Improvement In Performance Metrics Research Articles

Subjective and Objective Monitoring Markers: Are They Related to Game Performance Indicators in Elite Female Volleyball Players?

This cohort study aimed to investigate the relationship between subjective (wellness and internal training load [ITL]) and objective (neuromuscular fatigue) monitoring markers and performance aspects (reception quality [RQ] and attack efficiency [AE]) in professional female volleyball players. The study was conducted over an 8-week period during the final mesocycle of the competitive phase. A total of 24 training sessions and 10 matches were included in the analysis. Subjective measures of wellness and ITL were assessed, and neuromuscular fatigue was evaluated using countermovement-jump (CMJ) height. RQ and AE were determined based on game statistics. The study found a positive relationship between wellness and RQ, particularly affecting outside hitters and liberos. ITL showed a positive association with AE, primarily impacting outside hitters, opposite hitters, and middle blockers. Additionally, ITL demonstrated a negative correlation with RQ, mainly affecting outside hitters and liberos. CMJ performance was associated with AE, where a decrease in CMJ height was linked to reduced AE. The findings highlight the importance of considering players' wellness scores in training and match strategies for different positions. Careful management of training loads, considering both physical and technical demands, is crucial for optimizing performance outcomes. Monitoring neuromuscular fatigue, as indicated by CMJ performance, is particularly relevant for outside hitters, opposite hitters, and middle blockers involved in attack actions. Coaches, trainers, and sports practitioners can use these insights to develop position-specific training protocols and implement effective strategies for maintaining or improving performance metrics under various stressors.

Comparative multi-aspect study of two scenarios and optimization of the biomass-based cogeneration system integrated with the carbon capture and utilization technology

Combined Heat and Power (CHP) system based on bio-energy is an energy-saving and environment friendly way to utilize energy, but in the background of the "carbon peaking and carbon neutrality" strategy, ensuring low-carbon operation of CHP systems becomes imperative. In this paper, we introduce two distinct CHP system scenarios based on post-combustion carbon capture technologies: a syngas-based gas turbine cycle CHP system (scenario 1) and a biogas-based steam turbine cycle CHP system (scenario 2). Then, a techno-economic evaluation has been conducted to investigate their performance under varying operational conditions of heating and power load, as well as carbon capture technology. The findings reveal that, given the same biomass input rate, the syngas-based CHP system yields superior energy and economic outcomes. To simultaneously mitigate CO2 emissions and reduce electricity costs, a process of multi-objective optimization is employed. This optimization endeavor also focuses on maximizing the overall exergy efficiency. Consequently, scenario 1 experiences substantial improvements in performance metrics. Notably, the overall energy and exergy efficiencies reach 17.32 % and 14.58 % respectively. Additionally, the power and heating load are estimated at 2.58 and 2.17 MW correspondingly. The levelized cost of electricity and heating are assessed at 30.55 USD/MWh and 36.05 USD/GJ, respectively, coinciding with a reduction in CO2 emissions to 98.27 kgCO2/MWh.

Securely Running High-Performance Workloads as Microservices in Cloud Environments

Over the past few years, the paradigm shift towards cloud computing has transformed and revolutionized how organizations manage high- performance workloads. The microservices architecture, renowned for its modularity and scalability, is increasingly being adopted to run these workloads in cloud environments. However, this transition is not without its challenges, particularly in security. This research article delves into the methods for securely running high-performance workloads as microservices in cloud environments, presenting the advantages and the challenges involved. The study aims to develop a comprehensive framework that not only addresses these security concerns but also optimizes performance, a crucial aspect in today's digital landscape. This research is a testament to our commitment to thoroughness and precision, as it combines both qualitative and quantitative approaches. Qualitative data were meticulously gathered through interviews with 15 cloud security experts, providing invaluable insights into prevalent security practices and challenges. Quantitative data, on the other hand, were collected from performance benchmarks that rigorously compared traditional monolithic applications with microservices- based applications in a cloud setting. The study employs robust statistical analysis tools such as SPSS and Grafana to analyze the collected data, ensuring the validity and reliability of our findings. Key interview findings highlighted critical security measures necessary for microservices, including service authentication, data encryption, and vulnerability management. The performance benchmarks revealed that microservices- based applications significantly outperformed monolithic applications, with notable improvements in CPU utilization, memory usage, and response time. For instance, the microservices architecture demonstrated a 21% reduction in CPU utilization and a 12% decrease in memory usage compared to its monolithic counterpart. The proposed framework integrates robust security practices, ensuring secure authentication, encrypted data transmission, and regular updates to mitigate vulnerabilities. This framework enhances security and optimizes resource allocation, leading to improved performance metrics.

Improved YOLO v7 for Sustainable Agriculture Significantly Improves Precision Rate for Chinese Cabbage (Brassica pekinensis Rupr.) Seedling Belt (CCSB) Detection

Precise navigation in agricultural applications necessitates accurate guidance from the seedling belt, which the Global Positioning System (GPS) alone cannot provide. The overlapping leaves of Chinese cabbage (Brassica pekinensis Rupr.) present significant challenges for seedling belt fitting due to difficulties in plant identification. This study aims to address these challenges by improving the You Only Look Once (YOLO) v7 model with a novel approach that decouples its network head deriving from the Faster-Regions with Convolutional Neural Network (Faster R-CNN) architecture. Additionally, this study introduced a BiFormer attention mechanism to accurately identify the centers of overlapping Chinese cabbages. Using these identified centers and pixel distance verification, this study achieved precise fitting of the Chinese cabbage seedling belt (CCSB). Our experimental results demonstrated a significant improvement in performance metrics, with our improved model achieving a 2.5% increase in mean average precision compared to the original YOLO v7. Furthermore, our approach attained a 94.2% accuracy in CCSB fitting and a 91.3% Chinese cabbage identification rate. Compared to traditional methods such as the Hough transform and linear regression, our method showed an 18.6% increase in the CCSB identification rate and a 17.6% improvement in angle accuracy. The novelty of this study lies in the innovative combination of the YOLO v7 model with a decoupled head and the BiFormer attention mechanism, which together advance the identification and fitting of overlapping leafy vegetables. This advancement supports intelligent weeding, reduces the reliance on chemical herbicides, and promotes safer, more sustainable agricultural practices. Our research not only improves the accuracy of overlapping vegetable identification, but also provides a robust framework for enhancing precision agriculture.

2D materials for marine corrosion protection: A review

The pervasive issue of metal corrosion in various industrial environments, especially in marine settings, incurs significant economic costs and hazardous failures. Traditional protection methods, although diverse, fall short in durability and environmental adaptability, necessitating advanced solutions, such as two-dimensional (2D) materials, including graphene, layered double hydroxides, boron nitride, MXenes, and MoS2. These materials offer exceptional barrier properties against corrosive agents due to their high surface area and minimal thickness, enhancing the lifespan and reliability of metal assets under harsh conditions. Innovations such as self-repairing coatings and advanced application techniques, such as automated spraying and rapid photocuring, further augment their effectiveness. In this Review, recent advancements in the development and application of 2D material-enhanced coatings are reviewed, underscoring their potential in revolutionizing marine corrosion protection through improved performance metrics and sustainability. The challenges and prospects of these novel materials are also discussed, highlighting the need for further research to overcome application hurdles and realize their full potential in industrial uses.

Improved pediatric ICU mortality prediction for respiratory diseases: machine learning and data subdivision insights

The growing concern of pediatric mortality demands heightened preparedness in clinical settings, especially within intensive care units (ICUs). As respiratory-related admissions account for a substantial portion of pediatric illnesses, there is a pressing need to predict ICU mortality in these cases. This study based on data from 1188 patients, addresses this imperative using machine learning techniques and investigating different class balancing methods for pediatric ICU mortality prediction. This study employs the publicly accessible “Paediatric Intensive Care database” to train, validate, and test a machine learning model for predicting pediatric patient mortality. Features were ranked using three machine learning feature selection techniques, namely Random Forest, Extra Trees, and XGBoost, resulting in the selection of 16 critical features from a total of 105 features. Ten machine learning models and ensemble techniques are used to make accurate mortality predictions. To tackle the inherent class imbalance in the dataset, we applied a unique data partitioning technique to enhance the model's alignment with the data distribution. The CatBoost machine learning model achieved an area under the curve (AUC) of 72.22%, while the stacking ensemble model yielded an AUC of 60.59% for mortality prediction. The proposed subdivision technique, on the other hand, provides a significant improvement in performance metrics, with an AUC of 85.2% and an accuracy of 89.32%. These findings emphasize the potential of machine learning in enhancing pediatric mortality prediction and inform strategies for improved ICU readiness.

MoS2-based polarization-sensitive photodetectors with asymmetric plasmonic structures and decreased detection time

This study analyzes the response time and sensitivity of photosensors that use 2D MoS2 semiconductor films with a high concentration of defects resulting from technological process sequences. In the developed photosensors, we introduce asymmetric plasmonic gratings providing their sensitivity enhancement as well as light polarization sensitivity. The photodetectors, as initially fabricated, exhibit an exceptionally long response time, surpassing a duration of a thousand seconds. This prolonged response time significantly impedes the practical application of these detectors. Nevertheless, the implementation of a specifically designed algorithm enables a dramatic reduction in detection time, by several orders of magnitude in comparison to the response time. It is based on a mathematical model that accounts for the dynamic features of these devices. Our study demonstrates that photodetectors, when enhanced with asymmetric plasmonic gratings, exhibit significantly improved performance metrics. These include a photosensitivity of approximately 60 mA/W and a degree of linear polarization at 0.78. Furthermore, the implementation of our proposed algorithm, which optimizes the calculation of detection time, dramatically reduces the characteristic detection time from over a thousand seconds to just around 10 s.

Coplanar Waveguide (CPW) Loaded with Symmetric Circular and Polygonal Split-Ring Resonator (SRR) Shapes

This paper investigates the performance of coplanar waveguide (CPW) structures loaded with symmetric circular and polygonal split-ring resonators (SRRs) for microwave and RF applications, leveraging their unique electromagnetic properties. These properties make them suitable for metamaterials, sensors, filters, resonators, antennas, and communication systems. The objectives of this study are to analyze the impact of different SRR shapes on the transmission characteristics of CPWs and to explore their potential for realizing compact and efficient microwave components. The CPW-SRR structures are fabricated on a dielectric substrate, and their transmission properties and spectrogram are experimentally characterized in the frequency range of 4 GHz to 10 GHz with the rotation angles of the SRR gap. The simulation results demonstrate that the resonant frequencies and magnitude of the transmission coefficient of the CPW-SRR structures are influenced by the geometry of the SRR shapes and the rotation angles of the SRR gap, with certain shapes exhibiting enhanced performance characteristics compared to others. Moreover, the symmetric circular and polygonal SRRs offer design flexibility and enable the realization of miniaturized microwave components with improved performance metrics. Overall, this study provides valuable insights into the design and optimization of CPW-based microwave circuits utilizing symmetric SRR shapes, paving the way for advancements in the miniaturization and integration of RF systems.

Development of circular economy model using Industry 4.0’s machine learning techniques to improve performance metrics of MSMEs

Development of circular economy model using Industry 4.0’s machine learning techniques to improve performance metrics of MSMEs

Enhancing supply chain resilience facing partial and complete disruptions: The application in the cooking oil industry

Nowadays, food supply chains are more vulnerable to disruptions due to competition, uncertain environments, and global catastrophes such as the recent pandemic. Due to the importance of addressing disruption risks, this study develops a hybrid mechanism to enhance resilience in a multi-product supply chain. The proposed mechanism improves supplier resilience in a Cooking Oil Supply Chain (COSC) network design. First, we identify elements that affect supplier resilience and present a mechanism based on the Analytical Network Process (ANP) to evaluate resilience. In addition, several strategies are determined to enhance network resilience. Moreover, a stochastic mathematical formulation is proposed to utilize suppliers’ scores and a combination of identified strategies to strengthen supply chain resilience. The developed stochastic model encompasses three objectives and considers possible partial and complete disruptions of the supply chain network. Furthermore, we develop a robust counterpart formulation to handle the disruption uncertainty of the COSC and utilize the Normalized Normal Constraint (NNC) method to generate Pareto optimal solutions. Lastly, we put the implemented mechanism into practice in a real-world case to demonstrate its effectiveness in enhancing supply chain resilience. Likewise, we unveil that the implementation of suitable resilience strategies within the supply chain can lead to substantial improvements in performance metrics. For example, the results obtained illustrate that by embracing resilience strategies, the proposed mechanism has the potential to reduce the overall cost of the supply chain by as much as 56%.

NeoMUST: an accurate and efficient multi-task learning model for neoantigen presentation.

Accurate identification of neoantigens is important for advancing cancer immunotherapies. This study introduces Neoantigen MUlti-taSk Tower (NeoMUST), a model employing multi-task learning to effectively capture task-specific information across related tasks. Our results show that NeoMUST rivals existing algorithms in predicting the presentation of neoantigens via MHC-I molecules, while demonstrating a significantly shorter training time for enhanced computational efficiency. The use of multi-task learning enables NeoMUST to leverage shared knowledge and task dependencies, leading to improved performance metrics and a significant reduction in the training time. NeoMUST, implemented in Python, is freely accessible at the GitHub repository. Our model will facilitate neoantigen prediction and empower the development of effective cancer immunotherapeutic approaches.

Cascade operation-enhanced high-resolution representation learning for meticulous segmentation of bridge cracks

High-resolution (HR) crack images have proven valuable for bridge inspection using unmanned aerial vehicles (UAVs), offering fine details crucial for accurate segmentation. Traditional deep learning (DL) struggles with HR images due to downsampling issues and limited computational resources. To address this, we propose Cascade-FcaHRNet, a HR representation learning-based multiscale architecture. It incorporates a frequency-channel attention mechanism to capture tiny crack features, a two-stage cascade operation for global and local refinement, and a region-sensitive loss to avoid ambiguous predictions. Ablation studies confirm the effectiveness of these modifications. Robustness experiments show improvements in performance metrics for crack segmentation. In a field test, the Cascade-FcaHRNet accurately segments bridge cracks wider than 0.5 mm from 4 K resolution images, enhancing safety and efficiency in UAV-based bridge inspection. The approach holds potential for developing scientifically sound maintenance and management strategies.

Artificial intelligence in routine blood tests

Routine blood tests drive diagnosis, prognosis, and monitoring in traditional clinical decision support systems. As a routine diagnostic tool with standardized laboratory workflows, clinical blood analysis offers superior accessibility to a comprehensive assessment of physiological parameters. These parameters can be integrated and automated at scale, allowing for in-depth clinical inference and cost-effectiveness compared to other modalities such as imaging, genetic testing, or histopathology. Herein, we extensively review the analytical value of routine blood tests leveraged by artificial intelligence (AI), using the ICD-10 classification as a reference. A significant gap exists between standard disease-associated features and those selected by machine learning models. This suggests an amount of non-perceived information in traditional decision support systems that AI could leverage with improved performance metrics. Nonetheless, AI-derived support for clinical decisions must still be harmonized regarding external validation studies, regulatory approvals, and clinical deployment strategies. Still, as we discuss, the path is drawn for the future application of scalable artificial intelligence (AI) to enhance, extract, and classify patterns potentially correlated with pathological states with restricted limitations in terms of bias and representativeness.

Abstract 6826: Comparative analysis of a predictive transcriptomic model and functional gene expression signatures (FGES) for tertiary lymphoid structure (TLS) detection in lung adenocarcinoma (LUAD)

Abstract Background: TLS are recognized as significant biomarkers for patient prognosis and therapy selection. In current clinical practice, TLS are often detected by manual assessment of H&E slides, which has been associated with reproducibility issues. Here, we developed a transcriptomic model using TLS density to stratify LUAD patients into TLS-high and -low groups and compared it with 5 published TLS FGES to assess the predictive value for patient outcomes. Design: A regression model to predict TLS density (units(u)/mm²) was created using RNA expression data and histological mapping of 415 TCGA-LUAD samples. Features were chosen based on differential gene expression analysis of sample groups with low (<Q1, 0.26 u/mm²) and high (>Q3, 2.04 u/mm²) TLS density. The model was trained on 269 samples and independently validated on 146 TCGA and 9 internal samples. The RNA model and five previously reported TLS FGES were compared by Wilcoxon paired test to Spearman’s ρ and F1-scores from bootstrapped validation set. The resulting predictions stratified immunotherapy-treated samples from SU2C-MARK (n=58) and GSE182328 (n=28) into TLS-high and TLS-low groups by Q2 threshold for TLS density (0.78 u/mm²) for further univariate (Kaplan-Meier) and multivariate (Cox model, adjusted for age and sex) analyses for overall survival (OS) with log-rank tests. P-values were adjusted with Bonferroni correction, and 95% confidence intervals (CIs) were provided. Results: Statistically significant upregulated genes in samples with high TLS density included FDCSP, MS4A1, and CXCL13 (logFC ≥ 0.95, FDR<0.01). The transcriptomic model showed confident superiority in correlation with TLS density compared to the best FGES in TCGA samples (ρ=0.53±0.03 vs 0.41±0.03, p<0.001). The TLS model also outperformed the highest performing FGES when stratifying TCGA samples into TLS-high and TLS-low groups (F1=0.73±0.02 vs 0.69±0.02, p<0.005). In the internal cohort, the model demonstrated robust performance (F1=0.76±0.05, ρ=0.81±0.04) and outperformed all FGES except one (FGES F1=0.78±0.05, ρ=0.86±0.05). The TLS-high group defined by the TLS RNA model had a significantly better prognosis on immunotherapy cohorts (p=0.01; 1 year OS 49% for TLS-low vs 70% for TLS-high), whereas no discernible difference in OS was identified for the 5 TLS FGES. The TLS model had a log hazard ratio (logHR) of -3.11 (CI:-5.31,-0.91; p<0.05, Harrell's C-index=0.64), indicative of a stronger association with OS than of the TLS FGES (logHR=-2.11; CI:-4.1,-0.08; p=ns; C-index=0.59). Conclusions: Comparative analysis of the proposed transcriptomic model for TLS detection in LUAD samples demonstrated improved performance metrics and prognostic value compared to previously reported TLS FGES, affirming its potential for guiding therapeutic decision-making. Citation Format: Nadezhda Lukashevich, Daniil Dymov, Aleksandr Sarachakov, Sofya Kust, Anna Love, Ivan Valiev, Dmitry Ivchenkov, Alexander Bagaev, Viktor Svekolkin, Nikita Kotlov, Vladimir Kushnarev. Comparative analysis of a predictive transcriptomic model and functional gene expression signatures (FGES) for tertiary lymphoid structure (TLS) detection in lung adenocarcinoma (LUAD) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6826.

Prediction of antioxidant capacity in faba bean from individual phenolic constituents

The relationship between individual phytochemical constituents and overall antioxidant capacity or total phenolic content (TPC) is poorly understood in faba bean. This study used a range of linear and nonlinear regression techniques to investigate whether the antioxidant capacity and TPC of 60 faba bean samples (flour and methanolic extracts) could be predicted from 12 individual compounds (10 common polyphenols and 2 alkaloid glycosides) measured in the same samples. Nonlinear regression using machine learning with a Radial Basis Function showed the best performance for antioxidant and TPC prediction across all sample types, while multiple linear regression allowed moderately accurate predictions in most sample matrices. Improved performance metrics were seen for the methanolic extracts compared to the flour samples. The strongest predictors of antioxidant activity in the multiple linear regression models were protocatechuic acid, p-hydroxybenzoic acid, and ferulic acid, suggesting that these compounds are particularly important contributors to the high antioxidant activity of faba bean. Understanding the relationship between individual constituents and the antioxidant capacity may help food technologists and plant breeders develop faba bean products with maximal health benefits.

Advancing objective functions in hydrological modelling: Integrating knowable moments for improved simulation accuracy

Runoff prediction is a crucial aspect of water resource management and risk mitigation. Despite hydrological modelling plays a vital role in accurately representing catchment behaviour, calibration still poses a significant challenge. This study explores the use of knowable moments (KMoments), a category of high-order statistical moments, as part of the core of objective functions in hydrological model calibration. Traditional objective functions, such as Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE), often make assumptions about data distribution and are sensitive to outliers. KMoments offer a promising alternative by enabling reliable estimation and effective description of high-order statistics from typical hydrological samples and therefore, reducing uncertainty in their estimation and computation of the objective functions in question. Three daily lumped hydrological models (GR4J, VIC, and HYMOD) were employed to test the performance of different calibration strategies using KMoments-based objective functions and compare them with conventional approaches. The hydrological consistency of the simulations was also assessed through 27 hydrological signatures. Our findings highlight the advantages of using KMoments, including improved performance metrics and enhanced hydrological signature reproduction. The findings contribute to advancing hydrological modelling techniques and provide valuable insights for researchers and practitioners seeking to enhance simulation accuracy and reliability.

PHISHSNAP-A Chrome Extension Tool used for Detection of Phishing applying Machine Learning

This work introduces a novel approach aimed at strengthening the effectiveness of phishing detection systems in the face of evolving cyber threats. Leveraging the power of machine learning-based anomaly detection techniques, this proposed mechanism seeks to significantly enhance both the accuracy and adaptability of current detection methods to effectively combat emerging phishing attacks. Central to this methodology is the utilization of ensemble model mechanisms, which intelligently integrate predictions from a diverse array of machine learning models. Through cautious analysis of URLs utilizing distinct datasets, this system systematically compares and contrasts results with established approaches, thereby enriching the overall detection process. This approach showcases notable improvements in performance metrics, boasting higher success rates that substantially exceed conventional heuristic analysis and blacklist-based detection methodologies. By transcending the limitations inherent in traditional detection strategies, this innovative framework represents a promising leap forward in the ongoing battle against phishing exploits, offering enhanced resilience in safeguarding sensitive user information from malicious cyber threats.

Deep convolutional neural network framework with multi-modal fusion for Alzheimer’s detection

The biomedical profession has gained importance due to the rapid and accurate diagnosis of clinical patients using computer-aided diagnosis (CAD) tools. The diagnosis and treatment of Alzheimer’s disease (AD) using complementary multimodalities can improve the quality of life and mental state of patients. In this study, we integrated a lightweight custom convolutional neural network (CNN) model and nature-inspired optimization techniques to enhance the performance, robustness, and stability of progress detection in AD. A multi-modal fusion database approach was implemented, including positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, to create a fused database. We compared the performance of custom and pre-trained deep learning models with and without optimization and found that employing natureinspired algorithms like the particle swarm optimization algorithm (PSO) algorithm significantly improved system performance. The proposed methodology, which includes a fused multimodality database and optimization strategy, improved performance metrics such as training, validation, test accuracy, precision, and recall. Furthermore, PSO was found to improve the performance of pre-trained models by 3-5% and custom models by up to 22%. Combining different medical imaging modalities improved the overall model performance by 2-5%. In conclusion, a customized lightweight CNN model and nature-inspired optimization techniques can significantly enhance progress detection, leading to better biomedical research and patient care.

A data envelopment analysis model for opinion leaders’ identification in social networks

Through Online Social Networks (OSNs) such as Instagram, X (Twitter), and Facebook, employing Opinion Leaders (OLs) is becoming integral to companies’ strategies for influencing the public. The graph-based methods are one of the most important approaches for finding OLs in OSNs. Social Network Analysis (SNA)-based OLs finding methods deal with a considerable amount of data due to using entire relationships between all of the users in a network, which makes the algorithms time-consuming. Our main goal is to introduce a new method of OLs discovery that works with fewer data and maintains or improves performance metrics. Consequently, a new application of the Data Envelopment Analysis (DEA) method is presented here for OLs identification in social media. Another contribution of this paper is introducing a new framework (OL-Finder Evaluator or OLFE) for validating the OLs’ detection algorithms under imbalanced datasets. DEA methods, when compared with SNA methods, have the advantage of being able to apply over non-graph-based datasets and to work with substantially smaller datasets. In contrast, SNA methods require transparent relationships between people. In this study, we compare both DEA (including CCR and BCC) and SNA measures (including “Betweenness (BC),” “Degree (DC),” “Page Rank (PRC),” “Closeness (CC),” and “Eigenvector (EC)” centralities) on a real Instagram network for OLs detection. Compared with SNA, our proposed method can identify OLs with considerably fewer data. Besides the advantages of DEA for time-saving, close competition exists between the DEA and the SNA methods. On average, DEA performs better in accuracy, precision, recall, and F1-score performance metrics.

SCFS-securing flying ad hoc network using cluster-based trusted fuzzy scheme

AbstractFlying Ad hoc Networks have emerged as a promising technology for number of real-time applications. However, the flexible and unstructured characteristics of these networks make them vulnerable to security threats posed by malicious nodes, such as denial of service attacks, node impersonation, and information breaches. Another major issue is the consideration of those nodes being unable to prove their trustworthiness due to factors like hardware or software failure, or by link interruptions, during the processing of detection of false nodes in the network. The existing mechanisms encompassing encryption, authentication, and intrusion detection highlight limitations to secure real-time applications and services due to the high speed of flying nodes and the absence of fixed network structures. To overcome these constraints, this research paper incorporates a novel framework for evaluating and improving the security of network by introducing an innovative cluster-based approach. Moreover, it presents a fuzzy model that dynamically estimates the trust levels of both individual nodes and clusters, by assigning weight to the parameters to address vulnerabilities. Additionally, a trust reconfiguration mechanism is further proposed to address the issue of nodes unable to substantiate their trust by providing them with additional chances based on the collective trust from previous evaluations. Further, the paper incorporates a dynamic reputation system to proficiently identify and separate malicious and selfish nodes from the network. Simulation results indicate a significant improvement in performance metrics, with a considerable reduction in delay and drop ratio by 41.46% and 36.37%, respectively, while the sufficient rise of 54.71% and 46.05% in throughput and coverage, respectively, comparing with the considered state-of-art.