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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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AI Meets Cyber Defense: Enhancing Network Security with GAN-Driven NIDS 11

AI Meets Cyber Defense: Enhancing Network Security with GAN-...

引用

11th IEEE international conference on Advances in Computing and Communications, ICACC 2024

作者： Sarika, S.P. Paul, Arya Kumar, Ajay George, Anju Adi Shankara Institute of Engineering & Technology Electronics & Communication Engineering Ernakulam India

ISBN: (纸本)9798350379136

the field of cybersecurity has witnessed a growth in the breadth and intricacy of cyber threats due to the advancement of communication technology, which calls for the implementation of strong network intrusion detection systems (NIDS). Regrettably, a wide range of potential hazards might put computer users at risk when using the Internet. Users stand to lose a great deal, including control over machines, private information, and potentially even identity theft. the ongoing problem of inconsistent and changing threat data persists despite notable improvements in NIDS models. As more and more research has realized the need for improved detection skills, artificial intelligence (AI) has become a viable answer. In order to meet the challenges presented by emerging cyber threats, this study presents a thorough investigation into the integration of AI, specifically generative adversarial networks (GANs), into NIDS. GANs are a machine learning breakthrough that provides a fresh method for creating new data instances that closely mimic real data. this skill is especially useful in the context of network intrusion detection systems (NIDS), where identifying anomalies necessitates a sophisticated comprehension of intricate and dynamic threat patterns. Our study expands on earlier research in the field that mostly concentrated on deep learning methods like autoencoders, convolutional neural networks (CNN), and long short-term neural networks (LSTM), as well as conventional machine learning models like decision trees (DT) and support vector machines (SVM). Although these methods have shown potential, there hasn't been much of a practical use for them in actual NIDS systems. A promising approach to improving NIDS performance is provided by the suggested NIDS makes use of GANs, especially when it comes to anomaly detection. through the creation of artificial data instances that closely resemble real-world threats, GANs can enhance the resilience and flexibility of NIDS in dynami

关键词： Network intrusion

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An indirect approach for long term monitoring of bridge health 11th

An indirect approach for long term monitoring of bridge heal...

引用

11th international conference on Bridge Maintenance, Safety and Management, IABMAS 2022

作者： Corbally, R. Malekjafarian, A. Structural Dynamics and Assessment Laboratory School of Civil Engineering University College Dublin Ireland

ISBN: (纸本)9781032345314

Limited maintenance budgets, coupled with an ageing bridge-stock, mean that there is much appetite for efficient and inexpensive techniques for monitoring and detection of damage in bridges. It is not feasible or cost-effective to install individual Structural Health Monitoring systems on every bridge on the transport network and, as such, the concept of using sensors located within vehicles to monitor the condition of bridges has become the focus of much attention in recent years. this paper presents a drive-by bridge monitoring concept which utilizes in-vehicle measurements to infer bridge behavior, allowing changes in bridge condition to be monitored over time. Machine learning techniques are leveraged to allow the influence of vehicle speed to be considered. the proposed approach is shown to be capable of detecting midspan cracking and increased rotational stiffness at the support due to seized bearings. the use of a data-driven machine learning algorithm provides much more robust damage detection capabilities compared to existing drive-by approaches. the application of machine learning shows promising results, with a primary advantage of such an approach being the ability to train the algorithm to recognize and account for various external and environmental factors which typically causedifficulty when attempting to identify the dynamic properties of a bridge from a measured in-vehicle response. It is expected that this approach can be implemented on a large scale and act as an early warning tool for infrastructure owners to identify bridges which may be presenting signs of distress or structural deterioration. © 2022 the Author(s).

关键词： Deterioration

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Performance Evaluation of Modern Object Detection Models for automated Fruit Recognition in Smart Agriculture 11

Performance Evaluation of Modern Object Detection Models for...

引用

11th international conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Ahmad, Sarmad Bin Imran, Saad Asad, Usman Zeb, Ayesha National University of Sciences and Technology College of E&me Department of Mechatronics Engineering H-12 Islamabad Pakistan

ISBN: (纸本)9798331509293

An accurate and reliable image-based fruit detection system is essential for advancing agricultural tasks such as yield mapping and robotic harvesting. this paper benchmarks five state-of-the-art object detection frameworks on a merged dataset of 10 common fruit classes. the challenge of scarcity of high-quality fruit dataset detection of multiple classes of fruit is addressed. data merging and augmentation techniques are used to increase data samples and capture variability. Multiple architectures are used to explore computational trade-offs of varying deep learning networks. Model accuracy and inference speed are evaluated for considerations of future deployment in an agricultural environment. this study offers guidance on model selection for fruit detection based on accuracy and latency requirements. Future research should evaluate latencies on resource-constrained devices and should prioritize improving accuracy while reducing overall complexity and inference speed for fruit detection tasks. © 2025 IEEE.

关键词： Deep learning

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An Improved Ultra-Scalable Spectral Clustering Assessment with Isolation Kernel 1

引用

17th international conference on Knowledge Science, engineering and Management (KSEM)

作者： Liu, Jinzhu Wu, Peng Nanjing Univ Sci & Technol Sch Cyber Sci & Engn Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Intelligent Mfg Nanjing 210094 Peoples R China

ISBN: (数字)9789819754984

ISBN: (纸本)9789819754977;9789819754984

Spectral clustering is a well-established unsupervised learning technique that can discover clusters with complex shapes in a dataset. However, applying spectral clustering to large-scale data is often prohibitive due to its high computational cost. To address this issue, the ultra-scalable spectral clustering (U-SPEC) algorithm was recently proposed. In this paper, we improve the performance of U-SPEC by incorporating a data-dependent kernel method. We introduce the Isolation Kernel into the U-SPEC framework, resulting in a novel algorithm called IK-USPEC, which can handle datasets with heterogeneous densities. We evaluate IK-USPEC on 11 real-world and synthetic datasets, and show that it outperforms existing state-of-the-art clustering algorithms.

关键词： Isolation kernel data clustering Spectral clustering

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Proceedings of the 2022 11th international conference on System Modeling and Advancement in Research Trends, SMART 2022

Proceedings of the 2022 11th International Conference on Sys...

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11th international conference on System Modeling and Advancement in Research Trends, SMART 2022

ISBN: (纸本)9781665487320

the proceedings contain 289 papers. the topics discussed include: cloud bursting: intelligent technique in cloud computing;water-quality-analysis using machine learning;an optimized ensemble machine learning model for automobile risk factor prediction;a comparative evaluation of valine-leucine amino acid impact for disease identification using fuzzy C-means clustering and KNN classification;conversational interfaces for health consultation;visualization and prediction of heart disease using big data analytics;speech difficulties and clarification: a systematic review;an investigative study of long-term implication of extended reality applications in educational pursuits for learning-disabled population: a recommendation approach;an agile review of machine learning technique;scheduling cloudlets in a cloud computing environment: a priority-based cloudlet scheduling algorithm (PBCSA);an overview of supervised machine learning algorithm;and advanced blood management system community method based on deep learning.

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Discrete-time Recurrent Neural Network Algorithm with Different Discretization Formulas for Finding Solution of Discrete-Time Complex Division 11

Discrete-time Recurrent Neural Network Algorithm with Differ...

引用

11th IEEE data Driven Control and learning Systems conference, DDCLS 2022

作者： Shi, Yang Lu, Jiwen Zhao, Wenhan Sheng, Wangrong Li, Jian Gerontitis, Dimitrios K. School of Information Engineering Yangzhou University Yangzhou225127 China Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service Yangzhou University Yangzhou225127 China School of Computer and Information Technology Xinyang Normal University Xinyang464000 China International Hellenic University Department of Information and Electronic Engineering Thessaloniki57400 Greece

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In this paper, in order to solve discrete-time complex division, we propose several discrete-time recurrent neural network (DT-RNN) algorithms. First of all, the continuous-time complex division is transformed into matrix equation equivalently, which can be solved by the continuous-time recurrent neural network (CT-RNN) algorithm. Secondly, several discretization formulas developed by Taylor formula are used to establish the corresponding DT-RNN algorithms. Finally, numerical experiments are carried out, which present the feasibility and effectiveness of these DT-RNN algorithms. © 2022 IEEE.

关键词： Complex networks

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Analysis of Various Video-Based Human Action Recognition Techniques Using Deep learning Techniques 1

引用

11th international conference on Frontiers of intelligent Computing: theory and Applications, FICTA 2023

作者： Jandhyam, Lakshmi Alekhya Rengaswamy, Ragupathy Satyala, Narayana Department of Computer Science and Engineering Annamalai University Tamil Nadu Annamalai Nagar India Department of Computer Science and Engineering Seshadri Rao Gudlavalleru Engineering College Andhra Pradesh Gudlavalleru India

ISBN: (数字)9789819967063

ISBN: (纸本)9789819967056

Human action recognition is the ability to identify and naming activities using Artificial Intelligence (AI) from the collected movement raw information through variety of resources. Distinguishing human activities from images or video sequences is a challenging task because of problems, including background untidiness, biased occlusion, and scale changes. In this survey, a complete reassess of modern and high-tech research advances in the field of human motion categorization is explicated. In particular, human activity recognition methods are classified into four categories according to the methods used. Moreover, the review is prepared based on the published year of the article, the method used for research, and performance metrics. Finally, the research gaps and concerns of systems are explained for raising an efficient practice for human action recognition techniques using deep learning approaches. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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intelligent Monitoring Targets Using Visible and Infrared Sensors on UAV 1

引用

11th international conference on Robot Intelligence Technology and Applications, RiTA 2023

作者： Wei, Junyu Zhao, Zongqing Su, Shaojing Zuo, Zhen Sun, Xiaoyong Tong, Xiaozhong Chen, Wei College of Intelligence Science and Technology National University of Defense Technology Changsha China School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Jiangsu China School of Energy Science and Power Engineering Central South University Changsha Hunan China

ISBN: (数字)9783031706875

ISBN: (纸本)9783031706868

the unmanned air vehicle (UAV) demonstrates its prowess in capturing visual data within specific target domains and employing advanced algorithms, often grounded in deep learning or computer vision, to detect and estimate the geospatial coordinates of targets. UAV-based target monitoring, including detection and localization, come with numerous advantages, such as panoramic views, operational versatility, and compatibility with cutting-edge algorithms. Moreover, the fusion of visible and infrared imagery further enhances target detection and discrimination, even in varied environmental conditions. the fusion of dual-light technology with UAVs is introduced as a resilient and flexible framework for target detection and localization. this paper posits that this integration effectively harnesses the inherent strengths of both technologies, resulting in a proficient framework for target monitoring across diverse scenarios. We assert that the amalgamation of dual-light technology and UAVs optimizes their inherent capabilities, offering a robust and adaptable framework for precise target detection and localization across a spectrum of scenarios. the experimental results presented herein validate the efficacy of this approach in detecting and localizing targets under challenging conditions encompassing both infrared and visible imaging. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Unmanned aerial vehicles (UAV)

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Fliperentiation learning using a Mix of Tat Twam Asi and HOTS-Superitem Logic Based on the Kelase Platform as a Renewable learning Model 11

Fliperentiation Learning using a Mix of Tat Twam Asi and HOT...

引用

11th international conference on Electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Sugandini, Wayan Heryanda, Komang Krisna Sugiarta, I Made Divayana, Dewa Gede Hendra Universitas Pendidikan Ganesha Department of Health Singaraja Indonesia Universitas Pendidikan Ganesha Department of Management Singaraja Indonesia Universitas Pendidikan Ganesha Department of Mathematics Education Singaraja Indonesia Universitas Pendidikan Ganesha Department of IT Education Singaraja Indonesia

ISBN: (纸本)9798350355314

the decline in learning enthusiasm among students is caused by a monotonous learning process that does not accommodate students' learning needs adjusted to their differing abilities and interests. this problem must be overcome by presenting an innovation in the form of a Fliperentiation learning model design with a mixed Tat Twam Asi packaging and HOTS-Superitem logic based on the Kelase platform. this research aimed to show the initial design of the Fliperentiation learning model with mixed Tat Twam Asi packaging and HOTS-Superitem logic based on the Kelase platform as a renewable learning model. this research used the Borg and Gall model development approach. the development stages in this 2024 research only focus on the research & field data collection stage, planning stage, design development stage, initial trial stage, and revision stage of initial trial results. the subjects involved in obtaining data from initial trials on the initial design of the Fliperentiation learning model were ten evaluators. the data collection tool for the initial trial results was in the form of a questionnaire. the data analysis technique from this research compares the results of initial trials of the Fliperentiation learning model design with effectiveness standards referring to the eleventh scale. the impact of the research results is that there is a positive response from educational evaluators and stakeholders in the health sector regarding the existence of a renewable learning model that can be applied to health universities to support the improvement of students' cognitive, affective and psychomotor abilities based on their desires and needs. the results of this research have shown the effectiveness of the Fliperentiation learning model design included in the great category. this is proven by the percentage of effectiveness of the model design of 86.40%. © 2024 IEEE.

关键词： Federated learning

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Novel ARMF Integrated with Improved LSDA and Its Application in Fault Diagnosis 11

Novel ARMF Integrated with Improved LSDA and Its Application...

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11th IEEE data Driven Control and learning Systems conference, DDCLS 2022

作者： Zhu, QunXiong Zhang, Ning He, YanLin Xu, Yuan Beijing100029 China Engineering Research Center of Intelligent PSE Ministry of Education of China Beijing100029 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

the industrial process is developing to be intelligent and complex, thus the process data presents high dimension, nonlinearity and highly coupled features. Facing these features, this paper proposes a signal pattern-matching fault diagnosis method based on the adaptive rank-order morphological filter (ARMF) integrated with improved locality sensitive discriminant analysis (LSDA) named ILSDA-ARMF. this proposed methodology first fully extracts the variable features related to the fault using the improved LSDA;then the data after dimensionality reduction (DR) is used for signal pattern matching by using ARMF to achieve fault classification. the main advantage of the improved LSDA is that the Mahalanobis distance considers the correlation between samples and their nearest neighbor points. Meanwhile, the Tennessee Eastman (TE) chemical process is experimented with to verify the performance of the proposed ILSDA-ARMF. the simulation results show that the method proposed in this paper achieves more satisfactory results compared with other related methods. © 2022 IEEE.

关键词： Pattern matching

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