Optimal Feature Research Articles

An integrated framework for aspect category-based sentiment analysis using adaptive feature selection and category-aware decision fusion strategies

Purpose Aspect category-based sentiment analysis (ACSA) has been widely used in consumer preference mining and marketing strategy formulation. However, existing studies ignore the variability in features and the intrinsic correlation among diverse aspect categories in ACSA tasks. To address these problems, this paper aims to propose a novel integrated framework. Design/methodology/approach The integrated framework consists of three modules: text feature extraction and fusion, adaptive feature selection and category-aware decision fusion. First, text features from global and local views are extracted and fused to comprehensively capture the potential information in the different dimensions of the review text. Then, an adaptive feature selection strategy is devised for each aspect category to determine the optimal feature set. Finally, considering the intrinsic associations between aspect categories, a category-aware decision fusion strategy is constructed to enhance the performance of ACSA tasks. Findings Comparative experimental results demonstrate that the integrated framework can effectively detect aspect categories and their corresponding sentiment polarities from review texts, achieving a macroaveraged F1 score (Fmacro) of 72.38% and a weighted F1 score (F1) of 79.39%, with absolute gains of 2.93% to 27.36% and 4.35% to 20.36%, respectively, compared to the baselines. Originality/value This framework can simultaneously detect aspect categories and corresponding sentiment polarities from review texts, thereby assisting e-commerce enterprises in gaining insights into consumer preferences, prioritizing product improvements, and adjusting marketing strategies.

High-Resolution Mapping of Topsoil Sand Content in Planosol Regions Using Temporal and Spectral Feature Optimization

Soil sand content is an important characterization index of soil texture, which directly affects soil water regulation, nutrient cycling, and crop growth potential. Therefore, its high-precision spatial distribution information is of great importance for agricultural resource management and land use. In this study, a remote sensing prediction method based on the combination of time-phase optimization and spectral feature preference is innovatively proposed for improving the mapping accuracy of the sand content in the till layer of a planosol area. The study first analyzed the prediction performance of single-time-phase images, screened the optimal time-phase (May), and constructed a single-time-phase model, which achieved significant prediction accuracy, with a coefficient of determination (R2) of 0.70 and a root mean square error (RMSE) of 1.26%. Subsequently, the model was further optimized by combining multiple time phases, and the prediction accuracy was improved to R2 = 0.77 and the RMSE decreased to 1.10%. At the feature level, the recursive feature elimination (RF-RFE) method was utilized to preferentially select 19 key spectral variables from the initial feature set, among which the short-wave infrared bands (b11, b12) and the visible bands (b2, b3, b4) contributed most significantly to the prediction. Finally, the prediction accuracy was further improved to R2 = 0.79 and RMSE = 1.05% by multi-temporal-multi-feature fusion modeling. The spatial distribution map of sand content generated by the optimized model shows that areas with high sand content are primarily located in the northern and central regions of Shuguang Farm. This study not only provides a new technical path for accurate mapping of soil texture in the planosol area, but also provides a reference for the improvement of remote sensing monitoring methods in other typical soil areas. The research results can provide a reference for mapping high-resolution soil sand maps over a wider area in the future.

Optimized Watermelon Scion Leaf Segmentation Model Based on Hungarian Algorithm and Information Theory

In the fully automated grafting process of watermelon seedlings, it is crucial to ensure that the scion’s cotyledons maintain a perpendicular orientation with the rootstock cotyledons. To achieve precise segmentation of watermelon scion cotyledons and accurately extract parameters, such as cotyledon orientation angles, this study introduces enhancements to the Mask2Former network, aiming to improve segmentation accuracy for watermelon scion cotyledons. Specifically, two innovative modules are designed. Taking Swin-Former as the backbone, an Optimal Feature Re-ranking (OFR) module based on the Hungarian Algorithm is devised to re-rank the feature maps obtained from the feature extraction process. Grounded in information theory, the amount of information in semantic segmentation tasks is quantified as Shannon entropy, enabling the model to perceive the information distribution of the feature maps and dynamically adjust the output features. Experimental results demonstrate that the improved model achieves mIoU, mDice, mPrecision, and mRecall scores of 97.44%, 98.70%, 98.20%, and 99.21%, respectively, greatly outperforming Mask2Former, FCNN, and DeepLabv3. Furthermore, the enhanced network exhibits superior accuracy in low signal-to-noise ratio environments, highlighting its robustness in complex scenarios. This study provides a high-precision solution for agricultural automation in the watermelon industry, contributing to the development of fully automated grafting machines.

Advances in colorectal cancer diagnosis using optimal deep feature fusion approach on biomedical images.

Colorectal cancer (CRC) is the second popular cancer in females and third in males, with an increased number of cases. Pathology diagnoses complemented with predictive and prognostic biomarker information is the first step for personalized treatment. Histopathological image (HI) analysis is the benchmark for pathologists to rank colorectal cancer of various kinds. However, pathologists' diagnoses are highly subjective and susceptible to inaccurate diagnoses. The improved diagnosis load in the pathology laboratory, incorporated with the reported intra- and inter-variability in the biomarker assessment, has prompted the quest for consistent machine-based techniques to be integrated into routine practice. In the healthcare field, artificial intelligence (AI) has achieved extraordinary achievements in healthcare applications. Lately, computer-aided diagnosis (CAD) based on HI has progressed rapidly with the increase of machine learning (ML) and deep learning (DL) based models. This study introduces a novel Colorectal Cancer Diagnosis using the Optimal Deep Feature Fusion Approach on Biomedical Images (CCD-ODFFBI) method. The primary objective of the CCD-ODFFBI technique is to examine the biomedical images to identify colorectal cancer (CRC). In the CCD-ODFFBI technique, the median filtering (MF) approach is initially utilized for noise elimination. The CCD-ODFFBI technique utilizes a fusion of three DL models, MobileNet, SqueezeNet, and SE-ResNet, for feature extraction. Moreover, the DL models' hyperparameter selection is performed using the Osprey optimization algorithm (OOA). Finally, the deep belief network (DBN) model is employed to classify CRC. A series of simulations is accomplished to highlight the significant results of the CCD-ODFFBI method under the Warwick-QU dataset. The comparison of the CCD-ODFFBI method showed a superior accuracy value of 99.39% over existing techniques.

XGBoost-enhanced ensemble model using discriminative hybrid features for the prediction of sumoylation sites

Posttranslational modifications (PTMs) are essential for regulating protein localization and stability, significantly affecting gene expression, biological functions, and genome replication. Among these, sumoylation a PTM that attaches a chemical group to protein sequences—plays a critical role in protein function. Identifying sumoylation sites is particularly important due to their links to Parkinson’s and Alzheimer’s. This study introduces XGBoost-Sumo, a robust model to predict sumoylation sites by integrating protein structure and sequence data. The model utilizes a transformer-based attention mechanism to encode peptides and extract evolutionary features through the PsePSSM-DWT approach. By fusing word embeddings with evolutionary descriptors, it applies the SHapley Additive exPlanations (SHAP) algorithm for optimal feature selection and uses eXtreme Gradient Boosting (XGBoost) for classification. XGBoost-Sumo achieved an impressive accuracy of 99.68% on benchmark datasets using 10-fold cross-validation and 96.08% on independent samples. This marks a significant improvement, outperforming existing models by 10.31% on training data and 2.74% on independent tests. The model’s reliability and high performance make it a valuable resource for researchers, with strong potential for applications in pharmaceutical development.

Deep Feature Blend Attention: A New Frontier in Super Resolution Image Generation

Super Resolution (SR) images contain more useful information than Low Resolution (LR) images. Generally, SR images are highly preferred over LR images in the medical field due to their high quality. Generally, SR images are affected by many factors, like blur, noise, and decimation. Therefore, the present study proposes Deep Feature Blend Attention Mechanism for generating SR images with optimal outcomes. In this study, a Deep Learning (DL) based encoder-decoder is used to extract the detailed information from the LR image, assist in noise removal, and improve image quality. Using Gaussian blur, an LR image is produced, which is given as input to the encoder-decoder. The Attention mechanism with feature blending is performed to produce the reconstructed SR image. The low and high frequency component issues from feature map are improved using the attention mechanism. Also, feature blend attention mechanism is implied to reconstruct SR images from LR images. The present study utilizes the feature blend technique, which helps select the optimal features and blending to attain the optimal feature set. The study is performed with and without the feature blend technique in attention mechanism to expose the impact of the feature blend technique, and it is used to avoid overfitting issues and produce optimal results. The performance of the proposed system is assessed by using PSNR and SSIM to show the system's efficiency. The proposed system is compared with other state-of-the-art studies to expose the efficacy of the present system.

A Halton enhanced solution-based Human Evolutionary Algorithm for complex optimization and advanced feature selection problems

A Halton enhanced solution-based Human Evolutionary Algorithm for complex optimization and advanced feature selection problems

A feature fusion method based on radiomic features and revised deep features for improving tumor prediction in ultrasound images.

Radiomic features and deep features are both vitally helpful for the accurate prediction of tumor information in breast ultrasound. However, whether integrating radiomic features and deep features can improve the prediction performance of tumor information is unclear. A feature fusion method based on radiomic features and revised deep features was proposed to predict tumor information. Radiomic features were extracted from the tumor region on ultrasound images, and the optimal radiomic features were subsequently selected based on Gini score. Revised deep features, which were extracted using the revised CNN models integrating prior information, were combined with radiomic features to build a logistic regression classifier for tumor prediction. The performance was evaluated using area under the receiver operating characteristic (ROC) curve (AUC). The results showed that the proposed feature fusion method (AUC=0.9845) obtained better prediction performance than that based on radiomic features (AUC=0.9796) or deep features (AUC=0.9342). Our results demonstrate that the proposed feature fusion framework integrating the radiomic features and revised deep features is an efficient method to improve the prediction performance of tumor information.

Multi-Objective Feature Selection Algorithm using Beluga Whale Optimization

The advancement of science and technology has resulted in large datasets with noisy or redundant features that hamper classification. In feature selection, relevant attributes are selected to reduce dimensionality, thereby improving classification accuracy. Multi-objective optimization is crucial in feature selection because it allows simultaneous evaluation of multiple, often conflicting objectives, such as maximizing model accuracy and minimizing the number of features. Traditional single-objective methods might focus solely on accuracy, often leading to models that are complex and computationally expensive. Multi-objective optimization, on the other hand, considers trade-offs between different criteria, identifying a set of optimal solutions (a Pareto front) where no one solution is clearly superior. It is especially useful when analyzing high-dimensional datasets, as it reduces overfitting and enhances model performance by selecting the most informative subset of features. This article introduces and evaluates the performance of the Binary version of Beluga Whale Optimization and the Multi-Objective Beluga Whale Optimization (MOBWO) algorithm in the context of feature selection. Features are encoded as binary matrices to denote their presence or absence, making it easier to stratify datasets. MOBWO emulates the exploration and exploitation patterns of Beluga Whale Optimization (BWO) through continuous search space. Optimal classification accuracy and minimum feature subset size are two conflicting objectives. The MOBWO was compared using 12 datasets from the University of California Irvine (UCI) repository with eleven well-known optimization algorithms, such as Genetic Algorithm (GA), Sine Cosine Algorithm (SCA), Bat Optimization Algorithm (BOA), Differential Evolution (DE), Whale Optimization Algorithm (WOA), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Grasshopper Optimization Algorithm (MOGOA), Multi-Objective Non-dominated advanced Butterfly Optimization Algorithm (MONSBOA), and Multi-Objective Slime Mould Algorithm (MOSMA). In experiments using Random Forest (RF) as the classifier, different performance metrics were evaluated. The computational results show that the proposed BBWO algorithm achieves an average accuracy rate of 99.06% across 12 datasets. Additionally, the proposed MOBWO algorithm outperforms existing multi-objective feature selection methods on all 12 datasets based on three metrics: Success Counting (SCC), Inverted Generational Distance (IGD), and Hypervolume indicators (HV). For instance, MOBWO achieves an average HV that is at least 3.54% higher than all other methods.

A pilot study for speech assessment to detect the severity of Parkinson's disease: An ensemble approach.

Changes in voice are a symptom of Parkinson's disease and used to assess the progression of the condition. However, natural differences in the voices of people can make this challenging. Computerized binary speech classification can identify people with PD (PwPD), but its multiclass application to detect the severity of the disease remains difficult. This study investigated six diadochokinetic (DDK) tasks, four features (phonation, articulation, prosody, and their fusion), and three machine learning models for four severity levels of PwPD. The four binary classifications were: (i) Normal vs Not Normal, (ii) Slight vs Not Slight, (iii) Mild vs Not Mild and (iv) Moderate vs. Not Moderate. The best task and features for each class were identified and the models were ensembled to develop a multiclass model to distinguish between Normal vs. Slight vs. Mild vs. Moderate. For Normal vs Not-normal, logistic regression (LR) using the prosody from "ka-ka-ka" task, Random Forest (RF) using articulation from "petaka" for Slight vs Not Slight, RF for the fusion from "ka-ka-ka" for Mild vs Not Mild and Gradient Boosting (GB) using prosody from "ta-ta-ta" for Moderate vs Not Moderate gave the best results. Combining these using LR achieved an accuracy of 72%. Dividing the multiclass problem into four binary problems gives the optimum speech features for each class. This pilot study, conducted on a small public dataset, shows the potential of computerized speech analysis using DDK to evaluate the severity of Parkinson's disease voice symptoms.

Enhancing rice seed purity recognition accuracy based on optimal feature selection

Enhancing rice seed purity recognition accuracy based on optimal feature selection

Progression-Free Survival Prediction for Locally Advanced Cervical Cancer after Chemoradiotherapy with MRI-based Radiomics

Background and purposeA significant proportion of locally advanced cervical cancer (LACC) patients experience disease progression post-chemoradiotherapy (CRT). Currently existing clinical variables are suboptimal predictors of treatment response. This study reported a radiomics-based model leveraging information extracted from MR T2-weighted image (T2WI) to predict the progression-free survival (PFS) for LACC following CRT. Materials and methodsRadiomics features were extracted from pre-treatment MR T2WI in 105 LACC patients. Following pre-feature selection and a step-forward feature selection method, an optimal feature set was determined with a cox proportional hazard (CPH) model. The PFS predictions were generated through a radiomics-clinical combined model utilized five repeated nested 5-fold cross-validation (5-fold CV). Disease progression risk was stratified into high- and low-risk groups based on the predicted PFS and assessed by Kaplan-Meier analysis. ResultsThe radiomics texture feature extracted from MR T2WI significantly predict PFS in LACC after CRT. In comparison to the model using clinical variables alone, the radiomics-clinical combined model achieves significantly improved performance in testing patient cohort, achieving higher C-Index (0.748 vs 0.655) and AUC (0.798 vs 0.660 for 2-year PFS). Meanwhile, the proposed method significantly differentiated the high- and low-risk patients groups for disease progression (p<0.001). ConclusionAn MR T2WI-based radiomics and clinical combined model provided improved prognostic capabilities in predicting the PFS for LACC patients treated with CRT, outperforming a model using clinical variables alone. The incorporation of MR T2WI-based radiomics is promising in assisting in personalized management in LACC, indicating the potential of MR T2WI radiomics as imaging biomarker.

Development and validation of an explainable machine learning model for mortality prediction among patients with infected pancreatic necrosis.

Infected pancreatic necrosis (IPN) represents a severe complication of acute pancreatitis, commonly linked with mortality rates ranging from 15% to 35%. However, the present mortality prediction tools for IPN are limited and lack sufficient sensitivity and specificity. This study aims to develop and validate an explainable machine learning (ML) model for death prediction among patients with IPN. We performed a prospective cohort study of 344 patients with IPN consecutively enrolled from a large Chinese tertiary hospital from January 2011 to January 2023. Ten ML models were developed to predict 90-day mortality in these patients. A benchmarking test, involving nested resampling, automatic hyperparameter tuning and random search techniques, was conducted to select the ML model. Sequential forward selection method was employed to select the optimal feature subset from 31 candidate subsets to simplify the model and maximize predictive performance. The final model was internally validated with the 1000 bootstrap method and externally validated using an independent cohort of 132 patients with IPN retrospectively collected from another Chinese tertiary hospital from January 2018 to January 2023. The SHapley Additive exPlanations (SHAP) method was employed to interpret the model in terms of features importance and features effect. The final model constructed with optimal feature subset was deployed as an interactive web-based Shiny app. Random survival forest (RSF) model showed the best predictive performance than other 9ML models (internal validation, C-index=0.863 [95% CI: 0.854-0.875]; external validation, C-index=0.857 [95% CI: 0.850-0.865]). Multiple organ failure, Acute Physiology and Chronic Health Examination II (APACHE II) score ≥20, duration of organ failure ≥21 days, bloodstream infection, time from onset to first intervention <30 days, Bedside Index of Severity in Acute Pancreatitis score ≥3, critical acute pancreatitis, age ≥ 50 years, and hemorrhage were 9 most important features associated with mortality. Furthermore, SHAP algorithm revealed insightful nonlinear interactive associations between important predictors and mortality, identifying 9 features pairs with high interaction SHAP value and clinical significance. Two interactive web-based Shiny apps were developed to enhance clinical practicability: https://rsfmodels.shinyapps.io/IPN_app/ for cases where the APACHE II score was available and https://rsfmodels.shinyapps.io/IPNeasy/ for cases where it was not. An explainable ML model for death prediction among IPN patients was feasible and effective, suggesting its superior potential in guiding clinical management and improving patient outcomes. Two publicly accessible web tools generated for the optimized model facilitated its utility in clinical settings. The Natural Science Foundation of Hunan Province (2023JJ30885), Postdoctoral Fellowship Program of CPSF (GZB20230872), The Youth Science Foundation of Xiangya Hospital (2023Q13), The Project Program of National Clinical Research Center for Geriatric Disorders of Xiangya Hospital (2021LNJJ19).

Classification of Pear Varieties Using the K-Nearest Neighbor Algorithm and Extraction of Shape, Color, Texture, and Size Features

This study develops a pear variety classification system based on digital images using the K-Nearest Neighbor (KNN) algorithm. The data used included 195 images from three pear varieties, namely Century, Forel Afrika, and Singo, which were analyzed by utilizing various features such as color (RGB), texture (Local Binary Pattern), shape (area, circumference, length-width ratio), and size (bounding box dimensions). The preprocessing process removes the image's background to increase focus on the main object, thus allowing for more optimal feature extraction. The dataset is divided into 80% for training and 20% for model testing. The evaluation results show that the KNN model can achieve an accuracy of 85%, with an average precision value of 0.85, recall of 0.89, and F1-score of 0.85. These results prove that the KNN algorithm is effective in accurately classifying pear varieties, which can significantly contribute to applying digital image-based technology for automatic classification needs in the agricultural sector.

Maillard-derived mung bean protein-peach gum conjugates: A novel emulsifier to improve stability, antioxidants, and physicochemical properties of chia seed oil nanoemulsion.

This research is designed to enhance the physio-chemical properties, constancy, and antioxidant activities of water-in-oil (W/O) emulsions containing chia seed oil (CSO) by utilizing mung bean protein isolate (MBPI)-peach gum (PG) conjugates, which were created through the Maillard reaction (MR), as the emulsifying agents. The emulsions were prepared using MBPI-PG produced through the Maillard reaction (EMRP) at concentrations of 0.5%, 1%, and 1.5%. Another set of emulsions, serving as control samples, was prepared using MBPI-PG without the MR (EC) at the same concentrations. The EMRP samples demonstrated optimum characteristics during storage over 30days at 25°C, particularly at 1% concentration, including the droplet size (176.37nm), PDI (0.3), zeta potential (-47.52 Mv), quantity of absorbed protein (63.48%), creaming index (22.99%), and viscosity compared to EC. The emulsions prepared with MRP exhibited significantly lower POV (1.45mM/kg oil) and TBARS (59.17mM/kg oil) formation rates than EC. The EMRP1% formulation displayed the lowest release of antioxidant compounds among all formulations, suggesting low release control during storage. Molecular docking results confirmed that adding EMRP1% to the CSO emulsion significantly improved its quality and stability. This emulsifier could hold significant promise for future advancements in the food industry.

Optimizing Skin Cancer Diagnosis: A Modified Ensemble Convolutional Neural Network for Classification.

Skin cancer is recognized as one of the most harmful cancers worldwide. Early detection of this cancer is an effective measure for treating the disease efficiently. Traditional skin cancer detection methods face scalability challenges and overfitting issues. To address these complexities, this study proposes a random cat swarm optimization (CSO)with an ensemble convolutional neural network (RCS-ECNN) method to categorize the different stages of skin cancer. In this study, two deep learning classifiers, deep neural network (DNN) and Keras DNN (KDNN), are utilized to identify the stages of skin cancer. In this method, an effective preprocessing phase is presented to simplify the classification process. The optimal features are selected using the feature extraction phase. Then, the GrabCut algorithm is employed to carry out the segmentation process. Also, the CSO is employed to enhance the effectiveness of the method. The HAM10000 and ISIC datasets are utilized to evaluate the RCS-ECNN method. The RCS-ECNN method achieved an accuracy of 99.56%, a recall of 99.66%, a specificity value of 99.254%, a precision value of 99.18%, and an F1-score value of 98.545%, respectively. The experimental results demonstrated that the RCS-ECNN method outperforms the existing techniques.

Evaluation of the Effects of Mobile Applications on the Care Outcomes of Patients Undergoing Surgery for Breast Cancer : Systematic Review

Background: Breast cancer surgery requires comprehensive postoperative care to optimize recovery and patient outcomes. Mobile health applications have gained attention as tools to enhance patient engagement, provide education, and support recovery through features like symptom tracking and communication with healthcare providers. This systematic review aims to evaluate the impact of these applications on care outcomes in patients undergoing breast cancer surgery. Methods: This systematic literature study was conducted from January 2012 to December 2022 across PubMed, ProQuest and Google Scholar databases to identify randomized controlled trials assessing the effect of mobile applications on care outcomes in patients who underwent breast cancer surgery. Studies were included based on specific criteria: they had to evaluate mobile health interventions post-surgery and report on at least one outcome related to symptom management, quality of life, anxiety, depression, or distress. The content, methods and quantitative findings of all publications were analyzed. Results: The review included, four randomized controlled trial, covering a total of 318 participants. Across the studies, mobile health applications demonstrated a significant improvement in care outcomes. Symptom management effectiveness improved by 15-25%, anxiety and distress levels decreased by 10-20%, and patient-reported quality of life increased by an average of 15-25%. Additionally, mobile applications were associated with enhanced communication with healthcare providers, facilitating early intervention and support. Conclusion: Mobile health applications were found to significantly improve care outcomes for breast cancer patients undergoing surgery. These applications are also effective and safe in improving the quality of life, managing symptoms, and reducing psychological distress. The integration of mobile health tools can enhance patient care pathways and postoperative recovery. Future research should explore long-term impacts, optimal app features, and scalability in diverse patient populations.

Exploring the assessment of post-cardiac valve surgery pulmonary complication risks through the integration of wearable continuous physiological and clinical data

BackgroundPostoperative pulmonary complications (PPCs) following cardiac valvular surgery are characterized by high morbidity, mortality, and economic cost. This study leverages wearable technology and machine learning algorithms to preoperatively identify high-risk individuals, thereby enhancing clinical decision-making for the mitigation of PPCs.MethodsA prospective study was conducted at the Department of Cardiovascular Surgery of West China Hospital, Sichuan University, from August 2021 to December 2022. We examined 100 cardiac valvular surgery patients, where wearable technology was utilized to collect and analyze nocturnal physiological data at the 24-hour admission, in conjunction with clinical data extraction from the Hospital Information System’s electronic records. We systematically evaluated three different input types (physiological, clinical, and both) and five classifiers (XGB, LR, RF, SVM, KNN) to identify the combination with strong predictive performance for PPCs. Feature selection was conducted using Recursive Feature Elimination with Cross-Validated (RFECV) for each model, yielding an optimal feature subset for each, followed by a grid search to tune hyperparameters. Stratified 5-fold cross-validation was used to evaluate the generalization performance. The significance of AUC differences between models was tested using the DeLong test to determine the optimal prognostic model comprehensively. Additionally, univariate logistic regression analysis was conducted on the features of the best-performing model to understand the impact of individual feature on PPCs.ResultsIn this study, 22 patients (22%) developed PPCs. Across classifiers, models combining both physiological and clinical features performed better than physiological or clinical features alone. Specifically, including physiological data in the classification model improved AUC, ACC, F1, and precision by an average of 8.32%, 1.80%, 3.28% and 6.06% compared to using clinical data only. The XGB classifier, utilizing both dataset, achieved the highest performance with an AUC of 0.82 (± 0.08) and identified eight significant features. The DeLong test indicated that the XGB model utilizing the both dataset significantly outperformed the XGB models trained on the physiological or clinical datasets alone. Univariate logistic regression analysis suggested that surgical methods, age, nni_50, and min_ven_in_mean are significantly associated with the occurrence of PPCs.ConclusionThe integration of continuous wearable physiological and clinical data significantly improves preoperative risk assessment for PPCs, which helps to optimize surgical management and reduce PPCs morbidity and mortality.

Enhancing cybersecurity via attribute reduction with deep learning model for false data injection attack recognition

Cyberattacks have given rise to several phenomena and have raised concerns among users and power system operators. When they are built to bypass state estimation bad data recognition methods executed in the conventional grid system control room, False Data Injection Attacks (FDIA) pose a significant security threat to the operation of power systems. Therefore, real-time monitoring becomes inevitable with the quick implementation of renewables within the grid operator. The state estimation algorithm plays a major role in defining the grid’s operating scenarios. FDIA creates a significant risk to these estimation strategies by adding malicious information to the measurement obtained. Real-time recognition of these attack classes improves grid resiliency and ensures a secure grid operation. This study introduces a novel Attribute Reduction with a Deep Learning-based False Data Injection Attack Recognition (ARDL-FDIAR) technique. The primary goal of the ARDL-FDIAR technique is to improve security via the FDIA detection process. The ARDL-FDIAR technique uses Z-score normalization to scale the input data. The attribute reduction process gets invoked using the modified Lemrus optimization algorithm (MLOA) to choose optimal feature sets. Moreover, the FDIA detection process is performed by modelling an improved deep belief network (IDBN) model. Furthermore, the performance of the IDBN model is improved by the Cetacean Optimization Algorithm (COA)-based hyperparameter tuning process. A series of experiments were performed to ensure the enhancement of the ARDL-FDIAR technique. The results indicated the enhanced security performance of the ARDL-FDIAR technique compared to other DL approaches.

Identification of DNA damage repair-related genes in sepsis using bioinformatics and machine learning: An observational study.

Sepsis is a life-threatening disease with a high mortality rate, for which the pathogenetic mechanism still unclear. DNA damage repair (DDR) is essential for maintaining genome integrity. This study aimed to explore the role of DDR-related genes in the development of sepsis and further investigated their molecular subtypes to enrich potential diagnostic biomarkers. Two Gene Expression Omnibus datasets (GSE65682 and GSE95233) were implemented to investigate the underlying role of DDR-related genes in sepsis. Three machine learning algorithms were utilized to identify the optimal feature genes. The diagnostic value of the selected genes was evaluated using the receiver operating characteristic curves. A nomogram was built to assess the diagnostic ability of the selected genes via "rms" package. Consensus clustering was subsequently performed to identify the molecular subtypes for sepsis. Furthermore, CIBERSORT was used to evaluate the immune cell infiltration of samples. Three different expressed DDR-related genes (GADD45A, HMGB2, and RPS27L) were identified as sepsis biomarkers. Receiver operating characteristic curves revealed that all 3 genes showed good diagnostic value. The nomogram including these 3 genes also exhibited good diagnostic efficiency. A notable difference in the immune microenvironment landscape was discovered between sepsis patients and healthy controls. Furthermore, all 3 genes were significantly associated with various immune cells. Our findings identify potential new diagnostic markers for sepsis that shed light on novel pathogenetic mechanism of sepsis and, therefore, may offer opportunities for potential intervention and treatment strategies.