Feature Reduction Research Articles

Enhanced Epileptic Seizure Identification using Sparse ELM-ABO Fusion with Feature Reduction and Multi-class Classification

The interpretation of the electroencephalogram (EEG) signal is one method that can be utilized to diagnose epilepsy, which is one of the most prevalent brain illnesses. The length of an EEG signal is typically quite long, making it difficult to interpret manually. Extreme Learning Machine (ELM) is used to detection of Epilepsy and Seizure. But in ELM Storage space and training time is high. In order to reduce training time and storage space African Buffalo Optimization (ABO) algorithm is used. ABO is combined with Sparse ELM to improve the speed, accuracy of detection and reduce the storage space. First, Wavelet transform is used to extract relevant features. Due to their high dimensionality, these features are then reduced by using linear discriminant analysis (LDA). The proposed Hybrid Sparse ELM technique is successfully implemented for diagnosing epileptic seizure disease. For classification, the Sparse ELM-ABO classifier is applied to the UCI Epileptic Seizure Recognition Data Set training dataset, and the experimental findings are compared to those of the SVM, Sparse ELM, and ELM classifiers applied to the same database. The proposed model was tested in two scenarios: binary classification and multi-label classification. Seizure identification is the only factor in binary classification. Seizure and epilepsy identification are part of multi-label classification. It is observed that the proposed method obtained high accuracy in classification with less execution time along with performance evaluation of parameters such as prediction accuracy, specificity, precision, recall and F-score. Binary classification scores 96.08%, while multi-label classification achieves 90.89%.

L2NLF: a novel linear-to-nonlinear framework for multi-modal medical image registration.

In recent years, deep learning has ushered in significant development in medical image registration, and the method of non-rigid registration using deep neural networks to generate a deformation field has higher accuracy. However, unlike monomodal medical image registration, multimodal medical image registration is a more complex and challenging task. This paper proposes a new linear-to-nonlinear framework (L2NLF) for multimodal medical image registration. The first linear stage is essentially image conversion, which can reduce the difference between two images without changing the authenticity of medical images, thus transforming multimodal registration into monomodal registration. The second nonlinear stage is essentially unsupervised deformable registration based on the deep neural network. In this paper, a brand-new registration network, CrossMorph, is designed, a deep neural network similar to the U-net structure. As the backbone of the encoder, the volume CrossFormer block can better extract local and global information. Booster promotes the reduction of more deep features and shallow features. The qualitative and quantitative experimental results on T1 and T2 data of 240 patients' brains show that L2NLF can achieve excellent registration effect in the image conversion part with very low computation, and it will not change the authenticity of the converted image at all. Compared with the current state-of-the-art registration method, CrossMorph can effectively reduce average surface distance, improve dice score, and improve the deformation field's smoothness. The proposed methods have potential value in clinical application.

Incorporation of Titanium Carbide Nanoparticles into Epoxy Resin Composites to Enhance Mechanical and Thermal Properties

Titanium carbide (TiC) nanoparticles were included into epoxy resin composites to enhance their mechanical and thermal properties. Epoxy-TiC nanocomposites were produced using a solution mixing method, including different concentrations of TiC (0.5 weight percent, 1 weight percent, 2 weight percent, and 5 weight percent). The nanocomposites were then submitted to mechanical and thermal assessment. The results indicate that the addition of TiC nanoparticles enhanced the tensile strength from 50 MPa to 75 MPa, the flexural strength from 80 MPa to 110 MPa, and the impact strength from 10 kJ/m² to 15 kJ/m² at a TiC content of 2 weight percent. The initial deterioration temperature rose from 350 degrees Celsius to 375 degrees Celsius, while the glass transition temperature went from 120 degrees Celsius to 130 degrees Celsius. This led to enhanced thermal stability. A little reduction in features occurred due to the agglomeration of TiC nanoparticles when the concentration was raised to five weight percent. This study demonstrated that the optimal concentration of titanium carbide (TiC) for enhancing mechanical and thermal performance in epoxy composites is 2 weight percent. This suggests that these composites possess the capability for use in sophisticated engineering applications.

DQ-DeepLearn: Data Quality Driven Deep Learning Approach for Enhanced Predictive Maintenance in Smart Manufacturing

In the realm of smart manufacturing, predictive maintenance plays a pivotal role in ensuring equipment reliability, minimizing downtime, optimizing costs, and reducing product failure rate by detecting faulty products in the early stage. However, the efficacy of predictive maintenance hinges on the quality of training data employed for predictive modeling. Inadequate data quality can lead to erroneous predictions and, consequently, ineffective maintenance strategies. This research paper introduces a solution that leverages deep learning and data quality management to enhance predictive maintenance in smart manufacturing. Our proposed methodology encompasses two major components such as data quality management and faulty product detection. Data quality management includes data preprocessing, feature reduction, and data balancing, whereas faulty product detection is done using deep learning techniques. By combining these elements, a predictive model capable of accurately forecasting faulty products in early stages to reduce economic losses is developed. The proposed approach effectively addresses data quality issues and is tested on the SECOM dataset which indicates that it surpasses traditional models with an accuracy of 96.4% and perfect recall. Ultimately, this research contributes to the advancement of predictive maintenance and deep learning in the context of smart manufacturing, benefiting both industrial practitioners and researchers alike.

Data-Driven Modelling of Battery State-of-Health using Multi-Criteria-Based Feature Reduction

Data-Driven Modelling of Battery State-of-Health using Multi-Criteria-Based Feature Reduction

A machine learning radiomics based on enhanced computed tomography to predict neoadjuvant immunotherapy for resectable esophageal squamous cell carcinoma.

Patients with resectable esophageal squamous cell carcinoma (ESCC) receiving neoadjuvant immunotherapy (NIT) display variable treatment responses. The purpose of this study is to establish and validate a radiomics based on enhanced computed tomography (CT) and combined with clinical data to predict the major pathological response to NIT in ESCC patients. This retrospective study included 82 ESCC patients who were randomly divided into the training group (n = 57) and the validation group (n = 25). Radiomic features were derived from the tumor region in enhanced CT images obtained before treatment. After feature reduction and screening, radiomics was established. Logistic regression analysis was conducted to select clinical variables. The predictive model integrating radiomics and clinical data was constructed and presented as a nomogram. Area under curve (AUC) was applied to evaluate the predictive ability of the models, and decision curve analysis (DCA) and calibration curves were performed to test the application of the models. One clinical data (radiotherapy) and 10 radiomic features were identified and applied for the predictive model. The radiomics integrated with clinical data could achieve excellent predictive performance, with AUC values of 0.93 (95% CI 0.87-0.99) and 0.85 (95% CI 0.69-1.00) in the training group and the validation group, respectively. DCA and calibration curves demonstrated a good clinical feasibility and utility of this model. Enhanced CT image-based radiomics could predict the response of ESCC patients to NIT with high accuracy and robustness. The developed predictive model offers a valuable tool for assessing treatment efficacy prior to initiating therapy, thus providing individualized treatment regimens for patients.

Efficient Intrusion Detection System Data Preprocessing Using Deep Sparse Autoencoder with Differential Evolution

A great amount of data is generated by the Internet and communication areas’ rapid technological improvement, which expands the size of the network. These cutting‐edge technologies could result in unique network attacks that present security risks. This intrusion launches many attacks on the communication network which is to be monitored. An intrusion detection system (IDS) is a tool to prevent from intrusions by inspecting the network traffic and to make sure the network integrity, confidentiality, availability, and robustness. Many researchers are focused to IDS with machine and deep learning approaches to detect the intruders. Yet, IDS face challenges to detect the intruders accurately with reduced false alarm rate, feature selection, and detection. High dimensional data affect the feature selection methods effectiveness and efficiency. Preprocessing of data to make the dataset as balanced, normalized, and transformed data is done before the feature selection and classification process. Efficient data preprocessing will ensure the whole IDS performance with improved detection rate (DR) and reduced false alarm rate (FAR). Since datasets are required for the various feature dimensions, this article proposes an efficient data preprocessing method that includes a series of techniques for data balance using SMOTE, data normalization with power transformation, data encoding using one hot and ordinal encoding, and feature reduction using a proposed deep sparse autoencoder (DSAE) with differential evolution (DE) on data before feature selection and classification. The efficiency of the transformation methods is evaluated with recursive Pearson correlation‐based feature selection and graphical convolution neural network (G‐CNN) methods.

A Novel Comparison of Charotar Region Wheat Variety Classification Techniques using Purely Tree-based Data Mining Algorithms

Techniques for classifying data using data mining are now a day prevalent in agriculture. The method of classifying seeds involves grouping various seed varieties according to their morphological characteristics. To accomplish categorization of the typical Charotar region (generally comprising Anand and Kheda districts of the Gujarat State of India) Gujarat Wheat (GW) varieties (TRITICUM – AESTIVUM) viz. GW 273, GW 496, GW 322, LOK-1, and GDW 1255 (TRITICUM – DURUM), Weka Explorer was used. The features used are area, perimeter, solidity, aspect ratio, major and minor axis of seed kernel, Hue, Saturation, Value, and SF1 (empirical). Features reduction was done using Information Gain (IG) and its modified version Gain Ratio (GR). This paper compares performance of Tree based data mining algorithms in classifying wheat varieties. For classification we used purely tree-based machine learning algorithms viz. J48, Random Forest, Hoeffding Tree, Logistic Model Tree (LMT), and REPTree. LMT- logistics regression method gives higher accuracy 96.4% compared to other classifiers. Hoeffding Tree classifiers stood second with 96% accuracy. For validation 10-fold cross validation was used. By reducing the number of folds in cross validation performance of most algorithms decreased except J48. The percentage of correctly classified instance increased for all algorithms when features were selected by GR except for J48.

The problem of the Aral Sea drying up and its impact on the physical development of children in the Republic of Uzbekistan

Currently, one of the largest global environmental disasters in recent history is the Aral Sea tragedy, which by its environmental, climatic, socio-economic and humanitarian consequences poses a direct threat to the sustainable development of the region, health, gene pool and future of the people living there. The problem of the Aral Sea has left its imprint on the physical development of children in this region, and there is a tendency to change the physique of children in the direction of reduction of some dimensional features. This article is devoted to the results of the study of figures of preschool children in the Republic of Uzbekistan in order to identify changes in the dimensional characteristics of children.

Enhancing Security in Industrial IoT Networks: Machine Learning Solutions for Feature Selection and Reduction

Enhancing Security in Industrial IoT Networks: Machine Learning Solutions for Feature Selection and Reduction

Collaborative Reduction of Features and Instances in the Set-valued Decision System

Collaborative Reduction of Features and Instances in the Set-valued Decision System

Indicators for Hospitalization in Acute Pulmonary Embolism: Uncover the Association Between D-dimer Levels, Thrombus Volume and Radiomics

The advent of advanced computed tomography (CT) technology and the field of radiomics has opened up new avenues in diagnostic assessments. Increasingly, there is substantial evidence advocating for the incorporation of quantitative imaging biomarkers in the clinical decision-making process.This study aimed to examine the correlation between D-dimer levels and thrombus size in acute pulmonary embolism (PE) combining dual-energy CT (DECT) and radiomics and to investigate the diagnostic utility of a machine learning classifier based on dual-energy computed tomography (DECT) radiomics for identifying patients with a complicated course, defined as at least hospitalization at IMC. The study was conducted including 136 participants who underwent pulmonary artery CT angiography from January 2015 to March 2022. Based on DECT imaging, 107 radiomic features were extracted for each patient using standardized image processing. After dividing the dataset into training and test sets, stepwise feature reduction based on reproducibility, variable importance and correlation analyses were performed to select the most relevant features; these were used to train and validate the gradient-boosted tree models.Receiver operating characteristics (ROC) analysis was utilized to evaluate the association between volumetric, laboratory data and adverse outcomes. In the central PE group, we observed a significant correlation between thrombus volumetrics and D-dimer levels (p=0.0037), as well as between thrombus volumetrics and hospitalization at the Intermediate Care Unit (IMC) (p=0.0001). In contrast, no statistically significant differences were identified in thrombus sizes between patients who experienced complications and those who had a favorable course (p=0.3162). The trained machine learning classifier achieved an accuracy of 61% and 55% in identifying patients with a complicated course, as indicated by an area under the ROC curve of 0.63 and 0.58. In conclusion, our findings indicate a positive correlation between D-dimer levels and central PE's pulmonary embolic burden. Thrombus volumetrics may serve as an indicator for complications and outcomes in acute PE patients. Thus, thrombus volumetrics, as opposed to D-dimers, could be an additional marker for evaluating embolic disease severity. Moreover, DECT-derived radiomic feature models show promise in identifying patients with a complicated course, such as hospitalization at IMC.

An Interpretable Deep Learning-based Feature Reduction in Video-Based Human Activity Recognition

An Interpretable Deep Learning-based Feature Reduction in Video-Based Human Activity Recognition

MetricSifter: Feature Reduction of Multivariate Time Series Data for Efficient Fault Localization in Cloud Applications

MetricSifter: Feature Reduction of Multivariate Time Series Data for Efficient Fault Localization in Cloud Applications

Vision-and Tactile-Based Continuous Multimodal Intention and Attention Recognition for Safer Physical Human–Robot Interaction

Employing skin-like tactile sensors on robots enhances both the safety and usability of collaborative robots by adding the capability to detect human contact. Unfortunately, simple binary tactile sensors alone cannot determine the context of the human contact—whether it is a deliberate interaction or an unintended collision that requires safety manoeuvres. Many published methods classify discrete interactions using more advanced tactile sensors or by analysing joint torques. Instead, we propose to augment the intention recognition capabilities of simple binary tactile sensors by adding a robot-mounted camera for human posture analysis. Different interaction characteristics, including touch location, human pose, and gaze direction, are used to train a supervised machine learning algorithm to classify whether a touch is intentional or not with an F1-score of 86%. We demonstrate that multimodal intention recognition is significantly more accurate than monomodal analyses with the collaborative robot Baxter. Furthermore, our method can also continuously monitor interactions that fluidly change between intentional or unintentional by gauging the user’s attention through gaze. If a user stops paying attention mid-task, the proposed intention and attention recognition algorithm can activate safety features to prevent unsafe interactions. We also employ a feature reduction technique that reduces the number of inputs to five to achieve a more generalized low-dimensional classifier. This simplification both reduces the amount of training data required and improves real-world classification accuracy. It also renders the method potentially agnostic to the robot and touch sensor architectures while achieving a high degree of task adaptability. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Whenever a user interacts physically with a robot, such as in collaborative manufacturing, the robot may respond to unintended touch inputs from the user. This may be through body collisions or that the user is suddenly distracted and is no longer paying attention to what they are doing. We propose an easy-to-implement method to augment safety of physical human-robot collaboration by determining whether the touch from a user is intentional or not through the use of robot-mounted basic touch sensors and computer vision. The algorithm examines the location of the user’s hands relative to the touched sensors in addition to observing where the user is looking. Machine learning is then used to classify in real-time, with an F1-score of 86%, whether a touch is intentional or not such that the robot can react accordingly. The method is particularly applicable in collaborative manufacturing contexts, but can also be applied anywhere where a user physically interacts with a robot. We demonstrate the utility of the method in enhancing safety during human-robot collaboration through a simulated collaborative manufacturing scenario with the robot Baxter, but the method can easily be adapted to other systems.

Advanced Classifiers and Feature Reduction for Accurate Insomnia Detection Using Multimodal Dataset

Advanced Classifiers and Feature Reduction for Accurate Insomnia Detection Using Multimodal Dataset

A study of feature reduction techniques and classification for network intrusion detection

A study of feature reduction techniques and classification for network intrusion detection

Application of Support Vector Machine Classification Model to Identification of Vascular Endothelial Growth Factor Receptor Inhibitors.

Nowadays, with the increasing prevalence of cancer mortality, finding the best cancer inhibitors is vital. Angiogenesis, which refers to the formation of new blood vessels from existing ones, undergoes abnormal changes in the physiological process of solid tumors. Vascular endothelial growth factor receptor (VEGFR) plays a crucial role in angiogenesis. Hence, one of the suggestions in cancer treatment has been inhibiting VEGFR signaling to prevent angiogenesis. The computational approach as an in vitro alternative method is crucial to reduce time and cost. This study aimed to use classification algorithm to separate potent inhibitors from inactive ones. In order to apply the machine learning model, biological compounds were extracted from the BindingDB database. Due to the large number of molecular features, the classification model was susceptible to overfitting. To address this issue, a correlation-based feature selection algorithm was proposed as a means of feature reduction. Subsequently, for the classification step, a support vector machine model that utilizes both linear and non-linear kernels was employed. The implementation of the support vector machine model with the radial basis function kernel, along with the correlation-based feature selection method, resulted in a higher accuracy (81.8%, P value = 0.008) compared to other feature selection methods used in this study. Finally, two structures were introduced with the highest binding affinity to inhibit the second VEGFR. According to the results, the correlation-based feature selection method is more accurate than other methods.

Attention-based deep learning approach for detecting IoT botnetbased distributed denial of service attacks

Internet of Things (IoT) technology has evolved rapidly to become an integral part of our daily life. The numerous number of IoT devices and the limited security measures that could be applied on such devices attracted attackers to exploit these devices to act as botnets, generating massive Distributed Denial of Service (DDoS) attacks. IoT botnet-based DDoS attacks are growing dramatically both in frequency and sophistication and thus call for urgent development of powerful detection mechanism and deploy those mechanisms in an efficient way.In this paper, we propose attention-based deep learning approach for IoT botnet-based DDoS attacks. Specifically, we apply deep learning attention mechanism on two popular deep learning models LSTM and CNN for detection of IoT botnet DDoS attacks. We also examine the effect of using the deep Autoencoder for feature reduction and examine its effect on training time and data size needed to be exchanged through the network for training and updating the model, especially for solutions deployed at the Edge computing network. Performance evaluation shows that combining attention mechanism with LSTM model achieves superior performance with an accuracy reaching 100% which is due to combining the memorizing ability by the LSTM and the focusing ability of the attention in one model. In addition, results show that using Autoencoder for feature reduction resulted in reducing the training time up to 66.25%, 64.39%, 72.30% and 78.64% for ALSTM, LSTM, ACNN, CNN models respectively. Also achieved reduction in size of the dataset up to 67%.

MRI radiomics based machine learning model of the periaqueductal gray matter in migraine patients.

<p>The aim of the study was to investigate the question: Can MRI radiomics analysis of the periaqueductal gray region elucidate the pathophysiological mechanisms underlying various migraine subtypes, and can a machine learning model using these radiomics features accurately differentiate between migraine patients and healthy individuals, as well as between migraine subtypes, including atypical cases with overlapping symptoms?</p>. <p>The study analyzed initial MRI images of individuals taken after their first migraine diagnosis, and additional MRI scans were acquired from healthy subjects. Radiomics modeling was applied to analyze all the MRI images in the periaqueductal gray region. The dataset was randomized, and oversampling was used if there was class imbalance between groups. The optimal algorithm-based feature selection method was employed to select the most important 5-10 features to differentiate between the two groups. The classification performance of AI algorithms was evaluated using receiver operating characteristic analysis to calculate the area under the curve, classification accuracy, sensitivity, and specificity values. Participants were required to have a confirmed diagnosis of either episodic migraine, probable migraine, or chronic migraine. Patients with aura, those who used migraine-preventive medication within the past six months, or had chronic illnesses, psychiatric disorders, cerebrovascular conditions, neoplastic diseases, or other headache types were excluded from the study. Additionally, 102 healthy subjects who met the inclusion and exclusion criteria were included.&nbsp;</p>. <p>The algorithm-based information gain method for feature reduction had the best performance among all methods, with the first-order, gray-level size zone matrix, and gray-level co-occurrence matrix classes being the dominant feature classes. The machine learning model correctly classified 82.4% of migraine patients from healthy subjects. Within the migraine group, 74.1% of the episodic migraine-probable migraine patients and 90.5% of the chronic migraine patients were accurately classified. No significant difference was found between probable migraine and episodic migraine patients in terms of the periaqueductal gray region radiomics features. The kNN algorithm showed the best performance for classifying episodic migraine-probable migraine subtypes, while the Random Forest algorithm demonstrated the best performance for classifying the migraine group and chronic migraine subtype.</p>. <p>A radiomics-based machine learning model, utilizing standard MR images obtained during the diagnosis and follow-up of migraine patients, shows promise not only in aiding migraine diagnosis and classification for clinical approach, but also in understanding the neurological mechanisms underlying migraines.&nbsp;</p>.