Components Of Decision Support System Research Articles

Selection and adaptation to weed management methods: implications for non-chemical and integrated weed management approaches.

Herbicide-resistant weeds are a growing global concern, threatening food security. Non-chemical weed management approaches are becoming increasingly important. Furthermore, the adoption of non-conventional agricultural practices is on the rise, with regenerative farming practices aimed at rebuilding soil organic matter, restoring biodiversity, and reducing chemical use gaining traction. Consequently, non-chemical weed management methods are seen as essential solutions. However, excessive reliance on these methods may inadvertently lead to weed selection and adaptation, reducing their effectiveness. Here, we explore the mechanisms driving changes in efficacy due to selectivity and adaptation to non-chemical weed management practices. Additionally, we discuss potential integrated weed management (IWM) strategies that combine chemical and non-chemical methods to mitigate the risks of weed adaptation. This study highlights the role of evolutionary processes in shaping weed adaptation to non-chemical weed management methods and underscores the need to understand these processes to develop IWM approaches that remain effective over time. Monitoring phenological shifts and adaptations in the field should be a key component of decision support systems, tailored to the unique conditions of each site. Furthermore, a deeper understanding of weed adaptation mechanisms can enhance the efficacy of IWM strategies and help delay the inevitable adaptation to these control methods. © 2024 Society of Chemical Industry.

TECHNOLOGICAL ASPECTS OF ACCOUNTING AUTOMATION SYSTEM AS A DECISION SUPPORT SYSTEM

Nowadays all businesses need access to an extensive pool of information. Reporting, financial and accounting analysis, financial planning and budgeting are key factors for market-oriented corporate management and successful business. In the digital economy, strong competition, responsible entrepreneurship and corporate social responsibility the business feels a need for revolution and new decision sciences for complex systems that collect big data, analyze it and create reports that can be used by managers for decision-making. The present study aims to prove that the accounting automation system is an important tool for managing the economic activity of companies, because it mediates the interrelationships between managers/decision-makers, or subjects of management, economic activity and its results, or object of management, in support of Business intelligence, or BI, Data and Analysis, Power Platform, etc. The following research tasks have to be settled for reaching the above-mentioned goal: 1. to analyze the components of decision support systems; 2. to research the types of decision support systems; 3. to demonstrate the impact of the accounting automation system as a decision support system on the optimization and improvement of the working processes and making adequate timely management decisions; 4. to analyze the technological aspects of accounting automation system as a decision support system; 5. to research the challenges and solutions regarding ERP systems and SAP. In the course of the study shall be justified the thesis that the successful business is based on true, accurate and reliable information which is mostly generated by automated accounting system as a decision support system and contemporary information technologies. The results of the research are expressed in establishing the way to ensure and guarantee the unification, acceleration and optimization of the input-processing-output process, analysis, financial modelling, financial planning, etc. about decision support and respectively wealth management.

Accuracy versus reliability-based modelling approaches for medical decision making

Forecasting in the medical domain is critical to the quality of decisions made by physicians, patients, and health planners. Modeling is one of the most important components of decision support systems, which are frequently used to simulate and analyze under-studied systems in order to make more appropriate decisions in medical science. In the medical modeling literature, various approaches with varying structures and characteristics have been proposed to cover a wide range of application categories and domains. Regardless of the differences between modeling approaches, all of them aim to maximize the accuracy or reliability of the results in order to achieve the most generalizable model and, as a result, a higher level of profitability decisions. Despite the theoretical significance and practical impact of reliability on generalizability, particularly in high-risk decisions and applications, a significant number of models in the fields of medical forecasting, classification, and time series prediction have been developed to maximize accuracy in mind. In other words, given the volatility of medical variables, it is also necessary to have stable and reliable forecasts in order to make sound decisions. The quality of medical decisions resulting from accuracy and reliability-based intelligent and statistical modeling approaches is compared and evaluated in this paper in order to determine the relative importance of accuracy and reliability on the quality of made decisions in decision support systems. For this purpose, 33 different case studies from the UCI in three categories of supervised modeling, namely causal forecasting, time series prediction, and classification, were considered. These cases were chosen from various domains, such as disease diagnosis (obesity, Parkinson's disease, diabetes, hepatitis, stenosis of arteries, orthopedic disease, autism) and cancer (lung, breast, cervical), experiments, therapy (immunotherapy, cryotherapy), fertility prediction, and predicting the number of patients in the emergency room and ICU. According to empirical findings, the reliability-based strategy outperformed the accuracy-based strategy in causal forecasting cases by 2.26%, classification cases by 13.49%, and time series prediction cases by 3.08%. Furthermore, compared to similar accuracy-based models, the reliability-based models can generate a 6.28% improvement. As a result, they can be considered an appropriate alternative to traditional accuracy-based models for medical decision support systems modeling purposes.

Impact of soil moisture on cesium uptake by plants: Model assessment

Computational models of radioisotopes soil-to-plant transfer are an essential component of decision support systems for nuclear emergency and assessment impact of nuclear accidents on human beings and the environment. The soil water regime impact on cations behavior in the «soil-plant » system. Therefore, relationships between radioisotopes accumulation by plants and soil moisture should be included in the computational models. A kinetic model for simulation of Cs+ root uptake under alteration of soil moisture content in soil is presented in this paper. The model simulates Cs+ and K+ diffusion from bulk soil into rhizosphere under alteration of water content. The model results show a drop of Cs+ and K+ concentration in the rhizosphere with decreasing soil moisture. The magnitude of enhanced root uptake of Cs+ with soil moisture alteration depends on the exchangeable potassium content and soil texture. The Cs+ root uptake rate has a maximum at 60%–80% of field capacity under sufficient and high exchange potassium content. A similar dependence should also be seen for the RCs soil-to-plant transfer factor. The results indicate the importance of soil water content for modification of the RCs accumulation by plants and the necessity to include this factor into an appropriate semi-mechanistic model.

Model of decision support system in educational process of a university on the basis of learning analytics

In recent decades, the potential of analytics and data mining – the methodologies that extract valuable information from big data, transformed multiple fields of scientific research. Analytics has become a trend. With regards to education, these methodologies are called the learning analytics (LA) and educational data mining (EDM). Latterly, the use of learning analytics has proliferated due to four main factors: a significant increase in data quantity, improved data formats, achievements in the area of computer science, and higher complexity of available analytical tools. This article is dedicated to the description of building the model of decision support system (DSS) of a university based on educational data acquired from digital information and educational environment. The subject of this research is the development of DSS with application of learning analytics methods. The article provides a conceptual model of decision-making system in the educational process, as well as a conceptual model of the components of DSS component – forecasting subsystem. The peculiarity of forecasting subsystem model implies usage of learning analytics methods with regards to data sets of a higher educational institution, which contain the results of work of the digital information and educational environment, and include the characteristics of student activity. The main results of the conducted research is the examined and selected methods of clusterization and classification (KNN), the testing of which demonstrated palatable results. The author examined various methods of clusterization, among which k-prototypes method showed best results. The conclusion is made on favorable potential of application of the methods of learning analytics in Russian universities.

Средство эффективного взаимодействия экспертов в системах поддержки принятия решений электронного правительства

Решена проблема низкой эффективности традиционных методов организации взаимодействия экспертов в системах поддержки принятия решений электронного правительства. Показано, что деятельность высококвалифицированного эксперта, оценки которого оказывают существенное влияние на эффективность, результативность и экономичность властных решений, недостаточно организована, систематизирована и информационно обеспечена. На основании подходов, учитывающих специфику взаимодействия экспертов между собой и с лицом, принимающим решение, сформулированы требования к автоматизированным системам экспертного оценивания. Определено их место в системах поддержки принятия решений электронного правительства. Показано, что квалифицированные пользователи — эксперты, способные оценивать (преобразовать) входные данные, генерировать варианты решений и устанавливать правила выбора лучшего из них, являются неотъемлемым «элементом» архитектуры систем поддержки принятия решений, а автоматизированные системы экспертного оценивания — их обязательным компонентом. Разработано и реализовано программное средство экспертного оценивания – автоматизированную систему PsycheaEXPERTUS. Ее апробация показала, что использование таких систем в системе электронного правительства существенно упростит экспертные процедуры, сократит непроизводительные затраты времени на организацию и проведение очных консультаций и повысит эффективность привлечения специалистов к формированию и реализации государственной политики в условиях неопределенности внешней среды и ресурсных ограничений Дальнейшие исследования планируется направить на преодоление психологической неготовности лиц, принимающих решения, к использованию систем экспертного оценивания в Украине.

Geoscience Methods in Real Estate Market Analyses Subjectivity Decrease

Real estate management, including real estate market analysis, is part of a so-called geosystem. In recent years, the popularity of creating various types of systems and automatic solutions in real estate management, including those related to property classification and valuation, has been growing in the world, mainly to reduce the impact of human subjectivity, to increase the scope of analyses and reduce research time. A very important fact that should be underlined is that properties are strongly related to geolocation (space) and strongly determine it. Authors proposed in the paper solutions that highlight implementation of geoscience and “geo-approach” combined with fuzzy logic methods that allow to decrease subjectivity in property analyses and diminish uncertainty in decision making process. The proposed methodology involves three main problematic components of decision support system in property investment analyses development with the use of geo-technologies such as: determination of the database model; elaboration geo-property-zones with geoprocessing activities; identification of homogeneous group of properties transactions. The influence of spatial decision factor determined in the study lead to objective and precise calculation of value differentiation from 22 to 43% depending on the property’s remoteness to the sea.

A multiobjective Bayesian networks approach for joint prediction of tumor local control and radiation pneumonitis in nonsmall-cell lung cancer (NSCLC) for response-adapted radiotherapy.

Individualization of therapeutic outcomes in NSCLC radiotherapy is likely to be compromised by the lack of proper balance of biophysical factors affecting both tumor local control (LC) and side effects such as radiation pneumonitis (RP), which are likely to be intertwined. Here, we compare the performance of separate and joint outcomes predictions for response-adapted personalized treatment planning. A total of 118 NSCLC patients treated on prospective protocols with 32 cases of local progression and 20 cases of RP grade 2 or higher (RP2) were studied. Sixty-eight patients with 297 features before and during radiotherapy were used for discovery and 50 patients were reserved for independent testing. A multiobjective Bayesian network (MO-BN) approach was developed to identify important features for joint LC/RP2 prediction using extended Markov blankets as inputs to develop a BN predictive structure. Cross-validation (CV) was used to guide the MO-BN structure learning. Area under the free-response receiver operating characteristic (AU-FROC) curve was used to evaluate joint prediction performance. Important features including single nucleotide polymorphisms (SNPs), micro RNAs, pretreatment cytokines, pretreatment PET radiomics together with lung and tumor gEUDs were selected and their biophysical inter-relationships with radiation outcomes (LC and RP2) were identified in a pretreatment MO-BN. The joint LC/RP2 prediction yielded an AU-FROC of 0.80 (95% CI: 0.70-0.86) upon internal CV. This improved to 0.85 (0.75-0.91) with additional two SNPs, changes in one cytokine and two radiomics PET image features through the course of radiotherapy in a during-treatment MO-BN. This MO-BN model outperformed combined single-objective Bayesian networks (SO-BNs) during-treatment [0.78 (0.67-0.84)]. AU-FROC values in the evaluation of the MO-BN and individual SO-BNs on the testing dataset were 0.77 and 0.68 for pretreatment, and 0.79 and 0.71 for during-treatment, respectively. MO-BNs can reveal possible biophysical cross-talks between competing radiotherapy clinical endpoints. The prediction is improved by providing additional during-treatment information. The developed MO-BNs can be an important component of decision support systems for personalized response-adapted radiotherapy.

Evaluating the Effectiveness of Decision Support System: Findings and Comparison

Nowadays, regardless of the popularity and credibility of Decision Support Systems (DSS), measuring the efficacy of the decisions taken by the DSS is yet to be proven. As previous works identifies the complexities involved in measuring the efficiency of DSS, most of the time DSS efficiency is case dependent. The list of methods for collecting and analyzing data, building models, deployment models, data and model integration, and finally taking decisions are some of the major issues related to measuring DSS effectiveness. This paper focuses on measuring the effectiveness of DSS. The paper highlights the issues that still need to be addressed with efficient frameworks. Based on the literature review and discussion presented in Section I and II, this study proposed a framework and its implementation. Presents how the proposed model can improve the previous work. The major findings of this study reflect that every decision made by DSS is based on the collected data, analyzed by DSS tools, as well as depends on the developed models. Therefore, this study illustrated that each component of DSS plays vital role in measuring the effectiveness of DSS whatever the case and problem for which the DSS has been built and implemented for. In addition, the supporting methods and measuring factors for each component are other findings of this study. Any decision taken by DSS will be evaluated separately in order to measure the effectiveness of the system. The proposed framework resembles a new framework for the decision makers working in any industry.

Application of Decision Support System in Load Forecasting Management

Power load forecasting is a very important part of the operation of power grid companies. Load forecasting requires a variety of forecasting techniques and methods, as well as a certain amount of historical data. The decision support system can greatly improve the efficiency and accuracy of load forecasting. Data Warehouse (DW) and On Line Analytical Processing (OLAP), which are introduced in this paper, are the organic components of decision support system. The paper analyzed the way to use DW and OLAP to establish power decision support system for power load forecasting work.

Decision support systems in manufacturing: a survey and future trends

PurposeThis paper aims to propose a theoretical decision support framework, which integrates artificial intelligence (AI), discrete-event simulation (DES) and database management technologies so as to determine the steady state flow of items (e.g. fixtures, jigs, tools, etc.) in manufacturing.Design/methodology/approachThe existing literature was carefully reviewed to address the state of the arts in decision support systems (DSS), the shortcomings of pure simulation-based and pure AI-based DSS. A conceptual example is illustrated to show the integrated application of AI, simulation and database components of the proposed DSS framework.FindingsRecent DSS studies have revealed the limitations of pure simulation-based and pure AI-based DSS. A new DSS framework is required in manufacturing to address these limitations, taking into account the problems of flowing items.Research limitations/implicationsThe theoretical DSS framework is proposed using simple rules and equations. This implies that it is not complex for software development and implementation. Practical data are not presented in this paper. A real DSS will be developed using the proposed theoretical framework and realistic results will be presented in the near future.Originality/valueThe proposed theoretical framework reveals how the integrated components of DSS can work together in manufacturing in order to determine the stable flow of items in a specific production period. Especially, the integrated performance of case-based reasoning (CBR) and DES is conceptually illustrated.

R-GA: AN EFFICIENT METHOD FOR PREDICTIVE MODELING OF MEDICAL DATA USING A COMBINED APPROACH OF RANDOM FORESTS AND GENETIC ALGORITHM

Medical data mainly includes data of patients and their associated symptoms. Detecting a disease is becoming costly in terms of money and effort. Medical care will be much better if the predictions can be made with minimal efforts. Predictive modeling will help in detecting a disease early. Medical prediction methods which are computer based will help to improve diagnosis. These methods are the important components of decision support systems. This paper suggests the use of predictive modeling as a classifier. The records in dataset are used to construct classifiers using a combination of random forests and genetic algorithm. The inputs to the predictive model are records from the dataset. Genetic algorithm when used in field of computer science help to form methods that lead to a solution that is acceptable. The results of experimentation show that the random forests when used in combination with genetic algorithm gives better accuracy than random forests algorithm alone.

Decision Support System for Daily and Long Term Operations of the System of Milan, Italy

This study introduces the development of a Decision Support System (DSS) for daily and long term operations of the Water Distribution Network of Milan operated by the utility Metropolitana Milanese S.p.A (MM), developed during the European project ICeWater. The DSS has been developed for the two main problems of the utility by applying multi-objective optimization for pump scheduling and sectorization of the system. The DSS was built based on open source software on the server and the client side, making its applicability to other utilities possible. This paper presents the architecture of the DSS components and shows the advantages in the application of such tool in the operation for MM. A test for validation of the DSS for pump scheduling has been applied in a subsector named Abbiategrasso. Some results are presented showing the benefits for the utility by using the DSS in their daily operations.

From ER model to Star model: a systematic transformation approach

Data warehouse is an important component of decision support systems, because it provides data of interest for analysis and decision support purposes. This data of interest is useful for analysis due to the multidimensional nature of Star model. Designing a Star model requires ample amount of time, experience, expertise and adequate knowledge of data warehouse designing steps. As an alternative to data warehouse designing steps, transformation techniques are proposed in literature. These techniques transform entity-relationship ER model of online transaction process systems to Star model. In this study, we present and evaluate a systematic approach for transformation of ER model to Star model. We prove that by using our approach, inexperienced designers can produce Star models identical to the Star models produced by experienced data warehouse designers. Also, we prove that the Star model produced by existing approaches is different from the Star model produced by our transformation approach.

Using Natural Language Programming (NLP) Technology To Model Domain Ontology OTO by Extracting Occupational Therapy Concepts

Creation and development of formal domain ontology of Occupational Therapy (OTO) requires the prescription and formal evaluation of the results through specific criteria. UPON Methontology of development ontologies was followed to create OTO ontology, and was implemented by using Protege-OWL. Accuracy of OTO ontology was assessed using a set of ontology design criteria. This paper describes a software engineering approach to model domain ontology for occupational therapy resources (OTO) using Natural Language Programming (NLP) technology. The rules were written to annotate the domain concepts using Java Annotation Patterns Engine (JAPE) grammar. It is used to support regular expression matching and thus annotate OT concepts by using the GATE developer tool. This speeds up the time-consuming development of the ontology, which is important for experts in the domain who face time constraints and high workloads. The rules provide significant results: the pattern matching of OT concepts based on the lookup list produced 403 correct concepts and the accuracy was generally higher. Using NLP technique is a good approach to reducing the domain expert’s work, and the results can be evaluated. This study contributes to the understanding of ontology development and evaluation methods to address the knowledge gap of using ontology in the decision support system component of occupational therapy.

An Intelligent Decision Support System for the Operating Theater: A Case Study

From long to short term planning, decision processes inherent to operating theater organization are often subject of empiricism, leading to far from optimal results. Waiting lists for surgery have always been a societal problem, which governments have been fighting with different management and operational stimulus plans. The current hospital information systems available in Portuguese public hospitals, lack a decision support system component that could help achieve better planning solutions. Thus, an intelligent decision support system has been developed, allowing the centralization and standardization of planning processes, improving the efficiency of the operating theater and tackling the waiting lists for surgery fragile situation. The intelligence of the system derives from data mining and optimization techniques, which enhance surgery duration predictions and operating rooms surgery schedules. Experimental results show significant gains, reducing overtime, undertime, and better resource utilization.

Modeling Domain Ontology for Occupational Therapy Resources Using Natural Language Programming (NLP) Technology to Model Domain Ontology of Occupational Therapy Resources

Creation and development of a formal domain ontology of Ocuupational Therapy (OT) requires the prescription and formal evaluation of the results through specific criteria. Methontology of development ontologies was followed to create OTO ontology, and was implemented by using Protégé- OWL. Accuracy of OTO ontology was assessed using a set of ontology design cretria. This paper describes a software engineering approach to model domain ontology for occupational therapy resources (OT) using Natural Language Programming (NLP) technology. The rules were written to annotate the domain concepts using Java Annotation Patterns Engine (JAPE) grammar. It is used to support regular expression matching and thus annotate OT concepts by using the GATE developer tool. This speeds up the time-consuming development of the ontology, which is important for experts in the domain facing time constraints and high workloads. The rules provide significant results: the pattern matching of OT concepts based on the lookup list produced 403 correct concepts and the accuracy was generally higher. Using NLP technique is a good approach to reducing the domain expert's work, and the results can be evaluated. This study contributes to the understanding of ontology development and evaluation methods to address the knowledge gap of using ontology in the decision support system component of occupational therepy.

Selecting Relevant Information for Medical Decision Support with Application in Cardiology

Objectives: The aim of our work was to implement a prototype of a decision support system which has the form of a web-based classification service. Because the data analysis component of decision support systems often happens to be unsuitable for high-dimensional data, special attention must be paid to the sophisticated selection of the most relevant variables before learning the classification rule. Methods: We implemented a prototype of a diagnostic decision support system called SIR. The system has the ability to select the most relevant variables based on a set of high-dimensional measurements by means of a forward procedure optimizing a decision-making criterion. This allows to learn a reliable classification rule. Results: The implemented prototype was tested on a sample of patients involved in a cardiology study. We used SIR to perform an information extraction from a cardiological clinical study containing both clinical and gene expression data. The classification performance was evaluated by means of a cross validation study. Conclusions: The proposed classification system can be useful for clinicians in primary care to support their decision-making tasks with relevant information extracted from any available clinical study. It is especially suitable for analyzing high-dimensional data, e.g. gene expression measurements.

Intelligent Decision Support Systems For Admission Management In Higher Education Institutes

On the basis of their use, the DSS has received positive feedback from the University's decision makers. Making use of Intelligent Decision Support Systems (IDSS) technologies suited to provide decision support in the higher education environments, by generating and presenting relevant information and knowledge which are helpful in taking the decision regarding admission management in higher education colleges or universities. The university decision makers' needs and the DSS components are identified with the help of survey done. In this paper the components of a decision support system (DSS) for developing student admission policies in higher education institute or in the university and the architecture about DSS based on ERP are proposed followed by how intelligent DSS in conjunction with ERP helps to overcome the drawbacks , if ERP is used alone in higher education institutes .

Development and evaluation of an ontology for guiding appropriate antibiotic prescribing

ObjectivesTo develop and apply formal ontology creation methods to the domain of antimicrobial prescribing and to formally evaluate the resulting ontology through intrinsic and extrinsic evaluation studies. MethodsWe extended existing ontology development methods to create the ontology and implemented the ontology using Protégé-OWL. Correctness of the ontology was assessed using a set of ontology design principles and domain expert review via the laddering technique. We created three artifacts to support the extrinsic evaluation (set of prescribing rules, alerts and an ontology-driven alert module, and a patient database) and evaluated the usefulness of the ontology for performing knowledge management tasks to maintain the ontology and for generating alerts to guide antibiotic prescribing. ResultsThe ontology includes 199 classes, 10 properties, and 1636 description logic restrictions. Twenty-three Semantic Web Rule Language rules were written to generate three prescribing alerts: (1) antibiotic-microorganism mismatch alert; (2) medication-allergy alert; and (3) non-recommended empiric antibiotic therapy alert. The evaluation studies confirmed the correctness of the ontology, usefulness of the ontology for representing and maintaining antimicrobial treatment knowledge rules, and usefulness of the ontology for generating alerts to provide feedback to clinicians during antibiotic prescribing. ConclusionsThis study contributes to the understanding of ontology development and evaluation methods and addresses one knowledge gap related to using ontologies as a clinical decision support system component—a need for formal ontology evaluation methods to measure their quality from the perspective of their intrinsic characteristics and their usefulness for specific tasks.