Selection Method Research Articles

Groundwater salinity modeling and mapping using machine learning approaches: a case study in Sidi Okba region, Algeria.

The groundwater salinization process complexity and the lack of data on its controlling factors are the main challenges for accurate predictions and mapping of aquifer salinity. For this purpose, effective machine learning (ML) methodologies are employed for effective modeling and mapping of groundwater salinity (GWS) in the Mio-Pliocene aquifer in the Sidi Okba region, Algeria, based on limited dataset of electrical conductivity (EC) measurements and readily available digital elevation model (DEM) derivatives. The dataset was randomly split into training (70%) and testing (30%) sets, and three wrapper selection methods, recursive feature elimination (RFE), forward feature selection (FFS), and backward feature selection (BFS) are applied to train the data. The resulting combinations are used as inputs for five ML models, namely random forest (RF), hybrid neuro-fuzzy inference system (HyFIS), K-nearest neighbors (KNN), cubist regression model (CRM), and support vector machine (SVM). The best-performing model is identified and applied to predict and map GWS across the entire study area. It is highlighted that the applied methods yield input variation combinations as critical factors that are often overlocked by many researchers, which substantially impacts the models' accuracy. Among different alternatives the RF model emerged as the most effective for predicting and mapping GWS in the study area, which led to the high performance in both the training (RMSE = 1.016, R = 0.854, and MAE = 0.759) and testing (RMSE = 1.069, R = 0.831, and MAE = 0.921) phases. The generated digital map highlighted the alarming situation regarding excessive GWS levels in the study area, particularly in zones of low elevations and far from the Foum Elgherza dam and Elbiraz wadi. Overall, this study represents a significant advancement over previous approaches, offering enhanced predictive performance for GWS with the minimum number of input variables.

A Generalized Lightweight Intrusion Detection Model With Unified Feature Selection for Internet of Things Networks

ABSTRACTThe applicability of the Internet of Things (IoT) cutting across different domains has resulted in newer “things” acquiring IP connectivity. These things, technically known as IoT devices, are vulnerable to diverse security threats. Consequently, there has been an exponential increase in IoT malware over the past 5 years, and securing IoT devices from such attacks is a pressing concern in the current era. However, the traditional peripheral security measures do not comply with the lightweight security requirements of the IoT ecosystem. Considering this, we propose a lightweight intrusion detection model for IoT networks (LIDM‐IoT) that demonstrates similar efficiency in exposing malicious activities compared with the existing computationally expensive methods. The crux of the proposed model is that it provides efficient attack detection with lower computational requirements in IoT networks. LIDM‐IoT achieves the feat through a novel unified feature selection strategy that unifies filter‐based and embedded feature selection methods. The proposed feature selection strategy reduces the feature space by 94%. Also, we use only the records of a single attack type to build the model using the XGBoost algorithm. We have tested LIDM‐IoT with unseen attack types to ensure its generalized behavior. The results indicate that the proposed model exhibits efficient attack detection, with a reduced feature set, in IoT networks compared with the state‐of‐the‐art models.

Empirical comparison of genomic selection to phenotypic selection for biomass yield of switchgrass

AbstractSwitchgrass (Panicum virgatum L.) is one of several grass species being bred for use as a biomass crop to support the biofuel industry. Increases in biomass yield are imperative to ensure that crops such as switchgrass can sustainably meet the needs of this industry. Genomic selection is one strategy that can accelerate breeding gains for complex traits such as biomass yield. The goal of this study was to conduct three cycles of genomic selection in a previously trained Liberty switchgrass population and compare that to one cycle of phenotypic selection, both of which required 3 years to complete. The advanced lines were tested across five locations and three hardiness zones in the Central United States using a randomized complete block design with four replicates. There were strong genotype × location interactions, but the first two generations of genomic selection were superior to Liberty at four of the five evaluation locations. Conversely, phenotypic selection failed to result in significant gains in biomass yield for any of the five evaluation locations. Based on these results from Liberty switchgrass, genomic selection methods are expected to at least double the rates of gain in biomass yield relative to previous estimates using phenotypic selection methods.

Machine learning algorithms predict canine structural epilepsy with high accuracy.

Clinical reasoning in veterinary medicine is often based on clinicians' personal experience in combination with information derived from publications describing cohorts of patients. Studies on the use of scientific methods for patient individual decision making are largely lacking. This applies to the prediction of the individual underlying pathology in seizuring dogs as well. The aim of this study was to apply machine learning to the prediction of the risk of structural epilepsy in dogs with seizures. Dogs with a history of seizures were retrospectively as well as prospectively included. Data about clinical history, neurological examination, diagnostic tests performed as well as the final diagnosis were collected. For data analysis, the Bayesian Network and Random Forest algorithms were used. A total of 33 features for Random Forest and 17 for Bayesian Network were available for analysis. The following four feature selection methods were applied to select features for further analysis: Permutation Importance, Forward Selection, Random Selection and Expert Opinion. The two algorithms Bayesian Network and Random Forest were trained to predict structural epilepsy using the selected features. A total of 328 dogs of 119 different breeds were identified retrospectively between January 2017 and June 2021, of which 33.2% were diagnosed with structural epilepsy. An overall of 89,848 models were trained. The Bayesian Network in combination with the Random feature selection performed best. It was able to predict structural epilepsy with an accuracy of 0.969 (sensitivity: 0.857, specificity: 1.000) among all dogs with seizures using the following features: age at first seizure, cluster seizures, seizure in last 24 h, seizure in last 6 month, and seizure in last year. Machine learning algorithms such as Bayesian Networks and Random Forests identify dogs with structural epilepsy with a high sensitivity and specificity. This information could provide some guidance to clinicians and pet owners in their clinical decision-making process.

Secondary Cytoreductive Surgery in Relapsed Platinum-Sensitive Epithelial Ovarian Cancer: A Systematic Review of Randomized Controlled Trials.

Secondary cytoreductive surgery is a treatment option for relapsed platinum-sensitive epithelial ovarian cancer, but no clear indications are defined for the procedure. This systematic review aims to establish clear indications and compare outcomes versus standard-of-care chemotherapy. We conducted an electronic literature search across three databases and identified 2033 articles, including three phase 3 randomized controlled trials (RCT). The review adhered to PRISMA 2020 guidelines and was registered in PROSPERO (no. CRD42022379817). Despite varying patient selection methods, surgery plus chemotherapy demonstrated significantly prolonged progression-free survival compared to chemotherapy alone. However, overall survival outcomes were inconsistent: while GOG-0213 did not show extended overall survival, recent studies with stricter defined criteria for surgery (SOC-1 and DESKTOP-III) reported improved overall survival with the addition of surgery. Morbidity and mortality rates were low, with no difference in quality of life between the surgery and no-surgery groups. In conclusion, cytoreductive surgery presents a promising option for recurrent epithelial ovarian cancer treatment. Nonetheless, well-defined selection criteria appear crucial for achieving increased overall survival compared to conventional treatment.

Integrated Optimization of Route and Frequency for Rail Transit Feeder Buses under the Influence of Shared Motorcycles

In this paper, we develop a multi-objective integrated optimization method for feeder buses of rail transit based on realistic considerations. We propose a bus stop selection method that considers the influence of shared motorcycles, which can score the importance of alternative bus stops and select those with the highest scores as objectives. The objective of the model in this paper is to minimize both the travel costs of passengers and the operating costs of the bus company. This is achieved by optimizing feeder bus routes, the frequency of departures, and interchange discounts to enhance the connectivity between feeder buses and rail transit. In addition, to ensure the feasibility of generated routes in the real road network, a genetic algorithm encoded with priority is used to solve this model. We use the Xingyao Road subway station in Kunming as an example, and the results show that the optimization method is effective.

A taxonomy of unsupervised feature selection methods including their pros, cons, and challenges

A taxonomy of unsupervised feature selection methods including their pros, cons, and challenges

Towards Exploring the Limitations of Test Selection Techniques on Graph Neural Networks: An Empirical Study

Graph Neural Networks (GNNs) have gained prominence in various domains, such as social network analysis, recommendation systems, and drug discovery, due to their ability to model complex relationships in graph-structured data. GNNs can exhibit incorrect behavior, resulting in severe consequences. Therefore, testing is necessary and pivotal. However, labeling all test inputs for GNNs can be prohibitively costly and time-consuming, especially when dealing with large and complex graphs. In response to these challenges, test selection has emerged as a strategic approach to alleviate labeling expenses. The objective of test selection is to select a subset of tests from the complete test set. While various test selection techniques have been proposed for traditional deep neural networks (DNNs), their adaptation to GNNs presents unique challenges due to the distinctions between DNN and GNN test data. Specifically, DNN test inputs are independent of each other, whereas GNN test inputs (nodes) exhibit intricate interdependencies. Therefore, it remains unclear whether DNN test selection approaches can perform effectively on GNNs. To fill the gap, we conduct an empirical study that systematically evaluates the effectiveness of various test selection methods in the context of GNNs, focusing on three critical aspects: 1) Misclassification detection: selecting test inputs that are more likely to be misclassified; 2) Accuracy estimation: selecting a small set of tests to precisely estimate the accuracy of the whole testing set; 3) Performance enhancement: selecting retraining inputs to improve the GNN accuracy. Our empirical study encompasses 7 graph datasets and 8 GNN models, evaluating 22 test selection approaches. Our study includes not only node classification datasets but also graph classification datasets. Our findings reveal that: 1) In GNN misclassification detection, confidence-based test selection methods, which perform well in DNNs, do not demonstrate the same level of effectiveness; 2) In terms of GNN accuracy estimation, clustering-based methods, while consistently performing better than random selection, provide only slight improvements; 3) Regarding selecting inputs for GNN performance improvement, test selection methods, such as confidence-based and clustering-based test selection methods, demonstrate only slight effectiveness; 4) Concerning performance enhancement, node importance-based test selection methods are not suitable, and in many cases, they even perform worse than random selection.

Porosity Prediction Based on Ensemble Learning for Feature Selection and an Optimized GRU Improved by the PSO Algorithm

Accurate and reliable prediction of porosity forms the foundational basis for evaluating reservoir quality, which is essential for the systematic deployment of oil and gas exploration and development plans. When data quality of samples is low, and critical model parameters are typically determined through subjective experience, resulting in diminished accuracy and reliability of porosity prediction methods utilizing gated recurrent units (GRU), a committee-voting ensemble learning (EL) method, and an enhanced particle swarm optimization (PSO) algorithm are proposed to optimize the GRU-based porosity prediction model. Initially, outliers are eliminated through box plots and the min–max normalization is applied to enhance data quality. To address issues related to model accuracy and high training costs arising from dimensional complexity, substantial noise, and redundant information in logging data, a committee-voting EL strategy based on four feature selection algorithms is introduced. Following data preprocessing, this approach is employed to identify logging parameters highly correlated with porosity, thereby furnishing the most pertinent data samples for the GRU model, mitigating constraints imposed by single-feature selection methods. Second, an improved PSO algorithm is suggested to tackle challenges associated with low convergence accuracy stemming from random population initialization, alongside the absence of global optimal solutions due to overly rapid particle movement during iteration. This algorithm uses a good-point set for population initialization and incorporates a compression factor to devise an adaptive velocity updating strategy, thereby enhancing search efficacy. The enhanced PSO algorithm’s superiority is substantiated through comparison with four alternative swarm intelligent algorithms across 10 benchmark test functions. Ultimately, optimal hyper-parameters for the GRU model are determined using the improved PSO algorithm, thereby minimizing the influence of human factors. Experimental findings based on approximately 15,000 logging data points from well A01 in an operational field validate that, relative to three other deep learning methodologies, the proposed model proficiently extracts spatiotemporal features from logging data, yielding enhanced accuracy in porosity prediction. The mean squared error on the test set was 7.19 × 10–6, the mean absolute error stood at 0.0082, and coefficient of determination reached 0.99, offering novel insights for predicting reservoir porosity.

Interpretable deep residual network uncovers nucleosome positioning and associated features.

Nucleosomes represent elementary building units of eukaryotic chromosomes and consist of DNA wrapped around a histone octamer flanked by linker DNA segments. Nucleosomes are central in epigenetic pathways and their genomic positioning is associated with regulation of gene expression, DNA replication, DNA methylation and DNA repair, among other functions. Building on prior discoveries that DNA sequences noticeably affect nucleosome positioning, our objective is to identify nucleosome positions and related features across entire genome. Here, we introduce an interpretable framework based on the concepts of deep residual networks (NuPoSe). Trained on high-coverage human experimental MNase-seq data, NuPoSe is able to learn sequence and structural patterns associated with nucleosome organization in human genome. NuPoSe can bealsoapplied to unseen data from different organisms and cell types. Our findings point to 43 informative features, most of them constitute tri-nucleotides, di-nucleotides and one tetra-nucleotide. Most features are significantly associated with the nucleosomal structuralcharacteristics, namely, periodicity of nucleosomal DNA and its location with respect to a histone octamer. Importantly, we show that features derived from the 27 bp linker DNAflanking nucleosomescontribute up to 10% to the quality of the prediction model. This,alongwith the comprehensive training sets, deep-learning architecture, and feature selection method,may contribute totheNuPoSe's 80-89% classificationaccuracy on different independentdatasets.

Label distribution feature selection based on neighborhood rough set

SummaryIn label distribution learning (LDL), an instance is involved with many labels in different importance degrees, and the feature space of instances is accompanied with thousands of redundant and/or irrelevant features. Therefore, the main characteristic of feature selection in LDL is to evaluate the ability of each feature. Motivated by neighborhood rough set (NRS), which can be used to measure the dependency degree of feature via constructing neighborhood relations on feature space and label space, respectively, this article proposes a novel label distribution feature selection method. In this article, the neighborhood class of instance in label distribution space is defined, which is beneficial to recognize the logical class of target instance. Then, a new NRS model for LDL is proposed. Specially, the dependency degree of feature combining label weight is defined. Finally, a label distribution feature selection based on NRS is presented. Extensive experiments on 12 data sets show the effectiveness of the proposed algorithm.

A two-tier feature selection method for predicting mortality risk in ICU patients with acute kidney injury

Acute kidney injury (AKI) is one of the most important lethal factors for patients admitted to intensive care units (ICUs), and timely high-risk prognostic assessment and intervention are essential to improving patient prognosis. In this study, a stacking model using the MIMIC-III dataset with a two-tier feature selection approach was developed to predict the risk of in-hospital mortality in ICU patients admitted for AKI. External validation was performed using separate MIMIC-IV and eICU-CRD. The area under the curve (AUC) was calculated using the stacking model, and features were selected using the Boruta and XGBoost feature selection methods. This study compares the performance of a stacking model using two-tier feature selection with a model using single-tier feature selection (XGBoost: 85; Boruta: 83; two-tier: 0.91). The predictive effectiveness of the stacking model was further validated by using different datasets (Validation 1: 0.83; Validation 2: 0.85) and comparing it with a simpler model and traditional clinical scores (SOFA: 0.65; APACH IV: 0.61). In addition, this study combined interpretable techniques and causal inference to analyze the causal relationship between features and predicted outcomes.

Pairwise-Constraint-Guided Multi-View Feature Selection by Joint Sparse Regularization and Similarity Learning

Feature selection is a basic and important step in real applications, such as face recognition and image segmentation. In this paper, we propose a new weakly supervised multi-view feature selection method by utilizing pairwise constraints, i.e., the pairwise constraint-guided multi-view feature selection (PCFS for short) method. In this method, linear projections of all views and a consistent similarity graph with pairwise constraints are jointly optimized to learning discriminative projections. Meanwhile, the l2,0-norm-based row sparsity constraint is imposed on the concatenation of projections for discriminative feature selection. Then, an iterative algorithm with theoretically guaranteed convergence is developed for the optimization of PCFS. The performance of the proposed PCFS method was evaluated by comprehensive experiments on six benchmark datasets and applications on cancer clustering. The experimental results demonstrate that PCFS exhibited competitive performance in feature selection in comparison with related models.

The effect of pueraria isoflavones on bone binding around with osteoporosis state based on Micro-CT detection

ObjectivesThe osteoporosis (OP) is the biggest hidden danger for the oral implantation. To discussion the effect of Pueraria lobata isoflavones on osseointegration around implants with osteoporotic stat. Materials and methodsThe bilateral ovarian extraction is a selection method for modeling osteoporosis. The Low dose (25 mg/kg) gavage has no obvious effect. The Medium (50 mg/kg) and High (100 mg/kg) dose gavage can achieve the purpose of promoting new bone formation and bone mineralization around the implant. The three different doses of Pueraria isoflavones were administered for 12 weeks. The data of the experimental group and the Model group were recorded during the treatment. ResultsThe results shown that gavage of Pueraria lobata isoflavones can promote implants Peripheral osseointegration. The Low dose gavage has no obvious effect. The Medium and High dose gavage can achieve the purpose of promoting new bone formation and bone mineralization around the implant. When osseointegration is evaluated by a mechanical force, High dose Pueraria lobata treatment can reached the effect which is closely to the Normal group and Sham-operated group. ConclusionIt is demonstrated that Pueraria lobata isoflavones has important treatment value for postmenopausal osteoporosis.

Allergen Microarrays and New Physical Approaches to More Sensitive and Specific Detection of Allergen-Specific Antibodies.

The prevalence of allergic diseases has increased tremendously in recent decades, which can be attributed to growing exposure to environmental triggers, changes in dietary habits, comorbidity, and the increased use of medications. In this context, the multiplexed diagnosis of sensitization to various allergens and the monitoring of the effectiveness of treatments for allergic diseases become particularly urgent issues. The detection of allergen-specific antibodies, in particular, sIgE and sIgG, is a modern alternative to skin tests due to the safety and efficiency of this method. The use of allergen microarrays to detect tens to hundreds of allergen-specific antibodies in less than 0.1 mL of blood serum enables the transition to a deeply personalized approach in the diagnosis of these diseases while reducing the invasiveness and increasing the informativeness of analysis. This review discusses the technological approaches underlying the development of allergen microarrays and other protein microarrays, including the methods of selection of the microarray substrates and matrices for protein molecule immobilization, the obtainment of allergens, and the use of different types of optical labels for increasing the sensitivity and specificity of the detection of allergen-specific antibodies.

Wardrobe furniture color design based on interactive genetic algorithm

With the change in consumption environment and habits, the active feedback from users on online shopping platforms serves as a valuable source of information for analyzing user demand. Color design is an important factor in shaping product style and influencing user’s purchase decisions. This study combines the Latent Dirichlet Allocation (LDA) and an interactive genetic algorithm (IGA) to investigate the usability of the interactive genetic color selection method for wardrobe color design. Firstly, the LDA model was employed to cluster online review data to identify customer requirements (CRs), then summarize the perceptual evaluation factors (EFs) of color selection. Subsequently, the color selection information from market examples was used as reference to establish the initial population, and the interactive genetic color design process was completed with CorelDraw. Then, the fuzzy comprehensive evaluation method was employed to evaluate the color scheme generated from IGA. The empirical analysis demonstrated that the interactive genetic color selection method can effectively enhance both efficiency and satisfaction in wardrobe design. This study has substantial implications for both theory and practice in the field of wardrobe design and offers designers novel design concepts and methodologies.

Performance Evaluation and Comparison of Deep Neural Network Models for African Soil Properties Prediction

ABSTRACT The significance of soil in agriculture and human survival cannot be overstated. Given its limited supply, improving soil properties is imperative. This study addresses this challenge by predicting five major soil properties—organic carbon, pH values, Mehlich-3 extractable calcium, Mehlich-3 extractable phosphorus, and sand content—utilizing mid-infrared absorbance measurements from the African Soil Information Service (AfSIS) dataset, covering non-desert regions of Africa. With a pressing need for a reliable and efficient model to predict African soil properties using spectral measurements, this study fills a crucial gap, addressing the scarcity of functional soil property databases in Africa. The developed model eliminates costly soil sample preparation and lengthy chemical analysis, applicable in both onsite and laboratory settings for determining soil functional properties. By employing stacked autoencoders for feature dimensionality reduction and combining discrete wavelet transform with two feature selection methods (stepwise regression and random forest) to build robust multi-layer perceptron (MLP) models, the study offers a comprehensive approach. Evaluation metrics including root mean square error (RMSE), scatter index (SI), Variance Accounted For (VAF), Nash-Sutcliffe Model Efficiency (NSE), and Correlation Coefficient (R) demonstrate the superior performance of the MLP model informed by stacked autoencoder-selected features, outperforming models informed by wavelet-transformed features and partial least square regression. This best-performing autoencoder-based model presents a valuable tool for soil scientists tasked with modeling African soil properties.

Machine Learning-Driven Detection of Cross-Site Scripting Attacks

The ever-growing web application landscape, fueled by technological advancements, introduces new vulnerabilities to cyberattacks. Cross-site scripting (XSS) attacks pose a significant threat, exploiting the difficulty of distinguishing between benign and malicious scripts within web applications. Traditional detection methods struggle with high false-positive (FP) and false-negative (FN) rates. This research proposes a novel machine learning (ML)-based approach for robust XSS attack detection. We evaluate various models including Random Forest (RF), Logistic Regression (LR), Support Vector Machines (SVMs), Decision Trees (DTs), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), and ensemble learning. The models are trained on a real-world dataset categorized into benign and malicious traffic, incorporating feature selection methods like Information Gain (IG) and Analysis of Variance (ANOVA) for optimal performance. Our findings reveal exceptional accuracy, with the RF model achieving 99.78% and ensemble models exceeding 99.64%. These results surpass existing methods, demonstrating the effectiveness of the proposed approach in securing web applications while minimizing FPs and FNs. This research offers a significant contribution to the field of web application security by providing a highly accurate and robust ML-based solution for XSS attack detection.

Design Analysis to Enhance Financial Education for Student Entrepreneurs in University

Telkom University organizes an entrepreneurship program that serves entire students who want to start a business with a selection method to be incubated for one semester or more. Doing business for Student Entrepreneurship (SE) requires financial management expertise because it is very important to ensure that products are in line with expectations and efficient use of resources to maximize business profits. Financial skills are also important in running businesses. From that, the university holds learning finance management in the process of business incubation, but SE feels learning financial management is not interesting. Therefore, this research aims to discuss the needs of financial management for SE, lecturers, and universities with a qualitative approach through focus group discussion. The study's key findings are that SE needs to learn about financial statements. They also expect this to be done automatically on a digital basis that is easy to use, so entrepreneurial students can take advantage of the automatically generated data for proper decision-making and can focus on the creative and innovative aspects of the product. Financial apps for financial statements could potentially be made by digital software developers

Impact and Modal Characteristics of Steels Manufactured by a Novel Hybrid Selective Laser Melting Method

Impact and Modal Characteristics of Steels Manufactured by a Novel Hybrid Selective Laser Melting Method