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Classification of Attention Performance Post-Longitudinal tDCS via Functional Connectivity and Machine Learning Methods 9

Classification of Attention Performance Post-Longitudinal tD...

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9th ieee international conference for Convergence in Technology, I2CT 2024

作者： Rao, Akash K. Menon, Vishnu K. Bhavsar, Arnav Chowdhury, Shubhajit Roy Negi, Ramsingh Dutt, Varun Indian Institute of Technology Mandi Applied Cognitive Science Laboratory Mandi India Indian Institute of Technology Mandi School of Computing And Electrical Engineering Mandi India Institute of Nuclear Medicine And Allied Sciences Cognitive Control And Machine Learning Group Delhi India

ISBN: (纸本)9798350394474

Attention is the brain's mechanism for selectively processing specific stimuli while filtering out irrelevant information. Characterizing changes in attention following long-term interventions (such as transcranial direct current stimulation (tDCS)) has seldom been emphasized in the literature. To classify attention performance post-tDCS, this study uses functional connectivity and machine learning algorithms. Fifty individuals were split into experimental and control conditions. On Day 1, EEG data was obtained as subjects executed an attention task. From Day 2 through Day 8, the experimental group was administered 1mA tDCS, while the control group received sham tDCS. On Day 10, subjects repeated the task mentioned on Day 1. Functional connectivity metrics were used to classify attention performance using various machine learning methods. Results revealed that combining the Adaboost model and recursive feature elimination yielded a classification accuracy of 91.84%. We discuss the implications of our results in developing neurofeedback frameworks to assess attention. © 2024 ieee.

关键词： Electroencephalography

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Large Scale Image data Processing and Content Recognition Based on Deep Learning 3

Large Scale Image Data Processing and Content Recognition Ba...

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3rd international conference on data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Wan, Ruoyan Wei, Xiuzhuo Ma, Rui Zhao, Huinan Wu, Feng Changchun Humanities and Sciences College Jilin Changchun130000 China

ISBN: (纸本)9798331519032

As the cornerstone of computer vision (CV), image recognition is of great importance. It not only profoundly affects daily life fields such as facial recognition, intelligent security, and medical image analysis, but also plays a key role in breakthroughs in high-tech fields such as autonomous driving technology, intelligent robots, and industrial inspection. With the vigorous rise of artificial intelligence (AI) technology, especially the rapid development of deep learning (DL) technology, image recognition has ushered in unprecedented changes. This article focuses on the latest applications of DL in the field of image recognition and proposes an innovative large-scale image data processing and content recognition algorithm. This algorithm effectively captures complex features in images by constructing deep neural network models, and continuously optimizes model parameters using massive training data, thereby improving recognition accuracy and significantly enhancing the algorithm's ability to understand image content. The experimental results show that the algorithm exhibits excellent performance in processing large-scale image datasets, with fast recognition speed and industry-leading recognition accuracy, laying a solid foundation for the further application of image recognition technology. © 2024 ieee.

关键词： Medical robotics

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Semantic Reasoning and Knowledge Discovery in Biomedical Informatics Using Domain-Specific Ontologies 2

Semantic Reasoning and Knowledge Discovery in Biomedical Inf...

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2nd ieee international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Aishwarya, Bk Sharma, Smita Revathi, V. Singh, Navdeep Kumar, Ashwani Alwan, Adil A. Institute of Aeronautical Engineering Dundigal Hyderabad India Amity University Greater Noida Amity School of Engineering and Technology Uttar Pradesh India New Horizon College of Engineering Department of Applied Sciences Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Uttar PradeshKnowledge Park II Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350384277

The introduction of domain-specific ontologies is revolutionizing the rapidly developing area of Biomedical Informatics by improving semantic reasoning and knowledge discovery. Using these ontologies, a complete framework for the extraction, organization, and analysis of complicated biological data is presented in this research. The framework provides deeper insights into biomedical research and healthcare practices by interpreting and connecting enormous arrays of heterogeneous data via the use of web technologies and sophisticated data mining methods. Accurate interpretation of biological concepts and their connections is made possible by semantic reasoning, which is supported by the structured representation of domain-specific ontologies. This leads to improved query performance, more precise data retrieval, and the development of complex inferential processes. The study also looks at how ontology-based data mining may be used to find new links and patterns, which might help with predictive modelling and the creation of hypotheses in biomedical research. The implementation obstacles are also discussed, such as scalability issues, data quality assurance, and ontology alignment and integration. The approach's major contributions to drug development, personalized medicine, and the larger field of biomedical research are highlighted in the paper's conclusion, which will eventually lead to better healthcare results and well-informed decisions. © 2024 ieee.

关键词： Biomedical Informatics data Mining Domain-Specific Ontologies Knowledge Discovery Semantic Reasoning

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Ma-HGNN+: A Hypergraph Neural Network for Depth Analysis of Multimodal data 2

Ma-HGNN+: A Hypergraph Neural Network for Depth Analysis of ...

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2nd ieee international conference on Medical Artificial Intelligence, MedAI 2024

作者： Mei, Zhangyu Bi, Xiao Wen, Yating Kong, Xianchun Wu, Hao Shenzhen Research Institute School of Software Shandong University Shenzhen China School of Mathematics Shandong University Jinan China Traditional Chinese Medicine Hospital Department of Dermatology Chongqing Chongqing China Heze Municipal Hospital Department of Pediatric Surgery Heze China School of Software Shandong University Jinan China

ISBN: (纸本)9798350377613

The topic of data processing has experienced major advancements due to the emergence and rapid development of convolutional neural networks (CNNs). As the complexity and interconnectivity of real-world data continue to expand, there is a growing interest in mining deep relationships between the nodes in complex networks. Despite the substantial efforts and promising results achieved by prior studies, there is still a need to thoroughly leverage the intricate interconnections of data at higher order, particularly in the context of correlated and multi-modal information. Therefore, we propose Ma-HGNN+, a novel multimodal hypergraph neural network designed to comprehensively analyze the datasets containing multimodal data. The Ma-HGNN+ framework leverages the advantages of similarity within modal data and the modeling of high-order data correlation in hypergraphs to enhance data analysis, explore interconnections, and predict categories for similar data. Additionally, we introduce an attention mechanism module during the training process, which effectively utilizes high-order data correlation and crucial data. Evaluations on multiple real-world datasets demonstrate that Ma-HGNN+ outperforms existing state-of-the-art methods. Ma-HGNN+ is available at ***/HaoWuLab-Bioinformatics/Ma-HGNN. © 2024 ieee.

关键词： Convolutional neural networks

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AI-enabled digital twins: soft computing methods for industrial data mining 3

AI-enabled digital twins: soft computing methods for industr...

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3rd international conference on Intelligent computing and Next Generation Networks, ICNGN 2024

作者： Kunelbayev, Murat Amirkhanov, Bauyrzhan Zauranbek, Madiyar Abdulkhamit, Nazargozha Adilzhanova, Saltanat Nurgazy, Tomiris Artificial Intelligent and Big Data department Al-Farabi Kazakh National University Almaty Kazakhstan Cybersecurity and cryptology department Al-Farabi Kazakh National University Almaty Kazakhstan Artificial Intelligence and Big Data department Al-Farabi Kazakh National University Almaty Kazakhstan

ISBN: (纸本)9798331529222

Digital twins (DTs) have become a critical constituent in the continuous digital evolution of industrial systems, enabling instantaneous insight, anticipatory analytics, and tactical enhancement across various sectors. This paper provides an extensive examination of DT principles, historical progressions, market trends, enabling technologies, fields of application, economic impacts, modeling methodologies, challenges, and prospective developments. Special emphasis is placed on the food industry, which exemplifies how DTs can enhance traceability, ensure quality assurance, and build resilience in complex global supply chains. Based on a wide-ranging review of the scholarly literature and industrial analysis, this work highlights how DTs integrate IoT data, AI-driven models, cloud/edge computing, and robust engineering data management systems into cohesive decision-support frameworks. The result is a powerful skill that stimulates continuous improvement, goals toward sustainability, and strategic visioning in dynamic industrial settings. ©2024 ieee.

关键词： Supply chains

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Custom Accessors: Enabling Scalable data Ingestion, (Re-)Organization, and Analysis on Distributed Systems

Custom Accessors: Enabling Scalable Data Ingestion, (Re-)Org...

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2024 ieee international conference on Big data, Bigdata 2024

作者： Castellana, Vito Giovanni Mutlu, Burcu O. Dio Lavore, Ian Di Firoz, Jesun Wolf, Katherine Minutoli, Marco Feo, John Pacific Northwest National Laboratory RichlandWA United States Politecnico di Milano Milano Italy

ISBN: (纸本)9798350362480

The emerging class of high velocity and high volume data analytic workflows comprise interwoven data ingestion, organization, and processing stages, with ingestion and organization steps often contributing comparable or even higher computational costs than actual processing steps. Since complex workflows consist of a variety of phases that view and use data differently, being able to construct efficient, scalable, distributed data structures (arrays, vectors, sets, maps, and multi-maps) is essential and requires custom methods to extend and shrink containers, analyze and position data, and, maintain globally-consistent meta-data. In this paper, we propose a novel data-structure access paradigm based on the concept of Accessors. At a high level, accessors are customizable callable objects that can modify the behavior of insert, read, update, and delete operations for distributed containers while preserving atomicity guarantees. Accessors provide a very clean and natural way to implement a variety of programming patterns, e.g., conditional insertion/deletion and cascading computations, which would be otherwise hard (or even impossible) to express in parallel and distributed settings without using locks. We demonstrate the practicality and usefulness of our approach with two representative use cases and study the performance of these applications on a distributed High-Performance computing system. Our analysis highlights that our proposed abstraction allows for an effective overlapping and concurrent execution of different workflow steps (e.g., data ingestion and analysis), which in a conventional analytics pipeline would execute sequentially, contributing cumulatively to the overall latency. © 2024 ieee.

关键词： C++ (programming language)

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Enhancing Employee Motivation Through AI: The Role of Corporate Culture in the Workplace

Enhancing Employee Motivation Through AI: The Role of Corpor...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Prakash, S. Pavithrashree, S. Gokul, S. Karthick Raja, K. Dr.SNS Rajalakshmi College of Arts and Science Department of Commerce Tamil Nadu India

ISBN: (纸本)9798331543617

Motivating the employees is a vital factor for the success of an organization but measuring motivation is challenging, especially with the conventional tools and techniques that do not capture current situations and flexible working organizational environments. Presumably, this paper came up with an AI model that incorporates machine learning algorithms with corporate culture measures to estimate employees' motivation levels. The model relies on the IBM HR Analytics dataset that focuses on employee behavior and feedback data, for performance records. Approaches used herein are Principal Component Analysis for feature extraction and XGBoost and Logistic Regression for modeling. The proposed model obtained an accuracy of 90.5%, 89.2% precision, and an AUC of 0.92, surpassing typical models. Also, the system analyzed different employees and labeled them as high-risk employees with an accuracy level of 87.5%. The findings also show how AI can boost employee motivation by offering precise information on their requirements. Future research will involve applying the model with more substantial datasets and making the model more suitable for complex working contexts. © 2024 ieee.

关键词： data accuracy

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Object Detection and Categorization in complex Scenarios Using Deep Learning

Object Detection and Categorization in Complex Scenarios Usi...

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2024 international conference on Computer and Applications, ICCA 2024

作者： Chowdhury, Rayan Upadhyaya, Vikas Niit University Neemrana India

ISBN: (纸本)9798350367560

Images, which represent 2D depictions of 3D objects in our environment, are naturally decoded and categorized by the human neural network. In an effort to emulate this cognitive process, we employ machine learning algorithms, a subset of artificial intelligence focused on neural network training. This research work specifically explores the use of the YOLOv5 model in image identification and classification, aiming to enhance efficiency. Our investigation identifies key challenges such as intricate geometries, image resolution, real-time processing, environmental variations, and price considerations. Through phases involving data collection, model training, system integration, and assessment of performance, the research work successfully achieves proficient object categorization across diverse scenarios. These findings not only validate the approach's viability but also highlight areas for potential optimization and future exploration. © 2024 ieee.

关键词： Adversarial machine learning

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Application and Performance Analysis of Physics-Informed Neural Networks in Solving complex Physical Systems 2

Application and Performance Analysis of Physics-Informed Neu...

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2nd ieee international conference on Electrical, Automation and Computer Engineering, ICEACE 2024

作者： Chen, Xuyang University of California Letters and Science DavisCA United States

ISBN: (纸本)9798350368208

This study introduces a quantum-accelerated framework that integrates quantum optimization with physics-informed neural networks (PINNs) to solve PDEs more efficiently. By leveraging the Quantum Approximate Optimization Algorithm (QAOA), the proposed framework enhances the initialization and boundary parameter optimization of PINNs, addressing key challenges such as slow convergence and high computational cost. The hybrid approach combines the data efficiency and physical constraint embedding capabilities of PINNs with the computational advantages of quantum optimization, enabling faster and more accurate solutions for complex PDEs. The framework was validated using the Shuhao Cao PDE dataset, demonstrating significant improvements in training efficiency and accuracy compared to traditional numerical solvers and classical PINNs. Experimental results highlight that quantum optimization effectively accelerates convergence by optimizing initial conditions and boundary constraints, reducing the computational overhead associated with classical training processes. Despite its reliance on emerging quantum hardware and the sensitivity of PINNs to hyperparameter selection, this approach offers a scalable and robust solution for solving high-dimensional PDEs. The findings underscore the potential of hybrid quantum-classical methods in advancing scientific computing, providing a foundation for future research in stochastic PDEs, multi-physics problems, and other real-world applications. © 2024 ieee.

关键词： Stochastic systems

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Impacts of High Denisty Cloud computing on data Protection and Security Management for 6G Networking 2

Impacts of High Denisty Cloud Computing on Data Protection a...

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2nd international conference on Disruptive Technologies, ICDT 2024

作者： Mallikarjunaradhya, Vinay Yennapusa, Haritha Palle, Ranadeep Reddy Suganyadevi, K. Gupta, Nidhi Legal Tech Platform Thomson Reuters Toronto Canada University of Central Missouri Department of Computer Science AustinTX United States University of Houston Department of Computer Science AustinTX United States Sri Eshwar College of Engineering Department of Ece Tamil Nadu Coimbatore India G.L. Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

data protection and security management in the 6G era is a key challenge due to the emergence of high density cloud computing. The availability of high density cloud computing technologies creates a platform for massive data storage, processing, and analytics capabilities at a scale that has never been seen before. High density cloud computing facilitates the deployment of complex distributed systems consisting of hundreds or thousands of nodes in a single deployment. This can have a significant impact on data protection and security management as it increases the potential threat to the system from malicious actors. To protect data in this environment, organizations must adopt more advanced security practices such as access control, encryption, and defensive layers to protect against malicious actors. The use of encryption can provide a layer of security by making it difficult for outsiders to access information without authorization. Access control can also prevent unauthorized users from accessing certain parts of the system, while defensive layers can help detect and block malicious attacks. The results of this study showed that high density cloud computing can potentially improve the efficiency and performance of data protection and security management in 6G networks. © 2024 ieee.

关键词： 6G Networking Cloud data Storage data Protection Network Reliability Network Security Security Management

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