Perform Feature Selection Research Articles

Machine-Learning-Assisted Materials Discovery from Electronic Band Structure.

Traditional methods of materials discovery, often relying on intuition and trial-and-error experimentation, are time-consuming and limited in their ability to explore the vast design space effectively. The emergence of machine learning (ML) as a powerful tool for pattern recognition has opened exciting opportunities to revolutionize materials discovery. This work explores the application of ML techniques to assist in the discovery of materials using band structure data. The electronic band structure, which describes the energy levels of electrons in a material, holds vital information regarding its electronic and optical properties. The band structure data of 63,588 materials, including metals and insulators, have been retrieved from the Materials Project database. The data were grouped into 85 batches based on the band path in the first Brillouin zone. Three ML clustering algorithms were trained on the band structure data after performing feature selection and engineering, followed by noise reduction. The models were validated by comparing the materials' properties in a cluster.

A regularized Cox hierarchical model for incorporating annotation information in predictive omic studies

BackgroundAssociated with high-dimensional omics data there are often “meta-features” such as biological pathways and functional annotations, summary statistics from similar studies that can be informative for predicting an outcome of interest. We introduce a regularized hierarchical framework for integrating meta-features, with the goal of improving prediction and feature selection performance with time-to-event outcomes.MethodsA hierarchical framework is deployed to incorporate meta-features. Regularization is applied to the omic features as well as the meta-features so that high-dimensional data can be handled at both levels. The proposed hierarchical Cox model can be efficiently fitted by a combination of iterative reweighted least squares and cyclic coordinate descent.ResultsIn a simulation study we show that when the external meta-features are informative, the regularized hierarchical model can substantially improve prediction performance over standard regularized Cox regression. We illustrate the proposed model with applications to breast cancer and melanoma survival based on gene expression profiles, which show the improvement in prediction performance by applying meta-features, as well as the discovery of important omic feature sets with sparse regularization at meta-feature level.ConclusionsThe proposed hierarchical regularized regression model enables integration of external meta-feature information directly into the modeling process for time-to-event outcomes, improves prediction performance when the external meta-feature data is informative. Importantly, when the external meta-features are uninformative, the prediction performance based on the regularized hierarchical model is on par with standard regularized Cox regression, indicating robustness of the framework. In addition to developing predictive signatures, the model can also be deployed in discovery applications where the main goal is to identify important features associated with the outcome rather than developing a predictive model.

IMOABC: An efficient multi-objective filter–wrapper hybrid approach for high-dimensional feature selection

With the development of data science, the challenge of high-dimensional data has become increasingly prevalent. High-dimensional data contains a significant amount of redundant information, which can adversely affect the performance and effectiveness of machine learning algorithms. Therefore, it is necessary to select the most relevant features from the raw data and perform feature selection on high-dimensional data. In this paper, a novel filter–wrapper feature selection method based on an improved multi-objective artificial bee colony algorithm (IMOABC) is proposed to address the feature selection problem in high-dimensional data. This method simultaneously considers three objectives: feature error rate, feature subset ratio, and distance, effectively improving the efficiency of obtaining the optimal feature subset on high-dimensional data. Additionally, a novel Fisher Score-based initialization strategy is introduced, significantly enhancing the quality of solutions. Furthermore, a new dynamic adaptive strategy is designed, effectively improving the algorithm’s convergence speed and enhancing its global search capability. Comparative experimental results on microarray cancer datasets demonstrate that the proposed method significantly improves classification accuracy and effectively reduces the size of the feature subset when compared to various traditional and state-of-the-art multi-objective feature selection algorithms. IMOABC improves the classification accuracy by 2.27% on average compared to various multi-objective feature selection methods, while reducing the number of selected features by 88.76% on average. Meanwhile, IMOABC shows an average improvement of 4.24% in classification accuracy across all datasets, with an average reduction of 76.73% in the number of selected features compared to various traditional methods.

Chain hybrid feature selection algorithm based on improved Grey Wolf Optimization algorithm.

Hybrid feature selection algorithm is a strategy that combines different feature selection methods aiming to overcome the limitations of a single feature selection method and improve the effectiveness and performance of feature selection. In this paper, we propose a new hybrid feature selection algorithm, to be named as Tandem Maximum Kendall Minimum Chi-Square and ReliefF Improved Grey Wolf Optimization algorithm (TMKMCRIGWO). The algorithm consists of two stages: First, the original features are filtered and ranked using the bivariate filter algorithm Maximum Kendall Minimum Chi-Square (MKMC) to form a subset of candidate features S1; Subsequently, S1 features are filtered and sorted to form a candidate feature subset S2 by using ReliefF in tandem, and finally S2 is used in the wrapper algorithm to select the optimal subset. In particular, the wrapper algorithm is an improved Grey Wolf Optimization (IGWO) algorithm based on random disturbance factors, while the parameters are adjusted to vary randomly to make the population variations rich in diversity. Hybrid algorithms formed by combining filter algorithms with wrapper algorithms in tandem show better performance and results than single algorithms in solving complex problems. Three sets of comparison experiments were conducted to demonstrate the superiority of this algorithm over the others. The experimental results show that the average classification accuracy of the TMKMCRIGWO algorithm is at least 0.1% higher than the other algorithms on 20 datasets, and the average value of the dimension reduction rate (DRR) reaches 24.76%. The DRR reached 41.04% for 12 low-dimensional datasets and 0.33% for 8 high-dimensional datasets. It also shows that the algorithm improves the generalization ability and performance of the model.

Towards reliable hyperspectral imaging biomarkers of CT26 murine tumor model

The non-invasive monitoring of tumor growth can offer invaluable diagnostic insights and enhance our understanding of tumors and their microenvironment. Integrating hyperspectral imaging (HSI) with three-dimensional optical profilometry (3D OP) makes contactless and non-invasive tumor diagnosis possible by utilizing the inherent tissue contrast provided by visible (VIS) and near-infrared (NIR) light. Consequently, valuable information regarding tumors and healthy tissues can be extracted from the acquired hyperspectral images. Until now, very few methods have been used to monitor tumor models in vivo daily and non-invasively. In this research, we conducted a 14-day study monitoring BALB/c mice with subcutaneously grown CT26 murine colon carcinomas in vivo, commencing on the day of tumor cell injection. We extracted physiological properties such as total hemoglobin (THB) and tissue oxygenation (StO2) using the inverse adding-doubling (IAD) algorithm and manually segmented the tissues. We then selected the ten most relevant features describing tumors using the Max-Relevance Min-Redundancy (MRMR) algorithm and utilized 30 classic and advanced machine learning (ML) algorithms to discriminate tumors from healthy tissues. Finally, we tested the robustness of feature selection and model performance by smoothing tissue parameter maps extracted by IAD with a variable kernel and omitting selected training data. We could discriminate CT26 tumor models from surrounding healthy tissues with an area under the curve (AUC) of up to 1 for models based on the gradient boosting method, linear discriminant analysis, and random forests. Our findings help pave the way for precise and robust imaging biomarkers that could aid tumor diagnosis and advance clinical practice.

Machine Learning based Lung Cancer Diagnostic System using Optimized Feature Subset Selection

Lung is a vital organ that plays a major role in respiration. Without breathing, one may not survive in this world. Hence lung is an important organ that acts as filter to absorb oxygen and supply it to heart where pumping takes place through blood vessel in the circulatory system .The pumped blood takes oxygen and other nutrients to every other parts of the body. Hence one must take care of lung. There are various diseases associated with lungs. Lung Cancer is a deadly disease that spread across the countries all over the world. An early detection of lung cancer has been proved to improve the survival rate of human life. There are various resources are available to detect the lung cancer disease. They are low dose CT-scans, X-rays, blood-based screening, pathology slide reading, biopsy’s test, survey data(clinical dataset) etc. helps to predict the disease well in advance. Our proposed work uses two clinical datasets that has various features to detect how likely the persons get affected from the lung disease. Dataset1 includes features such as age, gender, smoking, yellow fingers, anxiety, peer-pressure, chronic disease, fatigue, allergy, wheezing, alcohol, coughing, shortness of breath, swallowing difficulty, and chest pain. Also, the work has experimented with another dataset2 that represents causes of lung cancer due to exposure of pesticide. Our proposed diagnostic system consider all these features in total and perform feature selection to extract optimal feature subsets using cuckoo search algorithm then perform classification using machine learning algorithms such as Linear Support Vector Machine, Logistic Regression and Random Forest algorithm. It is observed that with the cuckoo search algorithm, dataset 1 achieves an accuracy of 100%, precision of 100%, recall of 100%, and F1-score of 100% by LR Classifier. The Linear SVC classifier achieves an accuracy of 90%, a precision of 88%, a recall of 86%, and an F1-score of 87%.The Random forest Classifier achieves an accuracy of 91%, precision of 86%, recall of 93%, and F1-score of 90%. For dataset 2, both the LR classifier and Linear SVC classifier outperform with an accuracy of 100%, precision of 100%, recall of 100%, and F1-score of 100%. Whereas Random Forest provides accuracy of 97%, precision of 97%, recall of 96%, and F1-score of 97%.

FAStEN: An Efficient Adaptive Method for Feature Selection and Estimation in High-Dimensional Functional Regressions

Functional regression analysis is an established tool for many contemporary scientific applications. Regression problems involving large and complex data sets are ubiquitous, and feature selection is crucial for avoiding overfitting and achieving accurate predictions. We propose a new, flexible and ultra-efficient approach to perform feature selection in a sparse high dimensional function-on-function regression problem, and we show how to extend it to the scalar-on-function framework. Our method, called FAStEN, combines functional data, optimization, and machine learning techniques to perform feature selection and parameter estimation simultaneously. We exploit the properties of Functional Principal Components and the sparsity inherent to the Dual Augmented Lagrangian problem to significantly reduce computational cost, and we introduce an adaptive scheme to improve selection accuracy. In addition, we derive asymptotic oracle properties, which guarantee estimation and selection consistency for the proposed FAStEN estimator. Through an extensive simulation study, we benchmark our approach to the best existing competitors and demonstrate a massive gain in terms of CPU time and selection performance, without sacrificing the quality of the coefficients’ estimation. The theoretical derivations and the simulation study provide a strong motivation for our approach. Finally, we present an application to brain fMRI data from the AOMIC PIOP1 study. Complete FAStEN code is provided at https://github.com/IBM/funGCN. Supplementary materials for this article are available online.

Robust identification key predictors of short- and long-term weight status in children and adolescents by machine learning.

Early identification of high-risk individuals for weight problems in children and adolescents is crucial for implementing timely preventive measures. While machine learning (ML) techniques have shown promise in addressing this complex challenge with high-dimensional data, feature selection is vital for identifying the key predictors that can facilitate effective and targeted interventions. This study aims to utilize feature selection process to identify a robust and minimal set of predictors that can aid in the early prediction of short- and long-term weight problems in children and adolescents. We utilized demographic, physical, and psychological wellbeing predictors to model weight status (normal, underweight, overweight, and obese) for 1-, 3-, and 5-year periods. To select the most influential features, we employed four feature selection methods: (1) Chi-Square test; (2) Information Gain; (3) Random Forest; (4) eXtreme Gradient Boosting (XGBoost) with six ML approaches. The stability of the feature selection methods was assessed by Jaccard's index, Spearman's rank correlation and Pearson's correlation. Model evaluation was performed by various accuracy metrics. With 3,862,820 million student-visits were included in this population-based study, the mean age of 11.6 (SD = 3.64) for the training set and 10.8 years (SD = 3.50) for the temporal test set. From the initial set of 38 predictors, we identified 6, 9, and 13 features for 1-, 3-, and 5-year predictions, respectively, by the best performed feature selection method of Chi-Square test in XGBoost models. These feature sets demonstrated excellent stability and achieved prediction accuracies of 0.82, 0.73, and 0.70; macro-AUCs of 0.94, 0.86, and 0.83; micro-AUCs of 0.96, 0.93, and 0.92 for different prediction windows, respectively. Weight, height, sex, total score of self-esteem, and age were consistently the most influential predictors across all prediction windows. Additionally, several psychological and social wellbeing predictors showed relatively high importance in long-term weight status prediction. We demonstrate the potential of ML in identifying key predictors of weight status in children and adolescents. While traditional anthropometric measures remain important, psychological and social wellbeing factors also emerge as crucial predictors, potentially informing targeted interventions to address childhood and adolescence weight problems.

Abstract A008: Proof-of-Concept: A machine learning classifier to impute nativity status in Hispanic/Latino/a/x cancer patients in the California Cancer Registry

Abstract Background: Cancer disparities exist within the Hispanic/Latino/a/x (H/L) population by nativity status. Non-US born H/L colorectal cancer (CRC) patients and Prostate Cancer (PCa) patients in California have a statistically significant lower risk of cancer-specific survival when compared to Non-Hispanic White patients after controlling for confounders. However, birthplace information is largely incomplete in population-based cancer registries. For example, more than 40% of H/L patients have missing information on nativity in the California Cancer Registry (CCR) database. With the goal of better imputing missing birthplace, we built a Machine Learning (ML) model to predict nativity among CCR H/L cancer patients. Methods: H/L cases with primary invasive CRC (n = 75,613) and PCa (n = 93,042) at least 18 years old, diagnosed between 1988 and 2021 contained in the CCR research file were analyzed (46.36% had missing birthplace). A binary indicator variable assigning cases with known birthplace to US-born vs. Non-US born was constructed as the target for prediction. A stratified split by levels of the outcome was used to generate training (70%, n = 63,323) and testing (30%, n = 27,139) datasets. We used a pruned classification tree to perform feature selection, with the 10 features with the highest importance (measured by the overall reduction in impurity for any split containing each feature) used to predict nativity. Missing observations of the quantitative and qualitative features were mean, or mode imputed, respectively. The classification methods considered were classification trees, Random Forest (RF), and Boosting. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and the rate of correctly classified observations. Results: The top three features with the highest importance were the NHIA (NAACCR Hispanic/Latino Identification Algorithm) label, the Hispanic/Spanish identity, and the year of social security number issuance (SSN); the latter is part of a current SSN-based algorithm used to impute nativity status in this population. Classification models showed similar prediction performance. The highest AUC was reported by the RF (AUC = 0.9822), followed by Boosting (AUC = 0.9785), and the pruned classification tree (AUC = 0.9647). A similar rate of correctly classified observations was reported for the Boosting and RF models (∼94%). The RF model ranked feature importance in a similar order as the pruned classification tree. Ultimately, the RF model was the best performing classifier with a sensitivity and specificity of 0.61 and 0.90, respectively. This classifier substantially outperforms the SSN-based algorithm, which reported a correct classification rate of 81%, a sensitivity = 0.54, and a specificity = 0.41 in this dataset. Conclusions: This proof-of-concept analysis showcases a potential for developing a ML model to overcome missing nativity status in H/L cancer patients to enable more accurate classification by nativity in future research using CCR data. Citation Format: Proof-of-Concept: A machine learning classifier to impute nativity status in Hispanic/Latino/a/x cancer patients in the California Cancer Registry. Joel Sanchez Mendez, Lihua Liu, Juan P. Lewinger, Laura Fejerman, Mariana C. Stern [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A008.

Genetic Programming for Automatically Evolving Multiple Features to Classification

Abstract Performing classification on high-dimensional data poses a significant challenge due to the huge search space. Moreover, complex feature interactions introduce an additional obstacle. The problems can be addressed by using feature selection to select relevant features or feature construction to construct a small set of high-level features. However, performing feature selection or feature construction only might make the feature set suboptimal. To remedy this problem, this study investigates the use of genetic programming for simultaneous feature selection and feature construction in addressing different classification tasks. The proposed approach is tested on 16 datasets and compared with seven methods including both feature selection and feature constructions techniques. The results show that the obtained feature sets with the constructed and/or selected features can significantly increase the classification accuracy and reduce the dimensionality of the datasets. Further analysis reveals the complementarity of the obtained features leading to the promising classification performance of the proposed method.

Augmenting the availability of historical GDP per capita estimates through machine learning

Can we use data on the biographies of historical figures to estimate the GDP per capita of countries and regions? Here, we introduce a machine learning method to estimate the GDP per capita of dozens of countries and hundreds of regions in Europe and North America for the past seven centuries starting from data on the places of birth, death, and occupations of hundreds of thousands of historical figures. We build an elastic net regression model to perform feature selection and generate out-of-sample estimates that explain 90% of the variance in known historical income levels. We use this model to generate GDP per capita estimates for countries, regions, and time periods for which these data are not available and externally validate our estimates by comparing them with four proxies of economic output: urbanization rates in the past 500 y, body height in the 18th century, well-being in 1850, and church building activity in the 14th and 15th century. Additionally, we show our estimates reproduce the well-known reversal of fortune between southwestern and northwestern Europe between 1300 and 1800 and find this is largely driven by countries and regions engaged in Atlantic trade. These findings validate the use of fine-grained biographical data as a method to augment historical GDP per capita estimates. We publish our estimates with CI together with all collected source data in a comprehensive dataset.

A multi-task deep learning model based on comprehensive feature integration and self-attention mechanism for predicting response to anti-PD1/PD-L1

BackgroundImmune checkpoint inhibitor (ICI) has been widely used in the treatment of advanced cancers, but predicting their efficacy remains challenging. Traditional biomarkers are numerous but exhibit heterogeneity within populations. For comprehensively utilizing the ICI-related biomarkers, we aim to conduct multidimensional feature selection and deep learning model construction. MethodsWe used statistical and machine learning methods to map features of different levels to next-generation sequencing gene expression. We integrated genes from different sources into the feature input of a deep learning model, by means of self-attention mechanism. ResultsWe performed feature selection at the single-cell sequencing level, PD-L1 (CD274) analysis level, tumor mutational burden (TMB)/mismatch repair (MMR) level, and somatic copy number alteration (SCNA) level, obtaining 96 feature genes. Based on the pan-cancer dataset, we trained a multi-task deep learning model. We tested the model in the bladder urothelial carcinoma testing set 1 (AUC = 0.62, n = 298), bladder urothelial carcinoma testing set 2 (AUC = 0.66, n = 89), non-small cell lung cancer testing set (AUC = 0.85, n = 27), and skin cutaneous melanoma testing set (AUC = 0.71, n = 27). ConclusionOur study demonstrates the potential of the deep learning model for integrating multidimensional features in predicting the outcome of ICI. Our study also provides a potential methodological case for medical scenarios requiring the integration of multiple levels of features.

Data analytics in ensemble learning for effective crop yield prediction

In agricultural research, Crop Yield Prediction (CYP) offers the best decision-making to assist farmers in agricultural yield forecasting efficiently. Most of the existing studies have not considered the exhaustive exploration of data analytics techniques in Machine Learning (ML) for CYP due to which the existing models have not given the optimal results. The main objective of this study is to investigate the effectiveness of data analytics in Ensemble Learning (EL) techniques for more reliable and high-performance CYP models. This article proposes an expert system model, Blended Expert System for Crop Yield Prediction (BESCYP), designed to predict the precise crop yields for specific agricultural lands in the Assam state of India. The proposed BESCYP employs Expectation–Maximization (EM) algorithm to treat missing values, the Isolation Forest (IF) technique to analyze outliers, the Genetic Algorithm (GA) to perform feature selection, Robust Scaling (RS) technique to perform data normalization and the Extra Tree (ET) for classification that overcome the variance and overfitting problem commonly associated with standard ML algorithms. The evaluation of the proposed BESCYP model has been performed using accuracy, precision, recall, and F-1 score on a dataset obtained from International Crops Research Institute for Semi-Arid Tropics (ICRISAT). The proposed model is compared against different standard ML algorithms, EL algorithms and various existing models available in the literature, and the experimental results show that the proposed BESCYP model outperforms other models.

Improving genomic prediction of rhizomania resistance in sugar beet (Beta vulgaris L.) by implementing epistatic effects and feature selection

Background Rhizomania counts as the most important disease in sugar beet Beta vulgaris L. for which no plant protection is available, leaving plant breeding as the only defence strategy at the moment. Five resistance genes have been detected on the same chromosome and further studies suggested that these might be different alleles at two resistance clusters. Nevertheless, it was postulated that rhizomania resistance might be a quantitative trait with multiple unknown minor resistance genes. Here, we present a first attempt at genomic prediction of rhizomania resistance in a population that carries resistances at the two known resistance clusters. The sugar beet population was genotyped using single nucleotide polymorphism (SNP) markers. Methods First, genomic prediction was performed using all SNPs. Next, we calculated the variable importance for each SNP using machine learning and performed genomic prediction by including the SNPs incrementally in the prediction model based on their variable importance. Using this method, we selected the optimal number of SNPs that maximised the prediction accuracy. Furthermore, we performed genomic prediction with SNP pairs. We also performed feature selection with SNP pairs using the information about the variable importance of the single SNPs. Results From the four methods under investigation, the latter led to the highest prediction accuracy. These results lead to the conclusion that more than the two known resistance clusters are involved in rhizomania resistance and that genetic interactions affect rhizomania resistance. Finally, we have analysed which SNPs were repeatedly detected in the feature selection process and discovered four SNPs, two of which are located on chromosomes that were previously not associated with rhizomania resistance.

Sparse Bayesian learning using TMB (Template Model Builder)

Sparse Bayesian Learning, and more specifically the Relevance Vector Machine (RVM), can be used in supervised learning for both classification and regression problems. Such methods are particularly useful when applied to big data in order to find a sparse (in weight space) representation of the model. This paper demonstrates that the Template Model Builder (TMB) is an accurate and flexible computational framework for implementation of sparse Bayesian learning methods.The user of TMB is only required to specify the joint likelihood of the weights and the data, while the Laplace approximation of the marginal likelihood is automatically evaluated to numerical precision. This approximation is in turn used to estimate hyperparameters by maximum marginal likelihood. In order to reduce the computational cost of the Laplace approximation we introduce the notion of an “active set” of weights, and we devise an algorithm for dynamically updating this set until convergence, similar to what is done in other RVM type methods. We implement two different methods using TMB; the RVM and the Probabilistic Feature Selection and Classification Vector Machine method, where the latter also performs feature selection. Experiments based on benchmark data show that our TMB implementation performs comparable to that of the original implementation, but at a lower implementation cost. TMB can also calculate model and prediction uncertainty, by including estimation uncertainty from both latent variables and the hyperparameters. In conclusion, we find that TMB is a flexible tool that facilitates implementation and prototyping of sparse Bayesian methods.

Graph Representation Learning enhanced Semi-supervised Feature Selection

Feature selection is a key step in machine learning by eliminating features that are not related to the modeling target to create reliable and interpretable models. By exploring the potential complex correlations among features of unlabeled data, recently introduced self-supervision-enhanced feature selection greatly reduces the reliance on the labeled samples. However, they are generally based on the autoencoder with sample-wise self-supervision, which can hardly exploit the relations among samples. To address this limitation, this paper proposes Graph representation learning enhanced Semi-supervised Feature Selection (G-FS) which performs feature selection based on the discovery and exploitation of the non-Euclidean relations among features and samples by translating unlabeled “plain” tabular data into a bipartite graph. A self-supervised edge prediction task is designed to distill rich information on the graph into low-dimensional embeddings, which remove redundant features and noise. Guided by the condensed graph representation, we propose a batch-attention feature weight generation mechanism that generates more robust weights according to batch-based selection patterns rather than individual samples. The results show that G-FS achieves significant performance edges in fourteen datasets compared to twelve state-of-the-art baselines, including two recent self-supervised baselines. The source code is public available at https://github.com/Icannotnamemyselff/G-FS_Graph_enhacned_feature_selection .

State of Health Prediction in Electric Vehicle Batteries Using a Deep Learning Model

Accurately estimating the state of health (SOH) of lithium-ion batteries plays a significant role in the safe operation of electric vehicles. Deep learning (DL)-based approaches for estimating state of health (SOH) have consistently been the focus of study in recent years. In the current era of electric mobility, the utilization of lithium-ion batteries (LIBs) has evolved into a necessity for energy storage. Ensuring the safe operation of EVs requires a precise assessment of the state-of-health (SOH) of LIBs. To estimate battery SOH accurately, this paper employs a deep learning (DL) algorithm to enhance the estimation accuracy of SOH to obtain accurate SOH measurements. This research introduces the Diffusion Convolutional Recurrent Neural Network (DCRNN) with a Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithm (DCRNN + SVM-RFE) for enhancing classification and feature selection performance. The data gathered from the dataset were pre-processed using the min–max normalization method. The Center for Advanced Life Cycle Engineering (CALCE) dataset from the University of Maryland was employed to train and evaluate the model. The SVM-RFE algorithm was used for feature selection of pre-processed data. DCRNN algorithm was used for the classification process to enhance prediction precision. The DCRNN + SVM-RFE model’s performance was calculated using Mean Absolute Percentage Error (MAPE), Mean Squared Error (MAE), Mean Squared Error (MSE), and Root MSE (RMSE) metric values. The proposed model generates accurate results for SOH prediction; all RMSEs are within 0.02%, MAEs are within 0.015%, MSEs were within 0.032%, and MAPEs are within 0.41%. The mean values of RMSE, MSE, MAE, and MAPE were 0.014, 0.026, 0.011, and 0.32, respectively. Experiments confirmed that the DCRNN + SVM-RFE model has the highest accuracy among those that predict SOH.

Can computed tomography findings and radiomics analysis of mediastinal lymph nodes differentiate between sarcoidosis and lymphoma?

To assess the ability of computed tomography (CT) findings and radiomics analysis to differentiate mediastinal lymphadenopathies as sarcoidosis versus lymphoma. 94 patients with lymphoma and 97 patients with sarcoidosis, who had > 1cm mediastinal lymph node were included. Size, location of lymph nodes, and distribution of the largest lymph nodes in two groups were compared. A total of 636 lymphadenopathies in four different regions were segmented for radiomics. Lesion segmentation was semiautomatically performed with a dedicated commercial software package on chest CT images. 149 patients were grouped as a training cohort, while 42 patients who underwent CT in the oncology hospital were used for external validation. The least absolute shrinkage and selection operator (LASSO) analysis was used to perform feature selection. Using selected features, the classification performance of various data mining methods in separating groups of sarcoidosis and lymphoma was investigated. Distribution and size of lymphadenopathies were significantly different in sarcoidosis and lymphoma groups (<0.05). Radiomics and data mining methods showed excellent performance in differentiating lymph nodes of sarcoidosis and lymphoma according to both the largest lymphadenopathy and lymphadenopathies in four different mediastinal regions (AUC >0,95). Distribution and size of lymphadenopathies can help differential diagnosis in patients with sarcoidosis and lymphoma. CT radiomics analysis can discriminate the lymph nodes of sarcoidosis and lymphoma with great performance regardless of lymph node size and location and it can be used safely in the diagnosis of these diseases, which can sometimes be challenging to distinguish from each other.

Hybrid optimal feature selection-based iterative deep convolution learning for COVID-19 classification system

The COVID-19 pandemic has necessitated the development of innovative and efficient methods for early detection and diagnosis. Integrating Internet of Things (IoT) devices and applications in healthcare has facilitated various functions. This work aims to employ practical artificial intelligence (AI) approaches to extract meaningful information from the vast amount of IoT data to perform disease prediction tasks. However, traditional AI methods need help in feature analysis due to the complexity and scale of IoT data. So, this work implements the optimal iterative COVID-19 classification network (OICC-Net) using machine learning optimization and deep learning approaches. Initially, the preprocessing operation normalizes the dataset with uniform values. Here, random forest infused particle swarm-based black widow optimization (RFI-PS-BWO) algorithm was used to get the disease-specific patterns from SARS-CoV-2 (SC2), and other disease classes, where patterns of the SC2 virus are very similar to those of other virus classes. In addition, an iterative deep convolution learning (IDCL) feature selection method is used to distinguish features from the RFI-PS-BWO data. This iterative process enhances the performance of feature selection by providing improved representation and reducing the dimensionality of the input data. Then, a one-dimensional convolutional neural network (1D-CNN) was employed to classify and identify the extracted features from SC2 with no virus classes. The 1D-CNN model is trained using a large dataset of COVID-19 samples, enabling it to learn intricate patterns and make accurate predictions. It was tested and found that the proposed OICC-Net system is more accurate than current methods, with a score of 99.97 % for F1-score, 100 % for sensitivity, 100 % for specificity, 99.98 % for precision, and 99.99 % for recall.

A cosine similarity-based labeling technique for vulnerability type detection using source codes

Vulnerability detection is of great importance in providing reliability to software systems. Although existing methods achieve remarkable success in vulnerability detection, they have several disadvantages as follows: (1) The irrelevant information is removed from source codes, which have a high noise ratio, thereby utilizing deep learning methods and devising experiments featuring high accuracy. However, deep learning-based detection methods necessitate large-scale datasets. This results in computational hardship with respect to vulnerability detection in small-scale software systems. (2) The majority of the studies perform feature selection by processing vulnerability commits. Despite tremendous endeavors, there are few works detecting vulnerability with source codes. To solve these two problems, in this study, a novel labeling and vulnerability detection algorithm is proposed. The algorithm first exploits source codes with the help of a keyword vulnerability matrix. After that, an ultimate encoded matrix is generated by word2vec, thereby combining the labeling vector with the source code matrix to reveal a trainable dataset for a generalized linear model (GLM). Different from preceding studies, our method performs vulnerability detection without requiring vulnerability commits but using source codes. In addition to this, similar studies generally aim to bring sophisticated solutions for just one type of programming language. Conversely, our study develops vulnerability keywords for three programming languages including C#, Java, and C++, and creates the related labeling vectors by regarding the keyword matrix. The proposed method outperformed the baseline approaches for most of the experimental datasets with over 90% of the area under the curve (AUC). Further, there is a 7.7% margin between our method and the alternatives on average for Recall, Precision, and F1-score with respect to five types of vulnerabilities.