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Comparative Analysis of Machine Learning algorithms With Advanced Feature Extraction for ECG Signal classification

IEEE ACCESS 2024年 12卷 57727-57740页

作者： Subba, Tanuja Chingtham, Tejbanta Sikkim Manipal Univ SMU Sikkim Manipal Inst Technol Dept Comp Sci & Engn Gangtok 737132 Sikkim India

Electrocardiogram is a heartbeat signal that can be used for the application of Human-computer interaction. Electrocardiography (ECG) is a prominent way to analyze heart rate and to diagnose cardiovascular disease. However, its availability has been restricted, especially in contexts with limited resources, due to the cost associated with conventional ECG signal processing equipment. The importance of ECG signal processing classification for improving early diagnoses in clinical and remote monitoring contexts is highlighted here. The dataset considered for this work is MIT-BIH arrhythmia which has 15 categories and summarized in 5 classes Normal (N), Superventricular ectopic beats (SVEB), Ventricular ectopic beat (VEB), Fusion beats (F), and Unknown beats (Q). The work discusses the importance of automated classification techniques that make it possible to analyze vast amounts of ECG data effectively and objectively. This research presents an investigation into the classification of ECG signals using various Machine Learning (ML) methods. Specifically, the performance of Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), K Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms are examined. Among these classifiers, RF exhibits a remarkable accuracy of 98%. The results demonstrate the superior performance of the proposed approach for heartbeat classification systems.

关键词： Electrocardiography Arrhythmia Feature extraction Heart beat Peptides Convolutional neural networks classification algorithms Machine learning Biomedical signal processing Random forests Human computer interaction Electrocardiogram signal processing feature extraction machine learning random forest

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Automated Multi-Class Seizure-Type classification System Using EEG Signals and Machine Learning algorithms

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IEEE ACCESS 2024年 12卷 136524-136541页

作者： Abirami, S. Kathiravan, M. Yuvaraj, Rajamanickam Menon, Ramshekhar N. Thomas, John Karthick, P. A. Prince, A. Amalin Ronickom, Jac Fredo Agastinose Indian Inst Technol BHU Varanasi Sch Biomed Engn Computat Neurosci & Biol Lab Varanasi 221005 India Minist Def Def Bioengn & Electro Med Lab DRDO Bengaluru 560093 India Nanyang Technol Univ NTU Natl Inst Educ NIE Singapore 639798 Singapore Sree Chitra Tirunal Inst Med Sci & Technol Cognit Behav Neurol Ctr Dept Neurol R Madhavan Nayar Ctr Comprehens Epilepsy Care Thiruvananthapuram 695011 Kerala India McGill Univ Montreal Neurol Inst Montreal PQ H3A 0G4 Canada Natl Inst Technol Tiruchirappalli Dept Sensor & Biomed Technol Tiruchirappalli 620015 Tamil Nadu India BITS Pilani Dept Elect & Elect Engn KK Birla Goa Campus Zuarinagar 403726 Goa India

Epilepsy is a chronic brain disorder characterized by recurrent unprovoked seizures. The treatment for epilepsy is influenced by the types of seizures. Therefore, developing a reliable, explainable, and automated system to identify seizure types is necessary. This study aims to automate the process of classification of five seizure types: focal non-specific, generalized, complex partial, absence, and tonic-clonic using electroencephalogram (EEG) signals and machine learning algorithms. The EEG signals of 2933 seizures from 327 patients were obtained from the publicly available Temple University Hospital dataset. Initially, the signals were preprocessed using a standard pipeline, and 110 features from the time, frequency, and time-frequency domain were computed from each seizure. Further, the features were ranked using the statistical test and extreme Gradient Boosting (XGBoost) algorithm to identify the significant features. We built binary and multiclass seizure-type classification systems using the identified features and machine learning algorithms. Our study revealed that the EEG band power between 11-13 Hz, 27-29 Hz, intrinsic mode function (IMF) band power 19-21 Hz, and delta band (1-4 Hz) played a crucial role in discriminating the seizures. We achieved an average accuracy of 88.21% and 69.43% for the binary and multiclass seizure-type classification, respectively, using the XGBoost classifier. We also found that the combination of features performed well compared to any single domain. This automated system has the potential to aid neurologists in making diagnosis of epileptic seizure types. The proposed methodology can be applied alongside the established clinical approach of visual evaluation for the classification of seizure-types.

关键词： Electroencephalography Feature extraction Time-frequency analysis Epilepsy Machine learning algorithms classification algorithms seizure type classification electroencephalography feature extraction machine learning

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Applying Machine Learning algorithms for the classification of Sleep Disorders

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IEEE ACCESS 2024年 12卷 36110-36121页

作者： Alshammari, Talal Sarheed Univ Hail Coll Comp Sci & Engn Dept Informat & Comp Sci Hail 81481 Saudi Arabia

Sleep disorder classification is crucial in improving human quality of life. Sleep disorders and apnoea can have a significant influence on human health. Sleep-stage classification by experts in the field is an arduous task and is prone to human error. The development of accurate machine learning algorithms (MLAs) for sleep disorder classification requires analysing, monitoring and diagnosing sleep disorders. This paper compares deep learning algorithms and conventional MLAs to classify sleep disorders. This study proposes an optimised method for the classification of Sleep Disorders and uses the Sleep Health and Lifestyle Dataset publicly available online to evaluate the proposed model. The optimisations were conducted using a genetic algorithm to tune the parameters of different machine learning algorithms. An evaluation and comparison of the proposed algorithm against state-of-the-art machine learning algorithms to classify sleep disorders. The dataset includes 400 rows and 13 columns with various features representing sleep and daily activities. The k-nearest neighbours, support vector machine, decision tree, random forest and artificial neural network (ANN) deep learning algorithms were assessed. The experimental results reveal significant performance differences between the evaluated algorithms. The proposed algorithms obtained a classification accuracy of 83.19%, 92.04%, 88.50%, 91.15% and 92.92%, respectively. The ANN achieved the highest classification accuracy of 92.92%, and its precision, recall and F1-score values on the testing data were 92.01%, 93.80% and 91.93%, respectively. The ANN algorithm that achieved high accuracy than other tested algorithms.

关键词： Sleep apnea classification algorithms Machine learning algorithms Feature extraction Deep learning Brain modeling Radio frequency Artificial neural networks Support vector machines Random forests Optimization methods deep learning classification sleep disorder genetic algorithm

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classification algorithms on a large continuous random dataset using rapid miner tool

Classification algorithms on a large continuous random datas...

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International Conference on Electronics and Communication Systems (ICECS)

作者： Pooja Sharma Divakar Singh Anju Singh Department of Computer Science and Engineering Bhopal BU India Department of Computer Science and information technology Bhopal UTD-BU India

classification is widely used technique in the data mining domain, where scalability and efficiency are the immediate problems in classification algorithms for large databases. Now a day's large amount of data is generated, that need to be analyse, and pattern have to be extracted from that to get some knowledge. classification is a supervised machine learning task which builds a model from labelled training data. The model is used for determining the class; there are many types of classification algorithms such as tree-based algorithms (C4.5 decision tree, j48 decision tree etc.), naive Bayes and many more. These classification algorithms have their own pros and cons, depending on many factors such as the characteristics of the data. We can measure the classification performance by using several metrics, such as accuracy, precision, classification error and kappa on the testing data. We have used a random dataset in a rapid miner tool for the classification. Stratified sampling is used in different classifier such as J48, C4.5 and naïve Bayes. We analysed the result of the classifier using the randomly generated dataset and without random dataset.

关键词： classification algorithms Decision trees Data mining Accuracy Machine learning algorithms Training Data models

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Influence of Data Balancing on Transformer DGA Fault classification With Machine Learning algorithms

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IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION 2023年第1期30卷 385-392页

作者： Rajesh, Kandala N. V. P. S. Rao, U. Mohan Fofana, I. Rozga, P. Paramane, Ashish VIT AP Univ Sch Elect Engn Amaravati 522237 India Univ Quebec Chicoutimi Res Chair Aging Power Network Infrastructure ViAHT Dept Appl Sci Saguenay PQ G7H 2B1 Canada Lodz Univ Technol Inst Elect Power Engn PL-90537 Lodz Poland Lodz Univ Technol Inst Elect Power Engn PL-90537 Lodz Poland NIT Silchar Dept Elect Engn Silchar 788010 Assam India

The application of artificial intelligence algorithms for transformer incipient fault classification using dissolved gas analysis (DGA) is an interesting engineering approach. However, there are various factors that affect the performance of artificial intelligence algorithms. This article presents the influence of the data balancing approach on transformer DGA fault classification with the machine learning (ML) approach. In this work, a total of 4580 DGA samples from in-service transformers are considered for training various ML models. The main challenge for the DGA problem lies in the availability of the normal degradation transformer data and its uniformity corresponding to different faults is almost impossible. This is because DGA is not an exact science, but an empirical approach subjects to variability. Thus, it is a usual practice to apply data sampling techniques that largely influence the efficiency of the algorithms. The present work reports the impact of the data balancing schemes on the performance of the fault classification and demonstrates that a careful choice of the data sampling method and ML algorithm is essential for DGA problems. To demonstrate the global scale ability of the propose model, the model is tested on the IEC TC ten data (field inspection data), while these data are not exposed to the machine during the learning stage. The ability of the ADASYN method in significant enhancement of the global scale capability of AI-based transformer DGA fault classification is reported. This approach will be helpful for condition monitoring engineers in transformer insulation diagnosis in implementing the monitoring modules for large transformer fleets and understanding the insulation oil behavior over years.

关键词： Power transformer insulation Oil insulation classification algorithms Training Data models Testing Machine learning algorithms Dissolved gas analysis (DGA) fault diagnosis insulation liquids transformers

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Wrapper and Hybrid Feature Selection Methods Using Metaheuristic algorithms for English Text classification: A Systematic Review

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IEEE ACCESS 2022年 10卷 39833-39852页

作者： Alyasiri, Osamah Mohammed Cheah, Yu-N Abasi, Ammar Kamal Al-Janabi, Omar Mustafa Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Al Furat Al Awsat Tech Univ Karbala Tech Inst Karbala 56001 Iraq Mohamed Bin Zayed Univ Artificial Intelligence MB Machine Learning Dept Abu Dhabi U Arab Emirates

Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematically studied by exploring available studies of different metaheuristic algorithms used for FS to improve TC. This paper will contribute to the body of existing knowledge by answering four research questions (RQs): 1) What are the different approaches of FS that apply metaheuristic algorithms to improve TC? 2) Does applying metaheuristic algorithms for TC lead to better accuracy than the typical FS methods? 3) How effective are the modified, hybridized metaheuristic algorithms for text FS problems?, and 4) What are the gaps in the current studies and their future directions? These RQs led to a study of recent works on metaheuristic-based FS methods, their contributions, and limitations. Hence, a final list of thirty-seven (37) related articles was extracted and investigated to align with our RQs to generate new knowledge in the domain of study. Most of the conducted papers focused on addressing the TC in tandem with metaheuristic algorithms based on the wrapper and hybrid FS approaches. Future research should focus on using a hybrid-based FS approach as it intuitively handles complex optimization problems and potentiality provide new research opportunities in this rapidly developing field.

关键词： Metaheuristics Feature extraction Text categorization classification algorithms Systematics Search problems Business Feature selection text classification metaheuristic optimization algorithms wrapper-based approach hybrid-based approach

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classification of plastic materials using machine-learning algorithms and microwave resonant sensor

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JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS 2022年第12期36卷 1760-1775页

作者： Covarrubias-Martinez, Dania Lobato-Morales, Humberto Ramirez-Cortes, Juan M. Alvarez-Botero, German A. CICESE Ensenada 22860 Baja California Mexico INAOE Puebla Mexico

The identification of different plastic materials in pellet form using a microwave negative-order-resonance sensor along with the evaluation of classification algorithms from machine learning is presented in this paper. Operation of the sensor is within the unlicensed ISM 2.5 GHz band, and identification of the materials is based on the measured resonant parameters from the sensor. Several classifiers are used to process the resonant parameters having uncertainty factors involved in pellet measurements (air gaps, pellet positions, dimensions and shapes), and performance comparison between the algorithms is carried out in terms of accuracy in the classification. Moreover, the presented measurement method is proposed as a fast, non-destructive, and low-power consumption way to identify plastic raw materials using a low-profile circuit having a high potential of being used in industrial processes.

关键词： classification algorithms microwave sensors measurement uncertainty plastics resonant sensors

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A Review of classification Problems and algorithms in Renewable Energy Applications

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ENERGIES 2016年第8期9卷 607页

作者： Perez-Ortiz, Maria Jimenez-Fernandez, Silvia Gutierrez, Pedro A. Alexandre, Enrique Hervas-Martinez, Cesar Salcedo-Sanz, Sancho Univ Loyola Andalucia Dept Quantitat Methods Cordoba 14004 Spain Univ Alcala Dept Signal Proc & Commun Alcala De Henares 28805 Spain Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain

classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy ( RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field.

关键词： classification algorithms machine learning renewable energy applications

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Machine Learning Approach for Malware Detection and classification using Bio Inspired algorithms

Machine Learning Approach for Malware Detection and Classifi...

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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Mehta, Gaurav Jain, Shaily Das, Prasenjit Tripathi, Vikas Chitkara University Chitkara University School of Engineering & Technology Himachal Pradesh India Chitkara University Department of Computer Science and Engineering Himachal Pradesh India Chandigarh University Apex Institute of Technology Punjab Mohali India Graphic Era Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350313413

The prevalence and sophistication of malware today pose a challenge to conventional signature-based malware detection techniques. This paper provides an approach to the identification and classification of malware that makes use of bio-inspired algorithms in conjunction with machine learning (ML) approaches. The main purpose of this paper is to design a system that can accurately recognize and classify malware samples as they are encountered in real time. This paper uses different bio-inspired algorithms, such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), to achieve this. These algorithms take cues from biological processes. These techniques are used to fine-tune the feature selection process, which plays a major role in determining the quality of the detection and classification models. These methods improve the system's overall performance by simulating natural evolutionary processes to find the most discriminative and relevant features among a large set of candidate features. To predict the classification accuracy different ML algorithms are used like Logistic Regression, Gaussian Naive Bayes, K Nearest Neighbour, Decision Tree, Multilayer Perceptron, Support Vector Machine, JRip, Random Tree, REPTree along with ACO and PSO. The results shows that bio-inspired algorithms improved the accuracy of the malware detection as compared to the basic ML algorithms. © 2023 IEEE.

关键词： Bio Inspired algorithms classification algorithms Machine Learning Malware Detection

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classification of Terahertz Reflection Spectra using Machine Learning algorithms 47

Classification of Terahertz Reflection Spectra using Machine...

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47th International Conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz)

作者： Kristensen, Mathias Hedegaard Cielecki, Pawel Piotr Skovsen, Esben Aalborg Univ Dept Mat & Prod Sect Phys & Mech Skjernvej 4A DK-9220 Aalborg Denmark

ISBN: (纸本)9781728194271

A successful implementation of terahertz screening systems requires a development of reliable and efficient identification algorithms. Dimensionality reduction methods are applied to lower the dimensionality of multivariate data while retaining most of the information. Here, we focus on Principal component analysis (PCA) and linear discriminant analysis (LDA) for analysis and classification of terahertz reflection spectra. The complete data set consists of more than 5000 reflection spectra of six active materials. We found that LDA is better for grouping the spectra resulting in highly accurate classification of terahertz spectra. Furthermore, we compare the classification of referenced and non-referenced reflection spectra eligible for real-world applications of terahertz spectroscopy.

关键词： Dimensionality reduction Spectroscopy Machine learning algorithms Reflection Linear discriminant analysis classification algorithms Reliability

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