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检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

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Textionnaire: An NLP-Based Questionnaire Analysis Method for complex and Ambiguous Task Decision Support 21

Textionnaire: An NLP-Based Questionnaire Analysis Method for...

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21st ieee international conference on cognitive Informatics and cognitive computing, ICCI*CC 2022

作者： Rashidiani, Sajjad Doyle, Thomas E. Samavi, Reza Duncan, Laura Pires, Paulo Sassi, Roberto McMaster University Department of Electrical and Computer Engineering Hamilton Canada School of Biomedical Engineering McMaster University Hamilton Canada Vector Institute for Artificial Intelligence Toronto Canada Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering Toronto Canada McMaster University Department of Psychiatry and Behavioural Neurosciences Hamilton Canada University of British Columbia Department of Psychiatry Vancouver Canada

ISBN: (纸本)9781665490849

Questionnaires are one of the most popular means of gathering information in many domains. Traditional health surveys, particularly those focusing on specific populations of patients or certain conditions tend to have sample sizes that are too small to apply advanced artificial intelligence methods. In this study, we introduce a method that enables researchers to apply deep learning algorithms on small questionnaire datasets. We introduce a method to represent the information of fixed response questions of questionnaires in text format analogous to a word presentation for a number stored in expanded form. Then we use a pre-Trained, domain-relevant deep learning model for questionnaire classification. We applied this method to two databases: i) a hospital-maintained database of mental health questionnaires linked with hospital administrative resource uti-lization data, and ii) a similarly structured publicly available dataset, the National Survey on Drug Usage and Health. In all experiments, the deep pre-Trained model had a better Area Under Receiver Operating Characteristic Curve (AUROC) than a 3-layer artificial neural network (ANN). The proposed method enhanced classification performance by 22.95 % on Mental Health private data and 16.679% on small sub-sample of NSDUH data compared to a baseline ANN machine learning algorithm. Also, our technique improved the performance by up to 15.38% when tested for generalizability of classification. © 2022 ieee.

关键词： Natural language processing systems

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Knowledge-Enhanced Contextual Auction Design Based on Deep Neural Networks

Knowledge-Enhanced Contextual Auction Design Based on Deep N...

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cognitive computing and complex data (ICCD), international conference on the

作者： Mingxuan Liang Zhibo Guo Junwu Zhu Yuanyuan Zhang Xueqing Li Department of Information Engineering Yangzhou University Yangzhou China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

The optimal auction represents a fundamental challenge in auction design, primarily focused on maximizing the seller’s revenue. Recently, context-aware auction design has gained traction as it enhances the model’s understanding of the auction environment by incorporating contextual information about buyers and items. However, limited contextual information can hinder the model’s ability to capture potential correlations. Therefore, we propose a Knowledge-Enhanced Contextual Auction Design Model (KECA) based on neural networks. KECA aims to improve the comprehensive understanding of the auction environment by integrating external knowledge. Specifically, we first construct a structured knowledge graph of triples using Wikipedia. This knowledge graph is utilized to enhance the contextual feature representation of buyers and items, thereby providing a more comprehensive description of the information in the auction environment. Additionally, we employ the K-means clustering algorithm to enhance feature fusion between auction information and the knowledge graph within the locally sensitive hashing self-attention mechanism, thereby improving the model’s feature representation capabilities. Finally, extensive experimental results indicate that KECA outperforms current mainstream auction models under various settings, verifying the effectiveness of this approach.

关键词： Correlation Computational modeling Knowledge based systems Knowledge graphs Encyclopedias Real-time systems data models Internet Online services Context modeling

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Person Re-identification Method Based on Video Spatial Feature Enhancement

Person Re-identification Method Based on Video Spatial Featu...

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cognitive computing and complex data (ICCD), international conference on the

作者： Zunwang Ke Guozhi Sun Run Guo Minghua Du Yugui Zhang School of Software Xinjiang University Urumqi China Department of Dermatology Guang'anmen Hospital China Academy of Chinese Medical Sciences Beijing China Department of Emergency First Medical Center Chinese PLA General Hospital Beijing China Institute of Semiconductors Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Recently, video-based person re-identification has received more and more attention, and has played a very important role in public safety fields such as security monitoring and public security criminal investigation. However, it is still a great challenge for existing work to effectively overcome the occlusion problem and extract more abundant pedestrian spatial feature information. To tackle the problem of occlusion of pedestrians in real scenes, this paper proposes a person re-identification method based on video spatial feature enhancement. This method enhances the spatial information in video frames by using multi-angle feature aggregation of time attention and extracts continuous pedestrian detail features by using multi-frame spatial feature splicing of time domain information, so as to solve the difficult problem of pedestrian occlusion recognition. Experiments show that the proposed method has improved performance on MARS and DukeMTMC-VideoReID datasets, which verifies the effectiveness of the proposed method. The source code is available at https://***/sgzhi11/Video-Spatial-Feature-Enhancement.

关键词： Pedestrians Splicing Source coding Feature extraction Public security data mining Security Time-domain analysis Monitoring Identification of persons

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Quantitative Analysis of GPT-4 model: Optimizing Patient Eligibility Classification for Clinical Trials and Reducing Expert Judgment Dependency 24

Quantitative Analysis of GPT-4 model: Optimizing Patient Eli...

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8th international conference on Medical and Health Informatics, ICMHI 2024

作者： Devi, Arti Jha, Sarthak Punekar, Rajeshwari S. Uttrani, Shashank Dasgupta, Nataraj Pickett, Larry A. Singla, Aryansh Natarajan, Sayee Dutt, Varun Applied cognitive science laboratory Indian Institute of Technology Mandi Mandi175005 India Syneos Health Bengaluru122002 India Syneos Health MorrisvilleNC27560 United States

ISBN: (纸本)9798400716874

Objective: Generative Pre-trained Transformer 4 (GPT-4) is a large multimodal language model created by OpenAI and the fourth in its series of GPT foundation models. Although GPT-4 has been utilized in several applications, its abilities are less known for patient categorization based on their eligibility for clinical trials. The primary objective of this work is to evaluate the accuracy and efficacy of GPT-4 for patient eligibility evaluation. data: Ten US NSCLC drug-only interventional clinical trials were selected from ***. Ten patient profiles were manually created for each clinical trial using case presentations published in peer-reviewed medical journals by clinicians/epidemiologists. The dataset included two sets of adult patient profiles (50 eligible patients and 50 non-eligible patients for 100 patients) with a range of complexities, from complex to simple cases. The 100-case dataset was then analyzed, comparing the accuracy of the large language model—GPT-4, against the human expert’s evaluation. Analysis: Various data tuning scenarios (80% and 0%) were evaluated, explicitly examining the model’s capacity to mimic the performance of human experts in classifying patient eligibility. Model evaluations were compared to human evaluations to ensure reliable accuracy *** measure efficacy, age analysis, gender analysis, and sensitivity and specificity analyses were conducted, providing a comprehensive examination of the model’s performance across various dimensions. Results: GPT-4 showed 100% test accuracy in scenarios involving tuning in 80% of cases and 95% test accuracy without tuning in 80% of cases. GPT-4 showed © 2024 Copyright owner/author(s) ACM.

关键词： Zero-shot learning

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Alzheimer's Diseases Detection by Using Xception Deep Neural Network with data Augmentation

Alzheimer's Diseases Detection by Using Xception Deep Neural...

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2024 international conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Sivakumar, Soubraylu Bharadwaj, R. Abhichandra, S. SRM Institute of Science and Technology Computing Technologies School of Computing Chennai India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

In our modern age, amidst the whirlwind of progress and change, there exists a silent epidemic that transcends time and technology, affecting individuals and families in profound ways: Alzheimer's disease. This illness, which slowly causes memory loss and difficulty thinking, reminds us how delicate our minds are and how complex brain health can be. This work focuses on leveraging deep learning algorithms with data augmentation to enhance the accuracy of AD detection. The study examines four cutting-edge deep learning models: Xception, InceptionV3, EfficientNetB3, and ResNet50. Robust feature extraction from MRI images is accomplished by using ResNet50, which is renowned for its deep design with skip connections. EfficientNetB3, with its efficient design and high accuracy, contributes to effective classification of AD stages. InceptionV3's multi-level feature extraction capabilities are utilized to capture intricate patterns indicative of AD progression. Xception's depth wise separable convolutions enhance pattern recognition, aiding in precise disease detection. Utilizing deep learning algorithms, notably Xception, and employing data augmentation techniques, our study attained an exceptional accuracy of 97.18% in detecting Alzheimer's Disease, utilizing the "Alzheimer's Diseases Detection"dataset. In order to improve early diagnosis and treatment planning for Alzheimer's patients, the research intends to give a thorough examination of these deep learning methods in AD detection. © 2024 ieee.

关键词： Deep learning Accuracy data augmentation Feature extraction Robustness Classification algorithms Planning Pattern recognition Alzheimer's disease Residual neural networks

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Research on the Teaching Mode of Industrial Robots under the Modern Industrial College Model

Research on the Teaching Mode of Industrial Robots under the...

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cognitive computing and complex data (ICCD), international conference on the

作者： Yuefei Wang Jiaye Li Yudong Fang Junqi Luo School of Mechanical and Marine Engineering Beibu Gulf University Qinzhou China School of Education Beibu Gulf University Qinzhou China School of Electronic and Information Engineering Beibu Gulf University Qinzhou China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Modern industrial college is one of the achievements of the innovation of higher education organization in recent years, and it is an important hand to deepen the integration of industry with education and promote collaborative education. As one of the main courses in the industrial robot application direction of automation, the industrial robot practice course has a direct impact on the employment of graduates in this direction. Includes three parts: offline simulation, industrial robotics physical practice, and project innovation practice. However, different from the traditional practical training experimental courses, industrial robotics practice on the strict environmental requirements, industrial robots required supporting equipment is difficult to meet the diversified needs of the project, the practical project and the needs of enterprises do not match and other issues have been plagued by universities to carry out the relevant practical courses. Therefore, relying on the industrial robotics training course of Beibu Gulf University, we propose the design and realization of an industrial robotics practical education model based on the PDCA cycle theory to give fully to students' autonomy and to improve the collaborative ability. Relying on the needs of enterprises to realize the continuous iteration of practical projects, give full play to the utilization rate of practical training equipment, and enhance the professional and technical capabilities of graduates in the direction of industrial robot application.

关键词： Training Industries Hands Technological innovation Service robots Computational modeling Collaboration Organizations Industrial robots data models

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cognitive Observation of Multidimensional Stochastic Fractal Structures of complex Systems as a Tool of XAI

Cognitive Observation of Multidimensional Stochastic Fractal...

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international conference on Soft computing and Measurements (SCM)

作者： Vladimir L. Gorokhov Irina A. Brusakova Stanislav I. Shirokov Saint Petersburg Electrotechnical University “LETI” St. Petersburg Russia St. Petersburg Branch of the Special Astrophysical Observatory of the Russian Academy of Sciences St. Petersburg Russia

ISBN: (数字)9798350363708

ISBN: (纸本)9798350363715

The methods and means of cognitive dynamic projection of fractal stochastic structures of complex systems are considered. These tools can be used as an explanatory tool for interpreting the results of artificial intelligence (AI) systems working on fractal multidimensional data. In addition to the explanatory effect, the proposed methods can provide quantitative estimates of multidimensional statistical relationships in terms of non-additive statistics (such as: Rainier or Tsalis entropy, conditional concentration of the fractal process, spectrum of fractal dimensions) inherent in both the ideology of multifractal analysis and the ideology of deep learning.

关键词： Deep learning Explainable AI Mechanical variables measurement Fractals Entropy complex systems

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The Impact of Advanced Neural Network Architectures for Extracting Elusive Information 3

The Impact of Advanced Neural Network Architectures for Extr...

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3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Kachhoria, Renu Mittal, Love Divakaran, Prakash Singh, Vinay Thangarasu, N. Rajarajeswari, S. Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Computer Science Aligarh India Himalayan University Department of Bussiness & Management Itanagar India Usha Martin University Department of Coputing & Information Technology Ranchi India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai India

ISBN: (纸本)9798350319125

Advanced Neural community Architectures, consisting of Convolution Neural Networks (CNNs), have emerged as powerful solutions to demanding situations in extracting complicated and elusive information from large, high-dimensional datasets. CNNs have effective feature extraction abilities that may be used to identify subtle patterns, traits, and relationships in those datasets. but, effective architectures must be tailored to the particular nature of the hassle that allows you to maximize the potential of these fashions. CNNs were used effectively in some of domain names to discover styles in facts that could otherwise remain hidden. As an example, CNNs have been used inside the medical domain to quickly and accurately become aware of cardiologic patterns in electrocardiograms. Similarly, they have been used within the security domain to perceive capability threats earlier than they occur. In each domain names, the capability to extract cognitive facts from numerous datasets speedy and appropriately has saved lives and avoided failures. The exercise of the use of superior neural architectures to permit statistics extraction is complex and requires thoughtful layout. The structure must recall the traits of the project, which include the records kind, the range of dimensions concerned, and the amount of noise or outliers, to determine which strategies are maximum suitable for every specific utility. © 2023 ieee.

关键词： architectures cognitive Convolution particular strategies

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Learning Based Opportunistic data Transmission for cognitive IoT

Learning Based Opportunistic Data Transmission for Cognitive...

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Social computing and Networking (SocialCom), ieee international conference on

作者： Li Li Xiaoxiong Zhong School of Computer Science Shenzhen Institute of Information Technology Shenzhen China Tsinghua Shenzhen International Graduate School Shenzhen China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331521097

ISBN: (纸本)9798331521103

As the rapid development of communication technology, more and more smart devices will be connected in cognitive radio Internet of Things (CIoT) using an opportunistic manner. However, the dynamicity and heterogeneity of spectrum will make it more difficult in data transmission strategy design for unreliable IoT. Opportunistic routing exploits the broadcast property of wireless medium to improve network performance in an opportunistic communication manner. Inspired by this, in this paper, we propose a Nash-learning and Transfer Learning based Opportunistic data Transmission scheme, NTLODT, for differentiated services CIoT, which exploits Nash-learning for candidate set optimization and transfer learning for data transmission decision from a trust mechanism perspective. In addition, we prove that the convergence and complexity of the proposed algorithm. The simulation results demonstrate that the effectiveness of NTLODT in terms of average energy cost per bit, average delay, expected cost of routing and throughput.

关键词： Performance evaluation Social computing Costs Simulation Transfer learning Routing Throughput data communication Smart devices Optimization

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Navigating the Digital Landscape: Enhancing Service Discovery and Portability in Digital Marketing Strategies 23rd

Navigating the Digital Landscape: Enhancing Service Discover...

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23rd international conference on Next Generation Wired/Wireless Networks and Systems (NEW2AN) / 16th conference on Internet of Things and Smart Spaces (RuSMART)

作者： Djasurovna, Ergashxodjaeva Shaxnoza Obitovich, Kurolov Maksud Akramovich, Axmedov Ikrom Tashkent State Univ Econ Dept Mkt I Karimov St 49 Tashkent 100066 Uzbekistan

ISBN: (纸本)9783031609961;9783031609978

In today's fast-paced digital landscape, businesses are compelled to embrace digital marketing strategies to reach and engage their target audiences effectively. The evolution of digital marketing presents both opportunities and challenges, as organizations strive to maintain brand consistency and user engagement across diverse online platforms and channels. This research delves into the dynamic realm of digital marketing, with a primary focus on service discovery and portability as crucial components of contemporary marketing strategies. The study addresses several central research questions, investigating the evolution of digital marketing, the critical factors influencing service discovery, and the strategies for ensuring seamless portability across platforms. It explores how emerging technologies such as artificial intelligence, data analytics, and cloud computing are transforming the digital marketing landscape. Additionally, the research seeks to uncover the potential advantages and disadvantages of integrating these technologies into marketing practices. The significance of this research lies in its potential to guide businesses in optimizing their digital marketing efforts. By enhancing service discovery and portability, organizations can augment customer engagement, brand visibility, and maintain a competitive edge in the ever-evolving digital ecosystem. To achieve these objectives, the research utilizes a comprehensive methodology, combining qualitative and quantitative approaches. Case studies, surveys, interviews, and data analysis provide valuable insights, offering practical recommendations for businesses to adapt their digital marketing strategies. This research thus serves as a valuable resource for organizations navigating the complex digital marketing landscape, equipping them with the knowledge and strategies needed to thrive in the digital age.

关键词： Digital Marketing Service Discovery Portability Emerging Technologies Mobile-Centric Strategies data Privacy User Engagement

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