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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

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Robustness of ML-Based Seizure Prediction Using Noisy EEG data From Limited Channels 20

Robustness of ML-Based Seizure Prediction Using Noisy EEG Da...

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20th Annual international conference on Distributed computing in Smart Sensor Systems and the Internet of Things (DCOSS-IoT)

作者： Mohammad, Umair Saeed, Fahad Florida Int Univ Knight Fdn Sch Comp & Informat Sci Miami FL 33199 USA

ISBN: (纸本)9798350369458;9798350369441

Seizures pose a significant health hazard for over 50 million individuals with epilepsy worldwide, with approximately 56% experiencing uncontrollable seizures according to the CDC. Predicting seizures is challenging even with the availability of various sensors (gyroscopes, pulse rate sensors, heart rate monitors, etc). Electroencephalography (EEG) data can directly measure the activity of the brain and has been the choice of leveraging deep learning (DL) models for seizure prediction. Despite DL models achieving over 95% accuracy on retroactive clinical-grade EEG data, this performance fails to translate in real-world settings where the accuracy goes down to 66% which warrants further investigation. Moreover, consumer-grade wearable EEG headsets, characterized by lower data quality and a varying number of channels across brands, present additional challenges. In this paper, we estimate the robustness of DL models which are trained on clinical-grade EEG data but tested on the type of data expected from consumer-grade wearable EEG headsets. We select the previously published model SPERTL to estimate its robustness when: (1) predicting with data from less leads/channels, (2) predicting when faced with streaming data, (3) evaluating performance on imbalanced data with more interictal segments. Our results are compared against baseline results from the SPERTL model which we have re-configured to operate independently of the number of channels with an average baseline area under the curve (AUC) score of 98.56%. Our results demonstrate that though the model is surprisingly resilient to streaming and noisy data, reducing the number of channels and a higher class imbalance have a more severe degradation. The AUC across all cross-validation sets degrades only by 2% and 3% on average for noisy and streaming data, respectively. However, a performance reduction, on average, is observed by 32% when imbalance is increased with higher percentage of interictal samples, and up to 16

关键词： Electroencephalography (EEG) Wearable Seizure Prediction Robustness Lower Quality data

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Navigating learning Pathways: A Multifaceted Approach to Understanding Student Engagement and Progress in Online Programming Courses 12

Navigating Learning Pathways: A Multifaceted Approach to Und...

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12th international conference on Information and Education Technology, ICIET 2024

作者： Wang, Xiaonan Su, Yancong Sun, Yi Shigyo, Kento Ohtsuki, Kazuhiro Kiyomitsu, Hidenari Graduate School Of Intercultural Studies Kobe University Kobe Japan School Of Design And Art Xiamen University Of Technology Xiamen China Graduate School Of Information Technology Kobe Institute Of Computing Kobe Japan Hong Kong University Of Science And Technology Academy Of Interdisciplinary Studies Hong Kong Hong Kong

ISBN: (纸本)9798350371772

In an era when online education is becoming increasingly prevalent, and many institutions are transitioning onto online platforms, it has become crucial to understand online learning behaviors. The user data kept by online learning platforms has supported relevant research that has made great strides in analyzing both engagement and dropout rates in online courses. In online programming courses, learners struggle to understand the code and even code by themselves, while instructors barely have access to check everyone's code and give advice in real-time. It becomes critical for instructors to quickly grasp how learners are progressing and encountering difficulties in their online programming learning. This study combines learners' programming activity data, click stream data from video learning, and textual data such as accompanying notes and questions. A visual representation that combines multiple learning data is proposed, and learners' learning Trajectories Map in an online programming course is created from experimentally collected data. The results show a strong correlation between learners' programming activities and their engagement with the learning material. In addition, by comparing the learners' learning trajectories, it was possible to observe their different learning strategies, which provided more detailed clues for studying behavioral patterns and learner characteristics in online learning. This study introduces a new tool for educational evaluation, provides a more comprehensive perspective, and addresses the limitations of relying only on a single indicator for evaluation. It is of great significance to educational technology and computer-assisted education. © 2024 IEEE.

关键词： data visualization

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User Profile Based Movie Recommender System Using machine learning 2

User Profile Based Movie Recommender System Using Machine Le...

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2nd international conference on Recent Trends in Microelectronics, Automation, computing, and Communications Systems, ICMACC 2024

作者： Vemula, Punna Rao Jannu Soloman, Shyam Sunder Rao, Y. Chalapathi Baydeti, Nagaraju National Institute of Technology Nagaland Department of Computer Science and Engineering Nagaland Chumukedima797103 India VNRVJIET Department of Electronics and Communication Engineering Hyderabad500090 India

ISBN: (纸本)9798350366570

With the abundance of information online, users often struggle to make informed decisions. Recommendation systems (RMS) are crucial for helping users find relevant items among vast choices thereby using Content-Based Filtering and Collaborative Filtering algorithms. These systems are widely used across e-commerce, music apps, and streaming platforms like Netflix and YouTube to enhance user experience and drive business goals. Despite their benefits, RMS face some hazards such as cold start problems and scalability issues, which can impact the quality of recommendations. The proposed method uses the MovieLens-100k dataset, specifically the movies and ratings files, to recommend movies based on Content-Based Filtering. By preprocessing the data and calculating TF-IDF vectors and calculating the similarity for movie genres, the system builds user profiles and recommends movies with high similarity scores. Additionally, Single Value Decomposition (SVD) is used to address issues related to sparsity and scalability in collaborative filtering by reducing dimensionality. © 2024 IEEE.

关键词： Collaborative filtering

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Riddance of Weed Using an Agri-Robot Employed by Arduino and ESP32 CAM 15

Riddance of Weed Using an Agri-Robot Employed by Arduino and...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Vinod, Dasari Naga Kamalesh, A. Bhaskar, B. Teja, B. Ravi Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798350370249

This article presents the enhancement of a weed detection robot designed to automate the identification and riddance of unwanted vegetation in agricultural fields. The robot integrates a suite of hardware components, including an ultrasonic sensor, Arduino microcontroller, ESP32 CAM, robotic hand, L293D motor driver, battery, power supply module, Bluetooth module, and DC motor for locomotion. The DC motor is utilized to drive the wheels of the robot, enabling it to traverse the agricultural terrain efficiently. The ultrasonic sensor facilitates obstacle detection, ensuring safe navigation through the field while avoiding collisions. Meanwhile, the ESP32 CAM captures visual data of the surroundings, which is processed by machine learning algorithms to identify and classify weeds. Upon identifying weeds, the robotic hand, controlled by the Arduino microcontroller via the L293D motor driver, is deployed to remove the unwanted vegetation. The Arduino microcontroller acts as the central processing unit, orchestrating the operation of all components and executing decision-making algorithms. Additionally, the inclusion of a Bluetooth module enables remote monitoring and control of the robot, providing farmers with real-time updates on weed infestation levels and operational status. The weed detection robot offers a solution for weed management in agriculture and obtained 97% accuracy, promising increased crop yields and reduced labor costs through efficient weed riddance. ©2024 IEEE.

关键词： DC motors

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Application of Deep learning Techniques for Coronary Artery Disease Detection and Prediction: A Systematic Review 1

引用

1st international conference on Intelligent computing for Sustainable Development, ICICSD 2023

作者： Jayasree, M. Koteswara Rao, L. Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Hyderabad India

ISBN: (数字)9783031612985

ISBN: (纸本)9783031612978

Coronary artery disease, a leading cause of death, occurs when coronary arteries narrow due to plaque or blood clots. It is possible to visualize these blood clots in the blood vessels through angiograms, a medical imaging technique. An angiogram is a type of medical imaging procedure used to see where blood clots or plaques have formed inside the coronary arteries. The conventional coronary angiogram or X-ray angiogram has been measured as a gold standard to predict coronary artery disease. machine learning and deep learning techniques are widely deployed in many applications that include feature extraction, enhancement, and pattern analysis. The development of technology and the accessibility of vast amounts of data have made it relatively simple to analyze the condition of the coronary arteries, identify the disease, and make an early diagnosis. In this review paper, coronary artery disease assessment, angiograms, and various deep learning methods for coronary artery disease prediction are reviewed, and the assessment results are showcased. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Forecasting

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Enhancing Forensic Audio Transcription with Neural Network-Based Speaker Diarization and Gender Classification 10

Enhancing Forensic Audio Transcription with Neural Network-B...

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10th international conference on Engineering and Emerging Technologies, ICEET 2024

作者： Ullah, Rahmat Asghar, Ikram Malik, Hassan Evans, Gareth Ahmad, Jawad Roberts, Dorothy Anne School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Computing Engineering and Digital Technologies Teesside University MiddlesbroughTS1 3BX United Kingdom School of Computing Sciences University of East Anglia NorwichNR4 7TJ United Kingdom Uiversity of South Wales Faculty of Computing Engineering and Science PontypriddCF37 1DL United Kingdom Prince Mohammad Bin Fahd University Cybersecurity Center Al Khobar Saudi Arabia Posib Ltd y Gilfach Flintshire NercwysCH7 4EL United Kingdom

ISBN: (纸本)9798331532895

Forensic audio transcription is often compromised by low-quality recordings, where indistinct speech can hinder the accuracy of conventional Automatic Speech Recognition (ASR) systems. This study addresses this limitation by developing a machine learning-based approach to improve speaker diarization, a process critical for distinguishing between speakers in sensitive audio data. Previous research highlights the inadequacy of traditional ASR in forensic settings, particularly where audio quality is poor and speaker overlap is common. This paper presents a neural network specifically designed for gender classification, using 20 key acoustic features extracted from real forensic audio data. The model architecture includes input, hidden, and output layers tailored to differentiate male and female voices, with dropout regularization to prevent overfitting and hyperparameter optimization ensuring robust generalization across test data. The neural network achieved an average recall of 86.81%, F1 score of 85.67%, precision of 87.95%, and accuracy of 86.83% across varied audio conditions. This model significantly improves transcription accuracy, reducing errors in legal contexts and supporting judicial processes with more reliable, interpretable evidence from sensitive audio data. © 2024 IEEE.

关键词： Speech recognition

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Scalable Tree-based Register Automata learning 30th

Scalable Tree-based Register Automata Learning

引用

30th international conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS) Held as Part of the 27th European Joint conferences on Theory and Practice of Software (ETAPS)

作者： Dierl, Simon Fiterau-Brostean, Paul Howar, Falk Jonsson, Bengt Sagonas, Konstantinos Taquist, Fredrik Tech Univ Dortmund Dortmund Germany Uppsala Univ Uppsala Sweden Natl Tech Univ Athens Athens Greece

ISBN: (纸本)9783031572487;9783031572494

Existing active automata learning (AAL) algorithms have demonstrated their potential in capturing the behavior of complex systems (e.g., in analyzing network protocol implementations). The most widely used AAL algorithms generate finite state machine models, such as Mealy machines. For many analysis tasks, however, it is crucial to generate richer classes of models that also show how relations between data parameters affect system behavior. Such models have shown potential to uncover critical bugs, but their learning algorithms do not scale beyond small and well curated experiments. In this paper, we present SL lambda, an effective and scalable register automata (RA) learning algorithm that significantly reduces the number of tests required for inferring models. It achieves this by combining a tree-based cost-efficient data structure with mechanisms for computing short and restricted tests. We have implemented SL lambda as a new algorithm in RALib. We evaluate its performance by comparing it against SL*, the current state-of-the-art RA learning algorithm, in a series of experiments, and show superior performance and substantial asymptotic improvements in bigger systems.

关键词： Active automata learning Register automata

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical data 3

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Prasanna, V. Suresh Kumar, N. Divyaprabha, P. Sundaram, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Ganesh College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798331528201

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN-GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors' information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python. © 2024 IEEE.

关键词： Recurrent neural networks

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Advanced Fake News Detection in Chinese Using ECL (ERNIE-CNN-LSTM) Model 7

Advanced Fake News Detection in Chinese Using ECL (ERNIE-CNN...

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7th international conference on machine learning and Natural Language Processing, MLNLP 2024

作者： Yu, Bo Shan, Yiyu Abdalla, Hemn Barzan Ihnaini, Baha School of Mathematical Sciences College of Science Mathematics and Technology Wenzhou-Kean University Wenzhou China College of Science Mathematics and Technology Wenzhou-Kean University IAINLP Department of Computer Science Wenzhou China

ISBN: (纸本)9798350354973

With the proliferation of Internet technology and social media platforms, information can be easily exchanged across regions at unprecedented speeds, but this can also reduce the quality of information - especially in an era when false and misleading content spreads like wildfire on social media. False news spreads among people, misleads individuals and causes great harm in politics, economy, public health, and other fields. Despite the challenges of fake news detection and intervention, excellent technical support is provided in this regard thanks to recent advances in big data, natural language processing (NLP), deep learning paradigms, and more. Currently, however, the technology that can be used to determine whether news is true or false has been developed primarily in English - and there is an urgent need to optimize doing this in China at a non-minimal scale and complexity. The motivation and purpose of this study is to achieve the same high accuracy of fake news detection in the Chinese context. This article suggests a high-precision Chinese fake news detection system that innovatively combines the ERNIE 3.0, TextCNN, and BiLSTM models. Each model has a distinct role in our system: ERNIE 3.0 collects sophisticated semantic information using its robust pre-trained language model, which is particularly useful for comprehending Chinese text;TextCNN is good at extracting local features from text and identifying essential patterns. BiLSTM excels in identifying long-distance dependencies and contextual information in text. We employ a progressive training technique, initially training and optimizing the performance of each model independently, and then in the integrated model by weighting the outputs of these independent models so that they learn from one another, hence enhancing overall predictive power. As a result, our model can effectively integrate the benefits of each, improve the complete analysis capability of the complex characteristics of fake news, and i

关键词： Semantics

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Generating Personalized Imputations for Patient Health Status Prediction in Electronic Health Records

Generating Personalized Imputations for Patient Health Statu...

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2024 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Yang Zhang, Weiyu Si, Jiasheng Li, Zhao Peng, Xueping Lu, Wenpeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Evay Info Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China University of Technology Sydney Australian Artificial Intelligence Institute Sydney Australia

ISBN: (纸本)9798350386226

Electronic health records (EHRs) play a crucial role in the development of personalized treatment plans for patients. However, EHRs are often highly incomplete, posing significant challenges for predictive modeling. While existing deep learning models employ various imputation techniques to reconstruct missing values, they often fail to represent missing data in a personalized manner and do not learn from the missingness patterns in EHRs data. This limitation reduces their effectiveness in practical personalized healthcare applications. To address this issue, we propose SPIME, a self-supervised model that generates personalized imputations in EHRs data for patient health status prediction. We introduce a personalized missing mask (PMM) based on the frequency of feature measurements. Additionally, we incorporate a masked imputation task (MIT) loss that minimizes the loss of artificially introduced missing values, thereby enhancing the model's capability to handle missing data. SPIME adopts self-supervised pretraining to learn representations from personalized missing patterns and reconstructs missing data in the latent space. To further enhance representation learning, two independent attention mechanisms are applied separately across the feature and temporal dimensions. Experimental results on two real-world EHRs datasets show that SPIME outperforms existing state-of-the-art methods in predicting in-hospital mortality and decompensation, demonstrating its effectiveness in reconstructing missing data and predicting patient health status. The code will be published at https://***/cling6666/SPIME. © 2024 IEEE.

关键词： Electronic health record

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