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Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

computer systems science & engineering 2022年第9期42卷 1095-1107页

作者： Ayman Altameem Jaideep Singh Sachdev Vijander Singh Ramesh Chandra Poonia Sandeep Kumar Abdul Khader Jilani Saudagar Department of Computer Science College of Applied StudiesKing Saud UniversityRiyadh11495Saudi Arabia Department of Computer Science and Engineering Manipal University JaipurJaipur303007India Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Information Systems Department Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals;these signals can berecorded, processed and classified into different hand movements, which can beused to control other IoT devices. Classification of hand movements will beone step closer to applying these algorithms in real-life situations using EEGheadsets. This paper uses different feature extraction techniques and sophisticatedmachine learning algorithms to classify hand movements from EEG brain signalsto control prosthetic hands for amputated persons. To achieve good classificationaccuracy, denoising and feature extraction of EEG signals is a significant step. Wesaw a considerable increase in all the machine learning models when the movingaverage filter was applied to the raw EEG data. Feature extraction techniques likea fast fourier transform (FFT) and continuous wave transform (CWT) were usedin this study;three types of features were extracted, i.e., FFT Features, CWTCoefficients and CWT scalogram images. We trained and compared differentmachine learning (ML) models like logistic regression, random forest, k-nearestneighbors (KNN), light gradient boosting machine (GBM) and XG boost onFFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFTfeatures gave the maximum accuracy of 88%.

关键词： Machine learning brain signal hand motion recognition braincomputer interface convolutional neural networks

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Histogram-Based Decision Support System for Extraction and Classification of Leukemia in Blood Smear Images

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computer systems science & engineering 2023年第8期46卷 1879-1900页

作者： Neenavath Veeraiah Youseef Alotaibi Ahmad FSubahi Department of Electronics and Communications DVR&DHS MIC Engineering CollegeKanchikacharlaA.P.521180India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia

An abnormality that develops in white blood cells is called *** diagnosis of leukemia is made possible by microscopic investigation of the smear in the *** training is necessary to complete the morphological examination of the blood smear for leukemia *** paper proposes a Histogram Threshold Segmentation Classifier(HTsC)for a decision support *** proposed HTsC is evaluated based on the color and brightness variation in the dataset of blood smear *** operations are used to crop the nucleus based on automated *** Blood Cell(WBC)segmentation is calculated using the active contour model to determine the contrast between image regions using the color transfer *** entropy-adaptive mask generation,WBCs accurately detect the circularity region for identification of the *** proposed HTsC addressed the cytoplasm region based on variations in size and shape concerning addition and rotation *** in WBC imaging characteristics depends on the cytoplasmic and nuclear *** computation of the variation between image features in the cytoplasm and nuclei regions of the WBCs is used to classify blood smear *** classification of the blood smear is performed with conventional machine-learning techniques integrated with the features of the deep-learning regression *** designed HTsC classifier comprises the binary classifier with the classification of the lymphocytes,monocytes,neutrophils,eosinophils,and abnormalities in the *** proposed HTsC identifies the abnormal activity in the WBC,considering the color and shape *** exhibits a higher classification accuracy value of 99.6%when combined with the other *** comparative analysis expressed that the proposed HTsC model exhibits an overall accuracy value of 98%,which is approximately 3%–12%higher than the conventional technique.

关键词： White blood cells leukemia segmentation histogram blood smear

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Ovarian Cancer Prediction using Machine Learning 3

Ovarian Cancer Prediction using Machine Learning

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Agnihotri, Yashika Jain, Tarun Sharma, Vijay Shankar Manipal University Jaipur Department of Computer Science and Engineering India Manipal University Jaipur Department of Iot & Intelligent Systems India

ISBN: (纸本)9798350319125

Ovarian cancer is the type of cancer that has the highest recurrence rate in women and poses a serious threat to women. Due to the lack of observable signs, this quiet invader often goes undetected in the beginning, leaving women susceptible to its sneaky development. Once it has advanced and its symptoms are obvious, it becomes arduous to treat. Machine learning is emerging as a ray of hope in the healthcare scene in the current era of rapid technological innovation. Machine learning provides a promising path for the early diagnosis and prediction of ovarian cancer by harnessing the power of algorithms and data analytics, perhaps turning the tide against this tough enemy. The dataset [17] comprises of 349 instances and 49 features. The goal here is to derive a model that helps in classification of instances into two classes: Benign Ovarian Tumors (BOT) and Ovarian Cancer (OC). Therefore, methodical machine learning algorithms are employed to detect ovarian cancer. Our research findings revealed that out of all the classifiers, the Random Forest Model has the highest accuracy (91.43 %). This approach is capable of accurately classifying BOT and OC and could have immense effect on diagnosis of patients, leading to better treatments and higher survival rate. © 2023 IEEE.

关键词： Classification Decision Tree Model KNN Logistic Regression Model Random Forest Model SGD Model and Voting Classifier Model SVM Model Used - Machine Learning XGB Model

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AFuNet: an attention-based fusion network to classify texts in a resource-constrained language

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Neural Computing and Applications 2025年第9期37卷 6725-6748页

作者： Hossain, Md. Rajib Hoque, Mohammed Moshiul Dewan, M. Ali Akber Hoque, Enamul Siddique, Nazmul Department of Computer Science & Engineering Chittagong University of Engineering & Technology Chittagong4349 Bangladesh School of Computing and Information Systems Faculty of Science and Technology Athabasca University AthabascaABT9S 3A3 Canada School of Information Technology York University TorontoONM3J 1P3 Canada School of Computing Engineering and Intelligent Systems Ulster University Coleraine United Kingdom

In the era of widespread Internet use and extensive social media interaction, the digital realm is accumulating vast amounts of unstructured text data. This unstructured data often contain undesirable information, necessitating time-consuming manual classification efforts. An intelligent text classification system capable of automatically categorizing digitized texts based on semantic meaning is crucial. However, this task is particularly challenging for low-resource languages like Bengali due to a shortage of annotated corpora, issues with out-of-vocabulary words, lack of domain-specific hyperparameter tuning, limited ability to extract generalized text features, and class imbalances within the corpus. AFuNet: an attention-based fusion network to classify texts in a resource-constrained language. AFuNet undergoes a comprehensive four-phase experimental process, including baseline model evaluation and hyperparameter tuning, late fusion and model selection, attention-based early fusion and model identification, and an ablation study with impact analysis. Fine-tuned based on five Bengali text classification corpora, AFuNet achieves impressive accuracies: 96.60 ± 0.2 (BTCC11), 85.37 ± 0.2 (OSBC), 97.35 ± 0.2 (BARD), 93.74 ± 0.2 (IndicNLP), and 96.51 ± 0.2 (ProthomAlo). In comparison with previous state-of-the-art models on these corpora, AFuNet demonstrates significant accuracy improvements ranging from 0.54% to 4.49%, showcasing its effectiveness in advancing text classification capabilities for the Bengali language. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Semantics

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Compression with Unlabeled Graph Side Information

Compression with Unlabeled Graph Side Information

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Nikpey, Hesam Sarkar, Saswati Bidokhti, Shirin Saeedi University of Pennsylvania Department of Computer and Information Science United States University of Pennsylvania Department of Electrical and Systems Engineering United States

ISBN: (纸本)9781665475549

With the growth of big data in the past few decades, compression has become inseparable from data generation. The data generated daily across different platforms are correlated: friend networks on Facebook and Instagram, contact networks in subsequent days, and many more. This raises the question of compressing a dataset using another correlated dataset. For instance, can we compress the Facebook graph of friends when we know Instagram's graph? This can be cast as the classical problem of source coding with side information, and the answer is known to be positive when the graphs are "labeled"and/or aligned, meaning we need to know the node corresponding to Jon Doe in both Facebook and Instagram graphs. The classical idea is to utilize joint typicality to decide whether two graphs are correlated or not. In practice, graphs are often not aligned and/or the labels are concealed to keep the identity of the users private. In these scenarios, classical ideas are no longer applicable as joint typicality highly depends on the ordering of sequences. In this work, we prove for the first time the existence of lossless graph compression schemes that utilize unlabeled side information and improve the compression rate. In order to do that, we design binning along with a novel testing criterion that relies on graph matching, the closely related quadratic assignment problem and its asymptotic properties. © 2023 IEEE.

关键词： Combinatorial optimization

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Optimizing Facial Recognition: An Analytical Comparison of Traditional and Deep Learning Approaches 7

Optimizing Facial Recognition: An Analytical Comparison of T...

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7th International Conference on Data science and Its Applications, ICoDSA 2024

作者： Sakib, Shadman Siddiky, Md Nurul Absar Arifuzzaman, Md. Chowdhury, Md. Jalal Uddin University of Maryland Baltimore County Department of Information Systems Maryland21250 United States University of North Carolina at Charlotte Department of Electrical and Computer Engineering Charlotte United States Leading University Sylhet Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798350365351

In the rapidly evolving domains of AI and Internet tech, face recognition, a key machine learning application, is increasingly used in security, identity verification, and public monitoring. As this technology progresses, its applications are expanding. A critical factor in its growing popularity is the advancement of the underlying algorithms, which drives its effectiveness in various applications. This paper investigates facial recognition technologies using the Olivetti faces dataset. It compares the efficacy of Principal Component Analysis (PCA), Eigenface method, Support Vector Machine (SVM) with linear kernel, a standard Convolutional Neural Network (CNN), and a CNN-Transfer Learning approach with MobileNet. The transfer learning is trained with the Adam optimizer (learning rate 0.0004) and categorical cross entropy loss. Over 100 epochs with a batch size of 64, the model's performance, in terms of accuracy and loss, is meticulously tracked and visualized, providing insights into its learning progression. The final evaluation includes a detailed classification report, highlighting precision, recall, and F1-scores, and these metrics are graphically represented for a comprehensive understanding of the model's performance. The CNN model achieved 80% accuracy, while PCA and SVM exhibited high accuracies, reaching 91% and 97.5%, respectively. We have also applied the eigenface method and achieved 91% accuracy in face recognition. Notably, the CNN-Transfer Learning method demonstrates superior performance with a 95% accuracy, highlighting the potential of deep learning in facial recognition applications. © 2024 IEEE.

关键词： Contrastive Learning

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Incentive Designs for Learning Agents to Stabilize Coupled Exogenous systems 63

Incentive Designs for Learning Agents to Stabilize Coupled E...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Certorio, Jair Martins, Nuno C. La, Richard J. Arcak, Murat Institute for Systems Research University of Maryland College Park Department of Electrical and Computer Engineering College ParkMD20740 United States University of California Department of Electrical Engineering and Computer Science BerkeleyCA94720 United States

ISBN: (纸本)9798350316339

We consider a large population of learning agents noncooperatively selecting strategies from a common set, influencing the dynamics of an exogenous system (ES) we seek to stabilize at a desired equilibrium. Our approach is to design a dynamic payoff mechanism capable of shaping the population's strategy profile, thus affecting the ES's state, by offering incentives for specific strategies within budget limits. Employing system-theoretic passivity concepts, we establish conditions under which a payoff mechanism can be systematically constructed to ensure the global asymptotic stability of the ES's equilibrium. In comparison to previous approaches originally studied in the context of the so-called epidemic population games, the method proposed here allows for more realistic epidemic models and other types of ESs, such as predator-prey dynamics. The stability of the equilibrium is established with the support of a Lyapunov function, which provides useful bounds on the transient states. © 2024 IEEE.

关键词： Lyapunov functions

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Analysis of Life Expectancy Rate Post Lung Surgery Using Machine Learning 3

Analysis of Life Expectancy Rate Post Lung Surgery Using Mac...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Shreya Jain, Tarun Sharma, Vijay Shankar Manipal University Jaipur Department of Computer Science and Engineering India Manipal University Jaipur Department of Iot & Intelligent Systems India

ISBN: (纸本)9798350319125

It has become really important for patients to know the precise results that can happen post the treatment of lung cancer or that if they have any underlying health problem which can be the cause of death post lung cancer surgery. Doctors also need to know the full detailed report of patients' health with the risk factors that can affect their survival rate for better treatment of their patients. As such results are crucial, we need to implement such techniques which can generate the most accurate results. For such accurate analysis, we use machine learning techniques to generate better results. Several machine learning and deep learning approaches are being used to estimate the survival rate of lung cancer patients post the surgery. Examination of various health factors is used for prediction, as health factors could be significant predictors. Seven machine learning techniques that are linear regression, XGBooster, random forests, Decision Tree, KNN, SVM, voting classifier and two deep learning techniques that are ANN and BILSTM are used for analyzing performance. Analysis of accuracy has been done using F1 score, precision, and recall as measuring factors. For the analysis of outcome prediction, we have taken accuracy. © 2023 IEEE.

关键词： ANN Decision Tree Machine learning Random Forest Thoracic Surgery

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Advanced Multi-Spectral Image Processing Techniques for Enhanced Remote Sensing and Comprehensive Environmental Monitoring in Diverse Ecosystems

Advanced Multi-Spectral Image Processing Techniques for Enha...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Jagannathan, J. Ponmalar, A. Pandiarajan, Rajkumar Sudha, I. Ramesh, P.S. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India R.M.K. Engineering College Tamilnadu Chennai India School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350388602

The research is aimed at improvement of multi-spectral image analysis for remote sensing and monitoring the environment at various ecosystems. A combined spectral imagery encompasses several spectrums at a time compared to single spectral imagery that takes only one spectrum at an instance or at a given area to cover all these spectrums. The study employs digital high resolution satellite images and ground based data to quantify the vegetation vigour, water quality, soil characteristics and changes in land cover. Features, indexes, and classification routines are employed to process the data that is collected with the aid of avionics and other peripherals. This provincial level modelling highlights important gains in environmental status, which includes the general improvement in vegetation condition, water quality as well as physical properties of the soil in the province. This study focuses on the complementary possibilities of integration of multi-spectral imaging with other remote sensing technologies and the high performance of data processing. These techniques play significant roles if used to give current and comprehensive information in the management and conservation of the environment as highlighted in the study. © 2024 IEEE.

关键词： data analysis environmental monitoring image processing techniques machine learning Multi-spectral imaging remote sensing soil properties vegetation health water quality

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Out-of-distribution Object Detection through Bayesian Uncertainty Estimation 26

Out-of-distribution Object Detection through Bayesian Uncert...

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26th International Conference on Information Fusion, FUSION 2023

作者： Zhang, Tianhao Wang, Shenglin Bouaynaya, Nidhal Calinescu, Radu Mihaylova, Lyudmila The University of Sheffield Automatic Control and Systems Engineering United Kingdom Rowan University Department of Electrical and Computer Engineering United States University of York Department of Computer Science United Kingdom

ISBN: (纸本)9798890344854

The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distribution (OOD) instances are inevitable and usually lead to uncertainty in the results. In this paper, we propose a novel, intuitive, and scalable probabilistic object detection method for OOD detection. Unlike other uncertainty-modeling methods that either require huge computational costs to infer the weight distributions or rely on model training through synthetic outlier data, our method is able to distinguish between in-distribution (ID) data and OOD data via weight parameter sampling from proposed Gaussian distributions based on pre-trained networks. We demonstrate that our Bayesian object detector can achieve satisfactory OOD identification performance by reducing the FPR95 score by up to 8.19% and increasing the AUROC score by up to 13.94% when trained on BDD100k and VOC datasets as the ID datasets and evaluated on COCO2017 dataset as the OOD dataset. © 2023 International Society of Information Fusion.

关键词： Object detection

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