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International Conference on Big Data and information Analytics (BigDIA)

作者： Chaofan Dai Xiaoguang Yuan Zongkai Tian Xinyue Hu Zhen Luan Youchen Wang National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha China Beijing Institute of Computer Technology and Application Beijing China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

Generative models have become an exciting area of research in recent years. Generative Adversarial Networks (GANs) and Denoising Diffusion Probabilistic Models (DDPMs) have been utilized in various data augmentation applications. However, generative models can learn high-dimensional features of data through adversarial learning, making them suitable for nonlinear and nonstationary time series analysis, such as stock market prediction, high-frequency trading, and ocean current forecasting. In this paper, the researchers focus on using a wavelet-based GAN to predict stock market prices by generating synthetic stock market price trends. Historical stock price data from 2014 to 2024 is used for our experiments, and the results show that the wavelet-based GAN outperforms deep learning baseline models..

关键词： Wavelet transforms Analytical models Oceans Time series analysis Noise reduction Predictive models Generative adversarial networks Wavelet analysis Data models Stock markets

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Neural Networks for Natural Language Processing

Neural Networks for Natural Language Processing

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Deshpande, Sudhindra B. Velrani, K. Sundara Bhagyalakshmi, A. Vani, M. Sowmya Ananthi, S.N. Sahu, Devika Department of Information Science and Engineering KLS Gogte Institute of Technology Udyambag Karnataka Belagavi590006 India Department of Information Technology Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Computer Application Mohan Babu University Andhra Pradesh Tirupati517501 India Department of Computer Science and Engineering S.A Engineering College Tamil Nadu Chennai600077 India Department of Computer Science and Engineering Government Engineering College Chhattisgarh Raipur492001 India

ISBN: (纸本)9798350374933

Artificial neural networks have made Natural Language Processing (NLP) a lot different because they can encode language data more efficiently. A lot of different natural language processing tasks, like mood analysis, translation, and text production, have been done very well with these networks are based on the neuronal structure of the human brain. This abstract delves into the fundamental principles of neural networks in natural language processing, with a particular emphasis on notable structures, including the more recent Transformer models, CNNs, and RNNs. The investigation investigates the potential future trajectories, challenges, and applications of improving language comprehension and developing assignments. © 2024 IEEE.

关键词： Natural language processing neural networks neural networks NLP recurrent neural networks RNNs

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The Enhanced Analysis of FACT-Based Framework for Traffic Prediction in V2V Networks

The Enhanced Analysis of FACT-Based Framework for Traffic Pr...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Inzimam Mir Aadil Ankita Agarwal Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

The vehicular-to-car (V2V) communique networks have enabled a new generation of urban transportation phase. Vehicular conversation technology can offer scalability, dependability, and accuracy in various vehicle offerings. Even though a few modern studies exist on predicting traffic behavior in vehicular networks, only some are primarily based on an analytical method., this paper presents a better analytical method primarily based on the Fuzzy Adaptive Cluster-based total model (fact) for visitor prediction in V2V networks. The truth-based total version includes an extensive fuzzy-common sense-based device to degree the continued conduct and site visitors for a selected target node and then extrapolate the conduct and site visitors for the upcoming frames. Moreover, the model also contains a k-method algorithm to prepare the visitor's statistics into clusters for analysis. The effects of the simulation indicate that the proposed truth-based version is capable of addressing greater precisely and accurately the site visitors' prediction in dynamic site visitor's conditions, attaining higher performance metrics in terms of prediction accuracy, latency, and jitter as compared to techniques, like k-Nearest Neighbor (KNN), assist Vector Machines (SVM), and Multi-Layer Perceptron (MLP). The model has primary importance for its capability as it should be degree and expects V2V site visitors in V2V communications.

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-Net: Adaptively Aligned Multi-scale Moment for Few-Shot Action Recognition

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International Journal of computer Vision 2025年 1-16页

作者： Gao, Zilin Wang, Qilong Zhang, Bingbing Hu, Qinghua Li, Peihua School of Information and Communication Engineering Dalian University of Technology Dalian China College of Intelligence and Computing Tianjin University Tianjin China Haihe Laboratory of Information Technology Application Innovation Tianjin China School of Computer Science and Engineering Dalian Minzu University Dalian China

Thanks to capability to alleviate the cost of large-scale annotation, few-shot action recognition (FSAR) has attracted increased attention of researchers in recent years. Existing FSAR approaches typically neglect the role of individual motion pattern in comparison, and under-explore the feature statistics for video dynamics. Thereby, they struggle to handle the challenging temporal misalignment in video dynamics, particularly by using 2D backbones. To overcome these limitations, this work proposes an adaptively aligned multi-scale second-order moment network, namely $$\text {A}^2\text {M}^2$$ -Net, to describe the latent video dynamics with a collection of powerful representation candidates and adaptively align them in an instance-guided manner. To this end, our $$\text {A}^2\text {M}^2$$ -Net involves two core components, namely, adaptive alignment ( $$\text {A}^2$$ module) for matching, and multi-scale second-order moment ( $$\text {M}^2$$ block) for strong representation. Specifically, $$\text {M}^2$$ block develops a collection of semantic second-order descriptors at multiple spatio-temporal scales. Furthermore, $$\text {A}^2$$ module aims to adaptively select informative candidate descriptors while considering the individual motion pattern. By such means, our $$\text {A}^2\text {M}^2$$ -Net is able to handle the challenging temporal misalignment problem by establishing an adaptive alignment protocol for strong representation. Notably, our proposed method generalizes well to various few-shot settings and diverse metrics. The experiments are conducted on five widely used FSAR benchmarks, and the results show our $$\text {A}^2\text {M}^2$$ -Net achieves very competitive performance compared to state-of-the-arts, demonstrating its effectiveness and generalization.

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Identification of Malicious Access in IoT Network from Connection Traces by Using Light Gradient Boosting Machine 6th

Identification of Malicious Access in IoT Network from Conne...

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6th International Conference on Computational Intelligence in Data Mining, ICCIDM 2021

作者： Oram, Etuari Naik, Bighnaraj Senapati, Manas Ranjan Department of Information Technology Veer Surendra Sai University of Technology Odisha Burla Sambalpur768018 India Department of Computer Application Veer Surendra Sai University of Technology Odisha Burla Sambalpur768018 India

ISBN: (纸本)9789811694462

In this paper, light gradient boosting machine (light GBM) ensemble learning model based model has been used for identification of malicious access in IoT network from connection traces. This proposed approach starts with exclusive feature bundling (EFB) process to combine features that are mutually exclusive. Then the Gradient-based One-Side Sampling (GOSS) for generation of re-sampled dataset in which data selection is based on loss of the initial model and the absolute values of gradients. The final model is designed by using re-sampled data in which efficient splitting on data is achieved through information gain. The performance evaluation of the proposed model is conducted with state-of-the-art ensemble learning and machine learning based model in order to get overall generalized performance and efficiency. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning systems

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ARF-NET:Method of spinal segmentation in deformed spine radiographs 11

ARF-NET:Method of spinal segmentation in deformed spine radi...

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11th International Conference on information Systems and Computing technology, ISCTech 2023

作者： Lin, XiaTian Liu, Yingli Shen, Tao Gao, Ming Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650093 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China The First People's Hospital of Yunnan Province Kunming650500 China

ISBN: (纸本)9798350342406

Deformed spine radiograph spine segmentation can help doctors to analyze the disease site faster, which is a necessary part of the current development of competent healthcare. For scoliosis diagnosis, this paper designs an Attention-guided ResNet with an FPN-like Upsampling model(ARF-Net) for segmenting the cone part of the spine radiograph map. In this paper, ResNet50 is used as the backbone to propose the feature map. Then the Global-Coordinate Attention (GCA) attention mechanism is added to obtain the X-axis, Y-axis, and global features. Finally, through the SIMAMUP module, the feature map is upsampled to introduce more detailed information to prevent cone segmentation errors and to improve the cone segmentation capability. The final experimental findings demonstrate that the model described in this study has greater segmentation precision in the SpineWebl6 public dataset, with the Jaccard and Dice coefficients reaching 87.70% and 93.45%, respectively. © 2023 IEEE.

关键词： Image segmentation

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Intrusion Detection Based on Deep Belief Network and Stacking Method 3

Intrusion Detection Based on Deep Belief Network and Stackin...

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3rd International Conference on computer Communication and Network Security, CCNS 2022

作者： Wang, Jia Bao Miao, Xiang Hua Li, Xiang Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China Yunnan Key Laboratory of Computer Technology Application Kunming University of Science and Technology Yunnan Kunming650500 China

ISBN: (纸本)9781510660113

After data preprocessing, the feature dimension of NSL-KDD dataset increases from 42 dimensions to 122 dimensions. High dimensional data will make it more difficult for the model to learn the characteristics of the data, and there will be a lot of redundant data in the data set. Therefore, this paper uses the deep belief network to reduce the dimension of the characteristics of the intrusion detection data set after data preprocessing, and uses the stacking algorithm as the classifier to construct the intrusion detection model. Through comparative experiments, it is proved that the model has good performance in the four evaluation indexes of accuracy, precision, recall and F1 score, and effectively improves the performance of intrusion detection model. © 2022 SPIE.

关键词： Intrusion detection

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Big Data Analytics in IoT-Enabled Smart Healthcare Systems: A Machine Learning Survey

Big Data Analytics in IoT-Enabled Smart Healthcare Systems: ...

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2024 International Conference on Communication, Computing and Energy Efficient Technologies, I3CEET 2024

作者： Nigam, Charul Sharma, Priti Anand, Rahul V. Uttam, Arun Kumar Institute of Information Technology and Management Department of Information Technology New Delhi India Jagannath International Management School Department of Information Technology New Delhi India Psit College of Higher Education Department of Computer Application Kanpur India

ISBN: (纸本)9798331541583

The global epidemic of chronic illnesses, such as the COVID-19 pandemic, underscores the pressing need for readily available healthcare services. The flaws in the traditional healthcare systems that are centered on hospitals and clinics are no longer hard to recognize. Wearables that are connected and intelligent have thus emerged as a critical technological solution, leveraging advancements in the realm of the Internet of Issues. These Internet of Things (IoT)-enabled wearables collect a lot of data and provide context-specific behavioral, psychological, and physical health insights. The volume of data generated by wearables and other IoT healthcare devices is a major obstacle to proper management and may impede decision-making processes. Big data analytics is becoming a more and more common way to get valuable data and enable predictive analysis in order to get around this problem. Furthermore, machine learning (ML) has been used to a range of networking issues, such as resource allocation, routing, traffic engineering, and security. Despite the abundance of research on big data analytics and machine learning, there is a lack of specialized studies on the development of machine learning techniques for large-scale data analysis in the Internet of Things healthcare space. This paper offers a comprehensive analysis with a focus on big data analysis in the healthcare sector using machine learning techniques. It discusses a number of research questions and evaluates existing approaches, including the benefits and drawbacks of each. This project aims to educate government agencies and healthcare professionals on the latest advancements in machine learning (ML)-based data analytics for cognitive healthcare systems.. © 2024 IEEE.

关键词： Decision making

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A Digital Management System for Equipment Inventory Maintenance

A Digital Management System for Equipment Inventory Maintena...

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Advanced Electronic Materials, computers and Software Engineering (AEMCSE), International Conference on

作者： Xihua Liu Yadong Zheng Zhen Liu Guiyu Lai China Academy of Engineering Physic Institute of Computer Application Mianyang China Sichuan Zhongrui Information Technology Co. Ltd Mianyang China

ISBN: (数字)9798331510916

ISBN: (纸本)9798331510923

To overcome the persistent limitations inherent in conventional paper-based equipment maintenance systems and fragmented data management practices, a Digital Equipment Inventory Maintenance System is proposed. Centralized data governance is achieved through a meta-model-driven architecture that enforces standardized data acquisition protocols and automated validation procedures. Then this system establishes an integrated framework for comprehensive data lifecycle management, incorporating automated Extract-Transform-Load (ETL) pipelines with schema-mapping mechanisms to harmonize multi-source data streams. Cross-platform interoperability is enabled via WebGL-based digital-twin rendering, which facilitates interactive three-dimensional visualization of equipment inventories. A three-layer modular design - comprising Data Acquisition, Processing & Analytics, and Situational Awareness Dashboard - addresses the critical gap between industrial maintenance requirements and Industry 4.0 standards. Validation through industrial pilot cases demonstrates significant improvements in decision-making efficiency and error mitigation.

关键词： Systematics Data acquisition Pipelines Data visualization Rendering (computer graphics) Maintenance Data mining Streams Standards Software engineering

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Passive Detection for Multiple Underwater Targets with Linear Frequency Modulation Signals 17

Passive Detection for Multiple Underwater Targets with Linea...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Wang, Junfeng Cui, Yue Zhou, Mingzhang Sun, Haixin Liu, Lanjun Zhang, Ailing Tianjin University of Technology School of Integrated Circuit Science and Engineering Tianjin300384 China Mnr Key Laboratory of Southeast Coast Marine Information Intelligent Perception and Application China Tianjin Normal University College of Computer and Information Engineering Tianjin300387 China Xiamen University School of Information Science and Engineering Xiamen361005 China Ocean University of China College of Engineering Qingdao266100 China

ISBN: (纸本)9798350387384

Detecting the status of autonomous underwater vehicle, referred to hereinafter as an underwater target, is fundamental for successfully fulfilling cognition tasks or improving communication quality. In this article, we present a thorough study over the passive detection issue for multiple underwater targets emitting the linear frequency modulation signals. Specifically, hypothesis testing model, which comprises the signals received at the underwater detection node including periodic emission signals from the targets and additive noise, is firstly proposed for the practical scenarios. Then, the detection scheme has been investigated. Among this scheme, the behavior of the signals emitted by the underwater targets has been analyzed;and the primary aiming of the detection procedure, i.e., decision rule and its performance, has also been discussed and derived for the aforementioned scenarios. Towards the end, to confirm the advantages of our proposed studies, numerical simulation examples are carried out to examine the performance under different conditions. © 2024 IEEE.

关键词： Signal modulation

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