Search Results (2,028)

Accurate predictions of Initial Public Offerings (IPOs) aftermarket performance are essential for making informed investment decisions in the financial sector. This paper attempts to predict IPO short-term underperformance during a month post-listing. The current research landscape lacks modern models that address the needs of small and imbalanced datasets relevant to emerging markets, as well as the risk preferences of investors. To fill this gap, we present a practical framework utilizing tree-based ensemble learning, including Bagging Classifier (BC), Random Forest (RF), AdaBoost (Ada), Gradient Boosting (GB), XGBoost (XG), Stacking Classifier (SC), and Extra Trees (ET), with Decision Tree (DT) as a base estimator. The framework leverages data-driven methodologies to optimize decision-making in complex financial systems, integrating ANOVA F-value for feature selection, Randomized Search for hyperparameter optimization, and SMOTE for class balance. The framework’s effectiveness is assessed using a hand-collected dataset that includes features from both pre-IPO prospectus and firm-specific financial data. We thoroughly evaluate the results using single-split evaluation and 10-fold cross-validation analysis. For the single-split validation, ET achieves the highest accuracy of 86%, while for the 10-fold validation, BC achieves the highest accuracy of 70%. Additionally, we compare the results of the proposed framework with deep-learning models such as MLP, TabNet, and ANN to assess their effectiveness in handling IPO underperformance predictions. These results demonstrate the framework’s capability to enable robust data-driven decision-making processes in complex and dynamic financial environments, even with limited and imbalanced datasets. The framework also proposes a dynamic methodology named Investor Preference Prediction Framework (IPPF) to match tree-based ensemble models to investors’ risk preferences when predicting IPO underperformance. It concludes that different models may be suitable for various risk profiles. For the dataset at hand, ET and Ada are more appropriate for risk-averse investors, while BC is suitable for risk-tolerant investors. The results underscore the framework’s importance in improving IPO underperformance predictions, which can better inform investment strategies and decision-making processes. Full article

Background/Objectives: This paper is significant in highlighting the importance of early and precise diagnosis of Parkinson’s Disease (PD) that affects both motor and non-motor functions to achieve better disease control and patient outcomes. This study seeks to assess the effectiveness of machine learning algorithms optimized to classify PD based on vocal characteristics to serve as a non-invasive and easily accessible diagnostic tool. Methods: This study used a publicly available dataset of vocal samples from 188 people with PD and 64 controls. Acoustic features like baseline characteristics, time-frequency components, Mel Frequency Cepstral Coefficients (MFCCs), and wavelet transform-based metrics were extracted and analyzed. The Chi-Square test was used for feature selection to determine the most important attributes that enhanced the accuracy of the classification. Six different machine learning classifiers, namely SVM, k-NN, DT, NN, Ensemble and Stacking models, were developed and optimized via Bayesian Optimization (BO), Grid Search (GS) and Random Search (RS). Accuracy, precision, recall, F1-score and AUC-ROC were used for evaluation. Results: It has been found that Stacking models, especially those fine-tuned via Grid Search, yielded the best performance with 92.07% accuracy and an F1-score of 0.95. In addition to that, the choice of relevant vocal features, in conjunction with the Chi-Square feature selection method, greatly enhanced the computational efficiency and classification performance. Conclusions: This study highlights the potential of combining advanced feature selection techniques with hyperparameter optimization strategies to enhance machine learning-based PD diagnosis using vocal characteristics. Ensemble models proved particularly effective in handling complex datasets, demonstrating robust diagnostic performance. Future research may focus on deep learning approaches and temporal feature integration to further improve diagnostic accuracy and scalability for clinical applications. Full article

An efficient energy management strategy (EMS) is crucial for the energy-saving and emission-reduction effects of electric vehicles. Research on deep reinforcement learning (DRL)-driven energy management systems (EMSs) has made significant strides in the global automotive industry. However, most scholars study only the impact of a single DRL algorithm on EMS performance, ignoring the potential improvement in optimization objectives that different DRL algorithms can offer under the same benchmark. This paper focuses on the control strategy of hybrid energy storage systems (HESSs) comprising lithium-ion batteries and ultracapacitors. Firstly, an equivalent model of the HESS is established based on dynamic experiments. Secondly, a regulated decision-making framework is constructed by uniformly setting the action space, state space, reward function, and hyperparameters of the agent for different DRL algorithms. To compare the control performances of the HESS under various EMSs, the regulation properties are analyzed with the standard driving cycle condition. Finally, the simulation results indicate that the EMS powered by a deep Q network (DQN) markedly diminishes the detrimental impact of peak current on the battery. Furthermore, the EMS based on a deep deterministic policy gradient (DDPG) reduces energy loss by 28.3%, and the economic efficiency of the EMS based on dynamic programming (DP) is improved to 0.7%. Full article

The smart TV ecosystem is rapidly expanding, allowing developers to publish their applications on TV markets to provide a wide array of services to TV users. However, this open nature can lead to significant cybersecurity concerns by bringing unauthorized access to home networks or leaking sensitive information. In this study, we focus on the security of Android TVs by developing a lightweight malware detection model specifically for these devices. We collected various Android TV applications from different markets and injected malicious payloads into benign applications to create Android TV malware, which is challenging to find on the market. We proposed a machine learning approach to detecting malware and evaluated our model. We compared the performance of nine classifiers and optimized the hyperparameters. Our findings indicated that the model performed well in rare malware cases on Android TVs. The most successful model classified malware with an F1-Score of 0.9789 in 0.1346 milliseconds per application. Full article

Traffic emissions serve as one of the most significant sources of atmospheric PM2.5 pollution in developing countries, driven by the prevalence of aging vehicle fleets and the inadequacy of regulatory frameworks to mitigate emissions effectively. This study presents a Hybrid Population-Based Training (PBT)–ResNet framework for classifying traffic-related PM2.5 levels into hazardous exposure (HE) and acceptable exposure (AE), based on the World Health Organization (WHO) guidelines. The framework integrates ResNet architectures (ResNet18, ResNet34, and ResNet50) with PBT-driven hyperparameter optimization, using data from Open-Seneca sensors along the Nairobi Expressway, combined with meteorological and traffic data. First, analysis showed that the PBT-tuned ResNet34 was the most effective model, achieving a precision (0.988), recall (0.971), F1-Score (0.979), Matthews Correlation Coefficient (MCC) of 0.904, Geometric Mean (G-Mean) of 0.962, and Balanced Accuracy (BA) of 0.962, outperforming alternative models, including ResNet18, ResNet34, and baseline approaches such as Feedforward Neural Networks (FNN), Bidirectional Long Short-Term Memory (BiLSTM), Bidirectional Gated Recurrent Unit (BiGRU), and Gene Expression Programming (GEP). Subsequent feature importance analysis using a permutation-based strategy, along with SHAP analysis, revealed that humidity and hourly traffic volume were the most influential features. The findings indicated that medium to high humidity values were associated with an increased likelihood of HE, while medium to high traffic volumes similarly contributed to the occurrence of HE. Full article

Fingerprint recognition is one of the most effective and widely adopted methods for person identification. However, the computational time required for the querying of large databases is excessive. To address this, preprocessing steps such as classification are necessary to speed up the response time to a query. Fingerprints are typically categorized into five classes, though this classification is unbalanced. While advanced classification algorithms, including support vector machines (SVMs), multilayer perceptrons (MLPs), and convolutional neural networks (CNNs), have demonstrated near-perfect accuracy (approaching 100%), their high training times limit their widespread applicability across institutions. In this study, we introduce, for the first time, the use of a multilayer extreme learning machine (M-ELM) for fingerprint classification, aiming to improve training efficiency. A comparative analysis is conducted with CNNs and unbalanced extreme learning machines (W-ELMs), as these represent the most influential methodologies in the literature. The tests utilize a database generated by SFINGE software, which simulates realistic fingerprint distributions, with datasets comprising hundreds of thousands of samples. To optimize and simplify the M-ELM, widely recognized descriptors in the field—Capelli02, Liu10, and Hong08—are used as input features. This effectively reduces dimensionality while preserving the representativeness of the fingerprint information. A brute-force heuristic optimization approach is applied to determine the hyperparameters that maximize classification accuracy across different M-ELM configurations while avoiding excessive training times. A comparison is made with the aforementioned approaches in terms of accuracy, penetration rate, and computational cost. The results demonstrate that a two-layer hidden ELM achieves superior classification of both majority and minority fingerprint classes with remarkable computational efficiency. Full article

In complex marine environments, accurate prediction of maneuvering motion is crucial for the precise control of intelligent ships. This study aims to enhance the predictive capabilities of maneuvering motion for intelligent ships in such environments. We propose a novel maneuvering motion prediction method based on Long Short-Term Memory (LSTM) and Multi-Head Attention Mechanisms (MHAM). To construct a foundational dataset, we integrate Computational Fluid Dynamics (CFD) numerical simulation technology to develop a mathematical model of actual ship maneuvering motions influenced by wind, waves, and currents. We simulate typical operating conditions to acquire relevant data. To emulate real marine environmental noise and data loss phenomena, we introduce Ornstein–Uhlenbeck (OU) noise and random occlusion noise into the data and apply the MaxAbsScaler method for dataset normalization. Subsequently, we develop a black-box model for intelligent ship maneuvering motion prediction based on LSTM networks and Multi-Head Attention Mechanisms. We conduct a comprehensive analysis and discussion of the model structure and hyperparameters, iteratively optimize the model, and compare the optimized model with standalone LSTM and MHAM approaches. Finally, we perform generalization testing on the optimized motion prediction model using test sets for zigzag and turning conditions. The results demonstrate that our proposed model significantly improves the accuracy of ship maneuvering predictions compared to standalone LSTM and MHAM algorithms and exhibits superior generalization performance. Full article

Measuring breathing changes during exercise is crucial for healthcare applications. This study used wearable capacitive sensors to capture abdominal motion and extract breathing patterns. Data preprocessing methods included filtering and normalization, followed by feature extraction for classification. Despite the growing interest in respiratory monitoring, research on a deep learning-based analysis of breathing data remains limited. To address this research gap, we optimized CNN and ResNet through systematic hyperparameter tuning, enhancing classification accuracy and robustness. The optimized ResNet outperformed the CNN in accuracy (0.96 vs. 0.87) and precision for Class 4 (0.8 vs. 0.6), demonstrating its capability to capture complex breathing patterns. These findings highlight the importance of hyperparameter optimization in respiratory monitoring and suggest ResNet as a promising tool for real-time assessment in medical applications. Full article

Evapotranspiration (ET) plays a pivotal role in linking the water and carbon cycles between the land and atmosphere, with latent heat flux (LE) representing the energy manifestation of ET. Due to adverse meteorological conditions, data quality filtering, and instrument malfunctions, LE measured by the eddy covariance (EC) is temporally discontinuous at the hourly and daily scales. Machine-learning (ML) models effectively capture the complex relationships between LE and its influencing factors, demonstrating superior performance in filling LE data gaps. However, the selection of features in ML models often relies on empirical knowledge, with identical features frequently used across stations, leading to reduced modeling accuracy. Therefore, this study proposes an LE gap-filling model (SHAP-AWF-BO-LightGBM) that combines the Shapley additive explanations adaptive weighted fusion method with the Bayesian optimization light gradient-boosting machine algorithm. This is tested using data from three stations in the Heihe River Basin, China, representing different plant functional types. For 30 min interval missing LE data, the RMSE ranges from 17.90 W/m² to 20.17 W/m², while the MAE ranges from 10.74 W/m² to 14.04 W/m². The SHAP-AWF method is used for feature selection. First, the importance of SHAP features from multiple ensemble-learning models is adaptively weighted as the basis for feature input into the BO-LightGBM algorithm, which enhances the interpretability and transparency of the model. Second, data redundancy and the cost of collecting other feature data during model training are reduced, improving model calculation efficiency (reducing the initial number of features of different stations from 42, 46, and 48 to 10, 15, and 8, respectively). Third, under the premise of ensuring accuracy as much as possible, the gap-filling ratio for missing LE data at different stations is improved, and the adaptability of using only automatic weather station observation is enhanced (the improvement range is between 7.46% and 11.67%). Simultaneously, the hyperparameters of the LightGBM algorithm are optimized using a Bayesian algorithm, further enhancing the accuracy of the model. This study provides a new approach and perspective to fill the missing LE in EC measurement. Full article

In the context of neural network optimization, this study explores the performance and computational efficiency of learning rate adjustment strategies applied with Adam and SGD optimizers. Methods evaluated include exponential annealing, step decay, and SHAP-informed adjustments across three datasets: Breast Cancer, Diabetes, and California Housing. The SHAP-informed adjustments integrate feature importance metrics derived from cooperative game theory, either scaling the global learning rate or directly modifying gradients of first-layer parameters. A comprehensive grid search was conducted to optimize the hyperparameters, and performance was assessed using metrics such as test loss, RMSE, R² score, accuracy, and training time. Results revealed that while step decay consistently delivered strong performance across datasets, SHAP-informed methods often demonstrated even higher accuracy and generalization, such as SHAP achieving the lowest test loss and RMSE on the California Housing dataset. However, the computational overhead of SHAP-based approaches was significant, particularly in targeted gradient adjustments. This study highlights the potential of SHAP-informed methods to guide optimization processes through feature-level insights, offering advantages in data with complex feature interactions. Despite computational challenges, these methods provide a foundation for exploring how feature importance can inform neural network training, presenting promising directions for future research on scalable and efficient optimization techniques. Full article

Natural fiber composites have gained significant attention in recent years due to their environmental benefits and unique mechanical properties. These materials combine natural fibers with polymer matrices to create sustainable alternatives to traditional synthetic composites. In addition to natural fiber reinforcement, the usage of recycled aggregates in concrete has been proposed as a remedy to combat the rapidly increasing amount of construction and demolition waste in recent years. However, the accurate prediction of the structural performance metrics, such as tensile strength, remains a challenge for concrete composites reinforced with natural fibers and containing recycled aggregates. This study aims to develop predictive models of natural-fiber-reinforced recycled aggregate concrete based on experimental results collected from the literature. The models have been trained on a dataset consisting of 482 data points. Each data point consists of the amounts of cement, fine and coarse aggregate, water-to-binder ratio, percentages of recycled coarse aggregate and natural fiber, and the fiber length. The output feature of the dataset is the splitting tensile strength of the concrete. Extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) and extra trees regressor models were trained to predict the tensile strength of the specimens. For optimum performance, the hyperparameters of these models were optimized using the blended search strategy (BlendSearch) and cost-related frugal optimization (CFO). The tensile strength could be predicted with a coefficient of determination greater than 0.95 by the XGBoost model. To make the predictive models accessible, an online graphical user interface was also made available on the Streamlit platform. A feature importance analysis was carried out using the Shapley additive explanations (SHAP) approach. Full article

Babies express their needs, such as hunger, discomfort, or sleeplessness, by crying. However, understanding these cries correctly can be challenging for parents. This can delay the baby’s needs, increase parents’ stress levels, and negatively affect the baby’s development. In this paper, an integrated system for the classification of baby sounds is proposed. The proposed method includes data augmentation, feature extraction, hyperparameter tuning, and model training steps. In the first step, various data augmentation techniques were applied to increase the training data’s diversity and strengthen the model’s generalization capacity. The MFCC (Mel-Frequency Cepstral Coefficients) method was used in the second step to extract meaningful and distinctive features from the sound data. MFCC represents sound signals based on the frequencies the human ear perceives and provides a strong basis for classification. The obtained features were classified with an artificial neural network (ANN) model with optimized hyperparameters. The hyperparameter optimization of the model was performed using the grid search algorithm, and the most appropriate parameters were determined. The training, validation, and test data sets were separated at 75%, 10%, and 15% ratios, respectively. The model’s performance was tested on mixed sounds. The test results were analyzed, and the proposed method showed the highest performance, with a 90% accuracy rate. In the comparison study with an artificial neural network (ANN) on the Donate a Cry data set, the F1 score was reported as 46.99% and the test accuracy as 85.93%. In this paper, additional techniques such as data augmentation, hyperparameter tuning, and MFCC feature extraction allowed the model accuracy to reach 90%. The proposed method offers an effective solution for classifying baby sounds and brings a new approach to this field. Full article

In simulations of elastic flow using the lattice Boltzmann method (LBM), the steady-state behavior of the flow at low capillary numbers is typically poor and prone to the formation of bubbles with inhomogeneous lengths. This phenomenon undermines the precise control of heat transfer, mass transfer, and reactions within microchannels and microreactors. This paper establishes an LBM multiphase flow model enhanced by machine learning. The hyperparameters of the machine learning model are optimized using the particle swarm algorithm. In contrast, the non-dominated sorting genetic algorithm (NSGA-II) is incorporated to optimize bubble lengths and stability. This results in a coupled multiphase flow numerical simulation model that integrates LBM, machine learning, and the particle swarm algorithm. Using this model, we investigate the influence of elastic flow parameters on bubble length and stability in a T-shaped microchannel. The simulation results demonstrate that the proposed LBM multiphase flow model can effectively predict bubble elongation rates under complex conditions. Furthermore, multi-objective optimization determines the optimal gas–liquid two-phase inlet flow rate relationship, significantly mitigating elastic flow instability at low capillary numbers. This approach enhances the controllability of the elastic flow process and improves the efficiency of mass and heat transfer. Full article

Accurate travel time prediction is essential for improving urban mobility, traffic management, and ride-hailing services. Traditional link- and path-based models face limitations due to data sparsity, segmentation errors, and computational inefficiencies. This study introduces an origin–destination (OD)-based travel time prediction framework leveraging high-resolution ride-hailing trajectory data. Unlike previous works, our approach systematically integrates spatiotemporal, quantified weather metrics and driver behavior clustering to enhance predictive accuracy. The proposed model employs a Back Propagation Neural Network (BPNN), which dynamically adjusts hyperparameters to improve generalization and mitigate overfitting. Empirical validation using ride-hailing data from Xi’an, China, demonstrates superior predictive performance, particularly for medium-range trips, achieving an RMSE of 202.89 s and a MAPE of 16.52%. Comprehensive ablation studies highlight the incremental benefits of incorporating spatiotemporal, weather, and behavioral features, showcasing their contributions to reducing prediction errors. While the model excels in moderate-speed scenarios, it exhibits limitations in short trips and low-speed cases due to data imbalance. Future research will enhance model robustness through data augmentation, real-time traffic integration, and scenario-specific adaptations. This study provides a scalable and adaptable travel time prediction framework, offering valuable insights for urban traffic management, dynamic route optimization, and sustainable mobility solutions within ITS. Full article

Intelligent fault diagnosis (IFD) plays a crucial role in reducing maintenance costs and enhancing the reliability of safety-critical energy systems (SCESs). In recent years, deep learning-based IFD methods have achieved high fault diagnosis accuracy extracting implicit higher-order correlations between features. However, the excessive long training time of deep learning models conflicts with the requirements of real-time analysis for IFD, hindering their further application in practical industrial environments. To address the aforementioned challenge, this paper proposes an innovative IFD method for SCES that combines the particle swarm optimization (PSO) algorithm and the ensemble broad learning system (EBLS). Specifically, the broad learning system (BLS), known for its low time complexity and high classification accuracy, is adopted as an alternative to deep learning for fault diagnosis in SCES. Furthermore, EBLS is designed to enhance model stability and classification accuracy with high-dimensional small samples by incorporating the random forest (RF) algorithm and an ensemble strategy into the traditional BLS framework. In order to reduce the computational cost of the EBLS, which is constrained by the selection of its hyperparameters, the PSO algorithm is employed to optimize the hyperparameters of the EBLS. Finally, the model is validated through simulated data from a complex nuclear power plant (NPP). Numerical experiments reveal that the proposed method significantly improved the diagnostic efficiency while maintaining high accuracy. In summary, the proposed approach shows great promise for boosting the capabilities of the IFD models for SCES. Full article