Traditional Prediction Methods Research Articles

Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease

Due to the aging of the global population and lifestyle changes, cardiovascular disease has become the leading cause of death worldwide, causing serious public health problems and economic pressures. Early and accurate prediction of cardiovascular disease is crucial to reducing morbidity and mortality, but traditional prediction methods often lack robustness. This study focuses on integrating swarm intelligence feature selection algorithms (including whale optimization algorithm, cuckoo search algorithm, flower pollination algorithm, Harris hawk optimization algorithm, particle swarm optimization algorithm, and genetic algorithm) with machine learning technology to improve the early diagnosis of cardiovascular disease. This study systematically evaluated the performance of each feature selection algorithm under different population sizes, specifically by comparing their average running time and objective function values to identify the optimal feature subset. Subsequently, the selected feature subsets were integrated into ten classification models, and a comprehensive weighted evaluation was performed based on the accuracy, precision, recall, F1 score, and AUC value of the model to determine the optimal model configuration. The results showed that random forest, extreme gradient boosting, adaptive boosting and k-nearest neighbor models performed best on the combined dataset (weighted score of 1), where the feature set consisted of 9 key features selected by the cuckoo search algorithm when the population size was 25; while on the Framingham dataset, the k-nearest neighbor model performed best (weighted score of 0.92), and its feature set was derived from 10 features selected by the whale optimization algorithm when the population size was 50. The results of this study show that swarm intelligence algorithms can effectively screen key and informative feature sets, significantly improve model classification accuracy, and provide strong support for the early diagnosis of cardiovascular diseases.

CL-GNN: Contrastive Learning and Graph Neural Network for Protein-Ligand Binding Affinity Prediction.

In the realm of drug discovery and design, the accurate prediction of protein-ligand binding affinity is of paramount importance as it underpins the functional interactions within biological systems. This study introduces a novel self-supervised learning (SSL) framework that combines contrastive learning and graph neural networks (CL-GNN) for predicting protein-ligand binding affinities, which is a critical aspect of drug discovery. Traditional methods for affinity prediction are expensive and time-consuming, prompting the development of more efficient computational approaches. CL-GNN utilizes a contrastive learning strategy, a form of SSL, to learn from a large data set of 371 458 unique unlabeled protein-ligand complexes. By employing graph neural networks and molecular graph enhancement techniques, the model effectively captures protein-ligand interactions in a self-supervised manner. The fine-tuned model demonstrates competitive performance, achieving high Pearson's correlation coefficients and low root-mean-square errors on benchmark data sets. The proposed method outperforms existing machine learning models, showcasing its potential for accelerating the drug development process. The method effectively quantifies the similarity between protein-ligand complex representations learned in the pretraining and downstream testing phases through cosine similarity assessment. This approach not only revealed potential connections between complexes in their binding properties but also provided new insights into the understanding of drug mechanisms of action. In addition, the transparency of the model is significantly improved by visualizing the importance of key protein residues and ligand atoms. This visualization tool provides insight into the model's predictive decision-making process, providing key biological insights for drug design and optimization.

Data-knowledge hybrid driven intelligent prediction method of tunnel excavation profiles geometric deformation

ABSTRACT Applying digital twin technology to the tunnel excavation process enables intelligent tunnel construction, which is critical for ensuring construction safety and advancing intelligent building methods. However, the complex construction environment, involving numerous environmental factors and dynamic changes, poses challenges for traditional deformation prediction and analysis methods. These methods fail to systematically integrate the elements of the excavation scene to achieve accurate prediction. Therefore, this paper proposes a data-knowledge hybrid driven intelligent prediction method of tunnel excavation profile geometric deformation. A dynamic data-driven tunnel excavation knowledge extraction method is designed, along with the construction of a multi-process coupled tunnel excavation knowledge base. Additionally, a deep learning-based excavation profile deformation prediction model is developed, integrating the data-knowledge hybrid driven method to improve prediction performance. The experimental results demonstrate that the method proposed in this paper can significantly integrate and analyse the deformation elements of the tunnel excavation scene. It achieves intelligent prediction of the tunnel excavation profile geometric deformation, with reductions of 57.46%, 76.00%, and 53.12% in MAE, MSE, and RMSE, respectively, compared with the traditional prediction model. This method effectively enhances the deformation prediction accuracy of the tunnel excavation profile twin model.

A hybrid deep learning model based on signal decomposition and dynamic feature selection for forecasting the influent parameters of wastewater treatment plants.

Accurate prediction of influent parameters such as chemical oxygen demand (COD) and biochemical oxygen demand over five days (BOD5) is crucial for optimizing wastewater treatment processes, enhancing efficiency, and reducing costs. Traditional prediction methods struggle to capture the dynamic variations of influent parameters. Mechanistic biochemical models are unable to predict these parameters, and conventional machine learning methods show limited accuracy in forecasting key water quality indicators such as COD and BOD5. This study proposes a hybrid model that combines signal decomposition and deep learning to improve the accuracy of COD and BOD5 predictions. Additionally, a new dynamic feature selection (DFS) mechanism is introduced to optimize feature selection in real-time, reducing model redundancy and enhancing prediction stability. The model achieved R2 values of 0.88 and 0.96 for COD, and 0.75 and 0.93 for BOD5 across two wastewater treatment plants. RMSE and MAE values were significantly reduced, with decreases of 14.93% and 12.55% for COD at WWTP No. 5, and 20.89% and 20.40% for COD at WWTP No. 7. For BOD5, RMSE and MAE decreased by 3.56% and 5.28% at WWTP No. 5, and by 10.06% and 10.20% at WWTP No. 7. These results highlight the effectiveness of the proposed model and DFS mechanism in improving prediction accuracy and model performance. This approach provides valuable insights for wastewater treatment optimization and broader time series forecasting applications.

Advancements in Gene Structure Prediction: Innovation and Prospects of Deep Learning Models Apply in Multi-species

In recent years, advancements in gene structure prediction have been significantly driven by the integration of deep learning technologies into bioinformatics. Transitioning from traditional thermodynamics and comparative genomics methods to modern deep learning-based models such as CDSBERT, DNABERT, RNA-FM, and PlantRNA-FM prediction accuracy and generalization have seen remarkable improvements. These models, leveraging genome sequence data along with secondary and tertiary structure information, have facilitated diverse applications in studying gene functions across animals, plants, and humans. They also hold substantial potential for multi-application in early disease diagnosis, personalized treatment, and genomic evolution research. This review combines traditional gene structure prediction methods with advancements in deep learning, showcasing applications in functional region annotation, protein-RNA interactions, and cross-species genome analysis. It highlights their contributions to animal, plant, and human disease research while exploring future opportunities in cancer mutation prediction, RNA vaccine design, and CRISPR gene editing optimization. The review also emphasizes future directions, such as model refinement, multimodal integration, and global collaboration. By offering a concise overview and forward-looking insights, this article aims to provide a foundational resource and practical guidance for advancing nucleic acid structure prediction research.

Data and Knowledge Dual-Driven Creep Life Prediction for Austenitic Heat-Resistance Steel

Abstract: Traditional creep life prediction methods are generally difficult for researchers to fully consider the key factors affecting the creep performance, which limits their application in the research and development of new alloys. The artificial intelligence method can skip the complex mechanism and directly establish the mathematical correlation between the composition/process and the target performance. The accuracy, universality, and development efficiency of the model are better than the traditional material development strategy. In this study, we collected 216 creep data of austenitic heat-resistant steel, selected a variety of different machine learning algorithms to establish creep life prediction models, calculated and introduced a large amount of physical metallurgy knowledge highly related to creep based on Thermo-Calc, and converted the creep life into the form of the Larson–Miller parameter to optimize the data distribution, which effectively improved the prediction accuracy and interpretability of the model. In addition, the optimal model was combined with a genetic algorithm to obtain the best composition and process scheme with high-creep-performance potential, providing guidance for the design of austenitic heat-resistant steel.

An Integrated Framework for TFT-LCD Quality Prediction Based on L1 Regularized Feature Selection and SSA-Optimized MLP-XGBoost Ensemble

With the rapid development of display technology, accurate quality prediction of TFT-LCD is crucial because its performance directly affects the user experience. Traditional quality prediction methods rely on manual inspection or rule-based methods, facing the challenges of low efficiency, strong subjectivity, and lack of robustness to changes in production environment. To address these limitations, this paper proposes a novel regression model based on L1 regularized feature selection, Salp Swarm algorithm (SSA) optimization, and MLP-XGBoost integration. L1 regularization is used to automatically select key features from high-dimensional industrial data, reduce redundancy and enhance model generalization. Subsequently, SSA optimizes the hyperparameters of MLP and XGBoost, enabling effective exploration of the parameter space and thus improving model performance. The integrated MLP-XGBoost model combines the nonlinear feature extraction capability of MLP with the regression accuracy of XGBoost. Experimental results show that the proposed model outperforms traditional models in various indicators. Compared with the unoptimized MLP-XGBoost, the model achieves a significant reduction in prediction error, verifying its effectiveness in solving TFT-LCD quality prediction.

Machine learning-based spectrum occupancy prediction: a comprehensive survey

In cognitive radio (CR) systems, efficient spectrum utilization depends on the ability to predict spectrum opportunities. Traditional statistical methods for spectrum occupancy prediction (SOP) are insufficient for addressing the non-stationary nature of spectrum occupancy, especially with UEs’ increased mobility and diversity in the sixth-generation and beyond wireless networks. This survey provides a comprehensive overview of machine learning (ML)-based SOP methods that address these challenges. The paper begins with a brief discussion of problem definition and traditional statistical methods before delving into a detailed survey of ML-based methods. Various aspects of SOP are analyzed from a CR perspective, highlighting the multidimensional correlations in spectrum usage across time, frequency, space, etc. Key challenges and enabling methods for effective prediction are reviewed, focusing on deep learning methods that exploit these multidimensional correlations. The survey also covers dataset generation techniques for SOP. Additionally, the paper discusses CR threats that impair spectrum utilization and reviews ML methods for detecting these threats. The future directions for ML-based SOP are also given.

Machine-learning versus traditional methods for prediction of all-cause mortality after transcatheter aortic valve implantation: a systematic review and meta-analysis

BackgroundAccurate mortality prediction following transcatheter aortic valve implantation (TAVI) is essential for mitigating risk, shared decision-making and periprocedural planning. Surgical risk models have demonstrated modest discriminative value for patients undergoing...

Evaluating the advancements in protein language models for encoding strategies in protein function prediction: a comprehensive review.

Protein function prediction is crucial in several key areas such as bioinformatics and drug design. With the rapid progress of deep learning technology, applying protein language models has become a research focus. These models utilize the increasing amount of large-scale protein sequence data to deeply mine its intrinsic semantic information, which can effectively improve the accuracy of protein function prediction. This review comprehensively combines the current status of applying the latest protein language models in protein function prediction. It provides an exhaustive performance comparison with traditional prediction methods. Through the in-depth analysis of experimental results, the significant advantages of protein language models in enhancing the accuracy and depth of protein function prediction tasks are fully demonstrated.

An Evolutionary Deep Learning Framework for Accurate Remaining Capacity Prediction in Lithium-Ion Batteries

Accurate remaining capacity prediction (RCP) of lithium-ion batteries (LIBs) is crucial for ensuring their safety, reliability, and performance, particularly amidst the growing energy crisis and environmental concerns. However, the complex aging processes of LIBs significantly hinder accurate RCP, as traditional prediction methods struggle to effectively capture nonlinear degradation patterns and long-term dependencies. To tackle these challenges, we introduce an innovative framework that combines evolutionary learning with deep learning for RCP. This framework integrates Temporal Convolutional Networks (TCNs), Bidirectional Gated Recurrent Units (BiGRUs), and an attention mechanism to extract comprehensive time-series features and improve prediction accuracy. Additionally, we introduce a hybrid optimization algorithm that combines the Sparrow Search Algorithm (SSA) with Bayesian Optimization (BO) to enhance the performance of the model. The experimental results validate the superiority of our framework, demonstrating its capability to achieve significantly improved prediction accuracy compared to existing methods. This study provides researchers in battery management systems, electric vehicles, and renewable energy storage with a reliable tool for optimizing lithium-ion battery performance, enhancing system reliability, and addressing the challenges of the new energy industry.

MGATAF: multi-channel graph attention network with adaptive fusion for cancer-drug response prediction

BackgroundDrug response prediction is critical in precision medicine to determine the most effective and safe treatments for individual patients. Traditional prediction methods relying on demographic and genetic data often fall short in accuracy and robustness. Recent graph-based models, while promising, frequently neglect the critical role of atomic interactions and fail to integrate drug fingerprints with SMILES for comprehensive molecular graph construction.ResultsWe introduce multimodal multi-channel graph attention network with adaptive fusion (MGATAF), a framework designed to enhance drug response predictions by capturing both local and global interactions among graph nodes. MGATAF improves drug representation by integrating SMILES and fingerprints, resulting in more precise predictions of drug effects. The methodology involves constructing multimodal molecular graphs, employing multi-channel graph attention networks to capture diverse interactions, and using adaptive fusion to integrate these interactions at multiple abstraction levels. Empirical results demonstrate MGATAF’s superior performance compared to traditional and other graph-based techniques. For example, on the GDSC dataset, MGATAF achieved a 5.12% improvement in the Pearson correlation coefficient (PCC), reaching 0.9312 with an RMSE of 0.0225. Similarly, in new cell-line tests, MGATAF outperformed baselines with a PCC of 0.8536 and an RMSE of 0.0321 on the GDSC dataset, and a PCC of 0.7364 with an RMSE of 0.0531 on the CCLE dataset.ConclusionsMGATAF significantly advances drug response prediction by effectively integrating multiple molecular data types and capturing complex interactions. This framework enhances prediction accuracy and offers a robust tool for personalized medicine, potentially leading to more effective and safer treatments for patients. Future research can expand on this work by exploring additional data modalities and refining the adaptive fusion mechanisms.

AGRO-Cloud Model and Smart Algorithm to Increase Agriculture Production to Improve Agriculture Quality

Smart Agriculture is a revolutionary approach to farming that aims to increase crop yields, optimize resource usage, and reduce costs, through the use of technology the design and implementation of an AGRO-Cloud Model for crop yield prediction using hybrid deep learning. The proposed system aims to improve crop yield prediction accuracy and facilitate decision-making for farmers. The system utilizes a hybrid deep learning approach that associates the (CNNs) convolutional neural networks and (LSTM) long short-term memory networks to process multi-sensor data, including soil, moisture data, weather data, and data of crop growth. LSTMs are used to capture temporal dependencies in the input data, while CNNs are utilized to extract spatial features. The system is implemented on a cloud platform, allowing farmers to access the system from anywhere using a web-based interface. The system provides real-time crop yield prediction and alerts farmers to potential risks such as pests, disease, and adverse weather conditions. The system also provides data visualization tools that enable farmers to monitor the growth of their crops and make informed decisions about fertilization, crop management practices, and irrigation. Experimental results show that the proposed hybrid deep learning approach outperforms traditional machine learning methods for crop yield prediction, achieving a prediction accuracy of over 90%. The proposed model can increase agriculture production to improve the quality and profitability of farming operations and contribute to sustainable agriculture practices.

Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety.

This review explores the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in food safety detection and risk prediction. This paper highlights the advantages of CNNs in image processing and feature recognition, as well as the powerful capabilities of RNNs (especially their variant LSTM) in time series data modeling. This paper also makes a comparative analysis in many aspects: Firstly, the advantages and disadvantages of traditional food safety detection and risk prediction methods are compared with deep learning technologies such as CNNs and RNNs. Secondly, the similarities and differences between CNNs and fully connected neural networks in processing image data are analyzed. Furthermore, the advantages and disadvantages of RNNs and traditional statistical modeling methods in processing time series data are discussed. Finally, the application directions of CNNs in food safety detection and RNNs in food safety risk prediction are compared. This paper also discusses combining these deep learning models with technologies such as the Internet of Things (IoT), blockchain, and federated learning to improve the accuracy and efficiency of food safety detection and risk warning. Finally, this paper mentions the limitations of RNNs and CNNs in the field of food safety, as well as the challenges in the interpretability of the model, and suggests the use of interpretable artificial intelligence (XAI) technology to improve the transparency of the model.

Skillful subseasonal ensemble predictions of heat wave onsets through better representation of land surface uncertainties

Uncertainties in land surface processes notably limit subseasonal heat wave (HW) onset predictions. A better representation of the uncertainties in land surface processes using ensemble prediction methods may be an important way to improve HW onset predictions. However, generating ensemble members that adequately represent land surface process uncertainties, particularly those related to land surface parameters, remains challenging. In this study, a conditional nonlinear optimal perturbation related to parameters (CNOP-P) approach was employed to generate ensemble members for representing the uncertainties in land surface processes resulting from parameters. Via six strong and long-lasting HW events over the middle and lower reaches of the Yangtze River (MLYR), HW onset ensemble forecast experiments were conducted with the Weather Research and Forecasting (WRF) model. The performance of the CNOP-P approach and the traditional random parameter perturbation ensemble prediction method was evaluated. The results demonstrate that the deterministic and probabilistic skills of HW onset predictions show greater excellence using the CNOP-P approach, leading to much better predictions of extreme air temperatures than those using the traditional method. This occurred because the ensemble members generated by the CNOP-P method better represented the uncertainties in important land physical processes determining HW onsets over the MLYR, notably vegetation process uncertainties, whereas the ensemble members generated by the random parameter perturbation method could not. This finding suggests that the CNOP-P method is suitable for producing ensemble members that more appropriately represent model uncertainties through more reasonable parameter error characterization.

A Vegetable-Price Forecasting Method Based on Mixture of Experts

The accurate forecasting of vegetable prices is crucial for policy formulation, market decisions, and agricultural market stability. Traditional time-series models often require manual parameter tuning and struggle to effectively handle the complex non-linear characteristics of vegetable price data, limiting their predictive accuracy. This study conducts a comprehensive analysis of the performance of traditional methods, deep learning approaches, and cutting-edge large language models in vegetable-price forecasting using multiple predictive performance metrics. Experimental results demonstrate that large language models generally outperform other methods, but do not have consistent performance for all kinds of vegetables across different time scales. As a result, we propose a novel vegetable-price forecasting method based on mixture of expert models (VPF-MoE), which combines the strengths of large language models and deep learning methods. Different from the traditional single model prediction method, VPF-MoE can dynamically adapt to the characteristics of different vegetable types, dynamically select the best prediction method, and significantly improve the accuracy and robustness of the prediction. In addition, we optimized the application of large language models in vegetable-price forecasting, offering a new technological pathway for vegetable-price prediction.

Enhancing Precipitation Nowcasting Through Dual-Attention RNN: Integrating Satellite Infrared and Radar VIL Data

Traditional deep learning-based prediction methods predominantly rely on weather radar data to quantify precipitation, often neglecting the integration of the thermal processes involved in the formation and dissipation of precipitation, which leads to reduced prediction accuracy. To address this limitation, we introduce the Dual-Attention Recurrent Neural Network (DA-RNN), a model that combines satellite infrared (IR) data with radar-derived vertically integrated liquid (VIL) content. This model leverages the fundamental physical relationship between temperature and precipitation in a predictive framework that captures thermal and water vapor dynamics, thereby enhancing prediction accuracy. The results of experimental evaluations on the SEVIR dataset demonstrate that the DA-RNN model surpasses traditional methods on the test set. Notably, the DA-TrajGRU model achieves reductions in mean squared error (MSE) and mean absolute error (MAE) of 30 (9.3%) and 89 (6.4%), respectively, compared with those of the conventional TrajGRU model. Furthermore, our DA-RNN exhibits robust false alarm rates (FAR) for various thresholds, with only slight decreases in the critical success index (CSI) and Heidke skill score (HSS) when increasing the threshold. Additionally, we present a visualization of precipitation nowcasting, illustrating that the integration of multiple data sources effectively avoids overestimation of VIL values, further increasing the precision of precipitation forecasts.

Enhanced bearing RUL prediction based on dynamic temporal attention and mixed MLP

Bearings are critical components in machinery, and accurately predicting their remaining useful life (RUL) is essential for effective predictive maintenance. Traditional RUL prediction methods often rely on manual feature extraction and expert knowledge, which face specific challenges such as handling non-stationary data and avoiding overfitting due to the inclusion of numerous irrelevant features. This paper presents an approach that leverages Continuous Wavelet Transform (CWT) for feature extraction, a Channel-Temporal Mixed MLP (CT-MLP) layer for capturing intricate dependencies, and a dynamic attention mechanism to adjust its focus based on the temporal importance of features within the time series. The dynamic attention mechanism integrates multi-head attention with innovative enhancements, making it particularly effective for datasets exhibiting non-stationary behaviour. An experimental study using the XJTU-SY rolling bearings dataset and the PRONOSTIA bearing dataset revealed that the proposed deep learning algorithm significantly outperforms other state-of-the-art algorithms in terms of RMSE and MAE, demonstrating its robustness and accuracy.

A study on the acoustic propagation characteristics of circular expanding ducts using the finite element method

A circular expanding duct features an increasing cross-sectional area that tends to excite higher-order modes at low frequencies, thereby affecting the performance of acoustic devices. Studying the acoustic propagation characteristics of circularly expanding ducts is thus crucial for optimal design and noise control. Traditional prediction methods do not adequately account for three-dimensional wave effects inside a duct. This study investigates the sound attenuation performance of various circular expanding ducts in the frequency range of 10–2500 Hz using the three-dimensional finite element method in combination with acoustic theory. The results show that the geometric expansion of the duct changes the wavefront shapes of transmitted sound waves, causing varying degrees of wavefront distortion. Behind the expanding region, these distorted wavefronts interfere with each other to varying extents, affecting the transmission loss (TL) of the duct. When the length of the expanding section is shorter, the average attenuation is generally higher. However, at specific frequencies (e.g., 2100 Hz), a longer expanding section may enhance sound attenuation by exciting more resonant modes. As the expansion ratio of the cross-sectional area increases, wavefront distortion and modal resonance become more pronounced, leading to increased average attenuation and the appearance of more peaks in the TL curve. In addition, the incidence of higher-order modes induces complex modal coupling and interference, significantly reducing the average sound attenuation of the duct. This study provides theoretical foundations and reference values for optimizing the design of circular expanding ducts to improve acoustic performance.

Systematic Review of Methods for Individual Prediction of Postoperative Pain

Background: Acute postoperative pain is a common problem in clinical practice and merits attention considering its potential long‐term adverse effects. This systematic review covers current knowledge on methods for individual prediction of postoperative pain.Methods: A systematic literature search was conducted using the PubMed, EMBASE, and CINAHL databases for original studies with adult patients published in English between 2016 and 2022. Inclusion required assessment of risk factors preoperatively and assessment of postoperative pain. No reviews, meta‐analyses, or study protocols were included, nor studies with outcomes other than pain or where risk factor analysis was not performed preoperatively. A two peer‐reviewed system was utilized using the screening and data collection tool Covidence, with a focus on new tools for preoperative pain prediction. The results were only analyzed qualitatively.Results: The search yielded 1950 abstracts to be screened. In total, 208 articles were subjected to full‐text review, and 107 articles were included in the data synthesis of this review. The evaluated scientific methods were grouped and analyzed separately. Psychometric questionnaires and methods for quantitative sensory testing are still being studied. New methods proposed include the evaluation of pain induced by tourniquet inflation, venous cannulation, or pin‐prick stimulation, the analgesia/nociception index, electroencephalographic recording, and other new equipment developed for this purpose.Conclusion: Various screening methods have been proposed to identify patients prone to postoperative pain. The focus has shifted from procedure‐specific to individualized strategies to improve early management of pain. However, many traditional predictive methods still have a questionable role in clinical practice.Trial Registration: ClinicalTrials.gov identifier: CRD42022298479