Proposed Decision Support System Research Articles

A neutrosophic WENSLO-ARLON model for measuring sustainable brand equity performance

Sustainable brand equity denotes the perceived brand equity by consumers, influenced by companies' non-financial and environmental practices. To cultivate sustainable brand equity, corporations undertake green initiatives and engage in social responsibility endeavors, thereby communicating environmentally oriented messages to consumers. Companies feel the necessity to establish a competitive position in sustainability relative to their counterparts. Hence, the primary objective of this research is to conduct an inquiry aimed at identifying sustainable brand equity to meet this exigency of companies. In this context, the motivation of this study lies in treating the calculation of sustainable brand equity as a decision-making process and developing a decision support system for this purpose. The proposed decision support system for determining sustainable brand equity levels in this research has three fundamental inputsss: experts, criteria, and brands. The impact levels of experts on the decision-making process are quantified utilizing type-2 neutrosophic number (T2NN) sets. The weights of criteria are ascertained employing the weight by envelope and slope (WENSLO) method extended with T2NN sets. The ranking of sustainable brand equities is established through the innovative T2NN-based alternative ranking using two-step logarithmic normalization (ARLON) method. These methodologies are amalgamated, and the T2NN-WENSLO-ARLON hybrid model is posited as a decision support system for evaluating sustainable brand equity performance. An algorithm for T2NN-WENSLO-ARLON is formulated and elucidated alongside a case study focusing on companies operating in the cosmetics sector in Turkey. The case study identifies the “green product leadership” criterion as the most influential factor in determining sustainable brand equity. Furthermore, the cosmetic company “Misbahçe Inc.” is identified as the brand with the highest sustainable brand equity. Sensitivity and comparative analyses are conducted to ascertain the robustness of the results and the decision model. All scenario outcomes bolster the decision model. The research provides comprehensive insights into both the developed tool for calculating sustainable brand equity and develops implications for the cosmetics industry.

Evaluation of Ergonomic Risks for Construction Workers based on Multicriteria Decision Framework with the Integration of Spherical Fuzzy Set and Alternative Queuing Method

Ergonomic risks critically impact workers’ occupational health, safety, and productivity, and thereby the sustainability of a workforce. In the construction industry, the physical demands and dynamic environment exposes workers to various ergonomic hazards. While previous research has mainly focused on postural risks, there is a need to broaden the scope to include more relevant factors and assess them systematically. This study introduces a multi-criteria decision framework integrating the Spherical Fuzzy Sets (SFSs) and Alternative Queuing Method (AQM) to evaluate and prioritize ergonomic hazards. First, SFSs are employed to quantify the linguistic expressions of experts, addressing the inherent vagueness and uncertainty. Then, an entropy-based objective weighting method is adopted to determine the criteria weights. Finally, AQM is utilized to generate the risk priority. The proposed method has been implemented in a real-life construction project, where “overexertion due to unreasonable task organization”, “hypertension and heart diseases”, and “existing WMSD record” are identified as the top three ergonomic hazards. Then, a thorough discussion of intervention strategies regarding different risk categories is presented to facilitate ergonomic interventions. This proposed decision support system can promote effective ergonomic risk management, benefiting workers’ health and well-being and contributing to the sustainable workforce development of the construction industry.

The design and development of a dashboard for improving sustainable healthy food choices

Over the last decade, several digital tools have been designed to provide consumers with nutritional and environmental impact information about their food choices post-consumption. Many of these tools lack behavioral change modules, have low user engagement, and ignore inherent environmental nutrition trade-offs to stimulate dietary change. This study presents the design and development of a decision support system to enhance consumer health while meeting sustainability goals from a pre-consumption perspective. The proposed decision support system, Dashboard for Improving Sustainable Healthy (DISH) food choices, employs behavioral features, traffic light labels, and nudges to inform end-users about the nutritional health performance and environmental impact of meals. DISH uses a simple metric that allows end-users to explore the potential minutes of healthy and productive life gained or lost from consuming 100 kcal of a meal. The metric combines the positive or negative nutritional health effects (μ-DALYs) of consuming a meal and environmental damage (endpoint impact expressed in DALYs) on human health. In the DISH application, end-users are rewarded or deducted EnCoins, which represent the number of silver or gold coins lost or gained based on the cost ($) of environmental damage (midpoint impacts) of a meal compared to reference sustainable healthy and unsustainable and unhealthy meal. DISH's gamification module enables end-users to track the potential minutes of healthy and productive life gained/lost and gold or silver rewards or deductions from consuming 100 kcal of a selected meal through cumulative minutes gained or lost and EnCoins. In promoting a sustainable diet culture, the gamification module enables users to create groups and communities where friends and families can track their sustainability performance through meal decisions. The DISH application is currently available online and can be accessed by an end-user through any device. Further pilot studies will focus on testing the technology in partner campus cafeterias.

Applying a Genetic Algorithm to Implement the Fuzzy-MACBETH Method in Decision-Making Processes

This paper describes the development of an evolutionary algorithm for building cardinal scales based on the Fuzzy-MACBETH method. This method uses a triangular fuzzy numbers scale in the MACBETH method to incorporate the subjectivity of a semantic scale into mathematical modeling, which enables circumventing the cardinal inconsistency problem of the classical method, facilitating its application in complex contexts. A genetic algorithm is used in the fuzzy system developed here to build the basic fuzzy scale in a cardinally inconsistent decision matrix. The proposed technique is inspired by crossover and mutation genetic operations to explore potential solutions and obtain a cardinal scale aligned with the decision maker’s preferences. Finally, an illustrative example of the application of the proposed decision support system is presented. The results confirm that the FGA-MACBETH method aligns with the classical method. This study’s primary contribution is that circumventing the problem of cardinal inconsistency in a semantically consistent decision matrix enabled obtaining a cardinal scale without requiring the decision maker to redo his/her initial assessments.

A decision support system to recommend appropriate therapy protocol for AML patients.

Acute Myeloid Leukemia (AML) is one of the most aggressive hematological neoplasms, emphasizing the critical need for early detection and strategic treatment planning. The association between prompt intervention and enhanced patient survival rates underscores the pivotal role of therapy decisions. To determine the treatment protocol, specialists heavily rely on prognostic predictions that consider the response to treatment and clinical outcomes. The existing risk classification system categorizes patients into favorable, intermediate, and adverse groups, forming the basis for personalized therapeutic choices. However, accurately assessing the intermediate-risk group poses significant challenges, potentially resulting in treatment delays and deterioration of patient conditions. This study introduces a decision support system leveraging cutting-edge machine learning techniques to address these issues. The system automatically recommends tailored oncology therapy protocols based on outcome predictions. The proposed approach achieved a high performance close to 0.9 in F1-Score and AUC. The model generated with gene expression data exhibited superior performance. Our system can effectively support specialists in making well-informed decisions regarding the most suitable and safe therapy for individual patients. The proposed decision support system has the potential to not only streamline treatment initiation but also contribute to prolonged survival and improved quality of life for individuals diagnosed with AML. This marks a significant stride toward optimizing therapeutic interventions and patient outcomes.

Urinary Bladder Inflammation Prediction with the Gray Wolf Optimization Algorithm and Multi-Layer Perceptron-Based Hybrid Architecture

In this study, a decision support system for bladder inflammation prediction is presented. The proposed decision support system is built by establishing a hybrid architecture with Gray wolf optimization algorithm (GWO) and Multi-layer perceptron (MLP) networks. In addition to optimizing the hyperparameters in the MLP structure with GWO, the hybrid architecture also optimizes the order of input values to be presented to the MLP structure. The Acute Inflammations data set in the UCI Machine Learning repository was used as the data set in the study. Classification operations were carried out on this data set with the models obtained with hybrid architecture, Decision trees, k-Nearest Neighbors and Support Vector Machines methods. The controversial findings presented as a result of experimental studies have shown that the proposed hybrid architecture produces more successful results than other machine learning methods used in the study. In addition, the MLP network structure optimized with the hybrid architecture offers a new diagnostic method in terms of patient decision support systems.

A decision support system for autonomous ship trajectory planning

This study presents an approach to enhance human–machine collaboration in autonomous ship trajectory planning. A decision support system is developed, considering crucial environmental factors such as ocean currents, wind, and tidal information, alongside the integration of narrow channel geometry, squat effect, and Under-keel-clearance (UKC). The Dynamic Consequence Analysis (DCA) risk assessment method is utilized to establish dynamic safety domains, incorporating ship maneuvering characteristics and potential failure scenarios. The Multi-objective Particle Swarm Optimization (MOPSO) algorithm is then employed to generate alternative trajectories, optimizing five objective functions: minimizing safety domain violation, consecutive speed changes, path length, deviation from the initial plan, and deviation from the initial estimated time of arrival. Furthermore, two Multi-criteria Decision Making (MCDM) methods, Multi-Objective Optimization by Ratio Analysis (MOORA) with user preferences, and the Entropy Weight Method (EWM) with automatic weight allocation, are employed to rank alternative solutions from the Pareto front. Finally, a clustering method is employed on the Pareto front solutions. The outcomes that serve as representatives for these clusters are then merged with the highest-rated alternative solutions from MCDM methods. This combined set forms the basis for the final decision-making process carried out by the operator. While dynamic obstacles are not considered in this study, evaluation across three scenarios demonstrates the effectiveness of the proposed decision support system for trajectory planning.

Intelligent decision support system for assessing the risk of failures of complex technical systems

A decision support system has been developed that allows assessing the risk of failure of elements and components of complex technical systems using artificial intelligence. The proposed algorithm for the functioning of a decision support system implements the task of automating the process of assessing the technical condition of complex systems. The use of the proposed decision support system for assessing the technical condition of complex systems will improve the reliability of operating systems with insufficient information about their technical condition.

Electronic Health Record (EHR) System Development for Study on EHR Data-based Early Prediction of Diabetes Using Machine Learning Algorithms

Aims: This research work aims to develop an interoperable electronic health record (EHR) system to aid the early detection of diabetes by the use of Machine Learning (ML) algorithms. A decision support system developed using many ML algorithms results in optimizing the decision in preventive care in the health information system. Methods: The proposed system consisted of two models. The first model included interoperable EHR system development using a precise database structure. The second module comprised of data extraction from the EHR system, data cleaning, and data processing and prediction. For testing and training, about 1080 patients’ health record was considered. Among 1080, 1000 records were from the Kaggle dataset, and 80 records were demographic information from patients who visited our health center of Siddaganga organization for a regular checkup or during emergencies. The demographic information was collected from the proposed EHR system. Results: The proposed system was tested for the interoperability nature of the EHR system and accuracy in diabetic disease prediction using the proposed decision support system. The proposed EHR system development was tested for interoperability by random updations from various systems maintained in the laboratory. Each system acted like the admin system of different hospitals. The EHR system was tested for handling the load and interoperability by considering user view status, system matching with the real world, consistency in data updations, security etc. However, in the prediction phase, diabetes prediction was concentrated. The features considered were not randomly chosen; however, the features were those prescribed by a doctor who insisted that the features were sufficient for initial prediction. The reports collected from the doctors revealed several features they considered before giving the test details. The proposed system dataset was split into test and train datasets with eight proper features taken as input and one set as a target variable where the result was present. After this, the model was imported using standard “sklearn” libraries, and it fit with the required number of estimators, that is, the number of decision trees. The features included pregnancies, glucose level, blood pressure, skin thickness, insulin level, bone marrow index, diabetic pedigree function, age, weight, etc. At the outset, the research work concentrated on developing an interoperable EHR system, identifying the expectation of diabetic and non-diabetic conditions and demonstrating the accuracy of the system. Conclusion: In this study, the first aim was to design an interoperable EHR system that could help in accumulating, storing, and sharing patients' timely health records over a lifetime. The second aim was to use EHR data for early prediction of diabetes in the user. To confirm the accuracy of the system, the system was tested regarding interoperability to support early prediction through a decision support system.

Decision support system for power cable insulation vulcanization production line management

The paper proposes a structural model of a decision support system for controlling a cable insulation continuous vulcanization line. The proposed decision support system is based on a mathematical model of the cable insulation continuous vulcanization technological process, a database, and a mode correction algorithm. The DSS will make it possible to quickly develop a new regime in case of using new materials or structures, as well as to correct the current regime in case of unplanned changes during the production process. The core of the DSS is the proposed mathematical model, which is based on conservation laws and is presented as a system of differential equations closed by boundary conditions. The vulcanization process is described taking into account the temperature-time dependence of the kinetic parameters, which made it possible to take into account the uneven heating of the cable, both in the radial direction and along the length. The numerical implementation of the differential mathematical model made it possible to carry out a systematic analysis of the nature of the processes in the vulcanization pipe and to evaluate the influence of various technological, structural and material parameters on the cable insulation vulcanization completion degree. As a result of the analysis, significant parameters were identified that significantly affect the vulcanization process completion degree. Based on the obtained numerical results, technological parameters dependence technological surfaces on the geometry of the product and the properties of the materials used were constructed, a regression mathematical model was proposed that allows determining the values of the process control parameters without resorting to the use of a differential mathematical model. Based on the results of the analysis, an algorithm for correcting the technological regime is proposed, taking into account only significant process parameters. The proposed algorithm allows you to adjust the value of the isolation rate, depending on external influences and deviations from the specified parameters. The results of the study can be used in the production of cable and wire products with vulcanizable insulation, when it is necessary to quickly select the optimal technological mode, take into account changes in the cable design, properties of the insulation material, as well as possible pressure deviations inside the vulcanization pipe.

CLINICAL DECISION SUPPORT SYSTEM FOR EARLY DIAGNOSIS OF HEART ATTACK USING MACHINE LEARNING METHODS

Heart attack which is the main cause of death for both men and women is the leader among deaths due to heart diseases. Therefore, early diagnosis is very important for patients who are having a heart attack. Therefore, the study aimed to develop a clinical decision support system for the diagnosis of a heart attack to help physicians. In the study, variables were obtained accompanied by physicians by statistical analysis methods, where the optimum variables were selected from these variables considering the patient’s unconscious state in some cases. Different decision models were developed using probit regression, decision tree, SVM, and ANN methods. As a result, the developed clinical decision support models for heart attack diagnosis were compared and evaluated. Consequently, the best diagnosis model was obtained using ANN with selected variables. In addition to these, the proposed study is significantly noticed with a sensitivity of 98% and specificity of 93.7% for heart attack diagnosis with optimum variables compared to similar studies in the literature. By using the proposed decision support system, it is possible to determine whether a patient has a heart attack or not and help the physician in the process of diagnosis of a heart attack.

An integrated Multi-Criteria Decision Making and Multi-Choice Conic Goal Programming approach for customer evaluation and manager assignment

Companies adopt a customer-centric approach to maintain their competitive position or dominate the market. In this context, managers are pivotal in navigating the competitive markets in accomplishing these goals. Setting targets and conducting performance evaluations are vital for managers’ long-term success. This study aims to optimise customer evaluation and manager assignment in the defence industry. An integrated approach utilising the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed to conduct a comprehensive market analysis and conduct this evaluation. A bi-objective integer programming model is formulated to maximise​ total integrated scores and potential returns. The efficient number of visits is determined by combining Multi-Criteria Decision Making (MCDM) and Multi-Choice Conic Goal Programming (MCCGP). This study offers an objective alternative to traditional survey-based evaluations by proposing a novel decision support system for Goal-Based Performance Appraisals (GBPA) of managers. The proposed approach yields performance values that are considerably more reliable than questionnaire results. Moreover, the proposed decision support system can effectively benefit employees across diverse sectors, extending beyond the confines of the defence industry.

Fuzzy logic-based decision support system for automating ergonomics risk assessments

Workers in the modular construction industry are frequently exposed to ergonomic risks, which may lead to injuries and lower productivity. In light of this, researchers have proposed a number of ergonomics risk assessment methods to identify design flaws in work systems, thereby reducing ergonomic discomfort and boosting workplace productivity. However, organizations often disregard ergonomics risk assessments due to a lack of convenient tools and knowledge. Therefore, this study proposes a fuzzy logic-based decision support system to help practitioners to automatically and comprehensively assess the ergonomic performance of work systems. For comprehensive assessment of ergonomic risk, the proposed decision support system considers physical, environmental, and sensory factors. Specifically, the decision support system comprises eight fuzzy expert systems that output a composite risk score, called an “ergonomic risk indicator”, that indicates the overall level of ergonomic risk present in a given work system. The performance of the proposed decision support system is then evaluated using a real-world case study in a modular construction facility by comparing the results of the decision support system with the facility's occupational injury reports. The results prove the effectiveness of the decision support system. Overall, the decision support system is capable of generating a composite risk score, the ergonomic risk indicator, and the proposed high-level architecture and design represent significant contributions for the enhancement of health and safety in the modular construction industry.

Integrating Continuous and Batch Processes with Shared Resources in Closed-Loop Scheduling: A Case Study on Tuna Cannery.

Scheduling tasks in production facilities are usually hybrid optimization problems of a large combinatorial nature. They involve solving, in near-real time, the integration of the operation of several batch units of continuous dynamics with the discrete manufacture of items in processing lines. Moreover, one has to deal with uncertainty (process delays, unexpected stops) and the management of shared resources (energy, water, etc.) including decisions made by plant operators: still, some tasks in the scheduling layers are done manually. Manufacturing Execution Systems (MESs) are intended to support plant personnel at this level. However, there is still much work to do in terms of performing automatic scheduling, computed in real time, that guides managers to achieve an optimal operation of such complex cyber-physical systems. This work proposes a closed-loop approach to handle the uncertainty arising when facing the online scheduling of supply lines and parallel batch units. These units often share some resources, so effects due to concurrent resource consumption on the system dynamics are explicitly considered in the presented formulation. The proposed decision support system is tested onsite in a tuna cannery, to handle short-term online scheduling of sterilization processes that deal with limited steam, carts, and operators as shared resources.

Decision Support System for Predicting Mortality in Cardiac Patients Based on Machine Learning

Researchers have proposed several automated diagnostic systems based on machine learning and data mining techniques to predict heart failure. However, researchers have not paid close attention to predicting cardiac patient mortality. We developed a clinical decision support system for predicting mortality in cardiac patients to address this problem. The dataset collected for the experimental purposes of the proposed model consisted of 55 features with a total of 368 samples. We found that the classes in the dataset were highly imbalanced. To avoid the problem of bias in the machine learning model, we used the synthetic minority oversampling technique (SMOTE). After balancing the classes in the dataset, the newly proposed system employed a χ2 statistical model to rank the features from the dataset. The highest-ranked features were fed into an optimized random forest (RF) model for classification. The hyperparameters of the RF classifier were optimized using a grid search algorithm. The performance of the newly proposed model (χ2_RF) was validated using several evaluation measures, including accuracy, sensitivity, specificity, F1 score, and a receiver operating characteristic (ROC) curve. With only 10 features from the dataset, the proposed model χ2_RF achieved the highest accuracy of 94.59%. The proposed model χ2_RF improved the performance of the standard RF model by 5.5%. Moreover, the proposed model χ2_RF was compared with other state-of-the-art machine learning models. The experimental results show that the newly proposed decision support system outperforms the other machine learning systems using the same feature selection module (χ2).

DeepMiCa: Automatic segmentation and classification of breast MIcroCAlcifications from mammograms

Background and objectiveBreast cancer is the world’s most prevalent form of cancer. The survival rates have increased in the last years mainly due to factors such as screening programs for early detection, new insights on the disease mechanisms as well as personalised treatments. Microcalcifications are the only first detectable sign of breast cancer and diagnosis timing is strongly related to the chances of survival. Nevertheless microcalcifications detection and classification as benign or malignant lesions is still a challenging clinical task and their malignancy can only be proven after a biopsy procedure. We propose DeepMiCa, a fully automated and visually explainable deep-learning based pipeline for the analysis of raw mammograms with microcalcifications. Our aim is to propose a reliable decision support system able to guide the diagnosis and help the clinicians to better inspect borderline difficult cases. MethodsDeepMiCa is composed by three main steps: (1) Preprocessing of the raw scans (2) Automatic patch-based Semantic Segmentation using a UNet based network with a custom loss function appositely designed to deal with extremely small lesions (3) Classification of the detected lesions with a deep transfer-learning approach. Finally, state-of-the-art explainable AI methods are used to produce maps for a visual interpretation of the classification results. Each step of DeepMiCa is designed to address the main limitations of the previous proposed works resulting in a novel automated and accurate pipeline easily customisable to meet radiologists’ needs. ResultsThe proposed segmentation and classification algorithms achieve an area under the ROC curve of 0.95 and 0.89 respectively. Compared to previously proposed works, this method does not require high performance computational resources and provides a visual explanation of the final classification results. ConclusionTo conclude, we designed a novel fully automated pipeline for detection and classification of breast microcalcifications. We believe that the proposed system has the potential to provide a second opinion in the diagnosis process giving the clinicians the opportunity to quickly visualise and inspect relevant imaging characteristics. In the clinical practice the proposed decision support system could help reduce the rate of misclassified lesions and consequently the number of unnecessary biopsies.

A decision support system for the identification of metastases of Metastatic Melanoma using whole-body FDG PET/CT images.

Metastatic Melanoma (MM) is an aggressive type of cancer which produces metastases throughout the body with very poor survival rates. Recent advances in immunotherapy have shown promising results for controlling disease's progression. Due to the often rapid progression, fast and accurate diagnosis and treatment response assessment is vital for the whole patient management. These procedures prerequisite accurate, whole-body tumor identification. This can be offered by the imaging modality Positron Emission Tomography (PET)/Computed Tomography (CT) with the radiotracer F 18-Fluorodeoxyglucose (FDG). However, manual segmentation of PET/CT images is a very time-consuming and labor intensive procedure that requires expert knowledge. Most of the previously published segmentation techniques focus on a specific type of tumor or part of the body and require a great amount of manually labeled data, which is, however, difficult for MM. Multimodal analysis of PET/CT is also crucial because FDG-PET contains only the functional information of tumors which can be complemented by the anatomical information of CT. In this paper, we propose a whole-body segmentation framework capable of efficiently identifying the highly heterogeneous tumor lesions of MM from the whole-body 3D FDG-PET/CT images. The proposed decision support system begins with an Ensemble Unsupervised Segmentation of regions of high FDG-uptake based on Fuzzy C-means and a custom region growing algorithm. Then, a region classification model based on radiomics features and Neural Networks classifies these regions as tumors or not. Experimental results showed high performance in the identification of MM lesions with Sensitivity 83.68%, Specificity 91.82%, F1-score 75.42%, AUC 94.16% and Balanced accuracy 87.75% which were also supported by the public dataset evaluation.

Can NeuroIS improve executive employee recruitment? Classifying levels of executive functions using resting state EEG and data science methods

Based on the assumption that the success of an organization is largely determined by the knowledge and skills of its employees, human resource (HR) departments invest considerable resources in the employee recruitment process with the aim of selecting the best, most suitable employees. Due to the high cost of the recruitment process along with its high rate of uncertainty, HR recruiters utilize a variety of methods and instruments to improve the efficiency and effectiveness of this process. Thus far, however, neurological methods, in which neurobiological signals from an examined person are analyzed, have not been utilized for this purpose. This study is the first to propose a neuro-based decision support system to classify cognitive functions into levels, whose target is to enrich the information and indications regarding the candidate along the employee recruitment processes. We first measured relevant functional and cognitive abilities of 142 adult participants using traditional computer-based assessment, which included a battery of four tests regarding executive functions and intelligence score, consistent with actual recruitment processes. Second, using electroencephalogram (EEG) technology, which is one of the dominant measurement tools in NeuroIS research, we collected the participants' brain signals by administering a resting state EEG (rsEEG) on each participant. Finally, using advanced machine and deep learning algorithms, we leveraged the collected rsEEG to classify participants' levels of executive functions and intelligence score. Our empirical analyses show encouraging results of up to 72.6% accuracy for the executive functions and up to 71.2% accuracy for the intelligence score. Therefore, this study lays the groundwork for a novel, generic (non-stimuli based) system that supports the current employee recruitment processes, that is based on psychological theories of assessing executive functions. The proposed decision support system could contribute to the development of additional medium of assessing employees remotely which is especially relevant in the current Covid-19 pandemic. While our method aims at classification rather than at explanation, our intriguing findings have the potential to push forward NeuroIS research and practice.

JETSON NANO DEVKIT BASED DECISION SUPPORT SYSTEM DESIGN FOR SPRAYING STRATEGY AND SPRAYING SYSTEM OF AGRICULTURAL UNMANNED AERIAL VEHICLES

Aim: The aim of this paper is to create an intelligent decision support system for spraying and fertilization operations in agricultural lands with Agricultural Unmanned Aerial Vehicles. In this system, different pesticides will be sprayed in sparse or undeveloped areas, different pesticides will be sprayed in areas with normal development and different pesticides will be sprayed in areas with intensive development and plant health will be protected. Thus, inadequate spraying or burning of the plant due to over spraying will be prevented. Method: In this paper, a decision support system embedded in the Jetson Nano Devkit Artificial Intelligence Computer is developed for spraying and fertilization operations performed by Agricultural Unmanned Aerial Vehicles (UAVs). Three different image processing algorithms ("RGB Filter Method", " Pixelwise Classification Method" and "HSV Range Filter Method") were tested on 4000 agricultural field images in order to accurately analyze the snapshots obtained from UAVs. Solution time and solution reliability (green capture rate) were considered as two different objectives in this system where the images should be evaluated instantaneously and the amount of pesticide spraying should be changed. According to the image processing results, the working principle of the spraying system, which was created according to 3 different levels (low-medium-high level) and "Pulse Width Modulation" and "Pulse Width Modulation" techniques, was simulated through the "Proteus" simulation programme and transferred to the real system. Results: As a result of the analysis, it was determined that the "RGB Filter Method" is the ideal algorithm that simultaneously meets the objectives of solution time and solution reliability. In the spraying system, the amount of pesticide to be sprayed per second was determined for 3 stages. Finally, it was decided to take an image every 6 seconds from the camera on the APC and to spray accordingly. Conclusion: Thanks to the proposed decision support system, agricultural areas can be sprayed at dynamic levels according to the level of plant needs. In today's world of global warming, soil erosion and water scarcity, the study is expected to make a positive contribution to smart agricultural practices.

Simulation-Based Optimization Using Virtual Supply Chain Structured by the Configuration Platform

This study develops a decision support system for optimizing the performance of virtual supply chains. In the proposed framework, an exhaustive search algorithm is developed to search for all the possible solutions. For each candidate solution that is generated by the algorithm, a configuration platform is used to generate a virtual supply chain and evaluate its performance. The performance of the proposed decision support system is examined on a small size supply chain optimization problem. The experimental results show that the proposed algorithm can find the optimal solution for the optimal manufacturer selection and lot size problem.