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

共 712 条记录，以下是201-210 订阅

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Proceedings - 2023 3rd international conference on Smart data Intelligence, ICSMDI 2023

Proceedings - 2023 3rd International Conference on Smart Dat...

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3rd international conference on Smart data Intelligence, ICSMDI 2023

ISBN: (纸本)9781665464871

The proceedings contain 100 papers. The topics discussed include: automated shopping cart: reducing long queues one cart at a time;a guide towards implementing the effective algorithm for optimum topology in complex terrains;sarcasm detection: a systematic review of methods and approaches;decentralized e-commerce platform implemented using smart contracts;news text analysis using text summarization and sentiment analysis based on NLP;twitter sentiment analysis for bitcoin price prediction;a secure and privacy preserving telehealth solution in fog based environment;sustainable farming community using green marketing;exploring innovative methods for enhancing data security in computing;the social media implications on the sales of business products;a review on the capability and smart contract potential of block chain technology;and tackle outliers for predictive small holder farming analysis.

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LSTM deep learning model for Alzheimer's disease prediction based on cost-effective time series cognitive scores 5

LSTM deep learning model for Alzheimer's disease prediction ...

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5th international IEEE Novel Intelligent and Leading Emerging Sciences conference, NILES 2023

作者： Saleh, Hager El-Rashidy, Nora Abuhmed, Tamer El-Sappagh, Shaker South Valley University Faculty of Computers and Artificial Intelligence Hurghada Egypt Kafrelsheiksh University Faculty of Artificial Intelligence Machine Learning and Information Retrieval Department Egypt Sungkyunkwan University College of Computing and Informatics Korea Republic of Galala University Faculty of Computer Science and Engineering Suez435611 Egypt

ISBN: (纸本)9798350381030

Alzheimer's disease (AD) is a complex chronic neurodegenerative disease that propagates over time. Deep learning (DL) models can be used to learn time series data to extract deep temporal features and make robust decisions. The fusion of multimodal time series data has been proven to enhance model performance [1]. For instance, cognitive scores (CSs) including clinical dementia rating, and Alzheimer's disease assessment scores have been integrated with other modalities like MRI to predict the future status of AD patients [2]. They enhanced the performance of DL models. These scores can be collected easily and in a cost-effective way in hospitals. No study in the literature has built a deep learning model to predict AD based on the cost-effective and multimodal time series data of CSs. In this study, we propose an LSTM-based DL architecture to predict AD based on multiple time series CSs. The proposed model has been optimized using a Bayesian optimizer to select the best architecture, and it has been compared with multiple classical machine learning models like random forest and others. The proposed LSTM architecture achieved better results than other models and provided stable and robust performance. © 2023 IEEE.

关键词： data fusion

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Recognition of Masked Facial Expressions Based on Convolutional Neural Networks and data Augmentation

Recognition of Masked Facial Expressions Based on Convolutio...

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2023 international Annual conference on complex Systems and Intelligent Science, CSIS-IAC 2023

作者： Zhang, Xiao Liu, Yonggang Zhao, Ke Fu, Xiaolan State Key Laboratory of Brain and Cognitive Science Institute of Psychology Chinese Academy of Sciences Beijing China Department of Psychology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350309003

People sometimes intentionally change their facial expressions to mask their real emotions. Currently, there is relatively little research on the recognition of masked expressions. The effective identification of masked expressions helps to identify deception and lies and has important application value. Traditional machine learning methods require the manual design of feature extraction algorithms and have poor performance in facial expression recognition. This study performs masked expression recognition based on convolutional neural networks, including VGGNet, GoogLeNet, ResNet, and MobileNet. data augmentation and regularization are used to improve the performance of the network. The recognition rate of masked expressions has been significantly improved. Using leave-one-subject-out cross-validation, the recognition accuracy is 63.62% for required expressions (6R), 39.97% for experienced emotions evoked by video clips (6E), and 22.11% for 36 mixed expressions (6Ex6R), which is improved by 33.19%, 48.99%, and 96.52% compared to traditional methods, respectively. © 2023 IEEE.

关键词： Emotion Recognition

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Fast SVM-based One-Class Classification in Large Training Sets 9

Fast SVM-based One-Class Classification in Large Training Se...

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9th IEEE international conference on Information Technology and Nanotechnology, ITNT 2023

作者： Kurbakov, M. Yu. Sulimova, V.V. Tula State University Laboratory of Cognitive Technologies and Simulation Systems Tula Russia

ISBN: (纸本)9798350397338

SVM is one of the popular methods to solve One-Class classification problem. However, it is time and space-consuming. This fact makes it hard or even impossible to apply SVM for large training sets. In this paper we propose fast method (One-Class Kernel-based Mean Decision Rule method, OC-KMDR) to find an approximate decision of One-Class SVM problem. The main advantages of the proposed approach are: 1) the obtained decision is near exact (and in a number of cases the method can outperform the original SVM in quality);2) the obtained decision has absolutely the same structure as the original SVM;3) the absence of theoretical restriction for the training set size;4) it can be realized in an iterative manner but without inter-iteration data dependencies, and, as a result, provides the possibility for effective parallel computing. Experimental study of the proposed OC-KMDR-method in series of simulated large data sets shows that it outperforms existing methods for solving One-Class SVM problem in a computing time or(and) in a decision quality. © 2023 IEEE.

关键词： Iterative methods

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data Science with data Mining and Machine Learning A design science research approach

Data Science with Data Mining and Machine Learning A design ...

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2023 international conference on Industry Sciences and Computer Science Innovation, iSCSi 2023

作者： Duque, Jorge Godinho, António Moreira, José Vasconcelos, José ISLA - Instituto Politécnico de Gestão e Tecnologia Vila Nova de Gaia4400-107 Portugal Universidade Lusófona Centro Universitário do Porto Porto4000-098 Portugal COPELABS - Cognitive and People-centric Computing Research Unit Portugal

Access to large amounts of data, provided by continuous technological progress, becomes increasingly important for organisations. The access to data becomes even more useful when managers consult structured information in real time. This requires the adoption of data analysis techniques that, at a strategic level, allow the definition of business goals according to pre-defined objectives, and which help companies in their functional activities. With data Science, data Mining and Machine Learning platforms allow the development of decision support systems, capable of establishing patterns between data and making truly useful predictions for decision support. The methodology used in this work aims to combine data Science methods, such as data Mining and Machine Learning, in order to demonstrate the importance of transforming data into organised and oriented information to support decision making. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Decision support systems

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Design Novel FCM-based Forecasting Model for Interval-valued Time Series: From Distance-metric Perspective 18

Design Novel FCM-based Forecasting Model for Interval-valued...

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18th international conference on Intelligent Systems and Knowledge Engineering, ISKE 2023

作者： Yang, Fei Ouyang, Chenxi Yu, Fusheng Tang, Yuqing Jiang, Yanan School of Mathematical Sciences Beijing Normal University Beijing China

ISBN: (纸本)9798350318401

Predicting interval-valued time series (ITS) has long been a subject that has attracted researchers from a diverse range of fields including economics and finance. Since the data and relations in real world are usually highly sophisticated and inaccurate, modeling real complex systems is a challenging task especially for large scale, inaccurate and non stationary datasets. Fuzzy cognitive Map (FCM) has become a powerful tool for modeling and analyzing complex systems, and been widely used in various fields. As the increasing use of FCM, there is still very little known about implications of selecting the fitness function during the weight training process. Therefore, the purpose of this study is to explore the effects of different distances on the accuracy of learning algorithm in FCM by conducting experiments on different datasets. Findings reveal that the choice of distance indeed impacts on the final performance of the model. Moreover, this paper employs the enhanced overlap distance as the fitness function to optimize the weights. Suitability of this distance has been demonstrated, showcasing a significant advantage in accurately calculating the intervals' distance. © 2023 IEEE.

关键词： Fuzzy cognitive Maps

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EEG Channel Selection Based on Neuron Proportion with SNN for Motor Imagery Classification

EEG Channel Selection Based on Neuron Proportion with SNN fo...

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2023 international conference on Neuromorphic computing, ICNC 2023

作者： Sun, Zhihui Fan, Chaoqiong Jia, Tianyuan Li, Qing Wu, Xia Beijing Normal University School of Artificial Intelligence Beijing100875 China Beijing Normal University State Key Laboratory of Cognitive Neuroscience and Learning Beijing100875 China

ISBN: (纸本)9798350316889

The brain computer interface (BCI) technology based on motor imagery has great potential for various control and communication tasks. However, the presence of a large number of EEG channels leads to redundant information, which affects processing speed and classification accuracy. Spiking neural networks (SNN) have the potential to process EEG data by transmitting pulsing activity between synapses and neurons situated in space. Neucube is an SNN architecture inspired by the human brain structure that allows for end-to-end learning, classification, and understanding of spatiotemporal data at low power consumption, saving computing power and reducing operational complexity. By utilizing this model, the temporal and spatial information of EEG signals can be considered to explore the importance and correlation of spatial neurons corresponding to EEG channels during the classification process. Thus, this study aimed to use the Neucube model based on SNN to select the most influential EEG signal channels in the classification process. This improvement mainly focuses on improving classification accuracy and reducing energy consumption to enhance the practical application performance of BCI systems. The proposed method was tested on the BCI Competition IV dataset 2A. After deleting several unimportant EEG channels, the classification accuracy was improved, and the energy consumption was reduced. © 2023 IEEE.

关键词： Neural networks

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The Construction of Intelligent Classroom for Blended Teaching of cognitive Load and College English Spoken Language Based on data Mining 3

The Construction of Intelligent Classroom for Blended Teachi...

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3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Liu, Lu Wei, Yingying Peng, Nan East University of Heilongjiang Department of Basic Teaching and Research 150060 China

ISBN: (纸本)9798350338607

Building an intelligent classroom that combines cognitive load and college English teaching is a key step in improving students' learning experience. Blended learning combines traditional face-to-face teaching with online learning, creating a more flexible and interactive learning environment. The use of technology in the classroom can help reduce cognitive load and enable students to focus on understanding complex concepts. Intelligent classrooms should be equipped with modern technologies such as interactive whiteboards, projectors, and audio-visual aids to facilitate the delivery of course content. In addition, the use of artificial intelligence can provide tailored feedback and suggestions based on the individual needs of each student, thereby helping them personalize their learning experience. Incorporating blended learning into college English courses can also provide opportunities for real language use through online communication tools, thereby improving language acquisition. This method can help students develop language skills in real life while reducing cognitive load. In short, building an intelligent classroom for blended learning is an important step in improving the quality of higher education. It provides an opportunity to integrate technology into traditional teaching methods, creating a more attractive and effective learning experience for students. © 2023 IEEE.

关键词： Students

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GAMR: A GUIDED ATTENTION MODEL FOR (VISUAL) REASONING 11

GAMR: A GUIDED ATTENTION MODEL FOR (VISUAL) REASONING

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11th international conference on Learning Representations, ICLR 2023

作者： Vaishnav, Mohit Serre, Thomas Artificial and Natural Intelligence Toulouse Institute Université de Toulouse France Carney Institute for Brain Science Dpt. of Cognitive Linguistic & Psychological Sciences Brown University ProvidenceRI02912 United States Centre de Recherche Cerveau et Cognition CNRS Université de Toulouse France

Humans continue to outperform modern AI systems in their ability to flexibly parse and understand complex visual scenes. Here, we present a novel module for visual reasoning, the Guided Attention Model for (visual) Reasoning (GAMR), which instantiates an active vision theory - positing that the brain solves complex visual reasoning problems dynamically - via sequences of attention shifts to select and route task-relevant visual information into memory. Experiments on an array of visual reasoning tasks and datasets demonstrate GAMR's ability to learn visual routines in a robust and sample-efficient manner. In addition, GAMR is shown to be capable of zero-shot generalization on completely novel reasoning tasks. Overall, our work provides computational support for cognitive theories that postulate the need for a critical interplay between attention and memory to dynamically maintain and manipulate task-relevant visual information to solve complex visual reasoning tasks. © 2023 11th international conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Zero-shot learning

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Proceedings of 3rd Eclipse Security, AI, Architecture and Modelling conference on Cloud to Edge Continuum, eSAAM 2023

Proceedings of 3rd Eclipse Security, AI, Architecture and Mo...

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3rd Eclipse Security, AI, Architecture and Modelling conference on Cloud to Edge Continuum, eSAAM 2023, co-located with EclipseCon 2023

ISBN: (纸本)9798400708350

The proceedings contain 12 papers. The topics discussed include: NEMO: building the next generation meta operating system;scalable and portable federated learning simulation engine;cognitive architecture for process industries;on the containerization and orchestration of RISC-V architectures for edge-cloud computing;unikernels motivations, benefits and issues: a multivocal literature review;modular monoliths the way to standardization;tool support for architectural pattern selection and application in cloud-centric service-oriented ides: decision support for architectural pattern selection and application;enabling compute and data sovereignty with infrastructure-level data spaces;and feature estimation for punching tool wear at the edge.

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