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检索条件"任意字段=IEEE International Conference on Intelligent Computer Communication and Processing"

共 57141 条记录，以下是71-80 订阅

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Human-vehicle-road-environment-oriented intelligent Collision Warning Strategy 24

Human-vehicle-road-environment-oriented Intelligent Collisio...

引用

3rd international conference on Signal processing, computer Networks and communications, SPCNC 2024

作者： Zhang, Youwang Guo, Jianzhang Zhang, Biling School of Information and Communication Engineering Beijing University of Post and Telecommunications Beijing China China Telecom Digital Intelligence Technology Co. Ltd. Beijing China

ISBN: (纸本)9798400710834

Forward collision has become the main cause to the traffic accidents. To avoid forward collision, this paper proposes a single-lane longitudinal collision avoidance safe distance model and an intelligent collision warning strategy considering driver-vehicle-road-environment. First, the influence of driver characteristics, vehicle factors, road conditions and environment on the safe distance is analyzed. Then to obtain a more accurate reaction time, factors of driver, vehicle, road, and environment are taken into consideration, and the weights of different factors are adaptively adjusted using the attention mechanism. Finally, the longitudinal collision avoidance safe distance model and intelligent collision warning strategy are proposed. Simulation results show that the proposed model and strategy can improve traffic efficiency and reduce traffic accidents. © 2024 Copyright held by the owner/author(s).

关键词： Highway accidents

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Research on the Fusion of Autonomous Navigation Algorithms and Environmental Perception in intelligent Ship Technology 5

Research on the Fusion of Autonomous Navigation Algorithms a...

引用

5th ieee international conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Zhang, Zhijie Zong, Yonggang Binzhou Polytechnic Ocean Department Binzhou China

ISBN: (纸本)9798331533694

The research focus of this paper is on the organic combination of autonomous navigation algorithm and environmental perception technology, with the aim of enhancing the autonomous navigation capability of intelligent ships in complex marine environments. Autonomous navigation relies on the close coordination of multiple perception systems and algorithms, among which the simultaneous localization and mapping (SLAM) algorithm is particularly critical. The SLAM algorithm generates high-precision environmental maps in real time and provides accurate ship positioning by fusing multi-sensor information. This paper proposes and designs an autonomous navigation system that integrates environmental perception and SLAM technology. Its system architecture includes sensor data processing, path planning and control decision modules. The effectiveness of the proposed algorithm is verified through a series of model simulations, and experimental evaluation is carried out in multiple test scenarios. The simulation results show that compared with traditional navigation methods, the algorithm significantly improves the navigation accuracy and safety of ships in complex marine environments, and the positioning error rate is reduced by more than 15%. The research results of this paper provide a solid theoretical basis and technical support for the future research and development of autonomous navigation systems for intelligent ships. © 2025 ieee.

关键词： Marine navigation

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Artificial Intelligence to Enhance the Security of Internet of Things: A Comprehensive Review 3

Artificial Intelligence to Enhance the Security of Internet ...

引用

3rd international conference on intelligent Data communication Technologies and Internet of Things, IDCIoT 2025

作者： Dheekshaa, V.M. Harsha, V. Sindhu, M. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

The Internet of Things is the term we call when devices are connected to the network and work together to provide a better experience for users, facilitate better decision-making, and improve operations. As with any invention, this one also has its disadvantages. IoT devices have complex structures and low processing capacities. There are serious security risks in designing IoT like poor authentication, data leakage, and DDoS attacks. In this review paper, how AI can secure IoT is discussed. A few AI-driven strategies that help mask all these weaknesses are also discussed regarding intrusion detection systems (IDS), behavioural analytics, anomaly detection, and real-time threat monitoring. It presents a four-layered IoT architecture consisting of perception, network, support, and application layers. The use of the IoT systems will become more flexible and efficient in data security and collection, secure and encrypted communication, and predictive security analytics. AI will play the same role in improving the threat perception, real-time identification, and response, as this will make it indispensable to ensure the IoT environment security. This work emphasizes the urgent need for solid AI-powered, multilayered adaptive security frameworks to match the evolving demands of cybersecurity. © 2025 ieee.

关键词： Network intrusion

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A Hybrid Model for Parameter Tuning using SSO and CNN 3

A Hybrid Model for Parameter Tuning using SSO and CNN

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3rd international conference on intelligent Data communication Technologies and Internet of Things, IDCIoT 2025

作者： Patidar, Anil Bandhu, Kailash Chandra Department of Computer Applications Medi-Caps University Indore India Department of Computer Science and Engineering Medi-Caps University Indore India

ISBN: (纸本)9798331527549

Social Spider Optimization (SSO) and Particle Swarm Optimization (PSO) algorithms applied to Intrusion Detection Systems (IDS) across small and large datasets. The study aims to evaluate and compare both PSO and SSO and find best optimal parameters in order to improve the accuracy of IDS. Additionally, the SSO algorithm find the best hyperparameter settings which are kernel, filter, padding, learning rate, and stride to achieve optimal results that directly impact IDS security. The evaluation of algorithms in terms of accuracy, precision, recall, F1 score, and AUC score. Results indicate that, on small datasets, PSO comes under overfitting but not SSO. Even on larger datasets, SSO crosses PSO significantly in terms of accuracy. This finding shows the importance of improvement in scalability, efficiency, and robustness of IDS with help of Social Spider Optimization (SSO). © 2025 ieee.

关键词： Particle swarm optimization (PSO)

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Wormhole Attack Detection in the Internet of Things using a Hybrid Deep Learning Model 3

Wormhole Attack Detection in the Internet of Things using a ...

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3rd international conference on intelligent Data communication Technologies and Internet of Things, IDCIoT 2025

作者： Geo Francis, E. Sheeja, S. Antony John, E.F. Joseph, Jismy Dept. of Computer Science Karpagam Academy of Higher Education Tamil Nadu Coimbatore India Dept. of Data Science Sri Krishna Adithya College of Arts and Science Tamil Nadu Coimbatore India Kerala Thrissur India Kerala Mala India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Internet growth has led to widespread adoption and transmission of the IoT in numerous areas. From personal networks to industrial applications, healthcare to transportation, IoT is everywhere. Due to their widespread usage, attackers deploy sophisticated methods and software to infiltrate IoT networks. IoT devices' low processing power and resources can threaten security and privacy. This vulnerability has inspired attackers to undertake a variety of attacks against IoT networks. Wormhole attacks (WHA) are routing attacks which can interrupt IoT network packet routing and flow. To address the security issues related to IoT devices, a novel hybrid deep Learning (DL) method is proposed to classify potential security risks in IoT circumstances. The proposed module combines the Bidirectional Long Short-Term Memory with attention mechanism (ABS-BiLSTM) and AlexNet CNN to achieve higher detection accuracy. The security service provided by the proposed monitoring solution ensures compatibility among various IoT network communication protocols. The results clearly show that the effectiveness and excellence of the schemes in terms of network performance, accuracy, and detection speed enhancement. © 2025 ieee.

关键词： Network intrusion

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A Review on Oversampling Techniques for the Treatment of Class Imbalance Problem 3

A Review on Oversampling Techniques for the Treatment of Cla...

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3rd international conference on intelligent Data communication Technologies and Internet of Things, IDCIoT 2025

作者： Anand, Apurv Biswas, Saroj Kr. Majumdar, Sounak Thounaojam, Dalton Meitei Department of Computer Science and Engineering National Institute of Technology Silchar Silchar India

ISBN: (纸本)9798331527549

The Class Imbalance Problem (CIP) is a critical challenge in machine learning, particularly in applications such as medical diagnosis and fraud detection, where minority classes are underrepresented but crucial. This research addresses the issue by introducing a novel classification of oversampling techniques into 11 distinct categories, with a unique distinction between duplication and derivation mechanisms. Through an in-depth review of each category, this study identifies specific challenges such as overfitting and noise in imbalanced datasets and demonstrates how these categorized strategies effectively enhance model performance. The outcomes highlight practical solutions to improving classification accuracy and robustness in imbalanced scenarios. © 2025 ieee.

关键词： Contrastive Learning

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Maximizing Throughput and Minimizing Latency: Evolutionary Algorithms for Optimizing Wireless Cellular Networks 3

Maximizing Throughput and Minimizing Latency: Evolutionary A...

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3rd international conference on intelligent Data communication Technologies and Internet of Things, IDCIoT 2025

作者： Pechetti, Jyoshitha Yadav, Sneha Tarunya, G. Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

In the rapidly evolving world of wireless cellular network, optimizing key parameters like data throughput and latency is of critical importance for ensuring high quality communication services. The proposed presents a novel approach to multi-objective optimization to enhance overall network performance using Genetic Algorithms (GA), Pareto, and Particle Swarm Optimization (PSO). By employing optimization algorithms, a balance is sought in terms of trade-off of other parameters while maximizing data throughput and minimizing latency. Simulation layers indicate the comparative study further solidifies the efficacy of the value of evolutionary algorithms in optimizing wireless networks to satisfy the growing needs of high speed and less latency services in today’s communication landscape. © 2025 ieee.

关键词： Optimization algorithms

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Causal fMRI-Mamba: Causal State Space Model for Neural Decoding and Brain Task States Recognition

Causal fMRI-Mamba: Causal State Space Model for Neural Decod...

引用

2025 ieee international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Deng, Weihao Han, Fei Ling, Qinghua Liu, Qing Han, Henry School of Computer Science and Communication Engineering JiangSu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China School of Computer Jiangsu University of Science and Technology China School of Electronic and Information Engineering West Anhui University China School of Engineering and Computer Science Baylor University United States

ISBN: (纸本)9798350368741

Deep learning advances neural decoding in functional magnetic resonance imaging (fMRI) tasks with convolution and attention-based methods. However, these methods struggle with capturing global spatiotemporal information due to high dimensionality, noise and inter-individual difference of fMRI, which also increase computational complexity and prior bias. To this end, a novel causal state space model, Causal fMRI-Mamba, is proposed for neural decoding and task state mapping. It effectively captures global spatiotemporal information via eliminating local redundancies and capturing long-distance dependencies. Meanwhile, a causal representation framework is designed to extract invariant high-order features and disentangle related causal features, enhancing model performance. Furthermore, a dense connection module is expanded to prevent significant causal information loss in hidden states of inter layers. On the HCP brain task state classification task, Causal fMRI-Mamba achieves better performance and generalization than comparison methods. © 2025 ieee.

关键词： causal representation learning Neuro-decoding state space model task-based fMRI

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ResMobileNet: A Deep Ensemble Approach for Classification of Breast Cancer using Transfer Learning 3

ResMobileNet: A Deep Ensemble Approach for Classification of...

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3rd international conference on intelligent Data communication Technologies and Internet of Things, IDCIoT 2025

作者： Khattar, Sonam Kumar, Vishal Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science Chandigarh University Mohali India

ISBN: (纸本)9798331527549

Breast cancer remains one of the leading causes of death in women worldwide, and thus there is a great need for reliable diagnostic tools. This paper proposes an improved ensemble approach for breast cancer classification using mammography images, which integrates the strengths of two advanced transfer learning architectures: ResNet and MobileNetV2. The ensemble model combines the feature extraction capabilities of ResNet with the computational efficiency of MobileNetV2 to achieve higher diagnostic accuracy without compromising operational effectiveness. This approach was applied to an open mammography dataset with the use of preprocessing and data augmentation techniques for the better generalization of models. The metrics such as accuracy, precision, recall, and F1-score depicted that ensemble methods perform better than the single model, hence being a good approach toward the early detection of breast cancer. This work holds promise toward further integration of deep learning frameworks for advancing computer-aided diagnosis systems in the field of medical imaging. © 2025 ieee.

关键词： Mammography

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Natural Language processing in Geographic Information Systems 3

Natural Language Processing in Geographic Information System...

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3rd international conference on intelligent Data communication Technologies and Internet of Things, IDCIoT 2025

作者： Kushmitha, S. Kathrine, Jaspher W. Abisha, M.B. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798331527549

The integration of Geographic Information Systems (GIS) with Natural Language processing (NLP) has transformed how spatial data is accessed and utilized. By enabling GIS interfaces to interpret natural language queries, NLP enhances user experience, democratizes geospatial data, and improves query accuracy. This paper reviews state-of-the-art methodologies for geo-parsing, geo-coding, and spatial query interpretation, addressing challenges like geographic ambiguity, imprecise spatial relationships, and scalability limitations. Key contributions include a systematic literature review identifying research gaps such as insufficient context-awareness and a lack of annotated datasets, alongside case studies demonstrating the benefits of NLP integration in applications like disaster management and urban planning. Future opportunities are explored in leveraging deep learning, multimodal data integration, and adaptive user interfaces to further enhance GIS accessibility and usability. Future opportunities are explored in leveraging deep learning for spatial reasoning, integrating multimodal data sources for enhanced decision-making, and developing adaptive user interfaces tailored for diverse audiences. Furthermore, the paper highlights the potential of NLP in generating geo-descriptions for distributed sensor networks, supporting accessibility for impaired users, and enhancing real-time data analysis in smart city initiatives. Addressing these gaps and building scalable frameworks will pave the way for GIS systems that are not only powerful but also inclusive, fostering global accessibility and broader adoption of geospatial technologies. © 2025 ieee.

关键词： Structured Query Language

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