Ensemble System Research Articles

Adsorption of Polycyclic Aromatics on Crystal Surface of Coordination Cages: Photophysical Properties and Förster Resonance Energy Transfer.

Self-assembly reactions of PdX2 (X- = NO3-, BF4-, ClO4-, ReO4-, PF6-, and CF3SO3-) with 9,10-bis((isoquinolin-5-yloxy)methyl)anthracene (L) in Me2SO give rise to single crystals of coordination cages, [X@Pd2L4]X3, irrespective of X- anions, in high yields. The intracage Pd···Pd distance is significantly sensitive to the nestled X- anion, which can serve as a gauge for recognition of ubiquitous polyatomic anions. One interesting feature is that, via π-π interactions, various polycyclic aromatics (PAs) are characteristically adsorbed on the crystal surface of designed coordination cages with a wall of anthranyl moiety. This unprecedented ensemble system shows an efficient Förster resonance energy transfer (FRET) process from the anthracene (ANT) to pyrene (PYR) via a large degree of spectral overlap between the ANT emission and PYR absorption bands, in contrast to a simple mixture of ANT and PYR.

How Does the Tibetan Plateau Land Thermal Initial Condition Influence the Subseasonal Prediction of 2020 Record‐Breaking Mei‐Yu Rainfall

AbstractAccurate subseasonal prediction of heavy rainfall is helpful for disaster mitigation but challenging. The land thermal condition of Tibetan Plateau (TP), usually with climate memory ranging from weeks to seasons, has been seen as a potential predictability source for subseasonal prediction. Aiming at 2020 record‐breaking Mei‐yu rainfall, this study attempts to investigate whether and how the influence of initial TP surface thermal condition near late June influences the July rainfall prediction over the Middle and Lower Yangtze River Region (MLYR), based on two contrasting prediction experiments using a global climate ensemble prediction system. The results show that the most distinguishable change in the downstream prediction in July is the anomalous low‐tropospheric cyclone and the associated increased rainfall over MLYR corresponding to the warmer initial condition of surface TP. Influenced by the invasion of the positive potential vorticity (PV) center that generated over TP and propagated eastward, this low‐level cyclone anomaly over MLYR is formed within the first week of prediction, and persists for the next 3 weeks maintained by the positive feedback between the low‐level cyclone and middle‐tropospheric latent heating over MLYR in the prediction. This study confirmed the significant effect of TP initial thermal condition on downstream prediction ahead of 3 weeks during the Mei‐yu season (peak summer) with strong land–atmosphere coupling over TP.

Bag of Feature-Based Ensemble Subspace KNN Classifier in Muscle Ultrasound Diagnosis of Diabetic Peripheral Neuropathy

Muscle ultrasound quantification is a valuable complementary diagnostic tool for diabetic peripheral neuropathy (DPN), enhancing physicians’ diagnostic capabilities. Quantitative assessment is generally regarded as more reliable and sensitive than visual evaluation, which often necessitates specialized expertise. This work develops a computer-aided diagnostic (CAD) system based on muscle ultrasound that integrates the bag of features (BOF) and an ensemble subspace k-nearest neighbor (KNN) algorithm for DPN detection. The BOF creates a histogram of visual word occurrences to represent the muscle ultrasound images and trains an ensemble classifier through cross-validation, determining optimal parameters to improve classification accuracy for the ensemble diagnosis system. The dataset includes ultrasound images of six muscles from 53 subjects, consisting of 27 control and 26 patient cases. An empirical analysis was conducted for each binary classifier based on muscle type to select the best vocabulary tree properties or K values for BOF. The result indicates that ensemble subspace KNN classification, based on the bag of features, achieved an accuracy of 97.23%. CAD systems can effectively diagnose muscle pathology, thereby addressing limitations and identifying issues in individuals with diabetes. This research underscores muscle ultrasound as a promising diagnostic tool to aid physicians in making accurate diagnoses, streamlining workflow, and uncovering muscle-related complications in DPN patients.

Exploring the potential of forest snow modeling at the tree and snowpack layer scale

Abstract. Boreal and sub-alpine forests host seasonal snow for multiple months per year; however, snow regimes in these environments are rapidly changing due to rising temperatures and forest disturbances. Accurate prediction of forest snow dynamics, relevant for ecohydrology, biogeochemistry, cryosphere, and climate sciences, requires process-based models. While snow schemes that track the microstructure of individual snow layers have been proposed for avalanche research, so far, tree-scale processes resolving canopy representations only exist in a few snow-hydrological models. A framework that enables layer- and microstructure-resolving forest snow simulations at the meter scale is lacking to date. To fill this research gap, this study introduces the forest snow modeling framework FSMCRO, which combines two detailed, state-of-the art model components: the canopy representation from the Flexible Snow Model (FSM2) and the snowpack representation of the Crocus ensemble model system (ESCROC). We apply FSMCRO to discontinuous forests at boreal and sub-alpine sites to showcase how tree-scale forest snow processes affect layer-scale snowpack properties. Simulations at contrasting locations reveal marked differences in stratigraphy throughout the winter. These arise due to different prevailing processes at under-canopy versus gap locations and due to variability in snow metamorphism dictated by a spatially variable snowpack energy balance. Ensemble simulations allow us to assess the robustness and uncertainties of simulated stratigraphy. Spatially explicit simulations unravel the dependencies of snowpack properties on canopy structure at a previously unfeasible level of detail. Our findings thus demonstrate how hyper-resolution forest snow simulations can complement observational approaches to improve our understanding of forest snow dynamics, highlighting the potential of such models as research tools in interdisciplinary studies.

Landslide Hazard Is Projected to Increase Across High Mountain Asia

AbstractHigh Mountain Asia has long been known as a hotspot for landslide risk, and studies have suggested that landslide hazard is likely to increase in this region over the coming decades. Extreme precipitation may become more frequent, with a nonlinear response relative to increasing global temperatures. However, these changes are geographically varied. This article maps probable changes to landslide hazard, as shown by a landslide hazard indicator (LHI) derived from downscaled precipitation and temperature. In order to capture the nonlinear response of slopes to extreme precipitation, a simple machine‐learning model was trained on a database of landslides across High Mountain Asia to develop a regional LHI. This model was applied to statistically downscaled data from the 30 members of the Seamless System for Prediction and Earth System Research large ensembles to produce a range of possible outcomes under the Shared Socioeconomic Pathways 2‐4.5 and 5‐8.5. The LHI reveals that landslide hazard will increase in most parts of High Mountain Asia. Absolute increases will be highest in already hazardous areas such as the Central Himalaya, but relative change is greatest on the Tibetan Plateau. Even in regions where landslide hazard declines by year 2100, it will increase prior to the mid‐century mark. However, the seasonal cycle of landslide occurrence will not change greatly across High Mountain Asia. Although substantial uncertainty remains in these projections, the overall direction of change seems reliable. These findings highlight the importance of continued analysis to inform disaster risk reduction strategies for stakeholders across High Mountain Asia.

Enhancing land cover classification via deep ensemble network

The rapid adoption of drones has transformed industries such as agriculture, environmental monitoring, surveillance, and disaster management by enabling more efficient data collection and analysis. However, existing UAV-based image scene classification techniques face limitations, particularly in handling dynamic scenes, varying environmental conditions, and accurately identifying small or partially obscured objects. These challenges necessitate more advanced and robust methods for land cover classification. In response, this study explores ensemble learning (EL) as a powerful alternative to traditional machine learning approaches. By integrating predictions from multiple models, EL enhances accuracy, precision, and robustness in UAV-based land use and land cover classification. This research introduces a two-phase approach combining data preprocessing with feature extraction using three advanced ensemble models DenseNet201, EfficientNetV2S, and Xception employing transfer learning. These models were selected based on their higher performance during preliminary evaluations. Furthermore, a soft attention mechanism is incorporated into the ensembled network to optimize feature selection, resulting in improved classification outcomes. The proposed model achieved an accuracy of 97 %, precision of 96 %, recall of 96 %, and an F1-score of 97 % on UAV image datasets. Comparative analysis reveals a 4.2 % accuracy improvement with the ensembled models and a 1 % boost with the advanced hybrid models. This work significantly advances UAV image scene classification, offering a practical solution to enhance decision-making precision in various applications. The ensemble system demonstrates its effectiveness in remote sensing applications, especially in land cover analysis across diverse geographical and environmental settings.

Which classifiers are connected to others? An optimal connection framework for multi-layer ensemble systems

Ensemble learning is a powerful machine learning strategy that combines multiple models e.g. classifiers to improve predictions beyond what any single model can achieve. Until recently, traditional ensemble methods typically use only one layer of models which limits the exploration of different aspects in the classifiers’ predictions. On the other hand, the rise of deep learning has introduced multi-layer architectures that can learn complex functions by transforming data into multiple levels of representation. This characteristic of deep learning suggests that multi-layer ensembles may potentially provide better performance compared to single-layer ensembles. However, a problem which might arise is that in the subsequent layers, not all the inputs to a classifier are desirable, leading to lower performance. In this paper, we introduce a novel multi-layer ensemble of classifiers named COME in which each classifier at a specific layer is connected to multiple classifiers in the previous layer. These connections signify the use of the previous-layer-classifiers’ outputs as inputs for training the current layer's classifier. Each classifier can be connected to different classifiers in the previous layer, which allows inputs in each layer to be optimally selected. We propose a binary encoding scheme to encode the topology of the proposed multi-layer ensemble with defined connections between layers. Differential Evolution, a popular evolutionary computation method, is used as the optimisation algorithm to search for the optimal set of connections. Experimental results on 30 datasets from the UCI Machine Learning Repository and OpenML demonstrate that our proposed ensemble outperforms many state-of-the-art ensemble learning algorithms.

Numerical Analysis and Design of New Exhaust Section Downstream of Constant Volume Combustor

Abstract Pressure gain combustors (PGC) exploit either their isochoric or detonative combustion increasing the theoretical thermal efficiency of a gas turbine cycle. On this basis, a constant-volume combustor (CVC) is developed operating with rotary valves while the chamber is fed with mixture of air and liquid iso-octane. This work describes the numerical design of a new exhaust section after the CVC. First, the design parametrization of the transition duct and the resulting design of experiments (DOE) of 81 samples are introduced. Every case is numerically tested and the election of the best sample is based on the pressure losses and the oscillations characterization. In the last part, the LS89-VKI vane is added at the aft part of the best transition duct and the ensemble exhaust system is analyzed with the help of transient CFD analysis. Every component is evaluated in terms of pressure losses and oscillations, while the operation of the vane is investigated in details. The results of the stator's performance are discussed and compared with steady experimental data. During a CVC period, the cycle average outlet flow angle remains close to the outlet metal angle denoting promising results for the future numerical analysis of the subsequent rotor.

Deep Ensemble Learning using Transfer Learning for Food Classification

Aim: Deep learning models, such as deep convolutional neural networks (CNNs), have undergone extensive scrutiny in the context of food classification because of their exceptional feature extraction capabilities. Background: Similarly, ensemble-based learning approaches have exhibited great potential for achieving effective supervised classification. Objective: We suggest an innovative approach to improve the effectiveness of deep learningbased food classification. Method: Our proposal involves a novel deep learning ensemble framework that draws inspiration from the fusion of deep learning models with ensemble learning based on random subspaces. The random subspaces play a role in diversifying the ensemble system in a straightforward but impactful way. Moreover, to enhance the classification accuracy even more, we explore transfer learning, employing the migration of acquired weights from a single classifier to another (namely, CNNs). This approach expedites the process of learning. Result: Results from experiments conducted using well-established food datasets illustrate that the suggested deep learning ensemble system delivers competitive performance compared to state-of-the-art techniques, as evidenced by its classification accuracy. Conclusion: The amalgamation of deep learning and ensemble learning holds substantial promise for dependable food categorization.

Investigating the mechanisms of an intense coastal rainfall event during TAHOPE/PRECIP-IOP3 using a multiscale radar ensemble data assimilation system

Abstract The joint Taiwan-Area Heavy Rain Observation and Prediction Experiment (TAHOPE)/Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) field campaign between Taiwan and the United States took place from late May to mid-August in 2022. The field campaign aimed to understand the dynamics, thermodynamics, and predictability of heavy rainfall events in the Taiwan area. This study investigated the mechanisms of a heavy rainfall event that occurred on 6–7 June during the intensive observation period-3 (IOP3) of the field campaign. Heavy rainfall occurs on Taiwan’s western coast when a Meiyu front hovers in northern Taiwan. A multiscale radar ensemble data assimilation system based on the successive covariance localization (SCL) method is used to derive a high-resolution analysis for forecasts. Two numerical experiments are conducted with the use of convective-scale (RDA) or multiscale (MRDA) corrections in the assimilation of the radial velocity from operational radars at Chigu and Wufen, and the additional S-Pol radar deployed at Hsinchu during the field campaign. Compared with RDA, MRDA results in large-area wind corrections, which help reshape and relocate a low-level mesoscale vortex, a key element of this heavy rainfall event, offshore of western central Taiwan and enhances the front intensity offshore of northwestern Taiwan. Consequently, MRDA improves the 6-h heavy rainfall prediction over the coast of western Taiwan and better represents the elongated rainband in northern Taiwan during the 3- to 6-h forecast. Sensitivity experiments demonstrate the importance of assimilating winds from Chigu and S-Pol radar in establishing low-level mesoscale vortex and convergence zones.

Remote Interactions between tropical cyclones: The case of Hurricane Michael and Leslie's high predictability uncertainty

The study explores Hurricane Michael's impact on Hurricane Leslie's trajectory predictability using ECMWF and NCEP ensemble systems. A clustering method focused on tropical cyclones is used to identify potential paths for Leslie: Cluster 1 accurately predicted Leslie's direction towards the Iberian Peninsula, whereas Clusters 2 and 3 indicated a southern recurve near the Canary Islands. Analysis of potential vorticity and irrotational wind at upper levels showed a significant interaction between Michael, ridge, and trough across the jet stream from +12 h after initialization. Cluster 1 showed a stronger Michael promoting upper-level wind divergence greatest, modifying the jet stream configuration around the ridge and downstream. Alterations in the jet stream's configuration, functioning as a waveguide, propagated downstream, guiding Leslie towards the Iberian Peninsula. Clusters 2 and 3 indicated the trough's failure to incorporate Leslie, resulting in a recurve of the trajectory around the Azores anticyclone. This research enhances comprehension of the interaction between two tropical cyclones via synoptic Rossby wave flow. Moreover, the conceptual framework can aid operational meteorologists in identifying the sources of uncertainty, particularly in track forecasts under synoptic conditions analogous to those examined in this study.

An Ensemble Learning System Based on Stacking Strategy for Survival Risk Prediction of Patients with Esophageal Cancer

Background: Predicting the prognosis of esophageal cancer (EC) patients is crucial for optimizing the treatment plan and allocating medical resources effectively.Methods: This study proposes a novel ensemble learning-based EC survival prediction model. Firstly, recursive feature elimination (RFE) is used to determine the key feature subsets from the dataset. Based on the determined key features, the improved clustering by fast search and find of density peaks (IDPC) is proposed to construct a novel indicator related to EC survival risk. The cosine distance is introduced in IDPC to cluster samples with similar characteristics. Then, the adaptive synthetic (ADASYN) technique is used to generate more high-risk samples to balance high-risk and low-risk samples. Finally, the hyperparameters of the three models, including extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and random forest (RF), are optimized by whale optimization algorithm (WOA) and a new stacking model is constructed to evaluate the survival risk of patients.Results: The proposed stacking model achieved an area under the receiver operating characteristic curve (AUC) of 0.952 and Accuracy of 0.899, on the dataset from the First Affiliated Hospital of Zhengzhou University.Conclusions: The survival prediction model the proposed ensemble learning system is much more accurate and convenient, providing a basis clinical judgment and decision making and improving the survival status of esophageal cancer patients.

From ensemble learning to deep ensemble learning: A case study on multi-indicator prediction of pavement performance

Recently, Big data analytics approaches were combined with emerging machine learning techniques, which can provide more sophisticated insights into information-intensive activity. Compared to traditional shallow-architecture machine learning algorithms, Deep learning could excavate more potential information from the raw features. However, its powerful representational capacity relies on the support of enormous samples. The ensemble trees system performs superior on small sample problems due to better generalization capacity. To merge both benefits of deep learning and ensemble tree system, this paper developed a deep ensemble algorithm applied to multiple indicators prediction of pavement performance, including International Roughness Index and pavement 3-layer modulus. The deep ensemble algorithm is developed by merging a deep neural network (DNN) with the decision manifold property of the decision trees (TabNet) into a cascade ensemble system, combined with a sliding window algorithm to extract dependency information from raw data. During the training stage, the Bayesian Optimization Algorithm (BOA) is used to search for the optimal combination of sub-decision makers in the cascade ensemble. And equipped with GPU, it can speed up by 2.6–4.0 times. In the case study of pavement engineering, with sufficient training samples, it can achieve an average accuracy of 98.74 %, higher than DNN (97.49 %) and XGBoost (96.12 %) in predicting pavement indicators. With insufficient training samples, it can achieve an accuracy improvement of 12 % than XGBoost (75 %) and 24.5 % than DNN (62.5 %).

A human-in-the-loop ensemble fusion framework for road crash prediction: coping with imbalanced heterogeneous data from the driver-vehicle-environment system

ABSTRACT Road accidents are an inevitable aspect of daily life, and predicting crashes is crucial for minimizing disruptions and advancing intelligent transportation technologies. This study aims to design an ensemble fusion decision system using various base classifiers and a meta-classifier to improve crash prediction efficiency within the driver-vehicle-environment system. We adopted a data-driven strategy to analyze four categories of features—driver demographics, vehicle telemetry, driver inputs, and environmental conditions—collected from a driving simulator. Optimized modeling strategies using AdaBoost, XGBoost, GBM, LightGBM, and CatBoost were implemented. Moreover, statistical logit models were also used to assess the likelihood of crashes and the correlations among key variables. Furthermore, three resampling strategies, SMOTE-TL, SMOTE-ENN, and ADASYN, were employed to address class imbalance. The best performance was achieved with GBM, XGBoost, and AdaBoost as base classifiers, SMOTE-TL for balancing, and CatBoost as the meta-classifier, with 89.78% precision, 95.69% recall, and 92.64% F1-score.

Moment-Based Reinforcement Learning for Ensemble Control.

Problems involving controlling the collective behavior of a population of structurally similar dynamical systems, the so-called ensemble control, arise in diverse emerging applications and pose a grand challenge in systems science and control engineering. Owing to the severely under-actuated nature and the difficulty of placing large-scale sensor networks, ensemble systems are limited to being actuated and monitored at the population level. Moreover, mathematical models describing the dynamics of ensemble systems are often elusive. Therefore, it is essential to design broadcast controls that excite the entire population in such a way that the heterogeneity in system dynamics is robustly compensated. In this article, we propose a reinforcement learning (RL)-based data-driven control framework incorporating population-level aggregated measurement data to learn a global control signal for steering a dynamic population in the desired manner. In particular, we introduce the notion of ensemble moments induced by aggregated measurements and derive the associated moment system to the original ensemble system. Then, using the moment system, we learn an approximation of optimal value functions and the associated policies in terms of ensemble moments through RL. We illustrate the feasibility and scalability of the proposed moment-based approach via numerical experiments using a population of linear, bilinear, and nonlinear dynamic ensemble systems. We report that the proposed method achieves the desired control objectives of various ensemble control tasks and obtains significantly better averaged-reward when compared with three existing methods.

A Combined Scheme based on the Multiscale Stochastic Perturbed Parameterization Tendencies and Perturbed Boundary Layer Parameterization for a Global Ensemble Prediction System

Abstract Stochastic representations of model uncertainties are of great importance for the performance of ensemble prediction systems (EPSs). The stochastically perturbed parameterization tendencies (SPPT) scheme with a single-scale random pattern has been used in the operational global EPS of China Meteorological Administration (CMA-GEPS) since 2018. To deal with deficiencies in this operational single-scale SPPT scheme, a combined scheme based on the multiscale SPPT (mSPPT) scheme and the stochastically perturbed parameterization for the planetary boundary layer (SPP-PBL) scheme is developed. In the combined scheme, the mSPPT component aims to expand model uncertainties characterized by SPPT at mesoscale, synoptic scale, and planetary scale. The SPP-PBL component with six vital parameters is used to capture uncertainties in PBL processes, which is underrepresented by SPPT for the tapering treatment within PBL. Comparisons between the operational SPPT scheme and the mSPPT scheme reveal that the mSPPT scheme can generate more improvements in both ensemble reliability and forecast skills mainly in tropics. Besides, additional benefits from SPP-PBL on top of mSPPT are shown to be primarily distributed in tropics at the lower layers below 850 hPa and surface. Furthermore, the combined scheme of mSPPT and SPP-PBL is suggested to yield better spread–error relationships and forecast skills than the operational SPPT scheme in terms of objective verification scores for standard upper-air variables and surface parameters. A case study for the extreme precipitation event on 20 July 2021 in Henan Province of China also demonstrates the better ability of the combined scheme in forecasting the precipitation intensity and location. Significance Statement A comprehensive and reasonable representation of model uncertainties helps to improve the performance of ensemble prediction systems (EPSs). Despite the popular usage in simulating model uncertainties, the stochastically perturbed parameterization tendencies (SPPT) scheme possesses several shortcomings. To overcome this, a combined scheme based on the multiscale SPPT (mSPPT) scheme and the stochastically perturbed parameterization for the planetary boundary layer (SPP-PBL) scheme is proposed. Based on the global EPS of China Meteorological Administration (CMA-GEPS), additional benefits from the independent usage of mSPPT and SPP-PBL are disclosed. And the combined scheme can inherit the merits of mSPPT and SPP-PBL and generate more improvements on ensemble performance than the original single-scale SPPT scheme. This research provides a guideline for future upgradation of CMA-GEPS.

A boundary perturbation approach for regional wave ensemble forecast

In this study, we developed and validated two wave ensemble prediction systems (WEPS) to forecast wave conditions along the southeastern coast of Australia. Using the SWAN model (GEN3 ST6), we integrated complex bathymetric features with an unstructured grid and validated model outputs against buoy observations from Sydney, Port Kembla, and Batemans Bay. The two WEPS, SWAN-WW3 and SWAN-Pert, utilize different methodologies: SWAN-WW3 derives boundary conditions from NCEP’s Global Wave Ensemble System, while SWAN-Pert employs Latin Hypercube Sampling for boundary perturbations based on historical data. Our results demonstrate that both systems effectively predict significant wave height (Hs), with SWAN-Pert showing improved forecast accuracy in certain metrics compared to SWAN-WW3. Despite underdispersion in spread-skill diagrams, both WEPS exhibited good agreement with observed data. Additionally, rank histograms revealed that SWAN-Pert is more reliable at shorter lead times. This study highlights the potential of integrating statistical sampling methods and ensemble systems for enhancing regional wave forecasting accuracy.

Decision fusion-based system to detect two invasive stink bugs in orchards

Accurate and early detection of insect pests plays an important role in crop protection and pest management in agriculture, especially in orchards. This paper is focused on evaluating and improving the performance of insect detection algorithms by adopting an ensemble approach of artificial neural networks. A set of advanced object detection models including YOLOv8, Faster R-CNN, RetinaNet, SSD, and FCOS were selected, and the models were trained and evaluated on a common dataset representing digital images of different insect species pests. Two classes were considered represented by quite similar invasive stink bugs, Halyomorpha Halys and Nezara Viridula. These architectures were optimized to identify significant peculiarities and variations between reference insects, including size, shape, and color. Each model has been implemented and optimized to achieve the best possible performance before integrating into an ensemble system. By integrating the predictions of these models through a weighted ensemble mechanism that leverages the F1 Score of each model, a more performant global system was developed capable of detecting insect pests with improved performance over individual models. This significant improvement in insect detection highlights the potential of the proposed ensemble system in efficient and rapid insect pest identification, ultimately providing valuable opportunities for implementing crop monitoring technologies. The research highlights the importance of implementing and developing deep-learning technologies for solving specific challenges in agriculture and brings innovative ways of strategic pest management for sustainable agricultural practices.

Towards replacing precipitation ensemble predictions systems using machine learning

Towards replacing precipitation ensemble predictions systems using machine learning

Achieving High Accuracy in Android Malware Detection through Genetic Programming Symbolic Classifier

The detection of Android malware is of paramount importance for safeguarding users’ personal and financial data from theft and misuse. It plays a critical role in ensuring the security and privacy of sensitive information on mobile devices, thereby preventing unauthorized access and potential damage. Moreover, effective malware detection is essential for maintaining device performance and reliability by mitigating the risks posed by malicious software. This paper introduces a novel approach to Android malware detection, leveraging a publicly available dataset in conjunction with a Genetic Programming Symbolic Classifier (GPSC). The primary objective is to generate symbolic expressions (SEs) that can accurately identify malware with high precision. To address the challenge of imbalanced class distribution within the dataset, various oversampling techniques are employed. Optimal hyperparameter configurations for GPSC are determined through a random hyperparameter values search (RHVS) method developed in this research. The GPSC model is trained using a 10-fold cross-validation (10FCV) technique, producing a set of 10 SEs for each dataset variation. Subsequently, the most effective SEs are integrated into a threshold-based voting ensemble (TBVE) system, which is then evaluated on the original dataset. The proposed methodology achieves a maximum accuracy of 0.956, thereby demonstrating its effectiveness for Android malware detection.