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Neural encoding of multidimensional handwriting movements for brain-computer interfaces 24

Neural encoding of multidimensional handwriting movements fo...

引用

11th international conference on Biomedical and Bioinformatics engineering, ICBBE 2024

作者： Wang, Zebin Xu, Guangxiang Wang, Yueming Hao, Yaoyao The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China Nanhu Brain-computer Interface Institute Hangzhou China

ISBN: (纸本)9798400718274

Neural representations of handwriting persist even years after paralysis, which was previously employed to build high performance brain-computer interfaces (BCI) for brain-to-text communication. However, handwriting was traditionally viewed as two-dimensional (2D) movements, ignoring other key kinematic and kinetic dimensions. How brain encodes these additional dimensions during handwriting and the potential of this information to enhance BCI performance were largely unexplored. Here, we recorded intracortical neural signals from a paralyzed individual engaged in handwriting imaginary and tried to align these signals with the multidimensional handwriting movements from healthy subjects. We achieved successful decoding of neural signals from the paralyzed individual, enabling reconstruction of 2D handwriting trajectories with high accuracy. We then characterized handwriting from heathy subjects as multidimensional movements which include 3D velocity of pen tip, pen grip strength, pen tip pressure on the paper, and 8-channel electromyography (EMG) on forearm. Employing a neural encoding model, we found that more variance of the neural signals could be accounted by these additional variables, suggesting brain encodes handwriting in multiple dimensions rather than solely in 2D. Together, these results shed light on the complex neural mechanisms underlying handwriting, offering new insights into the potential for improving related BCI applications. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Electromyography

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Brain Tumour Detection using MRI Images 1

Brain Tumour Detection using MRI Images

引用

1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Jayashree Mallick, Ahmed Faraz Das, Ashutosh Singh, Abhiraj Shivaprakash, T. Preethi Salian, K. Nitte Meenakshi Institute of Technology Dept of Computer Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Dept of Computer Science and Engineering India Vijaya Vittala Institute of Technology Dept of Computer Science and Engineering Bangalore India Nmamit Dept of Information Science and Engineering Nitte Udupi India

ISBN: (纸本)9798331529246

An essential diagnostic technique for brain tumors, Magnetic Resonance Imaging (MRI) enables early detection and improved patient outcomes. However, manual interpretation of MRI scans can be time-consuming and subject to human error. Deep learning has revolutionized medical image analysis by providing automated and accurate classifications, significantly enhancing diagnostic processes. This study leverages the pre-trained Convolutional Neural Network (CNN) model Xception to classify MRI brain data, demonstrating its effectiveness in identifying brain tumors. The model was trained and validated using a diverse dataset, achieving a high test accuracy of 98.78% and training accuracy of 99.96%. These findings highlight the potential of the Xception model in supporting clinical decision-making and reducing diagnostic workloads. The study contributes to the field by showcasing Xception's implementation, dataset preparation, model design, training, and evaluation. Furthermore, it emphasizes the importance of using deep learning techniques for reliable and efficient brain tumor classification, setting the groundwork for future advancements in automated diagnostic systems. © 2025 IEEE.

关键词： Convolutional neural networks

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Texture- and Symptom-Based Coffee Leaf Disease Classification Using CNN and ResNet algorithms 4

Texture- and Symptom-Based Coffee Leaf Disease Classificatio...

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4th international conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Surendran, R. Sree, C. Pranitha Sumathy, K. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Saveetha Institution of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798350364729

This work aims to improve plant disease prediction through the implementation of an intriguing and revolutionary model that combines refined algorithms for machine learning with predictive analytics strategies. The goal is to strengthen utilities' resilience by providing precise and trustworthy forecasts of the potential classification of diseases. This study aims to push the limits of present prediction skills, analyze cutting-edge technology, and test unique features to improve the accuracy and timeliness of plant pathogen prophecies significantly. The ultimate goal in a rapidly evolving technological environment is to contribute to the development of proactive and effective mitigation techniques that reduce the impact of electrical glitches on equipment, infrastructure, and system-wide consistency. Materials required: The core database for the present investigation was obtained from the reputable data-gathering and analytics website Kaggle. The dataset includes a wide range of line voltages and currents for various failure scenarios. In Group 1, the convolutional neural network approach was applied. The statistical power of the 48 sample sizes utilized in the study was 0.8, the alpha value was 0.05, and the beta value was 0.2. ResNet is used by Group 2. Clinical is the method used to compute these figures. To run the accurate prediction software, Google Colab is employed. It analyzes data and displays accuracy as a percentage and in a graphical format for simple interpretation. Result: The convolutional neural network technique yielded an accuracy of 78.65% percent for Group 1, while the ResNet strategy yielded an accuracy of 70.61% for Group 2. SPSS data analysis program is employed for data set statistical analysis and correctness. A two-tailed test is used to appraise to what extent there is a statistically significant discrepancy in accuracy between both sets of data. Conclusion: Convolutional neural networks are more accurate than ResNet algorithms. © 2024 I

关键词： Convolutional neural networks

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software engineering Experiences of an Optimist 6th

Software Engineering Experiences of an Optimist

引用

6th international School on engineering Trustworthy software Systems, SETSS 2024

作者： He, Jifeng Tongji University Shanghai China

This edited transcript records a keynote address at the SETSS 2024 Workshop. It provides some thoughts on formal software engineering approach in the light of experience and recent developments such as Artif... 详细信息

ISBN: (纸本)9789819646555

关键词： software design

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An Automated Grading System for Competition Theses Assisted by NLP Models 24

An Automated Grading System for Competition Theses Assisted ...

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8th international conference on Electronic Information Technology and computer engineering, EITCE 2024

作者： Xue, Sijuan Duan, Xu Yang, Manqi College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

As the digital transformation of education continues to advance, the inefficiency and subjectivity of traditional manual scoring methods have become increasingly prominent. To address this issue, this study developed an automated scoring process. The process includes several key steps: reading text files, training latent Dirichlet allocation (LDA) topic models, matching texts with conditional sentence libraries, and performing natural language processing (NLP) analysis. The scoring method used in this study follows the standards proposed by advocates of text scoring conditions to comprehensively evaluate the completeness and accuracy of the text. The entire paper is comprehensively evaluated from four dimensions: completeness, content, summary quality, and writing ability. © 2024 Copyright held by the owner/author(s).

关键词： Natural language processing systems

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Nozzle design and flow field analysis of low recoil gun 3

Nozzle design and flow field analysis of low recoil gun

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3rd international conference on Machine Learning and computer Application, ICMLCA 2022

作者： Liang, Zhengang Shan, Junyu Yue, Shuai Du, Zhonghua Shenyang Ligong University Liaoning Shenyang China Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9781510664814

Based on the actual engineering requirements, this paper designs a nozzle for low recoil gun to reduce recoil. Based on the empirical formula, the nozzle throat diameter, outlet diameter, convergent section angle and divergent section angle of the nozzle are numerically calculated. Then the flow field at the nozzle outlet is simulated and analyzed by Fluent software. The maximum thrust of the nozzle is 70,000 N, and the total impulse is 267487.9935N·s, which is 31.9% less than that of the same type gun without nozzle, reaching the design requirements. The temperature at the outlet of the nozzle is 1330K, and the average temperature at the transition zone is about 775K. This nozzle can meet the application requirements. © 2023 SPIE.

关键词： Flow fields

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computer NETWORKS CYBER SECURITY VIA AN INTRUSION DETECTION SYSTEM

COMPUTER NETWORKS CYBER SECURITY VIA AN INTRUSION DETECTION ...

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2023 IEEE international conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Latha, J. KarthickMurugan, S. Logalakshmi, A. Department of Computer Science and Engineering R.M.K. Engineering College India Department of Computer Science and Engineering R.M.D. Engineering College India Department of Computer Science and Engineering Thiruthangal Nadar College India

ISBN: (纸本)9798350305708

Effectively identifying computer network attacks is still a challenge. This is due to recent attempts by cybercriminals to spoof intrusion detection systems (IDS) by altering packet contents. Additionally, the computer networks constantly add a large number of additional devices. Additionally posing a security risk to computer networks are these novel gadgets. To regulate computer network flows and offer security beforehand, you must do a thorough examination of the elements of your network, including the IDSs, the methodologies and devices used, particular kinds of attacks, and the tools that are used. This essay discusses intrusion detection methods, methodology, and tactics in addition to exploring innovative attack types, defenses, and current scientific study in the subject. © 2023 IEEE.

关键词： IDS methodologies IDS technologies IDS tools Intrusion detection system

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Enhancing Metaverse using Quantum Technologies

Enhancing Metaverse using Quantum Technologies

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2024 international conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Doshi, Jash Shaikh, Sanober Sultana Thadomal Shahani Engineering College Dept.of Computer Engineering Mumbai India Thadomal Shahani Engineering College Dept.of Artifical Intelligence and Data Science Mumbai India

ISBN: (纸本)9798350388800

Recently, metaverse technology has seen a boom in users. However, data privacy, security, connectivity, and computational complexity are still the main issues that ruin the user experience and make the metaverse less likely to be used. In this context, we propose a 3-layer quantum-enabled architecture system for the metaverse that holds the promise of overcoming these issues. We also cover the applications of the metaverse, the current challenges faced by the metaverse, and the exciting potential of Quantum Computing to revolutionize the metaverse. © 2024 IEEE.

关键词： Quantum computers

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System for Managing Online Crime and Enhancing Public Services 1

System for Managing Online Crime and Enhancing Public Servic...

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1st IEEE international conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, P. Mary, J. Josphin Dhivyabharathi, D. Nakra, Monica Thongam Awasthi, Hritik Kumar, Rupendra Chandigarh India Meenakshi A.H.E.R Dept. of Computer Science Chennai India Dhanalakshmi Srinivasan College of Engineering Dept. of CSE Coimbatore India Lingayas Vidyapeeth Dept. of Psychology Faridabad India Chandigarh University Dept. of CSE Chandigarh India J.S. University Dept. of Computer Science Shikohabad India

ISBN: (纸本)9798350356816

An Online Crime Management System has been developed to enhance the effectiveness and efficiency of criminal investigations and data management for law enforcement agencies. This web-based platform provides multiple access levels, including administrative roles for police officers who can review and analyze user reports, and functionalities for users to report crimes, manage cases, track and analyze evidence, and communicate with other participants in investigations. The system is designed to securely and efficiently handle criminal investigation data, featuring a user-friendly interface that facilitates easy navigation and information retrieval. The platform requires specific technical specifications for both hardware and software to ensure optimal performance. Overall, the Online Crime Management System represents a valuable tool for law enforcement agencies, improving the management and execution of criminal investigations. © 2024 IEEE.

关键词： Crime

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SO(3) Equivariant Framework for Spatial Networks

SO(3) Equivariant Framework for Spatial Networks

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2024 IEEE international conference on Big Data, BigData 2024

作者： Xia, Tian Aykent, Sarp Microsoft CambridgeMA United States Auburn University Computer Science and Software Engineering Department AuburnAL United States

ISBN: (纸本)9798350362480

Representation learning on spatial networks is emerging as a distinct area in machine learning and is attracting much attention in diverse domains. Some applications include molecular graphs for drug discovery and screening, brain networks for neuroscience, social networks for recommender systems, etc. However, most proposed approaches either use only spatial or network data that cannot distinguish certain types of graphs and limit their network expressivity or are tied to specific input data domains and network architectures. To address this gap, we introduce an equivariant message-passing network architecture that simultaneously leverages the spatial and network properties. In addition, we propose to take advantage of the geometric representations and extend the classical scalar features with 3D vectors. To exploit the spatial and network features, we present a Spatial Vector Neuron, which can be easily incorporated into the existing graph neural network architectures and allow the model to scale up by stacking more layers for larger receptive fields. A comprehensive set of experiments on both synthetic and real-world datasets demonstrate the strength of our proposed method and the potential of geometric representation learning for Spatial Networks. © 2024 IEEE.

关键词： Adversarial machine learning

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