SWI-Prolog Research Articles

Advances in the diagnosis of ocular diseases: an innovative approach through an expert system

In the context of ophthalmic care, where early diagnosis of eye disorders plays a crucial role in patients' quality of life, this study focused on the development and evaluation of an expert system based on SWI Prolog. The main objective of this research was to provide an effective method for the preliminary diagnosis of ocular disorders, including cataract, trachoma, uveitis, glaucoma, and presbyopia. For the evaluation of the system, a confusion matrix was implemented and accuracy, sensitivity and specificity were calculated using a sample of 30 cases, of which 20 were positive and 10 negatives. The findings revealed an outstanding accuracy of 95%, with a sensitivity and specificity of 90%. This highlights the potential of the tool as an effective means of early detection of visual problems. In conclusion, this expert system represents a significant advance in ophthalmologic diagnosis, with important implications for clinical care and patients' quality of life, although expansion and validation of the tool in further clinical studies is suggested for its wider and more successful implementation in the field of ophthalmology.

Sequent calculi and an efficient theorem prover for conditional logics with selection function semantics

Abstract In this paper we present our final solution to the problem of designing an efficient theorem prover for Conditional Logics with the selection function semantics. Conditional Logics recently have received a renewed attention and have found several applications in knowledge representation and artificial intelligence. In order to provide an efficient theorem prover for Conditional Logics, we introduce labelled sequent calculi for the logics characterized by well-established axioms systems including the axiom of strong centering CS, the axiom of conditional identity ID, the axiom of conditional modus ponens MP, as well as the conditional third excluded middle CEM, rejected by Lewis but endorsed by Stalnaker, as well as for the whole cube of extensions. The proposed calculi revise and improve the calculi SeqS introduced in Olivetti et al. (2007, ACM Trans. Comput. Logics, 8). We also present an implementation of these calculi in SWI Prolog, including a graphical interface in Python as well as standard heuristics and refinements that allow us to obtain an efficient theorem prover for the logics under consideration. Moreover, we present some statistics about the performances of the theorem prover, which are promising and significantly better than those of its predecessor CondLean, an implementation of the calculi SeqS.

Designing an Exploratory Indigenous Knowledge Management Framework for Soil Conservation Mechanism in the Konso Community

Today, Indigenous knowledge (IK) has a lot of contribution all over the world which it is the traditional knowledge that didn’t codified and not supported by technology. Furthermore, in Ethiopia there are different Indigenous knowledge Management (IKM) in different regions and ethnicities. In addition, Konso people also have different indigenous knowledge that used for various purposes like for forecasting weather, traditional medicine, soil conservation for protecting their environment and productivity. The main goal of this research study is to explore and design the Indigenous knowledge management framework for soil conservation mechanism in Konso People. As far as the knowledge of the researchers, this very power full knowledge is not explored and also not supported by emerging technology for making easy knowledge management processes like sharing, transferring, utilization and preservation. Therefore, it is very import to explore and design the IKM framework and develop the prototype to make enabled knowledge management process easy. The exploratory research method and design science research design were used to explore the knowledge from sources. Hence, both qualitative and quantitative research approach used data were collected from the sources using data collection tool like interview (questions), survey (questionnaires), technical observation (check list) and existing documents analysis. From the collected data new knowledge was revealed and the newly proposed IKM framework for soil conservation were design and developed using SWI Prolog tool for implementation. In addition, the designed and developed IKM framework for soil conservation were evaluated and validated by using ISO-1826 I standards. As per this study revealed, this study is significantly very import for sharing, transferring utilization and preserving Knowledge and for police development that used to protect the soil erosion and land degradation in Ethiopia and in Konso people specifically. Accordingly, this research, user and expert evaluation was assessed and its result as 70% respondents were responded that knowledge deliverability, 87.5% respondents were assented with attractively, 75% of respondents were agreed with accessibility and 62.5% of respondents were responded with suitability of proposed IKM framework and prototype. Furthermore, the obtained result significantly shows that, the proposed IKM framework for Soil conservation in Konso People have the ability to share, transfer and preserve Indigenous knowledge for the next generation.
 Keywords: Indigenous Knowledge, Soil conservation, Knowledge Management, Knowledge Preservation.

An Expert System for Screening and Prognosis of Diseases: An Instance of Healthcare Management

The background of this study is that a Medical Expert System made in Visual Prolog is proposed. This expert system makes a differential diagnosis among heart, lung, kidney, skin, and brain diseases. This system is designed to give help to a medical expert in making the appropriate diagnosis of a patient. Based on a patient's symptoms and medical background, SWI Prolog offers the diagnosis through the declarative knowledge representation methodologies. On the basis of the diagnosis and current medical regulations, it might also offer other treatment options. Medical data analysis using SWI Prolog is used to spot trends or patterns in patient outcomes or disease development. Making better-educated choices concerning patient care and treatment could be made easier by healthcare providers as a result. An SWI Prolog-based medical expert system's output will be influenced by the quality of the data and code used to generate it, as well as by the medical specialists that worked on its design and implementation. The creation of more sophisticated expert systems can be particularly beneficial for early disease detection, helping to reduce the burden of diseases by detecting them more accurately and efficiently.

Developing Expert System for Diagnosis and Treatment of Monkey Pox Outbreak

The goal of an expert system is to transfer human knowledge to the computer so that it can tackle problems that are typically handled by experts. The goal of a medical expert system is to assist doctors in making diagnoses. To provide a diagnosis, it takes into account both facts and symptoms. This suggests that a medical expert system makes a diagnosis based on information about the patient and knowledge of the diseases. The purpose of this study is to develop a forward chaining expert system prototype for diagnosing diseases associated with MonkeyPox outbreaks. The system is developed using SWI Prolog, represented using decision tree and offer guidance to patients and experts.

Computer Vision-Based Hazard Identification of Construction Site Using Visual Relationship Detection and Ontology

Onsite systematic monitoring benefits hazard prevention immensely. Hazard identification is usually limited due to the semantic gap. Previous studies that integrate computer vision and ontology can address the semantic gap and detect the onsite hazards. However, extracting and encoding regulatory documents in a computer-processable format often requires manual work which is costly and time-consuming. A novel and universally applicable framework is proposed that integrates computer vision, ontology, and natural language processing to improve systematic safety management, capable of hazard prevention and elimination. Visual relationship detection based on computer vision is used to detect and predict multiple interactions between objects in images, whose relationships are then coded in a three-tuple format because it has abundant expressiveness and is computer-accessible. Subsequently, the concepts of construction safety ontology are presented to address the semantic gap. The results are subsequently recorded into the SWI Prolog, a commonly used tool to run Prolog (programming of logic), as facts and compared with triplet rules extracted from using natural language processing to indicate the potential risks in the ongoing work. The high-performance results of Recall@100 demonstrated that the chosen method can precisely predict the interactions between objects and help to improve onsite hazard identification.

A Self-Learning Knowledge Based System for Credit Evaluation of Loan Application: The Case of Commercial Bank of Ethiopia

This study on prototype self-learning knowledge based system (KBS) is focused on evaluation of loan application used to overcome the challenges that resulted from lack of domain experts and poor loan evaluations. We attempted to design and develop a prototype self-learning KBS that provide advisory services for any credit customers and assists the domain experts in evaluation of customer’s requests for the loan. To develop this prototype system, knowledge was acquired using semi-structured interview from domain experts which are selected using purposive sampling technique from Commercial Bank of Ethiopia (CBE) and critique the acquired knowledge. Explicit knowledge is acquired by analyzing the secondary source of knowledge by method of document analysis. Then, the acquired knowledge is modeled using decision tree that represents concepts and procedures involved in credit evaluation and production rules are used to represent the domain knowledge. The prototype system is implemented using SWI Prolog editor tool. To determine the applicability of the prototype system in the domain area, the system has been evaluated and tested by the domain experts. Eighteen (18) test cases were selected purposively. Test cases are equally selected from both ineligible and eligible cases. The overall total performance of the prototype system is 77.71%. The performance of the prototype system is hopeful and meets the objective of the study. The study concludes that the major credit production type that advanced to customer is import letter of credit facility, export credit facility, pre-shipment credit facility and merchandise. The eligibility of application is focused on general and specific criteria. Credit customer is classified as business, corporate and commercial based on the score sheet they achieved. Generally, in this study, the applicability of knowledge of prototype self-learning KBS is proved as hopeful approach in banking industry for credit evaluation. Keywords : KBS, self-learning and credit (loan). DOI: 10.7176/IKM/10-6-03 Publication date: September 30 th 2020

A Self-Learning Knowledge Based System for Credit Evaluation of Loan Application: The Case of Commercial Bank of Ethiopia

This study on prototype self-learning knowledge based system (KBS) is focused on evaluation of loan application used to overcome the challenges that resulted from lack of domain experts and poor loan evaluations. We attempted to design and develop a prototype self-learning KBS that provide advisory services for any credit customers and assists the domain experts in evaluation of customer’s requests for the loan. To develop this prototype system, knowledge was acquired using semi-structured interview from domain experts which are selected using purposive sampling technique from Commercial Bank of Ethiopia (CBE) and critique the acquired knowledge. Explicit knowledge is acquired by analyzing the secondary source of knowledge by method of document analysis. Then, the acquired knowledge is modeled using decision tree that represents concepts and procedures involved in credit evaluation and production rules are used to represent the domain knowledge. The prototype system is implemented using SWI Prolog editor tool. To determine the applicability of the prototype system in the domain area, the system has been evaluated and tested by the domain experts. Eighteen (18) test cases were selected purposively. Test cases are equally selected from both ineligible and eligible cases. The overall total performance of the prototype system is 77.71%. The performance of the prototype system is hopeful and meets the objective of the study. The study concludes that the major credit production type that advanced to customer is import letter of credit facility, export credit facility, pre-shipment credit facility and merchandise. The eligibility of application is focused on general and specific criteria. Credit customer is classified as business, corporate and commercial based on the score sheet they achieved. Generally, in this study, the applicability of knowledge of prototype self-learning KBS is proved as hopeful approach in banking industry for credit evaluation. Keywords : KBS, self-learning and credit (loan). DOI: 10.7176/IKM/10-5-02 Publication date: August 31 st 2020

English

Tuberculosis is a major public health problem in Ethiopia more than half a century ago. If the disease is not treated well and on time, it can lead to severe health problems like lungs, but can also affect other organs, including the central nervous system. Therefore, detecting those diseases at early stages enable us to overcome and treat them appropriately. However, among people in the developing countries like Ethiopia, permanent diseases are growing to be causes of death. These problems are becoming worse due to the scarcity of specialists, practitioners and health facilities. In Ethiopia, there has been observed a threat of increased prevalence of tuberculosis and the number of death rates imputed to tuberculosis reached above 29,000 deaths (35 per 100,000) due to TB, excluding HIV related deaths, during the same period, 2014. In an effort to address such problem, this study attempts to design an expert system model for tuberculosis disease diagnose pre-screen laboratory process that can provide advice for physicians and patients to facilitate the diagnosis before laboratory testing. To this end, knowledge is acquired using both structured and unstructured interviews from domain experts which are selected using purposive sampling technique from health agents. Relevant documents analysis method is also followed to capture explicit knowledge. Thereafter, the acquired knowledge is modeled using decision tree that represents concepts, procedures involved in diagnosis and treatment of tuberculosis and production rules are used to represent the domain knowledge, and knowledge-based system is developed using SWI Prolog editor tool. It uses backward chaining which begins with possible solutions or goals and tries to gather information that verifies the solution. Key words: Model, expert system, knowledge representation, knowledge acquisition, tuberculosis disease.

Деякі аспекти навчання мов та технологій програмування систем штучного інтелекту майбутніх магістрів інформатики

У статті аналізуються деякі аспекти навчання мов та технологій програмування майбутніх магістрів інформатики у процесі навчання основ штучного інтелекту. Обґрунтовується можливість впровадження в навчання основи штучного інтелекту модуля „Мови та технології програмування систем штучного інтелекту”, призначеного для підготовки майбутніх магістрів інформатики. Навчання курсу повинне сприяти засвоєнню студентами базових знань, що стосуються основ функціонування систем штучного інтелекту, формування відповідних умінь застосування таких систем для розв’язування прикладних задач та оволодіння навичками використання засобів проектування та розробки цих систем. В статті розглянуто можливі шляхи формування у студентів практичних навичок та умінь програмування мовою логічного програмування Prolog в он-лайн середовищі SWI Prolog.

PRISM revisited: Declarative implementation of a probabilistic programming language using multi-prompt delimited control

PRISM is a probabilistic programming language based on Prolog, augmented with primitives to represent probabilistic choice. It is implemented using a combination of low level support from a modified version of B-Prolog, source level program transformation, and libraries for inference and learning implemented in C. More recently, developers working with functional programming languages have taken the approach of embedding probabilistic primitives into an existing language, with little or no modification to the host language, often by using delimited continuations. Captured continuations represent pieces of the probabilistic program which can be manipulated to achieve a great variety of computational effects useful for inference. In this paper, I will describe an approach based on delimited control operators recently introduced into SWI Prolog. These are used to create a system of nested effect handlers which together implement a core functionality of PRISM—the building of explanation graphs—entirely in Prolog and using an order of magnitude less code. Other declarative programming tools, such as constraint logic programming, are used to implement tools for inference, such as the inside-outside and EM algorithms, lazy best-first explanation search, and MCMC samplers. By embedding the functionality of PRISM into SWI Prolog, users gain access to its rich libraries and development environment. By expressing the functionality of PRISM in a small amount of pure, high-level Prolog, this implementation facilitates further experimentation with the mechanisms of probabilistic logic programming, including new probabilistic modelling features and inference algorithms, such as variational inference in models with real-valued variables.

Development of Knowledge Based System for Wheat Disease Diagnosis: A Rule Based Approach

Wheat production in Ethiopia is widely affected by diseases and attacked by a number of insect pests. Wheat disease diagnosis needs sufficient and knowledgeable agricultural experts to identify the diseases and describe the methods of treatment and protection at early stage of infestation. But, agricultural specialist assistance may not always available and accessible to every farmer when the need arises for their help. Hence, this study presents a rule based knowledge based system for wheat disease diagnosis in order to identify wheat disease timely and apply the control measures effectively. The system aims to provide a guide for research centers and development agents to facilitate the diagnostic process. To develop the system, data and knowledge are acquired from documented and non- documented sources. The acquired knowledge is modeled by using decision tree structure that represents concepts and procedures involved in the diagnosis of wheat disease. The system is developed using SWI Prolog programming language. The system has been tested and evaluated to ensure whether the performance of the system is accurate and the system is usable by research centers and development agents. The system has been registered overall performance of 87.78%. So, the developed system has potential to use as a decision tool for diagnosing and treating wheat disease.

Localized Knowledge based System for Human Disease Diagnosis

Knowledge based system can be designed to solve complex medical problems. It incorporates the knowledge that has been coded into facts, rules, heuristics and procedures. Incorporation of local languages with the knowledge based system allows end- users communicate with the system in a simpler and easier way. In this study a localized knowledge based system is developed for TB disease diagnosis using Ethiopian national language. To develop the localized knowledge based system, tacit knowledge is acquired from domain experts using interviewing techniques and explicit knowledge is captured from documented sources using relevant documents analysis method. Then the acquired knowledge is modeled using decision tree structure that represents concepts and procedures involved in diagnosis of disease. Production rules are used to represent domain knowledge. The localized knowledge based system is developed using SWI Prolog version 6.4.1 programming language. Prolog supports natural language processing feature to localize the system. As a result, the system is implemented using Amharic language (the national language of Ethiopia) user interface. With Localization, users at remote areas and users who are not good in foreign languages are benefited enormously. The system is tested and evaluated to ensure that whether the performance of the system is accurate and the system is usable by physicians and patients. The average performance of the localized knowledge based system has registered 81.5%.

Difference lists in Prolog

Prolog is taught at Bradford University within the two-semester module Symbolic and Declarative Computing/Artificial Intelligence. Second year undergraduate students are taught here the basics of the functional and the logic programming paradigms, the latter by using the Linux implementation of SWI Prolog [6]. The topic 'Difference lists' is mentioned in traditional textbooks such as [2] and [5] but it was felt that the available texts do not quite serve our purposes. We present here a lecture handout and a laboratory sheet for the teaching sessions on Difference lists. It is believed that the lectures and lab sessions together with the handouts shown here are a gentle, self-contained and reasoned introduction into the topic. The figures here shown to illustrate the concepts are considered a special feature of the handouts which in this form do not seem to be well known.

Generate and Repair Machine Translation

We propose Generate and Repair Machine Translation (GRMT), a constraint–based approach to machine translation that focuses on accurate translation output. GRMT performs the translation by generating a Translation Candidate (TC), verifying the syntax and semantics of the TC and repairing the TC when required. GRMT comprises three modules: Analysis Lite Machine Translation (ALMT), Translation Candidate Evaluation (TCE) and Repair and Iterate (RI). The key features of GRMT are simplicity, modularity, extendibility, and multilinguality.An English–Thai translation system has been implemented to illustrate the performance of GRMT. The system has been developed and run under SWI–Prolog 3.2.8. The English and Thai grammars have been developed based on Head–Driven Phrase Structure Grammar (HPSG) and implemented on the Attribute Logic Engine (ALE). GRMT was tested to generate the translations for a number of sentences/phrases. Examples are provided throughout the article to illustrate how GRMT performs the translation process.