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检索条件"任意字段=2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023"

共 711 条记录，以下是91-100 订阅

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data FUSION cognitive computing FOR CHARACTERIZATION OF MECHANICAL PROPERTY IN FRICTION STIR WELDING PROCESS

DATA FUSION COGNITIVE COMPUTING FOR CHARACTERIZATION OF MECH...

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ASME international Design Engineering Technical conferences / 43rd Annual Computers and Information in Engineering conference (IDETC-CIE)

作者： Hoang, Danny Chen, Ruimin Mishra, Debasish Pal, Surjya K. Imani, Farhad Univ Connecticut Dept Mech Engn Storrs CT 06269 USA Univ Connecticut UTC Inst Adv Syst Engn Storrs CT 06269 USA Indian Inst Technol Kharagpur Dept Mech Engn Kharagpur W Bengal India

ISBN: (纸本)9780791887295

High intricacy of the friction stir welding (FSW) process and severe plastic deformation typically result in serious part quality concerns, including thinning, groove, and kissing bond. These defects, in turn, impact mechanical properties (e.g., Young's modulus and yield strength), thereby deteriorating the performance of a joint part. Machine learning methods such as feature-based support vector machines and end-to-end deep neural networks are recently coupled with in-situ sensing to link process parameters with the final properties, yet the low accuracy and precision, poor sample efficiency, and lack of interpretable learning limit their capabilities for reliable characterization. This research introduces hyperdimensional cognitive computing (HCC) that mimics human brain functionalities to fuse power, torque, and force data to provide robust, sample-efficient, and explainable learning for process-property characterization in FSW. data augmentation is integrated with HCC to tackle the imbalanced data problem. The experimental results show that HCC seamlessly fuses in-situ data to predict the ultimate tensile strength with an F1-score of 0.899 +/- 0.048, which is 21.579%, 47.779%, 1.933%, and 68.139% better than support vector machine, logistic regression, Naive-Bayes and multilayer perceptron, respectively. Besides the superior prediction capability, HCC symbolic representation reveals that measured force is the critical factor in the characterization of ultimate tensile strength. The proposed HCC is shown to be effective for data fusion and single-pass learning and eliminates the necessity of costly and long retraining in various manufacturing processes such as 3D printing and bio-fabrication.

关键词： Friction stir welding cognitive computing data fusion Property characterization

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5G geological data for seismic inversion data detection based on wide-angle reflection wave technology

5G geological data for seismic inversion data detection base...

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international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Thiyagesan, M. Irfan, B.Md. Raman, Ramakrishnan Ponnarasi, N. Ramakrishnan, P. Senthil, G.A. Department of Electrical and Electronics Engineering R.M.K. Engineering College Kavaraipettai Tamil Nadu Chennai India Department of Information Technology Nalsar University of Law Telengana Hyderabad India Symbiosis Institute of Business Management Pune and Symbiosis International Deemed University Maharashtra Pune India Department of Electronics and Communication Engineering Jkk Nattraja College of Engineering and Technology Komarapalam Tamil Nadu India Department of Electronics and Communication Engineering M. Kumarasamy College of Engineering Tamil Nadu Karur India Department of Information Technology Agni College of Technology Tamil Nadu Chennai India

ISBN: (纸本)9781032669663

With the development of Geophysics theory, inversion technology has stepped from post stack inversion to pre stack seismic inversion which can reflect abundant reservoir parameter information, and the resolution of conventional deterministic inversion method cannot meet the needs of current exploration and development. Mesozoic and Paleozoic carbonate layers are more difficult to image. For this reason, we detect seismic inversion data of complex geologic structures using wide-angle reflected wave technology. A comparative analysis of real seismic data and inversion simulation data in the South Yellow Sea area verifies that effective and interfering waves can be distinguished with the help of inversion simulation data under complex geological tectonic conditions. This will take into account the factors affecting the imaging accuracy ofMesozoic and Paleozoic marine carbonate layers and lead to the improvement of acquisition parameters in the hard-imaging area of deep target layers in the southern Yellow Sea. To improve the quality of parameters of seismic data. © 2024 The Author(s).

关键词： Seismic waves

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Advancements in AI-Assisted Plant Disease Diagnosis 4

Advancements in AI-Assisted Plant Disease Diagnosis

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4th IEEE international conference on computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Chhabra, Anmol Saroch, Tarun Gupta, Ruchika Shukla, Aditya Raghav Chandigarh University Centre of Excellence-Data Science and Cognitive Computing Computer Science & Engineering Department Mohali India

ISBN: (纸本)9798350306118

Plant disease detection is a crucial task in agriculture to ensure healthy crop production. It is vital to identify plant diseases early in order to avert economic and environmental damages. A Machine learning-based approach including Deep Learning techniques for identifying plant disease was developed using Grape leaf images in this work. Inception v3, Squeezenet, VGG-16, and VGG-19 are some embedders that are used in the suggested method to convert raw image data into a fixed-length vector representation, also known as a 'embedding' or 'feature vector.' Our research concludes that CNN stands out with exceptional accuracy above 99.70% compared to classifiers like Naive Bayes, Logistic Regression, and SVM. Additionally, Inception V3 proved to be an efficient feature extractor, enhancing feature representation and reducing processing time. © 2023 IEEE.

关键词： Embeddings

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Understanding the Impact of Longitudinal VR Training on Users with Mild cognitive Impairment Using fNIRS and Behavioral data 31

Understanding the Impact of Longitudinal VR Training on User...

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IEEE conference on Virtual Reality and 3D User Interfaces (VR)

作者： Qu, Jing Zhu, Shantong Shen, Yiran Zhang, Yanjie Bu, Lingguo Shandong Univ Sch Software Jinan Peoples R China Shandong Univ Joint SDU NTU Ctr Artificial Intelligence Res C F Jinan Peoples R China Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China

ISBN: (纸本)9798350374025;9798350374032

With the growing needs on rehabilitation of the mild cognitive impairment (MCI) users group and the advantages of virtual reality (VR) technologies in cognitive training, the development of VR-based rehabilitation training methods has become a hot spot recently. However, the challenges in accurately measuring users' needs and quantifying training system efficacy are still not well resolved, especially for longitudinal tracking. In this study, a VR-based cognitive training and evaluation system is designed and implemented, targeting at fulfilling the rehabilitation needs of MCI users. It evaluates the impact of longitudinal VR-based training on MCI users with a number of feedback methodologies including brain activation indicators, brain network connectivity indicators, behavioral indicators and the Montreal cognitive Assessment (MoCA) scale scores, extracted from multi-modal data collected while training. A two-month longitudinal tracking ergonomics experiment was conducted to validate the usability of the feedback methodologies and to explore the influence of the training duration on the rehabilitation efficacy. The results showed that our proposed VR-based cognitive training and evaluation system had a positively significant impact on the rehabilitation of the MCI group. Meanwhile, the multi-source feedbacks can also help the updates and iterations of VR-based rehabilitation training systems. Finally, this study provides guidance for the selection of rehabilitation cycles and emphasizes the importance of quantitative studies with longitudinal follow-up in assessing rehabilitation efficacy.

关键词： Human-centered computing-Human computer interaction (HCI)-HCI design and evaluation methods-User studies Applied computing-Life and medical sciences-Health care information systems Human-centered computing-Interaction design-Empirical studies in interaction design

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Certain Investigations on cognitive based Movie Recommendation system using Pairwise Cosine Similarity 9

Certain Investigations on Cognitive based Movie Recommendati...

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9th international conference on Advanced computing and Communication Systems, ICACCS 2023

作者： Sumathi, S. Madhumitha, S. Sushma, R. Suriya, M. Subitsha, M. Swetha, S. Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India Sri Eshwar College of Engineering Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350397376

The Internet serves as a major source in various sectors to communicate and share data. It is being used by every individual across the world for several purposes. The main objective of the recommender system is to provide users with suitable products and information. The movie recommendation system also helps in providing customized decisions to the user, thus improving the experience of customer or user. Content-based filtering manipulates item attributes to suggest other items that are relevant to user likes, this suggestion is made using past actions of customers or users. Cosine Similarity is an evaluation metric that helps in finding how similar the items are regardless of their dimensions. The proposed study, furnish users with suggestions that rest on the prevalence and category of a movie. Content based recommendation system is built using cosine similarity. The movie recommendation system has its separate algorithms for making suggestions by examining users' past data. Many attributes are considered like rating of each movie, name of the director and genre of the film. The system make recommendation by considering one attribute or mixture of attributes. For the proposed system, two different datasets used namely, the credits dataset and the movie dataset. The analysis of data is done with help of Python. © 2023 IEEE.

关键词： Python

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Dilated convolution model for lightweight neural network 2nd

Dilated convolution model for lightweight neural network

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2nd international conference on Next-Generation computing and Information Systems, ICNGCIS 2023

作者： Gupta, Sachin Goyal, Priya Vyas, Bhuman Shabaz, Mohammad Bala, Suchitra Sameen, Aws Zuhair Department of CSE Maharaja Agrasen Institute of Technology India Department Computer Science and Engineering Manipal University Jaipur India Credit Acceptance Canton United States Model Institute of Engineering and Technology J&K Jammu India ICT & Cognitive Systems Sri Krishna Arts And Science College India College of Medical Techniques Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9781032738659

Lightweight convolutional neural networks are being studied further in order to better deploy deep convolutional neural networks to edge devices, minimize the number of model parameters in deep neural networks, and reduce network complexity. On accomplish the lightweight goal, the full convolution is performed to the remaining network structure. To increase the light effect, it is merged with point-by-point convolution to build an improved dilated convolution lightweight approach. The enhanced dilated convolution is coupled with conventional convolution to lessen the attenuation of accuracy, and a lightweight fusion dilated convolution is presented. The improved dilated convolution light approach offers the most substantial lightweight benefit, according to the data. Furthermore, the lightweight fusion dilated convolution approach decreases the amount of model parameters and offers the best speed-accuracy trade-off. © 2025 The Author(s).

关键词： Convolution

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cognitive and Emotional Monitoring with Inexpensive Wrist-Worn Consumer-Grade Wearables

Cognitive and Emotional Monitoring with Inexpensive Wrist-Wo...

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2023 IEEE international conference on Pervasive computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

作者： Wu, Yunan Valdez, Roxana Forlines, Clifton Northwestern University EvanstonIL United States Bowdoin College BrunswickME United States Northeastern University PortlandME United States

ISBN: (纸本)9781665453813

High-cost and physical-limitations limit the use of Electroencephalogram sensing (EEG) with respect to Affective computing. Wrist-worn Wearable health devices, an intriguing alternative, are becoming cheap, inconspicuous, and common-place - driving down the cost of heart-rate, accelerometer, respiration, and similar sensing. In this paper, we describe a quantitative empirical study investigating the use of low-cost sensing as a replacement for EEG. Our results show significant correlation between wrist-sensed, low-cost physiological data features and established EEG measures of emotion and cognition. This work demonstrates the value of inexpensive sensors capable of inferring emotion and cognition in a wrist-worn form-factor. © 2023 IEEE.

关键词： Electroencephalography

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Digital Twin Space: The Integration of Digital Twins and data Spaces

Digital Twin Space: The Integration of Digital Twins and Dat...

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2023 IEEE international conference on Big data, Bigdata 2023

作者： Somma, Alessandra Benedictis, Alessandra De Zappatore, Marco Martella, Cristian Martella, Angelo Longo, Antonella University of Naples Federico II Naples Italy University of Salento Lecce Italy

ISBN: (纸本)9798350324457

Digital Twins (DTs) are the novel paradigm for the development of Cyber-Physical systems. The state of art presents several use cases in different domains, and one of the most complex examples is represented by the Urban Digital Twin (UDT), which aims to virtualize urban assets (e.g., buildings, mobility infrastructures, energy grids, waste management facilities, etc.) and build advanced analysis and prediction services upon a city's digital representation. UDTs represent a formidable example of system of systems, as they are structured into a hierarchy of interconnected DT instances that process and share a huge amount of data subject to different access and usage *** data management in complex distributed scenarios, data Spaces are an emerging paradigm that aims at building a secure and privacy-preserving infrastructure to pool, access, share, process and use data. As a matter of fact, existing DTs solutions (not only in the urban domain) do not present a clear software architecture characterization and, moreover, they pay little attention to the data management *** bridge this gap, in this paper we present the Digital Twin Space, an architectural proposal that aims to instantiate data Spaces into DTs according to the guidelines set by relevant international projects. We use the UDT as a running example and we validate the model in the case of a smartPV panel, showing that the model matches the real cyber-physical system. © 2023 IEEE.

关键词： data Spaces Digital Twin architectures international data Space architecture Urban Digital Twins

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2023 international conference on Evolutionary Algorithms and Soft computing Techniques, EASCT 2023

2023 International Conference on Evolutionary Algorithms and...

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2023 international conference on Evolutionary Algorithms and Soft computing Techniques, EASCT 2023

ISBN: (纸本)9798350313413

The proceedings contain 274 papers. The topics discussed include: application method for highway traffic flow prediction and speed optimization based on BP neural network;singular value decomposition (SVD) based optimal image compression technique;hybrid optimization algorithm for effective clustering algorithm and routing protocol in MANET;an optimized deep learning technique for enhance disease identification in tomato leaf to promote sustainable agriculture;LungVision in humans-an ensemble method of classification of cancer in lungs;SE-Resnet152 model: early corn leaf disease identification and classification using feature based transfer learning technique;advanced educational assessments: automated question classification based on bloom’s cognitive level;mobile application for M-learning among students in higher education;a comprehensive analysis of chronic health diseases using big data;and the influence of photovoltaic grid connection on power quality of power grid and control technology based on big data.

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Hyperdimensional computing with Spiking-Phasor Neurons 23

Hyperdimensional Computing with Spiking-Phasor Neurons

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2023 international conference on Neuromorphic Systems, ICONS 2023

作者： Orchard, Jeff Jarvis, Russell University of Waterloo Canada International Centre for Neuromorphic Systems Australia

ISBN: (纸本)9798400701757

A Vector Symbolic Architecture (VSA) is a powerful framework for representing compositional reasoning. It can be used to create neural networks that perform cognitive functions, like spatial reasoning, arithmetic, symbol binding, and logic. But the vectors involved can be quite large, hence the alternative label Hyperdimensional (HD) computing. In this paper, we describe a number of simple neuron models that implement all the operators of a Fourier Holographic Reduced Representation (FHRR, a type of VSA). These spiking phasor neurons represent the phase of a complex number using spike timing within a cycle. We demonstrate the capabilities of these models on a series of VSA queries, including a state transition model, and spatial memory. This spiking version of FHRR paves the way for efficiently running a variety of VSA networks on neuromorphic hardware. © 2023 ACM.

关键词： Neural networks

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