Performance Of Framework Research Articles

ADVANCING WEB DEVELOPMENT: A COMPARATIVE ANALYSIS OF MODERN FRAMEWORKS FOR REST AND GRAPHQL BACK-END SERVICES

This article conducts a detailed comparison of leading REST and GraphQL web development frameworks, including Node.js with Express, Spring Boot, Django, Flask, Ruby on Rails, Apollo Server, GraphQL-Ruby, and Graphene. It assesses each framework's performance, scalability, usability, and community support to highlight their strengths and application suitability. The discussion extends to the challenges of framework selection, such as learning curves and security, and anticipates future technological influences like cloud computing and AI. The analysis aims to guide developers and organizations in choosing frameworks that best meet their project needs and strategic objectives, providing a concise yet comprehensive resource in the evolving field of web development.

Enhancing microgrid sustainability: Dynamic management of renewable resources and plug-in hybrid electric vehicles

This paper addresses the challenges inherent in Microgrids (MGs), Renewable Energy Sources (RES), and Plug-in Hybrid Electric Vehicles (PHEVs), presenting a pioneering approach for a sustainable energy future. The focal point is a novel Multi-Stage Dynamic Energy Management (EM) framework, strategically designed to optimize the use of non-dispatchable RES and PHEVs while reducing dependency on the Utility Grid (UG). The research integrates Solar Photovoltaic arrays (SPVs), Wind Turbines (WTs), Micro Turbines (MTs), Battery Energy Storage Systems (BESS), PHEVs, and loads, forming a comprehensive MG to enhance overall efficiency and resilience. The core innovation lies in the dynamic EM framework, minimizing UG dependency and adapting to stochastic RES generation and PHEV charging patterns. The study underscores the significance of demand response in enhancing energy efficiency and grid reliability. Demonstrating effective RES utilization, the research showcases the potential for a greener and more resilient energy infrastructure. Through advanced simulations, the proposed framework's performance is rigorously evaluated under various scenarios, including high RES penetration. The results not only affirm the framework's effectiveness but also highlight its potential to revolutionize MG management by reducing grid dependency and maximizing renewable resource utilization.

Research on 5G User Perception Detection and Experience Improvement Optimization Based on Capsule Network

COVID-19 caused a global public disaster as well as an economic crisis, and other challenges. The fifth-generation network, or 5G, connects practically every machine, person, and thing on the planet. We can analyse the public's opinions and sentiments connected to COVID-19 from 5G user-generated content on social media, which will eventually aid in promoting health intervention strategies and designing successful projects based on public perceptions. The BERT language model is first used to preprocess data that has been obtained from Sina Weibo. Following that, the features of the preprocessed data are chosen using a class-wise information technique. Finally, a capsule network (CapsNet) approach is used to identify the 5G user perception and experience optimization. Dynamic routing algorithm is used for optimizing the capsule network. By comparing the suggested framework's performance with certain existing approaches, its effectiveness is evaluated. Simulation results show that the proposed method is more accurate than previous approaches at identifying 5G user experiences.

A generalized framework for agricultural field delineation from high-resolution satellite imageries

ABSTRACT Accurate digital data on agricultural fields are crucial for various agriculture applications. While deep learning methods have shown promise in delineating fields from high-resolution imagery, there is a lack of research evaluating key techniques for field boundary detection. Challenges also exist in converting detection results into high-quality fields. This study addresses these issues and proposes a generalized framework for agricultural field delineation (GF-AFD). First, we identify three key techniques for field boundary detection and apply them to MPSPNet model. Ablation and comparison experiments demonstrate significant performance enhancements due to techniques. They also prove effective for DeeplabV3+ model, which shares a similar architecture. The modified models outperform U-Net-based models and approach the state-of-the-art models. Second, we show that performing region segmentation on boundary results yields improved field shapes. To address weak boundary loss and unstable parameters issues in existing segmentation-based methods, we introduce the OWT method to enhance weak boundaries directionally before segmentation. We also develop a hierarchical merging method, leveraging the observational hierarchy of fields, resulting in stable parameters across regions and models. The proposed GF-AFD framework was validated cross three diverse Chinese counties. The results demonstrate the framework's robust performance, providing a valuable solution for delineating agricultural fields.

PDSE-Lite: lightweight framework for plant disease severity estimation based on Convolutional Autoencoder and Few-Shot Learning.

Plant disease diagnosis with estimation of disease severity at early stages still remains a significant research challenge in agriculture. It is helpful in diagnosing plant diseases at the earliest so that timely action can be taken for curing the disease. Existing studies often rely on labor-intensive manually annotated large datasets for disease severity estimation. In order to conquer this problem, a lightweight framework named "PDSE-Lite" based on Convolutional Autoencoder (CAE) and Few-Shot Learning (FSL) is proposed in this manuscript for plant disease severity estimation with few training instances. The PDSE-Lite framework is designed and developed in two stages. In first stage, a lightweight CAE model is built and trained to reconstruct leaf images from original leaf images with minimal reconstruction loss. In subsequent stage, pretrained layers of the CAE model built in the first stage are utilized to develop the image classification and segmentation models, which are then trained using FSL. By leveraging FSL, the proposed framework requires only a few annotated instances for training, which significantly reduces the human efforts required for data annotation. Disease severity is then calculated by determining the percentage of diseased leaf pixels obtained through segmentation out of the total leaf pixels. The PDSE-Lite framework's performance is evaluated on Apple-Tree-Leaf-Disease-Segmentation (ATLDS) dataset. However, the proposed framework can identify any plant disease and quantify the severity of identified diseases. Experimental results reveal that the PDSE-Lite framework can accurately detect healthy and four types of apple tree diseases as well as precisely segment the diseased area from leaf images by using only two training samples from each class of the ATLDS dataset. Furthermore, the PDSE-Lite framework's performance is compared with existing state-of-the-art techniques, and it is found that this framework outperformed these approaches. The proposed framework's applicability is further verified by statistical hypothesis testing using Student t-test. The results obtained from this test confirm that the proposed framework can precisely estimate the plant disease severity with a confidence interval of 99%. Hence, by reducing the reliance on large-scale manual data annotation, the proposed framework offers a promising solution for early-stage plant disease diagnosis and severity estimation.

Optimizing data processing for edge-enabled IoT devices using deep learning based heterogeneous data clustering approach

- Edge-based Internet of Things devices have transformed smart farming, aiding in efficient data collection and processing for optimal resource utilization and crop yields. However, task scheduling and resource allocation pose significant challenges due to the dynamic nature of agricultural environments. Our research introduces a novel framework that integrates deep reinforcement learning algorithm into an edge-enabled wireless sensor network for multi-objective optimization of the functionality of the Deep Q-Networks (DQNs). This framework extends the traditional Q-learning method to manage large state-action spaces efficiently. It employs a deep neural network to approximate the Q-value function, rather than relying on a Q-table, making it more capable of handling complex problems with high-dimensional state spaces. It forms heterogenous data clusters supports an optimal task scheduling and resource allocation policies, sustains key objectives such as minimal energy consumption, latency, efficient resource utilization, and reduced task completion time. The framework's performance is evaluated in a simulated environment mimicking real-world smart farming applications. Results confirm its superiority in enhancing performance metrics and lowering energy consumption, as opposed to traditional networks.

First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects.

Vibrational spectroscopy is a powerful approach to visualising interfacial phenomena. However, extracting structural and dynamical information from vibrational spectra is a challenge that requires first-principles simulations, including non-Condon and quantum nuclear effects. We address this challenge by developing a machine-learning enhanced first-principles framework to speed up predictive modelling of infrared, Raman, and sum-frequency generation spectra. Our approach uses machine learning potentials that encode quantum nuclear effects to generate quantum trajectories using simple molecular dynamics efficiently. In addition, we reformulate bulk and interfacial selection rules to express them unambiguously in terms of the derivatives of polarisation and polarisabilities of the whole system and predict these derivatives efficiently using fully-differentiable machine learning models of dielectric response tensors. We demonstrate our framework's performance by predicting the IR, Raman, and sum-frequency generation spectra of liquid water, ice and the water-air interface by achieving near quantitative agreement with experiments at nearly the same computational efficiency as pure classical methods. Finally, to aid the experimental discovery of new phases of nanoconfined water, we predict the temperature-dependent vibrational spectra of monolayer water across the solid-hexatic-liquid phases transition.

Presegmenter Cascaded Framework for Mammogram Mass Segmentation.

Accurate segmentation of breast masses in mammogram images is essential for early cancer diagnosis and treatment planning. Several deep learning (DL) models have been proposed for whole mammogram segmentation and mass patch/crop segmentation. However, current DL models for breast mammogram mass segmentation face several limitations, including false positives (FPs), false negatives (FNs), and challenges with the end-to-end approach. This paper presents a novel two-stage end-to-end cascaded breast mass segmentation framework that incorporates a saliency map of potential mass regions to guide the DL models for breast mass segmentation. The first-stage segmentation model of the cascade framework is used to generate a saliency map to establish a coarse region of interest (ROI), effectively narrowing the focus to probable mass regions. The proposed presegmenter attention (PSA) blocks are introduced in the second-stage segmentation model to enable dynamic adaptation to the most informative regions within the mammogram images based on the generated saliency map. Comparative analysis of the Attention U-net model with and without the cascade framework is provided in terms of dice scores, precision, recall, FP rates (FPRs), and FN outcomes. Experimental results consistently demonstrate enhanced breast mass segmentation performance by the proposed cascade framework across all three datasets: INbreast, CSAW-S, and DMID. The cascade framework shows superior segmentation performance by improving the dice score by about 6% for the INbreast dataset, 3% for the CSAW-S dataset, and 2% for the DMID dataset. Similarly, the FN outcomes were reduced by 10% for the INbreast dataset, 19% for the CSAW-S dataset, and 4% for the DMID dataset. Moreover, the proposed cascade framework's performance is validated with varying state-of-the-art segmentation models such as DeepLabV3+ and Swin transformer U-net. The presegmenter cascade framework has the potential to improve segmentation performance and mitigate FNs when integrated with any medical image segmentation framework, irrespective of the choice of the model.

A Stacked Ensemble Framework through Synergistic Modeling for Adolescent Delinquency Classification

Abstract: This study emphasizes how ensemble approaches can improve the precision and resilience of predictive models in assessing social problems like adolescent delinquency. To classify adolescent delinquency, this study looks into how effective a stacked ensemble framework is. The performance of the stacked ensemble model is compared to individual classifiers using data on different risk factors linked to delinquent behavior. The evaluation metrics that are used are AUC, F-score, and the area under the receiver operating characteristic curve (AUC). The stacked ensemble framework constantly outperforms individual classifiers, as evidenced by the high AUC scores it achieves, which range from 0.97 to 1.00 across all classes of delinquency severity. Moreover, in terms of classification accuracy and F-score, the proposed ensemble model outperforms individual classifiers, demonstrating greater discriminative power. Additionally, the stacked ensemble exhibits improved generalization and robustness, proving its effectiveness in locating underlying patterns in the data and lowering the chance of overfitting. The classification of adolescent delinquency is significantly impacted by the ensemble framework's superior performance, which implies that ensemble techniques like stacking can improve predictability and accuracy in identifying adolescents who are at risk at an early stage. The results demonstrate the potential of the suggested stacked ensemble approach to enhance predictive performance and provide reliable classification across a range of delinquency severity levels, thereby validating its efficacy in classifying juvenile delinquency.

SCALABILITY IMPROVEMENT OF ACTIVE PROBING FOR FAILURE DETECTION IN LARGE-SCALE SOFTWARE DEFINED NETWORKS USING COMMUNITY DETECTION ALGORITHMS

Software-Defined Networking (SDN) is a networking approach that separates the network's control plane and data plane to provide better network control. The nature of SDN requires an essential focus on its reliability and availability, which includes failure detection aspects. Several techniques have been proposed to enhance SDN failure detection, including passive and active approaches. However, the Route Inspection (RI) algorithm, an example of the active probing method in active detection, has scalability issues when deployed in large-scale networks as it generates inefficient and lengthy probe paths. To address this, we propose using community detection (CD) algorithms to improve the probe path generation of the RI algorithm, resulting in the 'Failure Detection using Community Detection and probing (FDCD)' framework. With the aim to reduce travel paths, the proposed framework minimizes the network topology using CD algorithms to reduce the topology size into smaller communities. Then, the RI algorithm calculates the probe path in the communities instead of the single large topology, thus improving the efficiency of the failure detection mechanism. This paper adopts three community detection algorithms: Louvain, Label Propagation, and Girvan-Newman. We evaluate the framework using three well-known network topologies, COST266, PIORO40, and GERMANY50, in Mininet to observe the framework's performance. The results indicate that the proposed approach improves the algorithm's scalability against its baseline approach, improving the average probe round trip time by 66.87% and the average path installation time by 2.67%.

A deep learning-based method for structural modal analysis using computer vision

Structural modal analysis aims to determine a structure's natural frequency, damping ratio, and mode shape, helping with structural condition assessment and maintenance. In this study, a computer vision-based framework for the identification of structural modal parameters is developed, which consists of two main procedures: First, the one-dimensional (1D) vibration signals of edge pixels on the structure in the video are extracted via edge detection and optical flow theory. Second, a 1D convolutional neural network (CNN) coupled with long short-term memory (LSTM) is generated to extract structural modal parameters from the input 1D signal. The framework's performance has been validated through comparison with baseline values, which were obtained from contact sensors. Additionally, the model's robustness and extrapolability has been analyzed. The good performance of the computer vision-based approach confirms its potential for precise and dependable contact-free modal analysis.

A novel hybrid adaptive framework for support vector machine-based reliability analysis: A comparative study

This study presents an innovative hybrid Adaptive Support Vector Machine - Monte Carlo Simulation (ASVM-MCS) framework for reliability analysis in complex engineering structures. These structures often involve highly nonlinear implicit functions, making traditional gradient-based first or second order reliability algorithms and Monte Carlo Simulation (MCS) time-consuming. The application of surrogate models has proven effective in addressing computational challenges associated with a large number of simulations. Support Vector Machine (SVM), as an emerging machine learning method suitable for small-sample scenarios, offers a well-established theoretical foundation and presents an effective model substitution approach for reliability analysis in engineering structures. However, the existing literature lacks a comprehensive and thorough comparative analysis of SVM's hybrid adaptive modeling approach, encompassing initial sampling methods and learning functions, with regards to both computational efficiency and accuracy. Additionally, there is a gap in adaptive modeling methods capable of accommodating diverse types of input uncertainty, the nonlinearity of limit state functions, and various application scenarios. In response to these gaps, this article introduces the ASVM-MCS framework, which addresses these challenges by considering different types of input variables and various failure modes. Moreover, this study provides a comprehensive evaluation of the ASVM-MCS framework's performance, including its initial sampling methods and learning functions, across a range of application scenarios, such as scenarios involving only random variables, mixed variables, and the reliability of series-parallel systems.

WSGMB: weight signed graph neural network for microbial biomarker identification.

The stability of the gut microenvironment is inextricably linked to human health, with the onset of many diseases accompanied by dysbiosis of the gut microbiota. It has been reported that there are differences in the microbial community composition between patients and healthy individuals, and many microbes are considered potential biomarkers. Accurately identifying these biomarkers can lead to more precise and reliable clinical decision-making. To improve the accuracy of microbial biomarker identification, this study introduces WSGMB, a computational framework that uses the relative abundance of microbial taxa and health status as inputs. This method has two main contributions: (1) viewing the microbial co-occurrence network as a weighted signed graph and applying graph convolutional neural network techniques for graph classification; (2) designing a new architecture to compute the role transitions of each microbial taxon between health and disease networks, thereby identifying disease-related microbial biomarkers. The weighted signed graph neural network enhances the quality of graph embeddings; quantifying the importance of microbes in different co-occurrence networks better identifies those microbes critical to health. Microbes are ranked according to their importance change scores, and when this score exceeds a set threshold, the microbe is considered a biomarker. This framework's identification performance is validated by comparing the biomarkers identified by WSGMB with actual microbial biomarkers associated with specific diseases from public literature databases. The study tests the proposed computational framework using actual microbial community data from colorectal cancer and Crohn's disease samples. It compares it with the most advanced microbial biomarker identification methods. The results show that the WSGMB method outperforms similar approaches in the accuracy of microbial biomarker identification.

An efficient framework for optic disk segmentation and classification of Glaucoma on fundus images

Accurately segmenting the Optic Disk is a crucial step in classifying Glaucoma using Fundus images. Machine learning and artificial intelligence techniques are widely used in Glaucoma detection, and the main indicators observed in Fundus images are the presence of Papillary Atrophy, Cup to Disc Ratio values, diminishing Neural Retinal Rim (NRR), the Inferior Superior Nasal Temporal (ISNT) rule, and Cup Diameter. In this research, we investigated various segmentation and classification techniques that can be applied to Optic disk segmentation and classification of normal and glaucomatous eyes. The proposed method will be beneficial to clinicians and healthcare workers in facilities with limited resources. In this paper, histogram processing is used to determine the type of image, and based on this information; we decide whether the image requires segmentation. Some datasets in the standard dataset contain complete retinal images while others include segmented optic disks. The segmented images are directly given as input for classification using the proposed Convolutional Neural Network (CNN). For complete retinal images, segmentation is performed using the Simple Linear Iterative clustering (SLIC) and normalized graph cut algorithm. The proposed framework's performance is compared with that of pretrained neural networks, including VGG19, InceptionV3, and ResNet50V2, using major metrics. We trained and tested these architectures with 3115 images from six standard datasets. Our proposed framework outperforms all with an accuracy of 96.33 %.

Application of personalized medicine implementation framework: a multi-country analysis

Abstract Background Personalized medicine (PM) is a rapidly evolving field, and its implementation varies widely across different countries. Some countries have invested significantly in PM research and development, while others still need to adopt these approaches. Methods To create a visual map of the PM implementation current state between countries. A framework was applied to determine the PM implementation level. The necessary information was identified and gathered from various sources, including policy documents, national guidelines, websites of major governmental agencies, research organizations, and health technology assessment agencies such as NICE, CADTH, AHRQ, and RedETS. Results Our search retrieved 184 full-text documents to fulfill the framework, generating a PM implementation country map. The map reflects the variability between countries based on different factors, particularly social-economic development. Conclusions Our PM framework was applied in a multi-country context, ranking countries based on their implementation levels. Our findings can reflect the adoption of best practices to improve implementation efforts. Policymakers, healthcare professionals, and researchers can use our framework to assess PM implementation in their respective regions. Further research is needed to evaluate the framework's challenges, limitations, and performance in different contexts. Key messages • Our PM framework was applied in a multi-country context, ranking countries based on their implementation levels. • Policymakers, healthcare professionals, and researchers can use our framework to assess PM implementation in their respective regions.

A power-speed hierarchical optimization framework of diesel/battery/supercapacitor vehicular hybrid propulsion systems

Ships' fuel consumption has emerged as the primary contributor to energy use and pollution in maritime transportation. This paper presents a novel marine vehicular hybrid propulsion system (VHPS) comprising a diesel generator and a hybrid energy storage device (HESD) that integrates batteries and supercapacitors (SCs). However, variations in the power and energy characteristics of the three sources result in heightened intricacies in energy management and system control. Therefore, a power-speed hierarchical optimization framework is proposed to optimize the fuel consumption and stability of the system. Within this framework, an AECMS-GRNN energy management strategy (EMS) is proposed to reduce the fuel consumption of VHPS. Based on the AECMS-GRNN strategy's power distribution results, a multi-zone target speed power hysteresis control strategy is proposed to ensure that the speed of the diesel generator is stable, so as to improve the stability of the system. Finally, an experimental hardware-in-the-loop (HIL) platform is employed to validate the proposed framework's performance. From experimental results, the AECMS-GRNN strategy demonstrates precise load prediction with a minimal predictive ARMSE value of 1.5886 kW. Compared with a conventional rule-based (RB) strategy, AECMS-GRNN strategy can reduce the fuel consumption by 0.077 kg, about 7.7%. Additionally, the proposed speed control strategy ensures a stable speed for the DG, contributing to the system's consistent power supply.

Assessment System for Child Head Injury from Falls Based on Neural Network Learning.

Toddlers face serious health hazards if they fall from relatively high places at home during everyday activities and are not swiftly rescued. Still, few effective, precise, and exhaustive solutions exist for such a task. This research aims to create a real-time assessment system for head injury from falls. Two phases are involved in processing the framework: In phase I, the data of joints is obtained by processing surveillance video with Open Pose. The long short-term memory (LSTM) network and 3D transform model are then used to integrate key spots' frame space and time information. In phase II, the head acceleration is derived and inserted into the HIC value calculation, and a classification model is developed to assess the injury. We collected 200 RGB-captured daily films of 13- to 30-month-old toddlers playing near furniture edges, guardrails, and upside-down falls. Five hundred video clips extracted from these are divided in an 8:2 ratio into a training and validation set. We prepared an additional collection of 300 video clips (test set) of toddlers' daily falling at home from their parents to evaluate the framework's performance. The experimental findings revealed a classification accuracy of 96.67%. The feasibility of a real-time AI technique for assessing head injuries in falls through monitoring was proven.

Pengembangan Aplikasi Kalkulator Multifungsi Menggunakan Flutter Framework

Flutter is an open-source user interface (UI) software development kit (SDK) created by Google, designed for building natively compiled applications for mobile, web, and desktop platforms from a single codebase. It employs the Dart programming language and provides a rich set of pre-built widgets, offering developers the flexibility to craft visually appealing and interactive user interfaces. This paper presents an overview of the Flutter framework, discussing its core features, architecture, and benefits. Flutter's "hot reload" feature allows for real-time code changes without the need for a full application restart, enabling faster development and experimentation. The framework's performance is optimized by leveraging Skia, a graphics engine for smooth rendering, resulting in fast and fluid UI experiences. The Flutter ecosystem encompasses a thriving community, extensive documentation, and a wide range of plugins that extend functionality and simplify integration with native platform features. Its cross-platform capabilities streamline the development process and reduce time-to-market, making it a popular choice among developers and businesses alike. The paper also highlights some potential challenges faced when adopting Flutter, such as platform-specific issues and limited native functionality access. However, the benefits of code reusability, fast development cycles, and consistent UI across platforms outweigh these challenges for many projects. Overall, Flutter has emerged as a robust and versatile framework for building high-performance applications across diverse platforms. Its continuous evolution, strong community support, and commitment from Google position it as a promising solution for modern app development in the rapidly evolving world of technology.

FDF-HybridFS: Towards design of a failure detection framework using hybrid feature selection method for IP core networks that connect 5G core in NFV-based test environment

With the advancement of recent technologies, high-speed and secure internet connectivity is required for 5th-generation mobile networks. A massive number of smart devices connected to the networks generate a large amount of data. Consequently, increasing traffic has generated more strain on networks and devices. Therefore, to handle the huge amount of data generated by the various devices and to detect network and device failures, automation is expected to play an important role. Hence, these requirements have accelerated the necessity of automation. This study presents a novel hybrid feature-selection technique for detecting network and device failure in IP core networks that connect 5 G core networks for internet connectivity. This approach first performs feature ranking using the MI (Mutual information), CC (Correlation coefficient), and GI (Gini index) to get the three different feature sets. Subsequently, in order to acquire a single optimized feature set, features are combined using a union operation. This optimized feature set is fed to the wrapper-based Boruta feature selection algorithm. In addition, to handle the data imbalance problem, we have applied the SMOTETomek method. Finally, obtained optimized feature set is fed to the three most popular ensemble approaches, Random forest, LGBM (Light gradient boosting machine), and xGBoost for the detection of failure. The proposed detection system's performance is evaluated using the KDDI dataset provided by KDDI Corporation. The effectiveness of the proposed FDF-HybridFS is tested and compared with several recent state-of-the-art methods cited in the related work in terms of detection rate, precision, accuracy, and F1 score. An analysis of the proposed failure detection framework's performance on the KDDI dataset shows that it outperforms state-of-the-art methods in terms of both detection rate and detection accuracy.

Improving accuracy of medical data handling and processing using DCAF for IoT-based healthcare scenarios

The Internet of Things (IoT), uses communication technologies and intelligent computing to improve data analysis and computation accuracy, which is essential to the healthcare industry. The sensitivity and requirement for precision in managing and processing medical data make it a difficult task for diagnosing patients. To handle medical data in a healthcare scenario based on the IoT, a Data Classification and Analysis Framework (DCAF) is proposed in this research. The proposed framework seeks to improve the precision and dependability of handling and processing medical data, which are essential components of biomedical signal processing. By reducing errors and accelerating the processing of medical data, the framework can help in improving the accuracy of medical diagnosis and therapy, which is a key objective of biomedical signal processing and control. The goal of this framework is to increase the reliability of message delivery by concentrating on data classification and analysis. The earlier procedure is in charge of decreasing the volume of medical data transfers and ensuring the accuracy and delivery of consent data. The framework's latter process is in charge of allotting suitable processing time slots to increase delivery speed and lower data handling faults. A linear decision-making process based on accessibility, sequence order, and accuracy constraints and conditions supports the framework's joint process. Utilizing measures such as an accuracy ratio 0f 93.09 %, message delivery rate of 0.949 %, error with a range of 0.052 %, classification of 73.81 ms, and analysis time over fluctuating flow instances and analysis slots, the suggested framework's performance is validated.