Expert System Approach Research Articles

Enhancing gas drainage and ventilation efficiency in underground coal mines: A hybrid expert decision approach for booster fan prioritization

Expanding mining operations in goaf zones heightens gas production potential, posing challenges in maintaining adequate ventilation within development panels, consequently impacting coal production. Various strategies have been explored to enhance mine ventilation and gas drainage effectiveness. However, deficiencies persist in the proposed ventilation system for the Okaba underground coal mine, prompting this study’s necessity. Addressing these concerns, the study evaluates the feasibility of employing booster fans to mitigate the identified drawbacks. Prioritizing booster fans for airflow distribution in underground mines is a complex decision-making process, requiring an advanced expert system approach. To address this, the study proposes an intuitionistic-based fuzzy TOPSIS (IFT) method for booster fan prioritization in the Okaba mine. Results indicate that booster fan 4 (BF4) ranks highest, followed by booster fan 3 (BF3), consistent with fuzzy TOPSIS findings. Sensitivity analysis supports the predicted importance order, affirming the efficacy of the hybrid expert decision method in selecting a booster fan capable of enhancing the overall efficiency of gas drainage and ventilation systems in underground mines. This study introduces a Hybrid Expert Decision Approach that integrates IFT and traditional fuzzy TOPSIS methodologies. This hybrid approach is particularly novel because it combines the strengths of both methods to prioritize booster fans in underground coal mines.

A Systematic Review of Expert Systems for Improving Energy Efficiency in the Manufacturing Industry

Against the backdrop of the European Union’s commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electrical energy demand while simultaneously facing a growing shortage of skilled workers crucial for meeting established goals. Expert systems (ESs) offer the chance to overcome this challenge by automatically identifying potential energy efficiency improvements, thereby playing a significant role in reducing electricity consumption. This paper systematically reviews state-of-the-art ES approaches aimed at improving energy efficiency in industry with a focus on manufacturing. The literature search yields 1668 results, of which 62 articles published between 1987 and 2024 are analyzed in depth. These publications are classified according to the system boundary, manufacturing type, application perspective, application purpose, ES type, and industry. Furthermore, we examine the structure, implementation, utilization, and development of ESs in this context. Through this analysis, this review reveals research gaps, pointing toward promising topics for future research.

Prediction of Water Production Rate Using Fuzzy Logic Approach

Abstract—this paper describes a fuzzy expert system approach for prediction of water production rate in a desalination plant. The goal of the study is to predict water production rate based on several inlet variables determined by steam flow, steam pressure, seawater temperature, and seawater flow. The data used were collected from plant history records of the operation department log sheets. These data were analyzed and a model was constructed using fuzzy expert system. In the proposed model, both input and output variables are parameterized and classified into several fuzzy sets. A set of fuzzy rules are constructed based on the knowledge extracted from the data collected. Once the rules are evaluated, the variables are defuzzified and converted into corresponding output variable (water production rate). The results showed that inlet variables of the plant could predict water production rate with 95% prediction accuracy.

Choosing Optimal Supply Radius of Transformer Substations (TSs) in Iraq’s Cities Using Geometric Programming with Neutrosophic Coefficients

Numerous uncertainties exist in various electricity power system problems due to the size, complexity, geographical distribution, and influence of unforeseen events in these systems, making it difficult for traditional mathematics tools based on crisp set theory to have an impact on and solve many power system problems. As a new branch of mathematical uncertainty techniques, the neutrosophic expert systems approach has therefore emerged with the development of electric power systems and has proven successful when correctly linked. The expert typically uses ambiguous or neutrosophic language to describe their empirical knowledge, such as "very likely," "quite likely," "if x is large, then y is very likely to occur," "x should not be less than a," etc. To design an optimal radius of power supply in the electrical transformer substation, this article presents a new method for creating primal and dual neutrosophic geometric programming problems. It also provides a numerical example to evaluate the approximate optimal economic power supply radius.

Classification of the high‐rank syntaxa of the Central and Eastern Balkan dry grasslands with a new hierarchical expert system approach

AbstractAimsDeveloping a hierarchical classification system for classes, orders and alliances of the diverse dry grasslands of the Central and Eastern Balkan Peninsula and translating this into an electronic expert system (ES) for the automatic assignment of plots.LocationSerbia, Kosovo, North Macedonia, Bulgaria and northern Greece.MethodsWe extracted 5734 plots from the Balkan Dry Grassland Database corresponding to eight classes of dry grasslands reported from the region, using the EuroVegChecklist ES. This data set and later the plots within each derived subunit were subjected to a new numerical approach: starting with an initial partitioning (expert‐interpreted TWINSPAN classification), diagnostic species were determined based on their phi‐values for the target vegetation type and the differences in phi‐values to the next similar types. These diagnostic species were fed into an ES to create a new partitioning, a procedure which was iterated until diagnostic species and species of the ES converged. Then the same approach was applied within each of the derived units to define the units of the next‐lower level.ResultsThe iterative cluster optimisation (ICO) converged in all cases. The resulting hierarchical expert system (HES) classified 95% of all plots to alliances. We distinguished four classes with eight orders and 12 alliances: (1) Tuberarietea guttatae (Romuleion); (2) Stipo‐Brachypodietea distachyi (Clinopodio alpini‐Thymion striati); (3) Festuco‐Brometea with Brachypodietalia pinnati (Chrysopogono‐Danthonion calycinae and Cirsio‐Brachypodion pinnati), Festucetalia valesiacae (Festucion valesiacae), an unnamed order of rocky steppes (with Pimpinello‐Thymion zygioidis) and Koelerietalia splendentis (Centaureo‐Bromion fibrosi, Saturejion montanae and Diantho haematocalycis‐Festucion hirtovaginatae); (four) Koelerio‐Corynephoretea with Sedo acris‐Festucetalia (Festucion vaginatae) and Trifolio arvensis‐Festucetalia ovinae (Armerio rumelicae‐Potentillion and Minuartio montanae‐Poion molinerii all. nov.).ConclusionsWe created a unified hierarchical classification with an electronic ES using diagnostic species defined by phi‐values. Our new approach (ICO‐HES: iterative cluster optimisation for hierarchical expert systems) allows dividing large data sets into meaningful units at several hierarchical levels, and thus has high potential for complex classifications. Importantly, it overcomes the divide between ES species and diagnostic species and re‐unites them into one concept.

Green Supplier Assessment with Fuzzy Expert System Approach

Today, reasons such as increasing pollution on a global extent and limited raw material resources are increasing the interest in green supply chain management (GSCM). GSCM includes the processes starting from the very beginning of the production process of a product, completing the production, delivering the product to the customer, and recycling the product at the end of its useful life. Its main purpose is to eliminate or minimize the damages caused to the environment in all of these processes. In order to achieve this goal, it has great importance to assess the suppliers, which are one of the most important components of the production process, in terms of becoming a green supplier.
 
 In this study, a fuzzy expert system model has been developed to assess the green suppliers based on green production technology, environmental management system, pollution control, product cost, quality, and lead time criteria. Mean Square Error (MSE) and coefficient of determination (R2) have been calculated to measure the performance of the developed model. These calculated values show that the green supplier assessment performance of the developed model is quite high.

Modelling the risk of Japanese encephalitis virus in Victoria, Australia, using an expert-systems approach

Predictive models for vector-borne diseases (VBDs) are instrumental to understanding the potential geographic spread of VBDs and therefore serve as useful tools for public health decision-making. However, predicting the emergence of VBDs at the micro-, local, and regional levels presents challenges, as the importance of risk factors can vary spatially and temporally depending on climatic factors and vector and host abundance and preferences. We propose an expert-systems-based approach that uses an analytical hierarchy process (AHP) deployed within a geographic information system (GIS), to predict areas susceptible to the risk of Japanese encephalitis virus (JEV) emergence. This modelling approach produces risk maps, identifying micro-level risk areas with the potential for disease emergence. The results revealed that climatic conditions, especially the minimum temperature and precipitation required for JEV transmission, contributed to high-risk conditions developed during January and March of 2022 in Victora. Compared to historical climate records, the risk of JEV emergence was increased in most parts of the state due to climate. Importantly, the model accurately predicted 7 out of the 8 local government areas that reported JEV-positive cases during the outbreak of 2022 in Victorian piggeries. This underscores the model’s potential as a reliable tool for supporting local risk assessments in the face of evolving climate change.

Predicting the Remaining Useful Life of Light Aircraft Structural Parts: An Expert System Approach

This paper introduces an expert system approach for predicting the remaining useful life (RUL) of light aircraft structural components by analyzing operational and maintenance records. The expert system consists of four modules: knowledge acquisition, knowledge base, inference, and explanation. The knowledge acquisition module retrieves data from mandatory records, such as aircraft logbooks and mass and balance sheets. The knowledge base stores specific remaining useful lives (SRULs) for different load profiles that are determined using numerical strength analysis. The inference module utilizes the Palmgren-Miner rule to estimate the accumulated fatigue damage of the structural component based on the input data and the knowledge base. Lastly, the explanation module links the accumulated damage to the maintenance program and suggests the appropriate maintenance action. The Cessna 172R main landing gear leg is utilized as a case study, demonstrating the variance of RUL depending on the operating conditions. The objective of this approach is to enhance light aircraft maintenance decision making and advance operational safety.

A decision-making framework for optimal maintenance management: An integrated simulation-mathematical programming-expert system approach

In this study, an integrated simulation- mathematical programming- expert system approach is proposed to develop an optimal solution for maintenance management in a complex public infrastructure system. As the first step, a model of maintenance system is made through a computerized simulation with respect to the collected data on maintenance programs and unexpected failures. Then, the Taguchi method is adopted to develop different scenarios for each of which a precise measurement of cost and reliability based on customer dissatisfaction rate (i.e. modified reliability) is made. Regression method is applied to observe the association of components’ maintenance workload with cost and modified- reliability. After that, the best solution for maintenance management is achieved by developing a mathematical model, with the aim of reliability improvement and cost savings. Finally, after identifying the most significant components in terms of cost and modified- reliability by sensitive analysis, the maintenance actions taken during the program are determined using an expert system. Results demonstrated that the optimization model Outdo Taguchi scenarios achieved the optimum one as it met both targets simultaneously. Having determined the significant components of the system and enhanced the methodology by signaling maintenance specialists to devote additional attention to them, it can be especially helpful in assisting administrators in lowering the accident rate arising from the shortcomings in maintenance plans.

Expert Systems for Farmed Fish Disease Diagnosis: An Overview and a Proposal

The expert system approach, although quite old, is still quite effective in scientific areas where experts are required to make diagnoses and predictions. One of those areas is fish disease diagnosis. It is an application domain that currently employs complicated processes, which require high level skills for making accurate diagnoses. On the other hand, complete datasets for full diagnosis to be able to use machine learning techniques are not available. Therefore, in aquaculture, now more than ever, fish farmers do not have the required expertise or equipment to accurately diagnose a fish disease. For that reason, expert systems that can help in the diagnosis, prevention, and treatment of diseases have been developed. In this paper, we attempt to give an overview of the expert system approaches for fish disease diagnosis developed in the last two decades. Based on the analysis of their technical and non-technical characteristics, we propose an expert system architecture and a fish disease diagnosis process aiming at improving the deficiencies of the existing systems. The proposed system can handle all types of fish diseases based on image and non-image data as well as on molecular test results and can provide explanations. The diagnosis process goes through four consecutive levels, where each next level considers an additional category of parameters and provides diagnoses with a higher certainty.

Expert system approach to improve the accuracy of prediction and solution of various agricultural scenarios

The proposed research on developing expert systems for accurately predicting and recommending solutions for various agricultural scenarios is novel in several ways: Integration of Multiple Technologies: The research involves the integration of multiple technologies such as knowledge representation, artificial intelligence and machine learning algorithms, data integration and analysis techniques, and evaluation and validation techniques to develop a comprehensive and effective expert system for agriculture. Interdisciplinary Approach: The research is an interdisciplinary approach that brings together experts from various fields such as computer science, agriculture, and data science to develop an expert system that takes into account the needs of farmers and agriculture professionals. Use of Real-World Data: The research uses real-world data to test and validate the performance of the expert system, which increases its applicability and effectiveness in practical agricultural scenarios. Customization and Personalization: The proposed expert system can be customized and personalized based on the unique needs of individual farmers and agriculture professionals, which will make it more useful and user-friendly. Potential to Enhance Agriculture Productivity and Sustainability: The development of an effective expert system for agriculture has the potential to enhance productivity and sustainability in agriculture, which will benefit not only the farmers and agriculture professionals but also the wider society by improving food security and reducing environmental impact. In summary, the proposed research on developing expert systems for accurately predicting and recommending solutions for various agricultural scenarios is a novel and interdisciplinary approach that has the potential to transform agriculture by improving productivity, sustainability, and profitability. Future research in the development of expert systems for agriculture can build on the proposed research in several ways: Integration of Internet of Things (IoT) Technology: The integration of IoT technology can provide real-time data on various parameters such as soil moisture, temperature, and humidity, which can be used to improve the accuracy of the expert system predictions and recommendations. Integration of Remote Sensing Technology: The integration of remote sensing technology such as satellite imagery can provide a broader view of agricultural landscapes and enable the expert system to predict and recommend solutions for large-scale agricultural scenarios. Development of User-Friendly Interfaces: Future research can focus on developing user-friendly interfaces that enable easy access and understanding of expert system predictions and recommendations by farmers and agriculture professionals. Use of Explainable AI Techniques: Future research can explore the use of explainable AI techniques that enable the expert system to provide explanations for its predictions and recommendations, which can improve user trust and confidence in the system. Implementation of Expert System in Developing Countries: Future research can focus on the implementation of expert systems in developing countries where smallholder farmers face significant challenges in accessing and utilizing agricultural technology. The expert system can be adapted to the local context and needs of smallholder farmers to improve their productivity and livelihoods. Ffuture research in the development of expert systems for agriculture can focus on the integration of new technologies, development of user-friendly interfaces, use of explainable AI techniques, and implementation in developing countries to improve the effectiveness and accessibility of the expert system.

Industry 4.0 Maturity Models to Support Smart Manufacturing Transformation: A Systematic Literature Review

With increasing pressure to revitalize manufacturing industries with Smart Manufacturing capability within the Industry 4.0 (I4.0) context, companies have uneven readiness reflecting their gaps and barriers for transforming to the I4.0 state. Understanding factors and measuring a company’s maturity in addressing the I4.0 transformation is crucial to diagnose the company’s current condition and provide corresponding prescriptive action plan effectively. Despite the positive trend of maturity models for the industries, companies still face challenges with low I4.0 adoption rate. Designing a corresponding diagnostic framework into an intelligent maturity model will ultimately lead the company’s pathways toward the desired capabilities. In response, we systematically review and select the state-of-the-art research through a Systematic Literature Review (SLR) conduct to scrutinize the main characteristics of 14.0 Maturity Models. Subsequently, 35 exceptional articles published between 1980-2020 were selected for in-depth analysis of their structure, dimensions, and analytical features. Our analysis revealed the descriptive method have been widely used in many maturity models while few more-advanced prescriptive models design adopt fuzzy rule-base analytical hierarchy, knowledge based, Monte-Carlo methods, and even expert-system approaches. Furthermore, people, culture, organization, resources, information system, business processes, and smart technology, products and services have been treated as the popular evaluation dimensions which will define the state of an industry’s maturity level.

Design of a Fuzzy Expert System for Organizational Performance and Innovation Evaluation Based on the Role of Knowledge Management and Intellectual Capital

Objective: This study aimed to evaluate organizational performance and innovation based on the role of knowledge management and intellectual capital. Methodology: This study was conducted using a qualitative approach (interviews with experts). Through expert interviews and grounded theory, research criteria including causal conditions, intervening factors, strategies, and outcomes were derived. The criteria and sub-criteria were then ranked using the fuzzy Analytical Hierarchy Process (AHP) method. Causal factors and strategies were used as inputs to the model, while outcomes were treated as output variables. The expert system in this study had two inputs and one output. For the input sub-criteria, rules were defined based on expert opinions, and fuzzy inference was performed. The outputs of the expert system were the sub-criteria of the outcomes, which included five variables, and the fuzzy section utilized MATLAB software. Findings: Among the identified factors, causal factors were found to be more important than contextual and intervening factors. Within the causal factors, social factors were of greater significance. In the strategies category, human resources were identified as the most important, and ultimately, organizational outcomes were considered the most significant results. Conclusion: Overall, it can be stated that evaluating organizational performance and innovation based on the role of knowledge management and intellectual capital is of great importance. The utilization of a fuzzy expert system approach in this context can significantly assist managers and decision-makers in assessing organizational performance and innovation.

Design of Islamic Agricultural Insurance Model: Evidence from Indonesia

The agricultural sector is a role model that can be the basis for policymakers to integrate Islamic insurance patterns in agriculture. This research aims to design a model of the Islamic agricultural insurance system based on Islamic law in the context of Indonesia. This study is expected to contribute for Indonesian government to accommodate the implementation of the Islamic agricultural insurance system and pattern in Indonesia. This study used an expert system approach through data collection and information as a reference in the formulation of model designs, including interviews, questionnaires, direct observation, and data synthesis in the field. This research stage used a systems development conceptual model described in the research process, including planning, analysis, design, verification, validation, and building models. The research hypothesis is confirmed that Islamic agricultural insurance can be implemented based on risk and investment with tabarru’ funds (mutual financial aid funds) and investment funds. Islamic agricultural insurance is a way out for farmers, especially Muslim farmers, in ensuring agriculture management by managing the level of risk due to crop failure. Islamic agricultural insurance systems can guarantee risks that arise in agricultural businesses to provide inner peace to farmers in getting the good and right protection for businesses that run following Islamic principles. This research has resulted in innovative features and development models of Islamic agricultural insurance for the Protection and Empowerment of Farmers in Indonesia.

The Role of Institutions in the Downstream Agribusiness Palm oil (Elaeis Sp) Subsystem

Palm oil is one of the leading commodities of Nunukan Regency. However, problems related to institutions that play a role in the downstream agribusiness subsystem are not running optimally. This is due to limited information, weak coordination between institutions, weak capacity and optimization of farmer institutions. Therefore, this study aims to analyze the institutions that play a role in the downstream agribusiness subsystem. The research was conducted in Nunukan Regency, North Kalimantan Province. Data collection was carried out using a descriptive method through an expert system approach with a survey method. Data analysis with Interpretative Structural Modeling (ISM). The results showed that 11 institutions that have an important role in the Downstream Oil Palm Agribusiness Subsystem in Nunukan Regency are (1). Department of Agriculture and Plantation, (2). Agricultural Extension, (3). Bank Rakyat Indonesia Nunukan Regency, (4).GAPOKTAN/Women’s Farmer’s Group/Farmers’ Group, (5) Middlemen, (6) Cooperative Service, (7).Community Empowerment Agency and Village Government, (8). Cooperative Business Unit, (9). Palm Oil Company, (10). Central Bureau of Statistics (11). Agricultural Quarantine Center. Pemberdayaan Masyarakat dan Pemerintah Desa (BPMD), (8). Unit Usaha Koperasi, (9). Perusahaan Sawit, (10). Badan Pusat Statistik (11). Balai Karantina Pertanian.

Internet-delivered management of pain among cancer treatment survivors (IMPACTS WF-1901).

TPS12151 Background: Pain is a common symptom among cancer patients and often is inadequately treated. Treatment guidelines recommend patients have access to behavioral interventions that educate about pain and pain management. Pain coping skills training (PCST) accomplishes these goals through teaching cognitive and behavioral coping skills shown to reduce pain. When delivered in in-person, PCST can substantially improve chronic pain conditions. Yet, these interventions are underused due to myriad barriers (high costs, shortage of therapists, travel needs). There is a critical need for improved options to help reduce cancer-related pain and related impairment that should include evidence-based PCST interventions capable of overcoming access barriers. To address this need, we developed a web-based PCST program using a novel expert systems approach that retains critical features of in-person PCST in an automated program that requires no therapist. PainTRAINER, is an 8-week, interactive PCST program using tailoring algorithms, a knowledge database, and a virtual coach to guide development of essential skills for coping with chronic pain. Methods: With funding from the NIH HEAL Initiative, we have undertaken a randomized, prospective, comparative effectiveness trial through the Wake Forest NCI Community Oncology Research Program (NCORP) Research Base to determine the impact of painTRAINER on pain outcomes when compared to Enhanced Usual Care (EUC). Participants have a documented diagnosis of invasive cancer who are undergoing anticancer therapy or within 5 years of completing all cancer therapy. Participants must report cancer-related pain most days of the week of 4 or greater on the PROMIS Pain Intensity Scale; with pain of new onset or significantly exacerbated since cancer diagnosis. All participants receive usual care provided by their physician along with pain education materials. PainTRAINER arm participants have access to the painTRAINER program and a tutorial on how to use the program, and complete the painTRAINER modules on their own (1 session/week for 8 weeks). To enhance study access, patients without internet availability are provided a WiFi/cellular-enabled tablet during the intervention period. This trial examines short- and long-term outcomes measured immediately post-intervention and 3- and 6- months post-intervention. Primary outcomes are: pre- to post-intervention change in pain interference/severity. Secondary outcomes are: pain severity/interference at 3- and 6-month follow-up, opioid/analgesic use, health-related quality of life, and pain management self-efficacy. Qualitative interviews are conducted with a random sample of diverse participants who have completed the painTRAINER, and all who exit the study early, to subjectively assess experiences with pain and the clinical trial. Enrollment for this trial has begun (n = 36 of 456 patients enrolled) and is ongoing at 12 sites. Clinical trial information: NCT04462302.

Predictive analytics for fault reasoning in gas flow control facility: A hybrid fuzzy theory and expert system approach

Gas pressure reduction stations are essential in energy distribution networks because even a minor failure of these systems causes disruptive consumer problems. This study aims to introduce and implement a new knowledge-based platform that uses the synthesized expert's opinions to improve gas pressure control facilities. Given the record of failure of gas transmission system components and the data's uncertain nature, a new fuzzy expert system is developed that takes advantage of the object-oriented programming paradigm to analyze failure modes and conditions. The artificial intelligent model is designed in C# programming language, and a user-friendly interface is developed for ease of implementation. The knowledge-based model's validity has been tested by implementing real-world case studies adapted from the Iranian gas industry. Implementing the designed expert system shows that it can minimize the probability of a breakdown and improve safety conditions.

Four Circuit Transmission Line Location for Inter Circuit Faults Using Fuzzy Expert System

The inter circuit faults in four circuit power transmission line (FCPTL) have a significant influence on the conventional relay performance. Due to the special nature of faults, the protective relaying has been a challenging work. This paper investigates a fuzzy expert system (FES) approach for FCPTL to locate the inter circuit faults and to improve the accuracy of shunt faults location. This approach adopts the fundament component of current (FCC) of the FCPTL at only one end. With the goal of attaining improved inputs–outputs mapping capacity of FES for datasets, an optimization approach, i.e., Mamdani type, has been used for finding the optimal values of tuning parameters. The FES with If-Then rules has been framed for inter circuit faults location. The reported studies are performed in the LabVIEW platform using a 200 km, 500 kV, 50 Hz, FCPTL MATLAB test system. The MATLAB and LabVIEW simulation conform that the proposed approach can correctly locate faults considering various fault types and fault distances within the FCPTL. The proposed approach is easy to design and low cost for existing and new FCPTL installations.

Antilogic, a new supervised machine learning software for the automatic interpretation of antibiotic susceptibility testing in clinical microbiology: proof-of-concept on three frequently isolated bacterial species

ObjectiveAntibiotic susceptibility testing (AST) is necessary in order to adjust empirical antibiotic treatment, but the interpretation of results requires experience and knowledge. We have developed a machine learning software that is capable of reading AST images without any human intervention and that automatically interprets the AST, based on a database of antibiograms that have been clinically validated with European Committee on Antimicrobial Susceptibility Testing rules. MethodsWe built a database of antibiograms that were labelled by senior microbiologists for three species: Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus. We then developed Antilogic, a Python software based on an original image segmentation module and supervised learning models that we trained against the database. Finally, we blind tested Antilogic against a validation set of 5100 photos of antibiograms. ResultsWe trained Antilogic against a database of 18072 pictures of antibiograms. Overall agreement against the validation set reached 97% (16 855/17 281) regarding phenotypes. The severity rate of errors was also evaluated: 1.66% (287/17 281) were major errors and 0.80% (136/17 281) were very major errors. After implementation of uncertainty quantifications, the rate of errors decreased to 0.80% (114/13 451) and 0.42% (51/13 451) for major and very major errors respectively. DiscussionAntilogic is the first machine learning software that has been developed for AST interpretation. It is based on a novel approach that differs from the typical diameter measurement and expert system approach. Antilogic is a proof of concept that artificial intelligence can contribute to faster and easier diagnostic methods in the field of clinical microbiology.

Expert System Approach for Online Admissions

In current pandemic situation online admission system is the need of the hour. This research paper first presents importance of domain of Online Admission System. It mentions in brief research work and contribution of researcher in this domain required for this research paper. Paper briefs first what is prototype system and expert system. Researcher is presenting prototype system useful for stakeholders, organizations that conduct online admissions in framing the requirements for the system, and they can also get idea of what features in the system they are going to get at what approximate cost and third party organizations which are in design and development of the system. Three point formula is used for approximate cost estimation. Expert system asks questions to the user, according to input system suggests type of online admission system along with approximate cost.