Decision Support System Model Research Articles

Comparison of discriminant analysis coupled with principal component analysis (PCA-DA) and back propagation neural network (BPNN) as classification model for decision support system in porous ceramic matrix (PCM) based burner

Two classification models discriminant analysis coupled with principal component analysis (PCA-DA) and back propagation neural network (BPNN), are for the first time applied for decision support system in porous ceramic matrix (PCM) based burner. The PCM based burner is simulated numerically, and 121 pairs of gas and solid temperature profiles are generated as input data. The operation of PCM based burner is classified into four, on the basis of important properties of PCM like extinction coefficient and convective coupling. With the help of the data, the classification models are developed. The classification models are monitored and analyzed through different plots and classification parameters like specificity, sensitivity and precision. Further, new samples are correctly allocated to their corresponding class by the classification models. The classification models are also explored and compared under noisy data (2% and 5%). The performances of both the classification models are found to be good for no noise case with all the parameters like sensitivity, specificity, and precision values greater than 0.69, for both the models. However, with 2% noise case, BPNN performs better than PCA-DA. The minimum value of parameters (sn, sp, & pr) is 0.67 with BPNN and 0.50 with PCA-DA, respectively. Under 5% noise, the minimum values of the parameters dropped to 0.47 for PCA-DA and 0.50 for BPNN, respectively. With the help of plots though, the new samples are easily identified to their correct class 3.

Computational complexity in explainable decision support system: A review

The rise of decision processes in various sectors has led to the adoption of decision support systems (DSSs) to support human decision-makers but the lack of transparency and interpretability of these systems has led to concerns about their reliability, accountability and fairness. Explainable Decision Support Systems (XDSS) have emerged as a promising solution to address these issues by providing explanatory meaning and interpretation to users about their decisions. These XDSSs play an important role in increasing transparency and confidence in automated decision-making. However, the increasing complexity of data processing and decision models presents computational challenges that need to be investigated. This review, therefore, focuses on exploring the computational complexity challenges associated with implementing explainable AI models in decision support systems. The motivations behind explainable AI were discussed, explanation methods and their computational complexities were analyzed, and trade-offs between complexity and interpretability were highlighted. This review provides insights into the current state-of-the-art computational complexity within explainable decision support systems and future research directions.

Hybrid Analysis of Road Service Level Determination Decision Support System

Traffic congestion on the highway is one of the problems often faced by road users, as on the highway traffic in the city of Padang often experiences congestion due to the growth of several highways that are relatively smaller than the growth of traffic volume, causing a decrease in road service levels. This study aimed to determine the level of road services in several traffic lanes in the city of Padang. Data analysis using Hybrid Decision Support System (HDSS) modeling by combining the Analytical Hierarchy Process (AHP) method with the Weighted Aggregated Sum Product Assessment (WASPAS) This method is included in the Multi-Criteria Decision Making (MCDM) group. The AHP method can consistently determine the weight value of each criterion, and the WASPAS method can analyze alternative data to obtain decision results by ranking with the Weighted Sum Model (WSM) and Weighted Product Model (WPM) processes. The ranking results show that there are three types of road services in the city of Padang, namely B, C, and D with an accuracy rate of 0.6947%, this the results of this study using HDSS modeling can provide a better analysis process.

Inventory Optimization Using an AI-Powered Holistic Model for Decision-Support System

Abstract: This study presents a groundbreaking approach to inventory optimization through the implementation of an AIpowered holistic model within a decision-support system. Leveraging machine learning algorithms, specifically Logistic Regression (LR) and Decision Trees, the methodology explores historical sales data, supplier metrics, and market trends during an extensive Exploratory Data Analysis phase. The implementation of an AI-powered holistic model for inventory optimization in a decision-support system presents a transformative approach to managing and enhancing supply chain efficiency. The proposed methodology, integrating machine learning algorithms such as Logistic Regression and Decision Trees, has demonstrated its efficacy in achieving superior results compared to traditional models, as evidenced by higher accuracy, precision, recall, and F1 scores. The comprehensive exploration of historical sales data, supplier metrics, and market trends during the exploratory data analysis phase has facilitated a nuanced understanding of inventory dynamics. The seamless integration of the AI-powered model into the decision-support system has empowered organizations with timely and data-driven insights, fostering more agile and informed decision-making. The seamless integration of the AI-powered model into the decision-support system provides organizations with timely and data-driven insights, fostering agile and informed decisionmaking. Performance evaluation, including accuracy, precision, recall, and F1 scores, reveals that the proposed LR and Decision Tree models outperform the existing Support Vector Machine (SVM) model across all metrics. The LR model exhibits commendable precision, recall, and F1 score values of 0.95, 0.97, and 0.98, respectively, while the Decision Tree model demonstrates even higher values, with precision and recall at 0.96 and an exceptionally high F1 score of 0.99. These outcomes underscore the practical utility and robustness of the AI-powered holistic model in revolutionizing inventory management practices. While acknowledging challenges and limitations, this research signifies a crucial advancement in establishing responsive and intelligent decision-support systems for inventory optimization, paving the way for future innovations in supply chain management

AI-Based Holistic Model for Decision-Support System in Inventory Optimization using deep Learning

Abstract: This study introduces an innovative artificial intelligence-driven comprehensive model designed for a decision-support system in inventory optimization. This model was developed using advanced deep learning techniques. The model aims to surpass the constraints of typical inventory management systems by effectively capturing temporal correlations and nuanced patterns in the data. This is achieved by integrating Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures. To navigate modern supply chains effectively, the primary focus should be on offering real-time decision support. The approach aims to enhance the decision-making processes in inventory management. This brief introduction offers insight into a distinctive method aimed at addressing the challenges of modern supply networks. The strategy focuses on providing realtime decision support and optimization strategies to enhance inventory management efficiency. The model's effectiveness is demonstrated by thorough performance evaluations, showing higher accuracy, precision, and recall compared to existing logistic regression models. The suggested LSTM model achieves an accuracy of 0.99, precision of 0.96, recall of 0.94, and an F1 Score of 0.97, surpassing existing models with higher scores in all metrics. The GRU model, albeit somewhat less effective than the LSTM, shows great overall performance, especially in recall with a score of 0.97. This investigation compares the effectiveness of different models, focusing on the potential advancements made possible by LSTM and GRU architectures in improving decision-support systems for inventory optimization. The AI-Based Holistic Model provided is at the forefront of technology advancements, offering a comprehensive solution for making educated decisions and optimizing inventories sustainably in today's highly competitive corporate climate.

A system dynamics approach to management of water resources in Qatar

Rapid global population growth and extensive urbanization threaten water sustainability, hindering sustainable development. This issue is particularly acute in arid regions such as the Gulf Cooperation Council (GCC), where the scarce natural water resources are overexploited. Effective water resource management (WRM) is pivotal in overcoming this challenge. This holistic study presents an innovative approach to address water sustainability at a national scale by developing a WRM decision support system (DSS) leveraging system dynamics modeling (SDM). Employing a unique scenario design framework developed in this study, the DSS simulates Qatar's water resource system behavior for 2021 to 2070 according to nine clustered policy scenarios presenting different degrees of sustainability designed accounting for changes in physical, environmental, and socioeconomic patterns. Our results reveal that the “business-as-usual” (BAU) WRM policy can balance water supply and demand for only 32 years. According to the best policy scenario, the sustainability of the water supply could be ensured for up to 50 years by increasing the water supply by 10 % and reducing consumption by 10 %. Additionally, to aid policymakers in fostering water resource sustainability, groundwater conservation strategies are proposed using the unique scenario design framework by limiting the yield to the safe abstraction level and emphasizing the significance of preserving non-renewable groundwater resources as a “backstop” resource for the country. While marking the first holistic research study in Qatar by utilizing the SDM approach to tackle national-scale WRM challenges, the established WRM DSS model is equally applicable to other GCC countries and similar arid regions.

Spatial Decision Support System Model for Economic Resilience in East Java Province

Economic resilience has many dimensions and aspects to consider. The complexity of studying resilience ultimately requires simplification in the form of a model that can be applied in spatial decision-making. This study aims to find a model and simulation of economic resilience policy priorities for East Java Province in the form of a Spatial Decision Support System. The calculation is done by applying the TOPSIS algorithm on the vectorMCDA plugin in a geographic information system and qualitative descriptive analysis. The results of the calculation indicate that the focus of economic resilience policy is on the aspects of recovery and transformational innovation, while the ideal resilience policy alternative is the most appropriate priority policy alternative.

Models database of information technology for valuation of real estate

The goal of the research is to find and analyze ways to improve the results of real estate valuation based on a comparative approach. The chosen approach suggests the use of an information technology for the valuation of residential real estate, which is built on the basis of a database of models of decision support systems (DSS). The problem solved with the use of the discussed approach is to establish the degree of similarity between the object under consideration and its analogs by determining the value as an integral quality indicator based on the analysis of value-creating factors. This is followed by the construction of a generalized relationship between the value of this indicator and the prices of analogs. According to the logic of the comparative approach, it is assumed that the obtained relationship holds for the evaluated object as well and can be used in the calculation of its value.
 The formation of the database of models was based on the analysis of information technology processes, where details of functional aspects of process implementation, informational and logical connections between them, rights of access to information environment, and the implementation of processes by different types of users (their roles) were considered. The proposed database of models is built as a mathematical and logical basis of information technology that is applied as a computer tool for appraisers and aims to maximize formalization and efficiency improvement of process implementation based on expert evaluation methods. The construction of the database of models is based on methods of value estimation using multiple criteria, with consideration of their significance, analysis of hierarchies, ranking, Delphi, and user access control based on defined roles.

Modelling of Flood Hazard Early Warning Group Decision Support System

Early warning of flood hazards needs to be carried out comprehensively to avoid a higher risk of disaster. Every decision on early warning of a flood hazard is carried out in part by one party, namely the government or water resource managers. This research aims to provide a collaborative decision-making model for early warning of flood hazards through a Group Decision Support System Model (GDSS), especially in Indonesia. The novelty of this research is that the GDSS model involves more than one decision-maker and multi-criteria decision-making for early warning of flood hazards in the downstream Kali Sadar River, Mojokerto Regency, East Java Province, Indonesia. The GDSS model was developed using a hybrid method, namely the Analytical Network Process (ANP) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). There was more than one decision result; voting was carried out using the BORDA method to produce the decision. The test results of GDSS were obtained using a Spearman rank correlation coefficient of 0.8425 and matrix confusion, an accuracy value of 86.7%, a precision value of 86.7%, a recall value of 86.7%, and an f-measure of 86.7%. Based on the test results, good results were obtained from the GDSS model. Doi: 10.28991/CEJ-2024-010-02-018 Full Text: PDF

Digital Participatory Model as Part of a Data-Driven Decision Support System for Urban Vibrancy

Digital participation relies on computational systems as the instruments for expert engagement, data-driven insight, and informed decision-making. This study aims to increase expert engagement with the Bayesian-based decision support model in evaluating urban vibrancy decisions. In this study, urban vibrancy parameters are defined using “economic, use, and image value” measures. This article focuses on the visual aspect of vibrancy, defined as the image value of place. The image value is evaluated through likability and likability features. The case study area is the Eminönü Central Business District in the Istanbul Historic Peninsula due to its distinctive urban dynamics derived from the duality of being a cultural and cosmopolitan city center. This research presents a method as a decision support system (DSS) model based on the Bayesian belief network (BBN) and spatial BBN for supporting urban vibrancy decisions. The spatial BBNs monitor spatial outcomes of variables’ dependencies that form through the BBN relationship network. Spatial BBN tools monitors the spatial impact of decisions for informed urban interventions. The results demonstrate that urban greening, pedestrianization, and human-scaled streetscapes should be prioritized to make streets more likable. The most significant intervention areas are Tahtakale for signboard regulation, Sultanahmet and Vefa for cultural landscape improvement, and Vefa and Mahmutpaşa for planning building enclosures. The participation is achieved by evaluating urban vibrancy with what-if scenarios using BBN. The developed DSS model addresses which parameters should be prioritized, and what are their spatial consequences. The use of spatial BBN tools presents certain limitations in terms of interoperability and user interaction. Overall, this research contributes to participatory urban planning by incorporating both conditional and spatial dependencies. This unique approach not only promotes a more holistic understanding of urban vibrancy but also contributes to the advancement of digital participation in urban planning decisions.

The Determination of A Place of Popular Tourism on The Island of Madura Using Weighted Product (WP)

This research explored the diverse aspects of Madura Island, including its cultural, societal, and touristic facets. The primary focus was on developing a recommendation system to identify Madura's most popular tourist destinations. Utilizing the Weighted Product (WP) method, a decision support system model, this study assessed the popularity of various tourist attractions in Madura, aiding tourists in selecting destinations through a multi-criteria weighting process. Key parameters included the number of both foreign and local visitors, proximity to the city center, and visitor ratings. The study encompassed 62 tourist sites across four districts in Madura, evaluating the most popular attractions in each. Findings revealed the top destinations in each district: Bangkalan featured Makam Syeichona Cholil (preference value: 0.113), Sampang showcased Hutan Kera Nepa (0.127), Pamekasan highlighted Batu Ampar (0.171), and Sumenep was known for Makam Asta Tinggi (0.076). This research offered valuable insights for both tourists and stakeholders in the tourism industry of Madura Island.

Multi-criteria evaluation of reference terms requirements for development work performance

The specifics of carrying out research and development work (R&D), dictated by the requirements of legislation and standardization documents, impose a number of increased requirements on a potential contractor. The head of the organization, considering the possibility of performing R&D, makes a decision by analyzing the requirements of the terms of reference, the resources available to the organization and possible limitations. The article presents an approach to the formation of methodological support for assessing the possibility of implementing the requirements of the technical assignment for R&D within the time limits set by the contract, taking into account the resources available to the potential contractor and the level of qualification of personnel. To calculate the criteria, a modification of the PERT model is given and algorithms for analyzing the relevance of texts based on cosine similarity are applied. The proposed methodological apparatus can be used to create a model of a problem-oriented decision support system in preparation for R&D.

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.

MCDM Using Multi-Attribute Utility Theory and PIPRECIA in Customer Loan Eligibility Recommendations

Customer loan eligibility recommendations are an important step in the risk and financial assessment process. To ensure business continuity and customer satisfaction, it is important for financial institutions to consider several key factors. Problems in determining lending include various aspects that include credit risk that is difficult to assess appropriately, insufficient information about potential borrowers, potential fraud that can harm lenders. Regardless, financial institutions must ensure sound risk management and comply with applicable regulations and compliance standards to minimize potential losses and maintain fairness in decision-making related to lending. Cooperatives act as institutions that provide access to loans with more flexible terms than conventional financial institutions. The loan application process involves evaluating the customer's eligibility, which includes an analysis of financial condition, credit history, and repayment capacity. The contribution of this study provides a recommendation for savings and loan cooperatives in lending to customers using a decision support system model. The purpose of this study is to evaluate and compare customer loan feasibility using a combination of Multi-Criteria Decision Making (MCDM) methods, namely Multi-Attribute Utility Theory (MAUT) and Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA). This study aims to determine the ability of both methods to provide a comprehensive understanding of loan feasibility by considering a number of relevant criteria. The results of the customer loan eligibility rating recommend rank 1 with a final value of 0.5127 obtained by Customer K, rank 2 with a final value of 0.432 obtained by Customer I, and rank 3 with a final value of 0.3559 obtained by Customer F.

SISTEM PENDUKUNG KEPUTUSAN UNTUK KESESUAIAN LAHAN DAN TANAMAN PERTANIAN MENGGUNAKAN METODE PROFILE MATCHING

This study was made based on the body's requirements matching, because during this banya land cultivation cases that do not fit resulting in crop failures or even crop failure. The data used agricultural plant data consisting of rice, corn, soybeans and wheat. The right decision related to tersebuat is one of the determining factors in improving the agricultural sector.
 Determination of the appropriate plant based agriculture through three criteria: soil, climate and topography. Climate criteria has 4 sub-criteria which consists of precipitation, temperature, pH, and the intensity of the irradiation. This study shows how a decision support system profile matching can emberika recommendations on priorities for the agricultural crops of the land. In a profile matching methods are steps include finding the difference / gap, gap weighting, determine and calculate the value of core and secondary factor, calculate totals and calculates the ranking of land as a result. Interpolation calculations are also used in this system, which is to help find the value for the data used in the form of intervals. Interpolation is also used to help locate the weight values ​​gap gapnya values ​​in decimal form.            From the results of this study demonstrate the benefits of the profile matching method as a model for decision support system that can help fatherly determine the appropriate agricultural crops occur ring on one particular area.

A New Efficient and Privacy-Preserving Hybrid Classification model for Patient-Centric Clinical Decision Support System

A lot of focus has recently been placed on clinical decision support systems, that use advanced data mining methods to assist clinicians in making wise decisions. Along with increasing diagnosis accuracy, clinical decision support systems (CDSS) have the added benefit of speeding up diagnosis. Data security is crucial in this system. In this research, we offer EPPCD (Efficient and Privacy preserving Patient-centric Clinical Decision) support system to assist physicians in predicting illness risks of patients in a privacy-preserving manner. This system is proposed to solve the privacy difficulties present in the CDSS. The fine-grained access control enabled by the novel Double Encryption Ciphertext Policy Attribute-Based Encryption (DE-CPABE) technique is found to be a potential solution to this issue. In the proposed system, the past patients’ historical data are stored in cloud and can be used to train the hybrid Rotation Forest and AdaBoost classifier. Furthermore, extensive simulations used to evaluate performance show that our technology is capable of quickly and accurately determining a patient's disease risk while maintaining their privacy. The proposed system model is divided into five parties: Cloud Platform (CP), Trusted authority (TA), processing unit (PU), data provider (DP), and undiagnosed patient (PA).

Decision Support System For Admission Of New Employees With Weighting Method (Weight Product Model)

Firli Group is an e-commerce and digital marketing company. At every new employee recruitment, Firli Group is still manual or semi-computerized, making it difficult to determine prospective new employees. Therefore we need a system that can be petrified to facilitate decision making, namely by using the Weight Product Model (WPM). WPM is a model of Decision Support System (DSS), namely by ranking, this analysis is carried out with a sample of 5 people. Decision support system on the acceptance of new employees, is expected to help the company.

KAJIAN KARAKTERISTIK LAHAN UNTUK MENDUKUNG PENYUSUNAN MODEL DECISION SUPPORT SYSTEM (DSS) OPTIMALISASI LAHAN DI KABUPATEN PANGKEP

 
 The biophysical characteristics and environmental conditions of the land are required for the development of a Decision Support System (DSS) model for land optimization. The purpose of this study was to examine the characteristics of land cover, and the biophysical and environmental conditions of the land in Pangkep Regency. Suboptimal land identification and analysis of land and climate characteristics are carried out on the basis of secondary data obtained from satellite images, both land cover data in 2022, data in the form of three-dimensional DEM (Digital Elevation Model), and land characteristic data in the form of land system maps produced by the Geospatial Information Agency. The land cover in the Pangkep Regency area consists of fourteen types of land cover dominated by jungles, rice fields, and ponds. Flat topography with slopes of 0-2% and geological formations of alluvium and coastal deposits, Tonasa Formation, and Sekala Formation are dominant in Pangkep Regency. The dominant soil characteristic in this region is a lithosol soil type, soil depth more than 150 cm, soil pH is slightly acidic (5.6-6.5), clay texture, soil drainage criteria are inhibited and good, soil salinity in the range of 0.33%, C-Organic content with very low and low criteria, medium soil CEC, low total N value with a range of 0.1–0.21% and medium with 0.22–0.51%, Soil potassium content including very low criteria (<10 mg/100g) is in the range of 0.05 – 0.2 mg/100g and dominant 0.2 mg/100g, and rock abundance is 100+. The rainfall in this region is predominantly 2500 mm.th-1 and the average temperature is 23 - 32°C.

Adaptive Decision-Support System Model for Automated Analysis and Classification of Crime Reports for E-Government

Adaptive Decision-Support System Model for Automated Analysis and Classification of Crime Reports for E-Government

Digital twins and blockchain technology in the industrial Internet of Things (IIoT) using an extended decision support system model: Industry 4.0 barriers perspective

Blockchain technology (BT) can be considered a great help to the concept of digital twins (DT) as it can make sure of transparency, the immutability of data, decentralization of data storage, and peer-to-peer communication in industrial sectors. DT refers to the integrated multiphysics, multiscale, and probabilistic representation of a real-world physical component. The current study attempts to well envisage the ways blockchain can restructure and transform DTs to provide secure manufacturing, assuring compliance, traceability, quality, authenticity, and safety. However, to the adoption of DTs and BT in the industrial Internet of Things (IIoT), there are several barriers. To identify the key barriers, the present study discusses a survey approach through comprehensive literature and interviews with experts. To do so, this study introduced a novel approach using decision-making theory under the q-rung orthopair fuzzy set (q-ROFS) to analyze the identified barriers. This study developed a new methodology called q-ROF-subjective and objective weight integrated approach (SOWIA)-weighted aggregated sum product assessment (WASPAS). An empirical case study to assess the barriers to employing BT into DTs for IIoT implementation in the era of Industry 4.0 is taken. Also, comparative work and sensitivity analysis are discussed to demonstrate the advantage of the presented approach.