Predictive Capability Of Model Research Articles

Evaluate Multi-Objective Optimization Model for Product Supply Chain Inventory Control Based on Grey Wolf Algorithm

Enterprise supply chain inventory control plays a critical role in modern enterprise management. Yet achieving multi-objective optimization remains challenging. This paper proposes a deep learning integrated model based on grey wolf optimization (GWO-DLIM) aimed at optimizing inventory holding costs, stockout costs, and order costs. The model integrates multi-layer perceptron (MLP), DeBERTa, and GWO, leveraging the powerful predictive and feature extraction capabilities of deep learning models, combined with the global optimization ability of the grey wolf algorithm, to achieve multi-objective optimization. These results demonstrate that the GWO-DLIM model significantly outperforms other comparative models across all metrics, exhibiting lower prediction errors and root mean square errors, as well as higher service levels. This study provides an effective multi-objective optimization solution for supply chain inventory management, holding significant theoretical and practical implications.

DeepAge: Harnessing Deep Neural Network for Epigenetic Age Estimation From DNA Methylation Data of Human Blood Samples

Accurate prediction of biological age from DNA methylation data is a critical endeavor in understanding the molecular mechanisms of aging and developing age-related disease interventions. Traditional epigenetic clocks rely on linear regression or basic machine learning models, which often fail to capture the complex, non-linear interactions within methylation data. This study introduces DeepAge, a novel deep learning framework utilizing Temporal Convolutional Networks (TCNs) to enhance the prediction of biological age from DNA methylation profiles using selected CpGs by a Dual-Correlation based apparoach. DeepAge leverages a sequence-based approach with dilated convolutions to effectively capture long-range dependencies between CpG sites, addressing the limitations of prior models by incorporating advanced network architectures including residual connections and dropout regularization. The dual correlation feature selection enhances our model's predictive capabilities by identifying the most age-relevant CpG sites. Our model outperforms existing epigenetic clocks across multiple datasets, offering significant improvements in accuracy and providing deeper insights into the epigenetic determinants of aging. The proposed method not only sets a new standard in age estimation but also highlights the potential of deep learning in biologically relevant feature extraction and interpretation, contributing to the broader field of computational biology and precision medicine.

Research on imaging biomarkers for chronic subdural hematoma recurrence.

This study utilizes radiomics to explore imaging biomarkers for predicting the recurrence of chronic subdural hematoma (CSDH), aiming to improve the prediction of CSDH recurrence risk. Analyzing CT scans from 64 patients with CSDH, we extracted 107 radiomic features and employed recursive feature elimination (RFE) and the XGBoost algorithm for feature selection and model construction. The feature selection process identified six key imaging biomarkers closely associated with CSDH recurrence: flatness, surface area to volume ratio, energy, run entropy, small area emphasis, and maximum axial diameter. The selection of these imaging biomarkers was based on their significance in predicting CSDH recurrence, revealing deep connections between postoperative variables and recurrence. After feature selection, there was a significant improvement in model performance. The XGBoost model demonstrated the best classification performance, with the average accuracy improving from 46.82% (before feature selection) to 80.74% and the AUC value increasing from 0.5864 to 0.7998. These results prove that precise feature selection significantly enhances the model's predictive capability. This study not only reveals imaging biomarkers for CSDH recurrence but also provides valuable insights for future personalized treatment strategies.

Evaluation of the Predictive Capability of CMA Climate Prediction System Model for Summer Surface Heat Source on the Tibetan Plateau

Surface heat source (SHS) is a crucial factor affecting local weather systems. Particularly SHS on the Tibetan Plateau (TP) significantly influences East Asian atmospheric circulation and global climate. Accurate prediction of summer SHS on the TP is of urgent demand for economic development and local climate change. To evaluate the performance of SHS on the TP, the observed SHS data from the eleven sites on the TP verified against CRA40-land (CRA) is evidenced significantly better than ERA5-land (ERA5), another widely used reanalysis. The predictive capability of the CMA Climate Prediction System Model (CMA-CPS) for SHS on the TP was assessed using multiple scoring methods, including the anomaly correlation coefficient and temporal correlation coefficient, among others. Furthermore, relative variability and trend analysis were conducted. Finally, based on these assessments, the causes of the biases were preliminarily discussed. The CMA-CPS demonstrates a reasonable ability to predict the spatial distribution patterns of SHS, sensible heat (SH), and latent heat (LH) on the TP in summer. Specifically, the prediction results of SHS and LH exhibit an “east-high and west-low” distribution, while the distribution of the predicted SH is opposite. Nevertheless, the predicted values are generally lower than CRA, particularly in interannual variations and trends. Among the predictions, LH exhibits the highest temporal correlation coefficients, consistently above 0.6, followed by SHS, while SH predictions are less accurate. The spatial distribution and skill scores indicate that LH on the TP contributes more significantly to SHS than SH in summer. Furthermore, discrepancies in the predictions of surface temperature gradients, ground wind speed, and humidity on the TP may partly explain the biases in SHS and their components.

WS-BiTM: Integrating White Shark Optimization with Bi-LSTM for Enhanced Autism Spectrum Disorder Diagnosis

Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition marked by challenges in social communication, sensory processing, and behavioral regulation. The delayed diagnosis of ASD significantly impedes timely interventions, which can exacerbate symptom severity. With approximately 62 million individuals affected worldwide, the demand for efficient diagnostic tools is critical. This study introduces a novel framework that combines a White Shark Optimization (WSO)-based feature selection method with a Bidirectional Long Short-Term Memory (Bi-LSTM) classifier for enhanced autism classification. Utilizing the WSO technique, we identify key features from autism screening datasets, which markedly improves the model's predictive capabilities. The optimized feature set is then processed by the Bi-LSTM classifier, enhancing its efficiency in handling sequential data. We comprehensively address methodological challenges, including overfitting, generalization, interpretability, and computational efficiency. Furthermore, we conduct a comparative analysis against baseline algorithms such as Neural Networks, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, while also employing Particle Swarm Optimization (PSO) for feature selection validation. We evaluate performance metrics, including accuracy, F1-score, specificity, precision, and sensitivity across three ASD datasets: Toddlers, Adults, and Children. Our results demonstrate that the WS-BiTM model significantly outperforms baseline methods, achieving accuracies of 97.6%, 96.2%, and 96.4% on the respective datasets. Additionally, we implemented leave-one-dataset cross-validation and confirmed the statistical significance of our findings through a paired t-test, supplemented by an ablation study to detail the contributions of individual model components. These findings highlight the potential of the WS-BiTM model as a robust tool for ASD classification.

Thermodynamic calculation-assisted design of 500 MPa high performance steel by machine learning

In order to rationally design Q500 low-alloy wind power steel with stable microstructure and properties within a large cooling rate range, this study proposed a design system that utilizes thermodynamic database, machine learning (ML), and the multi-objective genetic optimization algorithm (MOGA). Thermodynamic calculation has been used to obtain a large amount of data on phase fractions and mechanical properties for various chemical composition at different cooling rates. By comparing the predictive capabilities of different ML models, the deep neural network (DNN) and MOGA were combined to design Q500 wind power steel. Subsequently, three groups of designed chemical compositions were appropriately selected for verification. The results indicate that the yield strength of the experimental steel was over 500 MPa, and the maximum error in microstructure and mechanical property prediction was 5.0%. The prediction results demonstrated the rationality of the composition design framework. Meanwhile, the method of combining the material database and ML proposed in this study can also be applied to the design of other low-alloy steels.

Single-cell and machine learning approaches uncover intrinsic immune-evasion genes in the prognosis of hepatocellular carcinoma

Background and aimsHepatocellular carcinoma (HCC) is a tumor of high heterogeneity and complexity, which poses significant challenges to effective treatment and patient prognosis because of its immune evasion characteristics. To address these issues, single-cell technology and machine learning methods have emerged as a promising approach to identify genes associated with immune escape in HCC. This study aimed to develop a prognostic risk score model for HCC by identifying intrinsic immune-evasion genes (IIEGs) through single-cell technology and machine learning, providing insights into immune infiltration, enhancing predictive accuracy, and facilitating the development of more effective treatment strategies. Materials and methodsThe study utilized data from The Cancer Genome Atlas database to analyze gene expression profiles and clinical data related to intrinsic immune evasion in patients with HCC. Various tools, including the Human Protein Atlas, cBioPortal, single-cell analysis, machine learning, and Kaplan-Meier plot, were used to analyze IIEGs. Functional enrichment analysis was conducted to explore potential mechanisms. In addition, the abundance of infiltrating cells in the tumor microenvironment was investigated using single-sample gene set enrichment analysis, CIBERSORT, xCELL, and tumor immunophenotype algorithms. The expression of glycosylphosphatidylinositol anchor attachment 1 (GPAA1) was examined in the clinical sample of HCC by quantitative real-time polymerase chain reaction, Western blotting, and immunohistochemical staining. ResultsUnivariate Cox analysis identified 63 IIEGs associated with the prognosis of HCC. Using random forest, least absolute shrinkage and selection operator regression analysis, and support vector machine, a risk score model consisting of six IIEGs (carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase (CAD), phosphatidylinositol glycan anchor biosynthesis class U (PIGU), endoplasmic reticulum membrane protein complex subunit 3 (EMC3), centrosomal protein 55 (CEP55), autophagy-related 10 (ATG10), and GPAA1) developed, which was validated using 10 pairs of HCC and adjacent non-cancerous samples. Based on the calculated median risk score, HCC samples were categorized into high- and low-risk groups. The Kaplan-Meier curve analysis showed that the high-risk group had a worse prognosis compared with the low-risk group. Time-dependent receiver operating characteristic analysis demonstrated the accurate predictive capability of the risk score model for HCC prognosis. Furthermore, immune infiltration analysis showed a positive correlation between the risk score model and 40 immune checkpoint genes as well as Th2 cells. ConclusionsA prognostic risk score model was formulated by six IIEG signatures and showed promise in predicting the prognosis of patients diagnosed with HCC. The utilization of the IIEG risk score as a novel prognostic index, together with its significance as a valuable biomarker for immunotherapy in HCC, provides benefit for patients with HCC in determining therapeutic strategies for clinical application.

Prediction of Energy-Related Carbon Emissions in East China Using a Spatial Reverse-Accumulation Discrete Grey Model

In order to better analyze and predict energy-related carbon emissions in East China to address climate change, this paper enhances the predictive capabilities of grey models in spatial joint prediction by creating the reverse-accumulation spatial discrete grey model RSDGM (1,1,m) and accumulation spatial discrete grey breakpoint model RSDGBM (1,1,m,t), which took the impact of system shocks into consideration. The efficiency of the models is confirmed by calculating the energy-related carbon emissions in East China from 2010 to 2022. Future emissions are predicted, and the spatial spillover effect of emissions in East China is discussed. The conclusions are as follows: (1) The RSDGM (1,1,m) theoretically avoids errors in background values and parameter calculations, reducing computational complexity. Empirically, the model exhibits high performance and reflects the priority of new information in spatial joint analysis. (2) The RSDGBM (1,1,m,t) captures the impact of shocks on system development, improving the reliability of carbon emissions prediction. (3) Jiangsu and Shandong are positively affected by spatial factors in terms of carbon emissions, while Shanghai and Zhejiang are negatively affected. (4) It is estimated that carbon emissions in East China will increase by approximately 23.8% in 2030 compared to the level in 2022, with the levels in Zhejiang and Fujian expected to increase by 45.2% and 39.7%, respectively; additionally, the level in Shanghai is projected to decrease. Overall, East China still faces significant pressure to reduce emissions.

Assessing the Predictive Capabilities of Autoregressive Integrated Moving Average and Linear Regression Models for Acute Changes in Clinical and Selected Laboratory Parameters in Children After Cardiac Surgery in the ICU

(1) Background: The main objective of this research was to assess the clinical factors related to the condition of pediatric patients with congenital heart defects after they underwent intensive care unit surgery. The information was gathered from the Congenital Heart Disease Surgery Unit at the National Heart Foundation Hospital and Research Institute in Dhaka, Bangladesh. We gathered and examined data from 288 ICU patients. Patients under the age of twelve who required more than a 24-h ICU stay were selected. (2) Methods: The dependent and independent variables were chosen in advance based on expert opinion. The relationships between these pre-specified ICU parameters were determined using the Pearson correlation model and assessed through linear regression and ARIMA modeling to predict subsequent acute changes in the patients’ ICU statuses. (3) Results: A statistically significant relationship (p value < 0.001) was found between CVP and BP (95% CI = 0.2113; 0.353 r = 0.2841249) and between PEEP and FiO2 (95% CI = 0.6992; 0.770 r = 0.7367744). Although the relationships between pH and PO2 were minor (95% CI = 0.161; 0.308 r = 0.2362575), they were statistically significant. The parameters considered statistically significant (p < 0.001) were chosen for forecasting. In this work, the linear regression model and the ARIMA model used the parameters BP, FiO2, and PO2 for prediction. We forecasted the patients’ statuses for the next hour. It was found that the ARIMA model had a lower error rate than the linear regression model. (4) Conclusions: This study helps identify the important parameters for predicting and monitoring patients’ statuses in the ICU, with the ultimate goal of providing physicians with an early warning system to anticipate deterioration in clinical and biochemical parameters. The ability to accurately forecast future patients’ conditions can enable proactive, targeted interventions, potentially improving outcomes and reducing the risk of adverse events.

Evaluating Machine Learning Models for Stroke Prognosis and Prediction in Atrial Fibrillation Patients: A Comprehensive Meta-Analysis.

Atrial fibrillation (AF) complicates the management of acute ischemic stroke (AIS), necessitating precise predictive models to enhance clinical outcomes. This meta-analysis evaluates the efficacy of machine learning (ML) models in three key areas: stroke prognosis in AF patients, stroke prediction in AF patients, and AF prediction in stroke patients. The study aims to assess the accuracy and variability of ML models in forecasting AIS outcomes and detecting AF in stroke patients, while exploring the clinical benefits and limitations of integrating these models into practice. We conducted a systematic search of PubMed, Embase, and Cochrane databases up to June 2024, selecting studies that evaluated ML accuracy in stroke prognosis and prediction in AF patients and AF prediction in stroke patients. Data extraction and quality assessment were performed independently by two reviewers, with random-effects modeling applied to estimate pooled accuracy metrics. The meta-analysis included twenty-four studies comprising 7,391,645 patients, categorized into groups for stroke prognosis in AF patients (eight studies), stroke prediction in AF patients (thirteen studies), and AF prediction in stroke patients (three studies). The pooled AUROC was 0.79 for stroke prognosis and 0.68 for stroke prediction in AF, with higher accuracy noted in short-term predictions. The mean AUROC across studies was 0.75, with models such as Extreme Gradient Boosting (XGB) and Random Forest (RF) showing superior performance. For stroke prognosis in AF, the mean AUROC was 0.78, whereas stroke prediction yielded a mean AUROC of 0.73. AF prediction post-stroke had an average AUROC of 0.75. These findings indicate moderate predictive capability of ML models, underscoring the need for further refinement and standardization. The absence of comprehensive sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) metrics limited the ability to conduct full meta-analytic modeling. While ML models demonstrate potential for enhancing stroke prognosis and AF prediction, they have yet to meet the clinical standards required for widespread adoption. Future efforts should focus on refining these models and validating them across diverse populations to improve their clinical utility.

Hydrodynamic model-driven evolutionary algorithm-based operation optimization of an experimental drainage pumping station

ABSTRACT The effective operation of pumping stations plays a crucial role in urban flood management. However, challenges persist in optimizing pumping station operations, including inaccuracies in characterizing flood propagation and the high computational costs associated with optimization. This study introduces a novel optimization approach for pumping station operation that integrates a hydrodynamic model with evolutionary algorithms, leveraging data-driven technology. The method iteratively computes operation rules using the adaptive particle swarm optimization (APSO) algorithm to identify optimal solutions. The hydrodynamic model accurately simulates flood propagation and provides hydraulic parameters for the objective function and constraints of the APSO algorithm. With the predictive capability of the Kriging model, the optimization enhances efficiency by reducing the frequency of calls to the hydrodynamic model. A study case of a flood management digital twin experimental platform was then taken for the application. Compared to initial operation rules, the objective function value of the proposed method is reduced by 28.7, 32.5, and 25%, respectively, under varying magnitudes of unsteady flood inflows, demonstrating high performances in both flood mitigation and operation cost control. Moreover, the method only requires 70 calls to the hydrodynamic model to formulate the decision operation rule.

From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases

The outbreak of emerging infectious diseases poses significant challenges to global public health. Accurate early forecasting is crucial for effective resource allocation and emergency response planning. This study aims to develop a comprehensive predictive model for emerging infectious diseases, integrating the blending framework, transfer learning, incremental learning, and the biological feature Rt to increase prediction accuracy and practicality. By transferring features from a COVID-19 dataset to a monkeypox dataset and introducing dynamically updated incremental learning techniques, the model's predictive capability in data-scarce scenarios was significantly improved. The research findings demonstrate that the blending framework performs exceptionally well in short-term (7-day) predictions. Furthermore, the combination of transfer learning and incremental learning techniques significantly enhanced the adaptability and precision, with a 91.41% improvement in the RMSE and an 89.13% improvement in the MAE. In particular, the inclusion of the Rt feature enabled the model to more accurately reflect the dynamics of disease spread, further improving the RMSE by 1.91% and the MAE by 2.17%. This study underscores the significant application potential of multimodel fusion and real-time data updates in infectious disease prediction, offering new theoretical perspectives and technical support. This research not only enriches the theoretical foundation of infectious disease prediction models but also provides reliable technical support for public health emergency responses. Future research should continue to explore integrating data from multiple sources and enhancing model generalization capabilities to further enhance the practicality and reliability of predictive tools.

Safedrive dreamer: Navigating safety–critical scenarios in autonomous driving with world models

Achieving stable and reliable autonomous driving in complex traffic environments while ensuring safety under unpredictable conditions is a critical challenge in autonomous driving technology. To address this issue, this study proposes the Safedrive Dreamer navigation framework, which aims to reduce the reliance on trial-and-error learning in real-world scenarios, thereby mitigating the risks associated with dynamic driving conditions and enhancing vehicle foresight. This framework integrates the predictive capabilities of world models with the constrained Markov decision process (CMDP) and safety reinforcement learning to accurately anticipate future environmental changes. This ensures the reliability of autonomous driving routes, thereby improving both safety and efficiency. Furthermore, to reduce trial-and-error costs in real-world applications, this study employs PAC-Bayesian methods to derive generalization error bounds between simulations and reality, enabling a more effective transfer of knowledge and experience from simulations to real-world scenarios. Validation experiments in simulated and real environments showed that Safedrive Dreamer significantly outperformed existing autonomous driving solutions by 3.8% in key safety metrics, excelling in collision avoidance and risk reduction. This study provides new insights into the integration of world models into decision-making processes to enhance decision-making capabilities in safety–critical applications, thereby contributing significantly to the improvement of autonomous driving safety and reliability.

Multimodal Representation Learning via Graph Isomorphism Network for Toxicity Multitask Learning.

Toxicity is paramount for comprehending compound properties, particularly in the early stages of drug design. Due to the diversity and complexity of toxic effects, it became a challenge to compute compound toxicity tasks. To address this issue, we propose a multimodal representation learning model, termed multimodal graph isomorphism network (MMGIN), to address this challenge for compound toxicity multitask learning. Based on fingerprints and molecular graphs of compounds, our MMGIN model incorporates a multimodal representation learning model to acquire a comprehensive compound representation. This model adopts a two-channel structure to independently learn fingerprint representation and molecular graph representation. Subsequently, two feedforward neural networks utilize the learned multimodal compound representation to perform multitask learning, encompassing compound toxicity classification and multiple compound category classification simultaneously. To test the effectiveness of our model, we constructed a novel data set, termed the compound toxicity multitask learning (CTMTL) data set, derived from the TOXRIC data set. We compare our MMGIN model with other representative machine learning and deep learning models on the CTMTL and Tox21 data sets. The experimental results demonstrate significant advancements achieved by our MMGIN model. Furthermore, the ablation study underscores the effectiveness of the introduced fingerprints, molecular graphs, the multimodal representation learning model, and the multitask learning model, showcasing the model's superior predictive capability and robustness.

Explainable machine learning models for predicting the ultimate bending capacity of slotted perforated cold-formed steel beams under distortional buckling

This study develops explainable machine learning (ML) models to predict the ultimate bending capacity of cold-formed steel (CFS) beams with staggered slotted perforations, focusing on distortional buckling behavior. Utilizing a dataset from 432 non-linear finite element analysis simulations of CFS Lipped channels, ten ML algorithms, including four basic and six ensemble models, were evaluated. Ensemble models, specifically CatBoost and XGBoost, demonstrated superior accuracy, with test-set performances reaching a coefficient of determination (R2) of 99.9%, outperforming traditional analytical methods such as the Direct Strength Method (DSM). SHapley Additive Explanations (SHAP) were applied to highlight how features like plate thickness and root radius critically influence predictions. The findings underscore the enhanced predictive capabilities of ML models for structural performance, suggesting a significant potential to refine traditional design methodologies and optimize CFS beam designs.

Subgrid-scale model considering the inverse energy cascade using an artificial neural network

For the closure of the subgrid-scale (SGS) stress tensor, an artificial neural network (ANN)-based SGS model that takes account of the inverse energy cascade in isotropic turbulence is developed. The data required for training this ANN-based SGS model are provided by direct numerical simulation of isotropic turbulence with an inverse energy cascade. Two input features, the root mean square of the rate-of-strain tensor and the product of the eigenvalues of the rate-of-strain tensor, are employed to characterize the inverse energy cascade. An a priori test reveals that the ANN-based model adequately predicts the SGS stress tensor in the backward energy transfer process, and the predictive capability of the gradient model is found to be slightly poorer than that of the ANN-based model, while that of the Smagorinsky model is not satisfactory. In comparison with the gradient model, the ANN-based model even predicts a few backward energy transfer events in the stage of excessive energy dissipation. In addition, the off-diagonal component of the SGS stress tensor, rather than the diagonal component, may be intimately associated with the inverse energy cascade. The ANN-based SGS model presented here is expected to provide inspiration for future investigations of the construction of SGS models that take account of the inverse energy cascade.

Utilising Artificial Intelligence to Predict Membrane Behaviour in Water Purification and Desalination

Water scarcity is a critical global issue, necessitating efficient water purification and desalination methods. Membrane separation methods are environmentally friendly and consume less energy, making them more economical compared to other desalination and purification methods. This survey explores the application of artificial intelligence (AI) to predict membrane behaviour in water purification and desalination processes. Various AI platforms, including machine learning (ML) and artificial neural networks (ANNs), were utilised to model water flux, predict fouling behaviour, simulate micropollutant dynamics and optimise operational parameters. Specifically, models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) and support vector machines (SVMs) have demonstrated superior predictive capabilities in these applications. This review studies recent advancements, emphasising the superior predictive capabilities of AI models compared to traditional methods. Key findings include the development of AI models for various membrane separation techniques and the integration of AI concepts such as ML and ANNs to simulate membrane fouling, water flux and micropollutant behaviour, aiming to enhance wastewater treatment and optimise treatment and desalination processes. In conclusion, this review summarised the applications of AI in predicting the behaviour of membranes as well as their strengths, weaknesses and future directions of AI in membranes for water purification and desalination processes.

A comprehensive investigation of multimodal deep learning fusion strategies for breast cancer classification

In breast cancer research, diverse data types and formats, such as radiological images, clinical records, histological data, and expression analysis, are employed. Given the intricate nature of natural phenomena, relying on the features of a single modality is seldom sufficient for comprehensive analysis. Therefore, it is possible to guarantee medical relevance and achieve improved clinical outcomes by combining several modalities. The presen study carefully maps and reviews 47 primary articles from six well-known digital libraries that were published between 2018 and 2023 for breast cancer classification based on multimodal deep learning fusion (MDLF) techniques. This systematic literature review encompasses various aspects, including the medical modalities combined, the datasets utilized in these studies, the techniques, models, and architectures used in MDLF and it also discusses the advantages and limitations of each approach. The analysis of selected papers has revealed a compelling trend: the emergence of new modalities and combinations that were previously unexplored in the context of breast cancer classification. This exploration has not only expanded the scope of predictive models but also introduced fresh perspectives for addressing diverse targets, ranging from screening to diagnosis and prognosis. The practical advantages of MDLF are evident in its ability to enhance the predictive capabilities of machine learning models, resulting in improved accuracy across diverse applications. The prevalence of deep learning models underscores their success in autonomously discerning complex patterns, offering a substantial departure from traditional machine learning approaches. Furthermore, the paper explores the challenges and future directions in this field, including the need for larger datasets, the use of ensemble learning methods, and the interpretation of multimodal models.

Embracing market dynamics in the post-COVID era: A data-driven analysis of investor sentiment and behavioral characteristics in stock index futures returns

This paper aims to enhance the understanding and prediction of stock market behavior during unexpected events like the COVID-19 pandemic, with a specific focus on the role of market attention, social media sentiment indicators, and the development and evolution of unexpected events. We highlight that the common trading and technical indicators used in forecasting the stock index futures prices often overlook investor sentiment and pandemic-related data, which can be instrumental in predicting stock market behavior during significant emergencies. In response, we propose a multi-faceted approach that incorporates these overlooked factors. First, we enhance the predictive index system by integrating investor sentiment, derived from stock message board commentary, and investor behavior influenced by the development and evolution of the pandemic. This innovative approach refines our model's predictive capabilities and is validated through comparative analysis. Second, we introduce a hybrid framework for predicting stock index futures closing prices. By decomposing the closing price series into long-term trends, cyclical variations, and random fluctuations, we create a more nuanced forecast. Each component is predicted separately using appropriate time-series algorithms, improving the overall predictive accuracy and offering generalizability and scalability. Third, we devise a dynamic trading strategy that recognizes pandemic-related data, evolving over time, as a pivotal factor. This strategy is adaptable to evolving market conditions, and our experimental evidence demonstrates its effectiveness in yielding higher returns and reducing associated risks. Our findings underline the importance of incorporating investor sentiment and pandemic-related data into stock market predictions, thus offering a more comprehensive and accurate approach to market forecasting and risk management.

Prediction of total organic acids concentration based on FOS/TAC titration in continuous anaerobic digester fed with food waste using a deep neural network model

In this work, the complexities of anaerobic digestion fed with highly degradable feedstock are investigated, focusing on accumulation of organic acids (OA) as a critical monitoring parameter, and the significance of prediction models for total OA concentration. The anaerobic digestion of food waste (FW) was conducted under the organic loading rate (OLR) range of 2.55–8.80 g COD/L/d and hydraulic retention time (HRT) of 30–15 days. The feasibility of flüchtige organische säuren (FOS), totales anorganisches carbonat (TAC), and the FOS/TAC was investigated by predicting the total OA using a deep neural network (DNN) model. Two digesters, Digester 1 and 2, were fed with FW from four distinct sites. When the OA concentration exceeded 2 g/L as CH3COOH, the feeding was paused to recover the methanogens activity. The total OA concentration was successfully predicted with FOS, TAC, and FOS/TAC using the DNN regression model even though applying on the datasets from two distinct digesters, indicating a R-value of 0.9557, R2 of 0.9133, and mean square error of 0.0329. The predictive capability of DNN regression model shows the feasibility of total OA prediction based on the titrimetric method for monitoring and optimizing continuous anaerobic digestion of highly degradable feedstock.