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检索条件"任意字段=3rd International Conference on Intelligent Computing in Data Sciences, ICDS 2019"

共 639 条记录，以下是1-10 订阅

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2019 3rd international conference on intelligent computing in data sciences, icds 2019

2019 3rd International Conference on Intelligent Computing i...

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3rd international conference on intelligent computing in data sciences, icds 2019

ISBN: (纸本)9781728100036

The proceedings contain 80 papers. The topics discussed include: optimizing electrical energy consumption in cloud data center;ams solar energy prediction: a comparative study of regression models;non-invasive health control through guided shopping: a rule-based approach;a new model based on global hybridization of machine learning techniques for 'customer churn prediction';a comparative study of similarity measurement in a new neural model of unsupervised clustering;EKF and UKF methods for the acoustic emission source localization in concrete;and deep learning based topics detection.

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3rd international conference on Security with intelligent computing and Big-data Services, SICBS 2019

3rd International Conference on Security with Intelligent Co...

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3rd international conference on Security with intelligent computing and Big-data Services, SICBS 2019

ISBN: (纸本)9783030468279

The proceedings contain 30 papers. The special focus in this conference is on Security with intelligent computing and Big-data Services. The topics include: Secure Authentication Key Agreement Protocol with eCK Model in Heterogeneous IoT Environment;robust Syndrome-Trellis Codes for Fault-Tolerant Steganography;audio Information Hiding Based on Cochlear Delay Characteristics with Optimized Segment Selection;A Watermarking Method for 3D Game Model Based on FCM Clustering and Density Tag Estimation of Vertex Set;A Reversible Information Hiding Scheme Based on Index Encryption Strategy in VQ-Image;introduction of Reversible data Hiding Schemes;study on the Factors Affecting Sharing Behavior of Social Media Platform Users;a Survey of Bus Arrival Time Prediction Methods;Applying Talent Quality-Management System (TTQS) and Information Visualization to e-Learning Partner Project;the Application of Traveling Gravity Model on international Tourism;AI-Based Farm Survey Technique for Efficient Fruit Harvesting;prediction of Breast Cancer Recurrence Using Ensemble Machine Learning Classifiers;an Impact of Capital Gains Tax for Securities on Taiwan Stock Market by Overreaction, Lock-in Effect and Market Microstructures;analysis of the Influence of Strengthening Visual Message Oriented Design on Way-Finding Problems;face Forgery Detection Based on Deep Learning;a Study of Text-Overlaid Image Compositions Information System About Enterprise Documents, Logo and Slogan;multimodal Presentation Attack Detection Based on Mouth Motion and Speech Recognition;unsupervised Learning: Using Clustering Algorithms to Detect Peer to Peer Botnet Flows;achieve Web Search Privacy by Obfuscation;cryptanalysis of Proxy Re-Encryption with Keyword Search Scheme in Cloud computing;The Research of Attacking TOR Network Users.

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Design of intelligent System for Puzzle Games Based on Machine Learning 3

Design of Intelligent System for Puzzle Games Based on Machi...

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3rd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Jiang, Xinli Fu, Peng Center Of Well-being Training Changchun Humanities And Sciences College Changchun China

ISBN: (纸本)9798350318609

In terms of data processing in puzzle games, this research paper studies and implements a set of data table workflows, with the aim of improving the flexibility of data configuration and increasing the convenience of data export, runtime loading, and data usage. This article implements a puzzle game framework that encapsulates the underlying modules and provides support for the upper logic modules. The three most important and commonly used modules in game frameworks are: state machine module, resource management module, and log module. The research results indicate that based on the puzzle game framework, the development efficiency of logical functions has been greatly improved, and the portability of the code has been greatly enhanced. Through overall testing, it can be seen from the overall performance that the game client is running well, and all parts meet the expected requirements. It also meets the requirements in non-functional aspects such as performance. © 2024 IEEE.

关键词： intelligent systems

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An intelligent Visualization Method for Classic and Famous Prescriptions Based on Big data 3

An Intelligent Visualization Method for Classic and Famous P...

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3rd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Yan, Xiangzhu Yu, Bing Changchun Humanities And Sciences College Center Of Well-being Training Changchun China

ISBN: (纸本)9798350318609

Relationships extraction using sMIL algorithm requires a set of classic and famous prescriptions data which is a data source that contains relational objects. The document data in this paper is the literature summary retrieved from the literature database such as PubMed. Therefore, in order to dig out the genetic data of the target syndrome of coronary heart in the literature as much as possible to improve the accuracy of excavation, data mining model where network decomposition and symptom combination are combined is proposed in this paper to mine the NEI gene data of the literature related to target syndrome in coronary heart disease with the help of a large number of previous literature research and previous research of the research group. Then final conclusion will be reached through comprehensively considering the mining results of these two methods. data source of this paper is a large number of biomedical literatures containing classic and famous prescriptions in some disease. © 2024 IEEE.

关键词： Visualization

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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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intelligent Control Technology in Rural Landscape Environment Design 3

Intelligent Control Technology in Rural Landscape Environmen...

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3rd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Li, Fenghua Zhu, Qicong Guangdong Baiyun University GuangDong GuangZhou China

ISBN: (纸本)9798350318609

Traditional rural landscape environment design is often based on static concepts, ignoring the dynamic changes of the environment. This makes it difficult for designs to cope with constantly changing climate, human flow, and ecological conditions. This article utilizes intelligent control technology to achieve dynamic design of rural landscape environment, adapting to changes in seasons, weather, and pedestrian flow, and providing more intelligent, sustainable, and user-friendly solutions for rural landscape environment design. It collects data information related to rural landscape environment through sensors and preprocesses the collected data. The experimental results show that the average accuracy of LSTM model for climate and human flow prediction is 95.3 % and 93.2%, respectively. Therefore, using LSTM can accurately predict the rural environment and improve the user's experience of rural landscape environment design. © 2024 IEEE.

关键词： Long short-term memory

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Integrating Big data Analytics in Soil Quality Assessment: Techniques, Challenges, and Applications 3

Integrating Big Data Analytics in Soil Quality Assessment: T...

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3rd international conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Hanif, Md Abu Lovely Professional University Department of Computer Applications Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Department of Computer Science 365 5th Ave New YorkNY110016 United States

ISBN: (纸本)9798331515911

This review paper investigates the application of Big data analytics, focusing on soil quality assessment, with an emphasis on the innovative benefits offered by modern data-driven techniques for improved soil management. With world food demands rising, the importance for real-time and exact soil quality monitoring has increased. Although still useful, traditional approaches to modeling are limited in their ability to capture the spatial and temporal variability of soil properties. Big data analytics sits as the answer to the above by facilitating extensive and large volume data processing resulting in better, on-demand and scalable soil quality assessment. In the paper, we describe machine learning algorithms, remote sensing and geospatial analysis as key techniques to mine soil data at scales and resolutions unprecedented so far. We also critically evaluate the challenge of data integration (heterogeneity, scalability and multi-disciplinarity). These analytics are scalable for assessment and decision-making in precision agriculture, sustainable land management and environmental conservation. Thus, this paper synthesizes current research and practical implications in support of the need for Big data analytics within soils and soil sciences to cover also future challenges. Future research directions are also highlighted, including the importance of improved data acquisition and algorithmic advances as well greater synergy between Big data and conventional soil science practices.. © 2025 IEEE.

关键词： data quality

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A hybrid CNN-GNN approach for enhanced early detection of schizophrenia using EEG data 3

A hybrid CNN-GNN approach for enhanced early detection of sc...

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3rd international conference on intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Reetha, L. Gnanajeyaraman, R. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of CSE TN Chennai602105 India

ISBN: (纸本)9798331523893

Schizophrenia, a complex neuropsychiatric disorder, poses significant challenges in early detection due to its impact on cognitive, emotional, and social functioning. Hybrid approach integrating CNNs and Graph Neural Networks has shown promise in addressing this challenge. CNNs excel at capturing local spatial-temporal features from EEG signals, while GNNs are designed to model the brain's connectivity patterns, providing a holistic representation of brain function. This study presents a hybrid CNN-GNN model that extracts both spatial-temporal features and models global brain connectivity for schizophrenia detection. Using EEG data from the Lomonosov Moscow State University (LMSU) dataset, this model achieves high accuracy (99.12%), sensitivity (98.83%), and specificity (99.35%). An improvement in schizophrenia detection, the CNN-GNN hybrid model gives doctors a more precise and trustworthy instrument for early diagnosis. © 2025 IEEE.

关键词： Convolutional neural networks

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Stock Price Prediction Using Gradient Boosting Machine with Technical Indicators 3

Stock Price Prediction Using Gradient Boosting Machine with ...

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3rd international conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Vinston Raja, R. Jayanthi, G. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai India Saveetha Nagar Tamil Nadu Chennai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

This research endeavors to design a predictive model to forecast stock prices using a Gradient Boosting Machine (GBM) regressor, specifically emphasizing the adjusted closing price to mitigate the autocorrelation issues intrinsic to time series data. The investigation encompasses a 12-month duration of historical stock data, integrating two technical indicators, namely Exponential Moving Averages (EMA-5 and EMA-10), to augment predictive precision. Gradient Boosting, recognized for its proficiency in managing intricate and non-linear data patterns, was employed to forecast forthcoming stock prices. Critical performance indicators, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R2), played a role in evaluating model accuracy, attaining an R2 score of 0.99, thereby signifying an almost perfect correlation between actual and predicted prices. Beyond numerical assessments, visual examinations through scatter plots, residual plots, and prediction error distribution further elucidated the model's efficiency. The findings reveal that the amalgamation of Gradient Boosting with Technical Indicators such as EMA-5 and EMA-10, coupled with an emphasis on the adjusted closing price, proves exceptionally effective for stock price forecasting. This methodology achieved an accuracy rate nearing 99%, illustrating its substantial promise for stock market prediction and its proficiency in alleviating autocorrelation challenges within time series datasets. © 2025 IEEE.

关键词： Autocorrelation

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Research on the Application and Development of intelligent Algorithm in the Field of New Media Communication 3

Research on the Application and Development of Intelligent A...

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3rd IEEE international conference on intelligent Technologies, CONIT 2023

作者： Guo, Kaimeng University of Macau Faculty of Social Sciences Humanities and Social Sciences Building Macau Taipa999078 China

ISBN: (纸本)9798350338607

Mobile Internet, Internet of things, big data, cloud computing, intelligent human-computer interaction and artificial intelligence algorithms jointly promote the development of current media platforms and systems in the direction of intelligence. In the development process of media intelligence, social information communication system turns to online social information communication *** application of intelligent algorithm in the field of new media communication is a human-computer interaction (HCI)which uses artificial intelligence (AI)and machine learning to improve user *** can be used in various applications, such as social network, search engine optimization, recommendation system and so *** main advantage of using artificial intelligence in human-computer interaction is that it can help users obtain more relevant information with less effort. When you use AI tools instead of manual processes,the process becomes faster and *** can also reduce costs by reducing the labor hours required to achieve the desiredgoals © 2023 IEEE.

关键词： Human computer interaction

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