Decision Algorithm Research Articles

Matrix Theory Approach to Fuzzy Hypersoft Set in Multi-Criteria Airline Selection

When we talk about decision-making then it means we have to tackle the attributes and alternatives, and it becomes difficult when attributes are further subdivided. The hypersoft theory allows us to handle further bifurcated attributes. This paper is aim to provide the legal way to deal with multi-criteria decision-making (MCDM) problems in the sense of further subdividing attributes by applying the concept of fuzzy hypersoft matrices (FHSM’s). Since matrices provide us with a fast and reliable way to solve problems. In this paper, we have proposed the concept of FHSM with their operators, theorems, propositions, and decision-making (DM) algorithms. The proposed algorithms have been supported with real-life application of airline selection while you have to take any flight. The concept of further bifurcation gives more accurate and refined results than the existing techniques, along with the implication of FHSM. In the future, the proposed concept can be implemented in other hybrids of hypersoft set theory with DM algorithms. The challenges faced in implication can be dealt with machine learning algorithms for fast and more accurate results.

Innovative Research on Physical Data Management of Football Players Under the Background of Smart Medical Treatment

With the intensification of the competition for public medical resources and the rapid development of smart medical technology, the demand for in-depth analysis of athletes’ basic information and decision support is growing day by day. However, the practice of applying smart medicine to athletes’ physical information management is still relatively limited. This paper aims to explore how to use smart medical decision technology to build an integrated athlete physical information management and decision support platform. Taking football players as the research object, this paper integrates data mining technology into a smart medical decision-making model, to achieve accurate analysis and effective management of athletes’ physical fitness information. The core of the research is to develop a smart healthcare decision model that can identify key information and patterns on the platform through data mining technology. In the selection of the decision algorithm, this paper adopts cluster analysis and association rule mining algorithms, which can deeply dig into the potential rules and correlations in the data. Through the application of an algorithm, we can accurately evaluate the physical condition of football players, and provide scientific guidance and suggestions for their subsequent physical training. The algorithm in this study is not only innovative in theory, but also shows good adaptability and effectiveness in practical application.

The environmental impact of inhalers: a framework for sustainable prescription practices in Spain

BackgroundThe healthcare sector contributes significantly to global greenhouse emissions, with inhalers being major contributors.ObjectiveTo develop a framework for reducing the environmental footprint of inhalers in Spain by implementing greener prescription...

Lower Extremity Soft Tissue Defect Reconstruction Using the SCIP Free Flap: A Comprehensive Review

The reconstruction of lower extremity soft tissue defects requires a flap capable of covering vital foot structures while withstanding the stresses of walking and shoe pressure. The Superficial Circumflex Iliac Artery Perforator (SCIP) flap provides finely detailed tissue and minimal donor site sequelae. Its surgical technique is safe, accessible, and benefits from preoperative echo-Doppler mapping for enhanced precision. The SCIP flap's numerous advantages make it an essential component in the plastic surgeon's decision-making algorithm for lower extremity soft tissue reconstruction, offering a renewed perspective on inguinal donor site management.

Mixture of Experts Framework Based on Soft Actor-Critic Algorithm for Highway Decision-Making of Connected and Automated Vehicles

Decision-making of connected and automated vehicles (CAV) includes a sequence of driving maneuvers that improve safety and efficiency, characterized by complex scenarios, strong uncertainty, and high real-time requirements. Deep reinforcement learning (DRL) exhibits excellent capability of real-time decision-making and adaptability to complex scenarios, and generalization abilities. However, it is arduous to guarantee complete driving safety and efficiency under the constraints of training samples and costs. This paper proposes a Mixture of Expert method (MoE) based on Soft Actor-Critic (SAC), where the upper-level discriminator dynamically decides whether to activate the lower-level DRL expert or the heuristic expert based on the features of the input state. To further enhance the performance of the DRL expert, a buffer zone is introduced in the reward function, preemptively applying penalties before insecure situations occur. In order to minimize collision and off-road rates, the Intelligent Driver Model (IDM) and Minimizing Overall Braking Induced by Lane changes (MOBIL) strategy are designed by heuristic experts. Finally, tested in typical simulation scenarios, MOE shows a 13.75% improvement in driving efficiency compared with the traditional DRL method with continuous action space. It ensures high safety with zero collision and zero off-road rates while maintaining high adaptability.

Automated Credit Card Risk Assessment using Fuzzy Parameterized Neutrosophic Hypersoft Expert Set

In the financial industry, financial fraud is an ever-evolving risk with extreme consequences. Data mining has been instrumental in the recognition of credit card fraud (CCF) during online transactions. CCF recognition, which is a data mining problem, become a challenge owing to its two main reasons - firstly, the profiles of fraudulent and normal behaviors modify continually and then, CCF dataset is extremely lopsided. The implementation of fraud recognition in credit card transactions is tremendously influenced by the sampling methodology on data, detection approach and variable selection utilized. The conception of the neutrosophic hypersoft set (NHSS) is a parameterized family that handles the sub-attributes of the parameter and is an appropriate extension of the NHSS to correctly evaluate the uncertainty, deficiencies, and anxiety in decision-making. In comparison to previous research, NHSS can accommodate additional uncertainty, which is the crucial approach to describe fuzzy datasets in the decision-making algorithm. This study introduces an Automated Credit Card Risk Assessment using Fuzzy Parameterized Neutrosophic Hypersoft Expert Set (ACCRA-FPNHES) technique. In the ACCRA-FPNHES technique, a three-step process is involved. As a primary step, the ACCRA-FPNHES technique designs sparrow search algorithm (SSA) for choosing features. In the second step, the detection of CCF takes place using FPNHES technique. Finally, in the third step, the parameters related to the FPNHES technique can be adjusted by arithmetic optimization algorithm (AOA). The simulation validation of the ACCRA-FPNHES technique can be studied on credit card dataset. The obtained values indicate that the ACCRA-FPNHES technique showcases better performance

Integrating Neutrosophic Theory for Improved Decision-Making in Wireless Body Area Networks: Enhancing Accuracy and Efficiency in Health Monitoring

Wireless Body Area Networks (WBANs) play a pivotal role in modern healthcare by enabling continuous monitoring of physiological data through sensors placed on or around the human body. Despite their significant benefits, WBANs face challenges such as data uncertainty, complex decision-making processes, and dynamic network conditions. These challenges can lead to inaccuracies and inefficiencies in health monitoring and diagnostics. The paper's main aim is to incorporate neutrosophic theory into Wireless Body Area Networks to provide enhancements in decision-making. In modern healthcare, the use of WBANs for monitoring physiological data by sensors, which are attached to or around the human body, can be continuous. Despite huge advantages, the main challenges that WBANs face are the uncertainties in data, complex decision-making processes, and dynamic network conditions, making health monitoring and diagnostics inaccurate and inefficient. In this paper, authors propose a robust framework to map sensor data into the neutrosophic domain and apply neutrosophic logic for enhanced accuracy and reliability of decision-making. In this paper, a Neutrosophic Decision-Making Algorithm is proposed, and its performance is compared with other decision-making techniques in terms of accuracy, response time, energy efficiency, and reliability. Experimental results show major improvements of around 95.3% in accuracy and a reduction of up to 25% in response time and energy consumption. Results underline the potential of neutrosophic theory for revolutionizing decision-making processes within WBANs to ensure more reliable and efficient health monitoring. This approach enables not only operational life but also improves patient outcome, avoiding a wrong diagnosis, during long-term health monitoring applications using WBAN devices.

Prevention and Management of Posterior Wound Complications Following Oncologic Spine Surgery: Narrative Review of Available Evidence and Proposed Clinical Decision-Making Algorithm.

Narrative Review. Contextualized by a narrative review of recent literature, we propose a wound complication prevention and management algorithm for spinal oncology patients. We highlight available strategies and motivate future research to identify optimal and individualized wound management for this population. We conducted a search of recent studies (2010-2022) using relevant keywords to identify primary literature in support of current strategies for wound complication prevention and management following spine tumor surgery. When primary literature specific to spine tumor cases was not available, data were extrapolated from studies of other spine surgery populations. Results were compiled into a proposed clinical algorithm to guide practice considering available evidence. Based on available literature, we recommend individualized stratification of patients according to identifiable risk factors for wound complication and propose several interventions which might be employed preventatively, including intrawound antibiotic administration, negative pressure wound therapy, and primary flap closure of the surgical wound. Of these, the available evidence, weighing possible risks vs benefits, most strongly favors primary flap closure of surgical wounds, particularly for patients with multiple risk factors. A secondary algorithm to guide management of wound complications is also proposed. Wound complications such as SSI and dehiscence remain a significant source of morbidity following spine tumor surgery. Triaging patients on an individualized basis according to risk factors for complication may aid in selecting appropriate prophylactic strategies to prevent these complications. Future research in this area is still needed to strengthen recommendations.

Управление результативностью микрофинансовых организаций предпринимательского финансирования с использованием алгоритмов машинного обучения

Article discusses theoretical problems of performance management of microfinance organizations as a hybrid form of entrepreneurship with dual objectives. The paper presents scoring models of decision-making on granting loans by public microfinance organizations of entrepreneurial finance based on forecasting the growth of borrowing companies. The models are built on the basis of data from 252 SME companies that received financial support from state microfinance organizations in Primorsky and Khabarovsk Krai for the period 2019-2022. The results demonstrate the high accuracy of the obtained models and their performance is confirmed by out-of-sample forecasting results. The theoretical result of the study is the development of an approach that allows integrating the principles of strategic management with the concept of value creation of public microfinance organizations. The practical significance stems from the development of a flexible decision-making algorithm that allows the management of microfinance organizations to adjust the criteria for selecting borrowers in accordance with the organizations’ strategic objectives. The algorithm assumes an ensemble structure of decision-making, where the manager of a microfinance organization receives an automated decision, but remains responsible for its result.

Autonomous Maneuvering Decision-Making Algorithm for Unmanned Aerial Vehicles Based on Node Clustering and Deep Deterministic Policy Gradient

Decision-making for autonomous maneuvering in dynamic, uncertain, and nonlinear environments represents a challenging frontier problem. Deep deterministic policy gradient (DDPG) is an effective method to solve such problems, but it is found that complex strategies require extensive computation and time in the learning process. To address this issue, we propose a node clustering (NC) method, inspired by grid clustering, integrated into the DDPG algorithm for the learning of complex strategies. In the NC method, the node membership degree is defined according to the specific characteristics of the maneuvering decision-making problem, and error handling strategies are designed to reduce the number of transitions in the replay database effectively, ensuring that the most typical transitions are retained. Then, combining NC and DDPG, an autonomous learning and decision-making algorithm of maneuvering is designed. The algorithm flow and the pseudo-code of the algorithm are given. Finally, the NC_DDPG algorithm is applied to a typical short-range air combat maneuvering decision problem for verification. The results show that the NC_DDPG algorithm significantly accelerates the autonomous learning and decision-making process under both balanced and disadvantageous conditions, taking only about 77% of the time required by Vector DDPG. The scale of NC impacts learning speed; the simulation results across five scales indicate that smaller clustering scales significantly increase learning time, despite a high degree of randomness. Compared with Twin Delayed DDPG (TD3), NC_DDPG consumes only 0.58% of the time of traditional TD3. After applying the NC method to TD3, NC_DDPG requires approximately 20–30% of the time of NC_TD3.

Comparative reinfection rate of one-stage versus two-stage revision in the management of periprosthetic joint infection following total hip arthroplasty: a meta-analysis

BackgroundDebates persist on the optimal surgical approach for treating Periprosthetic joint infection (PJI) following total hip arthroplasty (THA). This meta-analysis aimed to compare the reinfection rate of one-stage revision versus two-stage revision for PJI after THA.MethodsA comprehensive search was performed in four databases (PubMed, Embase, Web of Science, and Cochrane Library) to locate articles that assessed the reinfection rate of one-stage revision compared to two-stage revision. Meta-analyses of reinfection rate were performed.ResultsA total of 14 articles including of 1429 patients were chosen for inclusion in this meta-analysis, with 561 patients in the one-stage group and 868 patients in the two-stage group. The meta-analysis of the 14 trials revealed that there was no statistically significant disparity in the reinfection rate between the two groups(OR = 1.34, 95% CI 0.92 ~ 1.93, P = 0.12, I2 = 0). A subgroup analysis was conducted based on the presence of a well-defined algorithm for decision making in either a one-stage or two-stage revision. There was no statistically significant difference in reinfection rate between one-stage and two-stage revision if there was a decision algorithm(OR = 0.83, 95% CI 0.44 ~ 1.54, P = 0.55, I2 = 0). If not, the reinfection rate of one-stage revision was significantly higher than that of two-stage revision(OR = 1.79, 95% CI 1.11 ~ 2.88, P = 0.02, I2 = 0). Postoperative hip function score was significantly better in the one-stage revision group than that of the two-stage revision group(SMD = 0.54, 95% CI 0.31 ~ 0.78, P<0.05, I2 = 79%).ConclusionsA strategy that is clearly defined and can be used for decision making in one-stage or two-stage revision is necessary for the treatment of PJI after THA. When there is significant damage to the soft tissue and/or the presence of strong microorganisms, a two-stage revision is recommended in order to decrease the reinfection rate. One-stage revision is recommended for patients with low-toxic infections and intact soft tissue.Trial registrationPROSPERO (CRD42023450842, 17 August 2023) https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023450842.

Comparison of clinical results between transpulmonary thermodilution monitoring and conventional methods in cardiac surgery: An observational study.

The Pulse Index Contour Continuous Cardiac Output (PICCO) module provides advanced and continuous monitoring of cardiac output through the use of arterial pulse contour analysis and transpulmonary thermodilution. The objective of this study was to compare the early postoperative outcomes of patients who were monitored using the conventional method and the pulse contour analysis method. A prospective observational study was conducted involving 45 patients who underwent cardiac surgery between 2020 and 2022. Patients were randomly assigned to either Group P (PICCO) or Group C (conventional). In the PICCO cohort, a femoral artery cannula was inserted for the continuous recording and management of hemodynamic data, in accordance with the decision-making algorithm of the module. In the conventional group, cannulation of the radial artery and jugular vein was performed. The postoperative hemodynamic and clinical data are subjected to analysis. The utilization of dopamine was markedly diminished in Group P at both the 0- and 6-hour postoperative intervals, whereas the administration of dobutamine was observed to be elevated (P = .008). The frequency of red packed cell transfusions was higher in Group C at postoperative hour 0. Hemodynamic data indicated a 42% increase in cardiac index and a 33% decrease in systemic vascular resistance, along with a 33% increase in global ejection fraction in patients monitored with PICCO. The mortality rates observed in the 2 groups were not statistically different. The implementation of advanced monitoring techniques, specifically the PICCO module, led to notable enhancements in hemodynamic parameters. The utilization of this technique may prove advantageous in guiding inotrope selection and transfusion decisions during the initial postoperative period. However, it is important to note that morbidity and mortality rates remain comparable.

Anterior choroidal artery aneurysm case series: a clinical decision algorithm for endovascular and surgical treatment using a novel classification system.

Anterior choroidal artery (AChA) aneurysms represent 2-5% of intracranial aneurysms. The proximity of the origin of the AChA to the aneurysm neck poses a risk of thromboembolic complications following treatment. AChA occlusion can result in significant neurological deficits. We present a case-series of patients with AChA aneurysms and their treatment outcomes. We describe how treatment was chosen based on aneurysm and patient-specific factors to minimize risks, enhance efficacy of treatment, and improve patient outcomes. We retrospectively reviewed 27 patients who underwent treatment for AChA aneurysms at a single institution from 2015-2024. Clinical presentation, aneurysm characteristics, procedural details, and follow-up data were collected. Twenty-seven AChA aneurysms (median diameter 2.94mm), were treated in 29 procedures. Treatments included clipping (11 aneurysms), flow diversion (13), or coiling (5). The five coiled aneurysms were ruptured at presentation. Three patients experienced permanent infarction (10.3%), two in the setting of vasospasm after coiling and one after flow diversion in a patient who proved to have resistance to clopidogrel. At a median follow-up of 4.73months (IQR = 1.23-14.03), 25 patients (93%) had favorable functional recovery (mRS 0-2). Patients treated for AChA aneurysms experienced favorable functional outcomes by utilizing surgical and endovascular techniques carefully chosen based on aneurysm and patient-specific factors. Two patients did not achieve independence post-coiling due to disability from their initial subarachnoid hemorrhage. There were no treatment related causes of unfavorable functional outcomes.

Smart energy: application of the photovoltaic system using genetic algorithms for decision making in industry 4.0

The growing demand for sustainable solutions and the digitalization of industrial processes have driven the adoption of photovoltaic systems and advanced decision-making technologies. In the context of Industry 4.0, where automation and artificial intelligence are fundamental, these systems stand out as a clean energy alternative, promoting savings and reducing pollutant emissions. This study aims to develop a photovoltaic energy control model that uses genetic algorithms to optimize energy efficiency in industrial environments, reducing costs and dependence on non-renewable sources. The methodology included the computational modeling of a photovoltaic system and the application of genetic algorithms to optimize parameters such as panel angle and operating hours, adapting the system in real time to variable consumption and generation conditions. The results showed that the use of genetic algorithms increased the system's efficiency by up to 20% compared to traditional methods, as well as minimizing consumption from the electricity grid at peak times. This study reinforces the importance of artificial intelligence in optimizing renewable resources, contributing to energy efficiency and sustainability in Industry 4.0.

Multi-Agent Reinforcement Learning-Based Computation Offloading for Unmanned Aerial Vehicle Post-Disaster Rescue.

Natural disasters cause significant losses. Unmanned aerial vehicles (UAVs) are valuable in rescue missions but need to offload tasks to edge servers due to their limited computing power and battery life. This study proposes a task offloading decision algorithm called the multi-agent deep deterministic policy gradient with cooperation and experience replay (CER-MADDPG), which is based on multi-agent reinforcement learning for UAV computation offloading. CER-MADDPG emphasizes collaboration between UAVs and uses historical UAV experiences to classify and obtain optimal strategies. It enables collaboration among edge devices through the design of the 'critic' network. Additionally, by defining good and bad experiences for UAVs, experiences are classified into two separate buffers, allowing UAVs to learn from them, seek benefits, avoid harm, and reduce system overhead. The performance of CER-MADDPG was verified through simulations in two aspects. First, the influence of key hyperparameters on performance was examined, and the optimal values were determined. Second, CER-MADDPG was compared with other baseline algorithms. The results show that compared with MADDPG and stochastic game-based resource allocation with prioritized experience replay, CER-MADDPG achieves the lowest system overhead and superior stability and scalability.

Initial experience of parenchyma-sparing liver resection with systematic selective hepatic vein reconstruction for colorectal metastases

ObjectivesThis study aims to assess the feasibility and short-term and intermediate-term technical success rate of the concept of systematic selective hepatic vein (HV) reconstruction for parenchyma-sparing hepatectomies (PSHs) in patients...

Research on Low-Carbon Site Selection Decision Model and Algorithm for Green Logistics

This work proposes a low-carbon site selection decision model and algorithm for green logistics. The focus is to minimize carbon emissions while optimizing the distribution network. With the development of smart shared logistics and increasing environmental awareness, there is an urgent need to tackle issues in urban logistics, including pollution, resource inefficiency, high carbon energy usage, and greenhouse gas emissions. By integrating geographic, environmental, and operational data, the proposed site selection model incorporates multi-objective optimization to balance carbon efficiency with cost-effectiveness in logistics site selection. We employ a hybrid algorithm combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to solve the complex decision-making process. The model is tested using real-world logistics data, and the results demonstrate significant reductions in carbon emissions and transportation costs when compared to conventional logistics site selection methods. Further sensitivity analysis reveals that the model is robust under varying transportation demands and geographic constraints. Also, we highlight the role of green logistics in achieving corporate sustainability goals aligning with global carbon neutrality initiatives. We believe this model offers a practical approach to fostering environmentally responsible supply chains and will contribute to the advancement of sustainable logistics operations, helping organizations make informed, eco-friendly decisions in site selection.

Proof Theory and Decision Procedures for Deontic STIT Logics

This paper provides a set of cut-free complete sequent-style calculi for deontic STIT (‘See To It That’) logics used to formally reason about choice-making, obligations, and norms in a multi-agent setting. We leverage these calculi to write a proof-search algorithm deciding deontic, multi-agent STIT logics with (un)limited choice and introduce a loop-checking mechanism to ensure the termination of the algorithm. Despite the acknowledged potential for deontic reasoning in the context of autonomous, multi-agent scenarios, this work is the first to provide a syntactic decision procedure for this class of logics. Our proofsearch procedure is designed to provide verifiable witnesses/certificates of the (in)validity of formulae, which permits an analysis of the (non)theoremhood of formulae and act as explanations thereof. We show how the proof system and decision algorithm can be used to automate normative reasoning tasks such as duty checking (viz. determining an agent’s obligations relative to a given knowledge base), compliance checking (viz. determining if a choice, considered by an agent as potential conduct, complies with the given knowledge base), and joint fulfillment checking (viz. determining whether under a specified factual context an agent can jointly fulfill all their duties).

Statistical variable selection and causality in the social and behavioral sciences

AbstractThe problem of “variable selection” is a fundamental one across the sciences. In its broadest terms, this problem would be at least part of the general issue of theory selection and comparison. However, there is a more circumscribed problem that concerns primarily the choice of variables for the best fitting model, given some set of data, usually observational in nature, and specific statistical techniques, typically multiple regression. There is a deep strand in econometrics and other applied social, behavioral, and biomedical science statistics to want formal decision rules or algorithms to pick out variables. The paper examines seven such formal procedures using a simulated data set with known causal relations. The conclusion is that seven often-used procedures make systematic causal errors. Some suggestions about better alternatives conclude.

RETRACTION: The Analysis of Optimized Path Selection for Management Mode of Coastal Regional Circular Economy Based on Fuzzy Decision Algorithm

Retraction: W. Wei, D. Xiao, and W. Liu, “,” Expert Systems 41, no. (2024): . https://doi.org/10.1111/exsy.12985. The above article, published online on 04 March 2022, in Wiley Online Library (http://onlinelibrary.wiley.com/), has been retracted by agreement between the journal Editor‐in‐Chief, David Camacho; and John Wiley &amp; Sons Ltd. Following an investigation by the publisher, the parties have concluded that this article was accepted solely on the basis of a compromised peer review process. In addition, the investigation found significant flaws and logical inconsistencies in the article. Therefore, the article must be retracted. The authors did not respond to the notice regarding the retraction.