the rule base is made up of the set of IF-THEN rules depicting the domain expert knowledge. The expert system will fire the rules that matches the input queries and deliver the answers to the queries through the expert system output interface. The evaluation results of the presented system when compared with previous research showed a better accuracy of 95.36767% in terms of its efficiency in providing solutions to requests relating to complaints from campus students.

Paddy (Oryza sativa) is one of the most consumed food grains in the world. The process from its sowing to consumption via harvesting, processing, storage and management require much effort and expertise. The grain quality of the product is heavily affected by the weather conditions, irrigation frequency, and many other factors. However, quality control is of immense importance, and thus, the evaluation of grain quality is necessary. Since it is necessary and arduous, we try to overcome the limitations and shortcomings of grain quality evaluation using image processing and machine learning (ML) techniques. Most existing methods are designed for rice grain quality assessment, noting that the key characteristics of paddy and rice are different. In addition, they have complex and expensive setups and utilize black-box ML models. To handle these issues, in this paper, we propose a reliable ML-based IoT paddy grain quality assessment system utilizing affordable sensors. It involves a specific data collection procedure followed by image processing with an ML-based model to predict the quality. Different explainable features are used for classifying the grain quality of paddy grain, like the shape, size, moisture, and maturity of the grain. The precision of the system was tested in real-world scenarios. To our knowledge, it is the first automated system to precisely provide an overall quality metric. The main feature of our system is its explainability in terms of utilized features and fuzzy rules, which increases the confidence and trustworthiness of the public toward its use. The grain variety used for experiments majorly belonged to the Indian Subcontinent, but it covered a significant variation in the shape and size of the grain.

This paper aims to develop a framework for differentiating the selection of welding processes using specialized knowledge-based expert systems, which are particularly useful in dealing with issues that require complex decision-making. A program was created and programmed using the process information maps (PRIMAs) matrix in Visual Basic Access for selecting welding processes in a study. The program is in two stages. In the first stage, it excludes non-candidate operations according to several criteria, the most important of which is the type of metal and its thickness. The second stage is arranging operations using multi-criteria methods for decision-making through research to resolve complicated problems involving multiple factors. The hierarchical analysis process (AHP) was used. It is considered one of the most frequently used multi-criteria decision-making methods. The selection of analytic hierarchical process. AHP criteria depend on experience and knowledge rather than specific data for selecting alternatives and determining weights. The methodology is straightforward, easy to comprehend, and applicable to various domains needing intricate decision-making. The program results are compared with those of a previously published case to choose the best welding processes for a study case, which is a home radiator, and results matching the candidate processes were discovered.

One of the factors causing rice production disturbance in Indonesia is that farmers lack knowledge of early symptoms of rice plant diseases. These diseases are increasingly rampant because of the lack of experts. This study aimed to overcome this problem by providing an Expert System that helps farmers to make an early diagnosis of rice plant diseases. Data of rice plant pests and diseases in 2016 were taken from Samarinda, East Kalimantan, Indonesia using an in-depth survey, and rice experts from the Department of Food Crops and Horticulture of East Kalimantan Province were recruited for the project. The Expert System for Rice Plant Disease Diagnosis, ESforRPD2, was developed based on the pest and disease experiences of the rice experts and uses a Waterfall Paradigm and Unified Modeling Language. This Expert System can detect 48 symptoms and 8 types of diseases of rice plants from 16 data tests with a sensitivity of 87.5%. The system can also provide recommendations for the treatment of identified diseases. ESforRPD2 is available in Indonesian at http://esforrpd2.blog.unmul.ac.id

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.

This study explores the contemporary landscape of integrating numerical algorithms, artificial intelligence (AI), and expert methodologies within the domain of nephrology. Focusing on automation and decision support, we scrutinize the impact of numerical algorithms on the precise evaluation of kidney function. Furthermore, we delve into the transformative potential of artificial intelligence, particularly in the realms of machine learning and deep learning applications, elucidating its role in early disease detection and the formulation of personalized treatment strategies. The synergy between computer-based tools and expert-driven approaches is examined, underscoring their collaborative role in enhancing the accuracy and dependability of diagnoses. Additionally, we address the ethical considerations and challenges associated with the incorporation of automation in nephrology. The paper provides a concise overview of in-depth analysis while illustrating the promising prospects of these innovative methodologies for reshaping nephrology research and patient care.

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 Pharma-safe Index Dataset comprises comprehensive information on several aspects of pharmaceuticals, including general medication data, usage guidelines, dosage, adverse effects, pricing, drug interactions, duration of use, composition, and contraindications. The dataset is provided in both CSV and JSON file formats, and it is available in both English and Bahasa Indonesia. By conducting interviews, distributing flyers, and using pharmacy books, the dataset was collected from over-the-counter medications that were sold at three pharmacies located in Yogyakarta, Indonesia. A medical professional performed cleansing, standardization, and validation on it before it was exported to JSON and CSV formats. The data collected on drug efficacy, safety, and patient outcomes in Indonesia can be utilized by researchers in order to uncover trends and developing patterns of prescription drug resistance. It is possible that this will direct future research, lead to improvements in drug formulations, treatment strategies, and public health policies, and expand our understanding of how drugs work and how they affect patient health.

X Balikpapan merupakan usaha yang bergerak di bidang produksi furniture. Berdasarkan data cacat produk pada Tahun 2022. Diketahui terdapat rata-rata gap antara persentase jumlah cacat produk yaitu 13,8 % dengan standar cacat produk yang telah ditetapkan pada sofa sebesar 10%. Tujuan penelitian ini adalah untuk menentukan penyebab kegagalan yang harus ditangani dan usulan upaya penanggulangan berdasarkan metode FMEA berbasis expert system. Metode yang digunakan adalah FMEA dan Expert System yang berlandandaskan pada Kecerdasan Buatan. Metode FMEA berupa basis data dalam mengidentifikasi dan menganalisis kegagalan yang menghasilkan nilai Risk Priority Number (RPN). Metode expert system sebagai sistem komputer algoritma untuk meniru kemampuan yang ditransfer dari sebuah ahli pakar ke komputer untuk disimpan dalam memori sehingga dapat berkonsultasi dengan komputer. Hasil dari penelitian ini berupa sebuah perancangan website sistem pakar berbasis metode FMEA dengan persentase keakuratan data sebesar 86,66% dari 30 data pengujian sistem berdasarkan kondisi lingkungan pada jenis kegagalan proses yang telah terjadi pada produksi sofa yang menghasilkan jenis kegagalan yang harus ditangani yaitu pada proses produksi assembly dan jenis kegagalan prosesnya yaitu plywood atau kayu pecah dengan nilai RPN paling tinggi sebesar 216 beserta rekomendasi upaya penanggulangannya adalah sebelum produk sofa dikirim kepada pelanggan, lakukan pemeriksaan akhir terhadap plywood dan kayu yang digunakan dengan cara uji ketahanan sofa dengan beban maksimal yang telah ditetapkan. Pastikan tidak ada kelemahan atau kerusakan yang dapat menyebabkan pecahnya plywood dan kayu.

Preservation of cultural relics is crucial for cultural sustainability, particularly in the case of cave temples, which are a unique and immovable part of our heritage, nested within mountains. These structures bear immense historical significance and possess considerable economic value, making them vulnerable to criminal activities, notably theft. Establishing a robust physical protection system (PPS) is imperative to safeguard these relics from potential damage. This paper proposes a novel approach for assessing the vulnerability of cave temples' PPS. Based on the fuzzy Petri net (FPN) principle, a comprehensive vulnerability assessment index system was developed within the PPS framework, considering the unique characteristics of cave temples. This study refines the formal definition of the FPN, enhancing its precision and effectiveness for vulnerability assessment. The practicality and effectiveness of the proposed method are verified through simulation experiments. An illustrative example is provided to demonstrate this approach.

Chronic Energy Deficiency in pregnancy is a condition where pregnant women suffer from a lack of food that lasts for a long time. Pregnant women with chronic energy deficiency have poor eating behavior, which is influenced by knowledge, perception, beliefs and health education. Health education provided by health workers can increase knowledge and change eating behavior and can be done better if assisted by an expert system. The aim of this activity is to train health workers (doctors, midwives, nutritionists) to carry out early detection of chronic energy deficiency in pregnancy using an expert system, increasing knowledge, attitudes and eating behavior of pregnant women. The method that will be carried out is training using the web-based expert system modelangraini.com for health workers, health education for pregnant women, and increasing knowledge and changing eating behavior of pregnant women. This activity was carried out for 1 day and took place in Faculty of Medicine, University of Lampung on Saturday 30 September 2023 with 45 family doctors, midwives and nutritionists in Bandar Lampung City participating. The results of the evaluation of the implementation of the service showed that there was an increase in participants' understanding by 91.1% to good understanding, 8.9% of participants had a fairly good understanding, and no participants had a poor understanding of the causes, risk factors, impacts, prevention methods, and early detection of chronic energy deficiency in pregnancy with techniques that are fast, easy and can help health workers in providing education and interventions on the eating behavior of pregnant women. This training activity can improve the skills of health workers in an effort to increase knowledge, attitudes and eating behavior of pregnant women so that they can prevent, overcome and help reduce the prevalence of chronic energy deficiency in pregnant women and prevent stunting in toddlers. Keywords: early detection, chronic energy deficiency, training, health workers

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.

Abstract A tropical cyclone (TC) is extreme weather severely threatening coastal wind turbines. However, the non‐extreme wind speed period (nEWSP) before and after it can also bring considerable economic benefits to wind farms. Aiming at the working conditions of nEWSP‐TC, this paper analyzes the characteristics of the wind field based on historical actual TC data and constructs a pseudo‐Monte Carlo experiment, uses the experimental results to construct a knowledge base and an inference engine, and forms an expert system to guide the yaw control of wind turbines in nEWSP‐TC. The simulation results show that the yaw error has different effects on the fatigue load of the wind turbine under different working conditions of nEWSP‐TC, and the proposed improved yaw strategy can reduce the fatigue load of the wind turbine under the premise of limited power loss and improve safe operation capability of wind turbines under nEWSP‐TC conditions.

Despite being very old, the mining industry continues to be one of the major sources of pollution, with more people killed or injured than in all other industries. Prevention of incidents/accidents on machinery in mining pits and the issues of operator safety on mining machinery largely depend on the ergonomic adaptation of the workplace, compliance with safety procedures and policies, and organizational and other influential factors. Evidently, scarce consideration of those factors in the available literature has not given satisfactory results till now. The aim of this paper is to first set up a comprehensive model based on ergonomic factors and contextual theory, which takes into account all the influencing factors on the occurrence of incidents/accidents and represents a complex system of interdependence of influential variables of diverse, mostly stochastic nature, and then design a software solution on the given basis. In this research, based on the extensive data collected, a model was generated using the structural equations modelling methodology, which was then used to design the reasoning logic in the expert system for mitigating the risks of the operation of mining machines. An innovative solution incorporating a mathematical model of the interdependence of influential variables into the stored knowledge base offers a decision support system that provides recommendations for the maintenance of a particular mining machine, depending on the assessment of model factors in a specific decision-making situation at the higher organizational level and ergonomic suitability for the operator at the lower organizational level, and, in that manner, enables the mitigating of risky/unwanted events.

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.

Objective: To compare the laboratory tests conducted in real-life settings for patients with anemia with the expected prescriptions derived from an optimal checkup. Methods: A panel of experts formulated an “optimal laboratory test assessment'' specific to each anemia profile. A retrospective analysis was done of the laboratory tests conducted according to the type of anemia (microcytic, normocytic or macrocytic). Using an algorithmic system, the laboratory tests performed in real-life practice were compared with the recommendations suggested in the “optimal laboratory test assessment” and with seemingly “unnecessary” laboratory tests. Results: In the analysis of the “optimal laboratory test assessment”, of the 1179 patients with microcytic anemia, 269 (22.8%) had had one of the three tests recommended by the expert system, and only 33 (2.8%) had all three tests. For normocytic anemia, 1054 of 2313 patients (45.6%) had one of the eleven recommended tests, and none had all eleven. Of the 384 patients with macrocytic anemia, 196 (51%) had one of the four recommended tests, and none had all four. In the analysis of “unnecessary laboratory tests'', one lab test was unnecessarily done in 727/3876 patients (18.8%), i.e. 339 of 1179 (28.8%) microcytic, 171 of 2313 (7.4%) normocytic, and 217 of 384 (56.5 %) macrocytic anemias. Conclusion: Laboratory investigations of anemia remain imperfect as more than half of the cases did not receive the expected tests. Analyzing other diagnostic domains, the authors are currently developing an artificial intelligence system to assist physicians in enhancing the efficiency of their laboratory test prescriptions.

Rice is one of the most important cultivated plants for human survival. The activity of cultivating rice plants becomes a livelihood for most of these residents, so the success rate of the amount of rice harvested becomes very important because they depend on how much rice can be harvested, disease diagnosis is very important for farmers, this is very important to reduce economic losses due to diseases that cause crop failure. Therefore, when dealing with rice diseases, an expert is needed to make diagnoses or solutions to rice diseases. However, an expert does not know when to come to the village, and farmers also do not understand all rice diseases. Therefore, a web-based expert system application using the forward chaining method is proposed to represent an expert to help farmers diagnose and solve diseases of rice plants with existing symptoms.

Modern learning is impossible without automated knowledge testing systems. At present, the most progressive are adaptive testing models in which the complexity of tasks varies depending on the correctness of the patient’s answers. This article describes the development of an intelligent adaptive testing system using a fuzzy mathematics device. An intelligent adaptive testing system has been developed; the module that implements the expert system uses the production base of the rules. The input parameters of testing are the percentage of correct responses, the degree of correctness of the response, the duration of the response, and the number of attempts. The output is a change in the current level of training of the student on the basis of which test questions of related complexity are selected. As a method of logical inference, the Mamdani method is used which consists of six operational actions: phazification — conversion of exact values of input variables into values of linguistic variables through belonging functions, this served as the basis for designing a fuzzy base of rules of the expert system; aggregation of sub-conditions — determination of the truth of conditions for each linguistic rule of the fuzzy inference system; activating sub-conclusions — finding the degree of truth of each of the sub-conclusions in the linguistic rule; accumulation of conclusions — finding the belonging function for each of the output linguistic variables; defazzification — finding a numerical value for each of the output linguistic variables. A developed intelligent adaptive testing system (ISAT) is presented that allows, based on the analysis of test results, to determine the current level of training of students, to adapt the material to the level of their training. This system allows you to dynamically present questions of appropriate complexity in real time. When using the developed intelligent adaptive testing system, students will be provided with questions of the appropriate level of complexity, this will allow building an individual learning trajectory. The introduction of a predefined system will ensure the implementation of a personalized approach for organizing the learning process; will increase the accuracy of assessing students’ knowledge. The use of the technology of fuzzy expert systems allows for automated, intelligent control of students’ knowledge.

Abstract Background Accurate and timely decision-making in lung transplantation (LTx) programs is critical. The main objective of this study was to develop a mobile-based evidence-based clinical decision support system (CDSS) to enhance the management of lung transplant candidates. Method An iterative participatory software development process was employed to develop the ImamLTx CDSS. This study was accomplished in three phases. First, required data and standard clinical workflow were identified according to the literature review and expert consensus. Second, a rule-based knowledge-based CDSS application was developed. In the third phase, this CDSS was evaluated. The evaluation was done using the standard Post-Study System Usability Questionnaire (PSSUQ 18.3) and ten usability heuristics factors for user interface design. Results According to expert consensus, fifty-five data items were identified as essential data sets using the Content Validity Ratio (CVR) formula. By integrating information flow in clinical practices with clinical protocols, more than 450 rules and 500 knowledge statements were extracted. This CDSS provides clinical decision support on an Android platform regarding inclusion and exclusion referral criteria, optimum transplant time based on the type of lung disease, findings of initial assessment, and the overall evaluation of lung transplant candidates. Evaluation results showed high usability ratings due to the fact provided accuracy and sensitivity of this lung transplant CDSS with the information quality domain receiving the highest score (6.305 from 7). Conclusion Through a stepwise approach, the ImamLTx CDSS was developed to provide LTx programs with timely patient data access via a mobile platform. Our results suggest integration with existing workflow to support clinical decision-making and provide patient-specific recommendations.

Acute respiratory infections (ARI) encompass diseases with epidemic prevalence and mortality (measured number of deaths in a population) worldwide. Several types of diseases are classified as acute respiratory infections (ARI), i.e., pneumonia, diphtheria, tuberculosis, and COVID-19. The Ministry of Health of Indonesia suggests that one of the performance indicators for infectious disease control and management is disease detection. Disease detection constitutes the most significant factor for infectious diseases, and early detection based on the patient's symptoms is essential to classify and treat the diseases. The delayed treatment of these diseases has resulted in many cases and deaths, especially in ARI. Furthermore, to help support the Ministry of Health's program, an expert system was established to detect early acute respiratory infections (ARI) based on the patient's symptoms. Employing a combination of forward chaining and certainty factor as the expert system method could produce a percentage range of system accuracy of 95 to 97.5%.

Al-Qur’an secara tidak langsung memberikan metode dalam mendidik anak sejak usia dini yang tercantum pada surat Al-Ahqaf [46]: 30. Penerapan metode mendidik membutuhkan seorang pakar yaitu guru agama Islam yang bisa membantu menyarankan metode yang tepat digunakan. Oleh karena itu dalam hal ini terdapat kebutuhan untuk membuat aplikasi sistem pakar yang dapat membantu guru dalam menyarankan metode mendidik yang tepat kepada orang tua atau wali sesuai perilaku anak yang dimasukkan. Aplikasi pakar-mendidik-islam.com dibuat menggunakan metode forward chaining yang digunakan untuk melakukan penalaran didalam sistem basis data berupa rules hasis wawancara bersama pakar. Perilaku anak dan rules yang sudah ada dapat ditambahkan melalui halaman admin. Pengujian aplikasi dilakukan menggunakan quisioner model likert kepada orang tua atau wali siswa SDN 44 Amban dan SDN 48 Inggramui, Kab Manokwari. Hasil daripada quisioner tersebut keseluruhan memiliki nilai yang sama yaitu diatas 80% dengan kata lain user sangat setuju dengan quisioner yang diajukan untuk menguji aplikasi.

Abstract Purpose Wii Fit exergames have been less commonly used for the rehabilitation of athletes after Anterior Cruciate Ligament Reconstruction (ACLR). This study aims to investigate the effects of an expert system using Wii Fit exergames compared to conventional rehabilitation following ACLR. A forward-chaining rule-based expert system was developed which proposed a rehabilitation program that included the number and type of exercise in terms of difficulty and ease and the duration of each exercise in a progressive manner according to the patient's physical condition. Materials and methods Twenty eligible athletes aged 20–30 who underwent ACLR were enrolled in this study and randomly assigned to two groups; and received 12 sessions of either Wii Fit exergames as Wii group (n = 10) or conventional rehabilitation as CL group (n = 10). Results The main outcomes consisted of pain (Visual Analogue Scale (VAS)), knee effusion, knee flexion range (KFR), thigh girth (TG), single-leg hop for distance (SLHD), and for time (SLHT), static and dynamic balance tests. Both groups had considerable improvement in all outcomes, also there were significantly differences between Wii and CL groups as follows; VAS (P < 0.001), knee effusion (P < 0.001), TG (P = 0.001), KFR (P = 0.012), static balance in stable position (P < 0.001) and in unstable position (P = 0.001), dynamic balance in the anterior (P < 0.001), posteromedial (P < 0.001), posterolateral (P = 0.004) directions, symmetry index of SLHD (P < 0.001) and symmetry index of SLHT (P = 0.013). Conclusions The findings showed that using Wii Fit exergames in post-ACLR patients reduced pain and effusion while also improving function and balance significantly. Iranian Registry of Clinical Trials registration number is IRCT20191013045090N1, and the registration date is 03-03-2020.

This article discusses the issue of diagnosing low-power solar power plants using the five-valued (5VL) state evaluation {4, 3, 2, 1, 0}. We address in depth how the 5VL diagnostics built upon 2VL, 3VL, and 4VL—two-valued diagnostics, three-valued logistics, and four-valued diagnostics. Logic (5VL) assigns five state values to the range of signal value changes, and these states are completely operational ({4}), incomplete ({3}), critical efficiency ({2}), and pre-fault efficiency ({1}). For the identical ranges of diagnostic signal values, all three of the applied state valence logics interpret failure as changes outside of their permitted ranges. Diagnostic procedures made use of an AI-based DIAG 2 system. This article’s goal is to provide a comprehensive overview of the DIAG 2 intelligent diagnostic system, including its architecture, algorithm, and inference rules. Diagnosis with the DIAG 2 system is based on a well-established technique for comparing diagnostic signal vectors with reference signal vectors. A differential vector metric is born out of this examination of vectors. The input cells of the neural network implement the challenge of signal analysis and comparison. It is then possible to classify the object components’ states in the neural network’s output cells. Based on the condition of the object’s constituent parts, this approach can signal whether those parts are working, broken, or urgently require replacement.

Aim: Up to date many successful attempts to identify various types of lesions with machine learning (ML) were made, however, the recognition of Alzheimer’s disease (AD) from brain images and interpretation of the models is still a topic for the research. Here, using AD Imaging Initiative (ADNI) structural magnetic resonance imaging (MRI) brain images, the scope of this work was to find an optimal artificial neural network architecture for multiclass classification in AD, circumventing the dozens of images pre-processing steps and avoiding to increase the computational complexity. Methods: For this analysis, two supervised deep neural network (DNN) models were used, a three-dimensional 16-layer visual geometry-group (3D-VGG-16) standard convolutional network (CNN) and a three-dimensional residual network (ResNet3D) on the T1-weighted, 1.5 T ADNI MRI brain images that were divided into three groups: cognitively normal (CN), mild cognitive impairment (MCI), and AD. The minimal pre-processing procedure of the images was applied before training the two networks. Results: Results achieved suggest, that the network ResNet3D has a better performance in class prediction, which is higher than 90% in training set accuracy and arrives to 85% in validation set accuracy. ResNet3D also showed requiring less computational power than the 3D-VGG-16 network. The emphasis is also given to the fact that this result was achieved from raw images, applying minimal image preparation for the network. Conclusions: In this work, it has been shown that ResNet3D might have superiority over the other CNN models in the ability to classify high-complexity images. The prospective stands in doing a step further in creating an expert system based on residual DNNs for better brain image classification performance in AD detection.

Areca nut is a plant that is cultivated by the people of Papua as a means of livelihood. Areca nut has many benefits, starting from the fruit, leaves, roots, and the economic value of betel nut is also quite good. The problem often encountered by areca cultivators is the damage caused by areca plant diseases, causing losses both in terms of yield and control. In this study, the expert system for diagnosing areca plant diseases uses the Certainty Factor method, the essence of this method is to measure whether it is certain or uncertain in diagnosing disease based on the type of disease and disease symptoms found in areca plants, there are 8 types of areca plant diseases, namely spotting and yellowing, red leaf rust, stem base rot, fruit rot, shoot rot, root rot, flower and leaf fall and stunted plants. With the problems that occur, researchers will create an expert system that can help areca nut cultivators in diagnosing diseases in areca palm plants, it is hoped that this system can help areca cultivators by applying the Certainty Factor method which adopts expert knowledge into a computerized system so that the system can solve problems like an expert.

Speed limits should provide a balance between safety and traffic flow. However, setting a speed limit generally involves selecting a base speed (e.g., operational speed, design speed) and adjusting it with several other variables. For example, in Brazil, the standard guidelines list a series of factors that influence speed limits, but they do not provide a clear methodology to choose a speed limit for a highway segment. Therefore, under current Brazilian practice, the decision-maker is faced with a subject decision that mostly relies on expert judgment. This paper proposes a fuzzy expert system for setting Brazilian highway speed limits. Six input variables are considered in the system. Membership functions and fuzzy rules were extracted from experts’ evaluations of simulated highway scenarios. The experts evaluated the scenarios using linguistic variables and proposed speed limits. Then, a Mamdani fuzzy controller was developed. The outputs of the controller were compared to the expert’s answers for the simulated highway scenarios. The system was further validated considering two case studies of highway sections in Brazil. Results showed that the fuzzy system is able to provide outputs that agree with the experts’ evaluations and with the existing speed limits. The fuzzy controller developed in this study can be used to assist practitioners in setting highway speed limits on Brazilian highways.

Currently, the borescope is the most used method with non-destructive testing in the process of aero-engine inspections and is the only way to obtain borescope images. In recent years, the artificial intelligence technologies such as deep learning are applied to aero-engine damage classification and detection, and some effective methods are proposed to achieve intelligent inspection of aero-engines, which have significant value for industrial applications. The benefits and disadvantages of aero-engine borescope inspection and its development are reviewed. The progress in the application of artificial intelligence technologies such as expert system and machine learning to engine borescope detection images is over-viewed. Some of the challenges in achieving intelligent aero-engine borescope inspection are summarized.

This study presents a modular Fuzzy Expert System (FES) for chemotherapy drug dose scheduling. The computational model considers the patient’s Body Surface Area (BSA) and experts’ opinions to calculate chemo doses following the clinical practice. A proper balance between reducing cancerous cells and toxic side effects is required for effective drug scheduling. Still, in many cases, traditional clinical approaches fail to determine appropriate therapeutical doses that balance all restrictions. In our proposed system, FES-1 is developed to determine primary drug doses based on experts’ opinions and competing treatment objectives. To adjust the dose, FES-2 is developed based on clinical practices, the patient’s BSA, and experts’ opinions. The final chemotherapy drug dose schedule is generated by combining the outputs of FES-1 and FES-2, which is the proposed modular FES. A growth model is used in this work to observe response due to administration of chemotherapy drug doses and to determine the following doses by considering cancer patients’ three weight patterns (increasing, decreasing, and random order). Extensive simulation results and comparative assessment with other current computational chemotherapy drug scheduling models validate the effectiveness and the superiority of the model proposed in this study over the other methods reported in relevant studies.

Sustainability involves balancing the environment, equity, and economy, with a focus on the green or low-carbon economy. Four strategic targets for a green economy encompass green management, green production, green lifestyle, and green technologies. Adopting a green economy poses challenges for companies, necessitating innovative systems like green supply chain management, smart homes, and health-safety-environment systems. Companies' concern about financial effectiveness drives the need for investments in these systems, as financial performance is critical for survival and growth. This study investigates the influence of health-safety-environment practices on a firm's financial performance. By determining key health-safety-environment indicators and financial metrics, the research gathered insights through 97 questionnaires, which were completed by experts from four Iranian companies. The collected data was analyzed employing the analytic network process and Promethee techniques, enabling a robust evaluation of the relationship between HSE practices and financial performance. Finally, an expert system was designed based on decision matrices to provide suitable financial indicators derived from HSE data. Linking HSE practices to financial performance enhances overall effectiveness and sustainability, guiding businesses in making informed decisions while respecting growth, success, environmental issues, and finally low carbon economy. Received: 27 July 2023 | Revised: 25 September 2023 | Accepted: 13 October 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work.

Difteri adalah penyakit menular dan mematikan, penyakit ini dilaporkan oleh Dinas Kesehatan Jawa Barat kembali merebak akibat penanganan yang lambat. Jauhnya fasilitas layanan kesehatan dan keterbatasan jumlah dokter menjadi permasalahan. Tujuan dari penelitian ini adalah merancang dan membangun sistem pakar yang mampu melakukan diagnosis awal penyakit difteri melalui penerapan metode certainty factor. Aplikasi ini dirancang dalam bentuk web dengan mengikuti proses pengembangan aplikasi ESDLC (Expert System Development Life Cycle). Hasil dari penelitian ini adalah sebuah aplikasi sistem pakar berbasis web untuk mendiagnosis penyakit difteri dengan basis pengetahuan terdiri dari 12 gejala penyakit. Sistem pakar diagnosis penyakit difteri ini telah melalui proses uji coba menggunakan metode blackbox testing, hasilnya menunjukkan semua fitur dalam aplikasi yang sudah dibuat dapat beroperasi dengan baik. Selain itu, tingkat akurasi dari sistem pakar ini sebesar 90% berdasarkan akurasi yang telah dilakukan terhadap 10 data uji. Hasil tersebut menunjukkan bahwa diagnosis yang dihasilkan dari sistem pakar mempunyai hasil yang sejalan dengan diagnosis pakar.

Modern agricultural extension services help as a source of information for farmer queries. One of its applications are local hot lines where expert agents in specific crops or fields assist in farmer agricultural practices and decisions. As an already established technology in the field of customer services, conversational agents can help this solution by covering for experts when users have difficulties contacting them. These artificial agents can answer general queries at any hour, increasing the extension service accessibility. The creation of a contextualized solution is possible with the advancements of Natural Language Processing (NLP) and its accessibility in cloud services. We write this paper to collect state-of-the-art research on agricultural conversational agents and tools used for its design and deployment. General knowledge on current applications is gather and can later be used to help in the design of a localized solution when agricultural services are difficult to access. Lastly, we describe and analyze a chatbot prototype in the specific field of banana farming using IBM Watson services and the messaging platform Telegram App.

Guava (Psidium guajava) is a nutritious fruit known for its origin in Mexico, Central or South America, and the Caribbean. Its production faces declining levels, infections, and disease outbreaks. This dataset focuses on the early identification of guava diseases using image processing and computer vision. Farmers can detect and address diseases promptly by developing an expert system, increasing yields and reducing economic losses. The technology behind this dataset enables sustainable guava farming and disease prevention. This dataset consists of digital and thermal images of guava fruits, including healthy, damaged, and various diseased conditions such as wilt, Anthracnose, canker, and rot. The images are categorized based on the fruit's maturity level (mature, half-mature, and mature) and captured under different drop heights. The dataset also includes information on the damage-inducing methods, storage conditions, image capture schedule, and specific diseases present. The thermal images were acquired using hot air with controlled temperature and velocity.

This study aims to address the challenge of diagnosing mental disorders by using an expert system to provide accurate and efficient diagnosis. Mental health is crucial for achieving harmony in life, and the ability to face problems, feel happiness, and maintain positivity are all essential aspects of this. However, diagnosing mental disorders can be difficult and time-consuming, as symptoms may vary depending on the patient's condition and lifestyle. The study utilizes symptom data from 30 patients with 6 types of mental disorders, with each symptom assigned a weighted value of belief for the corresponding disorder. The Dempster Shafer algorithm is then used to process this data and obtain a total confidence value for each disorder. The results show a 97% confidence value for the patient's mental disorder, which can help psychologists make informed decisions and provide appropriate treatment.

In the modern economy, digitalization has become one of the key components of the Russian Federation regions socio-economic development. Enterprises of various industries are faced with the need to process large amounts of data, which greatly complicates data management, and therefore the relevance of the analysis of artificial intelligence technologies increases. Training employees for industrial processes is a major challenge in any industry. Effective human resource management requires an accurate assessment and presentation of available competencies, as well as an effective mapping of the required competencies for specific positions. Competences enable the company to achieve high production and economic results. The aim of the study is to develop a structural model of a predictive expert system for managing data on the competencies of a modern manager by combining artificial and human intelligence, which can serve as a decision support tool for managers in real conditions to improve the efficiency of a particular enterprise. The study of the demand for managers and requirements for candidates in the Russian Federation and the Republic of Tatarstan was conducted on the data of the largest Russian Internet recruitment company HeadHunter. To develop a structural model of the proposed expert system, information from specialized scientific publications published in the Russian and foreign scientific literature of the Web of science and Scopus databases was used. The expert system will allow the manager to find the best options for using employees, predict the development of the enterprise as a whole and its individual divisions, which will significantly increase the key performance indicators of any company.

In recent years and accelerated by the arrival of the COVID-19 pandemic, Learning Management Systems (LMS) are increasingly used as a complement to university teaching. LMS provide an important number of resources and activities that teachers can freely select to complement their teaching, which means courses with different usage patterns difficult to characterize. This study proposes an expert system to automatically classify courses and certify teachers’ LMS competence from LMS logs. The proposed system uses clustering to stablish the classification scheme. From the output of this algorithm, it defines the rules used to classify courses. Data registered from a university virtual campus with 3,303 courses and two million interactive events have been used to obtain the classification scheme and rules. The system has been validated against a group of experts. Results show that it performs successfully. Therefore, it can be concluded that the system can automatically and satisfactorily evaluate and certify the teachers’ LMS competence evidenced in their courses.

Auto insurance fraud detection plays an important role in promoting the healthy development of auto insurance.As the judgment of fraud involves the core content of civil rights,it is necessary for auto insurance experts to check the case and provide the reasons for fraud.Although the method based on machine learning have strongscalability and high accuracy,it lacks interpre-tability,while the rule method based on expert system has good interpretability,but it is limited by the trigger conditions of complex rules.To address the unexplainable problem of cases detected as“fraud” by machine learning methods without triggering the expert system fraud rules,this paper puts forward an analysis method of auto insurance fraud traceability based on network representationlear-ning.It first defines the abductive analysis task of auto insurance fraud.That is,for cases that are identified as “fraud ”ones by machine learning methods without triggering the expert system,it returns the ranking of the most likely fraud rules to auto insurance experts.Then,the method models the case-rule factor network based on the network representation lear-ning according to the fraud cases that have triggered the rules of the expert system,and learns the vector representation of these factors in fraud rules.To better measure the similarity between fraud cases and rules with incomplete triggering factors in the expert system,a weighted splicing strategy of factors in fraud rules is designed based on the principle of abductive reasoning,which can alleviate the problem of insufficient training data to some extent.Experimental results show that the proposed method can obtain better performances than existing methods in terms of three metrics.

Computer vision is a branch of computer science that studies how computers can ‘see’. It is a field that provides significant value for advancements in academia and artificial intelligence by processing images captured with a camera. In other words, the purpose of computer vision is to impart computers with the functions of human eyes and realise ‘vision’ among computers. Deep learning is a method of realising computer vision using image recognition and object detection technologies. Since its emergence, computer vision has evolved rapidly with the development of deep learning and has significantly improved image recognition accuracy. Moreover, an expert system can imitate and reproduce the flow of reasoning and decision making executed in human experts’ brains to derive optimal solutions. Machine learning, including deep learning, has made it possible to ‘acquire the tacit knowledge of experts’, which was not previously achievable with conventional expert systems. Machine learning ‘systematises tacit knowledge’ based on big data and measures phenomena from multiple angles and in large quantities. In this review, we discuss some knowledge-based computer vision techniques that employ deep learning.

Pineapple plants grow in tropical climates and have long been cultivated. Pineapple plants can be harvested 18-24 months after planting. Pineapple contains vitamins A and C and calcium, phosphorus, magnesium, iron, sodium, potassium, dextrose, sucrose (cane sugar), and bromelain enzymes beneficial for the body. Pineapples grow using fibrous roots to absorb organic matter and water from the soil. However, like other plants, pineapple plants also face problems with pests and diseases, causing a decrease in fruit quality and even leading to crop failure and losses for farmers. One of the causes of pests and diseases is no replanting for years because farmers lack knowledge in cultivating pineapple plants. For this reason, applying the expert system employing the Bayes Theorem is necessary to find suitable solutions in dealing with pests and diseases in pineapple plants. The system is built using a web-based programming language so that farmers can access the system created anytime and anywhere.

Abstract Background The diagnosis of primary membranous nephropathy (PMN) often depends on invasive renal biopsy, and the diagnosis based on clinical manifestations and target antigens may not be completely reliable as it could be affected by uncertain factors. Moreover, different experts could even have different diagnosis results due to their different experiences, which could further impact the reliability of the diagnosis. Therefore, how to properly integrate the knowledge of different experts to provide more reliable and comprehensive PMN diagnosis has become an urgent issue. Methods This paper develops a belief rule-based system for PMN diagnosis. The belief rule base is constructed based on the knowledge of the experts, with 9 biochemical indicators selected as the input variables. The belief rule-based system is developed of three layers: (1) input layer; (2) belief rule base layer; and (3) output layer, where 9 biochemical indicators are selected as the input variables and the diagnosis result is provided as the conclusion. The belief rule base layer is constructed based on the knowledge of the experts. The final validation was held with gold pattern clinical cases, i.e., with known and clinically confirmed diagnoses. Results 134 patients are used in this study, and the proposed method is defined by its sensitivity, specificity, accuracy and area under curve (AUC), which are 98.0%, 96.9%, 97.8% and 0.93, respectively. The results of this study present a novel and effective way for PMN diagnosis without the requirement of renal biopsy. Conclusions Through analysis of the diagnosis results and comparisons with other methods, it can be concluded that the developed system could help diagnose PMN based on biochemical indicators with relatively high accuracy. Graphical Abstract

This paper investigates a novel fuzzy Petri nets (FPNs) method based on q-rung orthopair fuzzy sets (q-ROFSs) to provide an efficient solution to uncertain knowledge representation and reasoning. It not only improves FPN’s flexibility in knowledge parameter representation and reasoning algorithms but also addresses the challenges that most FPNs cannot implement backward reasoning, which is a common reasoning task to infer condition statuses according to consequences reversely. Specifically, we first propose the q-rung orthopair FPNs (q-ROFPNs) by integrating q-ROFSs with FPNs. It achieves an intuitive evaluation of hesitancy information and a flexible adjustment of the knowledge representation ranges. And a reasoning algorithm based on the ordered weighted averaging-weighted average (OWAWA) operator is developed to accomplish the forward reasoning driven by q-ROFPNs, which can balance the proposition weights and its position weights flexibly. Building upon q-ROFPNs, we further propose the q-rung orthopair fuzzy reversed Petri nets (q-ROFRPNs) for backward reasoning task, where a decomposition algorithm for q-ROFRPNs is designed for reducing the inference complexity, and an ordered weighted backward reasoning (OWBR) algorithm is provided to backward reasoning suitable for different fuzzy environments. In addition, to ensure the accuracy and rationality of reasoning results, we propose a knowledge acquisition method by power average (PA) operator to eliminate the negative impact of outliers on knowledge parameter assessments. A simulation experiment on the fault diagnosis of the air conditioning system demonstrates that the proposed method can achieve a more flexible and reliable knowledge representation and reasoning than the state-of-the-art FPNs methods.

Wind power curve modeling is essential in the analysis and control of wind turbines (WTs), and data preprocessing is a critical step in accurate curve modeling. As traditional methods do not sufficiently consider WT models, this paper proposes a new data cleaning method for wind power curve modeling. In this method, a model-data hybrid-driven (MDHD) outlier detection method is constructed, and an adaptive update rule for major parameters in the detection algorithm is designed based on the WT model. Simultaneously, because the MDHD outlier detection method considers multiple types of operating data of WTs, anomaly detection results require further analysis. Accordingly, an expert system is developed in which a knowl-edgebase and an inference engine are designed based on the coupling relationships of different operating data. Finally, abnormal data are eliminated and the power curve modeling is completed. The proposed and traditional methods are compared in numerical cases, and the superiority of the proposed method is demonstrated.

Soon after the pandemic inflicted by Covid-19 was declared, different organizations and smart application development companies suggested that artificial intelligence could be an useful tool in helping the medical staff monitoring the virus spread for a better understanding of its behavior. This paper presents a smart application CovidWatch developed for users real-time warning regarding the possibility of contracting the Sars-CoV-2 virus from an infected person. For this purpose, the developed application uses the Bluetooth module of the user’s device in order to check the social distance and to determine the possible contacts. The developed application has a number of four modules (the expert system (ES), the data retrieval, the real-time information and warning and the interface), that work on Android devices. Using the artificial intelligence techniques (knowledge-based systems), the application has a knowledge base populated with user’s data, the user’s time of contact and distance and the users identified as being contacts. If a user warns the android application that he has been infected with Covid-19, it further triggers the expert system inference engine, allowing the knowledge to be inferred. Based on the implemented rules, the inference engine analyses the data regarding the users that were in contact with an infected person. If those meet the conditions taken into consideration for declaring a person as being contact (a certain distance and time) they receive in real time a warning message and some advice regarding how it is recommended to act. The main advantage of android application is that it provides real-time warning regarding the possibility or probability of being infected with Covid-19, warning that can be deactivated only after the user has confirmed that the took note of this. A set of CovidWatch application simulation results is also presented taking into consideration different scenarios.

Abstract The use of energy is important in all sectors, considered essential in industrial, commercial and residential activities. In view of this and the environmental problems caused by energy sources with fossil fuel origin, methods of energy production with renewable sources have been increasingly addressed. Studies that estimate the impact and evaluations of solar technologies in homes are scarce. In this sense, the applied method addresses the use of CLIPS for the development of a knowledge base for preliminary diagnosis and evaluation of solar energy for homes and businesses, thus obtaining a prototype of an expert system, the method also approaches the use of artificial intelligence with a heuristic characteristic and its target audience is people with little or no knowledge in the area, thus contributing to the social impact. As a result, the developed programming helps people with little or no knowledge about solar energy and its residential application to have access and thus verify the applicability. It is worth noting that this is the first empirical study carried out in Brazil, a country of crucial importance for the development of solar energy.