Network Based Decision Support System Research Articles

A SURVEY ON TOWARD EFFECTIVE RESPONSE TO NATURAL DISASTERS: A DATA SCIENCE APPROACH

Natural catastrophes have the potential to destroy large portions of infrastructure and kill thousands of people. Both the populace and the government find it challenging to deal with these situations. Particular attention must be given to the following two difficult problems: find a workable solution first evacuating people, then rebuilding homes and other infrastructure. Then, a successful recovery plan that prioritises the reconstruction of damaged areas and the evacuation of people can be a game-changer for overcoming those horrible circumstances. In this light, we introduce DiReCT, a method based on I a dynamic optimization model created to quickly develop an evacuation plan of an earthquake-stricken area, and ii) a double deep Q network-based decision support system capable of effectively guiding the rebuilding of the affected areas. The latter operates by taking into account the needs of the many stakeholders (such as citizens' social benefits and political priorities) as well as the resources available. The foundation for both of the aforementioned solutions is a specialized geographic data extraction Method called "GisToGraph," which was created expressly for this use. We used extensive GIS data, information on the vulnerability of urban land structures, and the historical city centre of L'Aquila (Italy) to test the applicability of the entire strategy.

A convolutional neural network-based decision support system for neonatal quiet sleep detection.

Sleep plays an important role in neonatal brain and physical development, making its detection and characterization important for assessing early-stage development. In this study, we propose an automatic and computationally efficient algorithm to detect neonatal quiet sleep (QS) using a convolutional neural network (CNN). Our study used 38-hours of electroencephalography (EEG) recordings, collected from 19 neonates at Fudan Children's Hospital in Shanghai, China (Approval No. (2020) 22). To train and test the CNN, we extracted 12 prominent time and frequency domain features from 9 bipolar EEG channels. The CNN architecture comprised two convolutional layers with pooling and rectified linear unit (ReLU) activation. Additionally, a smoothing filter was applied to hold the sleep stage for 3 minutes. Through performance testing, our proposed method achieved impressive results, with 94.07% accuracy, 89.70% sensitivity, 94.40% specificity, 79.82% F1-score and a 0.74 kappa coefficient when compared to human expert annotations. A notable advantage of our approach is its computational efficiency, with the entire training and testing process requiring only 7.97 seconds. The proposed algorithm has been validated using leave one subject out (LOSO) validation, which demonstrates its consistent performance across a diverse range of neonates. Our findings highlight the potential of our algorithm for real-time neonatal sleep stage classification, offering a fast and cost-effective solution. This research opens avenues for further investigations in early-stage development monitoring and the assessment of neonatal health.

A deep neural network-based decision support system for intelligent geospatial data analysis in intelligent agriculture system

A deep neural network-based decision support system for intelligent geospatial data analysis in intelligent agriculture system

Artificial Neural Network-Based Decision Support System for Development of an Energy-Efficient Built Environment

Implementing energy-efficient solutions in a built environment is important for reaching international energy reduction targets. For advanced energy efficiency-related solutions, computer-based decision support systems are proposed and rapidly used in a variety of spheres relevant to a built environment. Present research proposes a novel artificial neural network-based decision support system for development of an energy-efficient built environment. The system was developed by integrating methods of the multiple criteria evaluation and multivariant design, determination of project utility and market value, and visual data mining by artificial neural networks. It enables a user to compose up to 100,000,000 combinations of the energy-efficient solutions, analyze strengths and weaknesses of a built environment projects, provide advice for stakeholders, and calculate market value and utility degree of the projects. For visual data mining, self-organizing maps (type neural networks) are used, which may influence the choosing of the final set of alternatives and criteria in the decision-making problem, taking into account the discovered similarities of alternatives or criteria. A system was validated by the real case study on the design of an energy-efficient individual house.

Efficacy of Artificial Neural Network based Decision Support System for Career Counseling

Objectives: This paper presents the use of machine learning technique in order to eliminate the components affecting the human decision making process. To assist decision making in career counseling, an Artificial Intelligence model was implemented using Artificial Neural Network (ANN) in MATLAB for predicting vocational stream of pursuit based on the behavioral characteristic of the beneficiary. Methods/Statistical Analysis: The Differential Aptitude Test (DAT) battery; and Scientific Knowledge and Aptitude Test (SKAT) were used to assess an individual’s specific abilities in different areas. The training data set for the ANN model was procured in the form of normalized scores based on occupational/vocational profiles as given by the authors of DAT battery and SKAT. The trained ANN was tested with normalized stanine scores of 100 tenth class studentsraised through cluster random sampling technique for predicting a vocational stream of pursuit. In order to evaluate its accuracy and efficiency, three techniques of classification were employed. The unclassified data was classified by using Discriminant function analysis, ANN and two classifications were obtained from the trained counselors. Findings: The classification result obtained from the above mentioned techniques were compared and was found that the ANN system and Discriminant function analysis agreed approximately by 91% over all the test cases. The results of the statistical method support the classification made by the ANN. The two counsellors were in agreement with ANN’s classification output by approximately 81%. However, the counsellors disagreed with each other’s prediction approximately by 27% over all the test cases. The experimental results support the hypothesis that the proposed machine learning technique performed better than the prediction made by counsellors. Application/Improvements: In the Indian scenario, the developed machine learning system may be used as a standalone system in places where there is the paucity of counsellorsor can assist in the career decision making provided by human beings.

An artificial neural network based decision support system for energy efficient ship operations

Reducing fuel consumption of ships against volatile fuel prices and greenhouse gas emissions resulted from international shipping are the challenges that the industry faces today. The potential for fuel savings is possible for new builds, as well as for existing ships through increased energy efficiency measures; technical and operational respectively. The limitations of implementing technical measures increase the potential of operational measures for energy efficient ship operations. Ship owners and operators need to rationalise their energy use and produce energy efficient solutions. Reducing the speed of the ship is the most efficient method in terms of fuel economy and environmental impact. The aim of this paper is twofold: (i) predict ship fuel consumption for various operational conditions through an inexact method, Artificial Neural Network ANN; (ii) develop a decision support system (DSS) employing ANN-based fuel prediction model to be used on-board ships on a real time basis for energy efficient ship operations. The fuel prediction model uses operating data – ‘Noon Data’ – which provides information on a ship’s daily fuel consumption. The parameters considered for fuel prediction are ship speed, revolutions per minute (RPM), mean draft, trim, cargo quantity on board, wind and sea effects, in which output data of ANN is fuel consumption. The performance of the ANN is compared with multiple regression analysis (MR), a widely used surface fitting method, and its superiority is confirmed. The developed DSS is exemplified with two scenarios, and it can be concluded that it has a promising potential to provide strategic approach when ship operators have to make their decisions at an operational level considering both the economic and environmental aspects.

An artificial neural network based decision support system for solving the buffer allocation problem in reliable production lines

One of the major design problems in the context of manufacturing systems is the well-known Buffer Allocation Problem (BAP). This problem arises from the cost involved in terms of space requirements on the production floor and the need to keep in mind the decoupling impact of buffers in increasing the throughput of the line. Production line designers often need to solve the Buffer Allocation Problem (BAP), but this can be difficult, especially for large production lines, because the task is currently highly time consuming. Designers would be interested in a tool that would rapidly provide the solution to the BAP, even if only a near optimal solution is found, especially when they have to make their decisions at an operational level (e.g. hours). For decisions at a strategic level (e.g. years), such a tool would provide preliminary results that would be useful, before attempting to find the optimal solution with a specific search algorithm.The aim of this study is to create such a tool. More specifically, an Artificial Neural Network (ANN) based decision support system is developed to assist production line designers in making decisions concerning the Buffer Allocation Problem (BAP) in reliable production lines. The aim of the ANN is to predict the performance of the production line based on its characteristics. The decision support system has been designed to allow for these data to be outputted in a user friendly format. To develop such an ANN, a large number of training and test data is required. To collect these data, extensive experiments were performed on a carefully chosen set of production lines. Because of its speed, the myopic algorithm was used as the search algorithm for the experiments. The performance of the ANN is examined for test sets of production lines and an average accuracy close to 99% is found. The performance of the ANN is compared with that of other well established surface fitting methods and its superiority is confirmed. Based on the results from (a) the experiments and (b) the developed ANN, a decision support system, called BAPANN, is designed and implemented. BAPANN’s functionalities and capabilities are demonstrated via the use of illustrative scenarios, showing the effectiveness of the proposed method measured in terms of the required CPU time.In summary, BAPANN provides the production line designer with a powerful, efficient and accurate tool to make decisions on the buffer allocation problem for balanced reliable production lines. This is done in a convenient fashion without involving the designer in tedious and complex mathematical analysis.

A Bayesian Diagnostic System to Differentiate Glioblastomas from Solitary Brain Metastases

This paper aimed to construct a Bayesian network-based decision support system to differentiate glioblastomas from solitary metastases, based on multimodality MR examination. We enrolled 51 patients with solitary brain tumors (26 with glioblastomas and 25 with solitary brain metastases). These patients underwent contrast-enhanced T1-weighted magnetic resonance (MR) examination, diffusion tensor imaging (DTI), dynamic susceptibility contrast (DSC) MRI, and fluid-attenuated inversion recovery (FLAIR). We generated a set of MR biomarkers, including relative cerebral blood volume in the enhancing region, and fractional anisotropy measured in the immediate peritumoral area. We then generated a Bayesian network model to represent associations among these imaging-derived predictors, and the group membership variable, (glioblastoma or solitary metastasis). This Bayesian network can be used to classify new patients' tumors based on their MR appearance. The Bayesian network model accurately differentiated glioblastomas from solitary metastases. Prediction accuracy was 0.94 (sensitivity = 0.96, specificity = 0.92) based on leave-one-out cross-validation. The area under the receiver operating characteristic curve was 0.90. A Bayesian network-based decision support system accurately differentiates glioblastomas from solitary metastases, based on MR-derived biomarkers.

Decision support system for ultrasound inspection of fiber metal laminates using statistical signal processing and neural networks

The growth of the aerospace industry has motivated the development of alternative materials. The fiber–metal laminate composites (FML) may replace the monolithic aluminum alloys in aircrafts structure as they present some advantages, such as higher stiffness, lower density and longer lifetime. However, a great variety of deformation modes can lead to failures in these composites and the degradation mechanisms are hard to detect in early stages through regular ultrasonic inspection. This paper aims at the automatic detection of defects (such as fiber fracture and delamination) in fiber–metal laminates composites through ultrasonic testing in the immersion pulse-echo configuration. For this, a neural network based decision support system was designed. The preprocessing stage (feature extraction) comprises Fourier transform and statistical signal processing techniques (Principal Component Analysis and Independent Component Analysis) aiming at extracting discriminant information and reduce redundancy in the set of features. Through the proposed system, classification efficiencies of ∼99% were achieved and the misclassification of signatures corresponding to defects was almost eliminated.

Development of a decision support system to predict physicians’ rehabilitation protocols for patients with knee osteoarthritis

To design a medical decision support system (MDSS) that would accurately predict the rehabilitation protocols prescribed by the physicians for patients with knee osteoarthritis (OA) using only their demographic and clinical characteristics. The demographic and clinical variables for 170 patients receiving one of three treatment protocols for knee OA were entered into the MDSS. Demographic variables in the model were age and sex. Clinical variables entered into the model were height, weight, BMI, affected side, severity of knee OA, and severity of pain. All patients in the study received one of three treatment protocols for patients with knee OA: (a) hot packs, followed by electrotherapy and exercise, (b) ice packs, followed by ultrasound and exercise and (c) exercise alone. The resilient back propagation artificial neural network algorithm was used, with a ten-fold cross-validation. It was estimated that the MDSS is able to accurately predict the treatment prescribed by the physician for 87% of the patients. We developed an artificial neural network-based decision support system that can viably aid physicians in determining which treatment protocol would best match the anthropometric and clinical characteristics of patients with knee OA.

Refining classifiers with neural networks

This article is devoted to presentation of two approaches to the task of combining classification using decision rules with that of neural networks. Main stress is put on applying the knowledge derived from data with methods coming from area of rough sets and Boolean reasoning to construction of feedforward artificial network architecture. Such an approach allows for both easier construction of neural network based decision support system and better classification results for previously unseen cases. One more paradigm presented within the paper is the application of a simplified neural network to the task of resolving conflict that occur in rule-based decision support systems. The approaches presented in the paper are illustrated with examples and results of actual numerical experiments.

An intelligent model selection and forecasting system

In this paper we present an intelligent decision-support system based on neural network technology for model selection and forecasting. While most of the literature on the application of neural networks in forecasting addresses the use of neural network technology as an alternative forecasting tool, limited research has focused on its use for selection of forecasting methods based on time-series characteristics. In this research, a neural network-based decision support system is presented as a method for forecast model selection. The neural network approach provides a framework for directly incorporating time-series characteristics into the model-selection phase. Using a neural network, a forecasting group is initially selected for a given data set, based on a set of time-series characteristics. Then, using an additional neural network, a specific forecasting method is selected from a pool of three candidate methods. The results of training and testing of the networks are presented along with conclusions. Copyright © 1999 John Wiley & Sons, Ltd.

Treatment of missing data values in a neural network based decision support system for acute abdominal pain

In this study different substitution methods for the replacement of missing data values were inspected for the use of these cases in a neural network based decision support system for acute appendicitis. The leucocyte count had the greatest number of missing values and was used in the analyses. Four different methods were compared: substituting means, random values, nearest neighbour and a neural network. There were great differences in the substituted leucocyte count values between different methods and only nearest neighbour and neural network agreed about most of the cases. The importance of the substitution method for the final diagnostic classification of the patients by the neural network based decision support system was found to be small.

Concurrent design of manufacturing cell and control functions: A neural network approach

This paper presents a neural network based decision support system (DSS) for use in concurrently determining cell configuration, operation plans, and complexity requirements of cell control functions. Advanced simulators and neural network technology are used in developing the DSS. Simulation experiments were conducted with many possible combinations of design changes to generate training pairs for a neural network. Complexity of cell control functions required by each design option was assessed, based on operational requirements, and was used to train another neural net. Once both neural networks are properly trained, one network can be used to predict the cell design configuration given a set of desirable cell performance measures, while the other network can be used to identify complexity requirements of the cell control functions by using the output provided by the first network as input to the second neural net. An operation-driven cell design methodology was applied to sequentially predict requirements of both cell configuration and cell control functions from the trained neural networks. This innovative new design methodology was illustrated via a successful implementation exercise in acquiring a real automated manufacturing cell at industrial settings. The exercise proves that such a DSS serves well as an effective tool for cell designers and the management in determining appropriate cell configuration and cell control functions at the design stage.

Automated Knowledge Acquisition in a Neural Network-Based Decision Support System for Incomplete Database Systems

: A neural network–based decision support system has been designed and simulated to be used as a filter to improve the system performance of large incomplete databases enhanced with maybe algebra. To train the network, a knowledge-acquisition module equipped with a fuzzy logic technique was used to automatically generate a set of training pairs according to the semantics of the underlying database, the specific characteristics of the user query, and user requirements. Based on the notion of relative graded membership, a fuzzy logic–based controller was used to monitor and measure the quality of each training pattern as a means to generate a set of “good” training pairs. Finally, the proposed scheme has been simulated and analyzed to determine the effectiveness of the automatic training pairs generation process.

A decision support system for parking space assignment

This paper describes a network-based decision support system for a general form of assigning parking spaces. It is an integrated optimization system that generates the model, optimizes it, and produces reports. The problem is modeled as a pure assignment network, which minimizes the preference values. The preference values are penalty costs, which are a function of walking distance, cost and priority. The methodology presented here can be applied to a variety of assignment situations where there are competing objectives. Since the approach is based on a network algorithm, it is capable of solving very large systems in an efficient way. A description of how the model was used to assign parking spaces for Dunhill Apartments in Bloomington, Indiana is provided. The proposed system can be used for any multi-building and multi-lot situation.

OR Practice—An Efficient Decision Support System for Academic Course Scheduling

This paper describes a network-based decision support system approach to the most general form of the academic course scheduling problem. The dimensions of faculty, subject, time, and room are considered by incorporating a penalty function into a network optimization approach. The approach, based on a network algorithm, is capable of solving very large problems. This methodology can be applied to other scheduling situations where there are competing objectives and multiple resources. Such situations include: scheduling of exams, times, and rooms in an academic setting, and scheduling of clients, times, and facilities for physicians, hospitals, dentists, counselors, and clinics. Common problems in such settings include the utilization of available space, and dissatisfaction with assigned times and locations. The proposed system results in more effective room utilization patterns, improved instructor satisfaction levels, and streamlines the tedious scheduling process. We describe the use of the model to schedule all graduate and undergraduate courses in the College of Business Administration at Texas A&M University. This involves 175 faculty, over 300 sections, 20 rooms, and 16 time slots for each semester's scheduling problem.