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EEG-TCF2Net: A Novel Deep Interval Type-2 Fuzzy Model for Decoding SSVEP in Brain-computer Interfaces

EEG-TCF2Net: A Novel Deep Interval Type-2 Fuzzy Model for De...

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2024 IEEE international conference on systems, Man, and Cybernetics, SMC 2024

作者： Contreras, Marcelo Flores, Christian Andreu-Perez, Javier University of Alberta Node Lab at the Mechanical Engineering Edmonton Canada Utec Department of Mechatronics Engineering Lima Peru Utec Centro Bio Utec and Department of Electrical and Mechatronic Engineering Lima Peru University of Campinas DSPCom Laboratory from the Department of Electrical Engineering Campinas Brazil University of Essex Centre for Computational Intelligence Colchester United Kingdom

ISBN: (纸本)9781665410205

The Steady-State Visual Evoked Potential (SSVEP) is a robust method for creating a fast Brain-computer Interface (BCI);however, the time window of Electroencephalography (EEG) trials has to be reduced to improve the BCI's speed. This reduction leads to a decrease in the Signal-to-noise ratio (SNR), making it more difficult to classify these signals accurately. Conversely, combining Fuzzy Neural Block (FNB) that includes Type-l Fuzzy (T1F) in deep learning architecture has improved classification accuracy over data obtained in noisy environments. However, T1F has limitations in accurately modeling uncertainty and handling complex systems compared to Interval Type-2 Fuzzy (IT2F), which is particularly suitable for applications where robustness, adaptability, and accuracy are crucial. In this work, we proposed a deep learning framework that integrates the FNB using IT2F called FNB- IT2F. It is included parallel to the linear and final layers to assess their effectiveness. Thus, this study presents a unification of EEG- TCNet-LSTM with FNB-IT2F, which we call EEG- TCNet-LSTM-FNB-IT2F (i.e. EEG- TCF2Net). Our results reported a maximum recog-nition accuracy of 51.0% to 76.5% using the proposed method of EEG- TCF2N et in a subject-independent classification across all 10 subjects for 0.2 to 0.5 s time window. Overall, including FNB- IT2F in this deep learning architecture outperformed those without it, as well as baseline methods such as Filter-Bank Canonical Correlation Analysis (FBCCA) [1] and Task-related component analysis (TRCA) [2]. © 2024 IEEE.

关键词： Brain computer interface

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FROM GRAPHS TO HYPERGRAPHS: HYPERGRAPH PROJECTION AND ITS RECONSTRUCTION 12

FROM GRAPHS TO HYPERGRAPHS: HYPERGRAPH PROJECTION AND ITS RE...

引用

12th international conference on Learning Representations, ICLR 2024

作者： Wang, Yanbang Kleinberg, Jon Department of Computer Science Cornell University United States

We study the implications of the modeling choice to use a graph, instead of a hypergraph, to represent real-world interconnected systems whose constituent relationships are of higher order by nature. Such a modeling choice typically involves an underlying projection process that maps the original hypergraph onto a graph, and is common in graph-based analysis. While hypergraph projection can potentially lead to loss of higher-order relations, there exists very limited studies on the consequences of doing so, as well as its remediation. This work fills this gap by doing two things: (1) we develop analysis based on graph and set theory, showing two ubiquitous patterns of hyperedges that are root to structural information loss in all hypergraph projections;we also quantify the combinatorial impossibility of recovering the lost higher-order structures if no extra help is provided;(2) we still seek to recover the lost higher-order structures in hypergraph projection, and in light of (1)'s findings we propose to relax the problem into a learning-based setting. Under this setting, we develop a learning-based hypergraph reconstruction method based on an important statistic of hyperedge distributions that we find. Our reconstruction method is evaluated on 8 real-world datasets under different settings, and exhibits consistently good performance. We also demonstrate benefits of the reconstructed hypergraphs via use cases of protein rankings and link predictions. © 2024 12th international conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Graph theory

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Predict the Heart Disease Using a Logistic Regression Classifier Algorithm 12

Predict the Heart Disease Using a Logistic Regression Classi...

引用

12th international conference on System modeling and Advancement in Research Trends, SMART 2023

作者： Bhuvana, R. Maheshwari, S. Sasikala, S. Agurchand Manmull Jain College Department of Computer Science India

ISBN: (纸本)9798350369861

Machine learning (ML), a field of computer science, enables computer systems to interpret data similarly to how people do. Machine learning (ML) is an artificial intelligence technique that use an algorithm or technique to identify patterns in unstructured data. Making it possible for computers to learn from experience without explicit programming or human supervision is the main objective of machine learning (ML). Using well-labeled training data that has been used to train the machines, supervised learning is a type of machine learning in which the output is predicted by the computers. The phrase 'labelled data' describes input data that has already been given the proper output. One of the most common diseases affecting people today is cardiovascular disease, which can result in a variety of medical problems. Significant morbidity, mortality, and monetary loss are all results of it. If this disease were promptly recognized and predicted, patients might have the opportunity to employ the appropriate prophylactic and treatment measures. To better understand risk factors, we predict heart diseases using a supervised learning method as a logistic regression model. © 2023 IEEE.

关键词： Medical problems

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Factors Influencing Psychological Distress Mediated by E-Learning Courses in Times of Covid-19

Factors Influencing Psychological Distress Mediated by E-Lea...

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international conference on Communication and Applied Technologies (ICOMTA)

作者： Gutierrez-Aguilar, O. Ticona-Apaza, F. Ticona-Apaza, V. Chicana-Huanca, S. Catholic Univ Santa Maria Arequipa Peru Natl Univ San Agustin Arequipa Peru

ISBN: (纸本)9789811963469;9789811963476

The main objective of this research is to determine the influence of factors associated with psychological distress, such as the personal impact of e-learning, depression, and computer anxiety on the student, mediated by the courses in the methodology of e-learning in times of Covid-19. The methodology used for the study corresponds to a non-experimental investigation;the questionnaire was applied to a convenience sample of 397 university students (n = 41;a = 0.971. = 0.971), using validity and reliability tests, exploratory and confirmatory factor analysis through structural equation modeling of partial least squares (PLS-SEM). The results have shown a statistically significant effect between the student's computer anxiety and psychological distress, e-learning and depression and e-learning courses. While e-learning and depression establish, a statistically significant relationship in the individual impact of e-learning and psychological distress;while there is a significant influence relationship between e-learning and depression and the individual impact of e-learning and psychological distress. On the contrary, e-learning and depression do not influence, according to the study, e-learning courses;likewise, the individual impact of e-learning on psychological distress.

关键词： computer anxiety Covid-19 Depression E-learning Individual impact PLS-SEM Psychological distress

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Clinical Text Analysis with Natural Language Processing: A BERT-based Approach

Clinical Text Analysis with Natural Language Processing: A B...

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2024 international conference on Communication, computer Sciences and Engineering, IC3SE 2024

作者： Tiwari, Shashank Shekhar Goyal, Himanshu Rai Adnan, Myasar Mundher Shrivastava, Anurag Yadav, Kanchan Rao, A.L.N. Department Of Information Technology Rec U.P Ambedkar Nagar India Dr. Abdul Kalam Technical University Lucknow India Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Tamilnadu Chennai India Gla University Department Of Electrical Engineering Mathura India Lloyd Institute Of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350366846

This research investigates the transformative potential of Natural Language Processing (NLP) procedures, especially utilizing a BERT-based approach, for the comprehensive investigation of unstructured clinical content. The ponder centres on three key assignments: Named Entity Recognition (NER), Clinical Content Classification, and Subject modeling, each contributing to an all-encompassing understanding of accounts inside electronic health records. The BERT-based algorithm illustrates predominant execution, accomplishing exactness, review, and F1-score values of 0.87, 0.89, and 0.88, individually, near an amazing AUC-ROC score of 0.94. The NER calculation shows accuracy and review values of 0.92 and 0.88, highlighting its adequacy in recognizing and classifying substances in clinical content. The Clinical Content Classification algorithm, fine-tuned with BERT, achieves precision, recall, and F1-score values of 0.85, 0.87, and 0.86, with an AUC-ROC score of 0.92. The Subject modeling calculation, based on Latent Dirichlet Allocation (LDA), accomplishes a coherence score of 0.75, advertising profitable bits of knowledge into latent themes inside clinical accounts. This research contributes to the headway of data-driven healthcare by displaying the flexibility and viability of NLP strategies in extracting significant data from complex clinical content information. © 2024 IEEE.

关键词： Natural language processing systems

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Advances in Machine Learning-driven Quality Control within ERP systems: A Comprehensive Survey 11

Advances in Machine Learning-driven Quality Control within E...

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11th IEEE international conference on Intelligent Computing and Information systems, ICICIS 2023

作者： Qaddoori Aldoori, Shaymaa Ahmed Mabrouk, Mohamed Mahmoud, Abeer M. Cairo Egypt Faculty of Computer and Information Science Computer Science Department Cairo Egypt

ISBN: (纸本)9798350322101

Enterprise Resource Planning (ERP) systems have become essential instruments in contemporary corporate operations, offering significant benefits in process optimization and efficient data management. The assurance of quality and dependability in these intricate systems is of utmost significance given that errors or inconsistencies might yield extensive ramifications. This study highlights the importance of quality control within ERP systems, emphasizing the crucial role of accuracy and integrity in facilitating efficient decision-making processes and optimizing operational efficiency. This analysis explores the increasing importance of upholding ERP quality as firms become increasingly dependent on these systems to traverse intricate business environments. In addition, this study explores the capacity of machine learning to enhance quality control procedures. Machine learning techniques, owing to their ability to examine large datasets and identify subtle patterns, provide a transformative shift away from the traditional rule-based approaches. Machine learning enhances the capacity to anticipate problems, reduce interruptions, and maintain consistent operational performance by employing real-time anomaly detection, predictive modeling, and adaptation to shifting ERP settings. This study explores the complex confluence of technology and business imperatives, highlighting the transformative impact of the collaboration between machine learning and quality control on the evolution of ERP systems. © 2023 IEEE.

关键词： Decision making

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MICM: Rethinking Unsupervised Pretraining for Enhanced Few-shot Learning 24

MICM: Rethinking Unsupervised Pretraining for Enhanced Few-s...

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32nd ACM international conference on Multimedia, MM 2024

作者： Zhang, Zhenyu Chen, Guangyao Zou, Yixiong Huang, Zhimeng Li, Yuhua Li, Ruixuan School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

ISBN: (纸本)9798400706868

Humans exhibit a remarkable ability to learn quickly from a limited number of labeled samples, a capability that starkly contrasts with that of current machine learning systems. Unsupervised Few-Shot Learning (U-FSL) seeks to bridge this divide by reducing reliance on annotated datasets during initial training phases. In this work, we first quantitatively assess the impacts of Masked Image modeling (MIM) and Contrastive Learning (CL) on few-shot learning tasks. Our findings highlight the respective limitations of MIM and CL in terms of discriminative and generalization abilities, which contribute to their underperformance in U-FSL contexts. To address these trade-offs between generalization and discriminability in unsupervised pretraining, we introduce a novel paradigm named Masked Image Contrastive modeling (MICM). MICM creatively combines the targeted object learning strength of CL with the generalized visual feature learning capability of MIM, significantly enhancing its efficacy in downstream few-shot learning inference. Extensive experimental analyses confirm the advantages of MICM, demonstrating significant improvements in both generalization and discrimination capabilities for few-shot learning. Our comprehensive quantitative evaluations further substantiate the superiority of MICM, showing that our two-stage U-FSL framework based on MICM markedly outperforms existing leading baselines. The repository of this project is available at https://***/iCGY96/MICM. © 2024 ACM.

关键词： Contrastive Learning

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Tendencies and Challenges of Artificial Intelligence Development and Implementation 12

Tendencies and Challenges of Artificial Intelligence Develop...

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12th IEEE international conference on Intelligent Data Acquisition and Advanced Computing systems: Technology and Applications, IDAACS 2023

作者： Kondratenko, Yuriy Shevchenko, Anatoliy Zhukov, Yuriy Kondratenko, Galyna Striuk, Oleksandr Petro Mohyla Black Sea National University Mykolaiv54003 Ukraine Institute of Artificial Intelligence Problems Kyiv01001 Ukraine C-Job Nikolayev Mykolaiv54006 Ukraine

ISBN: (纸本)9798350358056

This paper discusses the problems and challenges of artificial intelligence (AI) implementation at the modern computer science and information technology development stage. The authors analyze the types of AI, tendencies, and current state of AI development as well as the challenges of AI implementation in the various fields of human activity. The paper focus on the National strategies of different countries directed to AI development for industry, agriculture, space exploration, education, medicine, military, automation of design practice in robotics, construction, machine-building and shipbuilding, planning and optimization of cargo transportation, etc. The AI's challenges are discussed in detail, including (a) AI's strong influence on the world's labor market shortly, (b) the ethical problems and dangers of AI implementation, and (c) the authors' perspective proposals and ethical approaches, including the structure of new-generation computer systems based on artificial intelligence and AI multi-software methodology for designing and 3D modeling. © 2023 IEEE.

关键词： AI multi-software design methodology AI-new-generation computer artificial intelligence challenges development ethical approach implementation tendencies

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Survey on Personalized Voice Assistant 6

Survey on Personalized Voice Assistant

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6th IEEE international conference on Recent Advances in Intelligent Computational systems, RAICS 2024

作者： Srikrishnan, S. Pushparaj, Ashwin Suresh, Advaith Nair, Akash Krishnan George, Teena Sharika, T.R. Adi Shankara Institute of Engineering and Technology Kalady Department of Computer Science and Engineering Kerala India

ISBN: (纸本)9798350381689

This survey reviews the current state of personalized voice assistant systems that utilize natural language processing, speech recognition, and AI techniques. It examines methodologies for developing intelligent assistants that can securely access local data, learn preferences, and control IoT devices via voice. The paper analyzes the capabilities, advantages, and limitations of existing voice assistant solutions. It also explores their integration with web technologies and discusses challenges like privacy, speech accuracy, and language modeling constraints. The goal is to provide an overview of personalized voice assistant technologies, their potential impact, and future research directions toward enabling seamless voice-based interactions. © 2024 IEEE.

关键词： Speech recognition

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Economical Quaternion Extraction from a Human Skeletal Pose Estimate using 2-D Cameras 9

Economical Quaternion Extraction from a Human Skeletal Pose ...

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9th IEEE international conference on Electronics, Computing and Communication Technologies, CONECCT 2023

作者： Radhakrishna, Sriram Balasubramanyam, Adithya P.E.S University Banashankari Department of Computer Science and Engineering Bangalore India Center for Internet of Things Department of Computer Science and Engineering Pesu Ring Road Campus Bangalore India

ISBN: (纸本)9798350334395

In this paper, we present a novel algorithm to extract a quaternion from a two dimensional camera frame for estimating a contained human skeletal pose. The problem of pose estimation is usually tackled through the usage of stereo cameras and inertial measurement units for obtaining depth and euclidean distance for measurement of points in 3D space. However, the usage of these devices comes with a high signal processing latency as well as a significant monetary cost. By making use of MediaPipe, a framework for building perception pipelines for human pose estimation, the proposed algorithm extracts a quaternion from a 2-D frame capturing an image of a human object at a sub-fifty millisecond latency while also being capable of deployment at edges with a single camera frame and a generally low computational resource availability, especially for use cases involving last-minute detection and reaction by autonomous robots and similarly modelled intelligent transport systems. The algorithm seeks to bypass the funding barrier and improve accessibility for robotics researchers involved in designing control systems. © 2023 IEEE.

关键词： Cameras

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