Intelligent and Expert Systems

Complex systems with nonlinearities and surrounding uncertainty are usually modeled sufficiently by Fuzzy Cognitive Maps (FCMs). FCMs work efficiently even with missing data. Experts, for each case study, support with their knowledge the developed FCMs. Nevertheless, the main drawback of FCMs is their convergence to the same equilibrium point regardless of the initial conditions. In this paper a different approach for modeling FCMs is proposed, where the inputs gain back their lost importance. Thus, Input-Sensitive Fuzzy Cognitive Maps (IS-FCMs), supported both by experts and by the appropriate Rule-Base, manage to converge to desired operating points. The Nonlinear Hebbian Learning algorithm (NHL) is used in order to optimize the values of the weights. A PV-System application is presented. The simulation results support the hypothesis of the proposed new IS-FCM model.

Technological Pedagogical and Content Knowledge (TPACK) is one of today’s frameworks that integrates technology in the teaching and learning process. However, little research has been done related to the investigation of in-service teachers’ TPACK, specifically in English language classroom setting. Therefore, this study aims at exploring TPACK in English language classroom from the prespectives of in-service English teachers. Qualitative  case study was involved as the approach of the study. The researchers utilized the triangulation of data sources including interview, observation, and document to get the data from in-service English teachers’ as the respondents of the study. The data acquired were then transcribed verbatim and analyzed using thematic analysis. The current study shows that there are three main themes as findings comprising English teachers’ understanding toward TPACK, the process of acquiring TPACK, and the English teachers’ challenges toward TPACK. The English te...

The present investigation was carried out to locate sustainable site for wells to provide continuous supply of water to limestone mine and cement plant. Total 11 locations were selected for study by vertical electric sounding (VES) using Schlumberger array survey at different point up to 200 m in and around Sehadpur, Itaura, Mahawa, Bhilampur, Saipura and Badhreta villages of Kailaras block, Morena district, Madhya Pradesh. The area is part of Upper Vindhyan formations and the rocks of Bhander Group are well exposed in the area. Maximum resistivity of VES was found in location number 8 (103 to 5000 Ω/m), where compact and thick layer occurs. Minimum resistivity of 25 Ω/m was recorded at location number 7, whereas comparatively less resistivities of 31 Ω/m and 33 Ω/m were recorded at VES location number 11 and 6, respectively. Resistivity values of VES of location number 6 ranges from 33 to 103 Ω/m in moderate to very good thickness of the layer. More or less similar resistivity values are found in VES 7 and 11.

Recent trends in agriculture yields a large number of researches and articles related to it. It has become a daily routine to access these articles by modern day researchers. But due to the fact that these information are in an unstructured form, readers might face difficulty to access these information. Text Categorization, a branch of Text Mining, is a very useful technique to represent these unstructured text in a structured way. In this research, a number of research articles has been categorized using text categorization by applying some popular machine learning algorithms.

The Balanced Scorecard (BSC) has been proposed as a strategic management evaluation system recently. Strategic management is important for organizational success and competitive advantage in an increasingly competitive business environment. Many organizations have adopted the BSC to gain business excellence and success in the marketplace. It is a practical tool for designing operational strategies. However, the BSC model has some drawbacks and problems which disable to identify the priorities in strategic plans (Initiatives). In this paper, the strategic plans is identified, using a simple additive weighting (SAW) decision model. Also, assessing and determining the strategic plans is developed in the BSC model, using expert panel opinion and SAW method. The results showed that the proposed model is more valid and acceptable and the experts verified the model for selecting strategic plans in the BSC in practice. The developed model has been used in a real case study and the results have been analyzed from different points of view. In this article initiative is called strategic plans. [Dodangeh J, Dehafarin E, Nasehifar V. A decision model for selecting of strategic plans in Balanced Scorecard model: A case study for a manufacturing firm J Am Sci 2012;8(8):402-407].

ABSTRAK Sistem pakar berbasis frame adalah kecerdasasn buatan yang menggunakan frame sebagai metode representasi pengetahuan. Sistem ini meniru cara kerja pakar manusia dalam memecahkan masalah pada domain tertentu dengan menyimpan pengetahuan dalam struktur data yang disebut frame. Frame berfungsi sebagai kerangka yang memuat semua pengetahuan tentang suatu objek, situasi, atau konsep, diorganisasikan dalam bentuk slot-slot atribut dan nilainya,yang mendeskripsikan karakteristik atau properti dari objek tersebut. Metode ini sangat efektif dalam memodelkan hubungan antar objek dan memungkinkan pewarisan atribut dari frame induk ke subframe. Sistem ini dibangun menggunakan bahasa pemrograman Python dan database SQLite, dengan struktur pengetahuan yang dikembangkan berdasarkan kerangka klasifikasi gejala, penyakit, dan tindakan penanganan. Setiap penyakit dimodelkan sebagai sebuah frame dengan atribut gejala, tingkat keparahan, dan tindakan pengobatan. Dengan demikian, sistem pakar berbasis frame menawarkan solusi yang efisien dan terstruktur dalam menyusun serta menggunakan pengetahuan, dan dapat diadaptasi untuk berbagai bidang aplikasi lainnya seperti pendidikan, kesehatan, dan industri. Kata kunci: sistem pakar, representasi pengetahuan, metode frame, kecerdasan buatan, diagnosis, [bidang aplikasi spesifik].

Sistem pakar merupakan sistem berbasis komputer yang dirancang untuk meniru proses pengambilan keputusan seorang pakar. Penelitian ini bertujuan mengembangkan sistem pakar berbasis aturan menggunakan metode Forward Chaining untuk membantu menentukan gaya belajar siswa. Sistem ini dibangun dengan basis pengetahuan berupa aturan produksi yang disusun berdasarkan wawancara dengan pakar pendidikan dan literatur terkait. Metode Forward Chaining dipilih karena efektif dalam penelusuran dari data awal menuju kesimpulan. Sistem ini dapat membantu guru dan konselor dalam mengidentifikasi gaya belajar dominan siswa, seperti visual, auditori, atau kinestetik. Hasil uji coba menunjukkan tingkat akurasi diagnosis sistem mencapai 92%. Sistem ini dapat menjadi alat bantu pengambilan keputusan dalam pengembangan metode pembelajaran yang sesuai.

___________________________________________________________________________ ABSTRAK Ketika sistem kekebalan tubuh kita dalam kondisi sehat, kita tidak akan gampang sakit. Manusia tidak bisa menjalani kehidupan tanpa adanya vitamin dan mineral. Kekurangan vitamin dan mineral dapat memperbesar kemungkinan seseorang mengidap penyakit serta menghambat fungsi tubuh agar tidak berjalan secara optimal. Vitamin dan mineral merupakan senyawa penting yang krusial untuk tubuh kita dan memiliki peran dalam mengatur berbagai proses di dalamnya. Pemeriksaan terhadap kadar kekurangan vitamin dan mineral di masyarakat sangat jarang dilakukan karena memerlukan analisis darah dan biaya yang relatif mahal. Dalam kajian ini, masalah tersebut diatasi dengan mengembangkan sebuah sistem pakar, yaitu sistem yang dapat mempercepat proses diagnosis kekurangan vitamin dan mineral dalam tubuh manusia, sehingga pasien dapat dengan mudah mengetahui jenis kekurangan yang mereka alami. Penerapan sistem ini memanfaatkan Teknik representasi Frame dan metode Backward Chaining.

Knowledge engineering in artificial intelligence (AI) is a discipline that focuses on the representation and processing of knowledge to enable computer systems to perform complex tasks such as medical diagnosis or natural language dialogue. The methods used include knowledge representation through ontologies, semantic networks, and rule-based systems, as well as automated reasoning using inference engines and theorem provers. The main outcome of these approaches is the ability of AI systems to understand, reason, and interpret knowledge, thereby increasing the efficiency and accuracy of problem solving. In conclusion, knowledge engineering plays a crucial role in the development of intelligent and adaptive AI systems.

Knowledge engineering in artificial intelligence (AI) is a discipline that focuses on the representation and processing of knowledge to enable computer systems to perform complex tasks such as medical diagnosis or natural language dialogue. The methods used include knowledge representation through ontologies, semantic networks, and rule-based systems, as well as automated reasoning using inference engines and theorem provers. The main outcome of these approaches is the ability of AI systems to understand, reason, and interpret knowledge, thereby increasing the efficiency and accuracy of problem solving. In conclusion, knowledge engineering plays a crucial role in the development of intelligent and adaptive AI systems.

ABSTRAK Sistem pakar merupakan salah satu cabang kecerdasan buatan yang bertujuan untuk meniru kemampuan seorang pakar dalam mengambil keputusan atau memecahkan masalah di suatu bidang tertentu. Salah satu metode yang umum digunakan dalam pengembangan sistem pakar adalah metode frame-based reasoning, yang menyajikan pengetahuan dalam bentuk struktur data yang disebut frame. Setiap frame merepresentasikan suatu objek, konsep, atau situasi lengkap dengan atribut dan nilainya, serta memiliki kemampuan untuk mewarisi sifat dari frame lain melalui hirarki. Sistem pakar ini juga mampu memberikan solusi kerusakan atau cara penangganan kerusakan dalam bentuk gambar sehingga memudahkan teknisi junior untuk mengikuti langkah-langkah dalam menangani kerusakan pada komputer. Sistem pakar berbasis frame terbukti efektif dalam merepresentasikan pengetahuan secara terstruktur dan memungkinkan pengambilan keputusan yang lebih efisien.

Pertumbuhan eksponensial data di berbagai bidang menuntut pendekatan analitik yang mampu menangani kompleksitas dan volume informasi secara efesien. Penelitian ini mengusulkan arsitektur kecerdasan hybrid yang mengintegrasikan machine learning (ML) dan system pakar (expert system) untuk menganalisis data kompleks. Sistem ini di rancang agar dapat menghubungkan kemampuan prediktif ML dengan logika aturan berbasis pengatahuan pada system pakar. Studi kasus dilakukan menggunakan data set transaksi keuangan untuk mendeteksi potensi anomali dan pola perilaku konsumen. Hasil eksperimen menunjukan bahwa pendekatan hybrid memberikan akurasi lebih tinggi dan interpretabilitas yang lebih baik disbanding dengan mode tunggal. System ini menunjukan potensi besar untuk aplikasi dalam bidang keuangan, dan e-commerce.

The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) is transforming various sectors, with libraries being one of the key areas experiencing profound changes. Technologies offer innovative solutions to long-standing challenges in libraries, such as managing vast amounts of digital information, enhancing user experiences, and optimizing operational efficiency. AI, with its ability to emulate human cognition, and ML, which allows systems to learn and improve from data, are reshaping how libraries operate and engage with users. AI-driven systems are automating routine tasks like cataloguing, metadata generation, and resource classification, reducing the workload of library staff and increasing efficiency. ML algorithms further personalize the user experience by recommending resources tailored to individual preferences, making it easier for users to discover relevant materials. Additionally, AI-powered chatbots and virtual assistants are enhancing user support by providing real-time assistance, reducing reliance on library personnel and improving accessibility. Predictive analytics, another key application, helps libraries forecast user demand and optimize allocation, ensuring that high-demand materials are readily available. The integration of these technologies also raises ethical concerns, particularly regarding data privacy, algorithmic bias, and potential job displacement. Libraries must navigate these challenges carefully to ensure that AI and ML systems are implemented transparently and responsibly. This paper explores the current and potential applications of AI and ML in libraries, examines real-world case studies of their implementation, and discusses the ethical implications of their use. As AI and ML continue to evolve, they hold the promise of further revolutionizing library services, improving access to information, and enhancing user engagement while presenting challenges that must be addressed for long-term success.

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.

Cervical cancer leads to major death disease in women around the world every year. This cancer can be cured if it is initially screened and giving timely treatment to the patients. This project focuses on A mobile based cervical cancer detection using neural network. The analysis of a digital image of the cervix; Oriented Local Histogram Technique (OLHT) is applied on the cervical image to enhance the edges and then Dual Tree Complex Wavelet Transform (DT-CWT) is applied on it to obtain multi resolution image. Then, features as wavelet, Grey Level Co-occurrence Matrix (GLCM), moment invariant and Local Binary Pattern (LBP) features are extracted from this transformed multi resolution cervical image. These extracted features are trained and also tested by feed forward back propagation neural network to classify the given cervical image into normal and abnormal. The morphological operations are applied on the abnormal cervical image to detect and segment the cancer region. The performance of the proposed cervical cancer detection system is analyzed in the terms of sensitivity, specificity, accuracy, positive predictive value, negative predictive value, Likelihood Ratio positive, Likelihood ratio negative, precision, false positive rate and false negative rate. The performance measures for the cervical cancer detection system achieves 97.42% of sensitivity, 99.36% of specificity, 98.29% of accuracy, PPV of 97.28%, NPV of 92.17%, LRP of 141.71, LRN of 0.0936, 97.38 % precision, 96.72% FPR and 91.36% NPR. From the simulation results, the proposed methodology outperforms the conventional methodologies for cervical cancer detection and segmentation process.

The abstract explores the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on modern library systems. It traces the evolution of digital libraries and highlights how AI enhances information retrieval, cataloging, and user interaction. The study reviews key AI technologies such as Natural Language Processing (NLP) for automated cataloging, Machine Learning algorithms for predictive analytics, and chatbots for personalized support. Through comparative analyses, the article shows how AI improves search accuracy, resource management, and user experiences over traditional methods. Case studies of AI in academic and special collections libraries provide real-world insights, while the article emphasizes the need for ethical AI guidelines and best practices for adoption, including staff training and collaboration with technology providers. The future of AI in libraries envisions them as dynamic information hubs, fostering more efficient and intuitive resource discovery, and maintaining their role as reliable knowledge stewards in an AI-driven landscape.

This project, An Expert system for diagnosis of COVID_19, is a software system use in the diagnosis of COVID_19 diseases. The software is an expert system with a database containing an expert knowledge. The user only uses it to determine whether user has COVID_19 diseases within its domain. The software has been designed to be interactive with interfaces capability generates from the user if they have symptoms of the diseases.
The user response helps the expert system to determine the level at which the disease is present. The user is further advised on what next to do. This software is implemented using PHP programing language, CSS and JavaScript, while my SQL database was used for data storage and manipulating. Health care facility should be accessible by all at all time. But some of the people that should access these facilities are far removed from these facilities. It would be of great necessity to provide a computerized system that will provide a complementary medical service, such as medical disease diagnosis in places where accessibility is a problem as well as health care facilities where qualified experts are lacking, hence this topic, An Expert System for Diagnose of COVID_19

Survival of the Fittest" as quoted by Charles Darwin fits and suits in all walks of life. As on date, big debate is on regarding the existence and survival of libraries. Libraries need to adopt emerging tools and technologies to provide best services to the users on time with precision. Artificial intelligence has placed itself everywhere in everyday life. This paper is an attempt to expedite AI applications in libraries, not only for providing services to the existing users but also finding innovative ways to attract new users. The paper discusses AI, basic requisites, its applications in libraries, and barriers in its implementations. The paper concludes that AI is the need of today for libraries.

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...

The paper reviews areas of application modern expert systems, on basis of which advantages and disadvantages of using ES are considered; main components of typical ES. The paper highlights features of expert systems it is proposed to include criteria for applicability of such systems. In course of review, classification was given, including nine features by which ES can be considered, which differs from existing ones in that it is proposed to take into account features of systems use and type of computer. A general overview of use of ES over past two years in various areas, from production to agriculture, has been carried out.

Artificial Intelligence is revolutionising the different areas of life and libraries and information centres are no exceptions. Many libraries are now looking for new ways to improve their services, enhance library activities and meet the changing needs of their patrons. Artificial Intelligence or Machine Learning can help achieve these objectives. This article talks about Artificial Intelligence and its major components and presents a review of literature trying to determine what is happening in this domain. It further discusses AI applications in different areas of library information services, especially cataloguing, acquisition, reference services, circulation, indexing,
robotics, etc. It further compares the benefits Vs drawbacks, as nothing comes without limitations or challenges. This basic exploration of AI applications in libraries could benefit a beginner or someone who has just started exploring the same.

Artificial Intelligence (AI) is one of computer science branches and is used to solve problems with symbolic reasoning. Expert systems (ESs) are one of the prominent research domains of AI. We developed declarative, online procedural rule-based expert system models for psychological diseases diagnosis and classification. The constructed system exploited computer as an intelligent and deductive tool. This system diagnoses and treats more than four types of psychiatric diseases, i.e., depression, anxiety disorder, obsessive-compulsive disorder, and hysteria. The system helps psychology practitioner and doctors to diagnose the condition of a patient efficiently and in short time. It is also very useful for the patients who cannot go to a doctor because they cannot afford the cast, or they do not have a psychological clinic in their area, or they are ashamed of discussing their situation with a doctor. The system consists of program codes that make a logic decision to classify the problem of the patient. The methodology for developing the declarative model was based on the backward chaining, also called goal-driven reasoning, where knowledge is represented by a set of IF-THEN production rules. The declarative programs were written in the PROLOG. While the design of the procedural model was based on using common languages like PHP, JavaScript, CSS, and HTML. The user of the system will enter the symptoms of the patients through the user interface and the program executes. Then the program links the symptoms to the pre-programmed psychological diseases, and will classify the disease and recommend treatment.

Third-generation robotic systems are intelligent in the sense that they can perform a large repertory of complex tasks under conditions not known a priori. They are usually sensor-coupled using multisensory (vision, force, tactiie, etc.) information about their initial state and environment, have decision-making capabilities, can monitor and modify their plans learning from past experience. They have the properties of flexibility and artificial intelligence. The aim of the book was to provide wide coverage of these concepts based on the latest developments of well-known experts working in academic and research institutions. The book is addressed to scientists, engineers, postgraduate students and professionals working or having interest in intelligent robotics and control. The book is divided in 5 parts containing 18 chapters. Part I is devoted to introductory issues and it consists of Chapter 1: Introduction to Intelligent Robotic Systems written by Tzafestas (National Technical University of Athens,. Greece). The general architecture of intelligent robotic control (consisting of organization, coordination and execution levels), the basic problems of robot modeling and motion control, and the classification of sensors including vision are introduced. Three examples of intelligen t robotic systems are selected from the field of sensor-based (Espirit 278 and Pardue system) and mobile (KAMRO) robots. Part II deals with the structure and programming of intelligent robotic systems. Chapter 2: Theory and Design of Intelligent Robotic Systems by Valavanis (Northeastern University, Boston, Massachusetts), presents a general theory of intelligent machines and robotic systems based on a heuristic probabilistic model and entropy functions to describe the system performance of structures composed of organization, coordination and execution levels. Chapter 3: Hierarchy oj Intelligent On-Line Execution by Kelley (Rensselaer Polytechnic Institute, Troy, New York) deals with a robotic assembly system that allows both automatic programming and execution despite uncertainties in the task environment. The principle ofleast commitment is followed, and the system is partitioned into off-line (planners, specialists) and on-line (supervisor, specialists, interfaces) modules. Some details are given for the design of the supervisor and its implementation with 4 specialists on VAX running under Unix. Chapter 4: Skill-Based Expert Systems in Robotics by Tomovic (University of Belgrade, Yugoslavia), deals with the skill level (functional motion of human extremities) of a hierarchical control structure using both numerical and non numerical models. It concentrates on multifinger robot hands and describes the hand mechanics, sensors and local controllers of the Belgrade-USC Hand, and the multilevel manipulator control using computer vision and expert system (for preshaping, grasping mode, etc.

Expert systems (ES) are systems that emanate from the new area of computing known as Artificial Intelligence (AI). An expert system is an application program that makes decisions to solve problems in a particular field, such as finance or medicine, by using knowledge and analytical rules defined by the expert of the field. It uses two components, a knowledge base and an inference engine to form conclusions. Additional tools include a user interface and explanation facilities, which enable the system to justify or explain its conclusions as well as allow developers to run check the operating system.

This paper is proposed as a model for ranking of fields, criteria and sub-criteria in the quality award model. It is based on the Analytic Hierarchy Process (AHP). The AHPis used to find the criteria's weightage and the sub criteria's priority, are applied to determine priorities and weightage, to recognize, apply and coordinate the factory resources. The results confirmed the efficient performance of the proposed model. The proposed model used an example problem and it provided a new tool for planning and analysing of excellence organizations more effectively.

Fuzzy merupakan logika yang berhadapan langsung dengan konsep sebagian, dimana logika klasik menyatakan bahwa segala hal dapat diekspresikan dalam salah atau benar. Logika fuzzy memungkinkan nilai keanggotaan antara salah dan benar. Logika fuzzy menyediakan cara sederhana untuk menggambarkan kesimpulan pasti dari informasi yang ambigu, sama-samar, atau tidak tepat. Sedikit banyak, logika fuzzy menyerupai pembuatan keputusan pada manusia dengan kemampuannya untuk bekerja dari data yang ditafsirkan dan mencari solusi yang tepat. Pengambilan keputusan dalam Teknik fuzzy dilakukan dalam beberapa tahapan yaitu pembentukan himpunan fuzzy (fuzzification), penentuan fungsi keanggotaan (membershipfunction), pembuatan aturan-aturan (rule), dan defuzzification. Rule merupakan konsep bagian utama dari fuzzy yang menjadi dasar untuk menentukan system menjadi pintar atau tidak. Salah satu faktor yang menentukan keakuratan hasil logika fuzzy adalah fungsi keanggotaan (membership function). Fungsi ...

Purpose-The study proposes a decision support tool (DST) that takes into account the readiness of an organization to accept and implement a consignment stock program (CSP) adoption in a 'leagile' supply chain. Methodology/approach-The paper includes a brief overview on multi-criteria decision making (MCDM) methods and uses Fuzzy logic theory to capture the uncertainty that usually occur in a decision process. Findings-A correct and realistic decision, which is well accepted by the organization team is easier to implement and creates a sooner positive outcome. Research limitations/implications-The proposed decisional model has a degree of limitation in the meaning that it must be adjusted according to the specific criteria of each business environment. Practical implications-The seven steps DST was designed to assist the practitioners, in their decision making processes, and to encourage them to use multi-criteria mathematical programming in their basic activities, as it is proved to increase their efficiency. Originality/value-The present research integrates the voices of the internal functions into a decisional process matrix. An organization readiness index is defined, to reflect the degree to which a business unit is prepared to adopt and implement a CSP.

Motivations: Ischemic Heart Disease (IHD) is one of the most common causes of the death in many countries [1]. The disease is unpredictable in onset and hence, requires rigorous diagnosis in regards to multiple signs and symptoms such as cardiac pain, breathlessness, sweating, palpitation, nausea vomiting and hypertension [2]. However, these signs and

Abstrak - Hewan ternak kambing merupakan hewan ternak banyak memberikan manfaatbagi kehidupan pangan manusia, seperti daging, dan susu. Hewan ternak kambing yang sehat akan menghasilkan daging, dan susu yang berkualitas bagus sehingga meningkatkan harga jual. Penyakit dapat mengganggu pertumbuhan hewan ternak kambing dan jika dibiarkan dapat membunuh hewan ternak kambing. Para peternak sebagian besar masih memiliki pengetahuan yang rendah tentang pengendalian penyakit sedangkan penyakit hewan ternak kambing berkembang subur di daerah yang beriklim tropis seperti Indonesia. Tenaga pakar yang tersedia di daerah pedesaan masih terbatas jumlahnya. Teknologi informasi seperti sistem pakar dapat membantu para peternak dalam melakukan penanganan dini terhadap penyakit yang menyerang hewan ternak kambing. Sistem pakar adalah sistem yang berusaha mengadopsi pengetahuan manusia ke komputer, agar komputer dapat menyelesaikan suatu masalah seperti yang biasa dilakukan oleh para ahli. Penelitian...

In this paper, a generic rule-base inference methodology using the evidential reasoning (RIMER) approach is proposed. Existing knowledge-base structures are first examined, and knowledge representation schemes under uncertainty are then briefly analyzed. Based on this analysis, a new knowledge representation scheme in a rule base is proposed using a belief structure. In this scheme, a rule base is designed with belief degrees embedded in all possible consequents of a rule. Such a rule base is capable of capturing vagueness, incompleteness, and nonlinear causal relationships, while traditional if-then rules can be represented as a special case. Other knowledge representation parameters such as the weights of both attributes and rules are also investigated in the scheme. In an established rule base, an input to an antecedent attribute is transformed into a belief distribution. Subsequently, inference in such a rule base is implemented using the evidential reasoning (ER) approach. The scheme is further extended to inference in hierarchical rule bases. A numerical study is provided to illustrate the potential applications of the proposed methodology.

Резюме. Развитие современных биомедицинских технологий позволяет обеспечить врача колоссальным объемом информации о состоянии организма пациента. Однако возможность использования этих данных для медицинской диагностики в полной мере пока видится только в качестве отдаленной перспективы. Причина этогоограниченные возможности мозга человека в оценке и интерпретации таких массивов данных. Решение проблемы видится в широком внедрении в медицину искусственного интеллекта и экспертных систем. В настоящее время в качестве способа реализации искусственного интеллекта практически всеми авторами рассматриваются различные варианты построения искусственных нейронных сетей. Этот подход, восходящий к фундаментальной теореме А.Н. Колмогорова, работам В.И. Арнольда и Хехт-Нильсена, демонстрирует прекрасные возможности в ряде задач распознания образов, сводящихся к выявлению скрытых деталей на фоне шумов входной информации. Значительно реже упоминается такой метод моделирования формального мышления, как экспертные системы, возникший в 1960-е гг., а потом ушедший в тень. С момента зарождения кибернетики разработчики компьютерных программ пытались воспроизвести механизм мышления человека, т. е. ставилась задача научить компьютер «думать». Первые известные результаты в области создания и использования интеллектуальных систем принесли работы Норберта Винера и Г.С. Альтшуллера. При этом создание интеллектуальных систем сводилось к разработке программ, решающих задачи с помощью разнообразных эвристических методов, основанных на свойстве человеческого мышления обобщать. Ключевые слова: искусственный интеллект; экспертная система; дифференциальная диагностика; база знаний; машина вывода; интерфейс доступа; клиническая диагностика; лабораторная диагностика; медицинская диагностика.

Tuberculosis is the second leading cause of death from an infectious disease worldwide, after the human immunodeficiency virus. The main aim of this research work is to develop a Neuro-Fuzzy system for diagnosing tuberculosis. The system is structured with to accept symptoms with the help of three domain Medical expertise as inputs that are used to automatically generate rules that are injected in to the knowledge based where the system would use to make decisions and draw a conclusion. MATLAB 7.0 is used to implement this experiment using fuzzy logic and Neural Network toolbox. In this experiment linguistic variables are evaluated using Gaussian membership function. This system will offer potential assistance to medical practitioners and healthcare sector in making prompt decision during the diagnosis of tuberculosis. In this work basic emblematic approach using Neuro-fuzzy methodology is presented that describes a technique to forecast the existence of mycobacterium and provides support platform to researchers in the related field.

This research is aimed at developing Neuro-fuzzy system that would determine the severity level of Osteomyelitis (OM) and control. Osteomyelitis is a major disease causing disability and reduction in quality of life. The symptoms of Osteomyelitis were analyzed based on the proper discussion with Experts to gather information about possible symptoms which are used as inputs parameters to the system four (4) inputs were used in this study that is pain, swelling, fever and age with their respective linguistic variables, one hundred and five (105) rules were generated for the system to draw conclusion Neuro-fuzzy methodology and triangular membership function is used throughout this experiment MATLAB 7.0 is used to implement this experiment. This study has paved a way to the creation of intelligent system that would determine the severity level of any kind of disease.

This study assess the performance and efficiencies of existing (4) gari production systems (profitability and marketing margins as well as the strengths, weakness, opportunities and constraints) in Sierra Leone. The Policy Analysis Matrix (PAM) was used for the analysis. Multistage random sampling was used to select study samples. Information was collected using a structured questionnaire from a total of 108 gari processors in 24 chiefdoms. Data collected included cost return and socioeconomic variables. The PAM was based on 50 Kg of fresh cassava storage roots and Leone (SSL) as money to evaluate costs and revenues. The analysis indicates that only three (3) cassava gari production systems (2, 3 & 4) present a Domestic Resource Cost Ratio of less than 1 (DRC < 1) and Cost-Benefit Ratio (CBR) also less than 1 (CBR within 0.64 to 0.96). Cassava gari

The industrial revolution 4.0 is Characterized by automation and digitalization in various sectors. This of course has an immediate impact on the development of education. The Science of Technology has been integrated into the education sector so that the modern era of education can Utilize technological developments to improve the quality of education. The quality of education is related to the learning process. Learners must maximize the learning process to improve learning outcomes. Learning style plays an important role in the learning process, learners must therefore Recognize the learning style they have. This study aims to analyze learning styles based on the type of learning style classification (visual, auditory and kinesthetic). The research uses waterfall method and developed based on the website by applying the principle of artificial intelligence by applying the forward chaining algorithm as an inference engine. The forward chaining is designed based on the rules of the...

Uterine cancer is one of the conspicuous cancers for women in both developed and developing countries including Bangladesh. Now, in the world it is the sixth most common cancer among women and fourteenth most common cancer overall. The high occurrence of uterine cancer in women has increased significantly in the last years. This involves many factors to be measured and evaluated, which are related to the signs and symptoms of this disease. These factors usually expressed in quantitative and qualitative ways. In addition, a hierarchical relationship exists among these factors. Since qualitative factors cannot be measured in a quantitative way, resulting various types of uncertainties such as incompleteness, vagueness, imprecision.

In 2015 the British government announced a number of major tax reforms for individual landlords. To give landlords time to adjust, some of these tax measures are being introduced gradually from April 2017, with full effect in tax year 2020/21. The changes in taxation have received much media attention since there has been widespread belief that the new measures were sufficiently skewed against landlords that they could signal the end of the Buy-To-Let (BTL) investment era in the UK. This paper assesses the prospective performance of BTL investments in London from the investor&#39;s perspective, and examines the impact of incoming tax reforms using a novel Temporal Bayesian Network model. The model captures uncertainties of interest by simulating the impact of changing circumstances and the interventions available to an investor at various time-steps of a BTL investment portfolio. The simulation results suggest that the new tax reforms are likely to have a detrimental effect on net p...

 Expert system is a system that seeks to adopt human knowledge into computers, this research was conducted to diagnose diabetes early. Disease data that were processed were sourced from the South Solok District General Hospital. The method used is Backward Chaining by using a goal-driven approach. The result of testing this method is that the user understands the danger of diabetes and knows how to revent it. Trials conducted on an expert system for diagnosing diabetes, most people understand the characteristics and symptoms of diabetes and understand how to prevent it. So that this expert system can be recommended for ordinary people

Ginekologi adalah cabang ilmu kedokteran yang berfokus pada tubuh wanita dan kesehatan reproduksinya mulai dari masa pubertas hingga dewasa. Ginekologi merupakan masalah kewanitaan yang asing ditelinga orang awam. Keterbatasan pengetahuan dan informasi yang dimiliki oleh maryarakat tentang kesehatan ginekologi disebabkan karena barbagai kondisi yang ada, dengan faktor utama yang menjadi permasalahan tersebut adalah rasa enggan ataupun malas untuk berkonsultasi secara langsung dengan seorang pakar atau ahli  dikarenakan merasa malu untuk membahas mengenai kesehatan pribadi apalagi yang berhubungan dengan organ vital. Oleh karena itu, dibuatlah sebuah sistem pakar yang digunakan sebagai alternatif solusi layakya seorang pakar atau ahli dalam mendiagnosis pasien. Penggunaan metode certainty factor dalam membangun sistem pakar diagnosa kanker ginekologi ini diharapkan dapat memberikan informasi yang jelas dengan memunculkan presentase kemungkinan user mengalami masalah kanker ginekologi...

The search for quick detection and solution of Lung cancer is traced back to the Stone Age by medical experts and health workers. Often time, patients are confronted with the challenges of running through different medical outfits and spending a lot of money to fight the deadly disease and still ended up in their untimely grave. This is not because the medical experts could not handle the patient appropriately, but because the disease has gotten to the final or terminal stage, and at this point it is irreversible. The quest to reduce the challenges faced by patients and medical experts to get a timely solution to the diagnosis or detection of lung cancer in other to save the life of the patient has motivated this research. The work aims at designing and implementing an intelligent-based system for the diagnosis of lung cancer driven by fuzzy c-means algorithm.The methodology used was Object-Oriente Analysis and Design (OOAD). The fuzzy model was designed in three main stages includingfuzzification, FCM inference system, and defuzzification. The triangular membership (Tmf) function was used to map the input parameters to the output parameter. The FIS model was designed using Mamdani's inference mechanism. C# programming language was used on a windows 10 platform to integrate the Matlab FIS output for better performance and MySQL database 5.7.14 from WAMP server 3.0.6 was used as the back-end engine. To validate this work, data was captured directly from the 50 volunteers. The training, testing, and checking FCM KMSI values of 0.00123, 0.03123, and 0.06301 respectively were observed in the process at 150 epochs and an average error of 0.0002102 was observed at 300 epochs.

Background: The EFQM Model, the most widely applied Business Excellence Model in Europe, can be used by any type of organization in order to conduct a thorough analysis of its strengths and its areas for improvement. The model can be used in two ways: both as a diagnostic tool for self-assessment and as a model for management control. Purpose: The main objective of this paper is to identify and analyze the modifications introduced to the latest version of the EFQM Model (version 2020). In an attempt to depict the main elements of the revised version, a comparison is made between the last and the previous version of the model aiming to identify the key features that need to be considered by applicant organizations when they decide to fulfill the model’s new requirements. Methodology:  A literature review is conducted in order to illustrate the evolution of the model to date and a comparative analysis is used as a key research method for studying the differences between the last two v...

Infectious disease is a very dangerous disease with a high mortality rate. Delays in handling the spread of an infectious disease can be minimized using an expert system. This study uses an expert system as a disease consulting service that is integrated with the health care system. Integration with the health care system is used for the knowledge acquisition process. The knowledge base on the expert system uses patient medical record data obtained through the health care system. The expert system can diagnose infectious diseases of sore throat (Pharyngitis), diphtheria, dengue fever, Typhoid fever, tuberculosis, and leprosy. The knowledge acquisition process produces 43 symptoms. These symptoms are used to diagnose new cases using Case-Based Reasoning (CBR) and Dempster-Shafer methods. In the CBR method, the similarity measurement process is determined by comparing the K-Nearest Neighbor, Minkowski Distance, and 3W-Jaccard similarity measurement methods. The expert system obtains a...

The accurate analysis of pests and diseases of the chili plants can determine the right solution to reduce the production failure of plants. But the number of horticulture experts who can help to diagnose pests and diseases of the chili plants is still limited. The expert system is built with the aim to help diagnosing pests and diseases of the chili plant. This expert system extracts expert's knowledge by using inference engine. The inference engine used is the Forward Chaining, which works by analyzing symptoms to achieve a demanded conclusion. The incompleteness of the experts' domain knowledge and the difference of the expert sources or the incompleteness of information provided by the expert system users, can lead into uncertain result of the expert system. The application of Certainty Factors in Expert System is able to anticipate Uncertainty from the Expert System result. The result presented by the expert system is in the form disease names, the definition, the solution and the certainty value from conclusion.

Asthma is a common chronic disease that affects millions of people around the world. The most common signs and symp-toms of asthma are cough, breathlessness, wheeze, chest tightness and respiratory rate. They cannot be measured ac-curately since they consist of various types of uncertainty such as vagueness, imprecision, randomness, ignorance and in-completeness. Consequently, traditional disease diagnosis, which is performed by a physician, cannot deliver accurate results. Therefore, this paper presents the design, develop-ment and application of a decision support system for as-sessing asthma under conditions of uncertainty. The Belief Rule-Based Inference Methodology Using the Evidential Rea-soning (RIMER) approach was adopted to develop this expert system, which is named the Belief Rule-Based Expert System (BRBES). The system can handle various types of uncertainty in knowledge representation and inference procedures. The knowledge base of this system was constructed by using re...

abstrack-rangkasbitung adalah salahsatu kabupaten tertinggal di provinsi banten, sulitnya mendapatkan alat alat kesehatan dan dokter spesialis menyulitkan warga masyarakat rangkasbitung mendapatkan pelayanan kesehatan yang maksimal,penelitian ini untuk memecahkan permasalahan yang ada di lingkungan RSUD adjidarmo rangkasbitung provinsi banten,system pakar di percaya dapat memecahkan permaslahan yang ada, di karnakan system pakar mampu membuat ke ahlian seorang pakar di aplikasikan ke dalam system yang dapat menggantikan tugas dokter spesialis,penyakit hipertensi termasuk salahsatu penyakit yang berbahaya jumlah penyandang hipertensi terus meningkat setiap tahunya,di perkirakan pada tahun 2025 akan ada 1,5 miliar orang yang terkena hipertensi,dan di perkirakan setiap tahunya 9,4 juta orang meninggal akibat hipertensi dan komplikasinya, tekanan darah lebih tinggi hipertensi adalah kondisi dimana tekanan darah lebih tinggi dari 130/80 milimeter ,Penelitian ini di hususkan terkait penya...

Sistem pakar adalah sistem yang berbasis komputer yang menggunakan pengetahuan, fakta dan tehnik penalaran dalam memecahkan masalah yang biasanya hanya dapat dipecahkan oleh seorang pakar dalam bidang tertentu. Sistem pakar memberikan nilai tambah pada teknologi untuk membantu dalam menangani era informasi yang makin canggih. Aplikasi sistem pakar ini menghasilkan keluaran berupa kemungkinan penyakit ginjal yang diderita berdasarkan gejala yang dirasakan oleh user. Sistem ini menggunakan metode Bayes. Sistem akan mencari nilai probabilitas tertinggi, dari berbagai kemungkinan jenis penyakit berdasarkan gejala yang dimasukkan user dan hasilnya ditampilkan kepada user. Sistem pakar untuk mendiagnosis penyakit ginjal ini menghasilkan keputusan penentuan jenis penyakit berdasarkan gejala yang dipilih user. Nilai Bayes yang mungkin dihasilkan adalah antara 0 sampai 1. Jika nilai Bayes yang dihasilkan semakin mendekati 1, maka semakin tinggi kepastian terkena penyakit terkait. Sebaliknya,...