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检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

共 1561 条记录，以下是121-130 订阅

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DeepRetina: A Deep Learning Approach for Diabetic Retinopathy Detection and Stage Classification

DeepRetina: A Deep Learning Approach for Diabetic Retinopath...

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2024 international conference on Advances in data Engineering and Intelligent computing Systems, ADICS 2024

作者： Nithishvikraman, H. Sudharsan, G. Anand Joseph Daniel, D.I. Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

Using color fundus imaging to identify diabetic retinopathy (DR) is a tough process that requires skilled doctors to comprehend the existence and significance of certain small characteristics. This effort is further complicated by a complex categorization system. This research aims to use convolution neural network (CNN) to consistently diagnose diabetic retinopathy and grade patients into five groups or stages. An automatically generated diagnostic may be provided by a data-enhanced CNN architecture network that can identify the complex components involved in the class task, as well as exudates, hemorrhages, and micro-aneurysms in the retina, without requiring human input. This work trained CNN using data that was made accessible to the public. When compared to other algorithms on the same dataset, it demonstrated an astounding performance. It was used to the global dataset and obtained a great accuracy of 97% on the validation image. © 2024 ieee.

关键词： complex networks

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cognitive Disorder Detection in Adolescents using Mental Health data 2

Cognitive Disorder Detection in Adolescents using Mental Hea...

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2nd ieee international conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2024

作者： Gowtham, R. Ramaraj, Vignesh Dayanand, V. Amrita Vishwa Vidyapeetham Amrita School of Computing Coimbatore Department of Computer Science and Engineering India

ISBN: (纸本)9798350354461

cognitive disorders among adolescents pose significant challenges to their overall well-being and prospects. Timely detection and intervention are crucial to mitigating the potential long-term impacts of these disorders. This research aims to develop a framework that can detect multiple cognitive disorders in adolescents by first collecting subjects' responses to various survey questions, which are derived from standard questionnaires used to assess the brain's functioning, then utilizing the Conditional Tabular Generative Adversarial Network (CTGAN) for treating the imbalance in the dataset, and finally using the TabNet deep neural network for classifying disorders. The proposed framework outperforms traditional machine learning (ML) models and is a promising tool for the online detection of cognitive disorders in adolescents. © 2024 ieee.

关键词： Adversarial machine learning

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Interpretation of Electroencephalogram using RNNs to Predict cognitive Disorders 3

Interpretation of Electroencephalogram using RNNs to Predict...

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3rd international conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Moovendran, Manikandan Surendran, S. Midhun, C. Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Chennai603203 India SRM Institute of science and technology Chennai603203 India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532420

EEG signals are very useful in giving an insightful view of what's going on inside the brain. However, such readings are still tricky and challenging to interpret because of the inherent non-linearity and dynamic behavior of the data. The present paper introduces a new approach to the analysis of EEG signals using Recurrent Neural Networks, with a particular focus on recognizing temporal dependencies that exist in sequential data. By exploiting the RNN property of information retention at different time steps, we demonstrate enhanced accuracy in pattern recognition and classification of cognitive states. Our model is trained over a massive dataset of EEG recordings using the LSTM units to get rid of vanishing gradients frequently encountered in deep-learning models. The results from the experiments are given below and illustrate how the proposed method compares with traditional machine learning techniques in terms of accuracy and robustness. It is a promising tool for all applications in neurological diagnosis, cognitive research, and developing a brain-computer interface. © 2024 ieee.

关键词： Recurrent neural networks

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Research on the Algorithm of Processing Related data for Automatic Generation of Computerized Reports Using complex Network Theory

Research on the Algorithm of Processing Related Data for Aut...

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2024 international conference on computing, Robotics and System Sciences, ICRSS 2024

作者： Chen, Qiwen School of Management Shanghai University shanghai China

ISBN: (纸本)9798331511296

This study focuses on the automatic generation of reports in a computerized environment and proposes an algorithm for processing related data based on complex network theory. By using the node and edge relationships in the complex network to construct a data association model, the dynamic association analysis of data in the report generation process is realized. The algorithm is designed based on the potential connection between data and relies on indicators such as degree centrality and clustering coefficient in the complex network to ensure the rationality and consistency of the data in the report. This paper comprehensively simulates the report generation and related data processing process by constructing a multi-level simulation model. The simulation results show that the proposed algorithm can meet the needs of automatic report generation in terms of accuracy and stability, and still show high robustness when processing data diversity and complexity. The algorithm is significantly superior to traditional methods in terms of association and processing efficiency. This study provides a novel technical idea for the automatic generation of computerized reports, and expands new possibilities for the application of complex network theory in the field of data processing. © 2024 ieee.

关键词： data assimilation

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Research on Visualization Technology of data Mining Based on RD-SOM (Radar-Self-Organizing Map) Clustering Algorithm 3

Research on Visualization Technology of Data Mining Based on...

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3rd international conference on Cloud computing, Big data Application and Software Engineering, CBASE 2024

作者： Ma, Bing Li, Dongsheng Liu, Xiaotan Mai, Yiyuan Qin, Shasha PLAA Infantry College PLAA Infantry College Shijiaozhuang Hebei China Hunan Changsha75841 China Hainan Hospital of Chinese PLA General Hospital Hainan Sanya China

ISBN: (纸本)9798331506582

With the continuous development of data storage, analysis, processing and other technologies, people are eager to visualize the mining process of complex data, and data mining visualization technology is gradually applied to all fields of society, which greatly improves the efficiency of data mining [1]. The author starts with data visualization technology, studies data preprocessing and algorithms, and proposes data mining visualization technology of RD-SOM clustering algorithm by analyzing the existing problems in the current SOM clustering algorithm, so as to improve the current algorithm and obtain higher data utilization rate. © 2024 ieee.

关键词： data assimilation

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Fault Detection in Transmission Production Lines Based on Imbalanced Multivariate Time Series 10

Fault Detection in Transmission Production Lines Based on Im...

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ieee 10th international conference on Big data computing Service and Machine Learning Applications (ieee BigdataService)

作者： Ndupuechi, Emeka Beecks, Christian Magna PT BV & Co Kg Magna Powertrain Untergruppenbach Germany Univ Hagen Dept Math & Comp Sci Hagen Germany

ISBN: (纸本)9798350366396;9798350366389

Traditional Noise, Vibration, and Harshness (NVH) testing is costly and complex, requiring manual limit setting by skilled NVH engineers. Additionally, production line data is mostly imbalanced, leading to bias toward the majority class in classification models. We addressed these problems by (a) proposing a machine learning (ML)-based solution for the classification of defect transmissions using multivariate time series data (vibration and encoder signals) from a production line test bench, and (b) introducing a new over-sampling method "Cyclic Exponential Averaging of Minority data". Our novel over-sampling method uses exponential average weights, with different time constants to generate new data points between minority data points of consecutive data pairs. Our classification model employs a Multi-layer Perceptron (MLP) for fault detection, using spectral order tracks and Short Time Fourier Transform (STFT). The spectral order tracks were extracted from the time series data as follows: First, using encoder pulses, we estimated the angle of the transmission's input shaft at each given time. Next, the vibration data were resampled into angle domain. Then, the Fast Fourier Transform (FFT) of the resampled vibration signal was computed to transform the signal to order domain. Finally, the relevant spectral order was tracked, and then the decibel (dB) average computed. To extract the STFT, first, the vibration signal of each transmission was divided into several small segments of equal length, then multiplied by a sliding window of same length, and then the FFT was computed. Next, the window was moved with a 50 percent overlap. Finally, the aforementioned steps were repeated until the end of the vibration signal is reached. To verify the effectiveness of our proposed over-sampling method with our developed model, we performed 6 experiments, comparing the performance of our proposed over-sampling method with the performance of 4 state-of-the-art over-sampling methods

关键词： Imbalanced data artificial neural networks multi-layer perceptron machine learning over-sampling fault diagnosis spectral order tracking short time Fourier transform exponential averaging

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Energy-Efficient Task Scheduling Using a Novel Metaheuristics Approach in Cloud data Centers 1

Energy-Efficient Task Scheduling Using a Novel Metaheuristic...

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1st international conference on cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Udatha, Chaitanya Lakshmeeswari, Gondi Department of Computer Science and Engineering Visakhapatnam India

ISBN: (纸本)9798350361780

Cloud computing operates under a centralized computing framework and employs a distributed computing paradigm that revolutionizes delivering computing services. With this transformative technology, users can access virtualized and elastic resources, including computation, platforms, and applications software, which can be provisioned and released instantaneously according to demand. The cloud infrastructure, comprising numerous data centers, requires substantial power consumption to efficiently deliver services to users on demand. The increased power consumption of these resources translates to higher costs for service provision. Current task scheduling methods predominantly focus on objectives like load balancing or makespan, but most of these approaches neglect to optimize power consumption for heterogeneous cloud platforms. The proposed EETSJMSPSO is a new novel metaheuristic approach known as Energy Efficient Task Scheduling using JAYA and Multi-Swarm Particle Swarm Optimization. The primary goal of EETSJMSPSO is to address the persistent challenge of high-power consumption when virtual machines are not subject to migration. The proposed algorithm allocates tasks intending to optimize energy consumption and makespan by implementing load balancing. It also aims to outperform existing schemes dynamically by optimizing other quality of service (QoS) parameters. © 2024 ieee.

关键词： Electric power utilization

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Architecture proposal for adding FOG processing capability to a Smart Building Management System 9

Architecture proposal for adding FOG processing capability t...

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9th ieee international conference on Fog and Mobile Edge computing (FMEC)

作者： Martinez Casas, David Rey Presas, Andrea Cotos Yanez, Jose M. Taboada Gonzalez, Jose A. Univ Santiago de Compostela Elect & Comp Sci Dept Comp Graph & Data Engn COGRADE Res Grp Santiago De Compostela Spain

ISBN: (纸本)9798350366495;9798350366488

This article examines the current state of the intelligent building monitoring system created for the University of Santiago de Compostela (USC) in the framework of the OPERE project and proposes a modification based on the Fog computing paradigm. The study is developed in the context of the European regulations for the energy efficiency of facilities and the reduction of greenhouse gases. The current system implements the data processing in DADIS modules, developed by this research group for the acquisition and flexible transmission of information. These modules provide the information to the monitoring system which offers functionalities such as energy consumption dashboards, configurable operation schedules and ad hoc data visualization. However, the limitations of the current system include the difficulty in scaling the processing of the acquired information and database queries. The described upgrade proposes the extensive use of MQTT to standardize communications and allow the development of stand-alone applications to scale processing. The same architecture facilitates the incorporation of Big data infrastructures that would solve even more complex query problems than those addressed in this scenario.

关键词： Smart Buildings Energy Fog computing

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Evaluation of Video-Assisted Annotation of Human IMU data Across Expertise, datasets, and Tools

Evaluation of Video-Assisted Annotation of Human IMU Data Ac...

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ieee international conference on Pervasive computing and Communications (PerCom)

作者： Hoelzemann, Alexander Bock, Marius Van Laerhoven, Kristof Univ Siegen Ubiquitous Comp Siegen Germany Univ Siegen Comp Vis Siegen Germany

ISBN: (纸本)9798350304367;9798350304374

Despite the simplicity of labels and extensive study protocols provided during data collection, the majority of researchers in sensor-based technologies tend to rely on annotations provided by a combination of field experts and researchers themselves. This paper presents a comprehensive study on the quality of annotations provided by expert versus novice annotators for inertial-based activity benchmark datasets. We consider multiple parameters such as the nature of the activities to be labeled, and the annotation tool, to quantify the annotation quality and time needed. 15 participants were tasked to annotate a total of 40 minutes of data from two publicly available benchmark datasets for inertial activity recognition, being simultaneously displayed both video and accelerometer data during annotation. We compare the resulting labels with the ground truth provided by the original dataset authors. Our participants annotated the data using two representative tools. Metrics like F1-Score and Cohen's Kappa showed experience did not ensure better labels. While experts were more accurate on the complex Wetlab dataset (51% vs 46%), Novices had 96% F1 on the simple WEAR dataset versus 92% for experts. Comparable Kappa scores (0.96 and 0.94 for WEAR, 0.53 and 0.59 for Wetlab) indicated similar quality for both groups, revealing differences in dataset complexity. Furthermore, experts annotated faster regardless of the tool. Given proven success across research, our findings suggest crowd-sourcing wearable dataset annotation to non-experts warrants exploration as a valuable yet underinvestigated approach, up to a complexity level beyond which quality may suffer.

关键词： Inertial sensors Activity recognition Sensor data annotation

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HiSec: Towards Cyber Threat Correlation and Discovery Based on Hierarchical Graph Neural Networks 22

HiSec: Towards Cyber Threat Correlation and Discovery Based ...

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ieee 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigdataSE conference / CSE conference / EUC conference / ISCI conference

作者： Xu, Liwen Lin, Xiang Li, Jianhua Bai, Min Wang, Liejun Shanghai Jiao Tong Univ Inst Cyber Sci & Technol Shanghai Peoples R China Qianxin Technol Grp Co LTD Qianxin Threat Intelligence Ctr Beijing Peoples R China

ISBN: (纸本)9798350381993;9798350382006

A multi-step targeted cyberattack refers to a sophisticated, systematic, and persistent form of attack aiming to compromise the security and integrity of a complex network system. These attacks exhibit spatial and temporal correlations in their execution sequences. However, conventional anomaly detection methods, which often focus on single facets such as network traffic or host behavior, lack the capacity to correlate and validate these steps. To address this deficiency, we introduce HiSec, a cyber threat correlation and discovery framework that leverages dynamic graph modeling and hierarchical graph neural networks. HiSec enhances the modeling of complex network systems and the analysis of spatio-temporal characteristics of multi-step targeted cyberattacks. Specifically, we introduce a novel dynamic graph modeling algorithm that employs overlapping samplers and sliding windows to establish long-term correlations among system activities. Aided by graph attention networks and the Transformer, HiSec uniquely exploits the spatio-temporal correlated edge feature representation, a capability inaccessible to traditional algorithms. Evaluation results reveal that HiSec surpasses existing benchmarks in unsupervised detection, achieving a high degree of accuracy (93.10%) and recall (94.77%). When deployed in our intranet for two rounds of evaluation, HiSec demonstrated remarkable efficiency, taking only 0.15 seconds to model 10,000 system activities occurring within approximately an hour.

关键词： complex Network System Multi-source Heterogeneous Cybersecurity data Graph Modeling Hierarchical Graph Neural Network Edge Feature Embedding Multi-step Targeted Cyberattacks

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