Improve Decision Support Systems Research Articles

Piping Material Selection in Water Distribution Network Using an Improved Decision Support System

This study introduces an integrated Multi-Criteria Decision Making (MCDM) methodology combining the Analytic Hierarchy Process (AHP), Entropy Weight Method (EWM), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to optimize the selection of municipal water supply pipeline materials. A comprehensive evaluation system encompassing thirteen criteria across technical, economic, and safety dimensions was developed to ensure balanced decision-making. The method employs a weight determination model based on Jaynes’ maximum entropy theory to harmonize subjective AHP-derived weights with objective EWM-derived weights, addressing inconsistencies in traditional evaluation approaches. This framework was validated in a case study involving a DN400 pipeline project in Jiaxing, Zhejiang Province, China, where five materials—steel, ductile iron, reinforced concrete, High-Density Polyethylene (HDPE), and Unplasticized Polyvinyl Chloride (UPVC)—were assessed using quantitative and qualitative criteria. Results identified HDPE as the most suitable material, followed by UPVC and reinforced concrete, with steel ranking lowest. Comparative analysis with alternative MCDM techniques demonstrated the robustness of the proposed method in balancing diverse factors, dynamically adjusting to project-specific priorities. The study highlights the flexibility of this approach, which can extend to other infrastructure applications, such as drainage systems or the adoption of innovative materials like glass fiber-reinforced plastic (GFRP) mortar pipes. By integrating subjective and objective perspectives, the methodology offers a robust tool for designing sustainable, efficient, and cost-effective municipal water supply networks.

Identifying the Current Status of Real Estate Appraisal Methods

Abstract Real estate appraisal, also known as property valuation, plays a crucial role in numerous economic activities and financial decisions, such as taxation assessment, bank lending, and insurance, among others. However, the current methods used in real estate appraisal face several challenges related to fundamental aspects such as accuracy, interpretation, data availability, and evaluation metrics. Therefore, the purpose of this research is to identify the current status of real estate appraisal methods, highlighting challenges and providing guidance for scholars to undertake further research in addressing them. The methodology retrieves the most recent papers published in the Scopus database over the past five years, covering the period from 2019 to the end of 2023, with an emphasis on empirical studies. These retrieved papers serve as references to capture the current status of real estate appraisal methods. The research findings confirm a clear trend towards increased utilization of artificial intelligence techniques, especially machine learning, but with unfinished work regarding related challenges. Artificial intelligence techniques enhance the accuracy of real estate appraisal, paving the way for improved decision support systems in business, financial, and economic sectors.

Interviews with farmers from the US corn belt highlight opportunity for improved decision support systems and continued structural barriers to farmland diversification

Diversifying high-input, monocropped landscapes like the US Corn Belt would provide both economic and ecosystem service benefits to the agricultural landscape. Decision support systems (DSS) and digital agriculture could help farmers decide if diversification is suitable for their operation. However, adoption of DSS by farmers remains low, likely due to lack of farmer engagement before and during the DSS development process. This study aimed to better understand the tasks, tools, and people involved in implementing farmland diversification with the goal to inform design of agricultural DSS. Semi-structured interviews were conducted with 11 farmers who had diversified their corn/soybean cropland with government-supported conservation programs (e.g., CRP, wetlands) and alternative crops (e.g., small grains, pasture) in the past four years. Interview data was transcribed and then analyzed using affinity diagramming. Results show farmers needed DSS to layer multiple sources of data and observations over several years to identify field productivity trends and drivers; spatial orientation of practices to fit management and field constraints; matching operation goals to alternative practices; financial planning and market exploration; and information on promising emerging practices like subsidized pollinator habitat. However, the interviews also highlighted structural barriers to diversification that DSS cannot or can only partially address. These included social pressures; market access; crop insurance policy; and quality of relationships with governmental agencies. Results indicate better DSS design can empower individual farmers to diversify cropland, but structural interventions will be needed to successfully diversify the agricultural landscape and support economic and ecosystem health.

Improved Decision Support System for College Sports Training Based on Id3 Algorithm

A Decision Support System (DSS) is a computer-based tool designed to assist individuals and organizations in making informed decisions. It utilizes data analysis, modeling techniques, and interactive interfaces to provide users with relevant information and insights. DSSs help streamline decision-making processes by synthesizing large amounts of data from various sources, generating forecasts, and evaluating alternative courses of action. They are particularly valuable in complex and uncertain situations where decisions have significant implications. DSSs can be used in a wide range of domains, including business, healthcare, finance, and logistics, to support strategic, tactical, and operational decision-making processes. This paper introduces an Integrated Hybrid Feature Subset Decision Support System (HFS-DSS) designed to optimize decision-making processes in student performance evaluation within educational institutions. The HFS-DSS combines feature subset selection techniques with decision support algorithms to identify key attributes and make accurate predictions regarding student performance. Through experimental evaluation, the system demonstrates its effectiveness in accurately assessing student performance based on selected features, with predictions closely aligning with actual observations. The results underscore the system's reliability and effectiveness in supporting informed decision-making processes, offering valuable insights for educators and administrators to enhance academic outcomes. The adaptability and versatility of the HFS-DSS make it well-suited for various educational contexts, providing potential applications in curriculum planning, student support services, and performance monitoring. The present paper highlights the significant contributions of the Integrated HFS-DSS towards enhancing decision-making processes and improving academic outcomes in educational institutions.

Cardiovascular Disease Preliminary Diagnosis Application Using SQL Queries: Filling Diagnostic Gaps in Resource-Constrained Environments

Cardiovascular diseases (CVDs) are chronic diseases associated with a high risk of mortality and morbidity. Early detection of CVD is crucial to initiating timely interventions, such as appropriate counseling and medication, which can effectively manage the condition and improve patient outcomes. This study introduces an innovative ontology-based model for the diagnosis of CVD, aimed at improving decision support systems in healthcare. We developed a database model inspired by ontology principles, tailored for the efficient processing and analysis of CVD-related data. Our model’s effectiveness is demonstrated through its integration into a web application, showcasing significant improvements in diagnostic accuracy and utility in resource-limited settings. Our findings indicate a promising direction for the application of artificial intelligence (AI) in early CVD detection and management, offering a scalable solution to healthcare challenges in diverse environments.

Individualized Models for Glucose Prediction in Type 1 Diabetes: Comparing Black-Box Approaches to a Physiological White-Box One.

Accurate blood glucose (BG) prediction are key in next-generation tools for type 1 diabetes (T1D) management, such as improved decision support systems and advanced closed-loop control. Glucose prediction algorithms commonly rely on black-box models. Large physiological models, successfully adopted for simulation, were little explored for glucose prediction, mostly because their parameters are hard to individualize. In this work, we develop a BG prediction algorithm based on a personalized physiological model inspired by the UVA/Padova T1D Simulator. Then we compare white-box and advanced black-box personalized prediction techniques. A personalized nonlinear physiological model is identified from patient data through a Bayesian approach based on Markov Chain Monte Carlo technique. The individualized model was integrated within a particle filter (PF) to predict future BG concentrations. The black-box methodologies considered are non-parametric models estimated via gaussian regression (NP), three deep learning methods: long-short-term-memory (LSTM), gated recurrent unit (GRU), temporal convolutional networks (TCN), and a recursive autoregressive with exogenous input model (rARX). BG forecasting performances are assessed for several prediction horizons (PH) on 12 individuals with T1D, monitored in free-living conditions under open-loop therapy for 10 weeks. NP models provide the most effective BG predictions by achieving a root mean square error (RMSE), RMSE = 18.99 mg/dL, RMSE = 25.72 mg/dL and RMSE = 31.60 mg/dL, significantly outperforming: LSTM, GRU (for PH = 30 minutes), TCN, rARX, and the proposed physiological model for PH=30, 45 and 60 minutes. Black-box strategies remain preferable for glucose prediction even when compared to a white-box model with sound physiological structure and individualized parameters.

Improving decision support systems with machine learning: Identifying barriers to adoption

AbstractPrecision agriculture (PA) has been defined as a “management strategy that gathers, processes and analyzes temporal, spatial and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production.” This definition suggests that because PA should simultaneously increase food production and reduce the environmental footprint, the barriers to adoption of PA should be explored. These barriers include (1) the financial constraints associated with adopting decision support system (DSS); (2) the hesitancy of farmers to change from their trusted advisor to a computer program that often behaves as a black box; (3) questions about data ownership and privacy; and (4) the lack of a trained workforce to provide the necessary training to implement DSSs on individual farms. This paper also discusses the lessons learned from successful and unsuccessful efforts to implement DSSs, the importance of communication with end users during DSS development, and potential career opportunities that DSSs are creating in PA.

Moving beyond agriculture and aquaculture to integrated sustainable food systems as part of a circular bioeconomy

The objective of this perspective paper is to present and discuss how systemic innovations can deliver a step change in the way food is produced in Europe. The production of healthy, safe and affordable food can contribute toward a just transition to net zero carbon (C) for Europe. A systemic and cross sectorial approach can contribute to climate mitigation by transfer of atmospheric CO2 to the terrestrial biosphere using low trophic species (LTS), including plants, seaweed and mussels (i.e. C sequestration) and increasing organic C stocks in soils and vegetation biomass (i.e. C storage). Innovative combinations of technologies applied to LTS, processed animal protein, new crops, and diversified and integrated production systems can link the high primary productivity rates of the marine environment to the C storage capability of the terrestrial food sector. Furthermore, the important roles of both private and public sector actors and better use of systemic approaches to further elucidate the multi-dimensional and multi-level interplays in complex food systems needs consideration. This can pave the way for linking and scaling up C-neutral marine and terrestrial food production systems into a future sustainable and circular bioeconomy. This systems-based approach can address some of the challenges associated with the current farming systems, as interdisciplinary research on aquaculture innovation can support the development of a resilient and sustainable food system. Examples of technologies provided include: a custom configured and digital user-oriented co-creation approach for Responsible Research and Innovation (RRI), a WebGIS tool on soil C storage, innovative composting methods, advanced breeding methods, new machinery for low greenhouse gas diversified orchard farming, AI model systems to improve decision support systems in management of soil, vertical farming, and animal feeding.

Evaluation of Drinking Water Quality for Salem District Using Weighted Product Method

The quality of drinking water is one of the most important factors affecting human health. However, in many countries, especially developing countries the quality of drinking water is not satisfactory and Poor drinking water quality causes many waterborne diseases. This special issue of Expression and Health focuses on drinking water quality Edited to better understand the implications for public health, thus improving many countries appropriate action may be taken. This editorial introduction, reviewed some recent research, briefly summarizing the main points of each contribution to this issue, then in to increase scientific proposed some research/directions. The articles in this magazine are interesting and this research covers many aspects of the topic, and is meaningful for sustainable drinking water quality protection. Using the correct linear mathematical equation Home selection problems cannot be solved and treated as semi-structured problem a heuristic process is required. Computer-based decision support systems can be used as an alternative to expert decision-making by inexperienced users. This article is about improving decision support systems for the household selection and Presents empirical work. For choosing a house based on weighted product method providing web-based decision support systems with the general objective of the research. As a result of the interview with the respondents, the calculation of choosing this house. In the weighted product method, help people choose the best alternative, Computational results show that the given alternatives can be sorted. The Alternative Parameters is Salem West, Salem South, Yercaud, Attur, Pethanaickenpalayam, Valapady, Sankari, Edapadi, Mettur, Omalur, Kadayampatti. The Evaluation Parameters is PH, TDS, TH, Calcium, Magnesium, Chloride, Sulphate. First rank is Pethanaickenpalayam and first rank is Salem West is lowest rank.

Quantifying risk assessments for monitoring control and surveillance of illegal fishing

Abstract One of the key international efforts to reduce Illegal, Unreported,and Unregulated (IUU) fishing is the United Nations Food and Agriculture Organization's Port State Measures Agreement. The agreement is designed to close off the opportunities for non-compliance, such as vessels engaging in illegal practices to land their catch. However, with thousands of vessels at sea and relatively limited inspection capacity, enhanced communication and information sharing to identify priority high-risk vessels is fundamental to successful implementation. Moving from traditional qualitative to quantitative risk assessments is one key step in ensuring the effective and efficient implementation. Risk profiling is in its infancy in the field of IUU fishing. Here, we present a transparent and repeatable method for eliciting quantitative weightings for indicators of IUU fishing using the Analytical Hierarchy Process. The method highlights the improvements in discrepancy with quantitative rankings, in comparison to traditional qualitative approach used, and the inherent ambiguity associated. This method can help to improve decision support systems used by fisheries surveillance agencies around the world. Knowing which vessels are high-risk is key to decision-making. Working towards a quantitative risk-based approach is fundamental step to improving oversight and control of IUU.

Artificial intelligence perspective in the future of endocrine diseases.

In recent years, artificial intelligence (AI) shows promising results in the diagnosis, prediction, and management of diseases. The move from handwritten medical notes to electronic health records and a huge number of digital data commenced in the era of big data in medicine. AI can improve physician performance and help better clinical decision making which is called augmented intelligence. The methods applied in the research of AI and endocrinology include machine learning, artificial neural networks, and natural language processing. Current research in AI technology is making major efforts to improve decision support systems for patient use. One of the best-known applications of AI in endocrinology was seen in diabetes management, which includes prediction, diagnosis of diabetes complications (measuring microalbuminuria, retinopathy), and glycemic control. AI-related technologies are being found to assist in the diagnosis of other endocrine diseases such as thyroid cancer and osteoporosis. This review attempts to provide insight for the development of prospective for AI with a focus on endocrinology.

Managing Sustainable Urban Public Transport Systems: An AHP Multicriteria Decision Model

The current combination of sustainable social awareness and the improved decision support systems, including multiple criteria decision models for sustainable development, creates the need for more efficient and accurate public policy decisions based on available technology. The continuous growth of urban public road transport in large cities, and therefore the worsening of air quality, along with recent economic crisis derived from the COVID-19 pandemic, is forcing public administrations to analyze the viability of current models, taking into consideration sustainable alternative energies. This study proposes a novel and consistent analytic hierarchy process (AHP) multicriteria decision-making (MCDM) model that combines both economic and environmental criteria, to evaluate public road transportation vehicles according to their alternative engine technologies and combustion characteristics. The proposed model has been applied to evaluate Madrid’s urban public road transport, based on 2020 data published by the Madrid City Council, compiled by authors, and assessed by a panel of 20 experts to identify criteria and factors included in the AHP-MCDM model. The findings illustrate the economic and environmental impact of alternative vehicles, show that the most sustainable alternative is the plug-in electric vehicle in economic and environmental terms, and assist policymakers and firms in future strategic decisions regarding sustainable urban transport policies.

An Improved Decision Support System for Identification of Abnormal EEG Signals Using a 1D Convolutional Neural Network and Savitzky-Golay Filtering

An Improved Decision Support System for Identification of Abnormal EEG Signals Using a 1D Convolutional Neural Network and Savitzky-Golay Filtering

Using Slightly Imbalanced Binary Classification to Predict the Efficiency of Winter Road Maintenance

The prediction of efficiency scores for winter road maintenance (WRM) is a challenging and serious issue in countries with cold climates. While effective and efficient WRM is a key contributor to maximizing road transportation safety and minimizing costs and environmental impacts, it has not yet been included in intelligent prediction methods. Therefore, this study aims to design a WRM efficiency classification prediction model that combines data envelopment analysis and machine learning techniques to improve decision support systems for decision-making units. The proposed methodology consists of six stages and starts with road selection. Real data are obtained by observing road conditions in equal time intervals via road weather information systems, optical sensors, and road-mounted sensors. Then, data preprocessing is performed, and efficiency scores are calculated with the data envelopment analysis method to classify the decision-making units into efficient and inefficient classes. Next, the WRM efficiency classes are considered targets for machine learning classification algorithms, and the dataset is split into training and test datasets. A slightly imbalanced binary classification case is encountered since the distributions of inefficient and efficient classes in the training dataset are unequal, with a low ratio between classes. The proposed methodology includes a comparison of different machine learning classification techniques. The graphical and numerical results indicate that the combination of a support vector machine and genetic algorithm yields the best generalization performance. The results include analyzing the variables that affect the WRM and using efficiency classes to drive future insights to improve the process of decision-making.

Identifying drivers of spatio-temporal dynamics in barley yellow dwarf virus epidemiology as a critical factor in disease control.

Barley yellow dwarf virus (BYDV) is one of the most important viral diseases of small grains worldwide. An understanding of its epidemiology is crucial to control this disease in a sustainable way. The virus moves through the agricultural landscape via cereal aphids as vectors. Understanding movement of these aphids in space and time is of key importance and in doing so, the spatial and temporal variables that influence BYDV epidemiology can be identified. The presence of summer hosts, crop rotation, crop diversity, agricultural practices and climate variables are crucial. Through digitalization, spatial (e.g. land‐use) and temporal (e.g. weather) information is becoming more readily available. Including this information into a prediction model could improve decision support systems that will rationalize the decision‐making process towards a more integrated control of the disease. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

An improved decision support system for ABC inventory classification

In this study, an Improved Decision Support System (IDSS) is developed to help the decision makers in their inventory classification decisions. For the first time in this paper, the novel IDSS for ABC classification is developed. Certain new algorithms regarding the manufacturing company’s features are applied in a framework of IDSS. The IDSS is developed as modular structure and provided the integrated modules of “Data-Base” and “ABC Analysis”. In the developed IDSS, the appropriate ABC classification models are considered among Annual Dollar Usage (ADU), Analytic Hierarchy Process (AHP), Scoring (SCR), Fuzzy C-means Algorithm (FCM), and Analytic Network Process (ANP). Some issues and applicability of the IDSS are illustrated with real case problems in the paper. The proposed IDSS software is considerably decreased the time for the inventory classification. In the meantime it could be easily used in various sectors. Therefore, the proposed IDSS significantly contributed to obtaining more accurate and quickly modifiable ABC classification in real cases. Furthermore, the user friendly software can be updated readily according to recent developments in the market.

HYBRID MORTALITY PREDICTION USING MULTIPLE SOURCE SYSTEMS

The use of artificial intelligence in clinical care to improve decision support systems is increasing. This is not surprising since, by its very nature, the practice of medicine consists of making decisions based on observations from different systems both inside and outside the human body. In this paper, we combine three general systems (ICU, diabetes, and comorbidities) and use them to make patient clinical predictions. We use an artificial intelligence approach to show that we can improve mortality prediction of hospitalized diabetic patients. We do this by utilizing a machine learning approach to select clinical input features that are more likely to predict mortality. We then use these features to create a hybrid mortality prediction model and compare our results to non-artificial intelligence models. For simplicity, we limit our input features to patient comorbidities and features derived from a well-known mortality measure, the Sequential Organ Failure Assessment (SOFA).

Modeling framework for representing long-term effectiveness of best management practices in addressing hydrology and water quality problems: Framework development and demonstration using a Bayesian method

Best management practices (BMPs) are popular approaches used to improve hydrology and water quality. Uncertainties in BMP effectiveness over time may result in overestimating long-term efficiency in watershed planning strategies. To represent varying long-term BMP effectiveness in hydrologic/water quality models, a high level and forward-looking modeling framework was developed. The components in the framework consist of establishment period efficiency, starting efficiency, efficiency for each storm event, efficiency between maintenance, and efficiency over the life cycle. Combined, they represent long-term efficiency for a specific type of practice and specific environmental concern (runoff/pollutant). An approach for possible implementation of the framework was discussed. The long-term impacts of grass buffer strips (agricultural BMP) and bioretention systems (urban BMP) in reducing total phosphorus were simulated to demonstrate the framework. Data gaps were captured in estimating the long-term performance of the BMPs. A Bayesian method was used to match the simulated distribution of long-term BMP efficiencies with the observed distribution with the assumption that the observed data represented long-term BMP efficiencies. The simulated distribution matched the observed distribution well with only small total predictive uncertainties. With additional data, the same method can be used to further improve the simulation results. The modeling framework and results of this study, which can be adopted in hydrologic/water quality models to better represent long-term BMP effectiveness, can help improve decision support systems for creating long-term stormwater management strategies for watershed management projects.

Case study for investigating groundwater and the future of mountain spring discharges in Southern Italy

Groundwater extraction is used to alleviate drought in many habitats. However, widespread drought decreases spring discharge and there is a need to integrate climate change research into resource management and action. Accurate estimates of groundwater discharge may be valuable in improving decision support systems of hydrogeological resource exploitation. The present study performs a forecast for groundwater discharge in Aquifer’s Cervialto Mountains (southern Italy). A time series starting in 1883 was the basis for long-term predictions. An Ensemble Discharge Prediction (EDisP) was applied, and the progress of the discharge ensemble forecast was inferred with the aid of an Exponential Smoothing (ES) model initialized at different annual times. EDisP-ES hindcast model experiments were tested, and discharge plume-patterns forecast was assessed with horizon placed in the year 2044. A 46-year cycle pattern was identified by comparing simulations and observations, which is essential for the forecasting purpose. ED is P-ES performed an ensemble mean path for the coming decades that indicates a discharge regime within ± 1 standard deviation around the mean value of 4.1 m3 s−1. These fluctuations are comparable with those observed in the period 1961–1980 and further back, with change-points detectable around the years 2025 and 2035. Temporary drought conditions are expected after the year 2030.

Comparing performances of intelligent classifier algorithms for predicting type of pain in patients with spinal cord injury.

Background and aimIn this study, performances of classification techniques were compared in order to predict type of pain in patients with spinal cord injury. Pain is one of the main problems in people with spinal cord injury. Identifying the optimal classification technique will help improve decision support systems in clinical settings.MethodsA descriptive retrospective analysis was performed in 253 patients. We compared performances of “Bayesian Networks”, “Decision Tree”, neural networks: “Multi-Layer Perceptron” (MLP), and “Support Vector Machines” (SVM). Predictor variables were collected in data set in SCI patients referred to Shefa Neuroscience Research Center, Tehran, Iran from 2010 through 2016. Performances of classification techniques were compared using “Accuracy”, “Sensitivity or True Positive Rate” (TPR), “Specificity or True Negative Rate” (SPC), “Positive Predictive Value” (PPV), “Negative Predictive Value” (NPV).ResultsMLP with Boosting technique was found to have the best accuracy (91%), best sensitivity (89%), best specificity (95%) best PPV (91%), and best NPV (96%) to predict spinal cord injury in this data set, given its good classificatory performance.ConclusionComputer-aided decision support systems (CAD) are dependent on a wide range of classification methods such as statistical methods, Bayesian methods, deductive classifiers based on the state or case, decision-making trees and neural networks: Multi-Layer Perceptron. Neural network classifiers especially, are very popular choices for medical decision-making, with proven effectiveness in the clinical field.