Rule-based Decision Support Research Articles

Enrollment Appraisal Using Rule-Based Decision Support System

This study explores the development and evaluation of a web-based enrollment appraisal system utilizing a rule-based decision support system for universities and colleges. Designed for the context of Antique, Philippines, the system leverages a two-tier architecture for efficient processing and secure data management. A user-friendly interface guides applicants through the enrollment process, while a secure server and rule-based engine analyze data against pre-defined eligibility criteria. The Rapid Application Development (RAD) methodology ensured a user-centered design with iterative development cycles incorporating user feedback. Evaluations using McCall's Software Quality Standard and the International Organization for Standardization and International Electrotechnical Commission (ISO/IEC) 25010 standard revealed the system's strengths in technical aspects (security, usability) and overall functionality. User feedback highlighted areas for improvement in interface design, clarity, and user perceptions of data security. The study concludes that the system offers a promising approach for more efficient and transparent enrollment processes. By addressing the feedback received, the system can be further enhanced to improve the user experience and build trust among applicants.

Architecture and Development Framework for a Web-Based Risk Assessment and Management Platform Developed on WordPress to Address Opioid Overdose.

The number of overdose-related fatalities continues to reach historic levels across Canada, despite ongoing efforts by authorities. To reduce mortality, a clinical trajectory ranging from preventative measures to crisis intervention, skill training to treatment, and risk assessment to risk management needs to be supported. The web-based Risk Assessment and Management Platform (RAMP) was developed to realize this concept and to empower people who use drugs through an integrated tool that allows them to better understand and manage their risk of overdose. This paper outlines the architecture and development of RAMP, which is built on the WordPress platform. WordPress components are mapped onto a 3-tier architecture that consists of presentation, application, and database layers. The architecture facilitates the development of a modular software that includes several features that are independent in functionality but interact with each other in an integrated platform. The relatively low coupling and high coherence of the features may reduce the cost of maintenance and increase flexibility of future developments. RAMP's architecture comprises a user interface, conceptual framework, and backend layers. The RAMP front end effectively uses some of the WordPress' features such as HTML5, CSS, and JavaScript to create a mobile, friendly, and scalable user interface. The RAMP backend uses several standard and custom WordPress plug-ins to support risk assessment and monitoring, with the goal of mitigating the impacts and eliminating risks together. A rule-based decision support system has been hard-coded to suggest relevant modules and goals to complement each user's lifestyle and goals based on their risk assessment. Finally, the backend uses the MySQL database management system and communicates with the RAMP framework layer via the data access layer to facilitate a timely and secure handling of information. Overall, RAMP is a modular system developed to identify and manage the risk of opioid overdose in the population of people who use drugs. Its modular design uses the WordPress architecture to efficiently communicate between layers and provide a base for external plug-ins. There is potential for the current system to adopt and address other related fields such as suicide, anxiety, and trauma. Broader implementation will support this concept and lead to the next level of functionality.

Golden Search Optimization based adaptive and diagonal kernel convolution neural network for disease prediction and securing IoT data in cloud

Due to their open architecture, Internet of Things (IoT) devices have limited user access and are susceptible to various security vulnerabilities. When healthcare data is stored in the cloud, it often becomes vulnerable to side-channel attacks, Denial of Service (DoS) attacks, and eavesdropping. Consequently, there is a need for an efficient access control scheme to enhance the data-sharing processes of IoT devices. Different machine learning and rule-based decision support systems have been proposed to improve the security of cloud-based data storage. However, these models have exhibited increased false alarm rates, complex processing, and longer response times. To address this research gap, we propose a Fuzzy TOPSIS model based on Golden Search Optimization (GSO) to enhance the security of patient information at both IoT and cloud server levels. Additionally, we introduce a revolutionary Diagonal Kernel Convolution Neural Network with an adaptable kernel (DKCNN-AK) to enhance the accuracy of lung disease diagnosis. The GSO algorithm categorizes the security ranks determined by the fuzzy TOPSIS model into the best and worst security principles. We conducted simulations using the COVID-19 X-Ray detection dataset, the lung cancer dataset, and the data collected from IoT devices for analysis.The experiments were conducted to assess various aspects, including computational time, security level analysis, information leakage, privacy level, prediction accuracy, sensitivity, specificity, and confusion matrix. The improved prediction accuracy and security provided by our proposed approach demonstrate its efficiency

Factors Hindering Solar Photovoltaic System Implementation in Buildings and Infrastructure Projects: Analysis through a Multiple Linear Regression Model and Rule-Based Decision Support System

Energy plays a predominant role in the development of society. With advancements in technology and the growth of society (buildings and infrastructures), the demand for energy is rapidly increasing. Developing countries typically rely on the import of fossil fuels and capital investments in infrastructure development to meet their energy needs. The execution of solar PV projects in developing countries is currently not being implemented promisingly. Therefore, the determination of the critical success factors hindering the implementation of solar PV projects is the need of the hour. The aim of this study is to determine the factors that hinder the implementation of solar PV projects through the use of a multiple linear regression model (MLRM) and a rule-based decision support system (RBDSS). Seven categories of factors were identified through a detailed literature review and interviews with energy experts. Four hundred and twenty-nine complete responses were collected in total through a questionnaire, and they were analyzed using relative importance indexing (RII) and MLRM and RBDSS approaches. A comparison was carried out against both methodologies to determine the most critical barriers to the implementation of solar PV projects. The findings regarding the MLRM approach showed that the top seven critical factors were economic conditions, encouraging policies, technological knowledge, organizational support, social awareness, market stability, and miscellaneous aspects. Similarly, the results for the RBDSS approach identified that the top seven critical factors were encouraging policies, economic conditions, organizational support, technological knowledge, market stability, social awareness, and miscellaneous aspects. The application of MLRM and RBDSS will help stakeholders in making timely decisions and corrections during the implementation phase, providing a systematic way to support the performance and execution of solar projects.

Conceptual fuzzy-based decision support tool for soil improvement under highway embankment - a case study of HaNoi–HaiPhong expressway

ABSTRACT In this research, a fuzzy rule–based decision support (FRBDS) tool is developed to assist selection of ground treatment techniques for road embankments. Using input variables such as embankment height, geological condition and soft soil thickness, the FRBDS evaluates the ground treatment level, based upon which the highway engineer determines a suitable treatment method taking into account project constraints. In the case study, the FRBDS is applied for a 10-km package of HaNoi–HaiPhong expressway construction project and is evaluated in comparison to expert judgements. The results show that the treatment methods provided by the two approaches are similar for 36 out of 52 sections of the package, but the FRBDS provides more consistent and rational solutions. In particular, the FRBDS significantly reduces technological fragmentation. It can be concluded that the FRBDS is a useful tool that establishes structured innovative solutions to improve the quality of decision-making for large transportation projects.

A Rule-Based Decision Support Method Combining Variable Precision Rough Set and Stochastic Multi-Objective Acceptability Analysis for Multi-Attribute Decision-Making

In an open environment, the demands of users are diverse and dynamic because users can participate in product design from beginning to end. Owing to this, the disorderly and unplanned participation of users will greatly increase the complexity of multi-attribute decision-making (MADM) in the product design process. In order to ensure the smooth development of the open design process, the decision support model and method need to repeatedly provide decision makers (DMs) with necessary decision support information in a relatively short period of time, which can realize the evaluation of the scheme and improve the utilization efficiency of community resources. However, the process of eliciting preference information is complex, exhausting, inefficient, and time-consuming in existing methods, which will result in a poor decision-making. With the purpose of optimizing the eliciting process in MADM, a rule-based decision support method is proposed in this paper, where the process of eliciting preference information and decision-making are synchronized and guided by pre-extracted decision rules. The rules are deduced from comparison relations on attributes and their outcomes through the combination of variable precision rough set approach (VPRS) and stochastic multi-objective acceptability analysis (SMAA). With the concept of attribute reduction and approximation accuracy in rough set theory, the extracted rules could eliminate redundant attributes and assign the relative priority of preference information. Based on the extracted rules, the multi-attribute decision-making process could be carried out step by step in an orderly manner. In each step, DMs only need to provide partial preference information by non-quantitative statements according to extracted rules. Once the decision result is reliable enough, the eliciting and decision-making process can be terminated promptly. In order to validate the proposed approach, experiments of decision rule extraction are implemented, and the results show that the proposed approach is effective both in the weak rule extraction and the strong rule extraction.

An Integrated Monitoring System for Coastal and Riparian Areas Based on Remote Sensing and Machine Learning

Nowadays, coastal areas are exposed to multiple hazards of increasing severity, such as coastal floods, erosion, subsidence due to a combination of natural and anthropogenic factors, including climate change and urbanisation. In order to cope with these challenges, new remote sensing monitoring solutions are required that are based on knowledge extraction and state of the art machine learning solutions that provide insights into the related physical mechanisms and allow the creation of innovative Decision Support Tools for managing authorities. In this paper, a novel user-friendly monitoring system is presented, based on state-of-the-art remote sensing and machine learning approaches. It uses processes for collecting and analysing data from various heterogeneous sources (satellite, in-situ, and other auxiliary data) for monitoring land cover and land use changes, coastline changes soil erosion, land deformations, and sea/ground water level. A rule-based Decision Support System (DSS) will be developed to evaluate changes over time and create alerts when needed. Finally, a WebGIS interface allows end-users to access and visualize information from the system. Experimental results deriving from various datasets are provided to assess the performance of the proposed system, which is implemented within the EPIPELAGIC bilateral Greece-China project. The system is currently being installed in the Greek case study area, namely Thermaikos Gulf in Thessaloniki, Greece.

Personalized Recommendations for Physical Activity e-Coaching (OntoRecoModel): Ontological Modeling.

BackgroundAutomatic e-coaching may motivate individuals to lead a healthy lifestyle with early health risk prediction, personalized recommendation generation, and goal evaluation. Multiple studies have reported on uninterrupted and automatic monitoring of behavioral aspects (such as sedentary time, amount, and type of physical activity); however, e-coaching and personalized feedback techniques are still in a nascent stage. Current intelligent coaching strategies are mostly based on the handcrafted string messages that rarely individualize to each user’s needs, context, and preferences. Therefore, more realistic, flexible, practical, sophisticated, and engaging strategies are needed to model personalized recommendations.ObjectiveThis study aims to design and develop an ontology to model personalized recommendation message intent, components (such as suggestion, feedback, argument, and follow-ups), and contents (such as spatial and temporal content and objects relevant to perform the recommended activities). A reasoning technique will help to discover implied knowledge from the proposed ontology. Furthermore, recommendation messages can be classified into different categories in the proposed ontology.MethodsThe ontology was created using Protégé (version 5.5.0) open-source software. We used the Java-based Jena Framework (version 3.16) to build a semantic web application as a proof of concept, which included Resource Description Framework application programming interface, World Wide Web Consortium Web Ontology Language application programming interface, native tuple database, and SPARQL Protocol and Resource Description Framework Query Language query engine. The HermiT (version 1.4.3.x) ontology reasoner available in Protégé 5.x implemented the logical and structural consistency of the proposed ontology. To verify the proposed ontology model, we simulated data for 8 test cases. The personalized recommendation messages were generated based on the processing of personal activity data in combination with contextual weather data and personal preference data. The developed ontology was processed using a query engine against a rule base to generate personalized recommendations.ResultsThe proposed ontology was implemented in automatic activity coaching to generate and deliver meaningful, personalized lifestyle recommendations. The ontology can be visualized using OWLViz and OntoGraf. In addition, we developed an ontology verification module that behaves similar to a rule-based decision support system to analyze the generation and delivery of personalized recommendation messages following a logical structure.ConclusionsThis study led to the creation of a meaningful ontology to generate and model personalized recommendation messages for physical activity coaching.

AvaLife-A New Multi-Disciplinary Approach Supported by Accident and Field Test Data to Optimize Survival Chances in Rescue and First Aid of Avalanche Patients.

Snow sports in the backcountry have seen a steep increase in popularity, and therefore preparedness for efficient companion and organized rescue is important. While technical rescue skills are widely taught, there is a lack of knowledge regarding first aid for avalanche patients. The stressful and time-critical situation for first responders requires a rule-based decision support tool. AvaLife has been designed from scratch, applying mathematical and statistical approaches including Monte Carlo simulations. New analysis of retrospective data and large prospective field test datasets were used to develop evidence-based algorithms exclusively for the avalanche rescue environment. AvaLife differs from other algorithms as it is not just a general-purpose CPR algorithm which has been slightly adapted for the avalanche patient. The sequence of actions, inclusion of the ≥150 cm burial depth triage criterion, advice to limit CPR duration for normothermic patients to 6 min in case of multiple burials and shortage of resources, criteria for using recovered subjects as a resource in the ongoing rescue, the adapted definition of “injuries incompatible with life”, reasoning behind the utmost importance of rescue breaths, as well as the updated BLS-iCPR algorithm make AvaLife useful in single and multiple burial rescue. AvaLife is available as a companion rescue basic life support (BLS) version for the recreational user and an advanced companion and organized rescue BLS version for guides, ski patrols and mountain rescuers. AvaLife allows seamless interoperability with advanced life support (ALS) qualified medical personnel arriving on site.

Refined Segmentation of K-Means Cluster Using Hidden-Markov Random Field (HMRF) for Leukemia Detection

Abstract: An important part in detecting and diagnosing leukaemia involves the microscopic examination of a patient’s blood sample. The type of malignant cells called the blasts is frequently determined by morphological alterations in White Blood Cells (WBCs). Real-time procedures are time-consuming, error-prone, and expensive. In order to overcome this problem the features such as the colour, shapes and texture can be extracted from the original image. This paper deals with the segmentation techniques. The segmentation process in automated approach is developed using k-means clustering (KMC) and Hidden-Markov Random Field (HMRF). This is also termed as Refined Segmentation (Re-Se). This analysis includes Aspirate Smear Images (ASI). First cell segmentation using a KMC is performed, then a Cell Image Representing Model (CIRM) using HMRF is build, estimating prototype variables using Expectation Maximization (EM) ratio, iterating until the best value is found, and finally achieving next phase called Refined Segmentation (Re-Se). Then, features including texture, geometry, colour, and statistical information are retrieved and used to categorize leukaemia cells from WBCs, by using Fuzzy Rule-Based Decision Support System (FRDSS). By incorporating more feature extraction techniques, the suggested method improves a blast of most immature cells identification for the Acute Myeloid Leukemia (AML) detection

Rule-based Decision Support for No-Code Digitalized Processes

Digitalized processes offer various advantages: uniform information flow, traceability of process progress, and enhanced analyzability. While these represent initial benefits from the digitalization of the process, a recurring evaluation and optimization of the process are required to sustain and improve process efficiency. For both non-digitalized and digitalized processes, the challenges for process owners are comparable. However, for digitalized processes, convenient tools facilitate process modifications. Particularly no-code digitalized processes exploit the opportunity for quick and agile process adaption with low efforts but require a structured and systematic method to ensure a quality-driven approach. This paper conceptualizes a rule-based decision support model for process improvement that takes advantage of digital traces recorded during process execution of digitalized processes. On the one hand, process mining techniques support data analysis and assist the extraction of process performance indicators as precursors to predict low process performance. On the other hand, process experts compile a list of recommendations for action in workshops and interviews to counteract low process efficiency. The approach to rule-based decision support developed in the scope of this research merges these two dimensions and constitutes an assistive tool for the continuous improvement of digitalized processes. Initial results of the model in practical application support this assessment and demonstrate further research needs.

A rule-based decision support system for aiding iron deficiency management

Iron is a vital mineral for the proper function of hemoglobin which is also a protein needed to transport oxygen in the blood. The lack of iron in human blood causes a range of serious health problems including "anemia." In this article, the COntAneRS (Clinical ONTology-based Iron Deficiency-ANEmia- Recommendation System) is proposed as a clinical decision support system to diagnose iron deficiency and manage its treatment. The applied methodologies and main technical contributions of this study are discussed in four aspects: (1) Iron Deficiency Domain Ontology (IDDOnt), (2) Semantic Web Rule Knowledgebase (SWRL), (3) Inference Engine, and (4) Physician Portal of the system. Experimental studies of the proposed system have been applied on a population of 200 people, consisting of real anemia patients and healthy individuals. First, a decision tree classifier is used to diagnose iron deficiency condition based on the patients' demographic information and certain medical data, as well as recently measured hemoglobin CBC levels of the patients. To check the effectiveness of the system, the data of 50 anonymous patients randomly selected from 200 patients are entered manually in the IDDOnt and the system is then verified according to the inferencing results. After inferencing step, the recommendations related to appropriate iron drugs, daily consumption dose, drug consumption periods, and additional medical suggestions about drug interactions are provided by the system to the responsible physician through system ontology, SWRL rules, and web services. As a result of experimental studies, our system has provided very good accuracy (99.5%) and robust results in producing patient-suitable suggestions. In addition, the applicability of the system on the cases is discussed as case studies in this paper. The results reported from the applied case studies are promising in demonstrating the applicability, effectiveness, and efficiency of the proposed approach.

Change Point Enhanced Anomaly Detection for IoT Time Series Data

Due to the exponential growth of the Internet of Things networks and the massive amount of time series data collected from these networks, it is essential to apply efficient methods for Big Data analysis in order to extract meaningful information and statistics. Anomaly detection is an important part of time series analysis, improving the quality of further analysis, such as prediction and forecasting. Thus, detecting sudden change points with normal behavior and using them to discriminate between abnormal behavior, i.e., outliers, is a crucial step used to minimize the false positive rate and to build accurate machine learning models for prediction and forecasting. In this paper, we propose a rule-based decision system that enhances anomaly detection in multivariate time series using change point detection. Our architecture uses a pipeline that automatically manages to detect real anomalies and remove the false positives introduced by change points. We employ both traditional and deep learning unsupervised algorithms, in total, five anomaly detection and five change point detection algorithms. Additionally, we propose a new confidence metric based on the support for a time series point to be an anomaly and the support for the same point to be a change point. In our experiments, we use a large real-world dataset containing multivariate time series about water consumption collected from smart meters. As an evaluation metric, we use Mean Absolute Error (MAE). The low MAE values show that the algorithms accurately determine anomalies and change points. The experimental results strengthen our assumption that anomaly detection can be improved by determining and removing change points as well as validates the correctness of our proposed rules in real-world scenarios. Furthermore, the proposed rule-based decision support systems enable users to make informed decisions regarding the status of the water distribution network and perform effectively predictive and proactive maintenance.

Use of Decision Trees for the Development of Decision Support Systems for the Control of Grinding Circuits

Grinding circuits can exhibit strong nonlinear behaviour, which may make automatic supervisory control difficult and, as a result, operators still play an important role in the control of many of these circuits. Since the experience among operators may be highly variable, control of grinding circuits may not be optimal and could benefit from automated decision support. This could be based on heuristics from process experts, but increasingly could also be derived from plant data. In this paper, the latter approach, based on the use of decision trees to develop rule-based decision support systems, is considered. The focus is on compact, easy to understand rules that are well supported by the data. The approach is demonstrated by means of an industrial case study. In the case study, the decision trees were not only able to capture operational heuristics in a compact intelligible format, but were also able to identify the most influential variables as reliably as more sophisticated models, such as random forests.

An Automatic Ontology-Based Approach to Support Logical Representation of Observable and Measurable Data for Healthy Lifestyle Management: Proof-of-Concept Study.

BackgroundLifestyle diseases, because of adverse health behavior, are the foremost cause of death worldwide. An eCoach system may encourage individuals to lead a healthy lifestyle with early health risk prediction, personalized recommendation generation, and goal evaluation. Such an eCoach system needs to collect and transform distributed heterogenous health and wellness data into meaningful information to train an artificially intelligent health risk prediction model. However, it may produce a data compatibility dilemma. Our proposed eHealth ontology can increase interoperability between different heterogeneous networks, provide situation awareness, help in data integration, and discover inferred knowledge. This “proof-of-concept” study will help sensor, questionnaire, and interview data to be more organized for health risk prediction and personalized recommendation generation targeting obesity as a study case.ObjectiveThe aim of this study is to develop an OWL-based ontology (UiA eHealth Ontology/UiAeHo) model to annotate personal, physiological, behavioral, and contextual data from heterogeneous sources (sensor, questionnaire, and interview), followed by structuring and standardizing of diverse descriptions to generate meaningful, practical, personalized, and contextual lifestyle recommendations based on the defined rules.MethodsWe have developed a simulator to collect dummy personal, physiological, behavioral, and contextual data related to artificial participants involved in health monitoring. We have integrated the concepts of “Semantic Sensor Network Ontology” and “Systematized Nomenclature of Medicine—Clinical Terms” to develop our proposed eHealth ontology. The ontology has been created using Protégé (version 5.x). We have used the Java-based “Jena Framework” (version 3.16) for building a semantic web application that includes resource description framework (RDF) application programming interface (API), OWL API, native tuple store (tuple database), and the SPARQL (Simple Protocol and RDF Query Language) query engine. The logical and structural consistency of the proposed ontology has been evaluated with the “HermiT 1.4.3.x” ontology reasoner available in Protégé 5.x.ResultsThe proposed ontology has been implemented for the study case “obesity.” However, it can be extended further to other lifestyle diseases. “UiA eHealth Ontology” has been constructed using logical axioms, declaration axioms, classes, object properties, and data properties. The ontology can be visualized with “Owl Viz,” and the formal representation has been used to infer a participant’s health status using the “HermiT” reasoner. We have also developed a module for ontology verification that behaves like a rule-based decision support system to predict the probability for health risk, based on the evaluation of the results obtained from SPARQL queries. Furthermore, we discussed the potential lifestyle recommendation generation plan against adverse behavioral risks.ConclusionsThis study has led to the creation of a meaningful, context-specific ontology to model massive, unintuitive, raw, unstructured observations for health and wellness data (eg, sensors, interviews, questionnaires) and to annotate them with semantic metadata to create a compact, intelligible abstraction for health risk predictions for individualized recommendation generation.

Data based analysis and improvement of energy efficiency in the automotive body shop

The energy demand of body shop manufacturing lines is primarily determined by decisions that are made in the early planning phase. However, energy related data which is needed in order to make well founded decisions is mostly unavailable in that phase. The lack of information leads to overdimensioned equipment and inefficient processes. Therefore, in this paper energy related data is acquired and analyzed from existing manufacturing lines. With an established energy and equipment database, the prediction of the future manufacturing lines energy demand is possible. In combination with a rule-based decision support, improvements are already possible in the early planning process. In comparison with existing approaches, the developed holistic methodology addresses all levels in the planning process and is exemplified by a use case in the automotive industry.

Covid-19 Series: A rule-based decision support system for analysis behavior of people while working from home

COVID-19 makes the community must carry out activities such as school, work, and worship at home. However, the long-running activities from home make people experience boredom which can lead to stress. On the other side, the entertainment obtained by the public through smartphones by reading articles by their interests can reduce boredom. This paper proposed a rules-based decision support system to help the people to make choices of their activities from home while COVID-19 e rules-based approach for make an application decision support system. Ruled base used in application to selected process through characteristics following the interests of the community. This decision support system is implemented in mobile web applications. The system can display articles based on interests by questions or statements through the front end system. The results showed that most users of the application in a happy condition while working from home.

Evidential Reasoning Rule-Based Decision Support System for Predicting ICU Admission and In-Hospital Death of Trauma

We propose to employ evidential reasoning (ER) rule to construct a clinical decision support system (CDSS) to aid physicians to predict the probability of intensive care unit (ICU) admission and in-hospital death for trauma patients once they arrive at a hospital. A generalized Bayesian rule is used to mine evidence from historical data. Evidence is profiled using a format of belief distribution, where the belief degrees of different trauma outcomes are assigned with derived probabilities linked to the corresponding outcomes. Inputs to the CDSS are clinical data of a patient, and output from the system is predicted belief degree of severe trauma, including ICU admission and in-hospital death. The inner logic of the CDSS is that pieces of evidence that match the clinical data of a patient are identified from the evidence base first, and then the ER rule-based evidence aggregation mechanism is utilized to combine the matched evidences to arrive at a prediction. The reliability, weight, and interdependence of clinical evidence are taken into account. Moreover, an evidence weight training module is constructed. The ER rule-based prediction model has superior performance compared with logistic regression and artificial neural network models. An innovative and pragmatic ER rule-based CDSS for trauma outcome prediction is contributed by this article. In the era of big data, this CDSS helps predict patient outcomes based on historical data and helps physicians in emergency departments make proper trauma management decisions.

Knowledge Base and Rule Base Safety Management Systems Development Strategies and Applications for Petrochemical Industries: A Brief Outline

This review paper discussed about the development tactics and applications of knowledge based and rule-based decision support systems for health and safety in oil and gas sector. It recommends the users on the extent of health and safety concerns for risk and hazard investigation and prevention in which the system developers and researchers mostly face issues during selection and adaptation of development approaches. A complete and systematic review and classification of articles from last seven years has been conducted and briefly analysed, to investigate various methodologies employed in previously developed rule based (RB) and knowledge based (KB) decision support systems. All major bibliographic databases covering a broad series of health and safety fields were searched in various oil and gas production sector related studies. Eighteen articles met the KB and RB study. Twelve of these met the methodological prospective of required investigation. In this review paper, researchers briefly explain about the methodological aspects and industrial applications of rule and knowledge-based decision support systems for reducing work place risks and hazards in safety and health domain. Conclusions that can be extracted from this analytical review paper of the methodological variation and applications of rule based and knowledge-based decision support system is that both inferences are interrelated to each other. RB and KB inferences techniques and methods are used in both interpretations for developing decision support systems in the context of oil and gas extraction and production sector. But safety and health consider KB decision systems as more effective for taking suitable decisions due to real time information and experts’ opinions which facilities safety professionals to take appropriate decision then RB reasoning decision support systems.

A rule-based decision support approach for site selection of Automated Teller Machines (ATMs)

The solution of facility site selection problems is of utmost importance for the existence and future of the businesses. When planning for banking services, banks should ensure their accessibility standards by taking their customers and needs into account. For this reason, the accessibility of the services offered via ATM devices is very important. Contrary to the studies that have been widely done in the literature, in this study, the data mining techniques for determining locations of the ATMs, specifically decision tree algorithms were applied to select the best location among candidates considering new ATM installations. In practice, a data set of a private bank’s ATM’s, which in the boundaries of the districts connected to downtown of Kayseri, was used. A decision support model based on rule-based location detection was created from the data set discussed. The correct classification scales were evaluated by applying different algorithms for decision tree method. Among the decision tree algorithms, model estimation accuracy was found to be 92.96% on average. The results were obtained using STATISTICA 10.0 Software.