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Classification and detection of EEG data for tired driving based on Mamba framework

Classification and detection of EEG data for tired driving b...

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Digital Twins and Parallel Intelligence (DTPI), IEEE international conference on

作者： Chen Xiaohui Liu Jian University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

Fatigue driving is an important factor that causes frequent traffic accidents. the fatigue driving pre-detection system is a research direction at present. Compared to the driving habits of different people, EEG signals can show physiological consistency, and it is a major challenge to mine the characteristics of driver fatigue from the multi-channel EEG data. In this paper, based on Mamba framework, CNN feature extraction module is selected to realize coupling, and a CNN-Mamba model is constructed and applied to EEG fatigue driving detection task. In this paper, we selected a fatigue driving dataset published in 2019, which contains 30 channels of EEG data from 11 subjects. the results show that the average accuracy of the model is 72.73% in the cross-subject EEG classification of 11 subjects, which is significantly improved compared with other advanced methods, and the recognition accuracy performance is improved by 9.65% compared with EEGNet.

关键词： Training Accuracy pattern classification Fatigue Feature extraction Brain modeling Electroencephalography Physiology Digital twins data mining

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A Review of machine learning Techniques for Brain Cancer Diagnosis

A Review of Machine Learning Techniques for Brain Cancer Dia...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Shalini Verma Kuldeep Singh Kaswan Sunil Kumar Bharti Galgotia University Greater Noida India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Brain tumors pose a significant threat to life and disrupt the normal functioning of the human body. Early detection is key to precise diagnosis and effective treatment planning. MRI, or Magnetic resonance imaging is instrumental, in identifying, assessing, and tracking brain tumors. However, a definitive diagnosis still depends on surgical pathology. therefore, this work focusses on applying machine learning (ML) to automatically evaluate and categorize images in MRI data for brain tumor research. the aim is to potentially transform clinical practice and enhance patient’s outcomes. Tumor segmentation in MRI brain images is a highly focused area within the medical community, given that MRI is a noninvasive imaging technique. Segmenting brain tumors entails separating aberrant from healthy brain tissue. this paper systematically reviews the segmentation of brain tumor methods and anomaly categorization in MRI images utilizing various ML approaches. Various data points are extracted from 42 studies that met inclusion criteria and analyzed. this systematic review examines the latest advancements in the processing of brain medical imaging using ML techniques.

关键词： Support vector machines Radio frequency Image segmentation Magnetic resonance imaging Brain cancer machine learning Transforms data mining Medical diagnostic imaging Systematic literature review

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LoCoML: A Framework for Real-World ML Inference Pipelines

LoCoML: A Framework for Real-World ML Inference Pipelines

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AI Engineering - Software Engineering for AI (WAIN), IEEE/ACM Workshop on

作者： Kritin Maddireddy Santhosh Kotekal Methukula Chandrasekar Sridhar Karthik Vaidhyanathan Software Engineering Research Center IIIT Hyderabad India

ISBN: (数字)9798331502195

ISBN: (纸本)9798331502201

the widespread adoption of machine learning (ML) has brought forth diverse models with varying architectures, data requirements, introducing new challenges in integrating these systems into real-world applications. Traditional solutions often struggle to manage the complexities of connecting heterogeneous models, especially when dealing with varied technical specifications. these limitations are amplified in large-scale, collaborative projects where stakeholders contribute models with different technical specifications. To address these challenges, we developed LoCoML, a low-code framework designed to simplify the integration of diverse ML models within the context of the Bhashini Project - a large-scale initiative aimed at integrating AI-driven language technologies such as automatic speech recognition, machine translation, text-to-speech, and optical character recognition to support seamless communication across more than 20 languages. Initial evaluations show that LoCoML adds only a small amount of computational load, making it efficient and effective for large-scale ML integration. Our practical insights show that a low-code approach can be a practical solution for connecting multiple ML models in a collaborative environment.

关键词： Codes Computational modeling Pipelines Optical character recognition Collaboration machine learning Text to speech Stakeholders machine translation Software engineering

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DDoS Attack Detection Method Based on Improved Bagging

DDoS Attack Detection Method Based on Improved Bagging

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Communication Technology and Information Technology (ICCTIT), international conference on

作者： Haiyou Zhi Jingxian Li Mengyue Yu Jin Wang Ziyan Hu Yihan Li School of Computer Science Wuhan Donghu University Wuhan China

ISBN: (数字)9798331528973

ISBN: (纸本)9798331528980

In view of the complexity and concealment of distributed denial of service attacks, a DDoS attack detection method based on improved Bagging ensemble learning is proposed. In order to effectively deal with the changing attack modes and improve the stability and accuracy of the detection model, this method enhances the recognition ability of DDoS attacks by introducing a weighted voting mechanism and combining multiple machine learning algorithms. Experiments on the CIC-DDoS2019 dataset show that after preprocessing steps such as feature selection, data normalization and feature correlation analysis, the improved Bagging model using RF, DT and KNN as base learners has an accuracy of $\mathbf{9 9. 9 8 \%}$ on the test set, which is significantly better than the traditional Bagging method and other detection models. this study shows that the improved Bagging method not only improves the accuracy and robustness of DDoS attack detection, but also provides new ideas for solving challenges such as high-dimensional data processing and dynamic network traffic analysis.

关键词： Radio frequency Accuracy machine learning algorithms Telecommunication traffic Denial-of-service attack Robustness Stability analysis Ensemble learning Computer crime Bagging

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Analysis of Big data Processing Based on Mapreduce and Convolution Neural Network 4th

Analysis of Big Data Processing Based on Mapreduce and Convo...

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4th international conference on Innovative Computing, IC 2021

作者： Xu, Hongsheng Wang, Lan Zhang, Yi Luoyang Normal University Luoyang China Henan Key Laboratory for Big Data Processing & Analytics of Electronic Commerce Luoyang China

ISBN: (纸本)9789811642579

this paper first describes the principle of convolution neural network. Convolution neural network (CNN) is proposed to deal with pattern recognition problem on the basis of neural cognitive machine. Convolution NN is a multi-layer neural network, which each layer is composed of multiple two-dimensional planes, and each plane is composed of multiple independent neurons. In this paper, the principle and design idea of MapReduce based on large-scale data are described in detail. the paper presents analysis of big data processing by MapReduce and Convolution Neural Network. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： MapReduce

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Online Q&A for machine learning Courses Based on Formula recognition

Online Q&A for Machine Learning Courses Based on Formula Rec...

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Information Science and Education (ICISE-IE), international conference on

作者： Xiaoke Zhu Xiaopan Chen Minghao Li Tianxiang Zhang School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

Mathematical formulas are ubiquitous in courses such as machine learning. Understanding these formulas is the primary challenge for studying of students. If a searchable knowledge base can be built for these formulas, it will help students learn. Accurate identification of these formulas is an important step in building and querying the knowledge base. However, mathematical formulas have complex two-dimensional and nested structures, which makes them inconvenient to search like ordinary text. this paper employs Faster R-CNN to detect regions containing mathematical formulas and improves its backbone network, anchor box dimensions, and pre-training network to make it more accurate and efficient in identifing these formulas. Furthermore, in order to enhance the features and meaningful representation of mathematical formulas in the input image, we introduce the distance transformation. Finally, comparative experiments were conducted on the publicly dataset GTDB. Our proposed approach achieved recognition rates of 91.22% and 85.74% for isolated and hybrid formulas detection.

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Research on Knowledge Graph Extraction Methods for Chinese STEM Curriculum

Research on Knowledge Graph Extraction Methods for Chinese S...

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machine learning and Natural Language Processing (MLNLP), international conference on

作者： Changlong Wang Xiujuan Sang Xijie Wang Yuan Gao Yi Liu College of Computer Science and Engineering Northwest Normal University LanZhou China

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

STEM education, as an innovative teaching model, has gained widespread attention in recent years. However, the lack of relevant textbooks and learning resources has made its implementation challenging. Developing interdisciplinary knowledge graphs tailored for STEM education has become an urgent issue. To address this, a knowledge extraction framework named Llms4edu is proposed, which utilizes a series of effective prompts to guide large language models in knowledge extraction. Specifically, the knowledge extraction task is transformed into multiple rounds of question-and-answer interactions with the LLM, gradually identifying entity-relation triplets from subject data. through experiments, an F1-score of 89.4% was achieved on the named entity recognition task in the chemistry subject, and an F1-score of 66.7 % on the relation extraction task. Finally, a subject ontology model was built for subject text, and a subject data set was constructed using Llms4edu, which includes three subjects of junior high school mathematics, physics, and chemistry, a total of 2,511 entities, 2,010 relationship triples, and cross-disciplinary knowledge is linked to construct a cross-disciplinary knowledge graph.

关键词： Knowledge engineering Training Chemistry Annotations Large language models Knowledge graphs Named entity recognition Ontologies data mining Physics

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RetainEXT: Enhancing Rare Event Detection and Improving Interpretability of Health Records using Temporal Neural Networks

RetainEXT: Enhancing Rare Event Detection and Improving Inte...

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4th IEEE-EMBS international conference on Wearable and Implantable Body Sensor Networks (BSN) / 18th IEEE-EMBS international conference on Biomedical and Health Informatics (BHI)

作者： Ramchand, Suraj Tsang, Gavin Cole, Duncan Xie, Xianghua Swansea Univ Comp Sci Swansea W Glam Wales Cardiff Univ Sch Med Cardiff Wales

ISBN: (数字)9781665487917

ISBN: (纸本)9781665487917

A recurring theme during the pandemic was the shortage of hospital beds. Despite all efforts, the healthcare system still faces 25% of resource strain felt during the first peak of coronavirus. Digitisation of Electronic Healthcare Records (EHRs) and the pandemic have brought about many successful applications of Recurrent Neural Networks (RNNs) to predict patients' current and future states. Despite their strong performance, it remains a challenge for users to delve into the black box which has heavily influenced researchers to utilise more interpretable techniques such as 1D-Convolutional neural networks. Others focus on using more interpretable machine learning techniques but only achieve high performance on a select subset of patients. By collaborating with medical experts and artificial intelligence scientists, our study improves on the REverse Time AttentIoN EX model, a feature and visit level attention network, for increased interpretability and usability of RNNs in predicting COVID-19-related hospitalisations. We achieved 82.40% area under the receiver operating characteristic curve and showcased effective use of the REverse Time AttentIoN EXTension model and EHRs in understanding how individual medical codes contribute to hospitalisation risk prediction. this study provides a guideline for researchers aiming to design interpretable temporal neural networks using the power of RNNs and data mining techniques.

关键词： Artificial intelligence data mining Electronic health records COVID-19 Attention networks

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recognition and Classification of Cardiac Arrhythmias Using Discrete Wavelet Transform (DWT) and machine learning Techniques 4th

Recognition and Classification of Cardiac Arrhythmias Usin...

引用

4th international conference on Advances in Emerging Trends and Technologies, ICAETT 2022

作者： Ayala-Cucas, Hermes Andrés Mora-Piscal, Edison Alexander Mayorca-Torres, Dagoberto León-Salas, Alejandro José Peluffo-Ordoñez, Diego Hernán Grupo de investigación de Ingeniería Mecatrónica Universidad Mariana Pasto Colombia Departamento de Lenguajes y Sistemas Informáticos Universidad de Granada C/Periodista Daniel Saucedo Aranda s/n Granada18071 Spain Research Program Mohammed VI Polytechnic University Ben Guerir Morocco

ISBN: (纸本)9783031259418

Cardiac arrhythmias are heart rhythm problems that usually occur when the electrical impulses coordinated with the heartbeat do not work correctly. For this reason, detecting abnormalities in an electrocardiogram (ECG) plays a vital role in patient follow-up. Due to the presence of noise, the irregularity of the heartbeat, and the nonstationary nature of ECG signals, their interpretation can be difficult, requiring the use of advanced computer systems to support the diagnosis of cardiac disorders. therefore, the development of assisted ECG analysis systems is a current topic of study, and the main challenge is to achieve adequate accuracy for application in the clinical setting. therefore, this article describes a software tool for classifying ECG samples into the main classes of cardiac arrhythmias by removing noise from the ECG signal at the preprocessing stage using conventional digital filters;the location of the QRS complex is essential for the identification of the ECG signal. therefore, the position and amplitude of the R peaks are determined in the segmentation stage. then the selection of the most relevant features of the ECG signal is performed using the discrete wavelet transform (DWT). the ability of the extracted features to differentiate between different classes of data is tested using machine learning techniques such as k-Nearest Neighbors, Neural Networks, and Decision Trees with 10-fold cross-validation. these methods are evaluated and tested with the MIT-BIH arrhythmia database, achieving the best accuracy of 98.54% using the k-Nearest Neighbors classifier. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electrocardiograms

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Advanced Wheat Disease Detection: Utilizing an Integrated CNN-SVM Model for Leaf Image Classification

Advanced Wheat Disease Detection: Utilizing an Integrated CN...

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Sustainable Expert Systems (ICSES), international conference on

作者： Ravi Ranjan Kumar Gautam Rana Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Artificial Intelligence (AI) and machine learning (ML) has significantly advanced gender recognition technology, which automates the identification of a person's gender from various forms of data. this study explores the development and evaluation of a gender recognizer system leveraging the EfficientNet B0 architecture and transfer learning. the model processes input images through several steps, including dataset preprocessing, feature extraction, and classification. the effectiveness of the model is demonstrated through performance metrics such as precision, recall, F1 score, and accuracy. the results indicate a high accuracy rate of 93%, with precision and recall scores showcasing the model's robust and reliable performance in predicting gender. Additionally, the ethical considerations, including privacy concerns and potential biases in gender recognition algorithms, are discussed, emphasizing the need for fairness and transparency in the deployment of such technologies.

关键词： Support vector machines Precision agriculture Plant diseases Privacy Accuracy Transfer learning Crops Predictive models Prediction algorithms Reliability

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