Precise Prediction Research Articles

Predicting nursing workload in digestive wards based on machine learning: A prospective study

BackgroundThe process of assessing and allocating nursing staff, as well as evaluating performance, relies heavily on nursing workload, which is strongly associated with patient safety outcomes. Nevertheless, most previous studies have utilized cross-sectional data collection methods, which limit the precision of workload prediction. Static workload models do not incorporate longitudinal changes in influential factors, potentially resulting in delayed or erroneous nursing management decisions and ultimately causing imbalances in nurses’ workload.AimTo employ machine learning algorithms to facilitate the dynamic prediction of nursing workload on the basis of patient characteristics.MethodsThis prospective cohort quantitative study was conducted between March 2019 and August 2021 in two general hospitals located in China. Data on the characteristics of 133 patients over the course of 1339 hospital days, as well as direct nursing time, were collected. A longitudinal investigation of nursing workload was carried out, applying multiple linear regression to identify measurable factors that significantly impact nursing workload. Additionally, machine learning methods were applied to dynamically predict the nursing time needed for each patient.ResultsThe mean direct nursing workload varied greatly across hospitalizations. Significant factors contributing to increased care needs included complications, comorbidities, body mass index (BMI), income, history of past illness, simple clinical score (SCS), and activities of daily living (ADL). The predictive performance improved through machine learning, with the random forest model demonstrated the best performance (root mean square error (RMSE): 1148.38; coefficient of determination (R2): 0.74; mean square error (MSE): 1318744.64).ConclusionsThe variability in nursing workload during hospitalization is influenced primarily by patient self-care capacity, complications, and comorbidities. The random forest algorithm, a machine learning algorithm, effectively handles a wide range of features, such as patient characteristics, complications, comorbidities, and other factors. This algorithm has demonstrated good performance in predicting workload.Implications for nursing managementThis study introduces a quantitative model designed to evaluate nursing workload throughout the duration of hospitalization. By employing the model, nursing managers can consider multiple factors that impact workload comprehensively, resulting in enhanced comprehension and interpretation of workload variations. Through the application of a random forest algorithm for workload prediction, nursing managers can anticipate and estimate workload in a proactive and precise manner, thereby facilitating more efficient human resource planning.

Enhancing Vehicle Location Prediction Accuracy with Road-Aware Rectification for Multi-Access Edge Computing Applications

In future 6G networks, real-time and accurate vehicular data are key requirements for enhancing the data-driven multi-access edge computing (MEC) applications. Existing estimation techniques to forecast vehicle position aim to meet the real-time data needs but compromise accuracy due to a lack of context awareness. While algorithms such as the Kalman filter improve estimation accuracy by considering certainty-grading and current-state estimate of measurements, they do not include the road context, which is vital for more accurate predictions. Unfortunately, current implementations of linear Kalman filters are not road-aware and struggle to predict a two-dimensional movement accurately. To this end, we propose a significant road-aware rectification-assisted prediction mechanism that enhances the modified Kalman filter predictions by incorporating road awareness. The parameters used for the Kalman filter include vehicle location, angle, speed, and time. In contrast, road-aware location rectification incorporates predicted location and lane shape, increasing the accuracy and precision of vehicle location predictions, reaching up to 99.9%. Performance is evaluated by comparing actual, predicted, and rectified vehicular traces at different speeds. The results demonstrate that the prediction error is approximately 0.005, while the proposed rectification process further reduces the error to 0.001, highlighting the effectiveness of the proposed approach. Overall, results support the idea of provisioning accurate, proactive, and real-time vehicular location data at the edge using a road-aware approach, thereby revolutionizing 6G vehicle location provisioning in MEC.

The picasso gas model: Painting intracluster gas on gravity-only simulations

We introduce picasso, a model designed to predict thermodynamic properties of the intracluster medium based on the properties of halos in gravity-only simulations. The predictions result from the combination of an analytical gas model, mapping gas properties to the gravitational potential, and of a machine learning model to predict the model parameters for individual halos based on their scalar properties, such as mass and concentration. Once trained, the model can be applied to make predictions for arbitrary potential distributions, allowing its use with flexible inputs such as N−body particle distributions or radial profiles. We present the model, and train it using pairs of gravity-only and hydrodynamic simulations. We show that when trained to learn the mapping from gravity-only to non-radiative hydrodynamic simulations, picasso can make remarkably accurate and precise predictions of intracluster gas thermodynamics, with percent-level bias and ~20% scatter for r/R500c∈[0.1,1]. Training the model on hydrodynamic simulations including sub-resolution physics modeling yields robust predictions as well, albeit with the introduction of a radius-dependent bias and an increase in scatter. We further show that the model can be trained to make accurate predictions from very minimal halo information, down to mass and concentration, at the cost of modestly reduced precision. picasso is made publicly available on Github (https://github.com/fkeruzore/picasso) as a Python package, which includes trained models that can be used to make predictions easily and efficiently, in a fully auto-differentiable and hardware-accelerated framework.

An interpretable machine learning model for precise prediction of biomarkers for intermittent fasting pattern

Intermittent fasting is currently a highly sought-after dietary pattern. To explore the potential biomarkers of intermittent fasting, untargeted metabolomics analysis of fecal metabolites in two groups of mice, intermittent fasting and normal feeding, was conducted using UPLC-HRMS. The data was further analyzed through interpretable machine learning (ML) to data mine the biomarkers for two dietary patterns. We developed five machine learning models and results showed that under three-fold cross-validation, Random Forest model was the most suitable for distinguishing the two dietary patterns. Finally, Shapely Additive exPlanations (SHAP) were explored to perform a weighted explanatory analysis on the Random Forest model, and the contribution of each metabolite to the model was calculated. Results indicated that Ganoderenic Acid C is the potential biomarkers to distinguish the two dietary patterns. Our work provides new insights for metabolic biomarker analysis and lays a theoretical foundation for the selection of a healthieir dietary lifestyle.

Residue-Level Multiview Deep Learning for ATP Binding Site Prediction and Applications in Kinase Inhibitors.

Accurate identification of adenosine triphosphate (ATP) binding sites is crucial for understanding cellular functions and advancing drug discovery, particularly in targeting kinases for cancer treatment. Existing methods face significant challenges due to their reliance on time-consuming precomputed features and the heavily imbalanced nature of binding site data without further investigations on their utility in drug discovery. To address these limitations, we introduced Multiview-ATPBind and ResiBoost. Multiview-ATPBind is an end-to-end deep learning model that integrates one-dimensional (1D) sequence and three-dimensional (3D) structural information for rapid and precise residue-level pocket-ligand interaction predictions. Additionally, ResiBoost is a novel residue-level boosting algorithm designed to mitigate data imbalance by enhancing the prediction of rare positive binding residues. Our approach outperforms state-of-the-art models on benchmark data sets, showing significant improvements in balanced metrics with both experimental and AI-predicted structures. Furthermore, our model seamlessly transfers to predicting binding sites and enhancing docking simulations for kinase inhibitors, including imatinib and dasatinib, underscoring the potential of our method in drug discovery applications.

Predicting grip strength-related frailty in middle-aged and older Chinese adults using interpretable machine learning models: a prospective cohort study

IntroductionFrailty is an emerging global health burden, and there is no consensus on the precise prediction of frailty. We aimed to explore the association between grip strength and frailty and interpret the optimal machine learning (ML) model using the SHapley Additive exPlanation (SHAP) to predict the risk of frailty.MethodsData for the study were extracted from the China Health and Retirement Longitudinal Study (CHARLS) database. Socio-demographic, medical history, anthropometric, psychological, and sleep parameters were analyzed in this study. We used the least absolute shrinkage and selection operator (LASSO) regression to filter the model for the best predictor variables and constructed six ML models for predicting frailty. The feature performance of six ML models was compared based on the area under the receiver operating characteristic curve (AUROC) and the light gradient boosting machine (LightGBM) model was selected as the best predictive frailty model. We used SHAP to interpret the LightGBM model and to reveal the decision-making process by which the model predicts frailty.ResultsA total of 10,834 eligible participants were included in the study. Using the lowest quartile of grip strength as a reference, grip strength was negatively associated with the risk of frailty when grip strength was >29.00 kg for males or >19.00 kg for females (p < 0.001). The LightGBM model predicted frailty with optimal performance with an AUROC of 0.768 (95% CI 0.741 ~ 0.795). The SHAP summary plot showed that all features predicted frailty in order of importance, with cognitive function being considered the most important predictive feature. The poorer the cognitive function, nighttime sleep duration, body mass index (BMI), and grip strength, the higher the risk of frailty in middle-aged and older adults. The SHAP individual force plot clearly shows that the LightGBM model predicts frailty in the individual decision-making process.ConclusionThe grip strength-related LightGBM prediction model based on SHAP has high accuracy and robustness in predicting the risk of frailty. Increasing grip strength, cognitive function, nighttime sleep duration, and BMI reduce the risk of frailty and may provide strategies for individualized management of frailty.

Optimizing quality of service forecasting in mobile networks through modified walrus optimization and multivariate approaches

This paper presents Ensemble-based Service Quality Prediction (EAQP), an automated method for predicting service quality under changing mobile network conditions. EAQP incorporates data preparation methods such as transformation, purification, & imputation, and then performs feature extraction utilizing statistical, geographical, as well as temporal approaches. An improved feature selection method, using a unique weighting approach and optimized by a modified Walrus Optimization Algorithm, improves the accuracy of predictions. EAQP utilizes a variety of prediction models such as support vector regression, recurrent neural network models, bi-directional short-term long-term memory networks, extreme learning machines, along with multi-layer perceptron neural networks to enhance predictive accuracy. EAQP uses complex optimization algorithms and ensemble learning approaches to provide precise and dependable predictions about service quality in real-time. This helps in proactive network management as well as improvement. This comprehensive approach shows potential for boosting network efficiency, optimizing the distribution of resources, and enhancing the end-user experience when using mobile communications systems.

Precise Temperature Prediction for Small-Sample Fiber Optic Spectra Based on Deep Learning

Precise Temperature Prediction for Small-Sample Fiber Optic Spectra Based on Deep Learning

Evolution and Predictive Analysis of Spatiotemporal Patterns of Habitat Quality in the Turpan–Hami Basin

The expansion of urban areas and unsustainable land use associated with human activities have brought about a decline in habitat quality (HQ), especially in arid regions with fragile ecosystems. A precise prediction of land use and habitat quality changes across different scenarios is crucial for the sustainable maintenance of ecological diversity. In this article, the InVEST model was employed to assess both the quality and degradation levels of habitats in the Turpan–Hami Basin (THB) spanning 1990~2020. Additionally, the InVEST-PLUS coupling model was employed to forecast habitat conditions under three different scenarios in 2050. Specifically, it involved the comparison of land use changes and spatial distribution of HQ across natural development (ND) scenarios, town development (UD) scenarios, and ecological protection (EP) scenarios, along with the analysis of hot spots of HQ spanning 1990~2050. The outcomes revealed the following: (1) The primary land use in the THB was categorized as unused land, alongside notable expansions in cultivated land, grassland, and built-up land. Conversely, there was a considerable decline observed in forests, water bodies, and unused land spanning 1990~2020. (2) The HQ within the THB exhibited evident spatial clustering characteristics. Between 1990 and 2020, areas with low HQ accounted for over 85%, areas with unchanged HQ constituted 88.19%, areas experiencing deteriorated HQ comprised approximately 5.02%, and areas displaying improved HQ encompassed around 6.79%. (3) Through the comparison of HQ for the ND, UD, and EP scenarios in 2050, it was observed that the average HQ under the EP scenario ranked highest, exhibiting the lowest degree of degradation on average. This indicates that the EP scenario is most advantageous for preserving HQ. Conclusively, this research provides valuable viewpoints for making decisions aimed at enhancing HQ in ecologically fragile arid regions.

Energy-based approach to the assessment of traffic flow

This article focuses on modeling vehicle acceleration noise in different road conditions, emphasizing urban, highway, and rural roads in Ukraine. Acceleration noise, which refers to the fluctuations in a vehicle's acceleration, is a critical factor in vehicle safety, fuel efficiency, and driving comfort. The research aims to improve current vehicle dynamics models by integrating multi-body dynamics and machine learning algorithms, allowing for more precise predictions of acceleration variability in real-time. The study is based on the existing literature, showing that road surface quality significantly affects acceleration noise. With frequent stop-and-go traffic, urban roads produce moderate but irregular noise patterns. Highways show stable acceleration noise at moderate speeds, but noise increases sharply as vehicles approach higher speeds due to aerodynamic forces. Rural roads, especially those in poor condition, exhibit the highest variability in acceleration noise, even at low speeds. The proposed model has been validated using real-world data. It demonstrates a strong correlation between the predictions and actual vehicle behavior on various road types. One of the key innovations in this research is the use of machine learning to adjust model parameters in real-time dynamically. This adaptive approach enhances the model’s accuracy and applicability, especially in intelligent transport systems. The model can inform traffic management strategies, allowing for real-time adjustments to speed limits, traffic signals, and routing decisions based on road conditions. This contributes to safer, more efficient, and sustainable transport systems, particularly in regions with inconsistent road infrastructure. The research concludes that integrating acceleration noise modeling into intelligent transport systems can significantly improve traffic flow and vehicle safety. Future research will expand the dataset to include a broader range of vehicle types and road conditions, further refining the model's predictive capabilities.

Variant pathogenicity prediction based on the ESGMM algorithm

Modeling the functional impact of sequence variation is a critical issue for both understanding and developing proteins. An Evolutionary Sequence and Gaussian Mixture Model (ESGMM) for predicting variant pathogenicity is presented in this paper. The model is trained on 2715 clinical proteins and their homologous sequences, using a Transformer-based protein language model to discover evolutionary patterns of amino acids from multiple sequence alignment (MSA). To fully mine deep information of MSA two-dimensional data, an axial attention mechanism is introduced during training. The model estimates the probability of all variants compared to the wild type and calculates variant scores. To categorize variations as pathogenic or benign, a global–local Gaussian mixture model is then constructed for each variant, and ESGMM scores are produced for each variant employing a combination of global and local information. Particle swarm optimization (PSO) is introduced to optimize the local Gaussian mixture model and further quantify the uncertainty of the classification, which enhances the model prediction precision. Experimental results demonstrate the superiority of the optimized ESGMM algorithm in predicting the pathogenicity of variants.

Adaptive Learning in AI Agents for the Metaverse: The ALMAA Framework

This study investigates the adaptability of Artificial Intelligence (AI) agents in the Metaverse, focusing on their ability to enhance responsiveness, decision-making, and engagement through the proposed Adaptive Learning Model for AI Agents (ALMAA) framework. The research does not introduce new interventions to existing platforms like Epic Games or AltspaceVR but instead analyzes how their operations align with adaptive learning principles. By examining these platforms, the study demonstrates the alignment between real-world practices and theoretical constructs, offering insights into how adaptive AI systems operate in dynamic virtual environments. Case observations highlight key metrics such as user interaction efficiency, contextual decision accuracy, and predictive engagement strategies. The data, derived from detailed user interaction logs and feedback reports, underscore the practical application of adaptive learning in optimizing user satisfaction and system performance. Statistical analyses reveal notable gains in response speed, predictive precision, and user engagement, validating the theoretical framework’s relevance. This paper positions the ALMAA framework as a critical lens for understanding and analyzing adaptive AI in virtual settings. It emphasizes theoretical exploration rather than experimental application, providing a foundation for future research into scalable, user-centered AI systems tailored for the Metaverse’s evolving demands.

Estimation of heart dose in left breast cancer radiotherapy: Assessment of vDIBH feasibility using the supervised machine learning algorithm.

The volunteer deep inspiration breath hold (vDIBH) technique is used to reduce the heart dose in left breast cancer radiotherapy. Many times, it is faced that despite rigorous exercise and training, not all patients get benefited as expected. The primary objective of this study was to develop a machine learning program for prediction of mean heart dose before left breast radiotherapy under vDIBH. The present work is based on the dosimetric parameters of eighty-two left breast cancer patients, who have undergone modified radical mastectomy, enrolled for their radiation treatment. The trained machine learning algorithm employed linear regression to establish a correlation between Haller Index and heart mean dose (HMD) received during the ca left breast cancer radiotherapy. Subsequently, HMD values were used to model the regression relationship with maximum heart distance (MHD). The method adopted is beneficial in patient selection and assessment for suitability of patients' radiotherapy planning under vDIBH treatment technique. For data from 21 test patients, the mean of HMD obtained from the treatment planning system (TPS) and the mean of predicted HMD by developed program were found to be 468.76 cGy and 464.66 cGy, respectively. The present work facilitates precise HMD prediction in left breast cancer radiation therapy even before starting the treatment planning process. Additionally, this program offers suggestions in terms of modifications in treatment settings for even better results of vDIBH techniques if not matches with the anticipated results.

Artificial Intelligence‐Based Detection of Local Spots in Network Materials Prone to Mechanical Failure

ABSTRACTPredicting the failure of a noncrystalline solid such as silica glass is challenging due to the complex fracture phenomenon. Failure originates from particular local zones where the atomic structure is highly susceptible to local rearrangement. The local yield stress (LYS) method predicts such locations of plastic events during external deformation. Despite the precise prediction accuracy of the location of the failure initiation, the LYS method requires substantial computational resources as it evaluates each local zone separately for rearrangement and is time‐consuming. Owing to new age development in artificial neural networks, a relation can be established between the plastic events and the atomic structure. In this study, a neural network is trained to identify this relation and, hence, predict the locations of atomic rearrangements. This approach results in a heatmap indicating soft spots that highly correlate with elementary events of fracture and allows for reliable incorporation of structural identification to coarser mechanical frameworks that need local information for a physically meaningful description. The advantage of this method is that it can be trained on a specific material and can predict the local soft spots for unseen structural information without the need for multiple time‐consuming molecular simulations.

Advances and Challenges in Species Ecological Niche Modeling: A Mixed Review

Species distribution modeling (SDM) is a vital tool for ecological and biogeographical research, allowing precise predictions of species distributions based on environmental variables. This study reviews the evolution of SDM techniques from 1985 to 2023, focusing on model development and applications in conservation, climate change adaptation, and invasive species management. We employed a mixed review with bibliometric and systematic element approaches using the Scopus database, analyzing 982 documents from 275 sources. The MaxEnt model emerged as the most frequently used technique, applied in 85% of the studies due to its adaptability and accuracy. Our findings highlight the increasing trend in international collaboration, particularly between China, the United Kingdom, and Brazil. The study reveals a significant annual growth rate of 11.99%, driven by technological advancements and the urgency to address biodiversity loss. Our analysis also shows that while MaxEnt remains dominant, deep learning and other advanced computational techniques are gaining traction, reflecting a shift toward integrating AI in ecological modeling. The results emphasize the importance of global cooperation and the continued evolution of SDM methodologies, projecting further integration of real-time data sources like UAVs and satellite imagery to enhance model precision and applicability in future conservation efforts.

Monthly Prediction of Pine Stress Probability Caused by Pine Shoot Beetle Infestation Using Sentinel-2 Satellite Data

Due to the significant threat to forest health posed by beetle infestations on pine trees, timely and accurate predictions are crucial for effective forest management. This study developed a pine tree stress probability prediction workflow based on monthly cloud-free Sentinel-2 composite images to address this challenge. First, representative pine tree stress samples were selected by combining long-term forest disturbance data using the Continuous Change Detection and Classification (CCDC) algorithm with high-resolution remote sensing imagery. Monthly cloud-free Sentinel-2 images were then composited using the Multifactor Weighting (MFW) method. Finally, a Random Forest (RF) algorithm was employed to build the pine tree stress probability model and analyze the importance of spectral, topographic, and meteorological features. The model achieved prediction precisions of 0.876, 0.900, and 0.883, and overall accuracies of 89.5%, 91.6%, and 90.2% for January, February, and March 2023, respectively. The results indicate that spectral features, such as band reflectance and vegetation indices, ranked among the top five in importance (i.e., SWIR2, SWIR1, Red band, NDVI, and NBR). They more effectively reflected changes in canopy pigments and leaf moisture content under stress compared with topographic and meteorological features. Additionally, combining long-term stress disturbance data with high-resolution imagery to select training samples improved their spatial and temporal representativeness, enhancing the model’s predictive capability. This approach provides valuable insights for improving forest health monitoring and uncovers opportunities to predict future beetle outbreaks and take preventive measures.

Lithium-Ion Battery Life Prediction Using Deep Transfer Learning

Lithium-ion batteries are critical components of various advanced devices, including electric vehicles, drones, and medical equipment. However, their performance degrades over time, and unexpected failures or discharges can lead to abrupt operational interruptions. Therefore, accurate prediction of the remaining useful life is essential to ensure device safety and reliability. Conventional RUL prediction methods typically rely on regression analysis, signal processing, and machine learning techniques to assess battery conditions such as charge/discharge cycles, voltage, temperature, and durability. Although effective, these approaches are constrained by their dependence on large amounts of labeled data and the necessity for complex feature engineering to capture battery physical characteristics. In this study, we propose an approach that employs deep transfer learning to address these limitations. By leveraging pretrained model weights, the proposed method significantly improves the efficiency and accuracy of RUL prediction even under limited training data conditions. Furthermore, we investigate the impact of external environmental factors and physical battery characteristics on RUL prediction precision, thereby contributing to a more robust and reliable prediction framework.

Rapid Vehicle Trajectory Prediction Based on Multi-Attention Mechanism for Fusing Multimodal Information

Trajectory prediction plays a crucial role in autonomous driving tasks, as accurately and rapidly predicting the future trajectories of traffic participants can significantly enhance the safety and robustness of autonomous driving systems. This paper presents a novel trajectory prediction model that follows the encoder–decoder paradigm, achieving precise and rapid predictions of future vehicle trajectories by efficiently aggregating the spatiotemporal and interaction information of agents in traffic scenarios. We propose an agent–agent interaction information extraction module based on a sparse graph attention mechanism, which enables the efficient aggregation of interaction information between agents. Additionally, we introduce a non-autoregressive query generation method that accelerates the model inference speed by generating the decoding queries in parallel. Comparative experiments with the existing advanced algorithms show that our method improves the multimodal trajectory prediction metrics for the Minimum Average Displacement Error (minADE), the Minimum Final Displacement Error (minFDE), and the Miss Rate (MR) by an average of 9.1%, 11.8%, and 14.6%, respectively, while the inference time is only 33.7% of the average time taken by the other algorithms. Finally, we demonstrate the effectiveness of the various modules proposed in this paper through ablation studies.

Development of a PMGDNI model to predict the probability of three-month unfavorable outcome acute ischemic stroke after endovascular treatment: a cohort study

BackgroundPatients with acute large vessel occlusion stroke (ALVOS) may exhibit considerable variability in clinical outcomes following mechanical thrombectomy (MT). This study aimed to develop a novel statistical model predicting functional independence three months post-endovascular treatment for acute stroke and validate its performance within the cohort.MethodConsecutive patients undergoing endovascular treatment for acute stroke with large vessel occlusion were randomly divided into a modeling group and a validation group in a 7:3 ratio. Independent risk factors were identified through LASSO regression and multivariate logistic regression analyses, leading to the development of a prognostic model whose performance was rigorously validated.ResultsA total of 913 patients were screened, with 893 cases included. The modeling group comprised 625 cases, and the validation group included 268 cases. Identified independent factors for adverse outcomes after endovascular treatment of acute ischemic stroke (AIS) were pneumonia (OR = 4.517, 95% CI = 2.916–7.101, P < 0.001), mechanical ventilation (OR = 2.449, 95% CI = 1.475–5.148, P = 0.001), admission GCS ≥ 8 (OR = 0.365, 95% CI = 0.167–0.745, P = 0.008), dysphagia (OR = 2.074, 95% CI = 1.375–3.126, P < 0.001), and 72-hour highest Na ≥ 145 (OR = 2.794, 95% CI = 1.508–5.439, P = 0.002), along with intracranial hemorrhage (OR = 2.453, 95% CI = 1.408–4.396, P = 0.002). These factors were illustrated in a PMGDNI column chart. The area under the ROC curve for the modeling group was 82.5% (95% CI = 0.793–0.857), and for the validation group, it was 83.7% (95% CI = 0.789–0.885). The Hosmer-Lemeshow test indicates that there is no statistically significant difference (P > 0.05) between the predicted and actual probabilities of adverse outcomes. The clinical decision curve demonstrated optimal net benefits at thresholds of 0.30-1.00 and 0.25-1.00 for both training and validation sets, indicating effective clinical efficacy within these probability ranges.ConclusionWe have successfully developed a new predictive model enhancing the accuracy of prognostic assessments for acute ischemic stroke following EVT. It provides an individual, visual, and precise prediction of the risk probability of a 90-day unfavorable outcome.

Predicting the seasonal dynamics of Dalbulus maidis (Hemiptera: Cicadellidae) in corn using artificial neural networks.

This study addresses the challenge of predicting Dalbulus maidis (DeLong & Wolcott) (Hemiptera: Cicadellidae) density in cornfields by developing an artificial neural network (ANN). Over two years, we collected data on meteorological variables (atmospheric pressure, air temperature, dew point, rainfall, relative humidity, solar irradiance, and wind speed), plant age, and density of D. maidis in cornfields located in two Brazilian biomes (Atlantic Forest and Brazilian Tropical Savannah). Out of 1056 ANNs tested, the neural network featuring a 30-day time lag, six neurons, logistic activation, and resilient propagation demonstrated the lowest root mean squared error (0.057) and a high correlation (0.919) with observed D. maidis densities. This ANN exhibited an goodness of fit in low-density (Atlantic Forest) and high-density (Brazilian Tropical Savannah) scenarios for D. maidis. Critical factors influencing D. maidis seasonal dynamics, including corn plant age, rainfall, average air temperature, and relative humidity, were identified. This study highlights the potential of the ANN as a promising tool for precise predictions of pest seasonal dynamics, positioning it as a valuable asset for integrated pest management programs targeting D. maidis.