Decision Support Mechanism Research Articles

A Framework for Susceptibility Analysis of Brain Tumours Based on Uncertain Analytical Cum Algorithmic Modeling.

Susceptibility analysis is an intelligent technique that not only assists decision makers in assessing the suspected severity of any sort of brain tumour in a patient but also helps them diagnose and cure these tumours. This technique has been proven more useful in those developing countries where the available health-based and funding-based resources are limited. By employing set-based operations of an arithmetical model, namely fuzzy parameterised complex intuitionistic fuzzy hypersoft set (FPCIFHSS), this study seeks to develop a robust multi-attribute decision support mechanism for appraising patients' susceptibility to brain tumours. The FPCIFHSS is regarded as more reliable and generalised for handling information-based uncertainties because its complex components and fuzzy parameterisation are designed to deal with the periodic nature of the data and dubious parameters (sub-parameters), respectively. In the proposed FPCIFHSS-susceptibility model, some suitable types of brain tumours are approximated with respect to the most relevant symptoms (parameters) based on the expert opinions of decision makers in terms of complex intuitionistic fuzzy numbers (CIFNs). After determining the fuzzy parameterised values of multi-argument-based tuples and converting the CIFNs into fuzzy values, the scores for such types of tumours are computed based on a core matrix which relates them with fuzzy parameterised multi-argument-based tuples. The sub-intervals within [0, 1] denote the susceptibility degrees of patients corresponding to these types of brain tumours. The susceptibility of patients is examined by observing the membership of score values in the sub-intervals.

Optimizing Vaccine Access: A Web-Based Scheduling System with Geo-Tagging Integration and Decision Support for Local Health Centers

The system created aims to produce an online vaccination appointment scheduling system with geo-tagging integration and a decision-support mechanism for neighborhood health clinics. With a decision support mechanism that suggests the essential vaccines based on their account details, it is made to meet the unique vaccination needs of each patient. The system includes immunizations that are accessible locally, and patients and midwives can manage their own corresponding information through personal accounts. Viewers of websites can visualize the distribution of vaccines by purok thanks to geotagging. The Agile Scrum Methodology was modified by the researchers for early delivery, change flexibility, and continual system improvement in order to accomplish the study’s main goal. In order to assess the system’s acceptability in terms of functional adequacy, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability, it was designed in accordance with the ISO 25010 Product Software Quality Standards. Following the assessment, the system was given an average total weighted mean score of 4.62, which represents a verbal interpretation of “strongly agree”. This score demonstrates that the evaluators were in agreement that the system met the requirements of ISO 25010 for Product Software Quality Standards.

МАТЕМАТИЧНЕ МОДЕЛЮВАННЯ В ЕКОНОМІЦІ: МЕТА КЕРУВАННЯ І ПРОБЛЕМА ПРИЙНЯТТЯ РІШЕНЬ

In this article, the management purpose and the issue of decision-making are considered and disclosed. The research illustrates procedures and the essence of the main stages of the decision-making process for control systems in various conditions. The article reveals the issue of developing a controlled mechanism for decision support, which ensures the improvement of the procedures of making reasonable and objective decisions in situations of exceptional complexity. The study outlines simple and complex solutions necessary for management execution. The application of various decision-making methods and models, vital in increasing management efficiency, is illustrated. The issue of the purpose is a central issue of management. Management serves to achieve some goals. Goal formulation is one of the most challenging and debatable issues of mathematical modelling in economics, and, therefore, it confirms the relevance of its research. Management quality assessment occurs when it is impossible to implement it separately. So it is necessary to formulate a criterion by which we can compare different controls. It is natural to choose the optimal control. The problem of decision-making arises when there are difficulties in achieving some goal. Making decisions is not a simultaneous act. This is a process that takes place over time and consists of separate stages. On the one hand, the management system must ensure the transformation of resources following the set goal. However, management can be considered a relatively separate information system. When characterizing management systems in the economy, one should anticipate both parties in their relationship. This approach is convenient for studying the technology and organization of the & quot; production & quot; of information necessary for the execution of the control function. Therefore, one can consider information processes in management systems in two aspects: transformation and movement of information. In the first case, we are discussing ways to transform information in the management system, the essence of which is the decision-making process. All other subsidiary transformations arrange around it: observation and analysis, data filtering, data reproduction, and others. In the second case, we consider methods of information transfer in the control system itself and between the control system and the controlled object.

Machine Learning Applications in Dentistry

Artificial Intelligence has emerged as a breakthrough in many fields including medicine and dentistry where new approaches can be employed to solve challenging decision making processes faced in the dental field. Artificial intelligence can be used as a decision support mechanism to solve the increasing population and consequently the increasing dental treatment needs. It also assists dentists in diagnosis and treatment planning stages that require expert opinion. This mini-review covers some of the recent studies in this area and envisions future directions on the use of machine learning in dental problems.

Методика за определяне на вътрешни причини и управление на конфликти в организацията

The paper presents the results of the author’s research aimed at creating a methodology for identifying the internal causes of conflict in organizations. It also provides a summary of the results of applying this particular methodology as a key element of the management decision support mechanism for effective management of internal conflicts in organizations. In addition, the paper structures a tool useful to managers in seeking solutions to cope with the causes of conflict and to select the most appropriate strategies and methods for managing them in their organizations. Exploring the internal causes of conflict in an organization outlines what the objective needs are to adequately build on the existing conflict culture in organizations.

Flexible Learning Experience Analyzer (FLExA): Sentiment Analysis of College Students through Machine Learning Algorithms with Comparative Analysis using WEKA

The spread of the COVID-19 pandemic broughtsignificant changes in society. Emerging technologies like artificial intelligence and machine learning devices improved several industries, especially in academe and higher education institutions. In this study, a model to analyze and predict college students' sentiments from the Flexible Learning Experience portal was built using several supervised machine-learning techniques. Waikato Environment for Knowledge Analysis (WEKA) application was used to apply the Naive Bayes (NB), C4.5, Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms. Additionally, a comparative analysis of different machine-learning methods was applied. The experimental results revealed that the C4.5 algorithmobtained the highest accuracy than other algorithms. The effectiveness of each algorithm was evaluated and compared using 10-fold crossvalidation (CV), taking into account the major accuracy metrics, instances that were accurately or inaccurately classified, kappa statistics, mean absolute error, and modeling time. Moreover, results show that the C4.5 algorithm outperformed other algorithms by classifying the model with 98.13% accuracy, 0.0132 mean absolute error, and 0.00 seconds of training time. Furthermore, teachers and college administrations were well accustomed to the sentiments and problems of college students and might act as a decisionsupport mechanism mainly as they deal with the new setting during this time of crisis.

Pharmacogenomics driven decision support prototype with machine learning: A framework for improving patient care.

A growing number of healthcare providers make complex treatment decisions guided by electronic health record (EHR) software interfaces. Many interfaces integrate multiple sources of data (e.g., labs, pharmacy, diagnoses) successfully, though relatively few have incorporated genetic data. This study utilizes informatics methods with predictive modeling to create and validate algorithms to enable informed pharmacogenomic decision-making at the point of care in near real-time. The proposed framework integrates EHR and genetic data relevant to the patient's current medications including decision support mechanisms based on predictive modeling. We created a prototype with EHR and linked genetic data from the Department of Veterans Affairs (VA), the largest integrated healthcare system in the US. The EHR data included diagnoses, medication fills, and outpatient clinic visits for 2,600 people with HIV and matched uninfected controls linked to prototypic genetic data (variations in single or multiple positions in the DNA sequence). We then mapped the medications that patients were prescribed to medications defined in the drug-gene interaction mapping of the Clinical Pharmacogenomics Implementation Consortium's (CPIC) level A (i.e., sufficient evidence for at least one prescribing action) guidelines that predict adverse events. CPIC is a National Institute of Health funded group of experts who develop evidence based pharmacogenomic guidelines. Preventable adverse events (PAE) can be defined as a harmful outcome from an intervention that could have been prevented. For this study, we focused on potential PAEs resulting from a medication-gene interaction. The final model showed AUC scores of 0.972 with an F1 score of 0.97 with genetic data as compared to 0.766 and 0.73 respectively, without genetic data integration. Over 98% of people in the cohort were on at least one medication with CPIC level a guideline in their lifetime. We compared predictive power of machine learning models to detect a PAE between five modeling methods: Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), K Nearest neighbors (KNN), and Decision Tree. We found that XGBoost performed best for the prototype when genetic data was added to the framework and improved prediction of PAE. We compared area under the curve (AUC) between the models in the testing dataset.

Reducing Operation Costs of Thyroid Nodules Using Machine Learning Algorithms with Thyroid Nodules Scoring Systems

Continuous advancement in the health sector is essential to reduce costs and increase efficiency and quality of service. The widespread use of ultrasonography (USG) has made it possible to detect thyroid nodules with higher success rates. Some standard scoring systems have been developed to score thyroid nodules. Thyroid scoring systems are classification systems that determine the risk of cancer in thyroid nodules according to ultrasonographic characteristics and nodule size. Different scoring results for the same thyroid nodule may occur according to these different scoring systems, which can cause some unnecessary surgical interventions. In this study, some intelligent models are developed to assist thyroid scoring systems, with the aim to determine the correct surgical intervention and reduce operation costs by preventing unnecessary interventions and surgical procedures. The integration of current thyroid scoring systems (K-TIRADS, ACR-TIRADS, EU-TIRADS, ATA, and BTA) and machine learning methods provides radiologists and clinicians a decision-support mechanism in the evaluation of thyroid nodules. Correct diagnosis will help to reduce costs by helping prevent unnecessary procedures. The present dataset was retrospectively constructed using ultrasound images of thyroid nodules between 2014 and 2018. In determining the treatment process of thyroid nodules, Random Forest, Adaboost, J48 Decision Tree (J48 DT), and Support Vector Machine (SVM) models are used for increased prediction accuracy of thyroid scoring systems. The goal is to decrease redundant Fine Needle Aspiration (FNA) biopsies and surgical interventions of suspicious thyroid nodules. As a result of the study, higher degrees of accuracy are achieved in the determination of correct or incorrect surgical interventions of thyroid nodules using the J48 DT algorithm with the EU-TIRADS scoring system, with an accuracy rate of 99.7853%, compared to other classifiers.

IMPROVING THE VISUAL DIAGNOSIS OF DISEASES USING HYBRID NEURAL NETWORKS

Computer recognition algorithms based on microscopic images of blood particles can be used as a decision support mechanism to help specialists speed up the diagnostic process. The purpose of this work is to evaluate the quantitative analysis of hybrid neural networks (CNN + RNN). It can visually check the solution area of the input image used by CNN + LSTM. Based on the microscope image, the recognition results of blood composition particles according to their shape have been achieved up to 90%.

Smart Operation of Climatic Systems in a Greenhouse

The purpose of our work is to leverage the use of artificial intelligence for the emergence of smart greenhouses. Greenhouse agriculture is a sustainable solution for food crises and therefore data-based decision-support mechanisms are needed to optimally use them. Our study anticipates how the combination of climatic systems will affect the temperature and humidity of the greenhouse. More specifically, our methodology anticipates if a set-point will be reached in a given time by a combination of climatic systems and estimates the humidity at that time. We performed exhaustive data analytics processing that includes the interpolation of missing values and data augmentation, and tested several classification and regression algorithms. Our method can predict with a 90% accuracy if, under current conditions, a combination of climatic systems will reach a fixed temperature set-point, and it is also able to estimate the humidity with a 2.83% CVRMSE. We integrated our methodology on a three-layer holistic IoT platform that is able to collect, fuse and analyze real data in a seamless way.

IIoT Based Anomaly Detection and Maintenance Management of an Industrial Rotary System

Numerous challenges are being faced by implementing the Industrial Internet of Things (IIoT), which enables anomaly detection and optimal maintenance management of industrial production systems and their critical machines. Industries have started adopting this revolutionary technology along with other allied technologies to reap the full benefits out of Industry 4.0 environment. The objectives of this research work were to use the IIoT to develop a continuous monitoring system of the behavior of bottleneck facilities in a production system, to predict and avoid all possible failures, and to improve the overall productivity of the manufacturing system. The proposed prognostic health monitoring system employs IIoT sensors to measure the current values of the operating parameters of a machine, using built-in intelligent decision support mechanism to compare with optimal ranges of values, and to message the appropriate alarming signals as per the severity of the deviation. The system developed was tested with a prototype model comprising the Internet of Things, internet communication technology, and a machine learning algorithm, MEMS with standard input, output, storage and display of components, which was developed in a laboratory but implemented in a case study in a real industrial production plant. After the successful implementation of the developed system, the performance of the critical machine was evaluated in terms of metrics such as the average number of failures, average downtime and average service time spent. It was found that after the implementation, the downtime has decreased by nearly 22% and for the performance in terms of its output, the flow rate exhibited a steady increase with the passage of time.

Creating Meaningful Alerts and Reducing Alert Fatigue: Strategies Implemented by Informatics Pharmacists to Optimize Dose Range Checking Alerts in a Multihospital Health System.

Background: Among the many clinical decision support (CDS) mechanisms available in electronic health record (EHR) systems, dose range checking (DRC) is one of the most impactful safeguard tools integrated within most computerized provider order entry (CPOE) workflows. Unfortunately, improper configurations and lack of resources to maintain and monitor CDS systems can hinder and even disrupt daily clinical operations. Objective: This article seeks to highlight the impact that informatics pharmacists can make by implementing different strategies to decrease nuisance alerts and create clinically meaningful DRC alerts that guide clinicians in their practice. Methods: Following the activation of the DRC application for 3623 medication groupers (ie, generic drugs and all their dosage form variations), informatics pharmacists implemented strategies to monitor DRC alert output and decrease the number of inappropriate alerts. Such strategies included weekly monitoring of alerts, modification of order sentences (including dose, route, and age/weight filters), update to the rule triggering the alerts, and modifications of the preference settings. Results: From July to September 2018, an average of 70 DRC tables were reviewed by informatics pharmacists, reducing the number of overridden DRC alerts to 4796 in the first week of September-a 63% decrease in a 3-month period. Conclusions: By reducing the number of DRC nuisance alerts and improving the clinical content of DRC alerts, informatics pharmacists can contribute to lowering alert fatigue and improving providers' trust in CDS alerts.

Evolved model for early fault detection and health tracking in marine diesel engine by means of machine learning techniques

The Coast Guard Command, which has a wide range of duties as saving human lives, protecting natural resources, preventing marine pollution and battle against smuggling, uses diesel main engines in its ships, as in other military and commercial ships. It is critical that the main engines operate smoothly at all times so that they can respond quickly while performing their duties, thus enabling fast and early detection of faults and preventing failures that are costly or take longer to repair. The aim of this study is to create and to develop a model based on current data, to select machine learning algorithms and ensemble methods, to develop and explain the most appropriate model for fast and accurate detection of malfunctions that may occur in 4-stroke high-speed diesel engines. Thus, it is aimed to be an exemplary study for a data-based decision support mechanism.

Processing capacity‐based decision mechanism edge computing model for IoT applications

AbstractThe handling of complex tasks in IoT applications becomes difficult due to the limited availability of resources in most IoT devices. There arises a need to offload the IoT tasks with huge processing and storage to resource enriched edge and cloud. In edge computing, factors such as arrival rate, nature and size of task, network conditions, platform differences and energy consumption of IoT end devices impacts in deciding an optimal offloading mechanism. A model is developed to make a dynamic decision for offloading of tasks to edge and cloud or local execution by computing the expected time, energy consumption and processing capacity. This dynamic decision is proposed as processing capacity‐based decision mechanism (PCDM) which takes the offloading decisions on new tasks by scheduling all the available devices based on processing capacity. The target devices are then selected for task execution with respect to energy consumption, task size and network time. PCDM is developed in the EDGECloudSim simulator for four different applications from various categories such as time sensitiveness, smaller in size and less energy consumption. The PCDM offloading methodology is experimented through simulations to compare with multi‐criteria decision support mechanism for IoT offloading (MEDICI). Strategies based on task weightage termed as PCDM‐AI, PCDM‐SI, PCDM‐AN, and PCDM‐SN are developed and compared against the five baseline existing strategies namely IoT‐P, Edge‐P, Cloud‐P, Random‐P, and Probabilistic‐P. These nine strategies are again developed using MEDICI with the same parameters of PCDM. Finally, all the approaches using PCDM and MEDICI are compared against each other for four different applications. From the simulation results, it is inferred that every application has unique approach performing better in terms of response time, total task execution, energy consumption of device, and total energy consumption of applications.

Social influence-based contrast language analysis framework for clinical decision support systems

Depression is a leading mental health problem affecting 300 million people globally. Recent studies show that social networks provide a tremendous potential for mental health professionals as a source of supplemental information about their patients. This study presents a methodological framework for clinical decision support systems (CDSSs) through analysis of social network data to distinguish the language usage of individuals with early signs of depression (i.e., contrast language analysis). By analyzing the contrast language patterns of different user groups, we are able to uncover constructive and actionable insights into the pain points and characteristics of users with signs of depression as decision support mechanisms for clinicians during intervention, (early) diagnosis and treatment plans. First, we discover terms that represent contrasting language by analyzing the percentage difference of terms in two user groups, labeled as”depressed” and”non-depressed” for ease of reference. Second, by building topic models based on social network contents, the topic-level contrast features are discovered. Finally, we consider the structure of the social network to discover the network-level contrast features. To illustrate the effectiveness of the proposed framework, we present a case study on early depression detection using a real-world dataset. The proposed framework has methodological contributions in enhancing the features and functionalities of CDSS for clinicians. It also contributes to evidence-based health research through cost-effective data and analytical insights that can supplement or improve the traditional survey and time-consuming interview methods.

Support decision system based on invoices data mining to estimate commercial pent-up demands

In the world, all the time, commercial transactions are carried out. A large proportion of those commercial transactions are stored in some way from the companies. The data presented in those databases have the potential to enable several analyses about those transactions, for example, where customers came from to pick up the product. Therefore, liking stores and customers, it is possible to observe some patterns in their relationship. However, if the customer’s unfulfilled wish is not registered, how can pent-up demands be identified? Here, we aim to present a methodology developed to identify pent demand by analyzing commercial invoices. In this regard, we are going to use Brazil as a case study to apply our methodology. Most of the commercial transactions in Brazil are informed to the government by electronic invoices (NFe). These invoices are an electronic version of the register of Brazilian commercial transactions. In these invoices, there is information about the commercial transaction carried out. Using the information collected in the electronic invoices, it is possible to quantitatively evaluate the existence of pent-up demand about some product in a specific region and then create decision support mechanisms. Our experiment observed that 13,6% of products presented a strong indication of pent-up demand according to our methodology. This analysis shows the decision-makers opportunities to explore the potential in increasing the sales of some products.

Blockchain-based mass customization framework using optimized production management for industry 4.0 applications

Customers want to experience unique products that suit their individual preferences. Therefore, companies are looking for ways to produce personalized products by moving away from traditional production models. Technologies such as blockchain, IoT, and cyber-physical systems have an important role in adopting the personal custom production model. In this study, a reliable and optimized mass customization framework is proposed. The framework has three main contributions. First, the use of Blockchain provides advantages in terms of data persistence, traceability, transparency, and reliability. Second, the production process is optimized by using artificial intelligence methods. A decision support mechanism is proposed for customers who will place orders. As a combination of the contributions, the proposed mass customization framework covers the processes from ordering to production. In addition to contributions such as facilitated customization and reliable data, energy saving in production is increased with the proposed framework. Simulation results show that energy savings between 10 and 47% are achieved.

Grouped multi-attribute bid-evaluation decision-making method based on selection preferences and entropy weights

With recent developments in information technology and the extensive promotion of Internet Plus, the use of online centralized procurement by governments and enterprise groups has become progressively more common, and the winning bid evaluation decision making method is particularly important in this context. However, experts might not be completely rational during the process of bid evaluation, which may induce the enhancement or repression of bid scores. To address such behaviors during the process of bid evaluation, an automatic mechanism to identify and correct such tendencies is proposed in this study. Because experts have different preferences for different alternatives, which are directly reflected in the evaluation of attribute values. Based on selection preference, this paper proposes a selection preference method for solving the subjective weight of attributes. Firstly, the weights of relevant attributes are first determined via the entropy weight method and the selection preference method, and the weights corresponding to groups are determined according to the differences between the scores assigned by experts. Then, a grouped multi-attribute bid evaluation decision making method is proposed based on the selection preference. Finally, an example is used to verify the effectiveness of the method and its superiority over existing methods. Thus, a theoretical basis and a decision support mechanism are provided in this study for centralized procurement departments of governments and enterprises. Further, it also provides guidance for multi-attribute decision making problems with identical grouped features.

COVID-19 diagnosis on CT images with Bayes optimization-based deep neural networks and machine learning algorithms.

Early diagnosis of COVID-19, the new coronavirus disease, is considered important for the treatment and control of this disease. The diagnosis of COVID-19 is based on two basic approaches of laboratory and chest radiography, and there has been a significant increase in studies performed in recent months by using chest computed tomography (CT) scans and artificial intelligence techniques. Classification of patient CT scans results in a serious loss of radiology professionals' valuable time. Considering the rapid increase in COVID-19 infections, in order to automate the analysis of CT scans and minimize this loss of time, in this paper a new method is proposed using BO (BO)-based MobilNetv2, ResNet-50 models, SVM and kNN machine learning algorithms. In this method, an accuracy of 99.37% was achieved with an average precision of 99.38%, 99.36% recall and 99.37% F-score on datasets containing COVID and non-COVID classes. When we examine the performance results of the proposed method, it is predicted that it can be used as a decision support mechanism with high classification success for the diagnosis of COVID-19 with CT scans.

An integrated cyber security risk management framework and risk predication for the critical infrastructure protection

Cyber security risk management plays an important role for today’s businesses due to the rapidly changing threat landscape and the existence of evolving sophisticated cyber attacks. It is necessary for organisations, of any size, but in particular those that are associated with a critical infrastructure, to understand the risks, so that suitable controls can be taken for the overall business continuity and critical service delivery. There are a number of works that aim to develop systematic processes for risk assessment and management. However, the existing works have limited input from threat intelligence properties and evolving attack trends, resulting in limited contextual information related to cyber security risks. This creates a challenge, especially in the context of critical infrastructures, since attacks have evolved from technical to socio-technical and protecting against them requires such contextual information. This research proposes a novel integrated cyber security risk management (i-CSRM) framework that responds to that challenge by supporting systematic identification of critical assets through the use of a decision support mechanism built on fuzzy set theory, by predicting risk types through machine learning techniques, and by assessing the effectiveness of existing controls. The framework is composed of a language, a process, and it is supported by an automated tool. The paper also reports on the evaluation of our work to a real case study of a critical infrastructure. The results reveal that using the fuzzy set theory in assessing assets' criticality, our work supports stakeholders towards an effective risk management by assessing each asset's criticality. Furthermore, the results have demonstrated the machine learning classifiers’ exemplary performance to predict different risk types including denial of service, cyber espionage and crimeware.