Minimum Redundancy Maximum Relevance Algorithm Research Articles

Kruskal Wallis and mRMR Feature Selection based Online Signature Verification System using Multiple SVM and KNN

Signature verification is a very important research area. Signature has been widely accepted as a person authentication method for centuries. It is mostly used in financial transactions, document authentication and agreements. It is more susceptible to being forged than any other biometrics. Online signature verification is used in real-time applications like e-commerce, online resource access, online financial transactions, physical access into a restricted area and many more. In order to achieve high efficiency in online signature verification systems, feature extraction and feature selection play a significant role. A suitable signature verification system is needed to prevent forgery and accept the genuine signer. We have extracted 30 global features from all 40 signers for verification. Here, k fold cross-validation technique is used to enhance the model's performance on unseen data. User-specific feature selection and ranking are done using Kruskal Wallis and Minimum Redundancy Maximum Relevance (mRMR) algorithm to hunt which performs better in our case. Kruskal-Wallis method tests if two or more classes have an equal median and gives the value of P based on which discriminative features are selected, whereas the mRMR algorithm ranks the whole feature set according to its importance. It evaluates the relevance of a feature and penalizes redundancy. Finally, multiple SVM and KNN classifiers are trained and tested with various selected features using Kruskal Wallis and mRMR to determine which combination performs best for the online signature verification system. Our model is trained, validated and tested on the SVC 2004 Task 1 database, which consists of skill forgery signatures. Here, one to one verification is done using each user's genuine and skill forgery signatures, which is the hardest to detect. Best average testing accuracy achieved in our case is 90.25% using Weighted KNN and Kruskal Wallis selected 15 features.

Stack performance classification and fault diagnosis optimization of solid oxide fuel cell system based on bayesian artificial neural network and feature selection

Solid oxide fuel cell (SOFC) has attracted much attention because of its high efficiency and silent power generation, and can be applied to the field of fuel cell-powered unmanned boats. However, a fuel deficit failure in the system can lead to reduced stack performance, reducing the reliability of SOFC applications in the field of unmanned boats. Therefore, this paper proposes an efficient breakdown diagnosis model for SOFC system fault diagnosis. Firstly, Bayesian artificial neural network is constructed by using the best feature data subset after Relief-F combined with minimum Redundancy Maximum Relevance algorithm feature selecting. Secondly, the correct diagnosis rate under different number of optimal features is calculated, and the best feature combinations are combined to verify. Finally, the particle swarm algorithm selects the optimal hidden neuron count to optimization model. Experimental findings suggest that for correct diagnosis rate, the breakdown diagnosis model based on the optimal number of 3 features can reduce the training time and maintain high accuracy compared with the use of all 34 features. This research result provides a feasible way for the fault diagnosis application of SOFC system power generation in the field of unmanned boats.

[68Ga]Ga‑PSMA‑617 PET-based radiomics model to identify candidates for active surveillance amongst patients with GGG 1–2 prostate cancer at biopsy

PurposeTo develop a radiomics-based model using [68Ga]Ga-PSMA PET/CT to predict postoperative adverse pathology (AP) in patients with biopsy Gleason Grade Group (GGG) 1–2 prostate cancer (PCa), assisting in the selection of patients for active surveillance (AS).MethodsA total of 75 men with biopsy GGG 1–2 PCa who underwent radical prostatectomy (RP) were enrolled. The patients were randomly divided into a training group (70%) and a testing group (30%). Radiomics features of entire prostate were extracted from the [68Ga]Ga-PSMA PET scans and selected using the minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator regression model. Logistic regression analyses were conducted to construct the prediction models. Receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curve were employed to evaluate the diagnostic value, clinical utility, and predictive accuracy of the models, respectively.ResultsAmong the 75 patients, 30 had AP confirmed by RP. The clinical model showed an area under the curve (AUC) of 0.821 (0.695–0.947) in the training set and 0.795 (0.603–0.987) in the testing set. The radiomics model achieved AUC values of 0.830 (0.720–0.941) in the training set and 0.829 (0.624–1.000) in the testing set. The combined model, which incorporated the Radiomics score (Radscore) and free prostate-specific antigen (FPSA)/total prostate-specific antigen (TPSA), demonstrated higher diagnostic efficacy than both the clinical and radiomics models, with AUC values of 0.875 (0.780–0.970) in the training set and 0.872 (0.678–1.000) in the testing set. DCA showed that the net benefits of the combined model and radiomics model exceeded those of the clinical model.ConclusionThe combined model shows potential in stratifying men with biopsy GGG 1–2 PCa based on the presence of AP at final pathology and outperforms models based solely on clinical or radiomics features. It may be expected to aid urologists in better selecting suitable patients for AS.

EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning

Brain–computer interface (BCI) technology holds promise for individuals with profound motor impairments, offering the potential for communication and control. Motor imagery (MI)-based BCI systems are particularly relevant in this context. Despite their potential, achieving accurate and robust classification of MI tasks using electroencephalography (EEG) data remains a significant challenge. In this paper, we employed the Minimum Redundancy Maximum Relevance (MRMR) algorithm to optimize channel selection. Furthermore, we introduced a hybrid optimization approach that combines the War Strategy Optimization (WSO) and Chimp Optimization Algorithm (ChOA). This hybridization significantly enhances the classification model’s overall performance and adaptability. A two-tier deep learning architecture is proposed for classification, consisting of a Convolutional Neural Network (CNN) and a modified Deep Neural Network (M-DNN). The CNN focuses on capturing temporal correlations within EEG data, while the M-DNN is designed to extract high-level spatial characteristics from selected EEG channels. Integrating optimal channel selection, hybrid optimization, and the two-tier deep learning methodology in our BCI framework presents an enhanced approach for precise and effective BCI control. Our model got 95.06% accuracy with high precision. This advancement has the potential to significantly impact neurorehabilitation and assistive technology applications, facilitating improved communication and control for individuals with motor impairments

Research of Short-Term Wind Power Generation Forecasting Based on mRMR-PSO-LSTM Algorithm

A novel short-term wind power forecasting method called mRMR-PSO-LSTM was proposed to address the limitations of traditional methods in ignoring the redundancy and temporal dynamics of meteorological features. The methods employed the Minimum Redundancy Maximum Relevance (mRMR) algorithm to select relevant meteorological features while minimizing redundancy. Additionally, the Particle Swarm Optimization (PSO) algorithm was utilized to optimize the parameters of the Long Short-Term Memory (LSTM) network, thereby enhancing its forecasting accuracy. Experimental results demonstrated that the proposed mRMR-PSO-LSTM outperforms FNN, GRU, and PSO-LSTM in four key evaluation metrics.

Innovative approach towards early prediction of ovarian cancer: Machine learning- enabled XAI techniques

Globally, ovarian cancer affects women disproportionately, causing significant morbidity and mortality rates. The early diagnosis of ovarian cancer is necessary for enhancing patient health and survival rates. This research article explores the utilization of Machine Learning (ML) techniques alongside eXplainable Artificial Intelligence (XAI) methodologies to aid in the early detection of ovarian cancer. ML techniques have recently gained popularity in developing predictive models to detect early-stage ovarian cancer. These predictions are made using XAI in a transparent and understandable way for healthcare professionals and patients. The primary aim of this study is to evaluate the effectiveness of various ovarian cancer prediction methodologies. This includes assessing K Nearest Neighbors, Support Vector Machines, Decision trees, and ensemble learning techniques such as Max Voting, Boosting, Bagging, and Stacking. A dataset of 349 patients with known ovarian cancer status was collected from Kaggle. The dataset included a comprehensive range of clinical features such as age, family history, tumor markers, and imaging characteristics. Preprocessing techniques were applied to enhance input data, including feature scaling and dimensionality reduction. A Minimum Redundancy Maximum Relevance (MRMR) algorithm was used to select the features in the model. Our experimental results demonstrate that in Support Vector Machines, we found 85 % base model accuracy and 89 % accuracy after stacking several ensemble learning techniques. With the help of XAI, complex ML algorithms can be given more profound insights into their decision-making, improving their applicability. This paper aims to introduce the best practices for integrating ML and artificial intelligence in biomarker evaluation. Building and evaluating Shapley values-based classifiers and visualizing results were the focus of our investigation. The study contributes to the field of oncology and women's health by offering a promising approach to the early diagnosis of ovarian cancer.

Enhancement in the safety and reliability of Pressurized Water reactors using Machine Learning approach

The increasing interest in artificial intelligence and automation within the nuclear industry stems from the hope of elevating the safety and reliability of nuclear power plants by minimizing the impact of human errors. This paper explores the capabilities of machine-learning based fault detection and diagnosis (FDD) models in accurately classifying transient events in a nuclear power plant using the Classification Learners Application from MATLAB. The scope of the research is narrowed to the identification of transient events of the same nature occurring at various locations within the plant. To accomplish this, the Generic Pressurized Water Reactor (GPWR) from the Western Services Cooperation (WSC) was used to simulate 14 transients in the primary system and 10 transients in the secondary system, resulting in two distinct datasets of 108,000 and 79,200 observations. Nine types of classifiers, including Decision Trees, Discriminant Analysis, Support Vector Machines, Logistic Regression, Nearest Neighbors, Naive Bayes, Kernel Approximation, Ensembles, and Neural Networks, with a total of 33 predefined models, were trained and validated. The K-Nearest Neighbors model achieved the highest accuracy of 97% in the primary system, while the Efficient Linear Discriminant and Logistic Regression models achieved the highest average accuracy of 99% in the secondary system. The assessment of the top-performing models was conducted through a comprehensive analysis employing key classification model evaluation metrics, such as the confusion matrix, accuracy, precision, recall, and the F1 score. In an effort to optimize the models, the study integrated feature selection using the minimum Redundancy Maximum Relevance (mRMR) algorithm. Additionally, the investigation explored different validation schemes, including both holdout and k-fold cross-validation schemes, resulting in a substantial reduction in training time without significantly compromising the overall accuracy of the models. The optimized models in this study demonstrated remarkable prediction accuracies, consistently exceeding 94%. Despite the complexity of the models and the intricacies involved in the training process, the training times ranged from 10 to 1800 s, reflecting the efficiency and robustness of the implemented optimization techniques. The results of this study underscore the potential of machine learning models in identifying transient events in nuclear reactors, showcasing their promising capabilities while maintaining low computational and execution costs. This suggests their efficacy in optimizing safe and efficient operational practices within nuclear facilities.

Machine learning-based MRI radiomics for assessing the level of tumor infiltrating lymphocytes in oral tongue squamous cell carcinoma: a pilot study

BackgroundTo investigate the value of machine learning (ML)-based magnetic resonance imaging (MRI) radiomics in assessing tumor-infiltrating lymphocyte (TIL) levels in patients with oral tongue squamous cell carcinoma (OTSCC).MethodsThe study included 68 patients with pathologically diagnosed OTSCC (30 with high TILs and 38 with low TILs) who underwent pretreatment MRI. Based on the regions of interest encompassing the entire tumor, a total of 750 radiomics features were extracted from T2-weighted (T2WI) and contrast-enhanced T1-weighted (ceT1WI) imaging. To reduce dimensionality, reproducibility analysis by two radiologists and collinearity analysis were performed. The top six features were selected from each sequence alone, as well as their combination, using the minimum-redundancy maximum-relevance algorithm. Random forest, logistic regression, and support vector machine models were used to predict TIL levels in OTSCC, and 10-fold cross-validation was employed to assess the performance of the classifiers.ResultsBased on the features selected from each sequence alone, the ceT1WI models outperformed the T2WI models, with a maximum area under the curve (AUC) of 0.820 versus 0.754. When combining the two sequences, the optimal features consisted of one T2WI and five ceT1WI features, all of which exhibited significant differences between patients with low and high TILs (all P < 0.05). The logistic regression model constructed using these features demonstrated the best predictive performance, with an AUC of 0.846 and an accuracy of 80.9%.ConclusionsML-based T2WI and ceT1WI radiomics can serve as valuable tools for determining the level of TILs in patients with OTSCC.

Machine Learning Enabled Photoacoustic Spectroscopy for Noninvasive Assessment of Breast Tumor Progression In Vivo: A Preclinical Study.

Breast cancer is a dreaded disease affecting women the most in cancer-related deaths over other cancers. However, early diagnosis of the disease can help increase survival rates. The existing breast cancer diagnosis tools do not support the early diagnosis of the disease. Therefore, there is a great need to develop early diagnostic tools for this cancer. Photoacoustic spectroscopy (PAS), being very sensitive to biochemical changes, can be relied upon for its application in detecting breast tumors in vivo. With this motivation, in the current study, an aseptic chamber integrated photoacoustic (PA) probe was designed and developed to monitor breast tumor progression in vivo, established in nude mice. The device served the dual purpose of transporting tumor-bearing animals to the laboratory from the animal house and performing PA experiments in the same chamber, maintaining sterility. In the current study, breast tumor was induced in the nude mice by MCF-7 cells injection and the corresponding PA spectra at different time points (day 0, 5, 10, 15, and 20) of tumor progression in vivo in the same animals. The recorded photoacoustic spectra were subsequently preprocessed, wavelet-transformed, and subjected to filter-based feature selection algorithm. The selected top 20 features, by minimum redundancy maximum relevance (mRMR) algorithm, were then used to build an input feature matrix for machine learning (ML)-based classification of the data. The performance of classification models demonstrated 100% specificity, whereas the sensitivity of 95, 100, 92.5, and 85% for the time points, day 5, 10, 15, and 20, respectively. These results suggest the potential of PA signal-based classification of breast tumor progression in a preclinical model. The PA signal contains information on the biochemical changes associated with disease progression, emphasizing its translational strength toward early disease diagnosis.

Preoperative CT-based deep learning radiomics model to predict lymph node metastasis and patient prognosis in bladder cancer: a two-center study

ObjectiveTo establish a model for predicting lymph node metastasis in bladder cancer (BCa) patients.MethodsWe retroactively enrolled 239 patients who underwent three-phase CT and resection for BCa in two centers (training set, n = 185; external test set, n = 54). We reviewed the clinical characteristics and CT features to identify significant predictors to construct a clinical model. We extracted the hand-crafted radiomics features and deep learning features of the lesions. We used the Minimum Redundancy Maximum Relevance algorithm and the least absolute shrinkage and selection operator logistic regression algorithm to screen features. We used nine classifiers to establish the radiomics machine learning signatures. To compensate for the uneven distribution of the data, we used the synthetic minority over-sampling technique to retrain each machine-learning classifier. We constructed the combined model using the top-performing radiomics signature and clinical model, and finally presented as a nomogram. We evaluated the combined model’s performance using the area under the receiver operating characteristic, accuracy, calibration curves, and decision curve analysis. We used the Kaplan–Meier survival curve to analyze the prognosis of BCa patients.ResultsThe combined model incorporating radiomics signature and clinical model achieved an area under the receiver operating characteristic of 0.834 (95% CI: 0.659–1.000) for the external test set. The calibration curves and decision curve analysis demonstrated exceptional calibration and promising clinical use. The combined model showed good risk stratification performance for progression-free survival.ConclusionThe proposed CT-based combined model is effective and reliable for predicting lymph node status of BCa patients preoperatively.Critical relevance statementBladder cancer is a type of urogenital cancer that has a high morbidity and mortality rate. Lymph node metastasis is an independent risk factor for death in bladder cancer patients. This study aimed to investigate the performance of a deep learning radiomics model for preoperatively predicting lymph node metastasis in bladder cancer patients.Key points• Conventional imaging is not sufficiently accurate to determine lymph node status.• Deep learning radiomics model accurately predicted bladder cancer lymph node metastasis.• The proposed method showed satisfactory patient risk stratification for progression-free survival.Graphical

Deep learning and machine learning predictive models for neurological function after interventional embolization of intracranial aneurysms.

The objective of this study is to develop a model to predicts the postoperative Hunt-Hess grade in patients with intracranial aneurysms by integrating radiomics and deep learning technologies, using preoperative CTA imaging data. Thereby assisting clinical decision-making and improving the assessment and prognosis of postoperative neurological function. This retrospective study encompassed 101 patients who underwent aneurysm embolization surgery. 851 radiomic features were extracted from CTA images. 512 deep learning features are extracted from last layer of ResNet50 deep convolutional neural network model. The feature screening process pipeline encompassed intraclass correlation coefficient analysis, principal component analysis, U test, spearman correlation analysis, minimum redundancy maximum relevance algorithm and Lasso regression, to identify features most correlated with postoperative Hunt-Hess grading. In the model construction phase, three distinct models were constructed: radiomics feature-based model (RSM), deep learning feature-based model (DLM), and deep learning-radiomics feature fusion model (DLRSCM). The study also calculated the radiomics score and combined it with clinical data to construct a Nomogram for predictive modeling. DLM, RSM and DLRSCM model was constructed by 9 base algorithms and 1 ensemble learning algorithm - Stacking ensemble model. Model performance was evaluated based on the area under the Receiver Operating Characteristic (ROC) curve (AUC), Matthews Correlation Coefficient (MCC), calibration curves, and decision curves analysis. 5 significant radiomic feature and 4 significant deep learning features were obtained through the feature selection process. These features were utilized for model construction. Bootstrap resampling method was used for internal validation of the models. In terms of model evaluation, the DLM model, the stacking ensemble algorithm results achieved an AUC of 0.959 and MCC of 0.815. In the RSM model, the stacking ensemble model AUC was 0.935 and MCC was 0.793. The stacking ensemble model in DLRSCM outperformed others, with an AUC of 0.968 and MCC of 0.820. Results indicated that the ANN performed optimally among all base models, while the stacked ensemble learning model exhibited the highest predictive performance. This study demonstrates that the combination of radiomics and deep learning is an effective approach to predict the postoperative Hunt-Hess grade in patients with intracranial aneurysms. This holds significant value in the early identification of postoperative neurological complications and in enhancing clinical decision-making.

Adaptive state of health estimation for lithium-ion batteries using impedance-based timescale information and ensemble learning

Accurate state of health (SOH) estimation is vital to ensure safe, reliable, and efficient operation of lithium-ion batteries. Timescale information on internal kinetic processes is closely related to battery health and can be efficiently identified from broadband impedance by the distribution of relaxation times (DRT). Despite its bright prospects for onboard scenarios, the application of impedance-based timescale information in SOH estimation has rarely been explored. Motivated by this, this work proposes a novel SOH estimation method utilizing impedance-based timescale information and ensemble learning. Adaptive SOH estimation over a wide state of charge (SOC) range is achieved without knowledge of usage history or SOC. Specifically, timescale identification of 584 impedance spectra is conducted via DRT. Based on the extracted timescale features, an ensemble learning technique with a base learner of the regression tree is employed to estimate SOH. The average of the mean absolute errors at different SOCs can be within 1.87 % for all cells under different cyclic conditions. The minimum redundancy maximum relevance algorithm assists in the comparative investigation into SOH estimation using different feature combinations. This work exhibits excellent applications of impedance-based timescale information in SOH estimation and can provide a fresh viewpoint in promoting the health management development.

Interpretable Machine Learning Models for Severe Esophagitis Prediction in LA-NSCLC Patients Treated with Chemoradiation Therapy

Radiation esophagitis is a common adverse event that may occur during chemoradiotherapy (CRT) that can adversely affect survival. This study aimed to develop interpretable machine learning (ML) models to predict grade 3 and higher radiation esophagitis in patients receiving definitive CRT therapy for locally advanced non-small cell lung cancer (LA-NSCLC). A total of 335 patients with LA-NSCLC who received definitive concurrent CRT at a single institution from 2017 to 2021 were retrospectively identified. Patients with esophagitis were identified and graded according to CTCAE v5.0. For each patient, 31 clinical features and 1093 dose-volume histogram (DVH) parameters from 19 structures were collected. The data was then randomly split into training (n = 233) and testing (n = 102) datasets. Feature selection was performed on the training dataset using the minimum redundancy maximum relevance algorithm to find a set of relevant features while controlling for the redundancy within the selected features, which were then followed by the Boruta algorithm to remove unimportant features and make the ML model more accurate. Synthetic minority oversampling technique was used to handle class-imbalanced datasets by generating synthetic samples for the minority class. Four variants of the Generalized Additive Model (GAM), including Explainable Boosting Machine (EBM), neural GAM (NODE-GAM), eXtreme Gradient Boosting (XGB)-GAM, and Spline, were built with selected features. The models' performance in predicting esophagitis was evaluated using the area under the receiver operating characteristic curve (AUC) in the test dataset. Shape plots were used to interpret the models' output and explain the selected features' contribution to the prediction. NODE-GAM yielded the highest performance (F1 score = 0.57, accuracy = 0.8, and AUC = 0.837), followed by EBM (F1 score = 0.43, accuracy = 0.8, and AUC = 0.7), Spline (F1 score = 0.42, accuracy = 0.74, and AUC = 0.737), and XGB-GAM (F1 score = 0.42, accuracy = 0.76, and AUC = 0.71). Selected features included D95%[Gy], D90%[Gy], D65%[Gy] and V40Gy [%] for the esophagus, V10Gy [%] for the pulmonary artery, and the distance from GTVn to the ascending aorta. The analysis of the selected features indicated that an increased radiation dose delivered to the esophagus and a shorter distance between the ascending aorta and GTVn were associated with a higher risk of developing esophagitis. Our study demonstrates the feasibility of developing interpretable ML models to predict esophagitis in patients with LA-NSCLC patients treated with CRT. NODE-GAM provided the best accuracy while providing insights into the driving dosimetric factors that could be used to guide optimal RT planning.

Computed Tomography-derived intratumoral and peritumoral radiomics in predicting EGFR mutation in lung adenocarcinoma

ObjectiveTo investigate the value of Computed Tomography (CT) radiomics derived from different peritumoral volumes of interest (VOIs) in predicting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients.Materials and methodsA retrospective cohort of 779 patients who had pathologically confirmed lung adenocarcinoma were enrolled. 640 patients were randomly divided into a training set, a validation set, and an internal testing set (3:1:1), and the remaining 139 patients were defined as an external testing set. The intratumoral VOI (VOI_I) was manually delineated on the thin-slice CT images, and seven peritumoral VOIs (VOI_P) were automatically generated with 1, 2, 3, 4, 5, 10, and 15 mm expansion along the VOI_I. 1454 radiomic features were extracted from each VOI. The t-test, the least absolute shrinkage and selection operator (LASSO), and the minimum redundancy maximum relevance (mRMR) algorithm were used for feature selection, followed by the construction of radiomics models (VOI_I model, VOI_P model and combined model). The performance of the models were evaluated by the area under the curve (AUC).Results399 patients were classified as EGFR mutant (EGFR+), while 380 were wild-type (EGFR−). In the training and validation sets, internal and external testing sets, VOI4 (intratumoral and peritumoral 4 mm) model achieved the best predictive performance, with AUCs of 0.877, 0.727, and 0.701, respectively, outperforming the VOI_I model (AUCs of 0.728, 0.698, and 0.653, respectively).ConclusionsRadiomics extracted from peritumoral region can add extra value in predicting EGFR mutation status of lung adenocarcinoma patients, with the optimal peritumoral range of 4 mm.

Automated Diagnosis of Pulmonary Diseases Using Lung Sound Signals

Pulmonary diseases are the third biggest cause of deaths worldwide. A prominent method to detect these diseases is the observation of lung sound signals. There is an increasing need for an efficient technique that can automatically diagnose such diseases with high accuracy. In this paper, two popular public datasets are considered, and every lung sound signal is decomposed into 8 frequency bands using rectangular zero-phase filters. Features are extracted from every band, including energy, kurtosis, mean absolute deviation and Lp norm. The extracted features are utilized for classification using machine learning schemes. The proposed method achieves 99.9% accuracy for multi-class classification on the combined dataset, and for binary classification, we have evaluated normal signal versus pathogenic signal, which is found to be 100% accurate for most of the diseases. High accuracy is obtained for the individual datasets as well. Top 20 features selected using minimum redundancy maximum relevance algorithm also yield 98.6% accuracy. Therefore, the proposed method can be easily deployed in real-time systems.

Classification of T lymphocyte motility behaviors using a machine learning approach.

T lymphocytes migrate into organs and interact with local cells to perform their functions. How human T lymphocytes communicate with organ-specific cells and participate in pathobiological processes remains unresolved. Brain infiltration of T lymphocytes is associated with multiple neurological disorders. Thus, to characterize the behavior of human T lymphocytes reaching the human brain, we performed time-lapse microscopy on human CD8+ T lymphocytes co-cultured with either primary human astrocytes or neurons. Using traditional manual and visual assessment of microscopy data, we identified distinct CD8+ T lymphocyte motility behaviors. However, such characterization is time and labor-intensive. In this work, we trained and validated a machine-learning model for the automated classification of behaviors of CD8+ T lymphocytes interacting with astrocytes and neurons. A balanced random forest was trained for the binary classification of established classes of cell behaviors (synapse vs. kinapse) as well as visually identified behaviors (scanning, dancing, and poking). Feature selection was performed during 3-fold cross-validation using the minimum redundancy maximum relevance algorithm. Results show promising performances when tested on a held-out dataset of CD8+ T lymphocytes interacting with astrocytes with a new experimenter and a held-out independent dataset of CD8+ T lymphocytes interacting with neurons. When tested on the independent CD8+ T cell-neuron dataset, the final model achieved a binary classification accuracy of 0.82 and a 3-class accuracy of 0.79. This novel automated classification approach could significantly reduce the time required to label cell motility behaviors while facilitating the identification of interactions of T lymphocytes with multiple cell types.

Hydraulic-Pump Fault-Diagnosis Method Based on Mean Spectrogram Bar Graph of Voiceprint and ResNet-50 Model Transfer

The vibration signal of a pump is often used for analysis in the study of hydraulic-pump fault diagnosis methods. In this study, for the analysis, sound signals were used, which can be used to acquire data in a non-contact manner to expand the use scenarios of hydraulic-pump fault-diagnosis methods. First, the original data are denoised using complete ensemble empirical mode decomposition with adaptive noise and the minimum redundancy maximum relevance algorithm. Second, the noise-reduced data are plotted as mean spectrogram bar graphs, and the datasets are divided. Third, the training set graphs are input into the ResNet-50 network to train the base model for fault diagnosis. Fourth, all the layers of the base model are frozen, except for the fully connected and softmax layers, and the support set graphs are used to train the base model through transfer learning. Finally, a fault diagnosis model is obtained. The model is tested using data from two test pumps, resulting in accuracies of 86.1% and 90.8% and providing evidence for the effectiveness of the proposed method for diagnosing faults in hydraulic plunger pumps.

Urban land use land cover classification based on GF-6 satellite imagery and multi-feature optimization

Urban land use/land cover (LULC) classification has long been a hotspot for remote sensing applications. With high spatio-temporal resolution and multispectral, the recently launched GF-6 satellite provides ideal open imagery for LULC mapping. In this study, we utilized multitemporal GF-6 images to generate six types of land features, including spectral bands, texture features, built-up, waterbody, vegetation, and red-edge indices. The minimum Redundancy Maximum Relevance (mRMR) algorithm was employed to optimize feature selection. Subsequently, Random Forest (RF) and Extreme Gradient Boosting (XGBT) were assessed using different feature selections. Besides, various feature configurations were designed for LULC classification and comparison. The results indicate that the mRMR-based RF method achieved the highest overall accuracy of 91.37%. The temporal red-edge indices were important features for urban LULC classification and contributed mainly to grassland and cropland. These results supplement existing classification methods and assist in improving LULC mapping in urban areas with complex landscapes.

Estimation of non-alcoholic steatohepatitis (NASH) disease using clinical information based on the optimal combination of intelligent algorithms for feature selection and classification

The early diagnosis of NASH disease can decrease the risk of proceeding elements and treatment costs for patients. This study aims to present an optimal combination of intelligent algorithms using advanced machine learning methods, including different feature selections and classifications based on clinical data and blood factors. In this work, collected data were from 176 patients to investigate NASH disease, and 19 features were extracted. We then sought to find the best combination of features based on different feature selection algorithms such as Feature Forward Selection (FFS), Minimum Redundancy Maximum Relevance (MRMR), and Mutual Information (MI). Finally, we used nine classifier frameworks with different mathematical mechanisms, including random forest (RF), logistic regression (LR), Linear Discriminant Analysis (LDA), AdaBoost, K nearest neighbors (KNN), multilayer perceptron model (MLP), support vector machine (SVM), and decision tree (DT) to estimate NASH disease. Our investigation revealed that the combination of dominant features, namely body mass index (BMI), glutamic pyruvic transaminase (GPT), total cholesterol (TC), high-density lipoprotein (HDL), Ezetimibe, lipoprotein level Lp(a), Loge(Lp(a)), total triglyceride (TG), Creatinine (Cre), HbA1c, Fibrate, and Sex, selected by the MRMR algorithm and classified by the RF method can provide the most appropriate performance based on less computation effort and maximum performance with accuracy, AUC, precision, and recall indices, which are and respectively. This study investigated the configuration of feature selection and classifier that is most suitable for classifying NASH disease based on clinical data and blood factors. The proposed intelligent algorithm based on MRMR and RF classifier can automatically diagnose NASH disease with appropriate performance and present an initial report without any further invasive methods. It also clarifies the diagnostic process and, as a result, the continuation of their prevention and treatment cycle.

Parametrized division of exposure zone for marine reinforced concrete structures with a multi-class Boosting method

The analysis of marine reinforced concrete structures using chloride profile data is a commonly used exposure zone classification method. However, chloride profile data is multi-class, unbalanced and non-parametric, which makes it difficult for the commonly used machine-learning methods to construct an appropriate classification model. To solve this problem, chloride profile is parametrized by the minimum redundancy maximum relevance algorithm and a multi-class Boosting method using F-measure as inductive bias indicator to evaluate the weight of base classifiers is put forward. The method is based on field test data of the chloride profile over a period of 18 years in Hangzhou Bay, China. The method outperforms the original Boosting method with an average F-measure improvement of 6.2 %. The results show that parametric partitioning of the exposure zone is achieved and the distribution of the exposure zone satisfies the Weibull distribution.