Ensemble Of Machine Learning Classifiers Research Articles

Botnet Attack Detection in IoT Devices using Ensemble Classifiers with Reduced Feature Space

The Internet of Things (IoT) is an advancing important technology offers multiple perks, such as webcams, baby monitors, room temperature controllers, smart security cameras and intelligent home automations resulting in the creation of intelligent settings that greatly simplify daily living. However, there are cybersecurity dangers associated with IoT devices due to their lack of protection. For example, Internet of Things botnets have become a major risk. IoT has been a boon for attackers to perform malicious attacks like information theft, DDoS, sending junk data to disrupt networks. IoT devices face serious security issues, from having default weak and common passwords, and a lack of security, rarely and poorly monitored, to having open access to management systems, always connected to the internet. In this paper, we used the N-BaIoT dataset which includes datasets of 9 IoT devices infected with 2 Bot viruses Mirai and Bashlite, where each botnet has 5 sub-attacks and the benign datasets of 9 devices. An analysis with the N-BaIoT dataset which initially had 115 features were reduced to 35 features by using manual reduction and further reduced to single feature in 5-time instances equivalent to 5 features using heat map. We then classified the sub-attacks of 2 botnets and benign of 9 IoT devices by using 7 Machine Learning based classifiers in the Weka tool and Python and compared our results with the manually reduced 35 Features and Heat map based 5 features. Performance metrics like correctly classified, incorrectly classified instances and time taken to build the model were evaluated to verify the proposed work. We found out that over 3 ensemble machine learning classifiers performed extremely well with 99 % accuracies for all devices. In order to verify the logic of our work we tried implementing our proposed model in a different dataset with 3 ensemble classifiers and were able to achieve high detection rates.

Comparison of Random Forest and XGBoost Classifiers Using Integrated Optical and SAR Features for Mapping Urban Impervious Surface

The integration of optical and SAR datasets through ensemble machine learning models shows promising results in urban remote sensing applications. The integration of multi-sensor datasets enhances the accuracy of information extraction. This research presents a comparison of two ensemble machine learning classifiers (random forest and extreme gradient boost (XGBoost)) classifiers using an integration of optical and SAR features and simple layer stacking (SLS) techniques. Therefore, Sentinel-1 (SAR) and Landsat 8 (optical) datasets were used with SAR textures and enhanced modified indices to extract features for the year 2023. The classification process utilized two machine learning algorithms, random forest and XGBoost, for urban impervious surface extraction. The study focused on three significant East Asian cities with diverse urban dynamics: Jakarta, Manila, and Seoul. This research proposed a novel index called the Normalized Blue Water Index (NBWI), which distinguishes water from other features and was utilized as an optical feature. Results showed an overall accuracy of 81% for UIS classification using XGBoost and 77% with RF while classifying land use land cover into four major classes (water, vegetation, bare soil, and urban impervious). However, the proposed framework with the XGBoost classifier outperformed the RF algorithm and Dynamic World (DW) data product and comparatively showed higher classification accuracy. Still, all three results show poor separability with bare soil class compared to ground truth data. XGBoost outperformed random forest and Dynamic World in classification accuracy, highlighting its potential use in urban remote sensing applications.

Comprehensive Classification of Fetal Health Using Cardiotocogram Data Based on Machine Learning

In the realm of obstetrics, the evaluation of fetal health remains a paramount yet challenging endeavor. Traditional approaches, such as electronic fetal monitoring (EFM), despite their widespread adoption, continue to grapple with uncertainties regarding their impact on neonatal outcomes and the reduction of emergency cesarean deliveries. This ambiguity is compounded by a prevailing confusion within the obstetric community about interpreting fetal heart rate patterns, often leading to inconsistent and subjective assessments. Addressing these complexities, our study presents an innovative machine learning-based techniques for the comprehensive classification of fetal health using cardiotocogram (CTG) data, offering a more objective and nuanced alternative to conventional methods. The core of our proposed solution is a novel model employing a sophisticated ensemble of machine learning classifiers, including Multi-Support Vector Machine (Multi-SVM), Decision Tree, Random Forest with Hyperparameter Tuning, XGBoost, and Neural Networks. This model is unique in its application, processing datasets in four different forms: raw datasets, datasets processed with MinMaxScaler, datasets subjected to feature selection using SelectKBest, and a combination of MinMaxScaler processing and SelectKBest feature selection. Such meticulous preprocessing, encompassing normalization and feature selection, is pivotal in ensuring equitable contribution from each feature, thereby optimizing the model's learning process and predictive accuracy. The effectiveness of our model is rigorously evaluated using a dataset comprising 2126 individual records from CTG exams, classified by specialist obstetricians into three types: Normal, Suspect, and Pathological. These records are exhaustively analyzed using various metrics, including Accuracy, Precision, Recall, F1-Score, ROC AUC, and Confusion Matrix. Among the classifiers, XGBoost emerged as the most proficient, consistently outperforming others across multiple metrics. This indicates its superior ability to accurately identify and categorize the different states of fetal health. Our findings thus underscore the significant promise of machine learning in revolutionizing fetal health monitoring, offering a more reliable, objective, and comprehensive method for assessing fetal well-being, with profound implications for prenatal care and clinical decision-making.

Critical Review of Stack Ensemble Classifier for the Prediction of Young Adults’ Voting Patterns Based on Parents’ Political Affiliations

Aim/Purpose: This review paper aims to unveil some underlying machine-learning classification algorithms used for political election predictions and how stack ensembles have been explored. Additionally, it examines the types of datasets available to researchers and presents the results they have achieved. Background: Predicting the outcomes of presidential elections has always been a significant aspect of political systems in numerous countries. Analysts and researchers examining political elections rely on existing datasets from various sources, including tweets, Facebook posts, and so forth to forecast future elections. However, these data sources often struggle to establish a direct correlation between voters and their voting patterns, primarily due to the manual nature of the voting process. Numerous factors influence election outcomes, including ethnicity, voter incentives, and campaign messages. The voting patterns of successors in regions of countries remain uncertain, and the reasons behind such patterns remain ambiguous. Methodology: The study examined a collection of articles obtained from Google Scholar, through search, focusing on the use of ensemble classifiers and machine learning classifiers and their application in predicting political elections through machine learning algorithms. Some specific keywords for the search include “ensemble classifier,” “political election prediction,” and “machine learning”, “stack ensemble”. Contribution: The study provides a broad and deep review of political election predictions through the use of machine learning algorithms and summarizes the major source of the dataset in the said analysis. Findings: Single classifiers have featured greatly in political election predictions, though ensemble classifiers have been used and have proven potent use in the said field is rather low. Recommendation for Researchers: The efficacy of stack classification algorithms can play a significant role in machine learning classification when modelled tactfully and is efficient in handling labelled datasets. however, runtime becomes a hindrance when the dataset grows larger with the increased number of base classifiers forming the stack. Future Research: There is the need to ensure a more comprehensive analysis, alternative data sources rather than depending largely on tweets, and explore ensemble machine learning classifiers in predicting political elections. Also, ensemble classification algorithms have indeed demonstrated superior performance when carefully chosen and combined.

Detection of bipolar disorder by means of ensemble machine learning classifier

The accurate diagnosis of bipolar disorder is extremely challenging, due to unpredictable mood swings, behaviors, sleep, judgment, and inability to think, which makes it difficult to make a proper diagnosis. This paper aims to investigate the application of ensemble classifiers in classifying bipolar disorder and to compare their performance with existing methods. Herein, the work involves a thorough analysis of diagnostic precision and performance metrics. According to a study, an existing classifier achieved an accuracy rate of 87% in bipolar disorder classification. In addition, the two most widely used classifiers, which are Random Forest and Decision Tree, achieved accuracy rates of 90% and 86%, respectively. These results highlight the performance baseline against which the proposed ensemble classifier is evaluated. Notably, the proposed ensemble classifier shows excellent results in bipolar disorder classification thereby, achieving an impressive accuracy rate of 98%. This considerable improvement in accuracy marks a significant stride in diagnostic precision, showcasing the potential of ensemble classifiers in enhancing bipolar disorder detection. The results of this study have given substantial implications for the field of mental health diagnosis, offering a promising avenue for a more accurate and reliable classification of bipolar disorder. This research reinforces the significance of advanced machine learning techniques and their potential to revolutionize the approach to diagnose and to manage mental health conditions.

NIDS: An Efficient Network Intrusion Detection Model for Security of Big Data Using Different Machine Learning classifiers

Security of the big data is one of the important challenges which needs to be addressed by designing an efficient network intrusion model for detecting the unauthenticated intruders in the network. The model should be able to detect the validity of the packet. The detection of intrusions in network was already represented by multiple researchers using different algorithms which still needs instant addressing. Proposing a machine learning classifier algorithm for intrusion detection. The KDD intrusion dataset is used in training the machine for identifying the different intrusions of the network traffic. The machine must be trained efficiently using the different classification algorithms and the security for the data needs to be attained by identifying the invalid network packets. The experimental results demonstrate that the random forest ensemble machine learning classifier is having highest accuracy of 0.2 % when compared with the existing research results in the identification of different intrusions towards the network packets.

ANALYSIS OF ELECTROHYSTEROGRAM SIGNALS AND PREDICTION OF PRETERM BIRTHS USING MACHINE LEARNING

The World Health Organization (WHO) estimates that over 15 million infants are born before the entire period of pregnancy. Over a million neonatal deaths occurred in 2015 as a result of preterm delivery, which is the prime cause for most deaths among children under the age of five. Although preterm birth has no hereditary link, only 10% of preterm deliveries in high-income settings result in deaths, compared to a mortality rate of up to 90% in low-income nations. When preterm cases are discovered, in addition to enhanced care provided at home for the expecting mother, the growing foetus may also benefit from medication, hospitalization for the duration of the pregnancy, or both. Low-income countries also struggle with a lack of access to a comprehensive healthcare system, which makes it difficult to proactively detect premature births. Machine learning techniques have a great potential to improve this situation by providing an intelligent framework for the detection of critical situations and consequently alerting the individual in cases of anomalies. This will require further follow-up with medical specialists. In this work, we investigate the application of deep learning and machine learning techniques for the analysis of electrohysterogram (EHG) data, which are uterine electrical impulses used to detect preterm deliveries. The TPEHG dataset is used to train a variety of machine learning classifiers, such as Support Vector Machines, Logistic Regression, and Decision Trees, as well as Deep Neural Networks like Convolutional Neural Networks and LSTMs. Additionally, we use plurality voting to build an ensemble of various neural networks and binary classifiers that are trained to classify EHG signals. The ensemble machine learning classifier with five base classifiers produced the best results overall, with an accuracy of 98.99%, sensitivity of 98.3%, and specificity of 97.9% outperforming several state-of-the-art algorithms for preterm birth detection.

An improved ensemble machine learning classifier for efficient spectrum sensing in cognitive radio networks

SummaryCognitive radio network (CRN) is one form of wireless communication for solving the spectrum underutilization problem. It is mainly used for sensing and learning the electromagnetic environment and changes according to the environment. CRNs mainly depend on the cooperation functionality for making the network work efficiently. The main technology in cognitive radio is spectrum sensing. The cooperative spectrum sensing (CSS) scheme is used for estimating the high transmit power in the cognitive radio networks, and the result is that it satisfies the interference constraints. It develops the communication overhead for the local observation dissemination between the secondary users. It is used for speed and accurate detection techniques of the user, and it also identifies the spectrum holes without any interference to others while sharing with other users. It is based on the ensemble support vector machine (SVM) with CSS for producing high performance in CRNs with the use of artificial intelligence (AI) techniques.

Efficient Detection of Covid-19 from Chest X-ray Images using CNN Feature Extraction and an Ensemble of Machine Learning Classifiers

Efficient Detection of Covid-19 from Chest X-ray Images using CNN Feature Extraction and an Ensemble of Machine Learning Classifiers

Application of ultrasound-based radiomics models of breast masses to predict invasive components of encapsulated papillary carcinoma.

Axillary lymph node (ALN) metastasis is seen in encapsulated papillary carcinoma (EPC), mostly with an invasive component (INV). Radiomics can offer more information beyond subjective grayscale and color Doppler ultrasound (US) image interpretation. This study aimed to develop radiomics models for predicting an INV of EPC in the breast based on US images. This study retrospectively enrolled 105 patients (107 masses) with a pathological diagnosis of EPC from January 2016 to April 2021, and all masses had preoperative US images. Of the 107 masses, 64 were randomized to a training set and 43 to a test set. US and clinical features were analyzed to identify features associated with INVs. Then, based on the manually segmented US images to obtain radiomics features, the models to predict INVs were built with 5 ensemble machine learning classifiers. We estimated the performance of the predictive models using accuracy, the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity. The mean age was 63.71 years (range, 31 to 85 years); the mean size of tumors was 23.40 mm (range, 9 to 120 mm). Among all clinical and US features, only shape was statistically different between EPC with INVs and those without (P<0.05). In this study, the models based on Random Under Sampling (RUS) Boost, Random Forest, XGBoost, AdaBoost, and Easy Ensemble methods had good performance, among which RUS Boost had the best performance with an AUC of 0.875 [95% confidence interval (CI): 0.750-0.974] in the test set. Radiomics prediction models were effective in predicting the INV of EPC, whereas clinical and US features demonstrated relatively decreased predictive utility.

Comparative performance of ensemble machine learning for Arabic cyberbullying and offensive language detection

Since cyberbullying impacts both individual victims and entire society, research on abusive language and its detection has attracted attention in recent years. Because social media sites like Facebook, Instagram, Twitter, and others are so widely accessible, hate speech, bullying, sexism, racism, aggressive material, harassment, poisonous comments, and other types of abuse have all substantially increased. Due to the critical requirement to detect, regulate, and limit the spread of harmful content on social networking sites, we conducted this study to automate the detection of offensive language or cyberbullying. We created a new Arabic balanced data set to be used in the offensive detection process because having a balanced data set for a model would result in improved accuracy models. Recently, the performance of single classifiers has been improved using ensemble machine learning. The purpose of this study is to examine the effectiveness of several single and ensemble machine learning algorithms in identifying Arabic text that contains foul language and cyberbullying. Applying them to three Arabic datasets, we have selected three machine learning classifiers and three ensemble models for this aim. Two of them are offensive datasets that are readily accessible in the public, while the third one was created. The results showed that the single learner machine learning strategy is inferior to the ensemble machine learning methodology. Voting performs is the best performing trained ensemble machine learning classifier, outperforming the best single learner classifier (65.1%, 76.2%, and 98%) for the same datasets with accuracy scores of (71.1%, 76.7%, and 98.5%) for each of the three datasets used. Finally, we improve the voting technique’s performance through hyperparameter tuning on the Arabic cyberbullying data set.

A brain stroke detection model using soft voting based ensemble machine learning classifier

A brain stroke is a medical emergency that occurs when the blood supply to a part of the brain is disturbed or reduced, which causes the brain cells in that area to die. The process involves training a machine learning model on a large labelled dataset to recognize patterns and anomalies associated with strokes. The proposed model is an ensemble machine learning algorithm, which integrates predictions obtained from several individual classifiers like Random Forest, Extremely Randomized Trees and Histogram-Based Gradient Boosting to make a final prediction. Each classifier provides a probability estimate for each class, final prediction is based on weighted average of these probabilities. The weights assigned to each classifier can be based on their performance on a validation set or can be set uniformly. The proposed soft voting model improved accuracy and robustness of final prediction compared to a single classifier. The limitation of study classification of stroke type can lead to appropriate use of resources and help to reduce healthcare costs. To solve this issue, future introduced a swarm intelligence-based optimization for improve classification accuracy. The proposed model obtained an accuracy of 96.88%. In order to carry out the investigation, the stroke prediction dataset is collected from UCI machine learning repository.

Accident Attention System for Somnambulism Patients

Promising technologies such as sensors, networking, and edge have led to many smart healthcare solutions to monitor and track patient health status. The health sector is now experiencing a significant transformation from conventional patient care to a smart healthcare environment. Smart health care allows medical professionals to monitor patients remotely and visualize the disease prognosis effectively. The Internet of medical things connect patients, doctors, and medical equipment via wireless networking technologies to process the data with Artificial Intelligence models. One of the domains of automated health care systems is to alert the caregivers and hospital on emergency conditions. This research study is a novel work that aims to help the caregivers of somnambulism patients attend to them in case of emergency. Sleep quality improves the health and work efficiency of any person. The caregivers of sleepwalking patients suffer from lack of sleep as the patient gets active during the night hours. The model is based on fall detection and sleep detection from wearable sensor data. The fall detection model includes feature selection by LASSO and classification by ensemble classifier. The proposed methodology shows improved performance for the fall detection model for all ensemble machine learning classifiers.

Reducing False Negative Intrusions Rates of Ensemble Machine Learning Model based on Imbalanced Multiclass Datasets

In spite of the efforts to improve the efficiency of intrusion detection systems based on machine learning algorithms, these systems still need more. The false negative (FN) prediction outcome is of a major priority among other outcomes, when attacks are considered as normal by classifiers. FN outputs are highly a concern issue, especially for multiclass classification, where minor classes have less instances in imbalanced datasets. In this work, three types of well-known imbalanced multiclass classification datasets are used with ensemble machine learning classifiers. The datasets: KDD99, UNSW_NB15, and CICIDS2017 are balanced using different combination of oversampling and under-sampling techniques to improve false negative rates. Suitable performance metrics have been used to obtain significant outputs improvements in all three datasets types using Random Forest classifier. Achieved accuracies are 99.9852% for KDD99, 83.5451% for UNSW_NB15 and 99.8613% for CICIDS2017. The outcomes of the work using the mentioned datasets have been compared with state-of-the-art related works and the results show a clear improvement in false negative rates.

Spectrum sensing in cognitive radio networks using an ensemble of machine learning frameworks and effective feature extraction

From the signal received on a particular frequency band, spectrum sensing (SS) is used in cognitive radio (CR) to assess whether the primary user (PU) is using the spectrum and, consequently, whether the secondary user (SU) can utilize the spectrum. The main issue with SS is determining the presence of the primary signal in a low signal-to-noise ratio (SNR). Compared to conventional technologies, machine learning techniques are more effective and accurate at identifying the qualities of input data. This paper proposes a machine learning (ML) based SS model for CR with effective feature extraction and reduction techniques. The proposed work comprises five phases: noise removal, wavelet transform, feature extraction, dimensionality reduction, and classification. Firstly, noise filtering is done on the received signal to remove the noise present in the input signal using the filters such as moving median filter (MMF), Gaussian filter (GF), and Gabor filter (GBF). After that, the filtered signal is transformed into a wavelet domain using Discrete Wavelet Transform (DWT) algorithm. Then the statistical features such as average absolute value, wavelet energy, variance, standard deviation, and peak value features are extracted from the DWT. Next, the dimensionality reduction (DR) is performed using Linear Discriminant Analysis (LDA). Finally, the classification is performed using the ensemble ML classifiers such as Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbour (KNN), which classify whether the PU signal is active or not. Simulations are carried out to analyze the efficiency of the presented models for SS. The results proved that SVM obtains the best performance for SS with higher accuracy and lower SNR.

Dual quantum cascade lasers for noninvasive glucose detection using photoacoustic spectroscopy

The combination of mid-infrared and photoacoustic spectroscopy has shown promising developments as a substitute for invasive glucose detection technology. A dual single-wavelength quantum cascade laser system has been developed using photoacoustic spectroscopy for noninvasive glucose monitoring. Biomedical skin phantoms with similar properties to human skin have been prepared with blood components at different glucose concentrations as test models for the setup. The detection sensitivity of the system has been improved to ± 12.5 mg/dL in the hyperglycemia blood glucose ranges. An ensemble machine learning classifier has been developed to predict the glucose level in the presence of blood components. The model, which was trained with 72,360 unprocessed datasets, achieved a 96.7% prediction accuracy with 100% of the predicted data located in zones A and B of Clarke’s error grid analysis. These findings fulfill both the US Food and Drug Administration and Health Canada requirements for glucose monitors.

EEG rhythm based emotion recognition using multivariate decomposition and ensemble machine learning classifier

Recently, electroencephalogram (EEG) signals have shown great potential to recognize human emotions. The goal of effective computing is to assist computers in understanding various types of emotions via human–computer interaction (HCI). Multichannel EEG signals are used to measure the electrical activity of the brain in space and time. Automated emotion recognition using multichannel EEG signals is an interesting area of cognitive neuroscience and affective computing research. This research proposes EEG multichannel rhythmic features and ensemble machine learning (EML) classifiers with leave-one-subject-out cross-validation (LOSOCV) for automatic emotion classification from multichannel EEG recordings. Multivariate fast iterative filtering (MvFIF) is used to assess the EEG rhythm sequences. EEG rhythms delta(δ), theta(θ), alpha(α), beta(β), and gamma(γ) are separated based on the mean frequency of the EEG rhythm sequence. Three Hjorth parameters and nine entropy features were extracted from multichannel EEG rhythms. Extracted features are selected using the minimum redundancy maximum relevance (mRMR) approach. The experimental design was performed on two emotional datasets (GAMEEMO and DREAMER). The validation showed that gamma rhythm multichannel features with EML-based subspace K-nearest neighbor (SS KNN) were as high as 93.5%–99.8%, achieving high classification accuracy. The comparisons of δ, θ, α, β, and γ rhythms with EML, support vector machine (SVM), and artificial neural network (ANN) were performed. we also analyzed multi-class emotions (HVHA, HVLA, LVHA, LVLA) with an ensemble-based bagging tree on gamma rhythm. It provides a novel solution for multichannel rhythm-specific features in EEG data analysis.

Cell-type classification of cancer single-cell RNA-seq data using the Subsemble ensemble-based machine learning classifier

Background The advent of single-cell RNA sequencing (scRNA-seq) has provided a high-resolution overview of the cellular heterogeneity of different tissue types. Manual cell type annotation of gene expression datasets remains a useful but time-intensive task. Ensemble machine learning methods leverage the predictive power of multiple classifiers and can be applied to classify high-dimensional gene expression data. Here, we present a novel application of the Subsemble supervised ensemble machine learning classifier used to classify novel cells with known cell type labels using gene expression data. Methods First, we tested the classification performance of different pre-processing steps used to normalize and upsample the training dataset for the Subsemble using a colorectal cancer dataset. Second, we conducted a cross-validated performance benchmark of the Subsemble classifier compared to nine other cell type classification methods across five metrics tested, using an acute myeloid leukemia dataset. Third, we conducted a comparative performance benchmark of the Subsemble classifier using a patient-based leave-one-out cross-validation scheme. Rank normalized scores were calculated for each classifier to aggregate performance across multiple metrics. Results The Subsemble classifier performed best when trained on a dataset that was log-transformed then upsampled to generate balanced class distributions. The Subsemble classifier was consistently the top-ranked classifier across five classification performance metrics compared to the nine other baseline classifiers and showed an improvement in performance as the training dataset increased. When tested using the patient-based leave-one-out cross-validation scheme, the Subsemble was the top-ranked classifier based on rank normalized scores. Conclusions Our proof-of-concept study showed that the Subsemble classifier can be used to accurately predict known cell type labels from single-cell gene expression data. The top-ranked classification performance of the Subsemble across two validation datasets, two cross-validation schemes, and five performance metrics motivates future development of accurate ensemble classifiers of scRNA-seq datasets.

HH model based smart deep brain stimulator to detect, predict and control epilepsy using machine learning algorithm

BackgroundEpilepsy is the most common neurological disorder in the world. To control epilepsy, deep brain stimulation is one of the widely accepted treatment techniques. However, conventional deep brain stimulation technique provides continuous stimulation without optimizing the stimulation parameters, resulting in adverse side effects and unexpected death. Hence, understanding the dynamic behavior of brain neural networks at a cellular level is required for patient-specific epilepsy treatment. Considering the underlying mechanism of a single neuronal shift in the brain neural network, computational model-based techniques have a new face for healthcare, which aims to develop effective medical devices for preclinical investigations. New methodThis paper discusses the design of a Smart Deep Brain Stimulator (SDBS) using the Hodgkin-Huxley (HH) conductance-based cellular model of brain neurons to automatically detect, predict and regulate epilepsy against patient-specific conditions. Epileptic activity is simulated as a spike train of action potential due to sodium and potassium channel conductance variations in the single-neuron HH model. The proposed SDBS consists of three components:- i) seizure detection using bagging and boosting-based ensemble machine learning classifiers, ii) channel conductance prediction using Long Short Term Memory-Recurrent Neural Network (LSTM-RNN) based Deep Neural Network (DNN) for updating model parameters of brain neuron, and iii) model-based intelligent control of epileptic seizure with Nonlinear Autoregressive Moving Average-L2 (NARMA-L2) Controller and Nonlinear Model Predictive Controller (NMPC). ResultsFor effective treatment, improving the overall accuracy and efficiency of SDBS is essential. For epilepsy detection, the ensemble bagging machine learning algorithm provides better accuracy of 92.7% compared to the ensemble boosting algorithm. LSTM-RNN deep neural network model with four layers predicts the variations in channel conductance with Root Mean Square Error (RMSE) of 0.00568 and 0.009081 for sodium and potassium channel conductance, respectively. From the closed-loop performances of SDBS with an intelligent control scheme, it is observed that SDBS with NMPC provides efficient and accurate stimulation with minimum energy consumption. From a stability point of view, SDBS with NMPC provides better stability than SDBS with NARMA-L2 Controller. Comparison with existing methodThe proposed SDBS is designed to generate accurate stimulation pulses for epilepsy patients with specific conditions depending on the neuronal activity of a single neuron. Moreover, it will also adapt to the dynamic condition of epilepsy patients. The existing deep brain stimulator continuously provides stimulation pulses without adapting to the patient’s conditions. ConclusionThe proposed SDBS could provide patient-specific treatment based on sodium/potassium channel conductance variations of brain neurons. It will help increase the use of deep brain stimulation techniques and reduce sudden death. Furthermore, the proposed technique will be extended to neural network models with larger neuronal populations to improve the practical feasibility.

RR Interval-based Atrial Fibrillation Detection using Traditional and Ensemble Machine Learning Algorithms.

Atrial fibrillation (AF) is a life threatening disease and can cause stroke, heart failure, and sometimes death. To reduce the rate of mortality and morbidity due to increased prevalence of AF, early detection of the same becomes a prior concern. Traditional machine learning (TML) algorithms and ensemble machine learning (EML) algorithms are proposed to detect AF in this article. The performances of both these methods are compared in this study. Methodology involves computation of RR interval features extracted from electrocardiogram and its classification into: normal, AF, and other rhythms. TML techniques such as Classification and Regression Tree, K Nearest Neighbor, C4.5, Iterative Dichotomiser 3, Support Vector Machine and EML classifier such as Random Forest (RF), and Rotation Forest are used for classification. The proposed method is evaluated using PhysioNet challenge 2017. During the tenfold cross validation, it is observed that RF classifier provided good classification accuracy of 99.10% with area under the curve of 0.998. Apart from contributing a new methodology, the proposed study also experimentally proves higher performance with ensemble learning method, RF. The methodology has many applications in health care management systems including defibrillators, cardiac pacemakers, etc.