Decision Information System Research Articles

Semi-supervised attribute reduction for partially labeled categorical data based on predicted label

In many practical applications of machine learning, there are a large number of partially labeled categorical data due to the high cost of labeling data. The semi-supervised learning algorithm is needed to deal with such data. This paper studies the label prediction of partially labeled categorical data and considers semi-supervised attribute reduction in a partially labeled categorical decision information system (p-CDIS) with predicted labels. The labels of unlabeled data are first predicted by means of the conditional probability. Then, uncertainty measurement for a p-CDIS with predicted labels is studied, and the dependence and conditional information entropy (CIE) are defined. Next, based on the dependence and CIE, two attribute reduction algorithms are designed. In addition, the effect of label deletion rate (LDR) on the dependence, CIE and reduction results are also studied. Finally, the results of experiments and statistical tests on 16 categorical UCI datasets show that the designed algorithms are statistically better than some state-of-the-art algorithms in classification accuracy.

An information system for monitoring tuberculosis cases: implementation research protocol using RE-AIM for a health region in Brazil

Tuberculosis is an infectious bacterial disease and one of the biggest public health problems in the world despite being curable. Research is still needed that considers operational aspects of treatment and control of its spread. Considering this scenario, the need to develop software that provides real-time information about the patient's path along health information systems for clinical decision-making is fundamental. Because of this, a region made up of 26 municipalities will implement a computerized system to monitor tuberculosis cases, the SISTB. However, the implementation of new technology can result in the creation of new functions and cause changes in the existing ones. Implementation research can be used as an approach where evidence can be provided to guide the use of digital technology in tuberculosis care. The RE-AIM framework provides a model to inform this research. Thus, this work aims to report the protocol of a study to evaluate the implementation of a health information system to assist in the monitoring of tuberculosis treatment, using the RE-AIM. The implementation will be carried out in 5 phases: i) define the locations that will receive the intervention; ii) prepare people to receive the intervention iii) train key people; iv) adapt the SISTB and/or the training according to the discussions in the previous phase; v) follow up and monitor the support of the technology. The data collected for the evaluation will come from the database of the implemented system, questionnaires, and training meetings. The forecast for the study conclusion is the end of 2023. As a result, we hope that the SISTB implementation will increase the positive outcomes of tuberculosis patients' treatment.

GIS APPLICATION FOR OPTIMUM ROUTE PLANNING FOR METRO RAIL TRANSPORTATION IN ABUJA

Route planners face a complex task when designing rail networks, considering factors such as land use, slope, soil type, lithology, hydrology, and more. This complexity poses a challenge and can lead to inadequate decision-making processes. To simplify this, Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) are used to help stakeholders make informed decisions. In Abuja, a Metro Rail line alignment was proposed to alleviate congestion and accidents in the Zuba, Madalla, Dei Dei, Gwarimpa, Lifecamp, Jabi and Berger areas. The analysis for the selected rail track identified a minimum GRA value of 0.438 and a maximum of 0.781 with a mean and standard deviation of 0.062 and 0.085. A histogram showed GRA values between 0.60 to 0.80 to be predominant along the selected rail route. This study successfully demonstrates the effective application of GIS and MCA principles in optimizing rail route planning for Metro Rail Transportation in Abuja.

АВТОМАТИЗИРОВАННАЯ СИСТЕМА ИЗВЛЕЧЕНИЯ КЛИНИЧЕСКИ РЕЛЕВАНТНЫХ ТЕРМИНОВ UMLS ИЗ ТЕКСТОВ АНГЛОЯЗЫЧНЫХ СТАТЕЙ НА ПРИМЕРЕ АКСИАЛЬНОГО СПОНДИЛОАРТРИТА

Significance. Early detection of axial spondyloarthritis (axSpA) is a complex clinical task. Quality improvement of axSpA diagnostics in primary care settings is possible with the help of decision-making information systems based on the ontological approach application. The key stage of the decision-making system development consists of the elaboration of a set of clinical terms. This set should fully describe clinical area or sub-area under study. One of the essential requirements is compliance of the clinical terms used with the existing clinical nomenclatures. Currently, the largest set of clinical terms is the Unified Medical Language System (UMLS) metathesaurus. The majority of UMLS terms is presented in English only. The development of tools for the analysis of unstructured texts and recognition of clinically relevant UMLS entities make it possible to elaborate a set of terms describing axSpA diagnostic aspects. This will also help to compile a list of UMLS terminology nomenclatures for their priority adaptation and expert translation into Russian. The purpose of this study is to develop an automated system for recognizing clinically relevant UMLS terms in texts of the English-language articles. Material and methods. The research material has included English terms (11.2 million) aggregated from 76 nome6nclatures of the current UMLS (2022AB) version. In addition, the study has used texts of PubMed clinical abstracts in English. Queries to the UMLS graph model, semantic algorithms for unstructured texts and machine-learning methods have been applied for data collection and analysis. Results. The study has elaborated a set of high-accuracy regular expressions (F1-score=98%) for metadata elimination from the text corpus. Then the authors have identified patterns for searching clinically relevant terms in the aggregated set of UMLS concepts. Using a logistic regression algorithm, the authors have trained a binary classification model. Input data for the created classificatory are information about an UMLS term. Output data are a label indicating the presence or absence of clinical relevance. Conclusion. The binary classification model has been validated individually and double-tested on different data samples. Values of the accuracy, sensitivity and specificity of metrics equal to 91%, 90% and 91%, respectively, for the validation sample (a number of axSpA terms). In addition, this model has been tested on the sets of terms aggregated for any two diseases. Values of the accuracy metric equal to 91% and 90%, respectively. With the help of the developed machine-learning model, the study has estimated that UMLS contains 1.5 million unique terms applicable to describing a clinical picture. In addition, lists of priority UMLS data sources and thematic groups have been compiled. These clinically relevant UMLS terms should be adapted and translated into Russian as soon as possible.

Decision-Making Information System for Academic Careers in Congolese Universities: From Analysis to Design of a Data Warehouse

Decision-Making Information System for Academic Careers in Congolese Universities: From Analysis to Design of a Data Warehouse

Spatial Analysis of Groundwater Potential: A first hand Approach towards Groundwater Development in Moroto District, Uganda

Groundwater is a vital resource that helps mankind and development. However, in Moroto District challenges of groundwater development has increased arising from changes in rainfall patterns coupled with high abstraction from population increase. In this study, the spatial variability of groundwater potential was assessed to develop a tool that will aid decision on groundwater potential sites selection for groundwater development/exploration. The study utilized an integrated technique and tools such as Remote Sensing (RS), Geographic Information System (GIS) and Multi Criteria Decision Analysis (MCDA) in analysis and delineation of potential areas for successful groundwater development. The generated map through weighted overlay of thematic layers (slope, soil, lithology, rainfall, land use and land cover) produced five categories of potential zones indicating 90% (i.e., 35% very high, 40% high and 15% moderate) of the study area to be suitable for groundwater development and only 10% (i.e., 2% poor and 8% very poor) exists as poor groundwater potential zones. Maps produced from this study can hence be used to identify appropriate sites for groundwater development, and therefore minimizing unsuccessful boreholes development.
 Keywords: GIS; Groundwater Development; Groundwater Potential; MCDA; Spatial Analysis.

A comparative investigation of ethical decision-making information systems professionals versus students in UAE

Technological advancements have seen information technology develop and establish itself as a dominant field. This is owing to the fact that UAE has become a leading business hub, tourist destination as well as education. Information technology systems have been put in place in almost every sector of the economy. Information Systems (IS) professionals have to make ethical decisions every day. The current technological environment marred with rapid technological advancements has only made things worse. It has led to more ethical problems that need to be resolved. This study explores the perception information system professionals have about ethical issues and how their perception and attitude differs from students. It investigates what role ethical considerations play in the decision making process of information systems professionals and how different it is from students.

A Model-Driven IS 4.0 Development Framework for Railway Supply Chain

Railway Industry (RI) in Malaysia possess below-average Information System (IS) skills and seldom use the IS for decision making at their operation level while they likewise discover digital transformation adaption is crucial and hence RI in Malaysia are in the slow mass of adapter classification. Perceiving the significant task of IS to RI in the economy, the government is resolved to assist and support the improvement of IS to guarantee their sustainability and competitiveness. IS framework being significant because it set up the computerized industry, lively digital, who can structure with simple to utilize and basic dynamic interaction. The present IS model utilized in Malaysia depends on the knowledge and experience of the specialist like system developers and academicians. The maximum of these IS models to identify the visual view of performance in RI are precise and are not strategized toward railway utilize and do not give prescriptive evaluation. The issue is no transition development and the absence of industry capacity to do the transition phases. This research focuses on the technology parameters influencing the adaption of IS to assist decision-makers, administrative bodies, and IS analysis to approach the advantages of its continued and expected improvement in the RI.

Risk management for the functionality component of an agricultural enterprise's decision-making support information system

Ensuring food security and economic and social stability is a national issue for every state. The research examines the functional component of the information system model for supporting decision-making in the agrarian sphere in environmental uncertainty. This component includes those mathematical models and methods that ensure the functioning of the information system for supporting decision-making in the agricultural sphere in ecological uncertainty. The main assumptions of the study are that no activity can be avoided entirely, and there are no risks that cannot be managed. Risk management is considered in more detail as a process of taking measures aimed at ensuring an acceptable risk. A mathematical model of agricultural enterprise management is formalized, in which unclear input parameters, namely environmental and economic circumstances, are risk factors. Four stages of risk management are considered: identification, analysis, development of management measures, and operational risk management. The primary external and internal sources of risk in the activity of an agricultural enterprise have been identified. An algorithm for risk analysis is proposed, which consists of seven steps. A quantitative method implementing the risk analysis algorithm has also been developed. A fuzzy profit function of an agricultural enterprise is constructed. It is proposed to calculate the risk as a root mean square deviation. The method of risk management consists in minimizing risks.

Prioritizing the Best Potential Regions for Brine Concentration Systems in the USA Using GIS and Multicriteria Decision Analysis.

We propose a methodology for identifying and prioritizing the best potential locations for brine concentration facilities in the contiguous United States. The methodology uses a geographic information system and multicriteria decision analysis (GIS-MCDA) to prioritize the potential locations for brine concentration facilities based on thermodynamic, economic, environmental, and social criteria. By integrating geospatial data with a computational simulation of a real brine concentration system, an objective weighting method identifies the weights for 13 subcriteria associated with the main criteria. When considering multiple dimensions for decision making, brine concentration facilities centered in Florida were consistently selected as the best location, due to the high second-law efficiency, low transportation cost, and high capacity for supplying municipal water needs to nearby populations. For inland locations, Southeast Texas outperforms all other locations for thermodynamic, economic, and environmental priority cases. A sensitivity analysis evaluates the consistency of the results as the priority of a main criterion varies relative to other decision-making criteria. Focusing on a single subcriterion misleads decision making when identifying the best location for brine concentration systems, identifying the importance of the multicriteria methodology.

Quantitative Framework of Political Education Micro-communication Mode Based on Reachable Information Matrix Algorithm

Based on the generalized decision reduction and reachable information matrix algorithm of the set-valued decision information system, this paper establishes a quantitative framework for the micro-communication model of political education. First, this paper draws on the comparison between communication and psychology using matrix to extract all decision rules based on Marxist theory and ideological and political education theory. Then, taking the dissemination effect of ideological and political education of college students as the research object, by analyzing the factors affecting the effect of dissemination of ideological and political education of college students, the factors and characteristics of political education dissemination are extracted. The construction of educational discourse system.

National Health Observatory: A tool to strengthen the health information system for evidence-based decision making and health policy formulation in Cameroon

Despite the consensus on the importance of evidence based health information for decision28 making and health policy formulation, quality information is not always available. This situation led the WHO in 2009 to recommend the establishment of an African Health Observatory and National Health Observatories. The WHO recommended 4 phases approach for the establishment of National Health Observatories was used in Cameroon: the preliminary phase, start-up, strengthening and full operation. Despite a non-optimal institutional positioning, the Cameroon’s Health Observatory is in the strengthening phase and contributes to evidence-based decision making in the health system. A strong political commitment is fundamental from the beginning of the project. The establishment of National Health Observatories calls for genuine national ownership, both of the process as a whole and of the tool itself. WHO should regularly assess the progress made in the establishment of National Health Observatories in different countries and provide feedback to them. A step-by-step approach, jointly validated by national stakeholders in a roll-out plan, would be more appropriate for the sustainability of the work of National Health Observatories.

A three-way decision method with tolerance dominance relations in decision information systems.

In recent years, various medical diagnosis problems have been addressed from the perspective of multi-attribute decision making. Among them, the three-way decision theory can provide a novel scheme to solve medical diagnosis issues under the framework of multi-attribute decision making via considering transforming relationships between loss functions and decision matrices. In this paper, we primarily explore a three-way decision method with tolerance dominance relations in ordered decision information systems. In existing three-way decision models, all objects can be divided into two states, we utilize decision attributes to obtain the set of two states in ordered decision information systems. Then, in order to improve the accuracy of patient classifications, the paper simultaneously considers the influence of loss and gain functions for each object, and uses loss and gain functions to obtain net profit functions as new measurement functions. Meanwhile, a class of three-way decisions in terms of multi-attribute decision making rules based on a tolerance dominance relation is established. In light of the proposed three-way decision method, we further construct a multi-attribute decision making method by using tolerance dominance relations and the constructed method is applied to a medical diagnosis issue of Lymphography. Finally, a comparison analysis and an experimental evaluation are performed to illustrate the feasibility and effectiveness of the presented methodology.

Update of optimal scale in dynamic multi-scale decision information systems

The problem of optimal scale selection for multi-scale decision information systems is an important issue in the field of granular computing research, especially when data are dynamically updated. Determining the changes of the optimal scale for dynamic data is a goal that has drawn increasing attention from scholars in related fields. For the case of object updating, solving this problem requires finding which characteristic conditions should be satisfied by newly added objects when different cases of the optimal scale occur. However, existing studies only include the sufficient and necessary condition of the optimal scale becoming smaller for dynamically updating objects. Therefore, to complete the theory regarding how the optimal scale will be changed when objects are dynamically updated, it is still necessary to explore the sufficient and necessary conditions for the optimal scale being unchanged or becoming larger. In this paper, we use three-way decision theory to study this problem. Concretely speaking, the uncertain region of three-way decision is used to reveal the change of knowledge at different scales. That is, the obtained results, combined with existing work, provide a nice solution to the problem of finding the changing laws of the optimal scale for object updating in multi-scale decision information systems.

Sequential 3WD-based local optimal scale selection in dynamic multi-scale decision information systems

The multi-scale decision information system (MDIS) is a typical granular computing model. In the research of MDIS, uncertainty is an important factor in making decision analysis, and the selection of optimal scale is a core problem. Therefore, the uncertainty of decision is an important factor in the scale selection. With the rapid increase of data size, the amount of feature information will increase greatly, and the uncertainty of the system will become more and more complex, which makes the optimal scale selection more difficult. The purpose of this study is to investigate the updating law of the local optimal scale under the condition of the dynamic increase of objects. The criterion of scale selection is to keep the uncertainty of the system unchanged. Firstly, the updating law of uncertainty for the decision class in a decision information system under the case of object increment is explored. Secondly, the definition of local optimal scale which keeps the uncertainty of decision classes is given by the sequential three-way decision theory, and the updating law of optimal scale is given by using the updating mechanism of the uncertainty of decision classes. Finally, experiments are conducted to verify the correctness and effectiveness of the proposed method in the calculation of local optimal scale by comparing the algorithms for adding multiple objects directly and adding objects one by one.

A prospect-regret theory-based three-way decision model with intuitionistic fuzzy numbers under incomplete multi-scale decision information systems

As a new data representation in the big data era, a multi-scale decision information system (MSDIS) realizes “multi-level, multi-angle and multi-view” evaluations in various problems. However, most of existing multi-scale data analysis models are built for complete information systems (CISs), whereas the research on incomplete multi-scale decision information systems (I-MSDISs) is not perfect. Additionally, the irrational behavior of one decision-maker (DM) often has an impact on decision outcomes. On this basis, this paper develops a prospect-regret theory-based three-way decision (3WD) model with intuitionistic fuzzy numbers (IFNs) under an I-MSDIS, which is abbreviated as PR-3WD-I-MSDIS. Specifically, we first select the optimal scale combination of I-MSDISs by the matrix theory to complete the information extraction of incomplete optimal sub-systems. Then, we further propose an attribute weight evaluation strategy under the incomplete optimal subsystem, which fully considers the neighborhood information of each object. Subsequently, the concept and calculation method of aggregated weighted fuzzy conditional probabilities are constructed from the perspective of intuitionistic fuzzy sets. Furthermore, a prospect-regret theory-based relative profit function is proposed, which sufficiently takes into account the loss and utility in decision-making processes. Finally, a novel three-way classification and ranking method is developed for solving incomplete multi-scale problems. The experimental results on real-world datasets demonstrate that the PR-3WD-I-MSDIS model achieves excellent performance.

A decision framework for tidal current power plant site selection based on GIS-MCDM: A case study in China

To reach carbon neutrality, renewable energy development will gradually accelerate. Tidal current energy, with high energy density, long-term predictability and potentially large reserves, is an important research direction. As the prerequisite for tidal current energy development, Tidal Current Power Plant Site Selection (TCPPSS) affects its future benefits. Although some studies have been conducted on TCPPSS, further work is still needed on criteria system, research scales and methods. In this paper, a three-stage decision framework is proposed based on Geographic Information System (GIS) and Multi-Criteria Decision Making (MCDM) to determine potential marine areas, extract and evaluate site alternatives. In this framework, a scientific criteria system with exclusion and evaluation criteria is established. To reduce subjective deviations in Analytic Hierarchy Process (AHP), a combinative weighting method based on Fuzzy Group decision making AHP (FGAHP) and CRiteria Importance Through Intercriteria Correlation (CRITIC) is developed to calculate the criteria weights. Considering the maximum group utility and minimum individual regret, VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), a compromise method, is used to rank the alternatives. The proposed framework is applied to a case of Shandong Province, Eastern China. The results are verified through sensitivity analysis and can provide a direct reference for decision-makers.

A three-way decision approach with a probability dominance relation based on prospect theory for incomplete information systems

The processing scheme of typical incomplete information systems has been widely explored nowadays, however most of these approaches may change the original data information and can not guarantee the integrity of original information systems. Meanwhile, psychological behaviors of decision makers usually have an influence on the decision-making results. In light of these facts, the paper explores a new three-way decision approach by using prospect theory for an incomplete information system, which handles the default information of an incomplete information system by virtue of a probability dominance relation. First, a probability dominance relation of an incomplete information system is defined to process a binary relation between evaluation values, and the way for calculating the relative value function in the behavior decision-making process is also given. Moreover, the conditional probability with a decision attribute in an incomplete decision information system is put forward, and an objective weight function via prospect theory is further obtained. On this basis, a prospect-theory-based three-way decision approach with a probability dominance relation for an incomplete information system is proposed. Finally, the stability, effectiveness and superiority of the presented approach are validated via corresponding experimental studies and comparative analysis with two realistic cases.

Geographic information system and multi-criteria decision analysis for the determination of groundwater recharge potential: systematic review

Abstract The groundwater recharge area is one of the crucial components in regional development. Many studies in determining groundwater recharge areas with a combination of Geographic Information System and Multi-criteria Decision Analysis have been carried out. The development of studies over the last ten years is reviewed in this paper using the PRISMA systematic review method to find out the study's progress. A total of 31 studies were found in the previous ten years based on the review results. Information about the country of origin of the study, dominant geology, climatic conditions, criteria used, decision-making and weighting methods, and the validation process for each study were extracted. The results show 12 countries researching this topic, with five dominant geological classes and nine classes of climatic conditions, and 33 criteria are used in the 2011–2021 range with eight dominant criteria used. Four decision rules and the weighted method are used, and a validation process is commonly used with well data. From systematic review and meta-analysis, we conclude: India has become the country that has most researched this topic. Hard-rock-class geology is dominant and arid and semi-arid climate conditions have become the main focus of studies. The variability of criteria is up to 33 with 18 criteria still used only once in each study, and eight dominant criteria have been used. Analytical Hierarchy Process (AHP) and Multiple Influence Factor (MIF) have become the most-used methods for assigned weight, and Frequency Ratio (FR) is the newest method for assigned weight. Lithology has the highest weight compared with the other seven dominant criteria, and well data has become the most common data for verification of groundwater recharge.

Gaussian kernel based gene selection in a single cell gene decision space

Information system is a database that shows relationships between objects and attributes. A real-valued information system is an information system whose information values are real numbers. A real-valued information system with decision attributes is referred to as a real-valued decision information system. If objects, conditional attributes and information values in a real-valued decision information system are cells, genes and gene expression values, respectively, then this information system is said to be a gene decision space. In a gene decision space, people are faced with gene expression data. If gene expression data in a gene decision space changes to single cell RNA-seq data, then this space is referred to as a single cell gene decision space. This paper explores gene selection in a single cell gene decision space in terms of Gaussian kernel. In the first place, the distance between two cells in each subspace of a single cell gene decision space is constructed. Next, the fuzzy Tcos-equivalence relation on the cell set is obtained in terms of Gaussian kernel. After that, measures of uncertainty for a single cell gene decision space are investigated. Lastly, gene selection algorithms in a single cell gene decision space are presented in terms of the proposed information entropy and information granularity. The presented algorithms are testified in several publicly open single cell RNA-seq data sets. Experimental results reveal that the presented algorithms can select appropriate genes related to classification, and significantly improve classification performances.