Decision Support System System Research Articles

Effect of an Automated Advice Algorithm (CloudConnect) on Adolescent-Parent Diabetes-Specific Communication and Glycemic Management: A Randomized Trial.

Because adolescence is a time of difficult management of Type 1 diabetes (T1D) in part from adolescent-parent shared responsibility of T1D management, our objective was to assess the effects of a decision support system (DSS) CloudConnect on T1D-related communication between adolescents and their parents and on glycemic management. We followed 86 participants including 43 adolescents with T1D (not on automated insulin delivery systems, AID) and their parents/care-giver for a 12-week intervention of UsualCare + CGM or CloudConnect, which included a Weekly Report of automated T1D advice, including insulin dose adjustments, based on data from continuous glucose monitors (CGM), Fitbit and insulin use. Primary outcome was T1D-specific communication and secondary outcomes were hemoglobin A1c, time-in-target range (TIR) 70-180mg/dl, and additional psychosocial scales. Adolescents and parents reported a similar amount of T1D-related communication in both the UsualCare + CGM or CloudConnect groups and had similar levels of final HbA1c. Overall blood glucose time in range 70-180mg/dl and time below 70mg/dl were not different between groups. Parents but not children in the CloudConnect group reported less T1D-related conflict; however, compared to the UsualCare + CGM group, adolescents and parents in the CloudConnect reported a more negative tone of T1D-related communication. Adolescent-parent pairs in the CloudConnect group reported more frequent changes in insulin dose. There were no differences in T1D quality of life between groups. While feasible, the CloudConnect DSS system did not increase T1D communication or provide improvements in glycemic management. Further efforts are needed to improve T1D management in adolescents with T1D not on AID systems.

A crop water stress index based internet of things decision support system for precision irrigation of wine grape

The goal of irrigation for wine grape grown in arid or semiarid regions is to sustain vine survival and to optimize berry attributes for quality wine production. Precision irrigation of wine grape is impeded by the lack of a smart, decision support system (DSS) to remotely monitor vine water status. The objectives of this study were to: develop and field test an Internet of Things (IoT) DSS system for precision irrigation of wine grape. The IoT system was comprised of a suite of in situ sensors used to monitor real-time weather conditions, grapevine canopy temperature, soil moisture, and irrigation amount. Sensor data were collected and stored on a field deployed data logger that calculated a daily thermal Crop Water Stress Index (CWSI) for grapevine using a neural network model with real-time sensor data model inputs. The data logger also hosted, via a cellular modem, webpages showing a running, 12-day history of daily CWSI, fraction of available soil moisture (fASW), irrigation amount, and other sensor data. The webpages were accessible to vineyard managers via cell phone or computer. The CWSI based IoT DDS system was installed at two small acreage, commercial estate vineyards in southwestern Idaho USA over four growing seasons. At each vineyard site, the DSS was used daily by the vineyard irrigation manager to schedule irrigation events. Neither vineyard manager used any other quantitative vine water status monitoring tool for irrigation management decisions. The midday leaf water potential (LWP) of grapevines was routinely measured by research project personnel. Data collected over the study period at each vineyard showed a significant (p < 0.001) correlation with LWP and fASW, providing evidence that, under the conditions of this study, the daily CWSI based IoT provided automated, remote monitoring of vine water status. Both vineyard managers reported daily use of the DSS for irrigation scheduling decisions. Over the four-year study, each vineyard manager was able to maintain consistent seasonal average CWSI daily values and irrigation application amounts, despite yearly differences in climatic conditions. The results of this study demonstrate that a CWSI based IoT DSS can be used for precision irrigation of wine grape in a commercial vineyard under semiarid growing conditions. The CWSI based IoT DSS used a unique combination of neural network modeling, edge computing, and IoT for real-time vine water stress monitoring for precision irrigation.

A smart sustainable system for flood damage management with the application of artificial intelligence and multi-criteria decision-making computations

Decision Support System (DSS) is an approach for smart management of different man-made and natural phenomena such as flood disasters. In the present study, different stages of a novel DSS system are designed to achieve Sustainable Development Goals through monitoring, predicting and controlling the flood. First, the monitoring step is done based on scrutinizing hydrological gathered data through historical climatology archives. Then for the prediction stage, after clustering the precipitation and flood records in five provinces in Iran, with the application of Logistic Regression, Neural Network, and Support Vector Machine as Machine Learning (ML) computations, flood disaster is estimated based on rainfall in different climates. Consequently, according to three differen scenarios, the appropriate strategies in Pre-Flood Activities (Pre-FA), During Flood Activities (DFA), and Post-Flood Activities (Post-FA) are prioritized by three Multi-Criteria Decision-Making (MCDM) techniques. The outcomes of integrated Artificial Intelligence (AI) and Ward computations illustrate that the rainfall data of different provinces of Iran are heterogeneous because of various geographical and topographical conditions. Finally, the MCDM outputs demonstrate that through Pre-FA, DFA, and v, all strategies are ranked for implementation of early making-decision in flood disasters.

An Intelligent Sensor Based Decision Support System for Diagnosing Pulmonary Ailment through Standardized Chest X-ray Scans.

Academics and the health community are paying much attention to developing smart remote patient monitoring, sensors, and healthcare technology. For the analysis of medical scans, various studies integrate sophisticated deep learning strategies. A smart monitoring system is needed as a proactive diagnostic solution that may be employed in an epidemiological scenario such as COVID-19. Consequently, this work offers an intelligent medicare system that is an IoT-empowered, deep learning-based decision support system (DSS) for the automated detection and categorization of infectious diseases (COVID-19 and pneumothorax). The proposed DSS system was evaluated using three independent standard-based chest X-ray scans. The suggested DSS predictor has been used to identify and classify areas on whole X-ray scans with abnormalities thought to be attributable to COVID-19, reaching an identification and classification accuracy rate of 89.58% for normal images and 89.13% for COVID-19 and pneumothorax. With the suggested DSS system, a judgment depending on individual chest X-ray scans may be made in approximately 0.01 s. As a result, the DSS system described in this study can forecast at a pace of 95 frames per second (FPS) for both models, which is near to real-time.

A Decision Support System for Undecided and Exploratory Students

Choosing a major can be a difficult choice for undeclared students, depending on personal interests, job markets, program costs, and the complexity of graduation requirements. The current procedure for exploring possible majors/minors involves a tedious and time-consuming exploration of static data presented in a convoluted format. This work considers the complexity of degree requirements and presents the design and implementation of an efficient decision support system (DSS) for exploring majors and/or minors. The DSS presents a tool that enables college students to map their already completed courses to degree requirements and then view the majors/minors that yield the shortest path for graduation. Secondly, the DSS system provides data visualization tools to assist students in selecting courses that satisfy the remaining requirements of a selected major/minor.

A hybrid Decision Support System for automating decision making in the event of defects in the era of Zero Defect Manufacturing

Defects are unavoidable during manufacturing processes, and a tremendous amount of research aimed at improving defect prevention has been conducted by scholars. Zero Defect Manufacturing (ZDM) seeks to eliminate defects in production. In addition, technological advancements now allow the repair of defective products. This creates the need to re-schedule productions more frequently in order to take into account the actions necessary to fix defective parts. This study focuses on detection and repair-based ZDM strategies. It implements a newly developed, hybrid Decision Support System (DSS) that uses data-driven and knowledge-based approaches to detect defects and then automate the necessary decision-making processes. The system uses an ontology based on the MASON ontology in order to describe the production domain and enrich the available data with contextual information. Real time production data and past knowledge are utilized to analyse defects, identify their type and severity, and suggest alternative repair plans. Possible repair plans are evaluated using a dynamic multi-criteria approach that determines the plan most suited to production conditions at the time of defect detection. To test the efficacy of the DSS developed for this study, it was integrated with a dynamic scheduling tool and was also used in an industrial application in the semiconductor domain. The simulations and the real-world implementation both show that the proposed DSS system can efficiently detect defects and automate the post-detection decision-making process. The multi-criteria approach adopted by this study proves that the DSS can make well-adapted decisions, handle the dynamic nature of a production system, and help manufacturers move closer to Zero Defect Manufacturing.

An Experimental Comparison of IoT-Based and Traditional Irrigation Scheduling on a Flood-Irrigated Subtropical Lemon Farm.

Over recent years, the demand for supplies of freshwater is escalating with the increasing food demand of a fast-growing population. The agriculture sector of Pakistan contributes to 26% of its GDP and employs 43% of the entire labor force. However, the currently used traditional farming methods such as flood irrigation and rotating water allocation system (Warabandi) results in excess and untimely water usage, as well as low crop yield. Internet of things (IoT) solutions based on real-time farm sensor data and intelligent decision support systems have led to many smart farming solutions, thus improving water utilization. The objective of this study was to compare and optimize water usage in a 2-acre lemon farm test site in Gadap, Karachi, for a 9-month duration, by deploying an indigenously developed IoT device and an agriculture-based decision support system (DSS). The sensor data are wirelessly collected over the cloud and a mobile application, as well as a web-based information visualization, and a DSS system makes irrigation recommendations. The DSS system is based on weather data (temperature and humidity), real time in situ sensor data from the IoT device deployed in the farm, and crop data (Kc and crop type). These data are supplied to the Penman–Monteith and crop coefficient model to make recommendations for irrigation schedules in the test site. The results show impressive water savings (~50%) combined with increased yield (35%) when compared with water usage and crop yields in a neighboring 2-acre lemon farm where traditional irrigation scheduling was employed and where harsh conditions sometimes resulted in temperatures in excess of 50 °C.

Theory and Development of a VRI Decision Support System: The USDA-ARS ISSCADA Approach

HighlightsMulti-faceted research efforts converged to an automated irrigation decision support system (DSS).Low-cost, solar-powered, wireless plant abiotic and biotic stress sensors were developed to aid the DSS.Low-cost, accurate TDR soil water sensors and a wireless node and gateway system were developed for the DSS.Sensor systems and research-based algorithms were integrated into an automated irrigation DSS and control system.Abstract. Variable-rate irrigation (VRI) is now possible with every new center pivot irrigation system sold, either using sector (speed) control or both sector and zone (radial along the pipeline) control. However, decision support systems able to generate a prescription for spatially varying irrigation based on crop water need have lagged far behind VRI equipment. Irrigation based on crop water need has been shown to increase both crop water productivity and nutrient use efficiency, meaning that an effective VRI decision support system (DSS) could improve profitability while conserving resources. In this article, we report separately on a VRI DSS using sensor-based plant and soil water feedback as implemented in four U.S. states. This article describes the genesis and development of the Irrigation Scheduling Supervisory Control and Data Acquisition (ISSCADA) system, of the integral plant and soil sensors, and of its wireless sensor network subsystems, as well as the role of multi-location research efforts and cooperative research and development agreements in the development of the needed plant and soil sensors and the ISSCADA and wireless sensor network systems. Keywords: Crop water productivity, Decision support system, Product development, Sensors, Variable-rate irrigation, VRI.

Application of Decision Support System during the Emergency Exercises for Nuclear Emergency Management

In India, two independent decision support system (DSS) systems are developed and implemented at NAPS (Narora Atomic Power Station) and MAPS (Madras Atomic Power Station) sites for emergency response. The simulation results of the DSS systems guides in identification of emergency zones and distances and implementation of the protective actions. This paper describes application / testing of the three decision support systems developed by IGCAR, NPCIL and SRI (AERB) for dose projections and decision on the implementation of the protective action in the public domain during the recently conducted emergency exercise at NPP site. Thus three DSS systems developed in India were applied to perform atmospheric dispersion and dose assessment in public domain around the nuclear power plants (NPPs) during the recent emergency exercise. The hypothetical accident scenario was considered for the exercise. The source term estimated based on plant parameters and pre calculated source term for large number of accident scenarios was used during the exercise. The results of the assessments by using these Decision support systems were presented to the decision makers for recommendation and implementation of protective actions such as evacuation, sheltering, KIO3 distribution, contamination control etc. The Numerical Weather Forecast model was used for the area around the NPP to produce the meteorological parameters that were further used by these DSS system. Predictive assessments of the radiological situation in the vicinity of the NPP site was performed during emergency exercise with various source terms. Taking into account the high uncertainties in the source term estimation and inputs to the simulation models, the simulated results from these three DSS show a reasonable agreement. The study demonstrated the utility of DSS systems for the assessment of the radiological consequences of hypothetical nuclear accidents during the emergency exercises at different NPP sites. The experience gained in using the DSS systems for operational application to the Indian NPPs will be further used by the exercise planners and developers to improve the system continuously and their adaptation to all NPP sites in India.

Визначення нештатних ситуацій на підприємствах харчової промисловості та розробка системи підтримки прийнятя рішень

Визначення нештатних ситуацій на підприємствах харчової промисловості та розробка системи підтримки прийнятя рішень

Construction of a Decision Support System Based on GP Services, Using a Warning–Judgment Module as an Example

Decision-making departments need more detailed and timely data in order to meet the needs of emergency response. Sichuan province is an area that frequently suffers natural disasters, and many disasters are caused by rainfall. This study establishes a decision support system (DSS) based on geoprocessing (GP) services, which can locate the region that overran the rainfall threshold and provide the population or property analysis, query, map plot, and path analysis functions. Most of the functions of the system are developed on the basis of geoprocessing services. This paper uses the warning–judgment module as an example to introduce the structure and function of the DSS system. The system satisfies the demands of real-time data acquisition, calculation, analysis, and presentation.

Signal quality measures on pulse oximetry and blood pressure signals acquired from self-measurement in a home environment.

Recently, decision support system (DSSs) have become more widely accepted as a support tool for use with telehealth systems, helping clinicians to summarize and digest what would otherwise be an unmanageable volume of data. One of the pillars of a home telehealth system is the performance of unsupervised physiological self-measurement by patients in their own homes. Such measurements are prone to error and noise artifact, often due to poor measurement technique and ignorance of the measurement and transduction principles at work. These errors can degrade the quality of the recorded signals and ultimately degrade the performance of the DSS system, which is aiding the clinician in their management of the patient. Developed algorithms for automated quality assessment for pulse oximetry and blood pressure (BP) signals were tested retrospectively with data acquired from a trial that recorded signals in a home environment. The trial involved four aged subjects who performed pulse oximetry and BP measurements by themselves at their home for ten days, three times per day. This trial was set up to mimic the unsupervised physiological self-measurement as in a telehealth system. A manually annotated "gold standard" (GS) was used as the reference against which the developed algorithms were evaluated after analyzing the recordings. The assessment of pulse oximetry signals shows 95% of good sections and 67% of noisy sections were correctly detected by the developed algorithm, and a Cohen's Kappa coefficient (κ) of 0.58 was obtained in 120 pooled signals. The BP measurement evaluation demonstrates that 75% of the actual noisy sections were correctly classified in 120 pooled signals, with 97% and 91% of the signals correctly identified as worthy of attempting systolic and/or diastolic pressure estimation, respectively, with a mean error and standard deviation of 2.53±4.20 mmHg and 1.46±5.29 mmHg when compared to a manually annotated GS. These results demonstrate the feasibility, and highlight the potential benefit, of incorporating automated signal quality assessment algorithms for pulse oximetry and BP recording within a DSS for telehealth patient management.

Patients’, nurses’ and physicians’ perception of delays in emergency department care

Background: Patients often judge their experiences in the emergency department (ED) based upon how long they have to wait, the attitudes of staff, and the information provided them. Objective: The objective of this study was to assess the causes in constraints to patient flow in emergency departments by comparing staff, patient, and DSS data findings. Methods: A random sample of patients and their healthcare providers were administered a survey asking them to rank the reasons for delay during three points after triage (60, 120, 180 minutes). A comparison was then done using Spearman’s rank correlations and a regression model with independent indicators collected from the hospitals Decision Support System (DSS) which included: time to be seen by doctors, time to laboratory test results, time for radiological results, wait time for hospital bed and discharge in order to compare if the perceptions of constraints are related to the actual reasons for delays in the ED. This study was approved by the Internal Review Board. Results: There was a significant correlation in the ranking of the reason for delays within the first, second and third hours between patients, nurses and doctors. However, when comparing perceptions for delay and independent data, only nurses within the third hour were correct in their understanding of the constraints that lead to delays. Conclusions: Overall, patients and staff view similar reasons for constraints to their timely flow through the ED. There is, however, very little correlation between the survey responses and the independent factors that did constrain the flow of the ED. A more extensive use and integration of the DSS system by staff could provide more reliable information for reasons for delay that could be communicated to the ED patients which could improve customer service.

Modeling emergency evacuation of individuals with disabilities (exitus): An agent-based public decision support system

In this paper, we present a public decision support system (DSS) distinguished from various DSSs in the private business sector in terms of its ownership, data scarcity, and beneficiaries. In particular, our system is intended to play a vital role in assessing and optimizing emergency response plans for rare but catastrophic events such as the September 11th attacks. While taking an agent-based microscopic simulation approach in a hierarchical framework, we used our model to estimate the effectiveness of alternative evacuation strategies to support emergency response planning as a part of business continuity planning for all private business organizations. The presented model is unique because it considers individuals with disabilities explicitly in terms of speed, ability to negotiate the environment, and normalcy bias depending on type of disability. It is also capable of classifying the environment in terms of accessibility characteristics encompassing various conditions which have been shown to have a disproportionate effect upon the behavior of individuals with disabilities during an emergency.Through a series of simulation experiments, our system identified specific locations (e.g., the NW landing) on the 3rd floor of the test bed building as possible bottleneck spots under certain conditions (e.g., the sharp increase in individuals with disabilities among residents). This way, our system provides the architect with tools to test the structure’s design to determine how well it meets the identified requirements for emergency evacuation to accommodate this shifting demographic. In particular, our system strongly demonstrated the effectiveness of new emergency evacuation strategies for individuals with disabilities such as assisted evacuations which allows other healthy people to play more active roles compared to traditional strategies in which individuals with disabilities are helplessly waiting for assistants such as fire fighters at the designated area. Our system also revealed that people using wheelchairs and those with lower stamina were at the greatest risk. People with lower stamina such as the elderly, people with chronic health conditions, or those with temporary injuries are at a greater risk mainly because they are not easily identifiable. Ultimately, the proposed DSS system can be used to inform public policy professionals of more effective, evidence-based evacuation planning and environmental design methods based on a better understanding of the behavior of individuals with disabilities.

A Financial Assets and Liabilities Management Support System

This paper describes the design and implementation of a decision support system (DSS) based on the fund dispatching decision viewpoint from the financial division of a business group. An integrated data warehouse is established and the technique of online analytical processing (OLAP) is applied to analyze daily transaction data of an enterprise resource planning system with determined management goal. We adopt the Business Dimensional Lifecycle approach to accomplish the system design and development. The DSS system developed is to provide latest and timely information of financial asset and liability positions in each company within the case business group so that decision makers can have a clear decision support in fund dispatching. While most related researches on fund dispatching focused especially on efficient banking capital management and few studies were done for general financial department of traditional enterprise let alone for the business group, this study has made a progress in this issue and the resultant system is applicable to the similar business group. Keywords: Financial Asset and Liability, Fund Dispatching, Decision Support Systems, OLAP, Data Warehouse

Visual decision support system for the assessment of steel production efficiency

A novel web-based Decision Support System (DSS) for condition monitoring and continuous improvement of steelmaking production efficiency is presented in this paper. Production efficiency was measured and modelled through Key Performance Indicators (KPIs), which are assigned on-line to a single product (coil) and identified at different facilities throughout the integrated route. The systemic nature of the DSS system boosts the integration of every agent included in the decision chain of the company. It assures multi-agent decision convergence and the transformation of process information and expert knowledge into profitable results. The first case study is presented.

An ANN‐based DSS system for quality assurance in production network

PurposeThe purpose of this paper is to propose an integrated model of decision support system (DSS), artificial neural network, information and communication technologies and statistical process control (SPC) to facilitate agreement by different stakeholders with special interests to commit to the decision that will be taken to stop the production line when something goes wrong somewhere in a supplier network environment.Design/methodology/approachA DSS is proposed to capture exceptional signals from source on deterioration of product quality to alert preventive actions needed before the problems are getting out of hand. The supervisors are given a set of guidelines to support making the decision. Real‐time SPC and rule‐based decision support procedures are used to trigger pre‐defined exceptional signals for forwarding to the most appropriate person (the knowledge holder in the problem domain) to make a decision to stop the line. All servers in all remote sites are internet‐connected and provide real‐time quality data to the regional supply chain manager. A case study is described to show how this is implemented in a lens manufacturing company.FindingsA significant improvement in quality level can be achieved by holding the knowledge worker accountable for making the decision to stop the production line rather than made by default as is with most traditional operations.Practical implicationsTo provide a concept to structure activities for decision support so that the persons responsible for making the decision to stop the production line is held accountable by all stakeholders.Originality/valuePractitioners can replicate the approach used in this paper to their own situations involving decisions to be made to address un‐structured problems and unclear responsibilities.

Developing and assessing an intelligent forex rolling forecasting and trading decision support system for online e-service

An effective foreign exchange (forex) trading decision is usually dependent on effective forex forecasting. In this study, an intelligent system framework integrating forex forecasting and trading decision is first proposed. Based on this framework, an advanced intelligent decision support system (DSS) incorporating a back-propagation neural network (BPNN)-based forex forecasting subsystem and Web-based forex trading decision support subsystem is developed, which has been used to predict the directional change of daily forex rates and provide intelligent online decision support for financial institutions and individual investors. This article describes the forex forecasting and trading decision method, the system architecture, main functions, and operation of the developed DSS system. A comparative study is conducted between our developed system and others commonly used in order to assess the overall performance of the developed system. The assessment results show that our developed DSS outperforms some commonly used forex forecasting and trading decision systems and can provide intelligent e-service for forex traders to make useful trading decisions in the forex market. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 475–499, 2007.

A Decision Support System for a small scale industry in a developing country

This paper describes the design and development of a Decision Support System (DSS) for a small scale manufacturing company in a developing country. The DSS provides a solution for an integrated problem of planning, shopfloor scheduling, and control. The DSS system accepts the demand data with priorities, then uses the information from the imbedded data base system regarding product structure, production process, available resources with the status, and inventory. This in turn is used by the imbedded models to solve planning and scheduling problems, and provide solutions containing job order release, shopfloor scheduling and material requirement report. What if analysis feature provided by DSS allows the managers to incorporate their knowledge and experience to improve the quality of the solution by evaluating different scenarios presented by the manager. The laboratory equipment manufactured by the company is made to order. Product information regarding twenty-two high demand items is incorporated into the data base system, and the scheduling problem is solved for these items. The jobshop is a mixed shop problem containing a flow shop, an open shop, and a flow shop. A heuristic method used for solving this problem is presented.

An effective database formation for decision support systems

Managers' tasks have two aspects: to monitor (control) business activities and to plan the future based upon the monitored results. Thus a decision Support System (DSS) must have two kinds of databases appropriate for its needs. A management database, i.e., one for monitoring activities, is constructed mainly from the existing operational databases. A data cube should be employed for the logical data structure of the management database so that managers can share it and access it in multiple ways. Planning databases, i.e., those for planning activities, are constructed mostly from the management database. A table form should be employed for its logical data structure so that managers will find it easy to use. The management and planning databases should be connected through DSS's system architecture [1]. This makes the operational data (indicating business activities) directly and immediately available for management decision making. We have previously presented the DSS architecture. This paper discusses the management and planning databases from the viewpoint of it.