Selection Techniques Research Articles

Evolutionary-based ensemble feature selection technique for dynamic application-specific credit risk optimization in FinTech lending

Evolutionary-based ensemble feature selection technique for dynamic application-specific credit risk optimization in FinTech lending

Possible treatments for synchronous bilateral small renal masses

Currently, there is no established consensus on the best treatment approach for patients with bilateral synchronous renal masses (BSRM). The timing and method of managing these cases remain subjects of debate. This review aims to summarize the available literature and explore the ongoing controversies surrounding this topic. Three studies investigated non-surgical treatments within BSRM. Specifically, one study focused on active surveillance (AS) and showed no statistical differences in terms of progression and development of metastatic disease relative to their unilateral counterpart. Two studies investigated ablative techniques showing promising results. Eight papers have been published regarding robot assisted partial nephrectomy (RAPN) for BSRM. All these papers highlighted the safety, feasibility, and efficacy of bilateral RAPN for BSRM. Literature regarding treatments other than surgery such as AS and ablative therapies (ATs) for BSRM is scarce, but promising. Progression, rate of metastases and survival of BSRM are similar to unilateral disease, and AS is a safe option in these cases. Few studies focused on RAPN related outcomes for BSRMs, but all confirmed the safety, feasibility, and efficacy of this procedure. Finally, one step RAPN resulted as feasible as the two staged procedures, especially when selective clamping techniques can be chosen.

Height prediction of individuals with osteogenesis imperfecta by machine learning

BackgroundOsteogenesis imperfecta (OI) is a genetic disorder characterized by low bone mass, bone fragility and short stature. There is a significant gap in knowledge regarding the growth patterns across different types of OI, and the prediction of height in individuals with OI was not adequately addressed. In this study, we described the growth patterns and predicted the height of individuals with OI employing multiple machine learning (ML) models. Accurate height prediction enables effective monitoring and facilitates the development of personalized intervention plans for managing OI.MethodThis study included cross-sectional data for 323 participants with OI, and the median height Z-score for OI types I, III and IV were − 0.62 (-5.93 ~ 3.24), -3.97 (-10.44 ~ -0.02) and − 1.64 (-6.67 ~ 2.44), respectively. Based on the cross-sectional data of participants, the height curves across different gender and OI types were plotted and compared. Subsequently, feature selection techniques, specifically the filter and wrapper methods, were employed to identify predictive factors for the height of participants. Finally, multiple machine learning (ML) models were constructed for height prediction, and the performance of each model was systematically evaluated.ResultsThe analysis of height curves revealed that male with OI are significantly taller than female with OI from the age of 14 (p = 0.045), individuals with OI type III are statistically shorter than those with OI types I and IV starting from 3 years old (p = 0.006), and those with OI type IV are statistically shorter than those with OI type I from the age of 10 (p = 0.028). The application of filter and wrapper methods identified gender (p = 0.001), age (p < 0.001), Sillence types (p = 0.007), weight Z-score (p < 0.001) and aBMD Z-score (p = 0.021) as significant predictive factors for height. The optimal performance of predictive models was registered by gradient boosting classifier (GB) (bias = 5.783, accuracy = 92.59%, R2 = 0.828), random forest (RF) (bias = 6.155, accuracy = 90.12%, R2 = 0.788), ensemble machine learning (EML) (bias = 6.250, accuracy = 91.36%, R2 = 0.825) and deep neuron networks (DNNs) (bias = 6.223, accuracy = 90.12%, R2 = 0.821).ConclusionThis study analyzed a large cohort of individuals with OI and provided detailed height patterns across different gender and OI types that are crucial for assessing overall growth. Gender, age, Sillence types, weight Z-score and aBMD Z-score were identified as predictive factors for height. The predictive models of GB, RF, EML and DNNs had higher accuracy to evaluate the height of individuals with OI. This study allows guardians and physicians to timely monitor the height parameters, and facilitate the creation of personalized intervention schedules tailored to the needs of individuals with OI.

Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence

In this paper, we propose a method for optimizing the parameter values in iterative reconstruction algorithms that include adjustable parameters in order to optimize the reconstruction performance. Specifically, we focus on the power divergence-based expectation-maximization algorithm, which includes two power indices as adjustable parameters. Through numerical and physical experiments, we demonstrate that optimizing the evaluation function based on the extended power-divergence and weighted extended power-divergence measures yields high-quality image reconstruction. Notably, the optimal parameter values derived from the proposed method produce reconstruction results comparable to those obtained using the true image, even when using distance functions based on differences between forward projection data and measured projection data, as verified by numerical experiments. These results suggest that the proposed method effectively improves reconstruction quality without the need for machine-learning techniques in parameter selection. Our findings also indicate that this approach is useful for enhancing the performance of iterative reconstruction algorithms, especially in medical imaging, where high-accuracy reconstruction under noisy conditions is required.

A study of feature importance for king salmon health classification with feature selection

King salmon is important for aquaculture in New Zealand, contributing significant economic value. Fish health is a priority for the industry, and the change in the health status of king salmon needs to be accurately detected at the earliest possible stage. Many factors affect the health of king salmon, such as temperature. Identifying the key features that influence health prediction is a crucial step toward achieving this goal. This study utilizes trial data collected by the Cawthron Institute, which includes diverse information on king salmon, such as blood biochemistry and hematology. We explore the data by employing statistical methods and feature selection techniques in machine learning to identify the most relevant features for king salmon health prediction, aiming to classify individuals as healthy or unhealthy with a small number of features. The results show that although the most efficient feature selection techniques on different datasets vary, overall, feature selection approaches can successfully identify relevant and informative features for king salmon health classification. Through the incorporation of a few selected features, the learned classifiers could still achieve statistically equal or better classification performance. This study not only contributes to the understanding of the health indicators of king salmon but also provides crucial insights into health prediction, which will be beneficial to the improvement of the health of king salmon, leading to the development of more effective management strategies for aquaculture.

Quantile analysis for financial bubble detection and surveillance

Understanding and monitoring financial bubbles is critical, as they can lead to market instability, asset price crashes, and economic downturns with widespread consequences. This article explores the usefulness of quantile regression (QR) technique in detecting and surveilling financial bubbles, encompassing both global testing and real‐time monitoring. We demonstrate that the QR‐based quantile unit root test, coupled with an optimal quantile selection technique, serves as an effective tool for a global bubble test without necessitating additional recursive techniques. Moreover, we propose two QR‐based bubble monitoring techniques. We show that the monitoring statistics follow a random variate under the null hypothesis of no bubbles but diverge to positive infinity in the presence of a mildly explosive bubble, and hence consistently date the origination of a bubble. Monte Carlo simulations suggest that compared with their LS counterparts, in the presence of skewed distributions, the QR‐based global test delivers substantially greater power, while the QR‐based monitoring procedures offer higher bubble detection rate and more accurate dating of the bubble origination. As an illustration, we conduct a pseudo real‐time monitoring exercise with the S&amp;P 500 composite index.

General feature selection technique supporting sex-debiasing in chronic illness algorithms validated using wearable device data

In tasks involving human health condition data, feature selection is heavily affected by data types, the complexity of the condition manifestation, and the variability in physiological presentation. One type of variability often overlooked or oversimplified is the effect of biological sex. As females have been chronically underrepresented in clinical research, we know less about how conditions manifest in females. Innovations in wearable technology have enabled individuals to generate high temporal resolution data for extended periods of time. With millions of days of data now available, additional feature selection pipelines should be developed to systematically identify sex-dependent variability in data, along with the effects of how many per-person data are included in analysis. Here we present a set of statistical approaches as a technique for identifying sex-dependent physiological and behavioral manifestations of complex diseases starting from longitudinal data, which are evaluated on diabetes, hypertension, and their comorbidity.

FinSafeNet: securing digital transactions using optimized deep learning and multi-kernel PCA(MKPCA) with Nyström approximation.

With the swift advancement of technology and growing popularity of internet in business and communication, cybersecurity posed a global threat. This research focuses new Deep Learning (DL) model referred as FinSafeNet to secure loose cash transactions over the digital banking channels. FinSafeNet is based on a Bi-Directional Long Short-Term Memory (Bi-LSTM), a Convolutional Neural Network (CNN) and an additional dual attention mechanism to study the transaction data and influence the observation of various security threats. One such aspect is, relying these databases in most of the cases imposes a great technical challenge towards effective real time transaction security. FinSafeNet draws attention to the attack and reproductive phases of Hierarchical Particle Swarm Optimization (HPSO) feature selection technique simulating it in a battle for extreme time performance called the Improved Snow-Lion optimization Algorithm (I-SLOA). Upon that, the model then applies the Multi-Kernel Principal Component Analysis (MKPCA) accompanied by Nyström Approximation for handling the MKPCA features. MKPCA seeks to analyze and understand a non-linear structure of data whereas, Nyström Approximation reduces the burden on computational power hence allows the model to work in situations where large sizes of datasets are available but with no loss of efficiency of the model. This causes FinSafeNet to work easily and still be able to make accurate forecasts. Besides tackling feature selection and dimensionality reduction, the model presents advanced correlation measures as well as Joint Mutual Information Maximization to enhance variable correlation analysis. These improvements further help the model to detect the relevant features in transaction data that may present a threat to the security of the system. When tested on commonly used database for testing banks performance, for instance the Paysim database, FinSafeNet significantly improves upon the previous and fundamental approaches, achieving accuracy of 97.8%.

BreCML: identifying breast cancer cell state in scRNA-seq via machine learning

Breast cancer is a prevalent malignancy and one of the leading causes of cancer-related mortality among women worldwide. This disease typically manifests through the abnormal proliferation and dissemination of malignant cells within breast tissue. Current diagnostic and therapeutic strategies face significant challenges in accurately identifying and localizing specific subtypes of breast cancer. In this study, we developed a novel machine learning-based predictor, BreCML, designed to accurately classify subpopulations of breast cancer cells and their associated marker genes. BreCML exhibits outstanding predictive performance, achieving an accuracy of 98.92% on the training dataset. Utilizing the XGBoost algorithm, BreCML demonstrates superior accuracy (98.67%), precision (99.15%), recall (99.49%), and F1-score (99.79%) on the test dataset. Through the application of machine learning and feature selection techniques, BreCML successfully identified new key genes. This predictor not only serves as a powerful tool for assessing breast cancer cellular status but also offers a rapid and efficient means to uncover potential biomarkers, providing critical insights for precision medicine and therapeutic strategies.

Path Algebra-Driven Classification Solution to Realize User-Centric Performance-Oriented Virtual Network Embeddings

The intense diversity of the Next-Generation Networking environments like 6G and the forthcoming deployment of immersive applications with varied user-specific requirements transform the efficient allocation of resources into a real challenge. Traditional solutions like the shortest path algorithm and mono-constraint methodologies are inadequate to handle customized user-defined performance parameters and effectively classify physical resources according to these intricate demands. This research offers a new evaluation mechanism to successfully replace the aforementioned traditional path ranking and path selection techniques. Specifically, the proposed framework is integrated with optimization-oriented metrics, each indicating a unique aspect of performance for evaluating candidate network paths. The deployed metrics are then algebraically synthesized to provide a distinctive multidimensional description of the examined substrate resources. These primary and composite metrics adhere to the fundamental monotonicity and isotonicity properties of a Path Algebra; hence, the validity and optimality of the proposed evaluation mechanism is guaranteed by design. To tackle the complexity created by the variety of human-centric customization, a novel methodology that analyzes and determines the weighted influence of the synthesized metrics depending on the characteristics of the served user-centric application is also introduced. The chosen suitable weights address performance-oriented mission-critical tailored objectives for adaptive optimizations. Its innovative algebraic design allows it to successfully describe and rank candidate paths in a versatile way, whether in legacy or modern architectures. The experimental data of the first scenario show that 62.5% and 50% of highlighted path evaluations proposed by the shortest path and unidimensional constraint strategies, respectively, suffer from moderate performance-oriented values compared to the proposed framework. Likewise, the results of the second examined scenario reveal that the proposed composite metric yields more suitable path rankings by 50% in contrast to its traditional counterparts, rendering the contested evaluation mechanisms obsolete.

Identification of a Novel Biomarker Panel for Breast Cancer Screening

Breast cancer remains a major public health concern, and early detection is crucial for improving survival rates. Metabolomics offers the potential to develop non-invasive screening and diagnostic tools based on metabolic biomarkers. However, the inherent complexity of metabolomic datasets and the high dimensionality of biomarkers complicates the identification of diagnostically relevant features, with multiple studies demonstrating limited consensus on the specific metabolites involved. Unlike previous studies that rely on singular feature selection techniques such as Partial Least Square (PLS) or LASSO regression, this research combines supervised and unsupervised machine learning methods with random sampling strategies, offering a more robust and interpretable approach to feature selection. This study aimed to identify a parsimonious and robust set of biomarkers for breast cancer diagnosis using metabolomics data. Plasma samples from 185 breast cancer patients and 53 controls (from the Cooperative Human Tissue Network, USA) were analyzed. This study also overcomes the common issue of dataset imbalance by using propensity score matching (PSM), which ensures reliable comparisons between cancer and control groups. We employed Univariate Naïve Bayes, L2-regularized Support Vector Classifier (SVC), Principal Component Analysis (PCA), and feature engineering techniques to refine and select the most informative features. Our best-performing feature set comprised 11 biomarkers, including 9 metabolites (SM(OH) C22:2, SM C18:0, C0, C3OH, C14:2OH, C16:2OH, LysoPC a C18:1, PC aa C36:0 and Asparagine), a metabolite ratio (Kynurenine-to-Tryptophan), and 1 demographic variable (Age), achieving an area under the ROC curve (AUC) of 98%. These results demonstrate the potential for a robust, cost-effective, and non-invasive breast cancer screening and diagnostic tool, offering significant clinical value for early detection and personalized patient management.

APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES FOR ERROR PREVENTION AND QUALITY CONTROL IN MIG/MAG WELDING PROCESSES: A COMPREHENSIVE REVIEW

Welding techniques, particularly MIG/MAG (Metal Inert Gas/Metal Active Gas) processes, are essential for various industries, including automotive, construction, aerospace, and defense. Ensuring the quality and accuracy of welds is crucial for efficient production and product reliability. However, numerous issues can arise during MIG/MAG welding, often resulting in suboptimal quality. This study presents a comprehensive review of research efforts employing AI techniques to address errors and defects in MIG/MAG welding. The review systematically analyzes relevant studies published since 2018, focusing on three key aspects: datasets employed, methodologies and approaches adopted, and performance metrics reported. The findings reveal a significant adoption of both machine learning and deep learning techniques, with the choice of approach dependent on factors such as the nature of input data, welding process dynamics, and computational requirements. Deep learning models, particularly convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, have demonstrated superior performance in tasks like image-based defect detection and time-series analysis for quality prediction. However, traditional machine learning algorithms have also been utilized, often in conjunction with dimensionality reduction or feature selection techniques. The review highlights the diverse range of performance metrics employed, including accuracy, precision, recall, F1-score, mean squared error (MSE), and root mean squared error (RMSE), with the selection contingent upon the specific task (classification or regression) and desired trade-off between different performance aspects. While many studies have reported promising results, with accuracy rates frequently exceeding 90%, challenges remain in real-world industrial settings, where factors such as noise, occlusions, and rapidly changing welding conditions can pose significant hurdles. This review serves as a comprehensive guide for researchers and practitioners working in the field of AI-assisted error prevention and quality control for MIG/MAG welding processes, highlighting current trends, methodologies, and future research directions.

Development of New Electricity System Marginal Price Forecasting Models Using Statistical and Artificial Intelligence Methods

The System Marginal Price (SMP) is the cost of the last unit of electricity supplied to the grid, reflecting the supply–demand equilibrium and serving as a key indicator of market conditions. Accurate SMP forecasting is essential for ensuring market stability and economic efficiency. This study addresses the challenges of SMP prediction in Turkey by proposing a comprehensive forecasting framework that integrates machine learning, deep learning, and statistical models. Advanced feature selection techniques, such as Minimum Redundancy Maximum Relevance (mRMR) and Maximum Likelihood Feature Selector (MLFS), are employed to refine model inputs. The framework incorporates time series methods like Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Convolutional LSTM (ConvLSTM) to capture complex temporal patterns, alongside models such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Extreme Learning Machine (ELM) for modeling non-linear relationships. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE) across regular weekdays, weekends, and public holidays. XGBoost combined with MLFS consistently achieved the lowest MAPE values, demonstrating exceptional accuracy and robustness. Among all of the models, XGBoost combined with MLFS consistently achieved the lowest MAPE values, demonstrating superior accuracy and robustness. The results highlight the inadequacy of traditional models like ARIMA and SARIMA in capturing non-linear and highly volatile patterns, reinforcing the necessity of using advanced techniques for effective SMP forecasting. Overall, this study presents a novel and comprehensive approach tailored for complex electricity markets, significantly enhancing predictive reliability by incorporating economic indicators and sophisticated feature selection methods.

Mapping AGN winds: A connection between radio-mode AGNs and the AGN feedback cycle

We present a kinematic analysis based on the large integral field spectroscopy (IFS) dataset of SDSS-IV MaNGA (Sloan Digital Sky Survey/Mapping Nearby Galaxies at Apache Point Observatory; ∼10 000 galaxies). We have compiled a diverse sample of 594 unique active galactic nuclei (AGNs), identified through a variety of independent selection techniques, encompassing radio (1.4 GHz) observations, optical emission-line diagnostics (BPT), broad Balmer emission lines, mid-infrared colors, and hard X-ray emission. We investigated how ionized gas kinematics behave in these different AGN populations through stacked radial profiles of the [O III] 5007 emission-line width across each AGN population. We contrasted AGN populations against each other (and non-AGN galaxies) by matching samples by stellar mass, [O III] 5007 luminosity, morphology, and redshift. We find similar kinematics between AGNs selected by BPT diagnostics compared to broad-line-selected AGNs. We also identify a population of non-AGNs with similar radial profiles as AGNs, indicative of the presence of remnant outflows (or fossil outflows) of past AGN activity. We find that purely radio-selected AGNs display enhanced ionized gas line widths across all radii. This suggests that our radio-selection technique is sensitive to a population in which AGN-driven kinematic perturbations have been active for longer durations (potentially due to recurrent activity) than in purely optically selected AGNs. This connection between radio activity and extended ionized gas outflow signatures is consistent with recent evidence that suggests radio emission (expected to be diffuse) originated due to shocks from outflows. We conclude that different selection techniques can trace different AGN populations not only in terms of energetics but also in terms of AGN evolutionary stages. Our results are important in the context of the AGN duty cycle and highlight integral field unit data’s potential to deepen our knowledge of AGNs and galaxy evolution.

Using Marker-Assisted Selection to Develop a Drought-Tolerant Rice Line with Enhanced Resistance to Blast and Brown Planthopper

Rice is a major global staple crop, but rising temperatures and freshwater shortages have made drought one of the most severe abiotic stresses affecting agriculture. Additionally, rice blast disease and brown planthopper infestations significantly impact yields. Therefore, developing water-saving, drought-resistant, high-yielding, and disease-resistant rice varieties is critical for sustainable rice production. The new water-saving and drought-resistant (WDR) rice ‘Huhan 1516’, bred using marker-assisted selection (MAS) and marker-assisted backcrossing (MABC) techniques, addresses these challenges. This variety is highly adaptable to drought-prone and water-scarce regions such as the Yangtze and Huai River basins. With its high yield, drought tolerance, and broad-spectrum resistance to rice blast (conferred by the Pi2 gene) and brown planthopper (BPH), ‘Huhan 1516’ is suitable for various farming systems and environments. Field trials show that this variety outperforms control varieties by 2.2% in yield and exhibits moderate resistance to both rice blast and brown planthopper. ‘Huhan 1516’ has been recognized as a new water-saving and drought-resistant rice variety by the state, and as a released cultivar, it has great potential for market promotion and application.

Impact of gamma radiation on marginal adaptation of nanohybrid composite and composition of dental hard tissues – Scanning electron microscopy and X-ray diffraction analysis: An in vitro pilot study

Aims: This pilot study aimed to compare the marginal adaptation of composite resin at the tooth-restoration interface, before and after radiation. Subjects and Methods: Fifteen extracted premolars were divided into 2 experimental groups (based on the timing of irradiation) and 1 control group of 5 teeth each. In Group I (control group), teeth were restored but not exposed to radiation at any stage, Group II: teeth were irradiated before cavity preparation and restoration, and Group III: after cavity preparation and restoration employing selective etch technique, teeth were exposed to radiation. The samples were then sectioned buccolingually to analyze the extent of the marginal gap under scanning electron microscopy and compositional alteration of dental hard tissues by X-ray diffraction study. The data collected were analyzed statistically. Statistical Analysis Used: The statistical software used was IBM SPSS version 23 New York, USA, and analysis was done using two-way ANOVA followed by Turkey’s post hoc test, this difference in the mean marginal gap between all three groups was nonsignificant (P ≥ 0.05). Results: In the control group (Group I), a minimum gap (4.203 µm ± 0.533) was observed at the tooth-restoration interface, indicating the highest level of adaptation as compared to Group II (5.816 µm ± 0.762) and Group III (4.862 µm ± 1.018). This suggests that radiation adversely affected the bonding between composite materials and both enamel and dentin, attributed to the alterations induced by radiotherapy in the chemical, physical, and morphological properties of both tooth structure and composite resin. Conclusions: Ionizing radiations adversely affect the bonding between enamel, dentin, and composite resin. Hence, restorative procedures should be performed before undergoing radiotherapy.

Classification of faults in friction stir processed composites using a machine learning and ensemble learning approach

Abstract Aluminium alloy based surface composites with hard reinforcement particles have wide scope in aerospace and automobile manufacturing industries. In this paper, the aluminium composites, manufactured by friction stir processing (FSP) with varying parameters are investigated for the faults occurred during fabrication process. It explores a machine-learning approach to detect defects of surface hybrid composites with an Al6061 alloy matrix, reinforced with copper and graphene nano-powders, using friction stir processing and a tungsten carbide tool on a milling machine. Multi-sensor time series data (vibration, force, and current) collected during fabrication, is preprocessed and labelled with normal and defective categories (e.g., pin break, brazing break, rough surface, no composite) using visual inspection. The important time domain and frequency domain features are extracted using different libraries in python. Thenafter, various types of feature selection techniques, viz filter, wrapper and embedded methods are implemented to select most relevant features. The selected subset of features from all selection methods used, are applied to different machine learning and ensemble learning classifiers and their performances are evaluated. The optimal combinations of the type of feature selection method and classifier used, are obtained for efficient classification of surface defects in composited formed by FSP. The real time monitoring and defect detection system can be developed in future for the composites developed by FSP using the developed models.

Enhancing Accuracy in X-ray Radiation-based Multiphase Flow meters: Integration of Grey Wolf Optimization and MLP Neural Networks

This research investigates the development of an advanced predictive model aimed at accurately determining the volumetric percentages of water, oil, and gas within oil pipeline systems. Utilizing an innovative approach that incorporates an X-ray source alongside two sodium iodide detectors, the study leverages the Monte Carlo N-Particle (MCNP) simulation code to model the behavior of three-phase fluids under varied conditions. The model meticulously simulates various volumetric configurations of water, oil, and gas, resulting in a comprehensive dataset that provides key spectral information. The initial phase involved the extraction of ten temporal and frequency-related features from each detector, culminating in a pool of twenty features. The analytical process then applied the Grey Wolf Optimization (GWO) algorithm to select the most indicative features for predictive modeling. Out of the initial set, seven features—short-time energy, frequency deviation, relative spectral density, spectral margin, main peak position, spectral coefficient, and frequency intensity—were identified as critical for enhancing model accuracy. These features were subsequently fed into a meticulously structured multilayer perceptron (MLP) neural network. This network, designed with two hidden layers containing 20 and 10 neurons, respectively, demonstrated exceptional capability, achieving a root mean square error (RMSE) of less than 0.06 in the prediction of oil and gas volumetric percentages. The study emphasizes the significant impact of integrating refined feature selection techniques and robust neural network architectures on the precision and reliability of volumetric predictions in multiphase flow systems within oil pipelines. This approach not only enhances predictive accuracy but also contributes to more efficient resource management and operational planning in the oil and gas industry.

Integrating temporal deep learning models for predicting screen-out risk levels in hydraulic fracturing

Amid the transformative shift in global energy structures, the exploitation and utilization of shale gas, an essential unconventional natural gas resource, have drawn widespread attention from both industrial and academic circles. However, screen-out incidents during hydraulic fracturing operations pose significant obstacles to extraction efficiency and safety. Traditional prediction methods, which rely on empirical estimations and simplified models, are deficient in accuracy and real-time applicability. Addressing this, our study introduces a novel deep learning ensemble integrating Gated Recurrent Units (GRU), Transformer, and One-Dimensional Convolutional Neural Networks (1D-CNN) for precise screen-out prediction. This approach markedly improves predictive accuracy by efficiently processing time-series data and capturing the complex dynamics of fracturing processes. Furthermore, the application of the correlation coefficient method and random forest algorithm for feature selection optimizes model input and further enhances prediction accuracy and operational efficiency. Our comparative analysis demonstrates the model’s superiority, achieving an F1 score of 0.951 and a loss of 0.430, clearly surpassing traditional and other deep learning methods. This integration of advanced neural architectures and feature selection techniques not only advances screen-out prediction but also yields practical insights for optimizing shale gas extraction strategies and enhancing safety.

Enhancing early detection of autistic spectrum disorder in children using machine learning approaches

Diagnosing Autism Spectrum Disorder (ASD) presents a multifaceted challenge, demanding accurate and efficient screening methods. Applying machine learning techniques offers a promising avenue for enhancing diagnostic accuracy and efficiency. This research investigates the efficiency of machine learning in distinguishing individuals with ASD from those without, utilizing a comprehensive dataset comprising screening questions, demographic factors, and ASD related diagnostic classifications. We applied chi-square feature selection technique and also tested Random Forest, Logistic Regression, Gradient Boosting Classifier, and Extra Trees Classifier. Each model showed optimal performance and exhibit high precision, recall, and F1-score for both ASD-positive and ASD-negative instances. Additionally, AUROC curves further validated the models’ exceptional discriminatory abilities, with exceptional results. Our findings highlight the potential of machine learning algorithms for enhancing ASD diagnosis accuracy and efficiency in clinical settings. Further research and validation on larger datasets are required to understand the importance of machine learning methods in ASD diagnosis.