Set Of Algorithms Research Articles

Prevalence and diagnostic accuracy of different diagnostic tests for Chagas disease in an indigenous community of the Paraguayan Chaco.

Chagas disease (CD), caused by the protozoan Trypanosoma cruzi (T. cruzi), poses a major health challenge in Paraguay, especially in the resource-limited Chaco region. Rapid diagnostic tests (RDTs) are valuable tools to enhance diagnostic access. This study evaluates CD prevalence and risk factors in an indigenous community in the Paraguayan Chaco and validates the national RDT-based diagnostic algorithm for resource-limited settings against the recommended standard algorithm, which relies solely on conventional serological tests. A descriptive cross-sectional study was conducted in Casanillo, Presidente Hayes, Paraguay. In July 2023, a two-week field campaign was executed using a non-probability convenience sampling method targeting individuals aged over 9 months. Screening involved a single RDT, with positives confirmed via enzyme-linked immunosorbent assay (ELISA). Algorithm accuracy was validated externally at the National Reference Laboratory of Paraguay against the standard algorithm, which, in this study, included an ELISA and Hemagglutination test. Discordant cases were resolved with a second ELISA or Immunofluorescence. The study involved 999 participants, with a median age of 26 years (IQR 12-45), and 51.1% were female. The RDT-based diagnostic algorithm showed 97.1% agreement (κ = 0.94, 95%CI: 0.90-0.98) with the standard algorithm. The RDT alone had 96.0% agreement (κ = 0.91, 95%CI: 0.87-0.96), while the confirmatory ELISA had 94.3% agreement (κ = 0.88, 95%CI: 0.83-0.93). The algorithm's sensitivity/specificity (95%CI) were 94.6% (89.2-97.8)/98.6% (96.1-99.7), with the RDT at 94.6% (89.2-97.8)/96.8% (93.6-98.7) and the ELISA at 96.9% (92.3-99.2)/92.7% (88.5-95.8). T.cruzi infection seroprevalence was 12.6% (95%CI: 9.56-16.52). Age, Sanapaná ethnicity, and awareness of CD vectors were significantly associated with infection odds. No significant associations were found with other typical CD risk factors, clinical history, or health habits. The study underscores the high burden of T. cruzi infection in indigenous communities in the Paraguayan Chaco, urging immediate interventions for improved diagnosis and treatment. The combination of RDTs with conventional serology for diagnostic screening in resource-constrained settings proved useful, and its further use is encouraged.

Feature selection by utilizing kernel-based fuzzy rough set and entropy-based non-dominated sorting genetic algorithm in multi-label data

Feature selection by utilizing kernel-based fuzzy rough set and entropy-based non-dominated sorting genetic algorithm in multi-label data

A COMPARISON OF RANDOM FOREST AND DOUBLE RANDOM FOREST: DROPOUT RATES OF MADRASAH STUDENTS IN INDONESIA

Random forest algorithm allows for building better CART models. However, the disadvantage of this method is often underfitting, especially for small node sizes. Therefore, the double random forest method was developed to overcome this problem. The research was conducted by utilising Education Management Information System (EMIS) data, which is related to the incidence of school dropout. The data used consists of 2 data, namely MTs and MA dropout data. The initial testing procedure was carried out using the random forest algorithm for each data set, then the data was evaluated using the double random forest method. From this study, the underfitting case can be overcome well using the double random forest algorithm, while in the fit case, the difference in the goodness-of-fit value of the model is relatively the same. The results obtained show that MTs prioritise school quality more than MA, although family factors are more important at the MA level. Although the total number of factors used is basically the same, it should be noted that the two school levels have different relevance variables. It should be noted that no forecasting was done in this study given that the methodology used two different types of data.

Artificial intelligence and machine learning in production efficiency enhancement and sustainable development: a comprehensive bibliometric review

This research presents a comprehensive bibliometric review of the role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing production efficiency and fostering sustainable development. With the increasing focus on sustainability, AI and ML technologies have emerged as pivotal tools for optimizing industrial processes, improving resource management and minimizing environmental impacts. The study analyzes key ML algorithms in various production settings. This study conducts systematic bibliometric analysis using the Scopus database and Bibliometrix R package, examining global trends, key collaborations, and thematic focuses on AI and ML applications in production efficiency and sustainable development. Novel contributions include uncovering underexplored ethical dimensions of AI adoption and emphasizing the pivotal role of SMEs and developing economies in advancing sustainable practices. Key research trends identified include the integration of AI with sustainable energy management, circular economy practices, and precision agriculture. Furthermore, the analysis reveals geographical contributions, with countries like China, the United States, and the United Kingdom leading in research output and impact. Despite the promising advancements, the review identifies gaps in ethical considerations, especially in data privacy and labor market implications, and suggests avenues for future research, including the implementation of AI and ML in developing economies and Small and Medium Enterprises (SMEs).

Energy-Efficient Route Planning Method for Ships Based on Level Set.

To reduce the fuel consumption of ships' oceanic voyages, this study incorporates the influence of ocean currents into the traditional level set algorithm and proposes a route planning algorithm capable of identifying energy-efficient routes in complex and variable sea conditions. The approach introduces the influence factor of ocean current to optimize routing in dynamically changing marie environments. First, models for the energy consumption of ships and flow fields are established. The level set curve is then evolved by integrating the flow environment and energy consumption gradient, solving the Hamilton-Jacobi equation with energy consumption parameters. The optimal path is subsequently determined through backtracking along the energy consumption gradient, enabling energy-efficient route planning from the starting point to the endpoint in complex ocean conditions. To verify the effectiveness of the proposed algorithm, its performance is evaluated through two case studies, comparing energy consumption under different environmental conditions. The experimental results demonstrate that, compared to the shortest path method based on the level set algorithm, the proposed approach achieves an energy saving rate of approximately 2.1% in obstacle-free environments and 1.4% in environments with obstacles.

A Deep Learning-Based Watershed Feature Fusion Approach for Tunnel Crack Segmentation in Complex Backgrounds.

As highway tunnel operations continue over time, structural defects, particularly cracks, have been observed to increase annually. Coupled with the rapid expansion of tunnel networks, traditional manual inspection methods have proven inadequate to meet current demands. In recent years, machine vision and deep learning technologies have gained significant attention in civil engineering for the detection and analysis of structural defects. However, rapid and accurate defect identification in highway tunnels presents challenges due to complex background conditions, numerous interfering factors, and the relatively low proportion of cracks within the structure. Additionally, the intensive labor requirements and limited efficiency in labeling training datasets for deep learning pose significant constraints on the deployment of intelligent crack segmentation algorithms. To address these limitations, this study proposes an automatic labeling and optimization algorithm for crack sample sets, utilizing crack features and the watershed algorithm to enable efficient automated segmentation with minimal human input. Furthermore, the deep learning-based crack segmentation network was optimized through comparative analysis of various network depths and residual structure configurations to achieve the best possible model performance. Enhanced accuracy was attained by incorporating axis extraction and watershed filling algorithms to refine segmentation outcomes. Under diverse lining surface conditions and multiple interference factors, the proposed approach achieved a crack segmentation accuracy of 98.78%, with an Intersection over Union (IoU) of 72.41%, providing a robust solution for crack segmentation in tunnels with complex backgrounds.

Model-free robust reinforcement learning via Polynomial Chaos

In recent years, the Robust Markov Decision Process (RMDP) has become an important modeling framework to address the discrepancies between simulated and real-world environments in Reinforcement Learning (RL) training. The purpose of RMDP is to accommodate the uncertainty of the real-world environments, employing a conservative approach to enhance the robustness of policy decisions. However, due to the difficulty of robust value function estimation, the RMDP framework is challenging to generalize to environments with large continuous state–action spaces. Our work focuses on model-free robust RL and proposes a model-free algorithm for continuous space setting. We adopt a new perspective on uncertainty sets such that the uncertainty sets are parameterized and the parameters obey specific stochastic distributions. We present a novel approach RPC to estimate the robust value function utilizing generalized Polynomial Chaos(gPC). We provide a proof to guarantee the convergence of the algorithm. Our training framework is based on off-policy RL, which reduces the computation overhead by gPC and improves learning stability. Our algorithm can handle continuous tasks and guarantee the robustness of the algorithm without incurring excessive computational overhead. We combine RPC with the TD3 method and conduct several experiments to evaluate its performance in a continuous robot control task, and the experimental results provide further evidence of the robustness of our algorithm.

Optimizing Financial Fraud Detection: Understandings from Variable Selection with Neutrosophic Vague Soft Set

Neutrosophy is the neutralities study and prolongs the discussion of the truth of opinions. Neutrosophic logic might be used in all sectors, to provide the solution for the indeterminate challenges. Some real-time data experience issues like inconsistency, incompleteness, and indeterminacy. A fuzzy set (FS) offers an uncertain solution, and an intuitionistic fuzzy set (IFS) processes partial data, but both fail to handle uncertain data. Financial fraud, believed as a deceptive strategy to gain financial assistance, has recently become a common threat in organizations and companies. Traditional methods namely manual inspections and verifications are costly, time-consuming, and imprecise to identify such fraudulent actions. With the development of artificial intelligence (AI), machine learning (ML)-based algorithms are applied logically to identify fraud transactions by investigating a larger amount of financial data. Therefore, the study offers an Optimizing Financial Fraud Detection using Bayesian Optimization and Variable Selection with Neutrosophic Vague Soft Set (OFFDBO-VSNVS) Algorithm. The OFFDBO-VSNVS model presents an optimized framework for fraud detection by integrating advanced variable selection techniques and classification models. Initially, the OFFDBO-VSNVS technique applies the Z-score data normalization technique to transform input data into a compatible layout. Next, the grey wolf optimizer (GWO)--based feature selection to effectively reduce dimensionality and highlight the most relevant features. For the classification and detection of financial fraud, the neutrosophic vague soft set (NVS) model can be employed. Eventually, the Bayesian optimization (BO) model adjusts the hyperparameter values of the NVS algorithm optimally and outcomes in greater classification performance. The stimulated outcome study of the OFFDBO-VSNVS model occurs and the outcomes are examined in terms of changing features. The experimental study represented the superiority of the OFFDBO-VSNVS method across the existing state-of-the-art methods

Adaptive enhancement of shoulder x-ray images using tissue attenuation and type-II fuzzy sets.

Shoulder X-ray images typically have low contrast and high noise levels, making it challenging to distinguish and identify subtle anatomical structures. While existing image enhancement techniques are effective in improving contrast, they often overlook the enhancement of sharpness, especially when amplifying blurring and noise. These techniques may improve detail contrast but fail to maintain overall image clarity and the distinction between the target and the background. To address these issues, we propose a novel image enhancement method aimed at simultaneously improving both the contrast and sharpness of shoulder X-ray images. The method integrates automatic tissue attenuation techniques, which enhance the image contrast by removing non-essential tissue components while preserving important tissues and bones. Additionally, we apply an improved Type-II fuzzy set algorithm to further optimize image sharpness. By simultaneously enhancing contrast and sharpness, the method significantly improves image quality and detail distinguishability. When tested on certain images from the MURA dataset, the proposed method achieved the best or second-best results, outperforming five no-reference image quality assessment metrics. In comparative studies, the method demonstrated significant performance advantages over 10 contemporary X-ray image enhancement algorithms and was validated through ablation experiments to confirm the effectiveness of each module.

Analysis of the Malatya Centrality-Based Clique Method on DIMACS Benchmarks and Random Graphs

The Maximum Clique Problem (MCP) is an NP-complete problem that aims to find the largest clique (complete subgraph) within a given graph. This problem has widespread applications in fields such as social network analysis, bioinformatics, chemical information systems, and communication networks. Although various solution methods have been proposed in the literature, an effective method that provides a general solution across different graph types is not yet available. In this study, we demonstrate that our previously proposed maximum clique method provides an effective solution for MCP on DIMACS benchmark graphs and random graphs generated using different models such as Erdos-Renyi, Forest Fire, Watts-Strogatz, and Regular Random models. The proposed method begins by computing the complement of the original graph. In the complement graph, the maximum independent set is determined using the Malatya Independent Set Algorithm, an efficient algorithm well-recognized in the literature. The maximum independent set identified in the complement graph is then used to determine the subgraph corresponding to the maximum clique in the original graph. The effectiveness of the proposed method was validated through tests on widely recognized DIMACS benchmark graphs. Additionally, to evaluate its performance and robustness on unpredictable graphs, tests were conducted on random graphs of varying complexities generated using Erdos-Renyi, Forest Fire, Watts-Strogatz, and Regular Random models. The results of these tests demonstrated that the method produced optimal or near-optimal MCP solutions for both DIMACS and random graphs. The successful outcomes further highlight the efficiency of the proposed method and its potential applicability in MCP-related domains.

Performance of diagnostic algorithms in patients with invasive pulmonary aspergillosis.

Invasive pulmonary aspergillosis (IPA), once limited to immunocompromised patients, is now a severe complication in critically ill ICU patients without classic risk factors. Due to the difficulty of obtaining histological evidence, its diagnosis relies on poorly tested algorithms in real-world settings. We conducted a retrospective multicenter (n=9) cohort study including 202 patients with IPA. Patients were classified using a multistep process based on the European Organization for the Research and Treatment of Cancer/Mycosis Study Group (EORTC-MSG), Invasive-Fungal Diseases in Adult Patients in Intensive Care Unit (FUNDICU), Aspergillus ICU (Asp-ICU), and Asp-ICU with biomarkers (Asp-ICU-BM) criteria. We then evaluated the predictive performance of these criteria against the clinical cohort and histologically proven cases. Among 202 patients, 78 had EORTC-MSG host factors and were classified accordingly, with the EORTC-MSG criteria achieving 100% agreement in identifying clinical and histologically proven cases. In 112 ICU patients without EORTC-MSG host factors, overall agreement was 53% for FUNDICU, 4% for Asp-ICU, and 26% for Asp-ICU-BM compared to the clinical cohort. Validation against histologically proven cases showed FUNDICU had 44% sensitivity and 75% specificity, Asp-ICU 6% sensitivity and 100% specificity, and Asp-ICU-BM 28% sensitivity and 63% specificity. Adding acute respiratory distress syndrome (ARDS) and post-cardiac surgery to the FUNDICU criteria improved sensitivity to 97% with a specificity of 63%. The remaining 12 patients lacked EORTC-MSG host factors and were not in the ICU, highlighting a novel classification system. EORTC-MSG and FUNDICU IPA classification systems are useful for the assignment of most patients with IPA. Incorporating post-operative complications after cardiac surgery and ARDS enhanced the diagnostic accuracy of FUNDICU.

WQI Improvement Based on XG-BOOST Algorithm and Exploration of Optimal Indicator Set

This paper takes a portion of the Manas River Basin in Xinjiang Province, China, as an example and proposes an improved traditional comprehensive water quality index (WQI) method using Extreme Gradient Boosting (XG-BOOST) to analyze the groundwater quality levels in the region. Additionally, XG-BOOST is used to screen the existing dataset of ten water quality indicators, including fluoride (F), chlorine (Cl), nitrate (NO), sulfate (SO), silver (Ag), aluminum (Al), iron (Fe), lead (Pb), selenium (Se), and zinc (Zn), from 246 monitoring points, in order to find the dataset that optimizes model training performance. The results show that, in the selected study area, water quality categorized as “GOOD” and “POOR” accounts for the majority, with “GOOD” covering 48.7% of the area and “POOR” covering 31.6%. Regions with water quality classified as “UNFIT” are mainly distributed in the central–eastern parts of the study area, located in parts of the Changji Hui Autonomous Prefecture. Comparatively, water quality in the western part of the study area is better than that in the eastern part, while areas with “EXCELLENT” water quality are primarily distributed in the southern parts of the study area. The optimal water quality indicator dataset consists of five indicators: Cl, NO, Pb, Se, and Zn, achieving an accuracy of 98%, RMSE = 0.1414, and R2 = 0.9081.

Sequential Maximal Updated Density Parameter Estimation for Dynamical Systems With Parameter Drift

ABSTRACTWe present a novel method for generating sequential parameter estimates and quantifying epistemic uncertainty in dynamical systems within a data‐consistent (DC) framework. The DC framework differs from traditional Bayesian approaches due to the incorporation of the push‐forward of an initial density, which performs selective regularization in parameter directions not informed by the data in the resulting updated density. This extends a previous study that included the linear Gaussian theory within the DC framework and introduced the maximal updated density (MUD) estimate as an alternative to both least squares and maximum a posterior (MAP) estimates. In this work, we introduce algorithms for operational settings of MUD estimation in real‐ or near‐real time where spatio‐temporal datasets arrive in packets to provide updated estimates of parameters and identify potential parameter drift. Computational diagnostics within the DC framework prove critical for evaluating (1) the quality of the DC update and MUD estimate and (2) the detection of parameter value drift. The algorithms are applied to estimate (1) wind drag parameters in a high‐fidelity storm surge model, (2) thermal diffusivity field for a heat conductivity problem, and (3) changing infection and incubation rates of an epidemiological model.

Prediction of Disease Stage by Machine Learning Classification Methods for Covid-19 Patients

Supervised machine learning classificitaion algorithms have been widely used in many fields in recent years. Especially, health is one of the most important areas where machine learning studies are carried out successfully. The aim of this study is to develop models that predict the disease stage of people who apply to hospital with the diagnosis of Covid-19. Inadequacies such as intensive care occupancy, insufficiency of beds, and shortage of respiratory equipment are among these problems, and this has left healthcare workers faced with the overwhelming burden of patients. Therefore, estimating the disease stages of Covid-19 patients at an early stage is of great importance. The data set used in the study includes the clinical and laboratory data of the patients during in their admission to the hospital.It has been tried to develop models that predict disease stage by using Logistic Regression, Random Forest and Support Vector Machine algorithms in the data set. The random forest model with 9 variables was the best performing model. With the models obtained, it will be ensured that the hospital management receives information in order to see the necessary treatment for low-risk or high-risk patients and to avoid medical system inadequacies.

Cautiously optimistic: paediatric critical care nurses’ perspectives on data-driven algorithms in low-resource settings—a human-centred design study in Malawi

BackgroundPaediatric critical care nurses face challenges in promptly detecting patient deterioration and delivering high-quality care, especially in low-resource settings (LRS). Patient monitors equipped with data-driven algorithms that monitor and integrate clinical data can optimise scarce resources (e.g. trained staff) offering solutions to these challenges. Poor algorithm output design and workflow integration, however, are important factors hindering successful implementation. This study aims to explore nurses’ perspectives to inform the development of a data-driven algorithm and user-friendly interface for future integration into a continuous vital signs monitoring system for critical care in LRS.MethodsHuman-centred design methods, including contextual inquiry, semi-structured interviews, prototyping and co-design sessions, were carried out at the high-dependency units of Queen Elizabeth Central Hospital and Zomba Central Hospital in Malawi between March and July 2023. Triangulating these methods, we identified what algorithm could assist nurses and used co-creation methods to design a user interface prototype. Data were analysed using qualitative content analysis.ResultsWorkflow observations demonstrated the effects of personnel shortages and limited monitor equipment for vital signs monitoring. Interviews identified four themes: workload and workflow, patient prioritisation, interaction with guardians, and perspectives on data-driven algorithms. The interviews emphasised the advantages of predictive algorithms in anticipating patient deterioration, underlining the need to integrate the algorithm’s output, the (constant) monitoring data, and the patient’s present clinical condition. Nurses preferred a scoring system represented with familiar scales and colour codes. During co-design sessions, trust, usability and context specificity were emphasised as requirements for these algorithms. Four prototype components were examined, with nurses favouring scores represented by colour codes and visual representations of score changes.ConclusionsNurses in the LRS studied, perceived that data-driven algorithms, especially for predicting patient deterioration, could improve the provision of critical care. This can be achieved by translating nurses’ perspectives into design strategies, as has been carried out in this study. The lessons learned were summarised as actionable pre-implementation recommendations for the development and implementation of data-driven algorithms in LRS.

Meta surface Assisted Open radio access networks

The paradigm of the open radio access networks (O-RAN) seeks to carry intelligence and openness (multi-vendors) to the conventional proprietary and closed radio access networks (RAN) schemes and deliver performance enhancement, cost-efficiency, and flexibility, in both the network's operation and deployment. On the other hand, Reconfigurable Intelligent Surfaces (RIS) are suggested for future networks due to their effectiveness in terms of cost and energy consumption. However, due to the fast time-varying feature of the dense wireless systems, it becomes difficult to allow optimal user association and beamforming in terms of signalling overheads and processing in RIS assisted RANs with the limited capacity of the fronthaul. Therefore, the objective of this study is to attain a trade-off between the costs (signalling overhead/complexity) and the throughput performance. In other words, the study sets the challenges of the RIS aided O-RAN technology regarding the joint selection of user’s equipment (UEs)/open radio unit (O-RU)-RIS pairs and the designing of beamforming at the O-RU/RIS and open distributed unit (O-DU). From this point, to addressing the designing challenges, this work suggests a simple and potential beamforming strategy in RIS aided O-RAN architecture taking into account the specification of the interfaces between different O-RAN units to split opportunities between the radio and distributing units. In specific, a channel-gain-based selection of UEs/O-RU-RIS pairs joint with duality theory (DT) and transform of quadratic function (TQF) algorithm (namely DT_TQF) is proposed. Firstly, the non-convex optimization problem is relaxed via duality and transform of quadratic functions, and then an iterative approach is carried out for the active and passive beamformers via a simple alternating optimization approach. This approach can achieve flexibility in the environment of a high-traffic transmission while lowering the interference between radio units and the signalling burden required for beamforming tasks. Numerical simulation results justify the effectiveness of the algorithm for different systems' parameter settings and validate the important of installing RIS. For example, for a certain environment, the performance gain is about 52.9 % in comparison to the classic null-steering/random phase shifter scheme.

Simulation-Based Evaluation of Big Data Caching Mechanisms

This paper provides a simulation-based evaluation that addresses memory management problems throughout Big Data processing. A significant problem occurs with in-memory computing when there is not enough available memory for processing the whole chunk of data, and hence some data must be selected for deletion to make room for new ones. The selected research strategy is to use different cache selection and replacement algorithms, such as Adaptive Replacement Cache (ARC) and Low Inter-Reference Recency Set (LIRS) algorithms, besides the default one, which is Least Recently Used (LRU). A simulator was built by the authors to assess the use of different caching approaches on Big Data platforms. The evaluation showed that the LIRS and the ARC algorithms gave a better hit ratio for different workloads than the LRU algorithm.

Iterative quantum algorithms for maximum independent set

Iterative quantum algorithms for maximum independent set

Advancement of post-market surveillance of medical devices leveraging artificial intelligence: Infusion pumps case study

Background Analysis of data from incident registries such as MAUDE has identified the need to improve surveillance and maintenance strategies for infusion pumps to enhance patient and healthcare staff safety. Objective The ultimate goal is to enhance infusion pump management strategies in healthcare facilities, thus transforming the current reactive approach to infusion pump management into a proactive and predictive one. Method This study utilized real data collected from 2015 to 2021 through the inspection of infusion pumps in Bosnia and Herzegovina. Inspections were conducted by the national laboratory in accordance with the Legal Metrology Framework, accredited to ISO 17020 standard. Out of 988 samples, 790 were used for model training, while 198 samples were set aside for validation (20% of the dataset). Various machine learning algorithms for binary classification of samples (pass/fail status) were considered, including Logistic Regression, Decision Tree, Random Forest, Naive Bayes, and Support Vector Machine. These algorithms were chosen for their ability to handle large datasets and potential for high prediction accuracy. Results Through detailed analysis of the achieved results, it was found that all applied machine learning methods yielded satisfactory results, with accuracy ranging from 0.98% to 1.0%, precision from 0.99% to 1%, sensitivity from 0.98% to 1.0%, and specificity from 0.87% to 1.0%. However, Decision Tree and Random Forest methods proved to be the best, both due to their maximum achieved values of accuracy, precision, sensitivity, and specificity, and due to result interpretability. Conclusion It has been established that machine learning methods are capable of identifying potential issues before they become critical, thus playing a crucial role in predicting the performance of infusion pumps, potentially enhancing the safety, reliability, and efficiency of healthcare delivery. Further research is needed to explore the potential application of machine learning algorithms in various healthcare domains and to address practical issues related to the implementation of these algorithms in real clinical settings.

Day-ahead energy management in green microgrids: Impact of long-term scheduling of hydrogen storage systems

Day-ahead energy management systems focus on optimizing resource scheduling on a daily basis, which may not adequately address seasonal load or price fluctuations. Targeting these long-term fluctuations in day-ahead scheduling, this paper introduces a two-stage optimization methodology specifically designed for day-ahead scheduling with long-duration hydrogen storage systems (HSS) that effectively eliminates the need for scenario-reduction techniques by dividing the long-term scales into short-term ones. As the amount of stored hydrogen in the storage tank affects operational scheduling on consecutive days, the first stage introduces a new variable to represent variations in the stored hydrogen amount, effectively decoupling consecutive days. Subsequently, the second stage employs a developed active set algorithm. This algorithm adds hydrogen storage tank constraints to the objective function to ensure that the stored hydrogen amount does not exceed the tank’s capacity limits on any day. Using real-world data from South Australia State, simulation results validate the proposed algorithm’s effectiveness and demonstrate that employing large storage tanks within an HSS is viable for long-duration applications.