Inference Engine

The Facial Action Coding System (FACS) is an objective method for quantifying facial movement in terms of 44 component actions, i.e. Action Units (AUs). This system is widely used in behavioral investigations of emotion, cognitive process and social interaction. Highly trained human experts (FACS coders) presently perform the coding. This paper presents a system that can automatically recognize 30 AUs, their combinations and their intensity. The system employs a framework for hybrid facial feature detection and an expert system for facial action coding in static dual-view facial images. Per facial feature, multiple feature detection techniques are applied and the resulting redundant data is reduced so that an unequivocal facial expression geometry ensues. Reasoning with uncertainty is used to encode and quantify the encountered facial actions based on the determined expression geometry and the certainty of that data. Eight certified FACS coders tested the system. The recognition results demonstrated rather high concurrent validity with human coding.

This paper discusses our expert system called Integrated System for Facial Expression Recognition (ISFER), which performs recognition and emotional classification of human facial expression from a still full-face image. The system consists of two major parts. The first one is the ISFER Workbench, which forms a framework for hybrid facial feature detection. Multiple feature detection techniques are applied in parallel. The redundant information is used to define unambiguous face geometry containing no missing or highly inaccurate data. The second part of the system is its inference engine called HERCULES, which converts low level face geometry into high level facial actions, and then this into highest level weighted emotion labels.

The Facial Action Coding System (FACS) is an objective method for quantifying facial movement in terms of 44 component actions, ie Action Units (AUs). This system is widely used in behavioral investigations of emotion, cognitive process and social interaction. Highly trained human experts (FACS coders) presently perform the coding. This paper presents a system that can automatically recognize 30 AUs, their combinations and their intensity. The system employs a framework for hybrid facial feature detection and an ...

The diagnosis of diseases in their early stages can assist us in preventing life-threatening infections and caring for them better than in the last phase because prevention is better than cure. The death rate can be very high due to the unapproachability of diagnosed patients at an early point. Expert systems help us to defeat the problem mentioned above and enable us to automatically diagnose diseases in their early phases. Expert systems use a fuzzy, rule-based inference engine to provide forward-chain methods for diagnosing the patient. In this research, data have been gathered from different sources, such as a hospital, by performing the test on the patients’ age, gender, blood sugar, heart rate, and ECG to calculate the values. The proposed expert system for medical diagnosis can be used to find minimum disease levels and demonstrate the predominant method for curing different medical diseases, such as heart diseases. In the next step, the diagnostic test at the hospital with t...

Pada ilmu kedokteran umum, ilmu tentang telinga, hidung dan tenggorokan tidak dibahas dengan mendetail. Sehingga sangatlah mungkin mahasiswa kedokteran umum dalam proses pembelajaran untuk menangani pasien seringkali menjumpai kesulitan. Selain mahasiswa kedokteran umum, dokter umumpun kadang mengalami hambatan dalam mendiagnosa penyakit telinga, hidung dan tenggorokan. Hal ini dikarenakan minimnya pengetahuan penyakit dalam bidang tersebut, sehingga penanganan dan pendiagnosaan yang dilakukan oleh dokter umum akan terkesan lambat dan kurang akurat. Untuk itu, diperlukan adanya sistem pakar yang dapat membantu mahasiswa kedokteran umum dalam pembelajaran pendiagnosaan penyakit telinga, hidung dan tenggorokan dan juga dokter umum dalam mendiagnosa penyakit telinga, hidung dan tenggorokan. Berdasarkan analisa kebutuhan sistem pakar maka dibuat sekumpulan aturan yang berisi pertanyaan, pilihan jawaban, bobot, dan nomor aturan berikutnya. Dari hasil analisis perhitungan bobot, akan dike...

The death rate is caused by breast cancer in women is increasingly high and growing. A number of people are getting to lose this part of their body due to late diagnosis of this disease. This therefore requires the development of an efficient and accurate diagnosis approach that will aid providing the knowledge of the type of breast cancer type and severity in order to reduce the mortality rate through the disease. This need serves as the major motivation for this work. In this paper, we proposed a fuzzy expert system for diagnosis of and treatment recommendation of breast cancer problems which provide physicians and patients with information of the cancer type and treatment recommendation. The application was designed using JAVA programming language, MATLAB and SQLite database engine. This application permits update of new information as a means of knowledge. The evaluation showed that the inclusion of the fuzzy inference system improved the accuracy and precision of the system from 0.8 to 0.9. The system is user-friendly and has high level of acceptability from the validation conducted at the end of the research.

Proper food planning is essential to consume nutritious food that keeps people healthy. Hygienic food offers a favorable existence that helps us to cut the chance of illness and to save time in selecting food. Food planning may ensure a new way to change our buying nature as well as bring variation in our food consumption. Selecting the right food at the right time is necessary. This paper presents an expert system that is intended to select the right food for the right meal for any age people. The expert system also suggests the amount of energy in Kcal required per day. We use rule rule-based expert system to carry out the system. The knowledge base, inference engine, and user interface are combined to give the service. This system helps us to cut the amount of food wastage as well as gives the proper nutritious diet.

Sebagian besar masyarakat menganggap bahwa kesehatan adalah harta yang paling berharga. Manusia akan melakukan apa saja agar kesehatannya terjaga dengan baik. Anggapan inilah yang menyebabkan beberapa pakar komputer mengembangkan suatu sistem yang dapat mengatasi masalah kesehatan pasien serta untuk menambah pengetahuan masyarakat. Ketersediaan teknologi komputer yang memiliki banyak kemampuan, akan memberikan kemudahan bagi masyarakat yang ingin menambah pengetahuan seputar kesehatan, khususnya kesehatan THT serta membantu pihak medis dalam mengatasi keterbatasan mereka dalam menjalankan tugas. Dalam penelitian ini disusun suatu sistem yang berbasis komputer sebagai penyedia informasi yang dapat mengidentifikasi penyakit THT. Program bantu identifikasi THT ini dilengkapi fasilitas untuk mendiagnosa penyakit sesuai gejala yang dialami, mampu melakukan proses pencarian hingga pengeditan data serta menampilkan informasi anatomi THT secara umum. Data yang digunakan dalam program ini adalah semua data yang berhubungan dengan penyakit THT (daftar penyakit, gejala dan obat yang dapat digunakan). Sedangkan program aplikasi disusun dengan menggunakan software Visual Basic dan Microsoft Access. Program ini menggunakan bahasa Indonesia dengan tujuan untuk memudahkan pengguna dalam menjalankan program aplikasi.

An expert system is a computer program designed to simulate the problem-solving behavior of a human who is an expert in a narrow domain or discipline. An expert system is normally composed of a knowledge base (information, heuristics, etc.) and inference engine (analyzes the knowledge base), and the end user interface (accepting inputs, generating outputs). One of the attractive features of expert systems is the program's ability to review a consultation and provide the user with an explanation for how its conclusion was derived. In this research, the expert system is used to detect the errors in logic circuits. The program is written in visual basic programming language depending on the truth table of logic gates so that the user doesn't need to save these truth tables in his mind. This program is applied in combinational logic circuit, which doesn't contain feedback. The program is answered for the questions of user "Why" and "How" to provide the description and details about gates and their works.

This paper introduces a new technique to solve financial allocation in Distribution System Expansion Planning (DSEP) problem. The proposed technique will be formulated by using mean-variance analysis (MVA) approach in the form of mixed-integer programming (MIP) problem. It consist the hybridization of Hopfield Neural Network (HNN) and Boltzmann Machine (BM) in first and second phase respectively. During the execution at the first phase, this model will select the feasible units meanwhile the second phase will restructured until it finds the best solution from all the feasible solution. Due to this feature, the proposed model has a fast convergence and the accuracy of the obtained solution. This model can help planners in decision-making process since the solutions provide a better allocation of limited financial resources and offer the planners with the flexibility to apply different options to increase the profit.

In the area of East Jakarta, especially in the Sejahtera Clinic, the public can examine the initial symptoms of ENT. With the initial inspection that must be queued, can cause a long time. As a result, many people who do not know clearly the type of illness suffered and how to treat it. Because ENT disease is very related to organs where if not treated immediately, it will be difficult to move daily, therefore, the need for diagnosis of complaints from patients. These patients can directly access the website of a website where patients can diagnose without having to go to a hospital or clinic. By comparing previous studies or literature reviews, the problem to be discussed is to add more symptoms and diseases, then a solution for the disease. So that the website to be made is more detailed and clear, the results of this research is to produce an expert system website that can easily examine the symptoms of the patient and provide the patient's illness and solutions to overcome the disease.

The causes of damage to the chiller machine can be sorted in a logical sequence to find the cause of damage to the chiller machine, so that users can immediately find out the cause of the existing damage. An expert system is a computer program that contains knowledge from one or more human experts about a specific field. With this system, it is hoped that users can easily find out about damage and how to handle it on the machine. Observations were carried out directly on the research object, namely PT Indofood CBP, because the way to observe the research object is to understand the needs of the research object so that the system built can meet the needs of the user concerned. Starting from minor problems to serious problems, apart from that, we also test the running of the chiller until the operation of every machine that requires supply from this chiller machine. This system was created with the aim of making it easier for users if they experience difficulties in carrying out repairs or troubleshooting the chiller machine. So that you can get solutions based on previous experiences carried out by Engineers. After collecting analytical data to overcome troubleshooting, the design stage is carried out to create an expert system for detecting damage to machines and how to handle repairs based on the experience that has been provided.

In this report we describe a method to perform type inference over data stored in an RDBMS, where rules over the data are specified using OWL-DL. Since OWL-DL is an implementation of the Description Logic (DL) SHOIN (D), we are in effect implementing a method for SHOIN (D) reasoning in relational databases. Reasoning make be broken down into two processes of classification and type inference. Classification may be performed efficiently by a number of existing reasoners, and since classification alters the schema, it need only be performed once for any given relational schema as a preprocessor of the schema before creation of a database schema. However, type inference needs to be performed for each data value added to the database, and hence needs to be more tightly coupled with the database system. Previously, no technique has been proposed that implements SHOIN (D) type inference within an RDBMS. We propose such a technique, involving the use of triggers to perform reasoning over the data values as they inserted into the database. We demonstrate the soundness and performance of our approach by comparing an implementation of our technique against other existing approaches for less powerful reasoning over data in an RDBMS. The results show we provide the fastest query processing of any technique, despite having a more powerful reasoner.

Inaccuracies in the process of diagnosing a type of disease result in errors in handling so that it will pose a risk of death. Accurate diagnostic process results require a high level of confidence so that the results are truly convincing. Current technological developments are making more and more mindsets for the development of information technology in the field of computerization born. One of them is an expert system. This expert system is often used to analyze disease in laying hens. The deficiency in previous research is that there is no degree of confidence so what happens is that the diagnosis often only uses the value of the expert. The role of the system user is only to select the available symptoms without giving the weighted value of the selected symptoms. This study aims to build an expert system capable of detecting symptoms in laying hens by assigning a degree of confidence to each symptom. The system is built with a combination of forward chaining techniques with a certainty factor, the weight value is based on a combination of the weight of symptoms from users and experts to anticipate conditions that are not ideal. Several stages in the research include data collection, knowledge base modeling, implementation into applications and testing. The conclusion that can be drawn from the trial results is that the system can show a maximum validity value of up to 100% when compared to manual calculations.

Every member of the community can experience a variety of diseases. The disease can be known from the symptoms it produces, but to know the exact type of disease, needed a doctor or a health professional. Since the number of doctors or health professionals is very limited and can not overcome the problems of the community at the same time, a system that has the capability of a doctor or health professional is required, which in this system contains the expertise of a physician or health professional on diseases and diseases. In this study was designed expert system using rule base (reason based reasoning) with forward chaining and backward chaining inference method that is intended to assist the community in diagnosing the disease. This disease diagnostic expert system developed has advantages in ease of access and ease of use. With the features that are owned, expert systems for the diagnosis of diseases that built can be used as a tool for disease diagnosis and can be accessed by the public to overcome the problem of limited number of doctors or health experts in helping people diagnose the disease.

Measuring Text similarity problem still one of opened fields for research area in natural language processing and text related research such as text mining, Web page retrieval, information retrieval and textual entailment. Several measures have been developed for measuring similarity between two texts: such as Wu and Palmer, Leacock and Chodorow measure and others . But these measures do not take into consideration the contextual information of the text .This paper introduces new model for measuring semantic similarity between two text segments. This model is based on building new contextual structure for extracting semantic similarity. This approach can contribute in solving many NLP problems such as te xt entailment and information retrieval fields.

Measuring Text similarity problem still one of opened fields for research area in natural language processing and text related research such as text mining, Web page retrieval, information retrieval and textual entailment. Several measures have been developed for measuring similarity between two texts: such as Wu and Palmer, Leacock and Chodorow measure and others . But these measures do not take into consideration the contextual information of the text .This paper introduces new model for measuring semantic similarity between two text segments. This model is based on building new contextual structure for extracting semantic similarity. This approach can contribute in solving many NLP problems such as te xt entailment and information retrieval fields.

An expert system is a branch of artificial intelligence designed to mimic the ability of a human expert in making decisions or solving problems in a specific field. This study aims to design and develop a rule-based expert system to assist in diagnosing diseases in rice plants. The system is developed using a forward chaining approach, where the reasoning process begins with facts provided by the user and gradually traces through rules to reach a diagnostic conclusion. Expert knowledge is acquired through interviews and literature studies, then represented in the form of IF-THEN rules. The system is implemented using the Python programming language with a web interface based on Flask. Based on testing with 30 real cases, the system demonstrated an accuracy rate of 90% compared to manual diagnoses by agricultural experts. These results indicate that a rule-based expert system can be an effective solution to support decision-making in agriculture, particularly for early and efficient detection of rice plant diseases.

Perkembangan teknologi otomotif yang pesat menyebabkan meningkatnya kompleksitas komponen sepeda motor, sehingga proses diagnosis kerusakan menjadi semakin sulit, terutama bagi mekanik pemula atau bengkel kecil. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem pakar berbasis frame guna membantu proses identifikasi dan diagnosis kerusakan sepeda motor secara cepat dan akurat. Metode frame digunakan sebagai pendekatan representasi pengetahuan karena mampu menggambarkan objek, gejala, dan relasi antar komponen dalam struktur data yang terorganisir. Sistem dikembangkan menggunakan bahasa pemrograman PHP dengan basis data MySQL. Setiap gejala kerusakan direpresentasikan dalam slot-slot yang saling terhubung dalam kerangka frame, memungkinkan sistem melakukan proses inferensi berdasarkan input gejala yang diberikan pengguna. Hasil pengujian menunjukkan bahwa sistem mampu memberikan diagnosis dengan tingkat akurasi di atas 90% dan waktu respon kurang dari 5 detik. Sistem juga menyediakan solusi perbaikan dan tips pencegahan berdasarkan hasil diagnosis. Dengan adanya sistem ini, diharapkan proses perbaikan sepeda motor menjadi lebih efisien, meningkatkan produktivitas bengkel, serta memberikan kemudahan bagi pengguna awam dalam mengenali permasalahan sepeda motornya.

Support System) is typically a diagnostic application and a modern technology that can be employed to provide standardized and quality medical facilities to the medical patients especially when expert doctors are not available at the medical centres. These days the use of the CDSSs is quite common in medical practice at remote areas. A CDSS can be very helpful not only in preventive health care but also in computerized diagnosis. However, a typical problem of CDSS based diagnosis is uncertainty. Typically, an ambiguity can occur when a patient is not able to explain the symptoms of his disease in a better way. The typically used forward chaining mechanisms in rule based decision support systems perform reasoning with uncertain data. ML (Markov Logic) is a new technique that has ability to deal with uncertainty of data by integrating FOL (First-Order-Logic) with probabilistic graphical models. In this paper, we have proposed the architecture of a ML based inference engine for a rule based CDSS and we have also presented an algorithm to use ML based forward chaining mechanism in the proposed inference engine. The results of the experiments show that the proposed inference engine would be intelligent enough to diagnose a patient's disease even from uncertain or incomplete/partial information.

Accurate estimation of flank wear during any in-progress machining process is highly important for the purpose of controlling product quality and the production rate. Hard milling is among few of the recently popularized technologies of metal cutting domain and is found under intense research for the purpose of estimation and control of tool wear. In the presented paper two fuzzy rules based strategies are explained and compared for accurate estimation of tool's flank wear in hard milling process. The offline strategy uses length of cut (LoC) as major input besides tool helix angle and workpiece material hardness, while for the online strategy LoC is replaced with the cutting force. Series of hard milling experiments were performed in order to obtain data for the development of two fuzzy expert systems as well as for testing of both of the strategies. ANOVA showed LoC and cutting force were more significant than other input parameters for the estimation of flank wear, and the design of fuzzy sets for input parameters was based upon this analysis. Two expert systems were tested using experimental data and the results showed that online strategy was 67.9% more accurate than offline one in estimating flank wear.

Based on Cumulative Number of Confirmed Human Cases of Avian Influenza (H5N1) Reported to World Health Organization (WHO) in the 2011 from 15 countries, Indonesia has the largest number death because Avian Influenza which 146 deaths. In this research, the researcher built an Avian Influenza (H5N1) Expert System for identifying avian influenza disease and displaying the result of identification process. In this paper, we describe five symptoms as major symptoms which include depression, combs, wattle, bluish face region, swollen face region, narrowness of eyes, and balance disorders. We use chicken as research object. Research location is in the Lampung Province, South Sumatera. The researcher reason to choose Lampung Province in South Sumatera on the basis that has a high poultry population. Dempster-Shafer theory to quantify the degree of belief as inference engine in expert system, our approach uses Dempster-Shafer theory to combine beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the belief and plausibility in our identification result. The result reveal that Avian Influenza (H5N1) Expert System has successfully identified the existence of avian influenza and displaying the result of identification process.

Traditionally human experts were responsible for taking decisions in solving the medical problems. But it was very difficult for human expert to solve complex problems,for that expert systems were designed.There are lot of applications in artificial intelligence domain that try to help human experts offering solutions for a problem. Expert system is a part of artificial intelligence which increases the ability of decision making of the human expert. They are designed to solve complex problems.Rule based expert system uses rules as the knowledge representation for knowledge coded into the system. Rule based expert system is used by the human experts to diagnose the problem.This paper surveys research work accomplished in the field of medical sector using rule based expert system.

As indicated in the quote above, the great Sherlock Holmes (and his physician creator, Sir Arthur Conan Doyle) realized the limitation of human memory and thought. A recent editorial in the Annals of Internal Medicine discussed this problem in the context of modern medicine and similarly referred to "the brain’s limited capacity for storage and processing" of medical information.2 In the past, physicians have attempted to overcome these limitations by the use of referrals, journals and textbooks but with unsatisfactory results.3 The conclusions of a study undertaken by the Massachusetts Medical Society and the National Academy of Sciences suggested that the situation could be improved with computer technology.4 One type of advanced computer technology designed specifically to assist in decision making is expert systems, the product of research in artificial intelligence.

Parallel processing systems offer a major improvement in capabilities to AI programmers. However, at the moment, all such systems require the programmer to manage the control of parallelism explicitly, leading to an unfortunate intermixing of knowledge-level and control-level information. Furthermore, parallel processing systems differ radically, making a control regime that is effective in one environment less so in another. We present a means for overcoming these problems within a unifying framework in which 1) Knowledge level information can be expressed effectively 2) Information regarding the control of parallelism can be factored out and 3) Different regimes of parallelism can be efficiently supported without modification of the knowledge-level information. The Protocol of Inference introduced in [Rowley et al., 19871 forms the basis for our approach.

The critical period for the growth and development of infants can be referred to as a critical period. It is said that this critical period in infancy will be very detrimental to the environment around it and it is said that the future due to infancy is very short and cannot be repeated. Newborns are more susceptible to various diseases. Diseases in infants can be identified based on the symptoms that arise. This is caused by several factors including the lack of information about diseases in infants after birth that is known to the general public. Therefore, research is needed on diseases and symptoms that affect babies after birth. One alternative in order to find out the disease in the baby after birth can use an expert system. In building the system, the author uses the Backward Chaining method, uses the Waterfall system development method (SDLC) and the Object Oriented Approach (OOA). The result of this research is a web-based application that can later be accessed or used to make it easier for the public, especially parents of babies, to diagnose symptoms or diseases in babies after birth as early as possible.

This paper aims to diagnose Pneumonia infection using image processing algorithms and artificial neural network. A group of infected and normal x-ray images are prepared using segmentation and feature extraction using many processes then using Self Organizing Map algorithm to classified them. Also, artificial neural network is used to build a database of different cases of pneumonia infected and normal x-ray images, training the network to detect the infected image; the used network was the Learning Vector Quantization network, which has a high performance in classification and determination processes. This system shows a good performance in processing, comparing, and d e t e c t i n g the infected images that reached 97.45%.

Car problem detection is a complicated process which demands high level of knowledge and skills. Our aim is to develop an expert system on car maintenance and troubleshooting that is capable of assisting car's owner in dealing with their car problems and troubleshooting them whenever the time is limit and the human expert, also known as mechanics is not available at that very point of time. This paper provides affective design issues concerning the problems while driving a car and will give a logical solution which would help in rectifying those problems. The system would contain various set of rules for detecting different type of failures which can be easily handled by the driver and will give their causes. Here the system mainly deals with the starting problems of car and detecting various other large scale problems.

Bidang pertanian merupakan aspek pokok dalam mata pencaharian sebagian besar masyarakat Indonesia. Terdapat banyak tanaman pangan yang dikembangkan dan dibudidayakan di Indonesia, salah satunya yaitu padi. Pada tanaman padi terdapat banyak hama yang menyerang, sehingga apabila tidak dilakukan pengendalian dan penanggulangan yang tepat dapat mengakibatkan penurunan produksi padi.  Pada penelitian  ini  dirancang  suatu  sistem  berbasis  website  yang dimaksudkan  untuk membantu petani dalam menentukan hama tanaman padi. Perancangan  aplikasi  ini  meliputi  hal  teknis  seperti  membuat  database  baru dan  gejala  yang di timbulkan oleh  hama. Aplikasi penentuan hama ini di bangun menggunakan software PHP dengan database  MySQL  untuk  mempermudah  orang  lain  guna  pengembangan  aplikasi  pembelajaran ini agar menjadi lebih menarik. Dengan  aplikasi  tersebut,  diharapkan  bisa  bermanfaat  untuk  petani  atau  masyarakat lainnya sebagai pengetahuan dan pembelajaran.

Implementation of Expert System Diagnosis of motorcycle engine damage is a solution of motorcycle service at AHASS 00955 Mitra Perdana, Expert System is a system that is related to the knowledge possessed by experts and technology users to solve a problem. This article, discusses the design of decision-making applications that have an important role in the process of diagnosing motor engine damage. Because the system is currently running the user only hands over the motorcycle to the technician. The use of computer-based artificial intelligence can be a solution to problems in structured decision making. The design of this expert system is designed using the forward tracking inference method and is designed in the form of a website to facilitate users in using it.

The focus of this research is on prostate disease which is one of the most common diseases in men in the world. The prostate is part of the male reproductive system, which includes the penis, prostate, and testicles. If the prostate is too large or has a problem, it can cause health problems. One of the health problems in the prostate is a benign prostate gland disease, also called benign prostatic hyperplasia (BPH) and prostate cancer. This study aims to design application of expert systems for early detection of prostate diseases, so that it can help and facilitate users who want to know information about prostate diseases. The method used using the forward chaining method is based on the Knowledge Base System. The results obtained will be created application design expert system that can detect prostate disease early in the form of prototype in Android application using Java programming language and Firebase database.

The slow execution speed of current rule-based systems has restricted their application areas. Multiprocessor architectures have been proposed to overcome this limitation. However, as the number of processors in a multiprocessor system grows, so does the cost of communication between processors or between processor and memory units. The use of optics for a fast and parallel implementation of rule-based systems is proposed. The proposed optical system is hybrid in nature, using electronics for the user interface and optics for the rule-based inference engine. The proposed system uses twodimensional planes as basic computational entities and is therefore able to provide concurrent rule processing. Furthermore, it provides highly efficient implementation of the basic operations needed in rule-based systems; namely, matching, selection, and rule firing. The execution speed of the proposed system is theoretically estimated and is shown to be potentially of orders of magnitude faster than current electronic systems.

This paper addresses the problem of a knowledge based support, when designing business strategy and adequate key performance indicators (KPI). The business strategy designing is considered to be a business process and is a subject of modelling as well, while a linguistic modelling approach is applied for those purposes, where the business process model semantics plays a role of principal importance and that model is derived from text in natural language (TNL text), which describes structure and functionality of the business processes to be modelled quantified via linguistic sets, which create basis of business process model semantics and might be applied in design and implementation of business process linguistic modelling -expert system built up base on semantic technology principles (ST-LM expert system). The ST-LM expert system knowledge base operates based on semantic networks and (SNW) and reference databases and contains knowledge concerned to KPI Indicator generation and decomposition to lower levels of management. In that paper, the ST-LM expert system structure and functionality is described together with an appropriate knowledge base and inference mechanism.

Thls paper Presents the f lrst verslon of GENES IS, an expert system shell sultable for the development of alarm pattern recogn It Ion expert systems (APRES). GENES IS Includes a series of algorlthms and procedures especially deslgned for a rapld and systematic construct Ion of APRES . The Inputs required by GENESIS are the fault trees of the system under analys Is, the probab I I lty of occurrence of each fault In the trees, and the set of symptoms (alarms and measurements) associated to the occurrence of each Individual fault. Wlth thls lnformat Ion, GENESIS generates a set of productlon rules which relates faults and symptoms. The shell uses these rules and the probability of occurence of each of the faults In order to generate opt lmal alarm pattern recognltlon strategles (algorithm of the Inference engine). A strategy helps the operator to recognize which alarm pattern Is occurrlng wlthout havlng to search the entlre set of patterns. Al I the alarm pattern recognltlon strategtes are generated off-line, as a consequence, the response of the system WI I I be very fast. Thls feature makes GENESIS a powerful tool for the da% lopment of rea l-t I me APRES. The successful development of emert systems for such problems as medlcal diagnosls, interpretation of oil well data and conf lgurat Ion of computer systems has fostered wide Interest In a broad range of appllcatlons. In partlcular, Moore [I] has described several areas tit-e expert systems technology might be utlllzed for Improved power plant operat Ion. One of the proposed areas Is the analysis of slgnlflcant events, such as plant uosets. In a modern dlstributed process control system there may be Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Asociation for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission.

Résumé: The textual case-based cooking systemWIKITAAABLE participates to the second Computer cooking contest (CCC). It is an extension of the TAAABLE system that has participated to the first CCC. WIKITAAABLE's architecture is composed of a semantic wiki used for the collaborative acquisition of knowledge (recipe, ontology, adaptation knowledge) and of a case-based inference engine using this knowledge for retrieving and adapting recipes. This architecture allows various modes of knowledge acquisition for case-based ...

Over the last few years, context-aware computing has received a growing amount of attention among the researchers in the IoT and ubiquitous computing community. In principle, context-aware computing transforms a physical environment into a smart space by sensing the surrounding environment and interpreting the situation of the user. This process involves three major steps: context acquisition, context modelling, and context-aware reasoning. Among other approaches, ontology-based context modelling and rule-based context reasoning are widely used techniques to enable semantic interoperability and interpreting user situations. However, implementing rich context-aware applications that perform reasoning on resource-bounded mobile devices is quite challenging. In this paper, we present a context-aware systems development framework for smart spaces, which includes a lightweight efficient rule engine and a wide range of user preferences to reduce the number of rules while inferring persona...

The recent pandemic caused by the Coronavirus Disease (COVID-19) first surfaced in Wuhan, China in December 2019. This paper presents
an expert system for the diagnosis of COVID-19 based on its symptoms to aid people in taking precautionary measures. When experts are not
available, an expert system that can effectively diagnose the disease is crucial. It takes the place of one or more experts in decision-making
and problem-solving. An expert system for diagnosis of COVID-19 is a system developed to recognize early COVID-19 symptoms that
individuals may experience by allowing users to directly check the disease with results that can serve as a foundation for additional testing.
This study's primary goal is to identify useful COVID-19 detection patterns or knowledge by examining the historical data we have obtained
from the Kaggle dataset. The patterns are presented as rules, which are given to the expert system after consultation with a domain expert. A
total of 1,048,575 pieces of data were used for model training and testing. To detect COVID-19 disease, we employ a PART rule-based
algorithm, which performed 92.47% accurately in a 10-fold cross-validation test. We can therefore draw the conclusion that the algorithm
produces a promising result and that the expert system aids in the diagnosis of the disease. The system offers a suggestion in line with the
identified symptom

Este trabajo de fin de grado consiste en el desarrollo de un sistema en el lenguaje Python que realiza inferencia lógica, implementando una redefinición del dialecto de reglas de producción del estándar Rule Interchange Format, un lenguaje para la unificación de documentos de reglas en diferentes lenguajes. En adición, el motor de inferencia desarrollado usa ontologías escritas en un subconjunto de OWL 2, e implementa una versión del algoritmo Rete para la realización de matching en reglas. Este trabajo se engloba en las tecnologías de la web semántica, y pretende ser una alternativa cómoda y fácil de usar que no requiera de traductores entre lenguajes de reglas. También se ha realizado un estudio de rendimiento del motor, en el que se observa el peso que tiene el pobre rendimiento de Python sobre el motor y la influencia del tamaño de la red Rete sobre el tiempo de ejecución.

While large language models (LLMs) have demonstrated impressive performance in question-answering tasks, their performance is limited when the questions require knowledge that is not included in the model's training data and can only be acquired through direct observation or interaction with the real world. Existing methods decompose reasoning tasks through the use of modules invoked sequentially, limiting their ability to answer deep reasoning tasks. We introduce a method, Recursion based extensible LLM (REBEL), which handles open-world, deep reasoning tasks by employing automated reasoning techniques like dynamic planning and forward-chaining strategies. REBEL allows LLMs to reason via recursive problem decomposition and utilization of external tools. The tools that REBEL uses are specified only by natural language description. We further demonstrate REBEL capabilities on a set of problems that require a deeply nested use of external tools in a compositional and conversational setting.

Around ten percent of patients admitted to National Health Service (NHS) hospitals have experienced a patient safety incident, and an important reason for the high rate of patient safety incidents is medical errors. Research shows that appropriate increase in the use of clinical decision support systems (CDSSs) could help to reduce medical errors and result in substantial improvement in patient safety. However several barriers continue to impede the effective implementation of CDSSs in clinical settings, among which representation of and reasoning about medical knowledge particularly under uncertainty are areas that require refined methodologies and techniques. Particularly, the knowledge base in a CDSS needs to be updated automatically based on accumulated clinical cases to provide evidence-based clinical decision support.In the research, we employed the recently developed belief Rule-base Inference Methodology using the Evidential Reasoning approach (RIMER) for design and developm...

̶Wireless Sensor Network technologies have become a latest research area in health care industries due to rapid maturity in improving the quality of life of a patient. Wireless Sensor Networks when work in medical field provide continuous monitoring of vital health parameters which over a long period of time provide doctors much needed help to make accurate diagnosis and giving better treatment. In this paper we propose a model which monitors various health parameters like body temperature and blood pressure monitor (mm Hg) of an individual. The collected data through the system is then transferred over the internet to a smartphone application of the patient. In this project, we are going to build a temperature and pressure measuring system. In this project, we will use a pressure sensor model BMP180 to detect temperature and pressure, Arduino device and show in mobile application temperature and pressure. The pressure sensor BMP180 consists of a piezoresistive sensor, an analog and...

Musculoskeletal diseases (MSDs), encompasses various conditions affecting muscles, bones, tendons, ligaments, and joints, resulting to pain, inflammation, and limited mobility, significantly impacting individuals' quality of life. Diagnosing these diseases poses a challenge for healthcare professionals due to symptom similarities with other conditions. To address this, the development of expert systems tailored for musculoskeletal diagnosis has emerged as a promising approach to enhance clinical decision-making and improve patient outcomes. This study aims at developing and evaluating an expert system for musculoskeletal disease diagnosis, by leveraging a knowledge base containing information on common musculoskeletal diseases and symptoms. The system utilized a combination of rule-based and machine learning techniques to provide diagnostic recommendations to physicians. Comparative analysis with experienced physicians, using a dataset of patients with known musculoskeletal diseases, revealed the expert system’s diagnostic accuracy of 92%, recall of 98%, Precision of 91%, F1-Score of 94% and a quicker diagnosis compared to physicians. Additionally, the system demonstrated ease of use and user-friendliness. This project focuses on predictive algorithms, leveraging expert systems dating back to the 1970s, emulating human expert decision-making, particularly in disease diagnosis. The development of an expert system for musculoskeletal disease diagnosis symbolizes the convergence of medical expertise, computer science, and artificial intelligence. By integrating machine learning, natural language processing, and decision support systems, these expert systems have the potential to revolutionize musculoskeletal healthcare delivery. In conclusion, our results show that expert systems hold promise in transforming clinical practice and improving patient outcomes in musculoskeletal healthcare through interdisciplinary collaboration and continuous innovation.

The expert system solves problems within a specific area of the knowledge base. Prolog is a logical programming language which works on its knowledge base and effectively can be used to develop an expert system. Covid 19 is a pandemic deices and an expert system can be developed to diagnose this disease with the help of its symptoms that can be used as a knowledge base in Prolog. This expert system can make a fast diagnosis process for the covid 19 which is important to prevent the spread of the virus. Here we developed an expert system using prolog for diagnosis purposes. Like humans, these systems can get better with time as they gain more experience. Expert systems combine their experiences and expertise into a knowledge base that is then used by an inference or rules engine, a set of rules that the software employs, to apply to certain scenarios. Prolog is ideal for use with intelligent systems for a few reasons. Prolog can be viewed as a straightforward theorem prover or infere...