Mutual Information Feature Selection Research Articles

Abnormal Detection and Fault Diagnosis of Adjustment Hydraulic Servomotor Based on Genetic Algorithm to Optimize Support Vector Data Description with Negative Samples and One-Dimensional Convolutional Neural Network

Because of the difficulty in fault detection for and diagnosing the adjustment hydraulic servomotor, this paper uses feature extraction technology to extract the time domain and frequency domain features of the pressure signal of the adjustment hydraulic servomotor and splice the features of multiple pressure signals through the Multi-source Information Fusion (MSIF) method. The comprehensive expression of device status information is obtained. After that, this paper proposes a fault detection Algorithm GA-SVDD-neg, which uses Genetic Algorithm (GA) to optimize Support Vector Data Description with negative examples (SVDD-neg). Through joint optimization with the Mutual Information (MI) feature selection algorithm, the features that are most sensitive to the state deterioration of the adjustment hydraulic servomotor are selected. Experiments show that the MI algorithm has a better performance than other feature dimensionality reduction algorithms in the field of the abnormal detection of adjustment hydraulic servomotors, and the GA-SVDD-neg algorithm has a stronger robustness and generality than other anomaly detection algorithms. In addition, to make full use of the advantages of deep learning in automatic feature extraction and classification, this paper realizes the fault diagnosis of the adjustment hydraulic servomotor based on 1D Convolutional Neural Network (1DCNN). The experimental results show that this algorithm has the same superior performance as the traditional algorithm in feature extraction and can accurately diagnose the known faults of the adjustment hydraulic servomotor. This research is of great significance for the intelligent transformation of adjustment hydraulic servomotors and can also provide a reference for the fault warning and diagnosis of the Electro-Hydraulic (EH) system of the same type of steam turbine.

An intelligent diabetes classification and perception framework based on ensemble and deep learning method.

Sugar in the blood can harm individuals and their vital organs, potentially leading to blindness, renal illness, as well as kidney and heart diseases. Globally, diabetic patients face an average annual mortality rate of 38%. This study employs Chi-square, mutual information, and sequential feature selection (SFS) to choose features for training multiple classifiers. These classifiers include an artificial neural network (ANN), a random forest (RF), a gradient boosting (GB) algorithm, Tab-Net, and a support vector machine (SVM). The goal is to predict the onset of diabetes at an earlier age. The classifier, developed based on the selected features, aims to enable early diagnosis of diabetes. The PIMA and early-risk diabetes datasets serve as test subjects for the developed system. The feature selection technique is then applied to focus on the most important and relevant features for model training. The experiment findings conclude that the ANN exhibited a spectacular performance in terms of accuracy on the PIMA dataset, achieving a remarkable accuracy rate of 99.35%. The second experiment, conducted on the early diabetes risk dataset using selected features, revealed that RF achieved an accuracy of 99.36%. Based on our experimental results, it can be concluded that our suggested method significantly outperformed baseline machine learning algorithms already employed for diabetes prediction on both datasets.

GNSS jamming detection using attention-based mutual information feature selection

Global navigation satellite systems (GNSS) are extensively utilized for military and civilian applications. Unfortunately, because of the signal weakness, GNSS is susceptible to interference, fading, and jamming, which reduces the position accuracy. Therefore, it would be beneficial to have a simple and highly accurate model for detecting the jamming signals to improve the GNSS receiver accuracy. In this paper, we propose a hybrid deep learning (DL) model for predicting jamming signals. Initially, we utilize a feature selection algorithm that combines mutual information (MI) with the minimal redundancy maximum relevance (mRMR) technique to identify the most crucial features. Subsequently, the model undergoes training using a soft attention-double-layer bidirectional long short-term memory (A-DBiLSTM) model. This particular model has shown outstanding performance in comparison to other DL models when applied to datasets collected from both kinematic and static jamming scenarios. To assess the effectiveness and efficiency of the proposed MI feature selection algorithm, we evaluate its performance through the calculation of confusion matrices and conducting numerical simulations. The simulation results of the A-DBiLSTM model demonstrate higher accuracy, precision, recall, and F1Score\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathrm {F1_{Score}}$$\\end{document} of 98.82%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98.82\\%$$\\end{document}, 98.4%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98.4\\%$$\\end{document}, 98.68%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98.68\\%$$\\end{document}, and 98.36%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98.36\\%$$\\end{document}, respectively. By employing the MI feature selection algorithm, dimensionality reduction is achieved. Moreover, the MI feature selection algorithm reduces 19%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$19\\%$$\\end{document} of the learning time with almost the same accuracy.

Abstract 2582: Intra- and peri-tumoral radiomic features are predictive of pathologic response to multiple neoadjuvant therapy regimen in rectal cancers via pre-treatment MRI

Abstract Introduction: Rectal cancers undergo neoadjuvant chemoradiation prior to resection, with increasing evidence that neoadjuvant chemotherapy or immunotherapy can boost clinical or pathologic response rates. However, baseline clinical tumor staging and carcinoembryonic antigen levels are limited for identifying which patients will most benefit from a multimodal neoadjuvant regimen, and which will not. We evaluated whether radiomics (computational features from radiographic images) from pretreatment MRI could identify which rectal cancer patients will achieve clinical or pathologic response to different neoadjuvant treatment regimens. Methods: MRI scans were retrospectively obtained from 3 different institutions for rectal cancer patients who had either undergone neoadjuvant chemoradiation or an experimental immunotherapy + chemoradiation. After pre-processing all MRI scans, tumor was manually delineated with an automated annotation of a 10 mm peritumor region. 916 radiomic features were extracted from two planes of acquisition (axial, coronal) and two ROIs (tumor, peritumor). 5 top-ranked radiomic features were identified from each region and plane via multi-stage mutual information feature selection to train a linear discriminant machine classifier to assign a predicted likelihood of achieving complete response to each patient. Model performance was validated via ROC analysis against response groups defined via (i) tumor regression grade indicating no tumor present on surgical pathology, and (ii) 1-year clinical complete response for patients who underwent non-operative management. Clinical variables (CEA, clinical stage) were statistically compared between response groups. Results: Training cohort comprised 64 patients who underwent chemoradiation alone (2 institutions, median age 61 yrs, M:F:MtF = 36:27:1). Validation cohort included 37 patients who underwent experimental TGFß inhibitor therapy prior to chemotherapy + chemoradiation (1 institution, median age 51 yrs, M:F = 25:12). Area under the ROC curve for multi-plane tumor+peritumor radiomic features (0.765 ± 0.054) was significantly higher for distinguishing complete response vs non/partial response compared to tumor (AUC = 0.635 ± 0.057, p < 0.0001) or peritumor (AUC = 0.612 ± 0.064, p < 0.0001). In holdout validation, performance was maintained with AUCs of 0.742 (tumor) 0.596 (peritumor), and 0.700 (all), respectively. Baseline CEA levels (p=0.3385) and cT stage (p=0.3386) were found to lack statistical significance as a predictor of response. Conclusion: Radiomic features from tumor and peritumor regions on pre-treatment MRI scans in rectal cancer patients may enable robust and accurate identification of pathologic or near-complete response after multiple neoadjuvant treatment regimen, while outperforming baseline clinical variables. Citation Format: Leo Bao, Thomas DeSilvio, Benjamin N. Parker, Mohsen Hariri, Prathyush Chirra, Murad Labbad, Stephen Tang, Gregory M. O'Connor, Emily Steinhagen, Jennifer L. Miller-Ocuin, Amit Gupta, Eric L. Marderstein, Aaron Carroll, Marka Crittenden, Michael J. Gough, Smitha Krishnamurthi, Kristina H. Young, Satish E. Viswanath. Intra- and peri-tumoral radiomic features are predictive of pathologic response to multiple neoadjuvant therapy regimen in rectal cancers via pre-treatment MRI [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2582.

Predicting early Alzheimer’s with blood biomarkers and clinical features

Alzheimer’s disease (AD) is an incurable neurodegenerative disorder that leads to dementia. This study employs explainable machine learning models to detect dementia cases using blood gene expression, single nucleotide polymorphisms (SNPs), and clinical data from Alzheimer’s Disease Neuroimaging Initiative (ADNI). Analyzing 623 ADNI participants, we found that the Support Vector Machine classifier with Mutual Information (MI) feature selection, trained on all three data modalities, achieved exceptional performance (accuracy = 0.95, AUC = 0.94). When using gene expression and SNP data separately, we achieved very good performance (AUC = 0.65, AUC = 0.63, respectively). Using SHapley Additive exPlanations (SHAP), we identified significant features, potentially serving as AD biomarkers. Notably, genetic-based biomarkers linked to axon myelination and synaptic vesicle membrane formation could aid early AD detection. In summary, this genetic-based biomarker approach, integrating machine learning and SHAP, shows promise for precise AD diagnosis, biomarker discovery, and offers novel insights for understanding and treating the disease. This approach addresses the challenges of accurate AD diagnosis, which is crucial given the complexities associated with the disease and the need for non-invasive diagnostic methods.

Hybrid similarity relation based mutual information for feature selection in intuitionistic fuzzy rough framework and its applications

Fuzzy rough entropy established in the notion of fuzzy rough set theory, which has been effectively and efficiently applied for feature selection to handle the uncertainty in real-valued datasets. Further, Fuzzy rough mutual information has been presented by integrating information entropy with fuzzy rough set to measure the importance of features. However, none of the methods till date can handle noise, uncertainty and vagueness simultaneously due to both judgement and identification, which lead to degrade the overall performances of the learning algorithms with the increment in the number of mixed valued conditional features. In the current study, these issues are tackled by presenting a novel intuitionistic fuzzy (IF) assisted mutual information concept along with IF granular structure. Initially, a hybrid IF similarity relation is introduced. Based on this relation, an IF granular structure is introduced. Then, IF rough conditional and joint entropies are established. Further, mutual information based on these concepts are discussed. Next, mathematical theorems are proved to demonstrate the validity of the given notions. Thereafter, significance of the features subset is computed by using this mutual information, and corresponding feature selection is suggested to delete the irrelevant and redundant features. The current approach effectively handles noise and subsequent uncertainty in both nominal and mixed data (including both nominal and category variables). Moreover, comprehensive experimental performances are evaluated on real-valued benchmark datasets to demonstrate the practical validation and effectiveness of the addressed technique. Finally, an application of the proposed method is exhibited to improve the prediction of phospholipidosis positive molecules. RF(h2o) produces the most effective results till date based on our proposed methodology with sensitivity, accuracy, specificity, MCC, and AUC of 86.7%, 90.1%, 93.0% , 0.808, and 0.922 respectively.

EMIFS: A Normalized Hyperbolic Ransomware Deterrence Model Yielding Greater Accuracy and Overall Performance.

Early detection of ransomware attacks is critical for minimizing the potential damage caused by these malicious attacks. Feature selection plays a significant role in the development of an efficient and accurate ransomware early detection model. In this paper, we propose an enhanced Mutual Information Feature Selection (eMIFS) technique that incorporates a normalized hyperbolic function for ransomware early detection models. The normalized hyperbolic function is utilized to address the challenge of perceiving common characteristics among features, particularly when there are insufficient attack patterns contained in the dataset. The Term Frequency-Inverse Document Frequency (TF-IDF) was used to represent the features in numerical form, making it ready for the feature selection and modeling. By integrating the normalized hyperbolic function, we improve the estimation of redundancy coefficients and effectively adapt the MIFS technique for early ransomware detection, i.e., before encryption takes place. Our proposed method, eMIFS, involves evaluating candidate features individually using the hyperbolic tangent function (tanh), which provides a suitable representation of the features' relevance and redundancy. Our approach enhances the performance of existing MIFS techniques by considering the individual characteristics of features rather than relying solely on their collective properties. The experimental evaluation of the eMIFS method demonstrates its efficacy in detecting ransomware attacks at an early stage, providing a more robust and accurate ransomware detection model compared to traditional MIFS techniques. Moreover, our results indicate that the integration of the normalized hyperbolic function significantly improves the feature selection process and ultimately enhances ransomware early detection performance.

Multi-institutional prognostic modeling of survival outcomes in NSCLC patients treated with first-line immunotherapy using radiomics

BackgroundImmune checkpoint inhibitors (ICIs) have emerged as one of the most promising first-line therapeutics in the management of non-small cell lung cancer (NSCLC). However, only a subset of these patients responds to ICIs, highlighting the clinical need to develop better predictive and prognostic biomarkers. This study will leverage pre-treatment imaging profiles to develop survival risk models for NSCLC patients treated with first-line immunotherapy.MethodsAdvanced NSCLC patients (n = 149) were retrospectively identified from two institutions who were treated with first-line ICIs. Radiomics features extracted from pretreatment imaging scans were used to build the predictive models for progression-free survival (PFS) and overall survival (OS). A compendium of five feature selection methods and seven machine learning approaches were utilized to build the survival risk models. The concordance index (C-index) was used to evaluate model performance.ResultsFrom our results, we found several combinations of machine learning algorithms and feature selection methods to achieve similar performance. K-nearest neighbourhood (KNN) with ReliefF (RL) feature selection was the best-performing model to predict PFS (C-index = 0.61 and 0.604 in discovery and validation cohorts), while XGBoost with Mutual Information (MI) feature selection was the best-performing model for OS (C-index = 0.7 and 0.655 in discovery and validation cohorts).ConclusionThe results of this study highlight the importance of implementing an appropriate feature selection method coupled with a machine learning strategy to develop robust survival models. With further validation of these models on external cohorts when available, this can have the potential to improve clinical decisions by systematically analyzing routine medical images.

Comparative Analysis of Machine Learning Algorithms for Heart Disease Prediction

One of the more difficult ailments is heart disease, which affected a large number of people worldwide. Heart illness must be promptly and accurately diagnosed in order to be treated, especially in the field of cardiology. In this work, we suggested a machine learning-based approach for diagnosing cardiac disease that is both effective and accurate. The system was created using classification algorithms, which Standard feature selection algorithms like Relief, Minimal redundancy maximal relevance, Least absolute shrinkage selection operator, and Local learning have been used to omit unnecessary and redundant features. Other feature selection algorithms include Support vector machine, Logistic regression, Artificial neural network, K nearest neighbor, Nave bays, and Decision tree. Additionally, we provided a brand-new, quick conditional mutual information feature selection approach to address.

A COMPARATIVE STUDY ON FEATURE SELECTION METHODS IN QSAR

A quantitative structure-activity relationship (QSAR) relates quantitative chemical structure attributes (molecular descriptors) to a biological activity. QSAR studies have now become attractive in drug discovery and development because their application can save substantial time and human resources. Several parameters are important in the prediction ability of a QSAR model. On the one hand, different statistical methods may be applied to check the linear or nonlinear behavior of a data set. On the other hand, feature selection techniques are applied to decrease the model complexity, to decrease the overfitting/overtraining risk, and to select the most important descriptors from the often more than calculated. The selected descriptors are then linked to a biological activity of the corresponding compound by means of a mathematical model. Different modeling techniques can be applied, some of which explicitly require a feature selection. A QSAR model can be useful in the design of new compounds with improved potency in the class under study. Only molecules with a predicted interesting activity will be synthesized. In the feature selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus attention, while ignoring the rest. This paper studied the comparative analysis of the Chi-square, Mutual Information, Anova F-value, Fisher Score and SHAP feature selection methods used in QSAR modeling. The Python code written to get experimental results in this article has been uploaded to Github.

An Explainable MRI-Radiomic Quantum Neural Network to Differentiate Between Large Brain Metastases and High-Grade Glioma Using Quantum Annealing for Feature Selection

Solitary large brain metastases (LBM) and high-grade gliomas (HGG) are sometimes hard to differentiate on MRI. The management differs significantly between these two entities, and non-invasive methods that help differentiate between them are eagerly needed to avoid potentially morbid biopsies and surgical procedures. We explore herein the performance and interpretability of an MRI-radiomics variational quantum neural network (QNN) using a quantum-annealing mutual-information (MI) feature selection approach. We retrospectively included 423 patients with HGG and LBM (> 2 cm) who had a contrast-enhanced T1-weighted (CE-T1) MRI between 2012 and 2019. After exclusion, 72 HGG and 129 LBM were kept. Tumors were manually segmented, and a 5-mm peri-tumoral ring was created. MRI images were pre-processed, and 1813 radiomic features were extracted. A set of best features based on MI was selected. MI and conditional-MI were embedded into a quadratic unconstrained binary optimization (QUBO) formulation that was mapped to an Ising-model and submitted to D’Wave’s quantum annealer to solve for the best combination of 10 features. The 10 selected features were embedded into a 2-qubits QNN using PennyLane library. The model was evaluated for balanced-accuracy (bACC) and area under the receiver operating characteristic curve (ROC-AUC) on the test set. The model performance was benchmarked against two classical models: dense neural networks (DNN) and extreme gradient boosting (XGB). Shapley values were calculated to interpret sample-wise predictions on the test set. The best 10-feature combination included 6 tumor and 4 ring features. For QNN, DNN, and XGB, respectively, training ROC-AUC was 0.86, 0.95, and 0.94; test ROC-AUC was 0.76, 0.75, and 0.79; and test bACC was 0.74, 0.73, and 0.72. The two most influential features were tumor Laplacian-of-Gaussian-GLRLM-Entropy and sphericity. We developed an accurate interpretable QNN model with quantum-informed feature selection to differentiate between LBM and HGG on CE-T1 brain MRI. The model performance is comparable to state-of-the-art classical models.

Machine-learning-based predictions of imprinting quality using ensemble and non-linear regression algorithms

The molecularly imprinted polymers are artificial polymers that, during the synthesis, create specific sites for a definite purpose. These polymers due to their characteristics such as stability, easy of synthesis, reproducibility, reusability, high accuracy, and selectivity have many applications. However, the variety of the functional monomers, templates, solvents, and synthesis conditions like pH, temperature, the rate of stirring, and time, limit the selectivity of imprinting. The Practical optimization of the synthetic conditions has many drawbacks, including chemical compound usage, equipment requirements, and time costs. The use of machine learning (ML) for the prediction of the imprinting factor (IF), which indicates the quality of imprinting is a very interesting idea to overcome these problems. The ML has many advantages, for example a lack of human error, high accuracy, high repeatability, and prediction of a large amount of data in the minimum time. In this research, ML was used to predict the IF using non-linear regression algorithms, including classification and regression tree, support vector regression, and k-nearest neighbors, and ensemble algorithms, like gradient boosting (GB), random forest, and extra trees. The data sets were obtained practically in the laboratory, and inputs, included pH, the type of the template, the type of the monomer, solvent, the distribution coefficient of the MIP (KMIP), and the distribution coefficient of the non-imprinted polymer (KNIP). The mutual information feature selection method was used to select the important features affecting the IF. The results showed that the GB algorithm had the best performance in predicting the IF, and using this algorithm, the maximum R2 value (R2 = 0.871), and the minimum mean absolute error (MAE = − 0.982), and mean square error were obtained (MSE = − 2.303).

A new approach to prediction riboflavin absorbance using imprinted polymer and ensemble machine learning algorithms

The molecularly imprinted polymer (MIP) is useful for measuring the amount of riboflavin (vitamin B2), in various samples using UV/Vis instruments. The practical optimization of the MIP synthesis conditions has a number of drawbacks, like the need to spend money, the need to spend time, the use of the compounds that cause contamination, needing laboratory equipment and tools. Using machine learning (ML) to predict the amount of riboflavin absorbance is a creative solution to overcome the problems and shortcomings of optimizing polymer synthesis conditions. In fact, by using the model without needing real work in the laboratory, the optimum laboratory conditions are determined, and as a result the maximized absorption of the riboflavin is obtained. In this paper, MIP was synthesized for selective extraction of the riboflavin, and UV/Vis spectrophotometry was used to quantitatively measure riboflavin absorbance. Various factors affect the performance of the polymer. The effect of six important factors, including the molar ratio of the template, the molar ratio of monomer, the molar ratio of cross-linker, loading time, stirring rate, and pH, were investigated. Then, using ensemble ML algorithms, like gradient boosting (GB), extra trees (ET), random forest (RF), and Ada boost (Ada) algorithms, an accurate model was created to predict the riboflavin absorption. Also, the mutual information feature selection method was used to determine the important features. The results of using feature selection method was shown that variables such as the molar ratio of the template, the molar ratio of the monomer, and the molar ratio of the cross-linker had a high effect on riboflavin absorbance. The GB and Ada boost algorithms performed better than ET and RF algorithms. After tuning the n-estimator hyper parameter (n-estimator = 300), the GB algorithm was shown an excellent performance in predicting the absorbance of riboflavin and the maximum R2-scoring of the model was obtained at 0.965995, the minimum of the mean absolute error (MAE), and mean square error (MSE) of the model respectively were obtained −0.003711 and −0.000078. Therefore, by using the proposed model, it is possible to predict riboflavin absorbance theoretically, and with high accuracy by changing the inputs of model, and using the model instead of working in the lab saves time, money, chemical compounds, and lab ware.

Feature Selection, Clustering and IoMT on Biomedical Engineering for COVID-19 Pandemic: A Comprehensive Review

In this era, feature clustering is a prominent technique in data mining. Features clustering have also huge application in biomedical research for multiple purpose including grouping, features reduction and many more. The Internet of Medical Things (IoMT) is a promising and emerging field of research that is having a major impact on knowledge retrieval and networking. IoMT also has significant application in biomedical research regarding remote monitoring and remote healthcare services. In this COVID-19 pandemic situation, psychological effect and human reaction have become a major concern of further research. A dataset can be reduced in size by using feature selection techniques. To facilitate subsequent processing, this will make the data easier to manage. Feature selection is also used to clean, reduce, and reduce dimensions of data. The clustering method has proven to be a powerful tool for finding patterns and structure in both labeled and unlabeled data sets. Our study basically provides various state-of-the-art methods regarding medical IoMT for remote healthcare, feature clustering for information retrieval regarding biomedical science. In this study, we are used five different type of feature selection like Minimum Redundancy - Maximum Relevance (mRMR), Random Forest, Normalized Mutual Information Feature Selection (NMIFS), F-Test and Chi-Square and five different type of Clustering algorithms like Hierarchical Clustering, Density-based spatial clustering of applications with noise (DBSCAN) Clustering, K-Means Clustering, Shrinkage Clustering, and Fuzzy C-Means Clustering. Finally, this study is very useful to understand and apply the appropriate IoMT, feature clustering, and catharsis on the various biomedical applications for the benevolence of society.

An Improved Mutual Information Feature Selection Technique for Intrusion Detection Systems in the Internet of Medical Things

In healthcare, the Internet of Things (IoT) is used to remotely monitor patients and provide real-time diagnoses, which is referred to as the Internet of Medical Things (IoMT). This integration poses a risk from cybersecurity threats that can harm patient data and well-being. Hackers can manipulate biometric data from biosensors or disrupt the IoMT system, which is a major concern. To address this issue, intrusion detection systems (IDS) have been proposed, particularly using deep learning algorithms. However, developing IDS for IoMT is challenging due to high data dimensionality leading to model overfitting and degraded detection accuracy. Feature selection has been proposed to prevent overfitting, but the existing methods assume that feature redundancy increases linearly with the size of the selected features. Such an assumption does not hold, as the amount of information a feature carries about the attack pattern varies from feature to feature, especially when dealing with early patterns, due to data sparsity that makes it difficult to perceive the common characteristics of selected features. This negatively affects the ability of the mutual information feature selection (MIFS) goal function to estimate the redundancy coefficient accurately. To overcome this issue, this paper proposes an enhanced feature selection technique called Logistic Redundancy Coefficient Gradual Upweighting MIFS (LRGU-MIFS) that evaluates candidate features individually instead of comparing them with common characteristics of the already-selected features. Unlike the existing feature selection techniques, LRGU calculates the redundancy score of a feature using the logistic function. It increases the redundancy value based on the logistic curve, which reflects the nonlinearity of the relationship of the mutual information between features in the selected set. Then, the LRGU was incorporated into the goal function of MIFS as a redundancy coefficient. The experimental evaluation shows that the proposed LRGU was able to identify a compact set of significant features that outperformed those selected by the existing techniques. The proposed technique overcomes the challenge of perceiving common characteristics in cases of insufficient attack patterns and outperforms existing techniques in identifying significant features.

Hydraulic fracturing performance analysis by the mutual information and Gaussian process regression methods

In shale gas production rate prediction, data are commonly acquired through simulations, which offer benefits but cannot fully predict the outcome of real engineering projects. This paper applied a Gaussian process regression (GPR) model, which was trained on the geological and field data from 175 wells in the Fuling gas field, China. GPR was chosen because this method can be suitably applied to a small dataset. To improve the accuracy, reduce the computational consumption, and increase the interpretability of the model, the mutual information method was used for feature selection after Pearson coefficient analysis. Based on comparison of the GPR, random forest and artificial neural network models, GPR achieved the best performance with the mutual information feature selection method, exhibiting R2 values of 0.71 and 0.68 for the training and test datasets, respectively. After model training, the model applied the Shapley additive explanation (SHAP) value method. The results indicate that geological factors dominate the gas production rate in Fuling, China. The volume of fluid loss could exert a negative impact on production rates and increase the consumption of 40–70 mesh proppant could increase gas production in Fuling.

Functional Proteomic Profiling Analysis in Four Major Types of Gastrointestinal Cancers.

Gastrointestinal (GI) cancer accounts for one in four cancer cases and one in three cancer-related deaths globally. A deeper understanding of cancer development mechanisms can be applied to cancer medicine. Comprehensive sequencing applications have revealed the genomic landscapes of the common types of human cancer, and proteomics technology has identified protein targets and signalling pathways related to cancer growth and progression. This study aimed to explore the functional proteomic profiles of four major types of GI tract cancer based on The Cancer Proteome Atlas (TCPA). We provided an overview of functional proteomic heterogeneity by performing several approaches, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), t-stochastic neighbour embedding (t-SNE) analysis, and hierarchical clustering analysis in oesophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), and rectum adenocarcinoma (READ) tumours, to gain a system-wide understanding of the four types of GI cancer. The feature selection approach, mutual information feature selection (MIFS) method, was conducted to screen candidate protein signature subsets to better distinguish different cancer types. The potential clinical implications of candidate proteins in terms of tumour progression and prognosis were also evaluated based on TCPA and The Cancer Genome Atlas (TCGA) databases. The results suggested that functional proteomic profiling can identify different patterns among the four types of GI cancers and provide candidate proteins for clinical diagnosis and prognosis evaluation. We also highlighted the application of feature selection approaches in high-dimensional biological data analysis. Overall, this study could improve the understanding of the complexity of cancer phenotypes and genotypes and thus be applied to cancer medicine.

COMPARISON OF FEATURE SELECTION BASED ON COMPUTATION TIME AND CLASSIFICATION ACCURACY USING SUPPORT VECTOR MACHINE

The goal of this research to compare Chi-Square feature selection with Mutual Information feature selection based on computation time and classification accuracy. In this research, people's comments on Twitter are classified based on positive, negative, and neutral sentiments using the Support Vector Machine method. Sentiment classification has the disadvantage that it has many features that are used, therefore feature selection is needed to optimize a sentiment classification performance. Chi-square feature selection and mutual information feature selection are feature selections that both can improve the accuracy of sentiment classification. How to collect the data on twitter taken using the IDE application from python. The results of this study indicate that sentiment classification using Chi-Square feature selection produces a computation time of 0.4375 seconds with an accuracy of 78% while sentiment classification using Mutual Information feature selection produces an accuracy of 80% with a required computation time of 252.75 seconds. So that the conclusion are obtained based on the computational time aspect, the Chi-Square feature selection is superior to the Mutual Information feature selection, while based on the classification accuracy aspect, the Mutual Information feature selection is more accurate than the Chi-Square feature selection. The recommendations for further research can use mutual information feature selection to get high accuracy results on sentiment classification

Prediction of Telecom Customer Churn Based on MIPCA-XGBoost Method

In order to solve the problem that the nonlinear information of data in the field of telecom customer churn prediction is not fully used, or even ignored, which leads to inaccurate prediction, this paper introduces the mutual information feature selection method (MIPCA) to filter the features and reduce the dimensions of customer data, and proposes an XGBoost method based on the mutual information feature selection method(MIPCA-XGBoost), which improves the accuracy of the prediction results. By using the data set of telecom industry customers published on Kaggle website, compares the prediction result of this method with that of machine learning algorithms commonly used in this field, and proves the accuracy, recall and F_Score of MIPCA-XGBoost method is higher than other algorithms.

Anomaly-based Network Intrusion Detection System using Deep Intelligent Technique

Background and objectives: Computer systems and network infrastructures are still exposed to many security risks and cyber-attack vulnerabilities despite advancements of information security. Traditional signature-based intrusion detection systems and security solutions by matching rule-based mechanism and prior knowledge are insufficient of fully protecting computer networks against novel attacks. For this purpose, Anomaly-based Network Intrusion Detection System (A-NIDS) as cyber security tool is considered for identifying and detecting anomalous behavior in the flow-based network traffic alongside with firewalls and other security measures. The main objective of the research is to improve the detection rate and reduce false-positive rates of the classifier using anomaly-based technique. Methods: an intelligent technique using deep learning algorithm and mutual information feature selection (MIFS) method to select optimal features on the benchmark datasets. Proposed method accurately capable of classifying normal and anomalous states of the data packets in a comprehensive way by combination of Long-Short term memory (LSTM) algorithm and MIFS method. Results: The model achieved encouraging results in terms of accuracy 99.79%, 0.002 false-positive rate with minimum time compared to other models recorded only 81.75s on CSE-CIC-IDS2018 dataset. At the end of the study, comparative studies are conducted to verify the effectiveness of proposed method on three realistic and latest intrusion detection datasets, named CSE_CIC-IDS2018, CIC-IDS2017, and NF-CSE-CIC-IDS2018 dataset. Conclusions: Proposed model in a combination of LSTM NN and Feature selection method (MIFS) increased detection rate and reduced false-positive alarms, also the model able to detect low frequent attacks while other existing models are suffering from.