Integrated Decision Support System Research Articles

Enhanced Network Security Protection through Data Analysis and Machine Learning: An Application of GraphSAGE for Anomaly Detection and Operational Intelligence

With the Internet's rapid expansion, network security challenges have become increasingly complex and prominent. Traditional protection methods, largely dependent on predefined rules and patterns, demonstrate limited effectiveness against sophisticated and unknown network attacks, failing to harness the full potential of extensive network data. This study addresses the challenges faced by modern cybersecurity, particularly the limitations of traditional defense methods in countering unknown and complex attacks, by proposing a solution that integrates data analysis and machine learning technologies. The focus of this research is placed on network security anomaly detection as well as on intelligent network operations and maintenance exception management based on graph network algorithms, aiming to enhance security defense capabilities and operational efficiency. Specifically, the main contributions and innovations of this paper include: 1. Innovations in sampling, aggregation, and loss functions within the Graph Sample and Aggregation (GraphSAGE) model to improve the accuracy and robustness of the model for network anomaly detection; 2. The introduction of a novel network anomaly root cause analysis and localization model, which, combined with an optimized root cause likelihood assessment method and search scheme, significantly enhances the speed and accuracy of anomaly localization; 3. The design of an integrated decision support system that can automatically adjust protection strategies as network conditions change, achieving a high level of automation and intelligence in cybersecurity management. This work not only provides effective technical support for network security protection but also opens new avenues for future cybersecurity research.

Evolution of Digital Twin Frameworks in Bridge Management: Review and Future Directions

Over the last decade, the digital twin (DT) concept has effectively revolutionized conventional bridge monitoring and management. Despite their overall success, current bridge DTs encounter conceptual ambiguities, hindering their inherent potential for practical implementation. Moreover, intelligent decision support models have not been properly considered as a component of the bridge DTs framework to enhance the reliability of decisions for asset maintenance. Therefore, this paper conducts a scientometric analysis and a comprehensive state-of-the-art review, exploring current bridge DT research trends and architectures and introducing an enhanced conceptual framework for bridge DTs. To this end, more than 480 research publications have been reviewed, compared, and analyzed. The research result encompasses the redevelopment of a multilayer DT framework, fostering its implementation in the full lifecycle of bridge infrastructure while exploring the potential integration of decision support systems and data fusion from advanced technologies to improve the overall efficiency of implementing DT technology in bridges.

Advancing Efficiency Sustainability in Poultry Farms through Data Envelopment Analysis in a Brazilian Production System.

The production efficiency factor is widely used to measure the zootechnical performance of a batch of broilers. The unit cost of production brings new elements to improve efficiency evaluation and financial sustainability for this activity in agriculture. This research aims to evaluate the production efficiency level of the crop to maximize the return on investment. This study uses Data Envelopment Analysis (DEA) with the computational processing of the SIAD software (Integrated Decision Support System). The variables selected were poultry housing, age at slaughter, feed consumed, mortality, and unit cost. The chosen output variable was the total available weight. The analysis spans 31 decision-making units (DMUs) composed of integrated producers, unveiling a frontier of efficiency delineated by the most exemplary DMUs. Notably, only two DMUs, specifically DMU 4 and DMU 23, approached the threshold of maximum relative efficiency. This research illuminates the critical role of unit cost in enhancing the assessment of production efficiency and financial sustainability within the agriculture environment. By setting benchmarks for efficient management and operational protocols, our findings serve as a cornerstone for improving practices among less efficient DMUs, contributing significantly to the literature on agricultural efficiency and offering actionable insights for the poultry farming sector.

An Integrated Decision Support System for Low-Disturbance Surface Mining

Low-disturbance mining in surface mining (LDM) can transform traditional surface mine production systems into a more sustainable model by reducing the disturbance of surface mining, minimizing pollutant emissions, and reducing ecological impacts. The purpose of this paper is to explore the LDM evaluation method by applying multi-criteria decision-making to provide technical support for LDM implementation. Therefore, an evaluation method based on the combination of the fuzzy analytical hierarchy process (F-AHP) and grey clustering was proposed. Analyzed in terms of the current status of the evaluation indicators (reality) and the significance of the development of the LDM (desirability). Determined the weights and low-disturbance (LD) levels of the evaluation indicators. Combined with the fuzzy technique for order preference by similarity to an ideal solution (F-TOPSIS), the low-disturbance open pit mining paths are ranked, and finally, the decision support system for low-disturbance mining in surface mining is constructed. This study not only enriches the existing literature on related technologies but also lays the foundation for further research on LDM and provides exploratory insights for deeper improvement of LD level in surface mining.

Integrated Decision Support System for Flood Disaster Management with Sustainable Implementation

Emergency management of a flood catastrophe should involve not only an immediate reaction to the flooding but also the pre-flood disaster phase, the flood disaster phase, and the post-flood disaster phase. This is in addition to the immediate response to the flooding. This research investigates whether or not a decision support system (DSS) for flood emergency management is necessary and whether or not it is feasible. Previously, we presented a model for the development of modular disaster management decision support systems. The prior work that we did on the integration of DSS model decomposition-derived modular subroutines is expanded upon in this research. The act of merging many models into a single comprehensive logical model is referred to as “Observed and Forecasted.” After that, it offers a systematic framework for handling crises throughout the whole of the process, and it concludes with an in-depth analysis of the function architecture of each application subsystem. In this work, we create a disaster management metamodel with the goal of eventually building a disaster management (DM) language. It will function as a representational layer of DM data that, over time, will create a system for DM decision support that mixes and matches various DM actions in line with the evolution of the disaster as it develops. A decision support system that will contain an innovative metamodel as a core component will be developed in order to accomplish the aims of integrating, enabling, and accelerating access to DM information. These objectives will be reached via the creation of a decision support system.

Learning in the Digital Age: Harnessing Decision Support Systems and Case Study for Physics Tutoring Program

In the contemporary educational landscape, the integration of Decision Support Systems (DSS), particularly the Simple Additive Weighting (SAW) method, offers a structured approach to evaluating teaching methods. This study focused on applying the SAW DSS in a Physics Tutoring Program. The results highlighted Interactive Simulations as the most effective teaching approach, emphasizing the role of technology in modern education. Hands-on Experiments and Group Discussions followed closely, underscoring the importance of experiential learning. Traditional Lectures, while foundational, benefit from enhanced interactivity. The SAW DSS's systematic approach ensures objective decision-making, though weight assignment can introduce subjectivity. A notable research gap emerged around the integration of SAW DSS with advanced educational technologies and its application across diverse cultural contexts. Future research avenues include refining the SAW method with real-time feedback and AI integration. In conclusion, as education evolves, tools like the SAW DSS provide invaluable insights, necessitating continuous adaptation to meet dynamic educational needs.

Integrating Decision Support Systems in Education: A Comprehensive Approach

The current study examined educational Decision Support Systems (DSS) using the Simple Additive Weighting (SAW) method. This study evaluated traditional face-to-face teaching, online learning, and blended learning based on four criteria: effectiveness, cost efficiency, accessibility, and technological requirements. Each criterion was weighted according to its importance. After standardizing and weighting the selection matrix, each instructional style was given a composite score for direct comparison. Blended learning outperformed the other methods, scoring 0.620. The findings suggest that integrating digital and traditional education could boost efficiency. However, the model was sensitive to weighting assignments and couldn't account for nuanced qualitative factors. Refined weight assignment, qualitative variables, and AI could improve schooling and other decision-making in the future. This study showed that DSS and SAW can help make educated education decisions.

AN INTEGRATED DECISION SUPPORT SYSTEM FOR STOCK INVESTMENT BASED ON SPHERICAL FUZZY PT-EDAS METHOD AND MEREC

The stock investment selection could be deemed as a classic multiple attribute group decision making (MAGDM) problem involving multiple conflicts and interleaved qualitative and quantitative attributes. Spherical fuzzy sets (SFSs) can excavate the potential vagueness and intricacy in MAGDM more effectively and deeply. This article we propose an integrated decision support system (IDSS) based on SFSs, prospect theory (PT), distance from average solution (EDAS) method and the MEthod based on the Removal Effects of Criteria (MEREC). The proposed IDSS, called SF-PT-EDAS-MEREC model, uses SFSs to describe the uncertain and obscure assessment information of DMs. The combination of PT and EDAS (PT-EDAS) method adequately captures DMs’ psychological behavior characteristics to execute more reasonable alternative evaluation. The MEREC is utilized to efficaciously obtain unknown attribute weights. In addition, this paper also presents a novel score function to compare spherical fuzzy numbers (SFNs) more directly and efficiently. Eventually, in order to illustrate the practicability of the proposed IDSS, two numerical examples of stock investment selection are employed to achieve this. Meanwhile, the comparative study with existing approach further demonstrates the effectiveness and superiority of SF-PT-EDAS-MEREC model.

Vision-based normalized canopy area estimation for variable nitrogen application in apple orchards

Normalized canopy area is a measure of spatial canopy fill or canopy coverage in a tree. It is an important factor for growers to consider in making various farming decisions, such as timing and amount of fertilization, pesticide application, and irrigation, as it gives a sense of the overall canopy growth. This study focused on estimating the normalized canopy area of apple trees using a ground-based stereo-vision system in a commercial orchard under natural lighting conditions. A color, depth, and height threshold, followed by a point density-based outlier removal was applied to segment the target tree canopies. The segmented canopy area was compared with the area estimated with manual segmentation, which showed an F1 score of 0.78. The overall growth of the tree canopies was then represented using normalized canopy area, which was calculated as a fraction of pixels occupied by the tree foliage over the total possible area a tree could occupy for a given tree spacing. It was found that the normalized canopy area of individual trees was highly correlated with the surface volume of the canopy (r = 0.7), which is a traditionally used parameter to assess or represent canopy growth. The estimated normalized canopy area was then compared against experts' nitrogen recommendation for the sample trees, which was based on their visual assessment of the vigor/growth of the trees. Four individual experts (one apple farmer, one orchard manager, and two horticulturists with research and outreach experience) were asked to provide their independent nitrogen recommendation and an average recommendation level was calculated. The comparison showed an overall correlation coefficient of -0.5, indicating an opposite relationship (higher the canopy density, lower the need for nitrogen and vice versa) between the normalized canopy area and the average of the experts' nitrogen recommendation. When the relationship between the normalized area and nitrogen recommendation level was assessed for individual experts, the correlation coefficient was estimated to be -0.86, -0.84, -0.96, and -0.78, respectively, for Expert1, Expert2, Expert3, and Expert4 respectively. The results from this study identified a critical measure for assessing nitrogen needs at individual tree levels in apple orchards and provides an automated tool to estimate the measure. The outcomes of this study could be used as a part of an integrated decision support system for orchard operations (e.g., fertilization or irrigation) that may include additional canopy characteristics such as spectral signature, trunk diameter, and rate of change of leaf color in the fall.

Integrated Decision Support Framework of Optimal Scaffolding System for Construction Projects

Selecting the appropriate temporary facilities is important for reducing cost and improving the productivity and safety of craft professionals in construction projects. However, the manual planning process for scaffolding systems is typically prone to inefficiencies. This paper aims to develop a knowledge-based framework for a scaffolding decision support system for industry. An integrated two-phase system was established, including a technical evaluation module and a knowledge-based module. First, the system identifies feasible scaffolding alternatives from the database through a rule-based algorithm. Second, a knowledge-based module was designed to assess the alternative performance. The framework effectively generated the ranking of scaffolding alternatives, and the top three influential factors were identified, including the site accessibility, protection to workers and health risk. Thus, an application study of an industrial steel project was proffered to validate the effectiveness of the framework. The proposed framework may help decision-making regarding the implementation of temporary facility planning in industry practices. It has wider applicability because it simultaneously considers site conditions, productivity, safety, and financial benefits, and is designed and implemented through a computerized path. The paper contributes to the industry by developing an integrated decision support system for temporary facilities. Additionally, the practical contribution of this research is the provision of an optimized scaffolding planning method that could be utilized as a guide when implementing the decision support system.

A three-stage fuzzy neutrosophic decision support system for human resources decisions in organizations

This study proposes an integrated Decision Support System (DSS) with Multi-Criteria Decision-Making (MCDM) to evaluate trainers in organizations and choose the most suitable one(s) for a training program. The clustering stage determines the most appropriate number of trainees to be assigned to a trainer. The proposed model also considers the training budget and the constraint limiting the number of assignments. The proposed model has three stages: Delphi, the Interval-Valued Neutrosophic Analytic Hierarchy Process (IVN-AHP), and Fuzzy C-Means (FCM). The model’s input is expert opinions on various criteria, the candidate assessment score, and the bi-comparison of agreed criteria. Outputs are weights, and the members of each cluster represent possible candidates. We present a case study to demonstrate the applicability of the proposed DSS in a training program assessment. Additional applications of the proposed system include recruitment and promotion.

APPLICATION OF DIGITAL TECHNOLOGIES IN THE DELIVERY OF CARGO IN INTERNATIONAL TRAFFIC

The article analyzes the current state of the theory and practice of solving issues related to the delivery of goods by road in international traffic. It is determined that increasing the efficiency of cargo transportation in international traffic is possible through the development and implementation of new information systems and the development of innovative software products to solve the problems of planning and operational management of the transport process. The purpose of this article is to develop an innovative integrated management decision support system for organizing and managing the delivery of goods in international traffic based on the combination of cloud technologies for centralized management of real-time information exchange between participants in the transportation process. The exchange of information between participants in the process of cargo delivery in international traffic using a software product allows to improve the provision of transportation services to the client, increase the level of information exchange, implement real-time management of the cargo transportation process, improve the efficiency of decision-making and immediately respond to any changes in the process. The article develops an integrated management decision support system for organizing and managing the delivery of goods in international traffic, based on the approach of combining cloud technologies for centralized management of real-time information exchange between participants in the transportation process. The application of the proposed system with the use of appropriate software will improve the quality of transportation services to the client, increase the level of information exchange, implement real-time management of the cargo delivery process and improve the efficiency of management decision-making. As criteria for assessing the feasibility of using the proposed system, it is customary to consider indicators that are critical to maintaining the required level of sustainability and service and most fully characterize the results of the work performed from the point of view of both the consumer and the carrier. Keywords: digital technologies, cargo delivery, international transportation, efficiency.

Clinical Performance of Decision Support Systems in Anesthesia, Intensive Care, and Emergency Medicine: A Systematic Review and Meta-Analysis.

Medical technology is expanding at an alarming rate, with its integration into health care often reflected by the constant evolution of best practices. This rapid expansion of available treatment modalities, when coupled with progressively increasing amounts of consequential data for health care professionals to manage, creates an environment where complex and timely decision-making without the aid of technology is inconceivable. Decision support systems (DSSs) were, therefore, developed as a means of supporting the clinical duties of health care professionals through immediate point-of-care referencing. The integration of DSS can be especially useful in critical care medicine, where the combination of complex pathologies, the multitude of parameters, and the general state of patients require swift informed decision-making. The systematic review and meta-analysis were performed to evaluate DSS outcomes compared to the standard of care (SOC) in critical care medicine. This systematic review and subsequent meta-analysis were performed after the EQUATOR networks Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA). We systematically explored PubMed, Ovid, Central, and Scopus for randomized controlled trials (RCTs) from January 2000 to December 2021. The primary outcome of this study was to evaluate whether DSS is more effective than SOC practice in critical care medicine within the following disciplines: anesthesia, emergency department (ED), and intensive care unit (ICU). A random-effects model was used to estimate the effect of DSS performance, with 95% confidence intervals (CIs) in both continuous and dichotomous results. Outcome-based, department-specific, and study-design subgroup analyses were performed. A total of 34 RCTs were included for analysis. In total, 68,102 participants received DSS intervention, while 111,515 received SOC. Analysis of the continuous (standardized mean difference [SMD], -0.66; 95% CI [-1.01 to -0.30]; P < .01) and binary outcomes (odds ratio [OR], 0.64; 95% CI, [0.44-0.91]; P < .01) was statistically significant and suggests that health interventions are marginally improved with DSS integration in comparison to SOC in critical care medicine. Subgroup analysis in anesthesia (SMD, -0.89; 95% CI, [-1.71 to -0.07]; P < .01) and ICU (SMD, -0.63; 95% CI [-1.14 to -0.12]; P < .01) were deemed statistically supportive of DSS in improving outcome, with evidence being indeterminate in the field of emergency medicine (SMD, -0.24; 95% CI, [-0.71 to 0.23]; P < .01). DSSs were associated with a beneficial impact in critical care medicine on a continuous and binary scale; however, the ED subgroup was found to be inconclusive. Additional RCTs are required to determine the effectiveness of DSS in critical care medicine.

An integrated decision support system to achieve sustainable development in transportation routes with traffic flow.

Due to the growing population and demand, transportation planning has received special importance in the context of supply chain management. One of the major challenges in transportation planning is the traffic problem. This challenge affects the safety, environmental, and efficiency factors of transportation systems. Accordingly, in this study, the routes, which are important pillars of transportation planning, are examined from the perspective of sustainability. In this regard, a novel decision support system is developed, wherein at first, some decision-making methods including Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), entropy technique, Nash equilibrium point (NEP), and data envelopment analysis (DEA) are employed to analyze and determine unstable routes. Then, a bi-level leader-follower multi-objective optimization model is developed, based on the vehicle types, to evaluate the routes at different time intervals and identify the most efficient time intervals as a traffic pattern. Finally, the proposed models are implemented in a real case study based on the freeways in Tehran. According to the main finding, it is revealed that heavier and bulkier vehicles have a greater impact on road instability.

Collaborative Decision Making (CDM) in Emergency Caused by Captain Incapacitation

The authors present a new approach to decision-making to ensure the proper collaboration of different personnel. Models are used in intelligent integrated decision support systems, especially for actions in emergencies. Behavioral deterministic models are used for synchronization actions of all operators, to support and timely predicting of operators' actions in an emergency. To determine the quantitative characteristics of risk levels, models for collaborative decision making (CDM) under uncertainty by the operators (pilots, air traffic controllers, flight/dispatch), and other invited specialists, have been developed. The decision-making modeling of a group of operators in case of an emergency situation such as “pilot incapacitation” was presented. The methodological basis for CDM in certainty is network planning, in conditions of Stochastic uncertainty – is a decision tree, in conditions of non-stochastic uncertainty – is a matrix of decisions, and outcomes are made this using the expert judgment method for obtaining quantity estimations.

An integrated decision support system for the urban food-water-energy nexus: Methodology, modification, and model formulation

Making cities more sustainable relies on opportunities to optimally integrate and manage food, water, and energy resources, among other essential requirements for the thriving of every society, in a synergistic manner. By means of decision support tools and the development of policy scenarios, cities can better understand how sustainability may be achieved by the optimal integration of the natural resources. Although increasingly employed, the need remains for an integrative decision-making methodology and tool that supports the incorporation of food, water, and energy sectors and the corresponding environmental and social footprints into a general framework, and quantitatively investigating the complicated synergies to optimize nexus strategies from a holistic point of view. This research develops an integrated decision-support system by means of a spatial optimization game model that searches for optimal resource management solutions through a cooperative scenario-building environment. The design of the proposed system relies on an innovative combination of methods capable of navigating decision-making through complex systems modeling and planning. This includes multi-objective optimization and cooperative game theory in the frame of a spatial serious gaming environment for real-world implementation. Relying on such an algorithmic framework, this research provides the foundation for a spatial serious game that enables forecasting the impact of policy interventions based on socio-economic drivers of the demand for the resources, environmental carrying capacity, land management, and primary climate change drivers. The outcomes serve as strategic guidelines for policymakers and encourage effective decision-making related to maximizing socio-economic targets and minimizing environmental burdens.

Електронний облік деревної продукції лісового господарства в Україні: проблеми та шляхи удосконалення

Illegal logging leads to the destruction of a significant area of forests, which have economic and socio-ecological value, as an integral part of the country's natural capital. One of the ways to reduce illegal logging and control the use of forest resources is properly organized accounting, particularly e-accounting. The article aims to reveal the methods of e-accounting of wood products of forestry in Ukraine to prevent illegal logging, as well as to determine ways to improve it. Investigating the problems of e-accounting of wood products, the authors use abstract thinking, analysis, and synthesis to summarize the essential connections between forestry management issues and accounting methods. Empirical evidence in the study is data from the State Forest Resources Agency of Ukraine and the State Statistics Service of Ukraine. An analysis of the proposals of foreign scientists was carried out using the methods of analogy, comparison, systematization and generalization to determine ways to improve the e-accounting of wood products in Ukraine. The dynamics of illegal logging of wood was analyzed. Methods of e-accounting of wood products in Ukraine were disclosed. It was found that currently, e-accounting of wood products cannot be called systematic because it does not cover forest resources of wood origin at the root, by-products, by-products from wood, and all products of non-wood origin. Based on the analysis of the results presented in foreign publications, the key directions of the improvement of the e-accounting system in the forestry of Ukraine were determined: methodological (improvement of methods of collection, analysis and evaluation of accounting/management information); informational (expansion of data sources, in particular through more active use in the accounting of the natural meter); technological (implementation of innovative forest monitoring technologies and integrated decision support systems). It is vital to combine e-accounting of wood products with other components (various types of reporting, forest health monitoring data, cost accounting for the restoration and protection of forest areas, assessment of forest sustainability, etc.) to form an integrated forestry management system, the implementation of which will contribute to solving the problems of climate change.

Machine Learning Based Logistic Decision Support System for Intelligent Vehicles and Transportation Systems

One-third of the freight expense is spent on transportation and transport networks, thus significantly impacting the logistics sector's efficiency. Artificial Intelligent logistics strategies are designed to alleviate the impact on metropolitan areas exacerbated by increased freight transport. The city of logistics is widely employed as a modern study area, has a significant social and economic influence, and has extensively explored the key elements and components. Most recent experiments concentrate on traffic management and logistics monitoring, even though no research studies have tried to detect drivers' distractions. Since drivers are one of the major parts of logistic service, this study incorporates existing logistics DSS with the cognitive model for predicting driver distraction. This paper presents a Machine Learning Integrated Decision Support System (MLIDSS) architecture and core components based on simulationoptimization modules to assist the smart logistics DSS with an eye to service delivery driver distraction level. The ML algorithms perform real-time driver distraction predictions for smooth logistics transportation. The viability of such a transportation system is often illustrated in the real world. The findings result in the highest-performing intelligent decision support system compared to existing models.

Introduction of integrated decision support system for flood disaster management

Heavy precipitation events and fluvial flood can cause serious damage to property and fatality, and thus flood disaster management is essential to lower the risk, to prevent and to mitigate the crisis. Effective and efficient flood disaster management requires on-site information, forecast and appropriate corresponding response strategies and measures. Therefore, a concept of integrated decision support system (DSS) for flood disaster management is proposed and discussed in this paper. The DSS integrates observation, rainfall forecast, fast flood simulation, historical events and crisis scenarios, and response strategies and measures, by means of information and communication technology (ICT) and internet of things (IoT). The DSS can provide relevant information and offer suggestions of action before and during the crisis. The DSS can also be utilised for disaster drill, education, and demonstration with the help of virtual reality (VR), augmented reality (AR) or Metaverse technology. The aims of this DSS are to improve existing response strategies and emergency services, to enhance the community-based disaster risk management, and to raise public crisis awareness.

An integrated decision support system for heart failure prediction based on feature transformation using grid of stacked autoencoders

Heart failure (HF) is a life-threatening disease. Therefore, prediction of risks associated with HF is essential at the early stages of the disease in order to ensure safety and reduce the mortality rate associated with it. To meet this objective, numerous medical decision support systems (MDSS) have been developed for HF risk prediction. Most of these MDSS focus on feature transformation-based methods and their integration with machine learning models. However, careful investigation of these MDSS reveals that they improved the accuracy on the testing data while the performance on the training data was either compromised or not taken into account. Such an improvement is not pertinent from machine learning perspective. Furthermore, the produced HF prediction accuracy also needs considerable amount of improvement. To tackle such issues, this study proposes a more robust approach that integrates a grid of stacked autoencoders with neural network model. The stacked autoencoders transform features by yielding more robust set of features and the neural network classifies the newly extracted set of features. The strength of the proposed integrated MDSS is evaluated by using an online HF benchmark database namely Cleveland database. The performance of the proposed feature transformation method is also compared with other well known methods namely Principal Component Analysis (PCA), Kernel PCA (KPCA) and feature selection methods i.e., chi-square, Gini index, F-score and mutual information based feature selection. The proposed approach outperformed other state-of-the-art feature transformation methods. The proposed integrated MDSS achieves classification accuracy of 95.55%, specificity of 97.95%, and sensitivity of 92.68%.