Ensemble Classifier Research Articles

An Ensemble Model Health Care Monitoring System.

Internet of things (IoT) is utilized to enhance conventional health care systems in several ways, including patient's disease monitoring. The data gathered by IoT devices is very beneficial to medical facilities and patients. The data needs to be secured against unauthorized modifications because of security and privacy concerns. Conversely, a variety of procedures are offered by block chain technology to safeguard data against modifications. Block chain-based IoT-based health care monitoring is thus a fascinating technical advancement that may aid in easing security and privacy problems associated withthe collection of data during patient monitoring. In this work, we present an ensemble classification-based monitoring system with a block-chain as the foundation for an IoT health care model. Initially, data generation is done by considering the diseases including chronic obstructive pulmonary disease (COPD), lung cancer, and heart disease. The IoT health care data is then preprocessed using enhanced scalar normalization. The preprocessed data was used to extract features such as mutual information (MI), statistical features, adjusted entropy, and raw features. The total classified result is obtained by averaging deep maxout, improved deep convolutional network (IDCNN), and deep belief network (DBN) ensemble classification. Finally, decision-making is done by doctors to suggest treatment based on the classified results from the ensemble classifier. The ensemble model scored the greatest accuracy (95.56%) with accurate disease classification at a learning percentage of 60% compared to traditional classifiers such as neural network (NN) (89.08%), long short term memory (LSTM) (80.63%), deep belief network (DBN) (79.78%) and GT based BSS algorithm (89.08%).

Optimal feature selection based on OCS for improved malware detection in IoT networks using an ensemble classifier

The increasing amount of IoT devices increases the size of network traffic data, causing an increase in the incidence of security breaches in IoT networks. Cybercriminals have developed malware to compromise the security of sensitive data, among other cyber threats. In the presence of inadequate and robust security mechanisms, sensitive data is prone to vulnerability. Hence, protecting data in the IoT environment is becoming a mandatory task. Various approaches have addressed malware detection using network data features. However, there is still room for improvement in developing superior techniques and utilizing more comprehensive datasets. This paper presents a novel lightweight ensemble voting classifier to detect malware traffic by deploying the best possible network data. The merits of the correlation coefficient and Opposition-Based Crow Search Algorithm (OCS) have been leveraged to compute the best possible features. Another advantage of this proposed experiment is its focus on a dataset tailored to malware traffic features. This focus enables highly accurate malware detection. After feature selection using OCS, the proposed malware classifier is trained and validated with both 5-fold and 10-fold cross-validation techniques. The tested results confirm that the presented malware classifier performs best using a minimal feature set, which is highly advantageous for IoT networks due to resource constraints.

Machine learning algorithm for ventilator mode selection, pressure and volume control.

Mechanical ventilation techniques are vital for preserving individuals with a serious condition lives in the prolonged hospitalization unit. Nevertheless, an imbalance amid the hospitalized people demands and the respiratory structure could cause to inconsistencies in the patient's inhalation. To tackle this problem, this study presents an Iterative Learning PID Controller (ILC-PID), a unique current cycle feedback type controller that helps in gaining the correct pressure and volume. The paper also offers a clear and complete examination of the primarily efficient neural approach for generating optimal inhalation strategies. Moreover, machine learning-based classifiers are used to evaluate the precision and performance of the ILC-PID controller. These classifiers able to forecast and choose the perfect type for various inhalation modes, eliminating the likelihood that patients will require mechanical ventilation. In pressure control, the suggested accurate neural categorization exhibited an average accuracy rate of 88.2% in continuous positive airway pressure (CPAP) mode and 91.7% in proportional assist ventilation (PAV) mode while comparing with the other classifiers like ensemble classifier has reduced accuracy rate of 69.5% in CPAP mode and also 71.7% in PAV mode. An average accuracy of 78.9% rate in other classifiers compared to neutral network in CPAP. The neural model had an typical range of 81.6% in CPAP mode and 84.59% in PAV mode for 20 cm H2O of volume created by the neural network classifier in the volume investigation. Compared to the other classifiers, an average of 72.17% was in CPAP mode, and 77.83% was in PAV mode in volume control. Different approaches, such as decision trees, optimizable Bayes trees, naive Bayes trees, nearest neighbour trees, and an ensemble of trees, were also evaluated regarding the accuracy by confusion matrix concept, training duration, specificity, sensitivity, and F1 score.

Prediction of Diabetes Using Diverse Ensemble Learning Classifiers

Diabetes is classified as a chronic disorder characterized by elevated blood glucose levels resulting from insufficient insulin synthesis or inadequate responsiveness of body cells to insulin. The prevailing protocol at healthcare facilities involves the acquisition of necessary data for the diagnosis of diabetes through a range of diagnostic procedures, followed by the administration of treatment in accordance with the outcomes of those tests. The task of generating precise outcomes using diabetes prediction models is challenging due to limited data availability and the existence of outliers. This study presents a proposed prediction model for diabetes that involves preprocessing techniques applied to the raw data, followed by the utilization of Ensemble Classifiers. The Ensemble Classifiers consist of a combination of catboost, LDA, LR, Random Forest, and GBC. By employing pre-processing approaches and ensemble methodologies, we have achieved improved performance i.e. 90.62% accuracy. The weights of ML models are evaluated using their Area Under Curve (AUC) and receiver operating characteristic curve (ROC) results. This proposed approach helps for clinical trials and help to take precautions at early stage of diabetes.

Multi-Time-Scale Shapelet-Based Feature Extraction for Non-Intrusive Load Monitoring

The performance of non-intrusive load monitoring (NILM) system is closely related to the uniqueness and comprehensiveness of the load features extracted from the equipment. However, existing feature extraction methods for NILM are difficult to extract interpretable intuitive features representing the uniqueness of load, and lack of comprehensive attention to the features on different time scales. Therefore, this paper proposes a multi-time-scale shapelet based feature extraction framework. First, shapelet, the most unique segment of a sequence, which can distinguish it from other sequences is introduced as a feature and multi-time-scale current shapelets are proposed to extract comprehensive characteristics of electric appliances and offer intuitive interpretability. Meanwhile, a shapelet transform is constructed, so that the essence of shapelet based NILM is transformed from sequence matching to a general classification problem. Next, an ensemble bagging classifier is proposed on multi-time scales to effectively balance the classification errors on each time scale and enhance the performance of NILM system. In addition, this framework can support small sample training to facilitate rapid deployment of NILM applications at the initial practical stage. The results show that this framework can effectively focus on the uniqueness of load on different time scales, provide interpretability for NILM, and achieve good classification effects.

MLW-BFECF: a multi-weighted dynamic cascade forest based on bilinear feature extraction for predicting the stage of Kidney Renal Clear Cell Carcinoma on multi-modal gene data.

The stage prediction of kidney renal clear cell carcinoma (KIRC) is important for the diagnosis, personalized treatment, and prognosis of patients. Many prediction methods have been proposed, but most of them are based on unimodal gene data, and their accuracy is difficult to further improve. Therefore, we propose a novel multi-weighted dynamic cascade forest based on the bilinear feature extraction (MLW-BFECF) model for stage prediction of KIRC using multimodal gene datasets (RNA-seq, CNA, and methylation). The proposed model utilizes a dynamic cascade framework with shuffle layers to prevent early degradation of the model. In each cascade layer, a voting technique based on three gene selection algorithms is first employed to effectively retain gene features more relevant to KIRC and eliminate redundant information in gene features. Then, two new bilinear models based on the gated attention mechanism are proposed to better extract new intra-modal and inter-modal gene features; Finally, based on the idea of the bagging, a multi-weighted ensemble forest classifiers module is proposed to extract and fuse probabilistic features of the three-modal gene data. A series of experiments demonstrate that the MLW-BFECF model based on the three-modal KIRC dataset achieves the highest prediction performance with an accuracy of 88.92%.

Fine-Tuned T5 For Auto-Grading Of Quadratic Equation Problems

Assessments constitute a fundamental and inevitable component of any educational journey. Manual effort required for the evaluation of these assessments is very high. Automation of the evaluation process and grading helps in making the review process more efficient, objective, and scalable, thereby reducing the workload of human reviewers. Automating the grading process for multiple-choice and short-answer assessments is relatively straightforward, but it poses significant challenges when applied to the evaluation of formal languages, particularly in the context of mathematical assessments. In this paper a model that automatically evaluates and grades the Quadratic Equation problems is presented. The study is conducted using a manually curated dataset comprising 1200 solutions to various quadratic equation problems. Embeddings of the quadratic solutions are generated using Google’s T5 Model. These embeddings are then used to train different traditional and ensembled machine learning models along with complex Deep learning models like LSTM and Bi LSTM. An in-depth analysis of the fine-tuned T5 model’s performance, evaluating its effectiveness in comparison with the pretrained T5 model in automatic grading of quadratic equation problems has been explored. Fine-tuning significantly contributes to the reduction of error by 70% and a noticeable increase in the R2 value to 97%.

Interpretable Ensemble Classifiers for Reliable Prediction of Antibacterial Activities of Peptides Against Escherichia Coli and Staphylococcus Aureus

Interpretable Ensemble Classifiers for Reliable Prediction of Antibacterial Activities of Peptides Against Escherichia Coli and Staphylococcus Aureus

Investigating an Ensemble Classifier Based on Multi-Objective Genetic Algorithm for Machine Learning Applications

Investigating an Ensemble Classifier Based on Multi-Objective Genetic Algorithm for Machine Learning Applications

An ensemble classification method based on machine learning models for malicious Uniform Resource Locators (URL).

Web applications are important for various online businesses and operations because of their platform stability and low operation cost. The increasing usage of Internet-of-Things (IoT) devices within a network has contributed to the rise of network intrusion issues due to malicious Uniform Resource Locators (URLs). Generally, malicious URLs are initiated to promote scams, attacks, and frauds which can lead to high-risk intrusion. Several methods have been developed to detect malicious URLs in previous works. There has been a good amount of work done to detect malicious URLs using various methods such as random forest, regression, LightGBM, and more as reported in the literature. However, most of the previous works focused on the binary classification of malicious URLs and are tested on limited URL datasets. Nevertheless, the detection of malicious URLs remains a challenging task that remains open to research. Hence, this work proposed a stacking-based ensemble classifier to perform multi-class classification of malicious URLs on larger URL datasets to justify the robustness of the proposed method. This study focuses on obtaining lexical features directly from the URL to identify malicious websites. Then, the proposed stacking-based ensemble classifier is developed by integrating Random Forest, XGBoost, LightGBM, and CatBoost. In addition, hyperparameter tuning was performed using the Randomized Search method to optimize the proposed classifier. The proposed stacking-based ensemble classifier aims to take advantage of the performance of each machine learning model and aggregate the output to improve prediction accuracy. The classification accuracies of the machine learning model when applied individually are 93.6%, 95.2%, 95.7% and 94.8% for random forest, XGBoost, LightGBM, and CatBoost respectively. The proposed stacking-based ensemble classifier has shown significant results in classifying four classes of malicious URLs (phishing, malware, defacement, and benign) with an average accuracy of 96.8% when benchmarked with previous works.

A Hybrid Learning-Architecture for Mental Disorder Detection Using Emotion Recognition.

Mental illness has grown to become a prevalent and global health concern that affects individuals across various demographics. Timely detection and accurate diagnosis of mental disorders are crucial for effective treatment and support as late diagnosis could result in suicidal, harmful behaviors and ultimately death. To this end, the present study introduces a novel pipeline for the analysis of facial expressions, leveraging both the AffectNet and 2013 Facial Emotion Recognition (FER) datasets. Consequently, this research goes beyond traditional diagnostic methods by contributing a system capable of generating a comprehensive mental disorder dataset and concurrently predicting mental disorders based on facial emotional cues. Particularly, we introduce a hybrid architecture for mental disorder detection leveraging the state-of-the-art object detection algorithm, YOLOv8 to detect and classify visual cues associated with specific mental disorders. To achieve accurate predictions, an integrated learning architecture based on the fusion of Convolution Neural Networks (CNNs) and Visual Transformer (ViT) models is developed to form an ensemble classifier that predicts the presence of mental illness (e.g., depression, anxiety, and other mental disorder). The overall accuracy is improved to about 81% using the proposed ensemble technique. To ensure transparency and interpretability, we integrate techniques such as Gradient-weighted Class Activation Mapping (Grad-CAM) and saliency maps to highlight the regions in the input image that significantly contribute to the model's predictions thus providing healthcare professionals with a clear understanding of the features influencing the system's decisions thereby enhancing trust and more informed diagnostic process.

Ensemble Learning-based Multiple Sclerosis Detection Technique Using Magnetic Resonance Imaging

Multiple sclerosis (MS) is a complicated neurological disorder that leads to demyelination of nerve fibers in the central nervous system, causing severe symptoms and gradual impairment. Prompt and precise diagnosis of MS is essential for prompt intervention and individualized treatment planning. This research presents a new method for detecting MS: magnetic resonance imaging (MRI) data. Utilizing current progress in deep learning and ensemble learning methodologies, we use SWIN transformer and MobileNetV3-small for extracting features from MRI images. These features are then used for classification using CatBoost, XGBoost, and random forest algorithms. The suggested framework is tested and confirmed effective using the Kaggle MS database, which consists of various MRI images. The experimental findings show a remarkable average accuracy of 99.8% and a little loss of 0.07, highlighting the effectiveness of the suggested strategy in discriminating between aberrant and normal MRI pictures that indicate MS. This study enhances the field of medical image analysis by providing a precise and effective framework for automated diagnosis of MS. This framework has the potential to enhance diagnostic efficiency and improve patient outcomes. Combining deep learning feature extraction with ensemble classifiers offers a robust and easily understandable approach for diagnosing MS and has the potential to be used in clinical settings. Future research should prioritize validating the suggested technique on more extensive datasets and incorporating it into clinical practice to enhance early identification of MS and provide individualized patient treatment.

An Ensemble Classifier Based on Diverse Convolutional Neural Networks for Skin Lesions Classification

An Ensemble Classifier Based on Diverse Convolutional Neural Networks for Skin Lesions Classification

An Investigation into Ensemble Learning Techniques for Evaluating Soil Fertility through Analytical Approaches

At the heart of India’s livelihood lies agriculture, a foundational and ever-evolving sector. The realm of agriculture faces modern challenges, encompassing unpredictable climate fluctuations, insufficient irrigation infrastructure, and erratic weather conditions. Amidst these challenges, machine learning has emerged as a valuable tool for evaluating soil fertility and Crop assessment in agriculture. Ensemble machine learning methodologies have garnered attention for their potential to enhance predictive capabilities. These techniques involve constructing meta-classifiers that collectively contribute to improved predictive accuracy. This paper centers on the analysis of soil data acquired from testing laboratories, to predict fertility based on a comprehensive dataset. The study employs prominent ensemble machine learning algorithms, specifically focusing on boosting techniques, to elevate predictive accuracy and ensure heightened consistency.The assessment of soil fertility categories involved the examination of 12 carefully chosen attributes. Diverse soil parameters were measured to facilitate the prediction of soil fertility levels. The outcomes of the experimentation revealed that the application of the boosting technique using the Xgboost algorithm yielded superior accuracy compared to alternative ensemble classifiers, achieving a remarkable 96% accuracy rate.

Diagnosis of anemia using non-invasive anemia detector through parametrical analysis

Anemia, characterized by low hemoglobin levels, affects a significant portion of the global population, particularly children and women in low- and lower-middle-income countries. Traditional hematological tests for diagnosing anemia can be invasive, costly, and resource-intensive. This study presents the development of a wearable, non-invasive anemia detector utilizing infrared (IR) technology. The device integrates a NodeMCU microcontroller, an OLED display, a buck converter, an IR transmitter, and a receiver in a compact, wearable package. The IR system measures hemoglobin levels by analyzing the absorption of infrared light emitted through the skin, typically on a fingertip or earlobe. The NodeMCU processes the light absorption data using sophisticated algorithms to determine hemoglobin concentration and facilitates wireless data transfer to smartphones or cloud servers for further analysis. The device is designed with energy efficiency in mind, utilizing a buck converter to ensure optimal power distribution to sensitive components. The OLED display provides users with clear, real-time feedback on their hemoglobin levels, offering a user-friendly interface. Initial testing and calibration with known hemoglobin concentrations demonstrated strong correlations between device readings and traditional lab tests. The device employs a hierarchical ensemble classifier to categorize individuals as healthy, mildly anemic, or severely anemic, aligning with clinical standards. The non-invasive nature of this device, combined with its portability and cost-effectiveness, makes it an ideal solution for point-of-care anemia detection, particularly in resource-limited settings. The integration of IR technology, microcontroller processing, and wireless communication presents a practical tool for healthcare providers, facilitating rapid anemia screening and ongoing monitoring. This innovation holds the potential to improve patient outcomes by enabling early detection and intervention in anemia management.

Election-based optimization algorithm with deep learning-enabled false data injection attack detection in cyber-physical systems

<abstract> <p>Cyber-physical systems (CPSs) are affected by cyberattacks once they are more connected to cyberspace. Advanced CPSs are highly complex and susceptible to attacks such as false data injection attacks (FDIA) targeted to mislead the systems and make them unstable. Leveraging an integration of anomaly detection methods, real-time monitoring, and machine learning (ML) algorithms, research workers are developing robust frameworks to recognize and alleviate the effect of FDIA. These methods often scrutinize deviations from predictable system behavior, using statistical analysis and anomaly detection systems to determine abnormalities that can indicate malicious activities. This manuscript offers the design of an election-based optimization algorithm with a deep learning-enabled false data injection attack detection (EBODL-FDIAD) method in the CPS infrastructure. The purpose of the EBODL-FDIAD technique is to enhance security in the CPS environment via the detection of FDIAs. In the EBODL-FDIAD technique, the linear scaling normalization (LSN) approach can be used to scale the input data into valuable formats. Besides, the EBODL-FDIAD system performs ensemble learning classification comprising three classifiers, namely the kernel extreme learning machine (KELM), long short-term memory (LSTM), and attention-based bidirectional recurrent neural network (ABiRNN) model. For optimal hyperparameter selection of the ensemble classifiers, the EBO algorithm can be applied. To validate the enriched performance of the EBODL-FDIAD technique, wide-ranging simulations were involved. The extensive results highlighted that the EBODL-FDIAD algorithm performed well over other systems concerning numerous measures.</p> </abstract>

Ensemble Classifiers for Bankruptcy Prediction Using SMOTE and RFECV

Ensemble Classifiers for Bankruptcy Prediction Using SMOTE and RFECV

Suicidal Thought Detection using Max Voting Ensemble Technique

Online social networking (SN) data presents a data stream that is rich in context and temporal information. It holds promise for predicting suicidal thoughts and behaviors. The fusion of SN data with machine learning algorithms offers a potential path forward. This research proposes a Max Voting Ensemble classifier model applied to a Reddit dataset for the identification of suicidal ideation. The preprocessing involves data cleansing, tokenization, and lemmatization. Additionally, TF-IDF and Word2Vec word embedding techniques are applied. Diverse machine learning algorithms, including Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Multinomial Naive Bayes (MNB), AdaBoost, and XGBoost, are implemented. The results of selected Machine Learning Classifiers (MLCs) are amalgamated using a Max Voting Ensemble classifier. The research findings clearly indicate that the Max Voting Ensemble classifier yields improved precision of 91.39% coupled with a substantial accuracy of 87.5%. The application of Ensembling Techniques (ET) to SN data holds the potential to address the complexities and modeling challenges inherent in predicting acute Suicidal Ideation within these dynamic time scales.

Predictive modeling and socioeconomic determinants of diarrhea in children under five in the Amhara Region, Ethiopia.

Diarrheal disease, characterized by high morbidity and mortality rates, continues to be a serious public health concern, especially in developing nations such as Ethiopia. The significant burden it imposes on these countries underscores the importance of identifying predictors of diarrhea. The use of machine learning techniques to identify significant predictors of diarrhea in children under the age of 5 in Ethiopia's Amhara Region is not well documented. Therefore, this study aimed to clarify these issues. This study's data have been extracted from the Ethiopian Population and Health Survey. We have applied machine learning ensemble classifier models such as random forests, logistic regression, K-nearest neighbors, decision trees, support vector machines, gradient boosting, and naive Bayes models to predict the determinants of diarrhea in children under the age of 5 in Ethiopia. Finally, Shapley Additive exPlanation (SHAP) value analysis was performed to predict diarrhea. Among the seven models used, the random forest algorithm showed the highest accuracy in predicting diarrheal disease with an accuracy rate of 81.03% and an area under the curve of 86.50%. The following factors were investigated: families who had richest wealth status (log odd of -0.04), children without a history of Acute Respiratory Infections (ARIs) (log odd of -0.08), mothers who did not have a job (log odd of -0.04), children aged between 23 and 36 months (log odd of -0.03), mothers with higher education (log odds ratio of -0.03), urban dwellers (log odd of -0.01), families using electricity as cooking material (log odd of -0.12), children under 5 years of age living in the Amhara region of Ethiopia who did not show signs of wasting, children under 5 years of age who had not taken medications for intestinal parasites unlike their peers and who showed a significant association with diarrheal disease. We recommend implementing programs to reduce the incidence of diarrhea in children under the age of 5 in the Amhara region. These programs should focus on removing socioeconomic barriers that impede mothers' access to wealth, a favorable work environment, cooking fuel, education, and healthcare for their children.

Variational Mode Decomposition and Empirical Wavelet Transform-Based Feature Extraction and Ensemble Classifier for Lower Limb Movement Prediction With Surface Electromyography Signal

Variational Mode Decomposition and Empirical Wavelet Transform-Based Feature Extraction and Ensemble Classifier for Lower Limb Movement Prediction With Surface Electromyography Signal