EEG-Based Depression Detection in the Prefrontal Cortex Lobe using mRMR Feature Selection and Bidirectional LSTM

Daily physical activity monitoring and recognition have become a big health caring challenge in modern times. Fast recognition of physical activity from wearable sensors dataset with acceptable accuracy has got great research attention. In this paper, we have presented an optimization framework with feature selection techniques. The Bayesian Optimization algorithm has been employed to optimize hyper-parameters of Support Vector Machine(SVM), Random Forest (RF), Extreme Gradient Boosting(XGBoost). Two feature selection algorithms like Fast Correlation Based Filter (FCBF) and Maximum Redundancy Maximum Relevance(mRMR) feature selection have been applied to reduce the size of the extracted feature vector. Classification performances of the two feature selection techniques are compared in terms of accuracy and F1 score. For reduced feature vector the highest acceptable accuracy and F1 score of 95.4% and 94.76% respectively have been achieved by optimized XGBoost classifier with mRMR feature selection. In addition to this, it is shown that reduced computation time with considerable classification performance can be achieved by an efficient feature selection algorithm which is useful for designing a simplified system.

Human Activity Recognition is a research field that aims to identify the activities carried out by a person. Recognition can be done by using information that is retrieved from various sources, for example: using inertial sensors. Lately, many people use smartphone with built-in inertial sensors such as accelerometer and gyroscope which make these smartphone capable of recognizing human activities. But, an optimization must be done to minimize the computational process of the recognition system so that the system could be function properly in smartphone that has limited processing power. In this study optimization was done by reducing the numbers of features used in the dataset. Using an available public human activity recognition dataset, mRMR (Minimum Redundancy Maximum Relevance) feature selection method was applied to the dataset to reduce the numbers of features. Results from this study show that using mRMR method in dataset 7∶3, the numbers of features can be reduced from 561 features to 201 features, while still maintaining the accuracy at 95.15% using SVM classifier and 94.23% using MLP classifier. While using mRMR method in dataset 8∶2, the numbers of features can be reduced from 561 features to 154 features with the accuracy 95.18% using SVM and the numbers of features can be reduced from 561 to 211 with the accuracy 94.84% using MLP. Both dataset using the same threshold that is 95% for SVM and 94% for MLP. For the running time computation, in dataset 7∶3 the running time becomes 42.57% from the initial running time using SVM and 12.63% from the initial running time using MLP. While in dataset 8∶2 the running time becomes 60.5% from the initial running time using SVM and 14% from the initial running time using MLP.

Due to limited space and regulations, food labels often lack information on micronutrients, i.e., vitamins and minerals. Accurately predicting missing these micronutrient data is essential yet challenging. This study explores the feasibility of using machine learning to predict these missing nutrients based on a limited reported nutrient (protein and carbs). Using the Tabel Komposisi Pangan Indonesia (TKPI) dataset, we evaluated the performance of 12 diverse classifiers to predict binary classes ("low" or "high") for 13 target micronutrients. Random forest emerged as the best performing classifier with an average accuracy of 0.7421 across all target nutrients. Additionally, we introduced feature engineering techniques by incorporating polynomial and ratio features to enhance model performance. Minimum redundancy maximum relevance (mRMR) feature selection was then applied to identify the most informative features. This approach boosted the average accuracy of the random forest classifier to 0.7591. These findings highlight the efficacy of feature engineering and selection in enhancing nutrient prediction models, demonstrating the potential to improve consumer knowledge about unknown nutrients in food.

Introduction: Needless to say that correct and real-time detection and effective prognosis of the COVID-19 are necessary to deliver the best possible care for patients and, accordingly, diminish the pressure on the healthcare industries. Hence our paper aims to present an intelligent algorithm for selecting the best features from the dataset and developing Machine Learning(ML) based models to predict the COVID-19 and finally opted for the best-performing algorithm.
 Methods: In this developmental study, the clinical data of 1703 COVID-19 and non-COVID-19 patients Using a single-center registry from February 9, 2020, to December 20, 2020, were used. The Minimum Redundancy Maximum Relevance (mRMR) feature selection algorithm identified the most relevant variables. Then, chosen features feed into the several data mining methods, including K-Nearest Neighbors, AdaBoost Classifier, Decision Tree, HistGradient Boosting Classifier, and Support Vector Machine. A 10-fold cross-validation method and six performance evaluation metrics were used to evaluate and compare these implemented algorithms, and finally, the best model was implemented.
 Results: Out of the 34 included features, 11 variables were selected as the essential features. The results of using ML algorithms indicated that the best performance belongs to the AdaBoost classifier with mean accuracy = 92.9%, mean specificity = 89.3%, mean sensitivity = 94.2%, mean F-measure = 91.6 %, mean KAPA = 94.3% and mean ROC = 92.1 %.
 Conclusion: The empirical results reveal that the Adaboost model yielded higher performance than other classification models and developed our Clinical Decision Support Systems (CDSS) interface to discriminate positive COVID-19 from negative cases.

Convolutional Neural Networks based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mRMR

Computational identification of N6-methyladenosine sites in multiple tissues of mammals

Software Defined Network (SDN) is a programmable network architecture that provides innovative solutions to the problems of the traditional networks. Congestion control is still an uncharted territory for this technology. In this work, a congestion prediction scheme has been developed by using neural networks. Minimum Redundancy Maximum Relevance (mRMR) feature selection algorithm was performed on the data collected from the OMNET++ simulation. The novelty of this study also covers the implementation of mRMR in an SDN congestion prediction problem. After evaluating the relevance scores, two highest ranking features were used. On the learning stage Nonlinear Autoregressive Exogenous Neural Network (NARX), Nonlinear Autoregressive Neural Network, and Nonlinear Feedforward Neural Network algorithms were executed. These algorithms had not been used before in SDNs according to the best of the authors knowledge. The experiments represented that NARX was the best prediction algorithm. This machine learning approach can be easily integrated to different topologies and application areas.

Refined composite multiscale fluctuation-based dispersion Lempel–Ziv complexity for signal analysis

Schizophrenia is a serious psychiatric illness which needs early and accurate diagnosis. Difference in activation patterns of schizophrenia patients and healthy subjects can be identified with the help of functional magnetic resonance imaging (fMRI). However, manual diagnosis using fMRI depends on subjective observation and may be erroneous. This has motivated the pattern recognition and machine learning research community to develop a reliable and efficient decision model for classification of schizophrenia patients and healthy subjects. However, high dimensionality and low availability of fMRI data leads to the curse-of-dimensionality problem which may degrade the performance of decision model. In the present research work, a combination of feature extraction and feature selection techniques is utilised to obtain a reduced set of relevant and non-redundant features for classification of schizophrenia patients and healthy subjects. Features are extracted from pre-processed fMRI data using a general linear model approach. Next Fisher's discriminant ratio, a univariate method, is employed for feature selection i.e. To determine a subset of discriminative features. Further, minimum Redundancy Maximum Relevance (mRMR) feature selection, a multivariate method, is employed to obtain a set of relevant and non-redundant features which are used for learning a decision model using support vector machine. Two balanced and well-age matched datasets of auditory oddball task derived from a publicly available multisite FBIRN dataset are used for experiments. First dataset consists of fMRI scans of 34 schizophrenia patients and 34 healthy subjects acquired through 1.5 Tesla scanners while second dataset consists of 25 schizophrenia patients and 25 healthy subjects acquired through 3 Tesla scanners. The performance is evaluated in terms of sensitivity, specificity and classification accuracy, and compared with two well-known existing approaches for classification of schizophrenia patients and healthy subjects using fMRI. Experimental results demonstrate that the proposed model outperforms the existing approaches in terms of sensitivity, specificity and classification accuracy. The proposed approach achieves classification accuracy of 88.2% and 78.0% for 1.5 Tesla and 3 Tesla datasets respectively. In addition, the brain regions containing the discriminative features are identified which may be potential biomarkers for diagnosis of schizophrenia using fMRI.

The aim of this study is to develop machine learning based models for staff churn and staff lifetime prediction. Three different approaches are used in the development of these models. In the first approach, prediction models are developed without feature selection. In the second approach, prediction models are developed using the Minimum Redundancy Maximum Relevance (mRMR) feature selection algorithm. In the third approach, prediction models are developed using the Principal Component Analysis (PCA) feature selection algorithm. Two different datasets are used in the development of the models. For predicting staff churn in both datasets, Logistic Regression (LR), Categorical Boosting (CatBoost), and Extreme Learning Machine (ELM) are used. To predict staff lifetime, Support Vector Machine (SVM), Gradient Boosting Machine (LightGBM), and K-Nearest Neighbors (KNN) are used. In order to evaluate the performance of the prediction models, Accuracy and F-Score are used for classification-based models, while Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used for regression-based models. The results obtained in this study show that the feature selection algorithms have no significant effect on the performance of the models.

Register-transfer-level (RTL) information is hardly available for hardware Trojan detection. In this paper, four RTL Trojan features related to branching statement are proposed. The Minimum Redundancy Maximum Relevance (mRMR) feature selection is applied to the proposed Trojan features to determine the recommended feature combinations. The feature combinations are then tested using different machine learning concepts in order to determine the best approach for classifying Trojan and normal branches. The result shows that a Decision Tree classification algorithm with all the four proposed Trojan features can achieve an average true positive detection rate of 93.72% on unseen test data.

The early detection and accurate diagnosis of liver fibrosis, a progressive and potentially serious liver condition, are crucial for effective medical intervention. Invasive methods like biopsies for diagnosis can be risky and expensive. This research presents a novel computer-aided diagnosis model for liver fibrosis using a hybrid approach of minimum redundancy maximum relevance (MRMR) feature selection, bidirectional long short-term memory (BiLSTM), and convolutional neural networks (CNN). The proposed model involves multiple stages, including image acquisition, preprocessing, feature representation, fibrous tissue identification, and classification. Notably, histogram equalization is employed to enhance image quality by addressing variations in brightness levels. Performance evaluation encompasses a range of metrics such as accuracy, precision, sensitivity, specificity, F1 score, and error rate. Comparative analyses with established methods like DCNN, ANN-FLI, LungNet22, and SDAE-GAN underscore the efficacy of the proposed model. The innovative integration of hybrid MRMR-BiLSTM-CNN architecture and the horse herd optimization algorithm significantly enhances accuracy and F1 score, even with small datasets. The model tackles the complexities of hyperparameter optimization through the IHO algorithm and reduces training time by leveraging MRMR feature selection. In practical application, the proposed hybrid MRMR-BiLSTM-CNN method demonstrates remarkable performance with a 97.8% accuracy rate in identifying liver fibrosis images. It exhibits high precision, sensitivity, specificity, and minimal error rate, showcasing its potential for accurate and non-invasive diagnosis.

The radiological features of computed tomography (CT) images and the sequence of radiomics signatures in continuous slices of lung CT lesions are helpful in identifying subtypes of lung adenocarcinoma. A model based on bidirectional long short-term memory (Bi-LSTM) and multihead attention can learn the rules of this sequence well. In this study, 421 patients with 427 lesions confirmed as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) or invasive adenocarcinoma (IAC) were recruited from three hospitals. The radiomics signatures of the identified lesion regions in each CT image were extracted using 'PyRadiomics' software, and the corresponding radiological features were subsequently documented and collected. Then, the top 100 features were extracted by the minimum redundancy maximum relevance (mRMR) feature ranking method. A model based on the radiological and imaging features was established to classify the lesions using Bi-LSTM and multihead attention. The diagnostic performance of the model was measured by the area under the curve (AUC) of the receiver operating characteristic (ROC). The model combined radiological features and radiomics signatures. The AUCs of the model in the training, testing, and validation groups were 0.985, 0.94 and 0.981, respectively, and the accuracy was 0.92, 0.976 and 0.91, respectively. In addition, we trained two other models [convolutional neural network (CNN) + multihead attention, long short-term memory (LSTM) + multihead attention] and compared them using the testing dataset. The precision of the two models was 0.89 and 0.88, respectively, and the accuracy was 0.88 and 0.87, respectively. Bi-LSTM and multihead attention based on radiomics signatures and radiological features provide a way to distinguish AIS, MIA, and IAC.

Computational approaches are able to analyze protein-protein interactions (PPIs) from a different angle of view by complementing the experimental ones. And they are very efficient in determining whether two proteins can interact with each other. In this paper, KNNs (K-nearest neighbors) is applied to predict the PPIs by coding each protein with the physical and chemical properties of its residues, predicted secondary structures and amino acid compositions. mRMR (minimum-redundancy maximum-relevance) feature selection is adopted to select a compact feature set, features of which are considered to be important for the determination of PPI-nesses. Because the size of the negative dataset (containing non-interactive protein pairs) is much larger than that of the positive dataset (containing interactive protein pairs), the negative dataset is divided into 5 portions and each portion is combined with the positive dataset for one prediction. Thus 5 predictions are performed and the final results are obtained through voting. As a result, the prediction achieves an overall accuracy of 0.8369 with sensitivity of 0.7356. The predictor, developed by this research for the prediction of the fruit fly PPI-nesses, is available for public use at http://chemdata.shu.edu.cn/ppip.

Identification of disease genes is a hot topic in biomedicine and genomics. However, it is a challenging problem because of the complexity of diseases. Inflammatory bowel disease (IBD) is an idiopathic disease caused by a dysregulated immune response to host intestinal microflora. It has been proven to be associated with the development of intestinal malignancies. Although the specific pathological characteristics and genetic background of IBD have been partially revealed, it is still an overdetermined disease and the blueprint of all genetic variants still needs to be improved. In this study, a novel computational method was built to identify genes related to IBD. Samples from two subtypes of IBD (ulcerative colitis and Crohn's disease) and normal samples were employed. By analyzing the gene expression profiles of these samples using minimum redundancy maximum relevance and incremental feature selection, 21 genes were obtained that could effectively distinguish samples from the two subtypes of IBD and the normal samples. Then, the shortest-path approach was used to search for an additional 20 genes in a large network constructed using protein-protein interactions based on the above-mentioned 21 genes. Analyses of the 41 genes obtained indicate that they are closely associated with this disease.