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

共 715 条记录，以下是511-520 订阅

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Vehicle to Vehicle Communication Using cognitive Radio Technique

Vehicle to Vehicle Communication Using Cognitive Radio Techn...

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international conference on Advanced computing and Communication Systems (ICACCS)

作者： P Vinoth Kumar R. Sri Sangeetha Priya S S. Sivaranjani Mahalakshmi R Pragatheshwaran N Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Electrical and Electronics Engineering Sri Eswar College of Engineering Coimbatore India Department of Electrical and Electronics Engineering AMET Deemed to be University Chennai India

Vehicle to Vehicle (V2V) correspondence is achieved by sending information with the nearby vehicles to prevent mishaps. This is achieved by the communication of vehicle information on travel to each other with smart techniques. In this study, intellectual cognitive radio technology is used to prevent information crashes and channel destruction. The vehicle operator may receive a warning to reduce the risk of an accident depending on how the framework is designed. In order to obtain vehicle information, each vehicle can identify the closest vehicle among all the directions. The prototype communicates other vehicle by cognitive radio (CR) and this technique offers better recognition intellectually to separate each vehicle to keep away from interference so that the exact data is sent between vehicles without noise and false information. The information like speed, temperature, and vehicle braking ability is collaborative between each other vehicles are achieved in this work. The working is examined with MATLAB simulation to analyze the spectral range and data transfer range between the vehicles. The working performance and simulation results are verified with the hardware results.

关键词： Technological innovation Simulation Vehicular ad hoc networks Transportation Prototypes Switches Turning

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Cloud Technology Impact on Human Behavior Based on cognitive Theory

Cloud Technology Impact on Human Behavior Based on Cognitive...

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Electrical, Computer and Energy Technologies (ICECET), international conference on

作者： Drajad Wiryawan Basil Rattanto Muhammad Shabir Shaharudin Joni Suhartono Adele Bernadet Lingkan Mailangkay Stefanus Rumangkit School of Information Systems Bina Nusantara University Jakarta Indonesia School of Management Universiti Sains Malaysia Penang Malaysia School of Management Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

The relationship between cognitive Theory and Cloud Technology is a learning stage for humans, especially technology users, in various innovations used by users in modeling reasoning to develop human direction toward a better future. It includes learning on a large scale, such as objectively coordinated thinking, which provides for decision-making, social interaction, and access to various technological devices used. The above also focuses on human familiarity with various distributed computing services in cloud technology, including PaaS, SaaS, and IaaS. This research used a systems literature review model for over 20 years (2000 – 2023). Meanwhile, the applications used are Vos Viewer and Publish or Perish. Due to the author's limited access to IEEE, ACM, and WoS, the database used and the focus of the literature is the Scopus database. In five years (2019–2023), the writing purpose accounted for only 55 articles, with 39 explicitly addressing cognitive theory and human behavior in the context of cloud technology. In this research, five factors influence human behavior with cloud technology: social interaction, access and connectivity, problem-solving, decision-making, and data confidentiality security. However, of the five impacts entered and obtained from the papers and journals studied, the most critical findings from this research are social interaction, data privacy and security, and decision-making. People will use cloud technology for social interaction, to protect and maintain the confidentiality of their data, and to make various decisions related to their activities. This research also shows that most references come from journals, not conference proceedings articles.

关键词： Cloud computing data privacy Technological innovation databases Computational modeling Bibliographies Decision making Behavioral sciences Security Problem-solving

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Darwinian Lion Swarm Optimization-Based Extreme Learning Machine with Adaptive Weighted Smote for Heart Disease Prediction 1

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1st international conference on Intelligent computing for Sustainable Development, ICICSD 2023

作者： Sasirega, D. Krishnapriya, V. Sri Ramakrishna College of Arts and Science Coimbatore India KG College of Arts and Science Tamil Nadu Coimbatore India Department of Computer Science and Cognitive Systems Sri Ramakrishna College of Arts and Science Tamil Nadu Coimbatore India

ISBN: (数字)9783031612985

ISBN: (纸本)9783031612978

Predicting Cardiovascular Diseases (CVD) is essential in reducing the deaths associated with different heart diseases. However, there are complexity and class imbalance problems in existing methods that degrade the overall effectiveness. Therefore, advanced frameworks are essential for heart disease detection models. This paper proposes a competent heart disease recognition framework using proficient machine learning (ML)-based methods for each clinical data analysis stage. Initially, the pre-processing stage introduces three methods for enhancing the input clinical data quality. Firstly, the Hierarchical Density-based Spatial Clustering of Applications with Noise based on the Neighbor Similarity (HDBSCAN-NS) is proposed to eliminate the data outliers without increasing the complexity. Secondly, Class Median based Missing Value Imputation (CMMVI) approach is proposed to impute the missing values based on the class distribution. Thirdly, Adaptive Weighted Synthetic Minority Oversampling Technique with Natural Neighbors (AWSMOTE-NN) is proposed to resolve the drawbacks of collinearity in the imbalanced dataset and improve the class balancing. After pre-processing, the features are extracted, and optimal feature subsets are selected using the Owl Optimization algorithm (OOA). These selected features are used by the proposed hyper-parameter optimized classifier of Darwinian Lion Swarm Optimization-based Extreme Learning Machine (DLSO-ELM) is utilized for detecting heart disease. UCI heart disease datasets are used to validate the proposed framework, and the results showed that the OOA-DLSO-ELM-based approach provides better heart disease prediction with high accuracy and low complexity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Balancing

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Conversion from Mild cognitive Impairment to Alzheimer’s Disease: A Comparison of Tree-Based Machine Learning Algorithms for Survival Analysis 16th

Conversion from Mild Cognitive Impairment to Alzheimer’s Di...

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16th international conference on Brain Informatics, BI 2023

作者： Sarica, Alessia Aracri, Federica Bianco, Maria Giovanna Vaccaro, Maria Grazia Quattrone, Andrea Quattrone, Aldo Neuroscience Research Center Department of Medical and Surgical Sciences Magna Graecia University Catanzaro88100 Italy

ISBN: (纸本)9783031430749

Prediction of conversion from Mild cognitive Impairment (MCI) to Alzheimer’s disease (AD) is usually performed with Machine Learning (ML) supervised approaches. However, typical ML classifiers are not able to provide information on time and risk to AD conversion. Survival Analysis statistical methods as Cox Proportional Hazard (CPH) give this information and can handle censored data, but they were designed for small dataset and do not perform well on heterogeneous feature space. ML survival algorithms overcome these limitations, and in particular tree-based models showed promising results on biomedical data. Thus, we applied Random Survival Forest (RSF), Conditional Survival Forest (CSF) and Extra Survival Trees (XST) on demographic, clinical and neuroimaging data of stable and progressive MCI patients from ADNI to evaluate their accuracy in predicting time-to-AD and to stratify MCI patients based on their risk grade. Moreover, we quantitatively compared their feature importance with Rank-Biased Overlap (RBO), a similarity measure between rankings. RSF showed highest c-index (0.87), followed by CSF and XST (0.85), while CPH had worst performance (0.83). According to RSF, CSF and XST, high-risk group had probability of AD conversion within 48 months respectively of 76.9%, 74.8% and 72.3%. Most predictive features (FAQ, mPACCdigit, mPACCtrailsB) were in nearly same order, where highest RBO was between CSF and XST (80%), while RSF had low similarity with CSF (66.3%) and XST (67.5%). Our findings suggested that tree-based survival models represent an optimal compromise between performance and interpretability when predicting time-to-event and assessing individual risk score of conversion from MCI to AD. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Research on Natural User Interface Interaction Design Based on Computer Big data Technology

Research on Natural User Interface Interaction Design Based ...

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Telecommunications, Electronics and Informatics (ICTEI), international conference on

作者： Ruitong Che The University of Edinburgh Scotland UK

ISBN: (数字)9798350342963

ISBN: (纸本)9798350342970

Interface less interaction is the interaction without physical or virtual visual interface. This paper studies the status quo of interface less interaction, conceptual definitions and practical applications under the concept of pervasive computing. The article analyses the influence of habit patterns on the system interaction effect, and proposes a natural interaction design process based on the habit patterns of user groups. Define the user group habit form matrix, and establish a new natural interaction system design method based on this. This interactive design method can help designers develop automatic service interface interfaces that are easy for public users to understand and use, reduce the user’s cognitive load, and better meet the cognitive diversity needs of automatic service interface users.

关键词： Pervasive computing Visualization Design methodology User interfaces cognitive load User experience Telecommunications

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A modified TOPSIS method considering interference effect with quantum cognitive theory

A modified TOPSIS method considering interference effect wit...

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data, Information and computing Science (CDICS), international conference on

作者： Wangwang Yu Xinwang Liu Yingping Zi School of Economics and Management Southeast University Nanjing China

ISBN: (数字)9798350382778

ISBN: (纸本)9798350382785

The TOPSIS method is a very important part in decision making field. However, we find the interference effect between attribute values, which will make the positive ideal solution (PIS) and negative ideal solution (NIS), the separation and ranking index deviate from the actual situation, and further influence the result of best and worst alternatives. To solve this problem, we propose a modified TOPSIS method considering interference effect with quantum cognitive theory. In this paper, we first consider for using the interference effect with quantum cognition to express the interference degree between attribute value. Second, we apply the interference degree and the detailed steps of original TOPSIS method to propose a modified TOPSIS method considering interference effect. Third, we apply our proposed TOPSIS method to evaluate the credit risk of commercial banks.

关键词： Quantum computing Decision making Quantum mechanics Interference Cognition Indexes

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Deep Learning based Spectrum predictions for cognitive Healthcare system

Deep Learning based Spectrum predictions for Cognitive Healt...

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international conference on computing and Networking Technology (ICCNT)

作者： Parnika Kansal Amity Law School Noida Amity University Noida Noida India

The prevailing pandemic scenario and the constant explosion of wireless devices lead to demand for a large spectrum. There is a crucial need for regular supervision of patient's health in these dire situations of a pandemic. This demands a smart solution for healthcare based on the latest technologies. This research work presents a detailed review of the latest advancements in the context of smart healthcare and medical imaging. This research work also proposes a novel framework for cognitive healthcare based on Deep Learning. A unique spectrum prediction model has been proposed that combines the Grey model and Long Short Term Memory functionalities and allocates spectrum to the patients according to their risk classification (high, medium, and low). This research work describes the distinct novel applications of cognitive radio, Deep learning, and the Internet of things in the Smart healthcare and Medical Imaging context. The proposed LSTM-Grey model has also been compared with existing spectrum sensing methodologies based on LSTM and CNN. All simulation results based on detection probability, false alarm probability and Accuracy of prediction indicate that LSTM-Grey model outperforms the existing solutions for healthcare system. The unique solution proposed based on the Grey model and LSTM for spectrum prediction enables transmission of medical data on free spectrum slots for real-time supervision of patient's health. The Deep Learning-based cognitive Healthcare system is a promising candidate for Smart Healthcare supporting swift diagnosis, tracking, and patient health supervision.

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Empowering Generative AI with Knowledge Base 4.0: Towards Linking Analytical, cognitive, and Generative Intelligence

Empowering Generative AI with Knowledge Base 4.0: Towards Li...

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IEEE international conference on Web Services (ICWS)

作者： Amin Beheshti School of Computing Macquarie University Sydney Australia

Intelligence refers to the ability to acquire and apply knowledge and skills, which comprises three fundamental components, namely knowledge, experience, and creativity. Consequently, there exist three primary Artificial Intelligence (AI) systems, namely Analytical AI, cognitive AI, and Generative AI. Analytical AI is primarily concerned with comprehending the data and transforming it into contextualized data and knowledge. On the other hand, cognitive AI is centered on understanding experience and aims to annotate, enrich, and utilize the knowledge, to facilitate decision-making. Lastly, Generative AI delves into the neural mechanisms involved in creative thinking and problem-solving, with a focus on enhancing the process of acquiring and applying knowledge and skills. This paper presents Knowledge Base 4.0 as the backend data for AI engines, which allows for linking knowledge and experience to enable empowering generative AI. The objective is not only to facilitate generating new content (such as text and images) but also to generate new processes when/if needed. We present the architecture of Knowledge Base 4.0 and the design and development of data services that construct and maintain this robust Knowledge Base. Additionally, we provide use cases in various domains, including health, policing, banking, and education.

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CareD: Caregiver’s Experience with cognitive Decline in Reddit Posts

CareD: Caregiver’s Experience with Cognitive Decline in Red...

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IEEE international conference on Healthcare Informatics (ICHI)

作者： Muskan Garg Sunghwan Sohn Artificial Intelligence and Informatics Mayo Clinic Rochester MN USA

With advancements in analysis of cognitive decline in electronic health records, the research community witnesses a recent surge in social media posting by caregivers and/or loved ones of people with cognitive decline. The major challenges in this area are availability of large and diverse datasets, ethics of data collection and sharing, diagnostic specificity and clinical acceptability. To this end, we construct a new dataset, CAREgivers experiences with cognitive Decline (CareD), of 1005 posts with more than 194K words and 9541 sentences, highlighting discussions on people with dementia and Alzheimer’s disease on Reddit. We discuss the changing trends of discussions on cognitive decline in social media and open challenges for natural language processing and social computing. We first identify the Reddit posts reflecting substantial information as candidate posts. We further formulate the annotation guidelines, handle perplexities to investigate the existence of experiences, self-reported articles and potential caregiver in candidate posts, resulting in the discovery of latent symptoms, firsthand information, and prospective source of longitudinal information about the patient, respectively.

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The Study of complex Manipulation via Kinematic Hand Synergies: The Effects of data Pre-Processing

The Study of Complex Manipulation via Kinematic Hand Synergi...

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IEEE international conference on Rehabilitation Robotics (ICORR)

作者： A. Michael West Federico Tessari Neville Hogan Department of Mechanical Engineering Laboratory for Biomechanics and Human Rehabilitation Massachusetts Institute of Technology Cambridge MA USA Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA USA

The study of kinematic hand synergies through matrix decomposition techniques, such as singular value decomposition, supports the theory that humans might control a subspace of predefined motions during manipulation tasks. These subspaces are often referred to as synergies. However, different data pre-processing methods lead to quantitatively different conclusions about these synergies. In this work, we shed light on the role of data pre-processing on the study of hand synergies by analyzing both numerical simulation and real kinematic data from a complex manipulation task, i.e., piano playing. The results obtained suggest that centering the data, by removing the mean, appears to be the most appropriate preprocessing technique for studying kinematic hand synergies.

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