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In modern life, the massive number of vehicles makes it hard for a human being to process its related information. So, it is important to build an automatic system to collect information about vehicles. The license plate is the unique identifier of a vehicle. In this paper, we propose an automatic license plate recognition system. The proposed system was based on the Faster R-CNN improved by adding an adaptive attention network for the segmentation of the license plate to retrieve the numbers and the letters of identification. Also, we add a deconvolution layer at the top of the features extraction network to detect the small size of the target license plate. To train and evaluate the proposed system, a dataset was collected for Arabic countries such as Egypt, KSA, and UAE that have similar license plates with Arabic and Indian numbers, Arabic and Latin alphabets. The dataset was collected from the internet using a python script then it was filtered and annotated manually. The evaluation of the proposed model dataset results in achieving a recall of 98.65 % and a precision of 97.46 %. The developed system was able to process images in real-time with a processing speed of 23 FPS.

Lung cancer remains the leading cause of cancer-related mortality worldwide, necessitating accurate and efficient diagnostic tools to improve patient outcomes. Lung segmentation plays a pivotal role in the diagnostic pipeline, directly impacting the accuracy of disease detection and treatment planning. This study presents an advanced AI-driven framework, optimized through genetic algorithms, for precise lung segmentation in early cancer diagnosis. The proposed model builds upon the UNET3 + architecture and integrates multi-scale feature extraction with enhanced optimization strategies to improve segmentation accuracy while significantly reducing computational complexity. By leveraging genetic algorithms, the framework identifies optimal neural network configurations within a defined search space, ensuring high segmentation performance with minimal parameters. Extensive experiments conducted on publicly available lung segmentation datasets demonstrated superior results, achieving a dice similarity coefficient of 99.17% with only 26% of the parameters required by the baseline UNET3 + model. This substantial reduction in model size and computational cost makes the system highly suitable for resource-constrained environments, including point-of-care diagnostic devices. The proposed approach exemplifies the transformative potential of AI in medical imaging, enabling earlier and more precise lung cancer diagnosis while reducing healthcare disparities in resource-limited settings.

The main objective of the current endeavor is to monitor hypothetical processes utilizing a Sisko tri-hybrid fluid over a rotating disk with entropy generation suspended in Darcy-Forchheimer porous medium. Electro Magneto Hydro Dynamics (EMHD), non-linear thermal radiation and exponential and thermal- space dependent heat source/sink coefficients are considered with the intent of conceiving an Runge-Kutta-Fehlberg method with shooting procedures integrated with a combination of an Adaptive Neuro-Fuzzy Inference System (ANFIS) and Reptile Search Algorithm (RSA). Then, ANFIS-RSA, is used to predict the Nusselt number, skin friction co-efficient in radial and tangential velocities. Reliable self-similarity variables have reduced a non-linear partial differential set of equations into an ordinary differential equation. According to the empirical evidence, Sisko fluid parameter rises the radial velocity whereas for magnetic field and Darcy-Forchheimer the azimuthal and axial velocities visualizations decreasing trend, respectively. The entropy generation and Bejan number rises for electric and radiation effects. Also, ANFIS-RSA indicates that the model attained a high level of precision in terms of radial velocity (98.13%), tangential velocity (98.18%) and Nusselt number (98.91%). Thus, the longer rendering of the nanoparticles used here might, makes them potentially helpful for regulating the therapeutic impact in the management and treatment of cancer.

The Cerchar Abrasiveness Index (CAI) is a vital parameter in geotechnical engineering, especially when it comes to tunneling and mechanized excavations. The study employed a comprehensive dataset of 163 samples representing various rock types, including igneous, sedimentary, and metamorphic formations. The methodology included three base algorithms (XGBoost, LightGBM, and Random Forest), improved by three distinct metaheuristic techniques: Arithmetic Optimization Algorithm (AOA), Reptile Search Optimization (RSO), and Harris Hawks Optimization (HHO). The Brazilian tensile strength (BTS), uniaxial compressive strength (UCS), equivalent quartz content (EQC), and brittleness index (BI) were the four main rock parameters used to make the predictive models. The model was further evaluated by splitting the data into 80% training and 20% testing sets. Subsequently, the model was compared to 17 real-world hard rock TBM projects in different countries and geological conditions. The AOA-optimized versions performed nicely, with AOA-LightGBM doing the best on the held-out test set (R² = 0.952, RMSE = 0.290, MAE = 0.208, VAF = 0.952). External validation showed that AOA-XGBoost performed properly, with the highest correlation coefficient of 0.8308 compared to field measurements from international tunneling projects. Also, the AOA-XGBoost did well on tests with R² = 0.951, RMSE = 0.296, MAE = 0.223, and VAF = 0.951. Using SHAP values to examine feature importance revealed unique parameter influence signatures. EQC was the most important parameter in XGBoost models, while UCS had the greatest impact in LightGBM and Random Forest-based models. The new method described here is an important advancement in CAI prediction methodology. It is more accurate and efficient than traditional experimental testing methods, and it works well on different types of rock. Its engineering applicability has been proven through real-world operational scenarios.

Nanofluids achieve high thermal transport efficiency by uniformly dispersing small particles in base liquids, significantly enhancing the heat transfer coefficients and making them vital in various thermal engineering applications. The research examines non-uniform thermal conductivity and activation energy critical for accurately describing fluid behaviour. The study incorporates bioconvection to prevent nanoparticle settling and ensure fluid stability through motile microorganisms. The governing partial differential equations are converted into ordinary differential equations that are solved using the Homotopy Analysis Method (HAM), to provide a strong mathematical framework for the analysis. This study finds that the velocity of the fluid decreases with magnetic constraint intensification and time retardation. however, heat transfer increases at higher radiation, and heat absorption/emission parameters but decreases with a higher Prandtl number, while an increased Schmidt number leads to decreased concentration profiles. This paper investigates a nano-Williamson fluid (NWF) flow over an exponentially stretched surface in a permeable medium, considering essential variables such as mixed convection, electromagnetic forces, non-linear thermal radiation, heat production, Joule heating and ohmic dissipation that are essential for understanding its complicated behavior.

This paper leverages data from February 6, 2023, Kahramanmaras (Turkiye) Earthquake (M w 7.8) to evaluate seismic risk and assess bridge damage through a fuzzy synthetic approach (FSA). A novel hierarchical damage classification framework is introduced, integrating critical factors such as ground conditions, structural characteristics, and seismic intensity. By analyzing data from 331 bridges affected by eight major historical earthquakes, the study underscored the influence of foundation depth, construction quality, and distance to fault rupture on structural resilience. Notably, 65% of damaged bridges were within 40 km of the distance to fault rupture, with oblique span orientations (45° to 65°) showing heightened susceptibility to seismic forces. To enhance resilience against earthquakes, the findings advocated for the adoption of deep foundations, advanced materials, and optimized structural designs. Consistent with field observations, the study reinforces the utility of FSA in enabling informed decision-making for disaster risk mitigation and is also beneficial for future seismic resilience design of bridges.

This study employs Monte Carlo (MC) models and thermal-statistical analysis to investigate the production mechanisms of strange (KS0, Λ) and multi-strange (Ξ, Ω) hadrons in high-multiplicity proton–proton collisions. Through systematic comparisons with experimental data, we evaluate the predictive power of EPOS, PYTHIA8, QGSJETII04, and Sibyll2.3d. EPOS, with its hydrodynamic evolution, successfully reproduces low-pTKS0 and Λ yields in high-multiplicity classes (MC1–MC3), mirroring quark-gluon plasma (QGP) thermalization effects. PYTHIA8’s rope hadronization partially mitigates mid-pT multi-strange baryon suppression but underestimates Ξ and Ω yields due to the absence of explicit medium dynamics. QGSJETII04, tailored for cosmic-ray showers, overpredicts soft KS0 yields from excessive soft Pomeron contributions and lacks multi-strange hadron predictions due to enforced decays. Sibyll2.3d’s forward-phase bias limits its accuracy at midrapidity. No model fully captures Ξ and Ω production, though EPOS remains the closest. Complementary Tsallis distribution analysis reveals a distinct mass-dependent hierarchy in the extracted effective temperature (Teff) and non-extensivity parameter (q). As multiplicity decreases, Teff rises while q declines—a trend amplified for heavier particles. This suggests faster equilibration of heavier particles compared to lighter species. The interplay of these findings underscores the necessity of incorporating QGP-like medium effects and refined strangeness enhancement mechanisms in MC models to describe small-system collectivity.

The accurate prediction of compressive strength (CS) in steel fiber reinforced concrete (SFRC) remains a critical challenge due to the material’s inherent complexity and the nonlinear interactions among its constituents. This study presents a robust machine learning framework to predict the CS of SFRC using a large-scale experimental dataset comprising 600 data points, encompassing key parameters such as fiber characteristics (type, content, length, diameter), water-to-cement (w/c) ratio, aggregate size, curing time, silica fume, and superplasticizer. Six advanced regression-based algorithms, including support vector regression (SVR), Gaussian process regression (GPR), random forest regression (RFR), extreme gradient boosting regression (XGBR), artificial neural networks (ANN), and K-nearest neighbors (KNN), were benchmarked through rigorous model validation processes including hold-out testing, K-fold cross-validation, sensitivity analysis, and external validation with unseen experimental data. Among the tested models, GPR consistently outperformed all others, achieving a maximum coefficient of determination (R²) of 0.93 and the lowest root mean square error (RMSE) of 16.54, thereby demonstrating superior capability in capturing the underlying nonlinear relationships within the data. The generalization performance of each model was examined by systematically altering input variables (fiber type, fiber content, w/c ratio, and aggregate size) while holding other parameters constant. GPR showed remarkable agreement with empirical trends across all validation cases, accurately identifying strength peaks and non-linear behavioral shifts, such as the parabolic relationship between w/c ratio and CS. Models like XGBR, SVR, and RFR provided reasonable estimates but lacked the precision of GPR under complex conditions. In contrast, ANN and KNN demonstrated weaker performance, frequently underpredicting or failing to capture key trends. By leveraging the predictive power and interpretability of advanced machine learning models, this research promotes a paradigm shift in structural engineering workflows.

The Hoek–Brown (H‐B) criterion is widely recognized as a standard in geotechnical engineering for assessing rock mass strength across various rock mass qualities. However, challenges arise in explicitly defining the Mohr failure envelope, particularly when the strength parameter “ a ” deviates from the conventional value of 0.5. This study investigates the compressive strength of rock masses in the Himalayas, particularly in the context of deep tunneling and slope stability, using the H‐B and Mohr–Coulomb (MC) criteria. Initially, the MC and H‐B criteria were combined while varying the angle of internal friction, revealing an inconsistent trend in friction angles regarding rock mass compressive strength. The relationship between tunnel depth, slope height, and rock mass compressive strength was then examined by combining equations involving RMR, RQD, and modified H‐B criteria. The combination of H‐B and MC resulted in lower rock mass compressive strength values, while noncombined equations yielded higher values. Incorporating the geological strength index (GSI) provided higher and more suitable compressive strength values. For the Himalayas, the suggested H‐B equations with GSI are recommended for both surface and subsurface excavations.

With the advancement of drone technology, the availability of different sensors has become more reliable and cost-effective for monitoring large open-pit mine project activities. Key advantages of drone technology, including low operational expenses, rapid revisit capabilities, deployment flexibility, and high precision, have established these systems as powerful instruments for monitoring open-pit mine areas. This paper aims to provide a comprehensive review of drone technology utilization in open-pit mine reclamation monitoring. Mining 4.0 has shown promise in open-pit mine monitoring for drone deployment for use in green mining practices. This review synthesizes current research on drone survey platforms, various sensor technologies, and their practical field applications within open-pit mines for mine reclamation monitoring. This review study aims to establish a robust framework for the monitoring and management of mine reclamation. This study will provide a technically reliable reference, advancing the knowledge and application of drone technology for reclamation monitoring and management.