Efficient Framework Research Articles

Research and Analysis of the Small Disturbance Equation in Subsonic, Transonic, and Supersonic Regimes

This paper explores the application of the Small Disturbance Equation (SDE) across subsonic, transonic, and supersonic flow regimes. Derived from the Euler and Navier-Stokes equations, the SDE offers an efficient framework for analyzing aerodynamic behaviors, particularly through the utilization of discretization techniques and iterative solving methods executed in Python. The study assesses the accuracy and limitations of the SDE in detailing essential flow characteristics, revealing that while the equation performs effectively in subsonic and transonic flows, it encounters challenges in supersonic regimes. Nonlinear effects such as shock waves significantly hinder its performance at high speeds. Compared with conventional computational fluid dynamics (CFD) methods, the SDE stands out in scenarios where computational efficiency is paramount. However, its limitations in handling high-speed flows must be carefully considered, highlighting the need for further refinement in its application to supersonic dynamics. This analysis suggests that while the SDE is beneficial for certain aerodynamic studies, its scope and utility are constrained by the inherent complexities of high-speed fluid dynamics.

An Efficient Framework for Secure Dynamic Skyline Query Processing in the Cloud

AbstractThis study introduces an innovative framework named scale for processing dynamic skyline queries securely in cloud environments. Unlike previous approaches that require complex operations on encrypted data, scale simplifies dynamic skyline domination to mere comparisons, significantly improving query efficiency. Through empirical evaluations over four datasets, we show that scale accelerates query processing nearly 1000-fold compared to existing state-of-the-art methods. Specifically, scale shows significant efficiency improvements by simplifying query interactions to a single round between the user and the cloud, which is validated through empirical studies on multiple datasets. Moreover, we introduce two distributed versions of scale, dist-scale-s and dist-scale-e, which further optimize performance by facilitating parallel processing. This adaptation showcases a substantial reduction in response times and computational overhead, underpinning the scalability and effectiveness of our framework in handling large-scale, secure cloud-based queries.

Efficient Productivity-Aware Control Parameter Optimization in Cutter Suction Dredger Construction Using Machine Learning with Parallel Global Search

This paper proposes an efficient productivity-aware optimization framework that utilizes hybrid machine learning with parallel global search to timely and appropriately adjust the critical control parameters (CCPs) of a cutter suction dredger (CSD) during construction. This optimization framework consists of three main parts. First, a hybrid Jaya–multilayer perceptron (MLP) algorithm was developed to rapidly construct a model that captures the interaction between construction parameters and slurry concentration. Next, the preliminary coarse results for the CCPs are determined through multi-parameter sensitivity analysis. Finally, the proposed resilient-zone parallel global search algorithm was employed to further optimize the CCPs, yielding more precise optimization results. To validate the proposed optimization framework and implement the in-situ service, it is applied to a real-world case study involving “Tianda” CSD construction. The results demonstrated that the average optimization duration is 6.7 s, which is shorter than the data acquisition interval of 8 s. Our approach improves the computational efficiency by 9.4 times compared with traditional optimization control methods. Additionally, there is a significant increase in the slurry concentration, with the maximum growth rate reaching 81.64%.

A Framework for Prioritising the Performance Criteria of Natural Fibre Composite Materials: Incorporation of CRITIC-TOPSIS Method

Selecting an appropriate Multi-Criteria Decision-Making (MCDM) method to provide a solution to assist design engineers in prioritising the right criteria in the early design process is essential. Part of the aim of this study is to establish an integration CRITIC-TOPIS for selecting the most efficient framework to choose performance criteria, namely density, tensile strength, Young’s modulus, cellulose, and elongation at break for natural fibre material intended for cap toe shoes like abaca, bamboo, coir, jute, kenaf, sisal. Hence, a new framework was proposed and tested based on integrating Criteria Importance Through Inter Criteria Correlation (CRITIC)-Technique Order Preference by Similarity to Ideal Solution (TOPSIS). Therefore, this proposed framework consists of two phases: the first involves determining the weights of attributes using the CRITIC method, and the second consists of making material criteria decisions using the TOPSIS method. Meanwhile, to achieve this objective, numerical validation was obtained using data from selected past case studies, which were then replicated to validate the output of the proposed framework. According to the validation conducted using CRITIC-TOPSIS, the results show a significant level of similarity, with the rankings being consistent. Therefore, the proposed methodology may provide imprecise and ambiguous information for prioritising the performance criteria of natural fibre composite materials. Moreover, design engineers can utilise this framework in the composite industry to create the best possible evaluation model for composite material criteria selection for various applications.

Automated coordinate system estimation: A preliminary step toward computer-assisted radial head arthroplasty planning.

The success of radial head arthroplasty (RHA) relies on the design of the implant and precision of the surgical technique, with preoperative planning potentially playing a crucial role. The accurate establishment of a patient-specific anatomical coordinate system (ACS) is essential for this planning process. This study tested the hypothesis that an innovative automated method would be an accurate, reliable, and efficient framework to determine the ACS of the proximal radius, which would be a step toward improving the precision of RHA planning. We used advanced computational techniques to analyze 50 forearm CT scans, comparing the accuracy, reproducibility, reliability, and efficiency of the automated method with manually derived ACS using expert observers as benchmarks. The results showed that the automated approach was more accurate in identifying anatomical landmarks, with smaller mean distance discrepancies (0.6 mm) than manual observers (1 mm). Its reproducibility was also superior, with narrower reproducibility limits, particularly for ulnar notch landmarks (0.6 to 0.8 mm compared to manual selection 1.2 to 1.4 mm) (p = .01). In addition, the limits of agreement and the mean absolute rotational and translational differences of the axes were narrower for the automated method, which also reduced the construction time to an average of 46 s compared to 150 s manually (p < .001). These findings suggest that the automated method has the potential to enhance the accuracy and efficiency of preoperative and postoperative computer-assisted procedures for RHA. Further research is needed to fully understand the utility of this automated system for enhancing RHA computer-assisted surgical planning.

Advancing SWAT Model Calibration: A U-NSGA-III-Based Framework for Multi-Objective Optimization

In recent years, remote sensing data have revealed considerable potential in unraveling crucial information regarding water balance dynamics due to their unique spatiotemporal distribution characteristics, thereby advancing multi-objective optimization algorithms in hydrological model parameter calibration. However, existing optimization frameworks based on the Soil and Water Assessment Tool (SWAT) primarily focus on single-objective or multiple-objective (i.e., two or three objective functions), lacking an open, efficient, and flexible framework to integrate many-objective (i.e., four or more objective functions) optimization algorithms to satisfy the growing demands of complex hydrological systems. This study addresses this gap by designing and implementing a multi-objective optimization framework, Py-SWAT-U-NSGA-III, which integrates the Unified Non-dominated Sorting Genetic Algorithm III (U-NSGA-III). Built on the SWAT model, this framework supports a broad range of optimization problems, from single- to many-objective. Developed within a Python environment, the SWAT model modules are integrated with the Pymoo library to construct a U-NSGA-III algorithm-based optimization framework. This framework accommodates various calibration schemes, including multi-site, multi-variable, and multi-objective functions. Additionally, it incorporates sensitivity analysis and post-processing modules to shed insights into model behavior and evaluate optimization results. The framework supports multi-core parallel processing to enhance efficiency. The framework was tested in the Meijiang River Basin in southern China, using daily streamflow data and Penman–Monteith–Leuning Version 2 (PML-V2(China)) remote sensing evapotranspiration (ET) data for sensitivity analysis and parallel efficiency evaluation. Three case studies demonstrated its effectiveness in optimizing complex hydrological models, with multi-core processing achieving a speedup of up to 8.95 despite I/O bottlenecks. Py-SWAT-U-NSGA-III provides an open, efficient, and flexible tool for the hydrological community that strives to facilitate the application and advancement of multi-objective optimization in hydrological modeling.

Numerical simulation of phase transition with the hyperbolic Godunov-Peshkov-Romenski model

In this paper, a thermodynamically consistent numerical solution of the interfacial Riemann problem for the first-order hyperbolic continuum model of Godunov, Peshkov and Romenski (GPR model) is presented. In the presence of phase transition, interfacial physics are governed by molecular interaction on a microscopic scale, beyond the scope of the macroscopic continuum model in the bulk phases. The developed approximate two-phase Riemann solvers tackle this multi-scale problem, by incorporating a local thermodynamic model to predict the interfacial entropy production. Using phenomenological relations of non-equilibrium thermodynamics, interfacial mass and heat fluxes are derived from the entropy production and provide closure at the phase boundary. We employ the proposed Riemann solvers in an efficient sharp interface level-set Ghost-Fluid framework to provide coupling conditions at phase interfaces under phase transition. As a single-phase benchmark, a Rayleigh-Bénard convection is studied to compare the hyperbolic thermal relaxation formulation of the GPR model against the hyperbolic-parabolic Euler-Fourier system. The novel interfacial Riemann solvers are validated against molecular dynamics simulations of evaporating shock tubes with the Lennard-Jones shifted and truncated potential. On a macroscopic scale, evaporating shock tubes are computed for the material n-Dodecane and compared against Euler-Fourier results. Finally, the efficiency and robustness of the scheme is demonstrated with shock-droplet interaction simulations that involve both phase transfer and surface tension, while featuring severe interface deformations.

SVQ-MAE: an efficient speech pre-training framework with constrained computational resources

Self-supervised learning for speech pre-training models has achieved remarkable success in acquiring superior speech contextual representations by learning from unlabeled audio, excelling in numerous downstream speech tasks. However, the pre-training of these models necessitates significant computational resources and training duration, presenting a high barrier to entry into the realm of pre-training learning. In our efforts, by amalgamating the resource-efficient benefits of the generative learning model, Masked Auto Encoder, with the efficacy of the vector quantization method in discriminative learning, we introduce a novel pre-training framework: Speech Vector Quantization Masked Auto Encoder (SVQ-MAE). Distinct from the majority of SSL frameworks, which require simultaneous construction of speech contextual representations and mask reconstruction within an encoder-only module, we have exclusively designed a decoupled decoder for pre-training SVQ-MAE. This allows the additional decoupled decoder to undertake the mask reconstruction task solely, reducing the learning complexity of pretext tasks and enhancing the encoder’s efficiency in extracting speech contextual representations. Owing to this innovation, by using only 4 GPUs, SVQ-NAE can achieve high performance comparable to wav2vec 2.0, which requires 64 GPUs for training. In the Speech Processing Universal Performance Benchmark, SVQ-MAE surpasses wav2vec 2.0 in both keyword spotting and emotion recognition tasks. Furthermore, in cross-lingual ASR for Mandarin, upon fine-tuning on AISHELL-1, SVQ-MAE achieves a Character Error Rate of 4.09%, outperforming all supervised ASR models.

Private compression for intermediate feature in IoT-supported mobile cloud inference

In the emerging Internet of Things (IoT) paradigm, mobile cloud inference serves as an efficient application framework that relieves the computation and storage burden on resource-constrained mobile devices by offloading the workload to cloud servers. However, mobile cloud inference encounters computation, communication, and privacy challenges to ensure efficient system inference and protect the privacy of mobile users’ collected information. To address the deployment of deep neural networks (DNN) with large capacity, we propose splitting computing (SC) where the entire model is divided into two parts, to be executed on mobile and cloud ends respectively. However, the transmission of intermediate data poses a bottleneck to system performance. This paper initially demonstrates the privacy issue arising from the machine analysis-oriented intermediate feature. We conduct a preliminary experiment to intuitively reveal the latent potential for enhancing the privacy-preserving ability of the initial feature. Motivated by this, we propose a framework for privacy-preserving intermediate feature compression, which addresses the limitations in both compression and privacy that arise in the original extracted feature data. Specifically, we propose a method that jointly enhances privacy and encoding efficiency, achieved through the collaboration of the encoding feature privacy enhancement module and the privacy feature ordering enhancement module. Additionally, we develop a gradient-reversal optimization strategy based on information theory to ensure the utmost concealment of core privacy information throughout the entire codec process. We evaluate the proposed method on two DNN models using two datasets, demonstrating its ability to achieve superior analysis accuracy and higher privacy preservation than HEVC. Furthermore, we provide an application case of a wireless sensor network to validate the effectiveness of the proposed method in a real-world scenario.

Reduced-Order Modeling for Dynamic System Identification with Lumped and Distributed Parameters via Receptance Coupling Using Frequency-Based Substructuring (FBS)

Paper presents an effective technique for developing reduced-order models to predict the dynamic responses of systems using the receptance coupling and frequency-based substructuring (RCFBS) method. The proposed approach is particularly suited for reconfigurable dynamic systems across various applications, like cars, robots, mechanical machineries, and aerospace structures. The methodology focuses on determining the overall system receptance matrix by coupling the receptance matrices (FRFs) of individual subsystems in a disassembled configuration. Two case studies, one with distributed parameters and the other with lumped parameters, are used to illustrate the application of this approach. The first case involves coupling three substructures with flexible components under fixed–fixed boundary conditions, while the second case examines the coupling of subsystems characterized by multiple masses, springs, and dampers, with various internal and connection degrees of freedom. The accuracy of the proposed method is validated against a numerical finite element analysis (FEA), direct methods, and a modal analysis. The results demonstrate the reliability of RCFBS in predicting dynamic responses for reconfigurable systems, offering an efficient framework for reduced-order modeling by focusing on critical points of interest without the need to account for detailed modeling with numerous degrees of freedom.

Securing oil port logistics: A blockchain framework for efficient and trustworthy trade documents.

The oil port logistics involves multiple parties including oil tanker owners, port authorities, customs, oil suppliers, and shipping companies. These parties need to exchange a significant amount of data and documentation related to cargo, such as bills of lading, customs declarations, and cargo manifests. This huge amount of data and documentation provides ample opportunities for data manipulation and corruption. Moreover, physical documentation is slow and prone to errors and manipulation. This data can be securely stored and shared between different parties in a tamper-proof and transparent manner using blockchain. Blockchain is a decentralized technology that employs secure hashing and consensus algorithms that can detect any data modification. Hence, this work proposes a blockchain-enabled immutable, and efficient framework for trade documentation in oil port logistics. The proposed framework provides timely processing of oil trade documents and ensures immutability while increasing trust among the trade entities. In addition, this work implements a private blockchain for the execution of smart contracts, which can ensure that all parties involved in the logistics process comply with pre-agreed rules and regulations. Simulation results validate the effectiveness of the proposed framework in terms of transparency, immutability, network latency, throughput, and resource utilization.

Dynamic Analysis Techniques for Road Bridges Under Large Traffic Flows

This work focuses on the dynamic analysis of road bridges under large microsimulated traffic flows, contributing an efficient dynamic analysis framework for future fatigue, comfort or driving safety studies in which realistic interactions between vehicles are considered. Different strategies to define the loading vector in the governing dynamic equations are presented for bridges discretized with beam and shell elements. The proposed interpolation techniques for beam-like models include a recursive binary search and a novel vectorized strategy based on shape functions expanded to the entire length of the deck. These are applied to a short simply supported bridge and a very long cable-stayed bridge under traffic flows generated with a cellular automata microsimulation model. The strategies presented here reduce the computational time required in conventional load interpolation algorithms, particularly in long bridges subject to dense traffic flows. Finally, the binary search algorithm is applied to the study of bridge decks discretized with shell elements under microsimulated traffic, demonstrating that high vehicle densities increase the contribution of the transverse vibration of the slab and the potential discomfort of pedestrians.

Low-pressure superconductivity of Th-B-H ternary compounds: An insightful study of stable and metastable phases

The low-enthalpy structures of Th-B-H ternary compounds below 50 GPa are investigated by using an efficient framework that combines high-throughput screening, genetic algorithms, and first-principles calculations. Four thermodynamically stable hydrides (Th2BH11, Th2BH10, ThB2H10, and ThBH) and seven thermodynamically metastable hydrides (ThBH2, ThBH3, ThBH4, ThBH5, ThBH7, ThB2H4, and ThB3H3) are revealed. There are no stable thorium boron hydrides at ambient pressure. R3m-ThB2H10 and R3m-Th2BH11 are indirect bandgap semiconductors, while R3m-Th2BH10 and P6/mmm-ThBH show metallic nature. The relatively low λ values of R3m-Th2BH10 and P6/mmm-ThBH result in their Tc around 5.8 K and 0.1 K at 20 GPa, respectively. Two metastable phases, F4¯3m-ThBH5 and P63/mmc-ThBH7, exhibit excellent superconducting properties at low pressures. The values of Tc of F4¯3m-ThBH5 and P63/mmc-ThBH7 reach 46.9 K at 20 GPa and 40.0 K at 50 GPa, respectively. Our findings offer valuable insights for discovering and designing superconducting rare earth hydrides under low and even ambient pressures.

Optimizing pharmaceutical inventory management: A global framework for efficiency and cost reduction

Pharmaceutical inventory management is critical in ensuring continuous drug availability and cost efficiency in healthcare institutions globally. However, maintaining optimal inventory levels is challenging due to factors such as overstocking, stockouts, and global supply chain disruptions. This paper reviews best practices for efficient pharmaceutical inventory management, emphasizing the integration of data analytics and predictive models to forecast demand, reduce waste, and streamline procurement. It explores strategies for implementing unified inventory systems across diverse healthcare settings, balancing cost efficiency with uninterrupted drug availability. Additionally, the paper examines the impact of global supply chain disruptions on pharmaceutical supply and recommends solutions for building resilience. Through real-world examples and recommendations for future improvements, this review highlights the potential for enhanced global inventory management practices to reduce costs while ensuring the continuous availability of essential medications. Keywords: Pharmaceutical Inventory Management, Data Analytics, Cost Efficiency, Predictive Models, Supply Chain Resilience, Drug Availability

The impact of capital flight on economic development: An empirical analysis from Palestine

This study focused on the function of capital flight to ascertain the true impact of the phenomenon of capital flight on economic development in Palestine and aimed to analyze the short- and long-term dynamic relationship between capital flight and economic development, including other affected variables. This research employed a quantitative research design and the descriptive analysis method. The analysis uses quarterly data from 2004 to 2022. This study employed the autoregressive distribution lag (ARDL) bound testing approach. Actual data from Palestine spanning the years 2004-2022 yielded significant findings. Based on heteroskedastic dynamic regression as an ARDL panel model, important findings were reached. First, both local country fundamentals and global variables have an impact on economic development and its rates over the long term, but in the near term, global forces may be predominantly recognized as drivers. Second, the variable of interest, capital flight, has a favorable impact on tax advantages. The empirical findings revealed that the short- and long-run analyses are consistent with each other. This necessitates putting into practice a variety of tactics, from creating efficient judicial and political institutions to encouraging economic development through managing macroeconomic issues. This study provides a fresh insight for policymakers to evaluate the impact of capital flight on economic development factors when coordinating fiscal and monetary policy in Palestine. The monetary and fiscal authorities should create an efficient policy framework. Palestine must reduce and stop the outflow of cash from inside its borders to improve its capacity to pay back its loans and debts to foreign creditors.

Robust and accurate numerical framework for multi-dimensional fractional-order telegraph equations using Jacobi/Jacobi-Romanovski spectral technique

This paper presents a novel spectral algorithm for the numerical solution of multi-dimensional fractional-order telegraph equations, a critical model used to capture the combined effects of diffusion and wave propagation. The core innovation of this work is the application of Jacobi-Romanovski polynomials as the basis functions for spectral discretization. These polynomials offer unique advantages, including the ability to handle nonstandard domains and boundary conditions, making them particularly suitable for partial differential equation (PDE) applications. A comprehensive error analysis is conducted, providing deep insights into the convergence rates and factors affecting the accuracy of the numerical solutions. Extensive numerical experiments further demonstrate the superior performance of the proposed spectral algorithm in solving a wide range of multi-dimensional fractional-order telegraph equation models. The results show a significant improvement in accuracy and computational efficiency compared to traditional numerical methods, such as finite difference or finite element techniques. This research advances the field of computational science by offering a robust, efficient, and versatile numerical framework for the precise solution of complex multi-dimensional PDEs.

An efficient conditional GAN-based framework for high-resolution prediction of tyre-pavement contact stresses – a contribution towards a digital twin of the road system

ABSTRACT The high-resolution prediction of tyre-pavement contact stresses is crucial for advancing pavement design and vehicle safety. A significant knowledge gap exists in accurately and quickly predicting tyre-pavement contact stresses, especially under varying conditions. Addressing this challenge, this study is part of the concerted effort within the project CRC/TRR 339, contributing to the series ‘A Contribution towards a Digital Twin of the Road System’. It aims to bridge this gap by introducing a novel Multimodal Conditional Deep Convolutional Generative Adversarial Network (MC-DCGAN) model for forecasting tyre-pavement contact stresses, offering an efficient and accurate method. The incorporation of a multimodal conditional module within the GAN-based framework allows for controllable outputs tailored to specific tyre types, inflation pressures, and loads. Leveraging the DCGAN architecture significantly enhances prediction accuracy. Experimental validation confirms the method’s high accuracy, strong generalisation capabilities, and a substantial reduction in computational time – nearly two orders of magnitude lower compared to traditional Finite Element simulations. This research not only fills a critical void in the high-precision prediction of tyre-pavement contact stresses, but also holds significant implications for optimising pavement design and enhancing road traffic safety, marking a significant step towards realising a digital twin of the road system.

Blockchain Enabled Secure Data Transmission With NB‐IoT Deployment in Smart Agriculture Crop Watch

ABSTRACTThe integration of Narrowband Internet of Things (NB‐IoT) and blockchain technology presents a promising solution to address security and efficiency concerns in data transmission within the Internet of Things (IoT). This research focuses on its application in crop Watch, an agricultural monitoring system. The IoT has revolutionized various industries, including agriculture, by facilitating the collection and transmission of extensive data through sensors and devices. However, ensuring secure and efficient data transmission remains a critical challenge. NB‐IoT boasts advantages such as improved coverage in challenging areas, massive connectivity with up to 100 000 links, low energy usage through prolonged discontinuous receipt (eDRX), and cost‐effectiveness. Despite these benefits, NB‐IoT faces security issues, including authentication, anonymity, and fault tolerance. To enhance data security, public key cryptography is proposed to be incorporated into the NB‐IoT framework. Blockchain technology, with its decentralized and immutable nature, can potentially address these security concerns and enhance data accountability. By creating a trust‐less and transparent infrastructure, blockchain complements NB‐IoT's capabilities and provides a robust mechanism for data verification and integrity. This research paper emphasizes the logical integration of NB‐IoT, public key cryptography, and blockchain technology, offering a secure and efficient data transmission framework for agricultural applications like crop Watch. Bridging the gap between data connectivity and security holds promise for advancing agricultural monitoring and driving IoT innovations.

MWRD (Mamba Wavelet Reverse Diffusion)—An Efficient Fundus Image Enhancement Network Based on an Improved State-Space Model

This study introduces an efficient fundus image enhancement framework based on an improved Mamba model and the Denoising Diffusion Probabilistic Model (DDPM). By integrating wavelet transform for local feature extraction and applying a reverse diffusion process, this approach significantly improves the effectiveness and efficiency of enhancing low-quality fundus images. The model achieves high-precision enhancement of retinal vessel details while substantially reducing the number of parameters. Comprehensive tests on publicly available datasets show that the suggested approach surpasses various advanced low-light image enhancement methods in both quantitative and qualitative assessments.

AI-Generated Spam Review Detection Framework with Deep Learning Algorithms and Natural Language Processing

Spam reviews pose a significant challenge to the integrity of online platforms, misleading consumers and undermining the credibility of genuine feedback. This paper introduces an innovative AI-generated spam review detection framework that leverages Deep Learning algorithms and Natural Language Processing (NLP) techniques to identify and mitigate spam reviews effectively. Our framework utilizes multiple Deep Learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM), to capture intricate patterns in textual data. The system processes and analyzes large volumes of review content to detect deceptive patterns by utilizing advanced NLP and text embedding techniques such as One-Hot Encoding, Word2Vec, and Term Frequency-Inverse Document Frequency (TF-IDF). By combining three embedding techniques with four Deep Learning algorithms, a total of twelve exhaustive experiments were conducted to detect AI-generated spam reviews. The experimental results demonstrate that our approach outperforms the traditional machine learning models, offering a robust solution for ensuring the authenticity of online reviews. Among the models evaluated, those employing Word2Vec embeddings, particularly the BiLSTM_Word2Vec model, exhibited the strongest performance. The BiLSTM model with Word2Vec achieved the highest performance, with an exceptional accuracy of 98.46%, a precision of 0.98, a recall of 0.97, and an F1-score of 0.98, reflecting a near-perfect balance between precision and recall. Its high F2-score (0.9810) and F0.5-score (0.9857) further highlight its effectiveness in accurately detecting AI-generated spam while minimizing false positives, making it the most reliable option for this task. Similarly, the Word2Vec-based LSTM model also performed exceptionally well, with an accuracy of 97.58%, a precision of 0.97, a recall of 0.96, and an F1-score of 0.97. The CNN model with Word2Vec similarly delivered strong results, achieving an accuracy of 97.61%, a precision of 0.97, a recall of 0.96, and an F1-score of 0.97. This study is unique in its focus on detecting spam reviews specifically generated by AI-based tools rather than solely detecting spam reviews or AI-generated text. This research contributes to the field of spam detection by offering a scalable, efficient, and accurate framework that can be integrated into various online platforms, enhancing user trust and the decision-making processes.