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Statement of problem Artificial intelligence applications are increasing in prosthodontics. Still, the current development and performance of artificial intelligence in prosthodontic applications has not yet been systematically documented and analyzed. Purpose The purpose of this systematic review was to assess the performance of the artificial intelligence models in prosthodontics for tooth shade selection, automation of restoration design, mapping the tooth preparation finishing line, optimizing the manufacturing casting, predicting facial changes in patients with removable prostheses, and designing removable partial dentures. Material and methods An electronic systematic review was performed in MEDLINE/PubMed, EMBASE, Web of Science, Cochrane, and Scopus. A manual search was also conducted. Studies with artificial intelligence models were selected based on 6 criteria: tooth shade selection, automated fabrication of dental restorations, mapping the finishing line of tooth preparations, optimizing the manufacturing casting process, predicting facial changes in patients with removable prostheses, and designing removable partial dentures. Two investigators independently evaluated the quality assessment of the studies by applying the Joanna Briggs Institute Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). A third investigator was consulted to resolve lack of consensus. Results A total of 36 articles were reviewed and classified into 6 groups based on the application of the artificial intelligence model. One article reported on the development of an artificial intelligence model for tooth shade selection, reporting better shade matching than with conventional visual selection; 14 articles reported on the feasibility of automated design of dental restorations using different artificial intelligence models; 1 artificial intelligence model was able to mark the margin line without manual interaction with an average accuracy ranging from 90.6% to 97.4%; 2 investigations developed artificial intelligence algorithms for optimizing the manufacturing casting process, reporting an improvement of the design process, minimizing the porosity on the cast metal, and reducing the overall manufacturing time; 1 study proposed an artificial intelligence model that was able to predict facial changes in patients using removable prostheses; and 17 investigations that developed clinical decision support, expert systems for designing removable partial dentures for clinicians and educational purposes, computer-aided learning with video interactive programs for student learning, and automated removable partial denture design. Conclusions Artificial intelligence models have shown the potential for providing a reliable diagnostic tool for tooth shade selection, automated restoration design, mapping the preparation finishing line, optimizing the manufacturing casting, predicting facial changes in patients with removable prostheses, and designing removable partial dentures, but they are still in development. Additional studies are needed to further develop and assess their clinical performance.

The purpose of this study was to determine if an expertnetwork, a form of artificial intelligence, could effectivelystratify cardiac risk in candidates for renal transplant.Input into the expert network consisted of clinical riskfactors and thallium-201 stress test data. Clinical riskfactor screening alone identified 95 of 189 patients ashigh risk. These 95 patients underwent thallium-201stress testing, and 53 had either reversible or fixed defects.The other 42 patients were classified as low risk.This algorithm made up the ‘‘expert system,’’ and duringthe 4-year follow-up period had a sensitivity of 82%,specificity of 77%, and accuracy of 78%. An artificial neural network was added to the expert system, creatingan expert network. Input into the neural networkconsisted of both clinical variables and thallium-201stress test data. There were 5 hidden nodes and the output(end point) was cardiac death. The expert networkincreased the specificity of the expert system alone from77% to 90% (p

This paper proposes a semantic framework for Business Model evaluation and its application to a real case study in the context of smart energy and sustainable mobility. It presents an ontology based representation of an original business model and examples of inferential rules for knowledge extraction and automatic population of the ontology. The real case study belongs to the GreenCharge European Project, that in these last years is proposing some original business models to promote sustainable e-mobility plans. An original OWL Ontology contains all relevant Business Model concepts referring to GreenCharge’s domain, including a semantic description of TestCards, survey results and inferential rules.

This article presents a study on the development in the theory and application of the Z-numbers, since its inception in 2011. The review covers: a) formalization of Z-number based mathematical operators, b) role of Z-numbers in Computing With Words, decision-making and trust modeling, c) application of Znumbers in real-world problems like multisensor data fusion, dynamic controller design, safety analytics and natural language understanding, d) brief comparison with conceptually similar paradigms, and e) some potential areas of future investigation. The paradigm currently has at least four extensions to its definition multidimensional Z-numbers, parametric Z-numbers, hesitant-uncertain linguistic Z-numbers and Z*-numbers. The Z-numbers have also been used in conjunction with rough sets and granular computing for enhanced uncertainty handling. While, this decade has seen a plethora of theoretical initiatives towards its growth, there remains major work-scope in the direction of practical realization of the paradigm. Some challenges yet unresolved are: a) automated translation of (imprecise, sarcastic, and metaphorical) linguistic expressions to their Z-number forms, b) discernment of probability-possibility distributions to map realworld situations under consideration, c) analysis of linguistic equivalents of Z-operator results to intuitive human responses, d) the endogenous arousal of belief in intelligent agents, and e) analysis of biases embedded in expert-belief values that are primary inputs to Z-number based expert systems. After a decade of the Z-numbers, the paradigm has proved to be of use in expertinput based decision-making systems and initial value problems. Its applicability in high-risk, high-precision areas like deep sea exploration and space science, remains unexplored. have also been used in conjunction with rough sets and granular computing for enhanced uncertainty handling. While, this decade has seen a plethora of theoretical initiatives towards its growth, there remains major work-scope in the direction of practical realization of the paradigm. Some challenges yet unresolved are: a) automated translation of (imprecise, sarcastic, and metaphorical) linguistic expressions to their Z-number forms, b) discernment of probability-possibility distributions to map realworld situations under consideration, c) analysis of linguistic equivalents of Z-operator results to intuitive human responses, d) the endogenous arousal of belief in intelligent agents, and e) analysis of biases embedded in expert-belief values that are primary inputs to Z-number based expert systems. After a decade of the Z-numbers, the paradigm has proved to be of use in expertinput based decision-making systems and initial value problems. Its applicability in high-risk, high-precision areas like deep sea exploration and space science, remains unexplored.

The complex traffic and road environment pose considerable challenges to the accuracy, timeliness, and adaptive ability of connected and autonomous vehicles (CAVs) in making driving decisions. This paper uses vehicle collaboration and integrates the adaptive learning capabilities of machine learning and the interpretation capabilities of expert systems (ESs) in a unified architecture to form a hybrid autonomous driving guidance system, which not only solves the ``bottleneck'' of knowledge acquisition during the construction of expert systems but also solves the ``black box'' phenomenon of machine learning in the decision-making process. First, an autonomous driving strategy based on deep reinforcement learning (DRL) is proposed for CAVs to make decisions and extract corresponding rules. Next, we design an ES knowledge base expansion method including rule extraction, rule sharing, and rule test. Particularly, vehicular blockchain is adopted to ensure user privacy and data security during the rule-sharing process. Third, hybrid autonomous driving guidance combining ES and machine learning is proposed for CAVs to make accurate and efficient decisions in different driving environments. Once the strategy is well trained, it can effectively guide CAVs to cope with the complex traffic environment. Extensive simulations validate the performance of our proposal in terms of decision-making accuracy, effectiveness, and safety.

Exponentially growing number of workloads has been transferred from on-premises to the cloud environment during last decade. It incurs a constantly increasing load on the monitoring and troubleshooting capabilities of the platforms that host those applications nowadays. Data mining techniques and use of telemetry data have recently become an unavoidable means for tracking the behavior of cloud services. The research effort elaborated in this paper is focused on exploiting real-world data to build an automatic database troubleshooting system that exploits the combination of the more comprehensive statistical data science models and an expert system to perform the root cause analysis. An extensive evaluation study was conducted during the eight-month period with a plethora of Azure SQL production workloads for a varying number of databases. The obtained results confirmed the viability and cost-effectiveness of such an approach at the scale of the cloud.

Researchers have created several expert systems over the years to predict heart disease early and assist cardiologists to enhance the diagnosis process. We present a diagnostic system in this paper that utilizes an optimized XGBoost (Extreme Gradient Boosting) classifier to predict heart disease. Proper hyper-parameter tuning is essential for any classifier’s successful application. To optimize the hyper-parameters of XGBoost, we used Bayesian optimization, which is a very efficient method for hyper-parameter optimization. We also used One-Hot (OH) encoding technique to encode categorical features in the dataset to improve prediction accuracy. The efficacy of the proposed model is evaluated on Cleveland heart disease dataset and compared it with Random Forest (RF) and Extra Tree (ET) classifiers. Five different evaluation metrics: accuracy, sensitivity, specificity, F1-score, and AUC (area under the curve) of ROC charts were used for performance evaluation. The experimental results showed its validity and efficacy in the prediction of heart disease. In addition, proposed model displays better performance compared to the previously suggested models. Moreover, our proposed method reaches the high prediction accuracy of 91.8%. Our results indicate that the proposed method could be used reliably to predict heart disease in the clinic.

An expanded sequence-to-sequence (E- Seq2Seq) based data-driven security-constrained unit commitment (SCUC) expert system for dynamic multiple-sequence mapping samples is proposed in this paper. First,the dynamic multiple-sequence mapping samples of SCUC are reconstructed by analyzing the input-output sequence characteristics. Then,an E-Seq2Seq approach with a multiple-encoder-decoder architecture and a fully connected extension layer is proposed. On this basis,the simple recurrent unit is introduced as a neuron of the E-Seq2Seq approach to construct the deep learning model,and an intelligent data-driven expert system for SCUC is further developed. The proposed approach has been simulated on a typical IEEE 118-bus system and a practical system from Hunan province in China. The results indicate that the proposed approach possesses strong generality,high solution accuracy,and efficiency over traditional methods.

When we fall sick, we seek professional medical assistance if situations warrant. Medical diagnostic processes are usually based on signs, symptoms and laboratory investigations. Some patients respond to treatment while others react (or are allergic) to treatment. Substances or drugs that cause allergies are called Allergens. Allergies, when not managed, can be chaotic to human health. Considering these scenarios and a lot of others, an expert system (ES) for differential symptoms-based diagnosis and drug allergy management comes in handy. This paper implements ‘Dr. Flynxz – A First Aid Mamdani-Sugeno-type Fuzzy Expert System for Differential Symptoms-Based Diagnosis’. It also introduced three novel algorithms that were used to implement Dr. Flynxz’s Ailment Prediction (Forward Chaining Approach and Backward Chaining Approach) and Allergy Management mechanisms. A comparison of the proposed FMI Ailments Prediction Algorithm with ada 2.37.2 (a rule based approach) and fuzzy logic both confirm Dr. Flynxz’s prediction. Experimental results indicate performance improvement in terms of prediction accuracy. The implemented system can be useful in the area of medical diagnosis and allergy management while the proposed algorithms will be useful in the development of Mamdani-Sugeno-type fuzzy inference systems.

Bangladesh extensively depends on agriculture for the economy as well as food security owing to its huge population. In this connection, it becomes very important to efficiently grow plants and increase their yields. Quantity and quality of fruits can be degraded having attacked by various diseases. It is a matter of fact that not even a single research work has been conducted for automated recognition of jackfruit diseases to facilitate those distant farmers who need proper cultivation support. Presuming that our context is the recognition of jackfruit diseases, two challenging problems are mainly raised, i.e. detection of diseases and classification of diseases. In this research, we perform an in-depth investigation of an agro-medical expert system, which proceeds with a digital image acquired with a cellphone or other handheld device and recognizes the disease. Exhaustive experiments have been performed to assess the feasibility of our intended expert system. At first, a discriminatory feature set is selected. k-means clustering segmentation is put into action to detect disease-affected regions of an image of a disease-attacked jackfruit and extract the features from these regions. Then classification of the diseases is accomplished by using nine off-the-shelf classification algorithms in order to thoroughly assess the merits of the classifiers in the index of seven prominent performance metrics. Random forest is found outperforming all other classifiers to the amount of all metrics used by attaining an accuracy approaching to 90%. On the contrary, logistic regression shows not only the poorest result of an accuracy approaching to 75% but also some other poorest metric-values.

Expert systems and neural networks are new tools for the control of fermentation processes. With expert systems the fermentation plant and the process itself is modelled via a generalized, qualitative system description based on the experience of human experts. On the other hand neural networks and interpolating associative memories can learn the process behaviour directly by process observation. The paper at hand reports, how both control techniques can be combined for purposes like process supervision, modelling and optimization of biological plants.

Computers have become a huge requirement to support human performance. Computers also often experience Hardware damage such as processors, VGA, motherboards, memory, mouse, keyboards, hard disks, optical drives, monitors. Until now, many computer users still do not have sufficient knowledge of the initial diagnosis of computer Hardware damage which causes a lot of computer users to pay a lot of money to find out and repair the damage that occurs to Computer Hardware. In this Expert System application research development, the authors use a combination of two inference methods, namely the Forward Chaining method and the Certainty Factor Method. The programming tools used in the development of this Expert System application is using the Sublime Text 3 application while the database uses MySQL with PHP as the programming language. The purpose of this Expert System application is made to assist computer users in making an initial diagnosis of a damaged computer Hardware along with the causes and solutions to overcome the damage.

The growing importance of sustainability of operations and declaration by energy organizations (eg: oil companies) to move in the direction of net zero and carbon neutral have put the focus on the enhanced oil recovery techniques that they employ. An enhanced oil recovery technique that is driven by the composition of injected water has been gaining focus due to its relatively lower capital and carbon intensity. To make effective decisions on the selected oil recovery method it is essential to analyze the rock, brine, oil, injected water and their complex multiscale interactions. This work does a meta-analysis of different mechanisms and data leading to the development of an expert system workflow. This expert system will enable an all inclusive screening that will ensure efficient decision making and implementation of this low carbon intensity oil recovery and hence energy production systems.

Assessing the performance of academics at different levels is increasingly difficult to achieve using traditional methods based mainly on numerical scores in evaluating teaching and research activity. The indexing of academic performance in various international databases with impact indices at different scales has led to the need for advanced computer models, such as expert systems based on fuzzy logic, proposed in this research, which address the evaluation of teachers even in the face of imprecise information and under conditions of uncertainty. In this research, as a contribution and novelty, a fuzzy logic model was developed in which an algorithm was simulated and implemented in Matlab using the Mandami toolkit, which allows inference of the rules of fuzzy logic and visualization. 3D. The system implementation was done by software in Matlab environment, using systems with fuzzy Mandami logic. The result of this pilot study was to test and validate the proposed model through a graphical interface, giving the results according to minimum criteria and with additional explanations.

The importance of managing the material recourse supply has become apparent in the modern economy as it largely determines the survival of a corporation and its success in the market, which is especially important in a crisis. The procurement has a particular impact on this process. The problem of subjectivity when choosing a supplier is increasing in modern condition, which forces companies to use new tools, such as artificial intelligence system to make management decisions. The article proposes an expert supplier management system as an integral part of the material recourse supply management system for automotive enterprises. The possibility of improving the efficiency of procurement system, in particular supplier management. Based on a fuzzy expert system is considered. The fuzzy knowledge used to build the expert system will allow the company's management to take into account the uncertainty when making decisions about choosing a particular supplier, as well as see a description of the supplier's criteria that cannot be quantified. The use of an expert system becomes especially relevant when difficulties arise in objective decision making and choosing from a variety of alternatives. As a result of the work of the expert system, the top management of the company will receive an objective decision on choosing a supplier.

An expert system is a computer program designed to solve problems in a domain that has human expertise. The knowledge built into the system is usually obtained from experts in the field. Based on this knowledge, an expert system can replicate the thinking process of the human experts and make logical deductions accordingly. Malaria and Typhoid are major health challenge in our society today (Nigeria), its symptoms can lead to other illness which include prolonged fever, fatigue, headaches, nausea, abdominal pain and constipation or diarrhea. People in endemic areas are at risk of contracting both infections concurrently. According to the world malaria report 2011, there were about 216 million cases of malaria and typhoid and estimated 655,000 deaths in 2010. (WHO report, 2011). The main challenging issue confronting the healthcare is lack of quality of service at minimal cost implying from diagnosing to predicting patients correctly. This issue can sometimes lead to an unfortunate clinical decision that can result in devastating consequences that are unacceptable. Although many studies were carried out by different researchers in the medical domain using various data techniques. In this research work, an efficient expert system that diagnoses patients with malaria and typhoid was developed. A secondary data was collected from university of Maiduguri teaching hospital for the period of four years which ranges from 2017 to 2020. The work explored the potential benefits of proposing a new model for prediction and diagnosis of malaria and typhoid using symptoms. The model adopted the Naive bayes and was implemented using the python. The system diagnoses a patient in real time (within 30 minutes) without necessarily visiting the laboratory for a test. Three algorithms were used these are, Support vector machine, Artificial neural network and Naïve bayes. From our finding, it is observed that Naïve bayes and support vector machine give the best result which is 100% in terms of accuracy of diagnosis. Keywords: Diagnosis, Prediction, Expert System, Typhoid, Malaria

There are still many parents who do not have sufficient understanding in terms of toddler disease. One way to provide education is the availability of a system that can be used for consultation based on the symptoms of illness experienced by toddlers and the actions needed to overcome them. The system that will be built is an expert system that can relatively provide suggestions for solutions to children's health problems using the Case Based Reasoning (CBR) method. namely an expert system that uses case-based reasoning methods, namely looking for similarities of a disease compared to a disease that has existed before. In this study, the CBR method was combined with a weighting process using the pairwise comparison method which was within the scope of the AHP (Analytic Hierarchy Process) method. In comparing consultations with old diseases that already exist in the system, and looking for similarities from the comparison results, the Sorensen similarity algorithm is used. This study resulted in weights with 3 symptom categories, namely mild symptoms with a weight of 0.09, moderate symptoms with a weight of 0.24 and severe symptoms with a weight of 0.67 and will recommend several diseases with a similarity above 0.5 and diseases with a similarity below 0.5 will be entered into the revise table to find a solution.

Kucing merupakan hewan piaraan yang sangat digemari saat ini, tingkah lakunya yang lucu dan bentuk tubuhnya yang imut membuat orang-orang dari semua lapisan masyarakat menyukainya. Masyarakat, apalagi yang menyukai dan mengurus kucing harus mengecampkan keadaan kesehatan kucing tersebut, karena tidak menutup peluang penyakit infeksi virus yang diderita oleh kucing tersebut dapat menular. Pada saat sekarang ini masyarakat tidak cukup pengetahuan mengenai edukasi penyakit kucing akibat infeksi virus sehingga mengakibatkan kucing sering terlambat mendapatkan penanganan. Penelitian ini bertujuan untuk menganalisis penyakit kucing akibat infeksi virus menggunakan metode Forward Chaining berdasarkan gejala-gejala dan merancang Sistem Pakar dalam mengukur akurasi menganalisis penyakit kucing akibat infeksi virus. Data yang diperlukan saat penelitian ini adalah data kucing, data gejala dan data solusi atau penanganan yang butuhkan untuk mengambil keputusan yang bersumber dari dokter hewan dari Paw’s Vet Padang. Berdasarkan analisis data yang diberikan oleh pakar, pakar memiliki mode pengambilan keputusan, yaitu mengumpulkan fakta-fakta terlebih dahulu untuk mencapai suatu kesimpulan atau keputusan, sehingga metode Forward Chaining dapat digunakan untuk melakukan penelitian ini. Tahapan pengolahan datanya seperti mempersiapkan input data, tabel keputusan pakar, menentukan rule, melakukan proses pelacakan, membuat pohon keputusan dan hasil pelacakan. Hasil yag didapat berhasil menganalisa gejala-gejala dan dapat menentukan penyakit yang berhubungan dengan penyakit infeksi virus pada kucing sehingga dapat ditentukan solusi serta langkah awal untuk penanganannya. Hasil uji coba yang dilakukan dengan membanding data dengan sistem yang telah dirancang memiliki tingkat akurasi yang sangat baik.

The main problems in making a correct diagnosis are: subjectivity and insufficient qualifications of the doctor, difficulties in correctly assessing the patient’s complaints, signs and symptoms of the disease observed in the patient, as well as individual manifestations of the symptoms of the disease. In publications on the use of expert systems for medical diagnostics using fuzzy logic, the main attention was paid to the medical features of the problem. In this work, for the first time, general methodological aspects of building such systems, creating databases, representing by fuzzy sets of real numbers, digital scales, linguistic and Boolean data of symptom values are formulated. The types of membership functions that are advisable to use to represent the symptoms of diseases are proposed. In fuzzy-logical conclusions, not only the values of the characteristic functions of the logical terms of individual symptoms, but also complex arithmetic functions of their values are used.

Most robotic systems consist of heavy machine elements that help reduce the vibrations generated by motion and that support good positional accuracy. Large-sized actuators are needed for such systems. Energy consumption significantly increases in oversized robotic systems. The proposed solution to which is to design robotic systems in lightweight and flexible forms. This makes possible smaller overall structure and actuator sizes. However, such systems suffer from residual vibrations. One well-known control solution to residual vibration problem could be input shaping techniques. In this study, a new Fuzzy Logic-based intelligent input shaping selection technique has been proposed. With this proposed approach, it is possible to set optimum settling time, positioning accuracy of robotic systems, and minimum residual vibrations by implementing the crucial open-loop control approach. Comprehensive experience is needed to overcome design issues on command shaping and system modeling. Based on these design criteria constraints and extensive knowledge requirements, the recommended solution is a Fuzzy Logic-based intelligent input shaping technique for accurate modeling and parameter estimation of the systems. The proposed approach is a Fuzzy Logic-based intelligent input shaper selection and input shaper setting determination algorithm. The applicability of the proposed method was verified on the Quanser Flexible Link experimental setup.

As the generalization of fuzzy systems, the belief rule base (BRB) expert system is transparent and interpretable. However, the interpretability of BRB has almost been ignored recently and leads to the decrease of model credibility. The main reason is the lack of unified guidelines for establishing an interpretable BRB expert system. In this article, the interpretability characteristics of BRB are summarized systematically, which can be used as the guideline of BRB establishment. Four interpretability criteria are proposed to ensure the interpretability of BRB in the optimization. A modified optimization algorithm with the interpretability constraints transformed from the interpretability criteria is further developed. As such, an interpretable BRB can be established. A case study for health state evaluation of the aerospace relay is conducted to verify the effectiveness of the proposed method.

Background Sellar masses (SM) frequently present with insidious hormonal dysfunction. We previously showed that, by utilizing a combined reflex/reflecting approach involving a laboratory clinician (LC) on common endocrine test results requested by non-specialists, and subsequently adding further warranted tests, previously undiagnosed pituitary disorders can be identified. However, manually employing these strategies by an LC is not feasible for wider screening of pituitary disorders. Objective The aim of this study was to compare the accuracy and financial impact of an Artificial Intelligence (AI) based, fully computerized reflex protocol with manual reflex/reflective intervention protocol led by an LC. Methods We developed a proof-of-concept AI-based framework to fully computerize multi-stage reflex testing protocols for pituitary dysfunction using automated reasoning methods. We compared the efficacy of this AI-based protocol with a reflex/reflective protocol based on manually curated retrospective data in identifying pituitary dysfunction based on 12 months of laboratory testing. Results The AI-based reflex protocol, as compared with the manual protocol, would have identified laboratory tests for add-on that either directly matched or included all manual add-on tests in 92% of cases, and recommended a similar specialist referral in 90% of the cases. The AI-based protocol would have issued 2.8 times the total number of manual add-on laboratory tests at an 85% lower operation cost than the manual protocol when considering marginal test costs, technical staff and specialist salary. Conclusion/Discussion Our AI-based reflex protocol can successfully identify patients with pituitary dysfunction, with lower estimated laboratory cost. Future research will focus on enhancing the protocol’s accuracy and incorporating the AI-based reflex protocol into institutional laboratory and hospital information systems for the detection of undiagnosed pituitary disorders.

In recent few years, deep learning made a huge impact in engineering and scientific domains. One of the most-suited field is the image synthesis and editing techniques. Image Synthesis is an integral field of Computer vision and Expert systems. Generative adversarial networks (or GANs) have gained a lot of attention as it achieves better performance compared to the traditional methods. They can also be used in many image synthesis and editing areas like human image synthesis, face aging, text to-image synthesis and 3D image synthesis. In this survey, several state-of-the art image synthesis and editing techniques are discussed which uses convolutional neural networks to generate fake images. We also discuss the advantages, limitations and features of such methods along with how the image quality changes with the size of dataset used for learning. At last, we discuss some ways to detect fake images generated by image synthesis techniques.

Corn is one of the leading agricultural commodities that can be used as a staple plant other than rice. Constraints faced by corn farmers include the lack of information about diseases that attack the corn plants, which causes less productivity. In this study a system was developed that can automatically detect disease that attacks corn plants so that preventive measures can be taken to prevent corn plants from dying. Expert systems are designed to solve certain problems by imitating the work of experts. In addition, this expert system also helps farmers who are experiencing problems regarding diseases and pests and their solutions without relying on an expert. The method used is the Naive Bayes method, Naive Bayes is a method used to predict probabilities and has several characteristics that are istitively in accordance with the way of thinking of an expert and accompanied by a strong mathematical basis. From the tests carried out with diagnoses obtained from the comparison between the results of expert diagnoses and the diagnosis of the system to diagnose diseases and pests in corn plants is 90%.

In the big data era, there is a necessity for effective frameworks to collect, retrieve, and manage data. As not all tweets are hashtagged by users, retrieving them is a complicated task. To address this issue, we present a rule-based expert system classifier that uses the well-known concept of fingerprint in the judicial sciences. This expert system using defined rules first takes a fingerprint from the tweets of an emerging topic. After that, for being robust the fingerprint, using a rule-based search, the fingerprint with its neighbor features is to be updated. For detecting the unhashtagged tweets of the topic, each tweet in question checks itself with the generated fingerprint. By using the Twitter APIs of Streaming API and REST API, there is no way to access old Twitter data. To address this issue, we present a hybrid approach of Web scraping and Twitter streaming API. When the presented framework is compared to other similar works, there are (1) a novel two-class classification using an expert system approach that can intelligently and robustly detect the most of tweets of the emerging topics although they do not have the hashtag of the topic.; (2) a practical method for extracting old Twitter data. Also, we made a comparative text mining in 195649 collected Persian and English tweets about JCPOA. The JCPOA is one of the most important international treaties about the nuclear program between the Islamic Republic of Iran and the USA, China, France, Russia, Germany, and England.

Undoubtedly, the factor leading to the death of cattle is the spread of diseases that can worsenthe health of animals. Diagnosing animal diseases is difficult if a qualified veterinary professional is notavailable, which is usually due to the remoteness of livestock farms from scientific veterinary centres. Thus,the use of expert systems for solving veterinary medicine problems is proposed as a solution. An expertsystem for diagnosing animal diseases is required to simplify the work of veterinarians and subsequentlyreduce the incidence of animal diseases.The consequence is that the need to implement decision support toolsis undeniable, as veterinary medicine is a science that involves possible diagnostic errors, decision-makingcomplexity and an extensive knowledge base. As a result, the use of cognitive maps, the functionality ofwhich is described in this article, is proposed. The development of these decision support tools includesboth discrete analytical information and intuitive elements that optimise their impact on everyday veterinarypractice. Cognitive maps are active knowledge resources that use animal data to generate case-specificrecommendations to support diagnostic decision-making. The knowledge sources used and the process ofobtaining, analyzing and evaluating knowledge are described in detail. Productive rules are used as a toolto represent knowledge, which is also used to address the uncertainties that arise in the decision-makingprocess. Introducing one-dimensional tables when structuring a knowledge library is an appropriate solutionfor system design. Creating a knowledge base in the way described can solve the initial goals of developinga software product for veterinary applications.

It can occur on many occasions that you or a loved one requires urgent medical assistance, but they are unavailable due to unforeseen circumstances, or that we are unable to locate the appropriate doctor for the care. As a result, we will try to incorporate an online intelligent Smart Healthcare System in this project to solve this issue. It's a web-based programmed that allows patients to get immediate advice about their health problems. The aim of the smart healthcare system is to create a web application that can take a user's symptoms and predict diseases, as well as serve as an online consultant for various diseases. We created an expert system called Smart Health Care System, which is used to make doctors' jobs easier. A machine examines a patient at a basic level and recommends diseases that may be present. It begins by inquiring about the patient's symptoms; if the device is able to determine the relevant condition, it then recommends a doctor in the patient's immediate vicinity. The system will show the result based on the available accumulated data. We're going to use some clever data mining techniques here. We use several intelligent data mining techniques to guess the most accurate illness that could be associated with a patient's symptoms, and we use an algorithm (Naive Bayes) to map the symptoms with potential diseases based on a database of many patients' medical records. This system not only makes doctors' jobs easier, but it also benefits patients by getting them the care they need as soon as possible. Keywords: Disease Prediction, Naïve Bayes, Machine Learning Algorithm, Smart Healthcare System.

Pusat Kesehatan Masyarakat (Puskesmas) merupakan kesatuan organisasi fungsional yang menyelenggarakan upaya kesehatan yang bersifat menyeluruh, terpadu, merata dapat diterima dan terjangkau oleh masyarakat. Tujuan penelitian ini adalah membuat Aplikasi Sistem Pakar untuk mendeteksi penyakit kulit pada balita sesuai dengan data-data yang ada pada Puskesmas. Metode pengembangan sistem yang digunakan yaitu metode Extreme Programing (XP) dengan tahapan pengerjaan meliputi planning, design, coding dan testing. Sistem dirancang dengan menggunakan Unified Modeling Language (UML) yang meliputi use case, activity diagram, dan class diagram, perangkat lunak yang digunakan adalah PHP (Hypertext Preprocessor) dengan database MySQL dan menggunakan metode Naïve Bayes dan Forward Chaining. Hasil akhir dari pembuatan Aplikasi ini adalah memudahkan pasien, dokter dan bidan mendeteksi penyakit kulit pada balita di Puskesmas

Talent management is a factor that determines success in the business environment because talent management requires quantitative and qualitative skills. This study aims to implement the certainty factor (CF) method of an expert system for employee development talent management. This research using a certainty factor (CF) method to design an expert system framework. Due to the focus on our research aim, we provide a certainty factor calculation with mathematical modeling for calculating talent management employee development in X Company. The confidence level is 93.55% for a recommendation of not promotion of the job; for 52.38% is a recommendation that can be proposed for promotion, but HRD will evaluate in some time; for 98.73% is a recommendation for promotion of the job. We used CF calculation that can provide the level of confidence (in %). The calculation of the certainty factor (CF) method can be used for recommending job promotion in some companies.

The medical informatics field gets wide attention among the research community while developing a disease diagnosis expert system for useful and accurate predictions. However, accuracy is one of the major medical informatics concerns, especially for disease diagnosis. Many researchers focused on the disease diagnosis system through computational intelligence methods. Hence, this paper describes a new diagnostic model for analyzing healthcare data. The proposed diagnostic model consists of preprocessing, diagnosis, and performance evaluation phases. This model implements the water wave optimization (WWO) algorithm to analyze the healthcare data. Before integrating the WWO algorithm in the proposed model, two modifications are inculcated in WWO to make it more robust and efficient. These modifications are described as global information component and mutation operator. Several performance indicators are applied to assess the diagnostic model. The proposed model achieves better results than existing models and algorithms.

This paper discusses a new automated training system called CATS. The proposed system covers all the main components of the educational process, including the simple and convenient formation of educational material, tasks for laboratory works, tests to check knowledge, allows you to monitor the progress of students, the process of studying educational content, check completed work for plagiarism, send incorrectly completed tasks to correction, keep an electronic journal and much more. The automated CATS system has been introduced into the educational process at the Belarusian National Technical University and is actively used, especially in the context of the COVID-19 pandemic. In the spring of 2020 alone, more than a thousand users have registered in the CATS system. The intellectual component in the CATS system allows you to implement a unique training program, which is based on the existing knowledge and the level of perception of the educational material by the students. As mathematical methods, it is proposed to use the analysis of expert systems, as well as artificial neural networks. These mathematical methods made it possible to develop adaptability algorithms, their software implementation and testing in the educational process. Users are provided with a web application and its mobile clients for iOS and Android operating systems. Mobile applications are localized in Russian, Belarusian, English and German. By formalizing the intellectual processes that are carried out by both the teacher and the student, it is possible to automate a certain part of their functions, reduce the cost of manual labor, which will make it easier to control the educational process, and make the training itself more effective.

Medical knowledge is expanding fast and it is difficult for general practitioners to remain abreast of all medical domains. Also, access to domain specialist is limited due to availability and geographical constraints. In many situations the diagnosis in [sic] upon the decision of the general practitioner and in cases this has resulted in the problem of patient's misdiagnosis. The purpose of this research is to create an expert system as a decision support model which is capable of risk analysis for diagnosis based on the patient's demography and laboratory tests. The expert system is designed in compliancy with medical communications protocol such as HL7 and can be integrated to any HL7 compliant Electronic Medical records system to provide more intelligence in diagnosis. Using linear scoring models and Fuzzy logic, the patient's demography and laboratory results will be used as rule bases. Such knowledge will be used as priors for a Bayesian engine to create the diagnostic spaces. Patient's information is compared in the space and the general practitioner can select between the possible hypotheses. Each diagnostic decision will be associated with a risk value. Using such scoring model provides a new semantic in diagnosis by providing risk values for every diagnosis made and by suggesting the most suitable treatment. Unlike many other existing expert systems, the architecture is designed in a generic standard which provides the capability to use the system for all medical domains. Achieving this generality has been a major goal achieved and its details are discussed in this document.

Coastal cattle are livestock that have economic value, such as selling beef and cattle breeds. Cow disease can cause the quality of its sales to decrease. This study aims to help cattle breeders to determine the type of cow disease, from the symptoms that exist in these cows. So that the prevention of the risk of cow disease can be avoided. All data used are sourced from experts. In determining the type of disease in cows, the Forward Chaining method is used. The fact-finding technique is then put into the predetermined rules to get a conclusion. Making a website based expert system makes it easy for breeders to access it online. The accuracy of the system has been tested by related parties so as to produce fast and efficient information. From research, it can help breeders in diagnosing the symptoms experienced by cows and the test results can detect the type of disease accurately.

Machine learning systems offer the key capability to learn about their operating environment from the data that they are supplied. They can learn via supervised and unsupervised training, from system results during operations, or both. However, while machine learning systems can identify solutions to problems and questions, in many cases they cannot explain how they arrived at them. Moreover, they cannot guarantee that they have not relied upon confounding variables and other non-causal relationships. In some circumstances, learned behaviors may violate legal or ethical principles such as rules regarding non-discrimination. In these and other cases, learned associations that are true in many – but not all – cases may result in critical system failures when processing exceptions to the learned behaviors. A machine learning system, which applies gradient descent to expert system networks, has been proposed as a solution to this. The expert system foundation means that the system can only learn across valid pathways, while the machine learning capabilities facilitate optimization via training and operational learning. While the initial results of this approach are promising, cases where networks were optimized into high error states (and for which continued optimization continued to increase the error level) were noted. This paper proposes and evaluates multiple techniques to handle these high error networks and improve system performance, in these cases.

The chapter discusses the basic concepts of computer security as well as the taxonomy and classification of the fundamental algorithms in the domains of artificial intelligence, machine learning, and data science in relation to their applications in computer security. It reviews the sources of security threats and the attacks, using the area of IoT and wireless devices as an example, as well as examines the possible protection mechanisms and tools. The module provides a general classification of intelligent approaches and their relationship to various computer security fields. It focuses on an introduction of the major intelligent techniques and technologies in computer security, such as expert systems, fuzzy logic, machine learning, artificial neural networks, and genetic algorithms. While presenting multiple techniques, the text emphasizes their advantage in comparison to each other as well as the obstacles in their further progress. Short algorithm descriptions and code examples are included.

This paper presents an expert system that is able to classify different types of power quality (PQ) events. The expert system uses the voltage waveforms and distinguishes the different types of voltage dips and swells. Both Fourier and Wavelet analyses are applied for extracting distinct features of various types of events as well as for classifying the events.

In a computer technology that can help people solve problems that are widely applied in the world of medicine is an expert system. a system that is adopted from human insight into computers, thus enabling problems to be solved by computers, as developed by an expert. In this study, the Expert System can diagnose hepatitis. The system can diagnose hepatitis, which is made using the PHP programming language and MySQL database using the Sorensen Cooficient method. With input in the form of symptoms and blood tests, then the results are possible diseases suffered by the patient and an explanation of the disease and treatment solutions. So that this expert system is able to diagnose hepatitis. This study uses the Sorensen Cooficient method which can be implemented into an expert system with complex or acute hepatitis, or simple hepatitis.

Kulit merupakan salah satu bagian tubuh yang memiliki sifat fleksibel untuk melindungi tubuh manusia dari dampak ekologis. Sebagian besar orang membutuhkan kulit wajah yang kencang dan terbebas dari penyakit kulit, terutama kulit berjerawat. Oleh karena itu, setiap orang harus melakukan perawatan wajah dan menggunakan beberapa bahan yang cocok untuk kulit wajah, ada lima jenis kulit normal, kulit berminyak, kulit kuring, kulit kombinasi dan kulit sensitif. Untuk mengkaji jenis kulit wajah dengan membandingkan teknik Forward Chaining dan Certainty Factor. Pemeriksaan ini menggunakan 30 data yang diperoleh dari wawancara dengan spesialis. Ada beberapa indikasi yang diperoleh dari berbagai masalah yang diidentifikasi pada kulit wajah yang digunakan sebagai informasi manifestasi untuk jenis kulit wajah yang dikendalikan oleh spesialis. Informasi tersebut digunakan untuk memeriksa jenis kulit wajah berdasarkan petunjuk dan informasi dari dokter spesialis. Langkah-langkah persiapan yang dilakukan adalah dengan membandingkan teknik Forward Chaining dan Certainty Factor. Hasil yang didapat setelah pengujian dan pemeriksaan teknik Forward Chaining dan Certainty Factor adalah 83,33% dan hasil temuan menyatakan bahwa klien memiliki jenis kulit yang khas. Hasil pengujian dapat dilihat dari dua teknik yang menguji jenis kulit wajah eksplisit, selanjutnya kerangka utama yang digunakan dapat ditentukan untuk membantu dokter kulit dalam membedah jenis kulit wajah.

In general, a person is difficult do dermine whether a lump or swelling of the lymph nodes in the body, is just a normal lump or whether it has become cancer. If the lumph does not go away whitin a few days, the patient is required to immediately consult a doctor to get detailed information about the disease and how to treat it. Lymph nodes scattered in the human body are round and are part of the lymphatic system that plays a role in fighting viruses that enter the body. The cause of these enlarged lymph nodes can be due to the type of food consumed, congenital disease in the patient’s body (comorbid), infection to lymph node cancer (lymphoma). For this reason, an Expert System is needed by applying the Certainty Factor method in order to assist the public in analyzing lymph node disease by answering several questions regarding the symptoms experienced. This system is implementad using the PHP programming language and MySQL database. The result of this study show that the Certainty Factor method can work well in the Expert System analysis process. From the result of system trials with several patient data, the disease accuracy rate in the patient named S is 63% and the disease accuracy rate in the patient named MR is 68%. Besides being useful for an expert, this Expert System can help patients to find out the type of disease they are suffering from, the accuracy of disease, the method of treatment and a guide for making decisions.

Health is a blessing that is very important for humans, most of us, many of us don't really care about skin diseases caused by fungi such as tinea versicolor, water fleas, ringworm and scabies. In general, people are quite aware of how to deal with the symptoms of skin diseases suffered. But it would be better to include medical participation in detecting a disease symptom, because many disease symptoms are considered trivial by some people but can be fatal to human skin. So it is necessary to make an application based on medical knowledge to diagnose skin diseases in humans which is used as a tool in obtaining information about skin diseases in humans and provide recommendations as the first action that must be taken to repeat skin diseases in humans. The knowledge base is structured in such a way into a database with several tables. Drawing conclusions in this expert system using the forward chaining inference method. The expert system will provide questions to the user in the form of symptoms of several diseases and the user will answer these questions. Until the user will get a solution from the results of the question earlier.

In this day and age, motorbikes have an important role in transportation facilities, motorbike users are increasingly dense, especially in the city of Semarang, which is not accompanied by information media. The lack of media information regarding damage to the motorbike makes it difficult for someone to know the cause of the damage to the motorbike, not to mention the Kawasaki KLX150 which happens a lot of engine damage. For this reason, the expert system diagnoses motor damage by knowing the type of motor damage, after that diagnostics and alternative solutions to the problem are carried out. With this, the method and algorithm used is Case-Based Reasoning (CBR) using the Similarity 3W-Jaccard calculation, this second method and algorithm can be used to diagnose damage from the symptoms in the database. Each symptom has a weighted value of each, including a value of 5 (five) for severe symptoms, relating to engine and electrical parts, a value of 3 (three) for moderate symptoms, relating to braking and chains, a value of 1 (one) mild symptom, relating to with the indicator on the speedometer. The system will display 5 (five) types of damage calculated using the 3W-Jaccard Algorithm sorted by the highest value. The revision process will appear if the similarity calculation results are less than 0.6 (zero point six) because it is considered that the results are not sufficiently similar to the solution to be repaired, it needs to be reviewed and will be entered into the review table, then the expert will find a solution.

Many high-risk industries identify non-technical skills as safety-critical abilities of the operational staff that have a protective function against human fallibility. Based on an established non-technical skills classification system, methods for expert knowledge elicitation were used to describe non-technical skills in the specific context of train traffic control in the Netherlands. The findings offer insights regarding the skill importance for good operational outcomes, skill difficulty, categorization, and attitudes based on subject matter experts’ opinions. Substantial overlap between the employed non-technical skills framework and the observed expert classification was found, which might indicate that the experts utilize a mental model of nontechnical skills similar to the one used. Furthermore, considerations concerning the organizational culture and the attitudes towards change provide a promising outlook when introducing novel solutions to non-technical skill training and assessment.

Interdisciplinary approaches to creating high-tech computer systems for the diagnosis of melanoma using artificial intelligence are presented. A model is proposed for the architecture of an interactive expert system. This includes a set of features for a contemporary medical algorithm (the Kittler algorithm) along with a knowledge base and a diagnosis evaluation score for the case under study.