Nonlinear Dependence Research Articles

A novel framework combining band selection algorithm and improved 3D prototypical network for tree species classification using airborne hyperspectral images

Fine-grained identification of forest types and tree species represents a critical aspect of forest resource inventory and monitoring. The use of airborne hyperspectral remote sensing imagery stands out for its ability to finely differentiate among tree species, leveraging its exceptional spatial resolution and rich spectral details. However, this approach is limited by several challenges (e.g., high spectral correlation and information redundancy). In accordance, the adoption of a lightweight deep learning approach in the form of a few-shot learning model can effectively resolve the challenges of multi-forest tree species classification. Therefore, integrating a data dimensionality reduction algorithm with a few-shot classification model presents a promising avenue for resolving the fine-grained classification of forest tree species. In this study, we propose the innovative classification framework FAST 3D-CNN P-Net. This framework utilizes CNN for band selection, enhances the fine-grained identification process in hyperspectral data, and integrates an optimized FAST 3D-CNN into the P-Net classifier (a few-shot classifier). First, a CNN-based band selection method is employed to learn the nonlinear dependencies between spectral bands, assign weights to rank the bands, and reconstruct the global spectral information using the most informative bands. It then constructs a novel classification model, designated FAST 3D-CNN P-Net, through the integration of an optimal 3D-CNN with a prototypical network. To enhance classification performance, the FAST 3D-CNN P-Net utilizes reconstructed hyperspectral images derived from the band selection results as input. The effectiveness of the proposed framework was assessed with the airborne GFF dataset and the widely accessible medium-resolution hyperspectral datasets, Indian Pines (IP) and Kennedy Space Center (KSC). The overall classification accuracy reached 98.33 % for the GFF dataset and 97.21 % and 99.43 % for the IP and KSC, respectively, exhibiting performance superiority compared to the standalone 3D-CNN classification network. This classification framework demonstrates efficiency in selecting a subset of hyperspectral bands with minimal redundancy, empowering the rapid and accurate classification and mapping of tree species in complicated, multi-species forest stands, even with a limited quantity of labeled samples.

A new coaxial flow-through probe for electromembrane extraction of methadone from clinical samples on-line coupled to capillary electrophoresis

BackgroundA new design of a flow-through coaxial electromembrane extraction (EME) probe that can be on-line coupled with CE instrument is described and tested. The supporting base of the probe is a PDMS microchip with T-shaped channels into which two coaxially arranged capillaries for inlet and outlet solutions are inserted. The extraction part of the probe is a porous polypropylene hollow fiber, sealed at one end and modified with nitrophenyloctyl ether (NPOE) extraction fluid. The internal volume of the extraction probe is 1.1 μL. ResultsThe EME probe was tested on laboratory samples and methadone was extracted into 3.0 M AcOH as acceptor. The concentration dependence was linear in the range of 0.1–1.0 μg mL−1 at EME 300 s/150 V and in the range of 0.5–10.0 μg mL−1 at EME 100 s/150 V. The enrichment factor was greater than 30 and the LOD was 0.21 μg mL−1. The EME of methadone in clinical samples showed a linear concentration dependence in human urine and a nonlinear concentration dependence in serum. The distribution of methadone in each phase of the extraction system and the effect of extraction membrane thickness on the enrichment factor were studied. The EME probe can be applied repeatedly. SignificanceThe supporting base of EME probe and flow gating interface (FGI) are realized by a microfluidic PDMS microchips cast in the laboratory without the use of lithography. A supporting PDMS chip with coaxially arranged capillaries and extraction membrane forms a compact analytical instrument. The entire EME/CE analysis process is performed on a laboratory-made instrument and automated by LabView.

Investigation of effects of process parameters on microstructure and fracture toughness of SLM CoCrFeMnNi

Selective Laser Melting (SLM) of multi-component alloy (MCA) such as CoCrFeMnNi is gaining significant attention due to its potential to exhibit an intriguing blend of mechanical properties, particularly strength-ductility combinations. However, limited attention has been directed toward the critical aspect of fracture toughness, which is indispensable for various engineering applications. This study delves into obtaining the fracture toughness of SLMed CoCrFeMnNi MCA, with a focus on the influence of microstructure, densification, and key printing parameters (volumetric energy density (VED), laser power, and scan speed). Experimental findings revealed that individual printing parameters rather portray better insights on the densification as opposed to the VED. Notably, slow scan speed of 800 mm/s significantly enhances densification irrespective of the VED. The alloy presents complex cellular structures with the morphology reflecting a non-linear dependence on VED. The presence of cellular dislocations analogous in scale to the cellular structures was observed with dark nanoprecipitates, recognized as Mn-rich oxides. Microhardness evaluations portrayed a non-linear relationship with varying VED, with values spanning between 230 HV and 280 HV. The fracture toughness did not exhibit a linear correlation also with VED, but better fracture toughness values were achieved at 200 W. Additionally, a notable observation of late crack initiation was observed on the load-displacement curves, indicative of the material's intrinsic ductility. The fracture toughness values ranged between 320 and 220 MPa.m−0.5. A correlation between the microstructure and densification with the fracture toughness was made. The paper also presents a comprehensive discussion on the mechanisms underpinning microstructure formation and their consequential effects on the fracture toughness of the material.

Applications of the kinetic theory for a model of a confined quasi-two dimensional granular mixture: Stability analysis and thermal diffusion segregation

The Boltzmann kinetic theory for a model of a confined quasi-two dimensional granular mixture derived previously [Garzó et al., “Navier–Stokes transport coefficients for a model of a confined quasi-two dimensional granular binary mixture,” Phys. Fluids 33, 023310 (2021)] is considered further to analyze two different problems. First, a linear stability analysis of the hydrodynamic equations with respect to the homogeneous steady state (HSS) is carried out to identify the conditions for stability as functions of the wave vector, the coefficients of restitution, and the parameters of the mixture. The analysis, which is based on the results obtained by solving the Boltzmann equation by means of the Chapman–Enskog method to first order in spatial gradients, takes into account the (nonlinear) dependence of the transport coefficients and the cooling rate on the coefficients of restitution and applies in principle to arbitrary values of the concentration, and the mass and diameter ratios. In contrast to the results obtained in the conventional inelastic hard sphere (IHS) model, the results show that all the hydrodynamic modes are stable so that the HSS is linearly stable with respect to long enough wavelength excitations. On the other hand, this conclusion agrees with previous stability analysis performed in earlier studies for monocomponent granular gases. As a second application, segregation induced by both a thermal gradient and gravity is studied. A segregation criterion based on the dependence of the thermal diffusion factor Λ on the parameter space of the mixture is derived. In the absence of gravity, the results indicate that Λ is always positive, and hence, the larger particles tend to accumulate near the cold plate. However, when gravity is present, our results show the transition between Λ>0 (larger particles tend to move toward the cold plate) to Λ<0 (larger particles tend to move toward the hot plate) by varying the parameters of the system (masses, sizes, composition, and coefficients of restitution). Comparison with previous results derived from the IHS model is carried out.

Disorder-induced non-linear growth of fingers in immiscible two-phase flow in porous media

Immiscible two-phase flow in porous media produces different types of patterns depending on the capillary number Ca and viscosity ratio M. At high Ca, viscous instability of the fluid–fluid interface occurs when the displaced fluid is the more viscous, and leads to viscous fingering, which is believed to exhibit the same growth behavior as the viscously-unstable fingers observed in Hele–Shaw cells by Saffman and Taylor [“The penetration of a fluid into a porous medium or Hele–Shaw cell containing a more viscous liquid,” Proc. R. Soc. London 245, 312 (1958)], or as diffusion-limited aggregates (DLA). In such Laplacian growth processes, the interface velocity depends linearly on the local gradient of the physical field that drives the growth process (for two-phase flow, the pressure field). However, a non-linear power-law dependence between the flow rate and the global pressure drop, reminiscent of what has also been observed for steady-state two-phase flow in porous media, was evidenced experimentally for the growth of viscously-unstable drainage fingers in two-dimensional porous media, 20 years ago. Here, we revisit this flow regime using dynamic pore-network modeling and explore the non-linearity in the growth properties. We characterize the previously unstudied dependencies of the statistical finger width and non-linear growth law's exponent on Ca, and discuss quantitatively, based on theoretical arguments, how disorder in the capillary barriers controls the growth process' non-linearity, and why the flow regime crosses over to Laplacian growth at sufficiently high Ca. In addition, the statistical properties of the fingering patterns are compared to those of Saffman–Taylor fingers, DLA growth patterns, and the results from the aforementioned previous experimental study.

Granular Aluminum Kinetic Inductance Nonlinearity

Granular Aluminum is a superconductor known for more than eighty years, which recently found its ap-plication in qubits, microwave detectors and compact resonators, due to its high kinetic inductance, critical magnetic field and critical current. Here we report on the nonlinear dependence of granular Aluminum inductance on current, which hints towards parametric amplification of the microwave signal in granular Aluminum films. The phase shift of the microwave signal reached 4 rad at a frequency of 7 GHz, which makes it possible to estimate the nonlinearity of the system as \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta \\phi {\ ext{/}}\\phi = 1.4{\\kern 1pt} \\% $$\\end{document} and the potential gain of the order of 17 dB.

A Sub-1-V Nanopower MOS-Only Voltage Reference

A novel low-power MOS-only voltage reference is presented. The Enz–Krummenacher–Vittoz (EKV) model is adopted to provide a new perspective on the operating principle. The normalized charge density, introduced as a new variable, serves as an indicator when trimming the output temperature coefficient. The proposed voltage reference consists of a specific current generator and a 5-bit trimmable load. Thanks to the good match between the current source stage and the output stage, the nonlinear temperature dependence of carrier mobility is automatically canceled out. The circuit is designed using 55 nm COMS technology. The operating temperature ranges from −40 °C to 120 °C. The average temperature coefficient of the output voltage can be reduced to 21.7 ppm/°C by trimming. The power consumption is only 23.2 nW with a supply voltage of 0.8 V. The line sensitivity and the power supply rejection ratio at 100 Hz are 0.011 %/V and −89 dB, respectively.

T-SPP: Improving GNSS Single-Point Positioning Performance Using Transformer-Based Correction

GNSS (global navigation satellite systems) technology enables high-precision single-point positioning (SPP) in open environments. However, the accuracy of GNSS positioning is significantly compromised in complex urban canyons due to signal obstructions and non-line-of-sight propagation errors. To address this challenge, we propose a GNSS displacement estimation algorithm. This method learns nonlinear dependencies between GNSS raw measurements and corresponding position changes, capturing dynamic and layered features in GNSS measurement data for displacement estimation. We introduce a denoising auto-encoder (DAE) to preprocess raw GNSS observations, reducing the impact of noise. The model simultaneously outputs estimated displacement and model confidence. The fusion process dynamically combines positioning results from the SPP algorithm and the D-Tran model, adaptively blending them to achieve accurate and optimal positioning estimation. This approach optimizes the accuracy of estimated positioning results while maintaining confidence in the estimation. Experimental results show a 61% reduction in root mean square error (RMSE) and 100% availability in urban canyon environments compared to traditional single-point positioning techniques.

Liquidity‐adjusted value‐at‐risk using extreme value theory and copula approach

AbstractIn this study, we propose the application of the GARCH‐EVT‐Copula model in estimating liquidity‐adjusted value‐at‐risk (L‐VaR) of energy stocks while modeling nonlinear dependence between return and bid‐ask spread. Using the L‐VaR framework of Bangia et al. (1998), we present a more parsimonious model that effectively captures non‐zero skewness, excess kurtosis, and volatility clustering of both return and spread distributions of energy stocks. Moreover, to measure the nonlinear dependence between return and spread series, we use multiple copulas: Clayton, Gumbel, Frank, Normal, and Student‐t. Based on the statistical backtesting and economic loss functions, our results suggest that the GARCH‐EVT‐Clayton copula is superior and most consistent in forecasting L‐VaR compared with other competing models. This finding has several implications for investors, market makers, and daily traders who appreciate the importance of liquidity in market risk computation.

On the dependence of electrical mobility on temperature, humidity and structure of alkylammonium ions

Insights into the effect of temperature (T) and relative humidity (RH) as well as structure and polarisation on ion mobility help the comparison and interpretation of mobility and mass-based data. We measured alkylammonium ions in air under different T (14 °C, 24 °C, 34 °C and 41 °C) and RH (0 %, 20 %, 40 %) conditions using two individual setups (in both cases a planar differential mobility analyser coupled with a time-of-flight mass spectrometer) and the results are in excellent agreement. Mobility increases with rising T and decreases with water vapour loading. When separating the measurement mobility by structures, clear mass dependence was observed. The measured mobilities exhibited large deviations from theoretically calculated results in dry conditions, which are possibly caused by adduct formation on the monomer ions via clustering (or reactions). This phenomenon seems to be unavoidably associated with light ions under atmospheric pressures, which is worth further exploration and bearing in mind when comparing measurements to calculations. Both methanol and oxygen (occasionally nitrogen or alkyl chain elongation) are possible candidates of the adduct. Under spherical assumption, we used the modified Mason–Schamp's approximation to link the measured mobility to the mobility equivalent diameter. The drag enhancement factor ξ and the effective gas-molecule collision diameter dg derived from our measurement data are comparable to literature values. Our data also exposed a non-linear dependence on the polarisation parameter ε*. Polarisation, ξ and dg were parameterised using linear models against ion structures, T, and RH for primary, secondary and tertiary alkylammonium ions with identical alkyl groups. Our model parametrisations predict mobilities within ±10 % deviation from the measured data. The model also has satisfying predicting power for alkylammonium ions with unidentical alkyl structures.

Forecasting Water Demand With the Long Short-Term Memory Deep Learning Mode

Traditional methods often fall short in modeling the nonlinear, seasonally variable nature of urban water demand. This proposed solution is an integrated ARIMA-LSTM deep learning model, combining ARIMA's proficiency in linear trend and seasonal modeling with LSTM's strength in capturing nonlinear time dependencies. In these experiments, the authors trained and evaluated using daily water demand data from 2015 to 2020, with its performance validated for the year 2021. The ARIMA-LSTM model demonstrates promising results, outperforming individual models in terms of accuracy. In validation, it achieves a high coefficient of determination (R2) of 0.98 and a significantly low root mean square error (RMSE) of 2.94. These metrics indicate an excellent fit to the data and a high level of precision in its predictions. The significance of this research lies in its potential to advance the field of urban water demand forecasting, ultimately contributing to better water resource management and sustainability in urban areas.

The impact of TV advertising on website traffic

AbstractWe propose a modeling procedure for estimating immediate responses to TV ads and evaluating the factors influencing their size. First, we capture diurnal and seasonal patterns of website visits using the kernel smoothing method. Second, we estimate a gradual increase in website visits after an ad using the maximum likelihood method. Third, we analyze the nonlinear dependence of the estimated increase in website visits on characteristics of the ads using the random forest method. The proposed methodology is applied to a dataset containing minute‐by‐minute organic website visits and detailed characteristics of TV ads for an e‐commerce company in 2019. The results show that people are indeed willing to switch between screens and multitask. Moreover, the time of the day, the TV channel, and the advertising motive play a great role in the impact of the ads.

An Alternative Calibration Method for Measuring N2O5 with an Iodide-Chemical Ionization Mass Spectrometer and Influencing Factors.

In this work, we developed an alternative calibration method for measuring N2O5 with an iodide adduct mass spectrometer (I-CIMS). In this calibration method, N2O5 is heated and then quantified based on the decrease in the amount of NO due to its reaction with the pyrolysis product (NO3). This alternative calibration method was compared with the commonly used method utilizing NOx analyzers equipped with a photolytic converter, which gauge NO2 reduction as a result of its reaction with O3 to quantify N2O5. It is notable that the two methodologies demonstrate favorable consistency in terms of calibrating N2O5, with a variance of less than 10 %. The alternative calibration method is a more reliable way to quantify N2O5 with CIMS, considering the instability of the NO2 conversion efficiency of photolytic converters in NOx analyzers and the loss of N2O5 in the sampling line. The effects of O3 and relative humidity (RH) on the sensitivity toward N2O5 were further examined. There was minimal perturbation of N2O5 quantification upon exposure to O3 even at high concentrations. The N2O5 sensitivity exhibited a nonlinear dependence on RH as it initially rose and then fell. Besides I(N2O5)-, the collisional interaction between I(H2O)- and N2O5 also forms I(HNO3)-, which may interfere with the accurate quantification of HNO3. As a consequence of the pronounced dependence on humidity, it is advisable to implement humidity correction procedures when conducting measurements of N2O5.

The Mechanism of Resonant Amplification of One-Dimensional Detonation Propagating in a Non-Uniform Mixture

The propagation of detonation waves (i.e., supersonic combustion waves) in non-uniform gaseous mixtures has become a matter of interest over the past several years due to the development of rotating detonation engines. It was shown in a number of recent theoretical studies of one-dimensional pulsating detonation that perturbation of the parameters in front of the detonation wave can lead to a resonant amplification of intrinsic pulsations for a certain range of perturbation wavelengths. This work is dedicated to the clarification of the mechanism of this effect. One-dimensional reactive Euler equations with single-step Arrhenius kinetics were solved. Detonation propagation in a gas with sine waves in density was simulated in a shock-attached frame of reference. We carried out a series of simulations, varying the wavelength of the disturbances. We obtained a non-linear dependence of the amplitude of these pulsations on the wavelength of disturbances with resonant amplification for a certain range of wavelengths. The gain in velocity was about 25% of the Chapman–Jouguet velocity of the stable detonation wave. The effect is explained using the characteristic analysis in the x-t diagram. For the resonant case, we correlated the pulsation period with the time it takes for the C+ and C− characteristics to travel through the effective reaction zone. A similar pulsation mechanism is realized when a detonation wave propagates in a homogeneous medium.

Kinetic evidence for multiple aggregation pathways in antibody light chain variable domains.

Aggregation of antibody light chain proteins is associated with the progressive disease light chain amyloidosis. Patient-derived amyloid fibrils are formed from light chain variable domain residues in non-native conformations, highlighting a requirement that light chains unfold from their native structures in order to aggregate. However, mechanistic studies of amyloid formation have primarily focused on the self-assembly of natively unstructured peptides, and the role of native state unfolding is less well understood. Using a well-studied light chain variable domain protein known as WIL, which readily aggregates in vitro under conditions where the native state predominates, we asked how the protein concentration and addition of pre-formed fibril "seeds" alter the kinetics of aggregation. Monitoring aggregation with thioflavin T fluorescence revealed a distinctly non-linear dependence on concentration, with a maximum aggregation rate observed at 8 μM protein. This behavior is consistent with formation of alternate aggregate structures in the early phases of amyloid formation. Addition of N- or C-terminal peptide tags, which did not greatly affect the folding or stability of the protein, altered the concentration dependence of aggregation. Aggregation rates increased in the presence of pre-formed seeds, but this effect did not eliminate the delay before aggregation and became saturated when the proportion of seeds added was greater than 1 in 1600. The complexity of aggregation observed in vitro highlights how multiple species may contribute to amyloid pathology in patients.

Interdependence and spillovers between big oil companies and regional and global energy equity markets

We examine spillovers and nonlinear dependence dynamics between big oil supermajors and regional and global energy equity markets. We derive our empirical results by fitting a directional spillover index, a conditional value-at-risk (CoVaR) method, and time-varying parameter copulas. Spillover index results indicate that big oil supermajors most largely spillover to the EU energy equity sector than to the US energy equity sector. A big oil supermajor British Petroleum (BP) consistently exerts some of the largest spillovers across regional and global energy markets. The CoVaR analysis reveals that on the downside, shocks from Royal Dutch Shell A most largely spillover to the US energy equity sector, while Royal Dutch Shell B does it on the upside. On the downside, Chevron most largely spillovers shocks to the EU energy equity sector, while BP does it on the upside. ExxonMobil most largely spillovers downside shocks to the world energy equity sector, while Royal Dutch Shell B does it on the upside. The Copula results show an asymmetric dependence between major oil companies and the US energy equity sector. The relationship between big oil companies and the EU energy equity sector is characterised by symmetric dependence dynamics.

Renewable energy consumption - financial globalization-tourism nexus with nonlinearity and cross-sectional dependence during Asia's carbon-free transition.

Adopting renewable energy consumption is one of the most important aspects of international efforts to combat climate change, improve energy security, and encourage the shift to a more robust and sustainable energy system. Therefore, the empirics and policymakers worldwide are searching for factors that can promote renewable energy consumption. This analysis intends to investigate the role of financial globalization and tourism on renewable energy consumption in Asia and sub-regions such as Central Asia, East Asia, South Asia, Southeast Asia, and West Asia. The analysis utilized the linear and nonlinear CS-ARDL methods. Long-run outcomes of the linear and nonlinear models confirm that a rise in financial globalization and tourism promotes renewable energy consumption in Asia and all sub-regions. However, the nonlinear model highlights that a fall in financial globalization hurts renewable energy consumption in Central and South Asian regions, and tourism only hurts renewable energy consumption in Asia. In addition, ICT, GDP, and GHG emissions help promote renewable energy consumption. These results suggest that Asian policymakers must increase collaboration in the financial sectors and promote sustainable tourism in the regions to promote renewable energy consumption.

ENSO phase locking, asymmetry and predictability in the INMCM Earth system model

Abstract Advanced numerical climate models are known to exhibit biases in simulating some features of El Niño–Southern Oscillation (ENSO), which is a key mode of interannual climate variability. In this study we analyze how two fundamental features of observed ENSO – asymmetry between hot and cold states and phase-locking to the annual cycle – are reflected in two different versions of the INMCM Earth system model (state-of-the-art Earth system model participating in the Coupled Model Intercomparison Project). We identify the above ENSO features using the conventional empirical orthogonal functions (EOF) analysis, which is applied to both observed and simulated upper ocean heat content (OHC) data in the tropical Pacific. We obtain that the observed tropical Pacific OHC variability is described well by two leading EOF-modes, which roughly reflect the fundamental recharge-discharge mechanism of ENSO. These modes exhibit strong seasonal cycles associated with ENSO phase locking while the revealed nonlinear dependencies between amplitudes of these cycles reflect ENSO asymmetry.We also assess and compare the predictability of observed and simulated ENSO based on linear inverse modelling. We find that the improved INMCM6 model has significant benefits in simulating described features of observed ENSO as compared with the previous INMCM5 model. The improvements of the INMCM6 model providing such benefits are discussed. We argue that proper cloud parameterization scheme is crucial for accurate simulation of ENSO dynamics with numerical climate models.

Fear of Cancer Progression: A Comparison between the Fear of Progression Questionnaire (FoP-Q-12) and the Concerns about Recurrence Questionnaire (CARQ-4).

As cancer patients often suffer from fear of cancer progression (FoP), valid screening for FoP is of high relevance. The aims of this study were to test psychometric properties of two FoP questionnaires, to determine their relationship to other anxiety-related constructs, and to analyze the impact of sociodemographic and clinical factors on the FoP. Our sample consisted of n = 1733 patients with mixed cancer diagnoses. For measuring FoP, the Fear of Progression questionnaire (FoP-Q-12) and the Concerns About Cancer Recurrence Questionnaire (CARQ-4) were used. The mean scores of the FoP-Q-12 and the CARQ-4 were 30.0 ± 10.4 and 16.1 ± 10.8, respectively, indicating relatively high levels of FoP. Both questionnaires showed excellent internal consistency coefficients, α = 0.895 and α = 0.915, respectively. The correlation between the two FoP questionnaires was r = 0.72. Female patients reported more FoP than male patients (d = 0.84 and d = 0.54, respectively). There was a nonlinear age dependency of FoP, with an increase found in the age range from 18 to 50 years and a decrease in the older age range. Radiation, chemotherapy, and antibody therapy, but not surgery, lead to an increase in FoP. Both questionnaires show good psychometric properties and can be recommended for use in an oncological routine. Female patients and patients in the middle-age range deserve special attention from healthcare providers.

Enhancing Heart Disease Prediction using Advanced Feature Engineering and Ensemble Learning Techniques

This study introduces a holistic model for predicting heart disease ,integrating advanced algorithms with a focus on feature engineering. The dataset encompasses a diverse range of patient parameters, including demographics, lifestyle factors, and medical history. Feature engineering involves a meticulous process of selecting, transforming, and augmenting relevant features to enhance the model's ability to discern patterns and relationships within the data. This stage is essential for increasing the predicted accuracy of the model and gleaning insightful information from intricate datasets. The logistic regression algorithm is employed to establish a baseline predictive model, providing insights into the individual contribution of each feature. Subsequently, a neural network is implemented to capture intricate non-linear dependencies and interactions within the data, further refining the predictive capabilities. Results indicate that the incorporation of feature engineering significantly improves the model's performance compared to traditional approaches. Early experiments demonstrate promising accuracy rates in heart disease prediction, showcasing the potential for early detection and proactive healthcare interventions. This method not only enhances predictive modeling for heart disease but also emphasizes the significance of feature engineering in maximizing the full capabilities of machine learning algorithms for medical applications.