Information Feature Selection Research Articles

Визначення предикторів діагнозу пацієнтів з ПТСР на основі параметрів одновимірних моделей першого порядку BOLD-сигналів структур мозку та МГУА

Introduction. The use of functional magnetic resonance imaging (fMRI) allows for the assessment of processes occurring in the brain. By analyzing the examination results, it is possible to establish the parameters of connections between brain structures, and changes in the values of these parameters can be used as diagnostic conclusion predictors for PTSD-patients. Purpose. To identify predictors for the classification of the PTSD diagnosis using the connectivity parameters of BOLD signals from brain structures. Methods. The technology for identifying predictors of PTSD diagnosis is based on a) the formation of connectivity parameters of BOLD signals from brain structures obtained during resting-state scanning, b) the use of classifier-oriented selection based on inter-class variance and mRMR criteria to select informative features, and c) the classification of PTSD diagnosis using a logistic regression algorithm optimized by the Group Method of Data Handling. Results. The technology proposed in this work enabled the selection of informative features and the identification of their predictive forms, resulting in the formation of classifiers for the diagnosis of PTSD with high accuracy, sensitivity, and specificity. Conclusion. A technology for the formation, selection, and use of connectivity parameters of BOLD signals from brain structures has been proposed for differentiating healthy individuals from those who suffer with PTSD. A list of the most informative features of PTSD and their predictive forms in the form of generalized variables has been obtained, which can be used for diagnostic conclusions. The results obtained indicate the presence of a specific type of connection between the brain areas identified in the study based on levels of excitation (parameters а0 of the models) and the alteration of these levels in the context of PTSD.

A Methodical Framework Utilizing Transforms and Biomimetic Intelligence-Based Optimization with Machine Learning for Speech Emotion Recognition.

Speech emotion recognition (SER) tasks are conducted to extract emotional features from speech signals. The characteristic parameters are analyzed, and the speech emotional states are judged. At present, SER is an important aspect of artificial psychology and artificial intelligence, as it is widely implemented in many applications in the human-computer interface, medical, and entertainment fields. In this work, six transforms, namely, the synchrosqueezing transform, fractional Stockwell transform (FST), K-sine transform-dependent integrated system (KSTDIS), flexible analytic wavelet transform (FAWT), chirplet transform, and superlet transform, are initially applied to speech emotion signals. Once the transforms are applied and the features are extracted, the essential features are selected using three techniques: the Overlapping Information Feature Selection (OIFS) technique followed by two biomimetic intelligence-based optimization techniques, namely, Harris Hawks Optimization (HHO) and the Chameleon Swarm Algorithm (CSA). The selected features are then classified with the help of ten basic machine learning classifiers, with special emphasis given to the extreme learning machine (ELM) and twin extreme learning machine (TELM) classifiers. An experiment is conducted on four publicly available datasets, namely, EMOVO, RAVDESS, SAVEE, and Berlin Emo-DB. The best results are obtained as follows: the Chirplet + CSA + TELM combination obtains a classification accuracy of 80.63% on the EMOVO dataset, the FAWT + HHO + TELM combination obtains a classification accuracy of 85.76% on the RAVDESS dataset, the Chirplet + OIFS + TELM combination obtains a classification accuracy of 83.94% on the SAVEE dataset, and, finally, the KSTDIS + CSA + TELM combination obtains a classification accuracy of 89.77% on the Berlin Emo-DB dataset.

Algorithm for Determination of Pepper Maturity Classes by Combination of Color and Spectral Indices

Abstract The aim of the present work is to propose methods and tools for classifying sweet pepper into groups according to their degree of maturity based on color and spectral characteristics extracted from color images on the surface of the vegetables. The investigated pepper is two varieties of sweet - red Banji and yellow Liri. Five groups were formed, depending on the degree of maturity, and 16 color and 11 spectral indices were calculated for each of the groups. By successively using the ReliefF and PLSR methods, a selection of informative features and subsequent reduction of the vector formed by them was carried out, thereby aiming to increase the predictive results and minimize the time for data processing. The obtained classification errors between the individual stages of ripening vary according to the type of pepper and depending on which of the two types of maturity the fruits are in - technical or biological. For red sweet pepper, the separation inaccuracy obtained using a discriminant classifier with a quadratic separation function is in the range of 8 - 19%, while for yellow it is from 5 to 23%. The results obtained in the present work for the classification of pepper into groups according to their degree of maturity would support decision-making in selective harvesting and overall more accurate and efficient management of the harvesting process from the point of view of precision agriculture. The work will continue with studies related to the prediction of various compounds indicating changes in the color of peppers, including chlorophylls, carotenes and xanthophylls. In this way, it is possible to increase the accuracy in determining the degree of ripeness, since in pepper the color does not always follow the same pattern of change from green to yellow to orange to red.

NOx concentration prediction based on multi-channel fused spectral temporal graph neural network in coal-fired power plants

Establishing an accurate and stable NOx concentration prediction model is the foundation for achieving environmental protection in coal-fired power plant denitrification. In this research, a data-driven hybrid prediction model (BMFE-MFST-GNN) is proposed to improve the prediction accuracy of inlet NOx concentrations. First, we develop the adaptive variational modal decomposition method (FEVMD) to decompose historical NOx concentration data into simple and smooth subsequences and extract time-frequency features. Second, we propose the boosting mutual information feature selection algorithm (BMIFS) to determine the best set of auxiliary variables. The delay time is calculated based on the maximal information coefficient (MIC) to reconstruct the datasets. Then, the multi-channel fused spectral temporal graph neural network (MFST-GNN) is created to build the graph feature information of decomposed subsequences and reconstructed auxiliary variables to predict the concentration subsequences. Finally, we integrate the subsequence prediction results to obtain the future NOx concentrations. The experimental results demonstrate that the proposed method outperforms several comparative models in predicting NOx concentrations.

Machine learning and integrative multi-omics network analysis for survival prediction in acute myeloid leukemia

BackgroundAcute myeloid leukemia (AML) is the most common malignant myeloid disorder in adults and the fifth most common malignancy in children, necessitating advanced technologies for outcome prediction. MethodThis study aims to enhance prognostic capabilities in AML by integrating multi-omics data, especially gene expression and methylation, through network-based feature selection methodologies. By employing artificial intelligence and network analysis, we are exploring different methods to build a machine learning model for predicting AML patient survival. We evaluate the effectiveness of combining omics data, identify the most informative method for network integration and compare the performance with standard feature selection methods. ResultsOur findings demonstrate that integrating gene expression and methylation data significantly improves prediction accuracy compared to single omics data. Among network integration methods, our study identifies the best approach that improves informative feature selection for predicting patient outcomes in AML. Comparative analyses demonstrate the superior performance of the proposed network-based methods over standard techniques. ConclusionsThis research presents an innovative and robust methodology for building a survival prediction model tailored to AML patients. By leveraging multilayer network analysis for feature selection, our approach contributes to improving the understanding and prognostic capabilities in AML and laying the foundation for more effective personalized therapeutic interventions in the future.

Label generation with consistency on the graph for multi-label feature selection

Multi-label feature selection involves the selection of informative features in high-dimensional data sets based on the relationships among different variables. However, large-scale data sets often contain unknown labels that hold latent information, posing a challenge for discovery. Explicitly uncovering latent information among labels not only helps establish robust mappings between features and labels but also expands the applicable knowledge. This paper introduces a novel feature selection method called Label Generation with Consistency on the Graph for Multi-label Feature Selection (LGCM) to effectively integrate label generation and feature selection processes. The proposed method incorporates a two-way flipping mechanism that leverages the consistency on the global graph to guide label generation for each instance. Additionally, the feature selection model utilizes a shared low-rank feature space to minimize deviation from the label generation, providing feedback to the label generation process. Extensive experiments validate the effectiveness of LGCM in improving state-of-the-art feature selection methods in multi-label learning.

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.

Maximally informative feature selection using Information Imbalance: Application to COVID-19 severity prediction

Clinical databases typically include, for each patient, many heterogeneous features, for example blood exams, the clinical history before the onset of the disease, the evolution of the symptoms, the results of imaging exams, and many others. We here propose to exploit a recently developed statistical approach, the Information Imbalance, to compare different subsets of patient features and automatically select the set of features that is maximally informative for a given clinical purpose, especially in minority classes. We adapt the Information Imbalance approach to work in a clinical framework, where patient features are often categorical and are generally available only for a fraction of the patients. We apply this algorithm to a data set of ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document} 1300 patients treated for COVID-19 in Udine hospital before October 2021. Using this approach, we find combinations of features which, if used in combination, are maximally informative of the clinical fate and of the severity of the disease. The optimal number of features, which is determined automatically, turns out to be between 10 and 15. These features can be measured at admission. The approach can be used also if the features are available only for a fraction of the patients, does not require imputation and, importantly, is able to automatically select features with small inter-feature correlation. Clinical insights deriving from this study are also discussed.

Prediction of Icing on Wind Turbines Based on SCADA Data via Temporal Convolutional Network

Icing on the blades of wind turbines during winter seasons causes a reduction in power and revenue losses. The prediction of icing before it occurs has the potential to enable mitigating actions to reduce ice accumulation. This paper presents a framework for the prediction of icing on wind turbines based on Supervisory Control and Data Acquisition (SCADA) data without requiring the installation of any additional icing sensors on the turbines. A Temporal Convolutional Network is considered as the model to predict icing from the SCADA data time series. All aspects of the icing prediction framework are described, including the necessary data preprocessing, the labeling of SCADA data for icing conditions, the selection of informative icing features or variables in SCADA data, and the design of a Temporal Convolutional Network as the prediction model. Two performance metrics to evaluate the prediction outcome are presented. Using SCADA data from an actual wind turbine, the model achieves an average prediction accuracy of 77.6% for future times of up to 48 h.

Interactive AI Virtual Teaching Resource Intelligent Recommendation Algorithm Based on Similarity Measurement on the Internet of Things Platform

Abstract To tackle the issues of low recommender system correctness and the long time of recommendation in the traditional intelligent recommendation algorithm of interactive artificial intelligence (AI) virtual teaching resources, a similarity measurement-based intelligent recommendation algorithm of interactive AI virtual teaching resources is proposed. According to the contact frequency of adjacent learners, the behavior patterns of learners are mined, and the user characteristics are selected on the basis of the mutual information feature selection method. In the context of the category attribute matrix and learner category attribute scoring matrix of interactive AI virtual teaching resources, the user interest matrix is constructed by the Relevance Feedback based on Inverse Learning Function (RF-ILF) method. The user similarity is calculated and the neighbor set is found in the similar user clusters after clustering. According to the results of the simulation, the suggested algorithm is more efficient and provides more accurate recommendations when recommending interactive AI virtual teaching resources.

Localization and grading of NPDR lesions using ResNet-18-YOLOv8 model and informative features selection for DR classification based on transfer learning

Complications in diabetes lead to diabetic retinopathy (DR) hence affecting the vision. Computerized methods performed a significant role in DR detection at the initial phase to cure vision loss. Therefore, a method is proposed in this study that consists of three models for localization, segmentation, and classification. A novel technique is designed with the combination of pre-trained ResNet-18 and YOLOv8 models based on the selection of optimum layers for the localization of DR lesions. The localized images are passed to the designed semantic segmentation model on selected layers and trained on optimized learning hyperparameters. The segmentation model performance is evaluated on the Grand-challenge IDRID segmentation dataset. The achieved results are computed in terms of mean IoU 0.95,0.94, 0.96, 0.94, and 0.95 on OD, SoftExs, HardExs, HAE, and MAs respectively. Another classification model is developed in which deep features are derived from the pre-trained Efficientnet-b0 model and optimized using a Genetic algorithm (GA) based on the selected parameters for grading of NPDR lesions. The proposed model achieved greater than 98 % accuracy which is superior to previous methods.

Enhancing Wireless Sensor Network Security Through Mutual Information Analysis for Intrusion Detection and Resilience

Intrusion detection is a critical aspect of network security, involving the identification and response to unauthorized access or malicious activities within a system. It plays a crucial role in safeguarding networks and ensuring data integrity and confidentiality. The Mutual Information (MI) analysis is a vital component in the realm of intrusion detection and security within Wireless Sensor Networks (WSNs). By assessing the information shared between pairs of sensor nodes, MI analysis reveals underlying patterns and relationships in network data. This process involves calculating the mutual information for each node pair based on statistical properties of observed values. Strong correlations or dependencies between nodes are identified, aiding in the detection of critical nodes or clusters vulnerable to compromise. Additionally, MI analysis informs feature extraction by guiding the selection of informative features that capture network structure. It also serves as a proactive tool for identifying anomalies or deviations from expected patterns, which may signal intrusion attempts or malicious activities. Through monitoring changes in mutual information over time, MI analysis facilitates prompt responses to emerging threats, enhancing the resilience and security of WSNs. The work exhibits high accuracy, recall rates, and detection rates across various attack scenarios, underscoring its efficacy in identifying and mitigating security threats. The algorithm's efficiency, effectiveness, and reliability make it a promising solution for enhancing WSN security. Through sophisticated methodologies and adaptive defensive strategies, the proposed algorithm strengthens the robustness and dependability of WSNs, minimizing the risk of potential security vulnerabilities and ensuring comprehensive threat detection in real-world deployment scenarios.

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.

Design of a Classifier model for Heart Disease Prediction using normalized graph model

Heart disease is an illness that influences enormous people worldwide. Particularly in cardiology, heart disease diagnosis and treatment need to happen quickly and precisely. Here, a machine learning-based (ML) approach is anticipated for diagnosing a cardiac disease that is both effective and accurate. The system was developed using standard feature selection algorithms for removing unnecessary and redundant features. Here, a novel normalized graph model (n – GM) is used for prediction. To address the issue of feature selection, this work considers the significant information feature selection approach. To improve classification accuracy and shorten the time it takes to process classifications, feature selection techniques are utilized. Furthermore, the hyper-parameters and learning techniques for model evaluation have been accomplished using cross-validation. The performance is evaluated with various metrics. The performance is evaluated on the features chosen via features representation. The outcomes demonstrate that the suggested (n – GM) gives 98 % accuracy for modeling an intelligent system to detect heart disease using a classifier support vector machine

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.

Слабо-контролируемая групповая классификация

В работе решается задача слабо-контролируемого обучения в постановке групповой бинарной классификации. Предполагается, что каждый объект выборки может включать набор подобъектов, относящихся к разным классам. Предлагаемый метод решения основан на выборе информативного признакового пространства и фильтрации обучающей выборки. Описывается применение разработанного метода для прогнозирования степени поражения участков головного мозга у пациентов с ишемическим инсультом по снимкам компьютерной томографии. Weakly supervised learning implies possible uncertainty or fuzziness of the labelling. Current study addresses this problem using the formulation of group binary classification. It is assumed that each sample object may include a set of sub-objects belonging to one of two classes. Objects are described by a set of features; the predicted feature determines the degree to which an object belongs to the “positive” class. It is required to construct a decision function from the training sample to predict the target feature for new objects. The proposed method is based on the selection of informative feature space and filtering the training sample. Both the selection of informative features and the removal of noise observations are carried out on the basis of analysis of the local environment of objects. The degree of similarity between the object and the class is determined by the k nearest neighbours of the object, taking into account their degree of belonging to the target class. For an experimental study of the developed method, the real problem of analyzing tomography images of the brain to predict the degree of damage to its areas in stroke is solved. The results are compared with a number of known methods. A method for constructing a decision function for predicting the degree of belonging of an object to the target class has been developed. The results of an experimental study and comparison with a number of well-known machine learning algorithms (random forest, support vector machine, kNN) confirmed the efficiency of the method for solving the problem of predicting the degree of damage to brain areas in stroke patients. Unlike other similar algorithms, the proposed method allows establishing a set of the most informative features in order to improve the interpretability of the solution and reduce the effect of overfitting.

Machine Learning-Based Classification of Turkish Music for Mood-Driven Selection

Music holds a significant role in our daily lives, and its impact on emotions has been a focal point of research across various disciplines, including psychology, sociology, and statistics. Ongoing studies continue to explore this intriguing relationship. With advancing technology, the ability to choose from a diverse range of music has expanded. Recent trends highlight a growing preference for searching for music based on emotional attributes rather than individual preferences or genres. The act of selecting music based on emotional states is important on both a universal and cultural level. This study seeks to employ machine learning-based methods to classify four different music genres using a minimal set of features. The objective is to facilitate the process of choosing Turkish music according to one’s mood. The classification methods employed include Decision Tree, Random Forest (RF), Support Vector Machines (SVM), and k-Nearest Neighbor, coupled with the Mutual Information (MI) feature selection algorithm. Experimental results reveal that, with all features considered in the dataset, RF achieved the highest accuracy at 0.8098. However, when the MI algorithm was applied, SVM exhibited the best accuracy at 0.8068. Considering both memory consumption and accuracy, the RF method emerges as a favorable choice for selecting Turkish music based on emotional states. This research not only advances our understanding of the interaction between music and emotions but also provides practical insights for individuals who want to shape their music according to their emotional preferences.