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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

共 8138 条记录，以下是471-480 订阅

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Enhancing Nutrition Tracking: Webcam-based Fruit and Vegetable Recognition and Calorie Measurement using Image processing and Convolutional Neural networks 5

Enhancing Nutrition Tracking: Webcam-based Fruit and Vegetab...

引用

5th IEEE international conference for Emerging Technology, INCET 2024

作者： Leo, Sam S. Benjamin, Ruth Moly Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350361155

the "Enhancing Nutrition Tracking" wander epitomizes a groundbreaking agreeable vitality between fake bits of knowledge (AI) and food science. through the utilization of Convenient Net convolutional neural frameworks, this creative system passes on real-time normal item affirmation and correct calorie estimations, in this way empowering clients to make taught dietary choices. Central to its value is the visual emphasis on carbohydrate substance, ensuring clients have a comprehensive understanding of their food choices and dietary intake. this project's trademark lies in its user-friendly interface, arranged to streamline the strategy of capturing characteristic item and vegetable pictures for incite dietary bits of information. By reliably joining AI advancement into conventional assignments, individuals can effortlessly get to basic wholesome information, empowering more advantageous eating affinities and way of life choices. Besides, continuous ask almost endeavors search for to refine the system's real-time characteristic item recognizable confirmation capabilities and examine the immersive potential of virtual reality for in-depth wholesome assessments. By initiating the integration of AI and virtual reality advancements, the "Enhancing Nutrition Tracking" amplify focuses to catalyze a worldview move in food watching. © 2024 IEEE.

关键词： Fruits

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An Ensemble based Convolutional Neural Network modelling for Classifying Medulloblastoma subtype 5

An Ensemble based Convolutional Neural Network modelling for...

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5th IEEE international conference for Emerging Technology, INCET 2024

作者： Sathwik, T. Reddy, Tadi Sai Vishruth Charitha, Talluri Singh, Rimjhim Padam Kanchan, Sneha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Internet Engineering and Computer Science Universiti Tunku Abdul Rahman Malaysia

ISBN: (纸本)9798350361155

Medulloblastoma a heterogeneous pediatric brain tumor, requires specific subtype identification in order to determine the most effective treatment strategies. To evaluate the accuracy of classification, this study examines state-of-art leading Convolutional Neural Network (CNN), models like ResNet-50, MobileNet, Xception model, ensembles, etc in differentiating between medulloblastoma subtypes using histopathological images. the models undergo rigorous training and fine-tuning using a comprehensive benchmark dataset encompassing a variety of medulloblastoma subtypes. We also employ advanced image pre-processing techniques to optimize feature extraction. through comparative analyses based on the accuracy, sensitivity, and specificity and precision of each model to uncover their unique strengths and weaknesses, the work identifies the top performing model for medulloblastoma subtypes classification. then considering unique strengths of these models, we propose an ensemble methodology that combines top performing models for the underlying problem. through experimentation, our ensemble approach proves to have the highest accuracy in medulloblastoma subtype classification. the combination of ResNet-50's depth, MobileNet's efficiency, and Xception's feature extraction abilities contribute to the ensemble's outstanding performance with 97% of acuuracy and F-score. © 2024 IEEE.

关键词： Convolutional neural networks

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Intrusion Detection for Internet of things networks using Attention Mechanism and BiGRU 5

Intrusion Detection for Internet of Things Networks using At...

引用

5th international conference on Electronic Engineering and Informatics, EEI 2023

作者： Song, Yalong Zhang, Dalong Li, Yitong Shi, Shijie Duan, Pengsong Wei, Junfei School of Cyber Science and Engineering Zhengzhou University Zhengzhou China School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Hanwei Electronics Group Corporation Zhengzhou China

ISBN: (纸本)9798350327076

the rapid growth of Internet of things (IoT) devices has led to increased vulnerability to serious security threats within IoT networks. As such, it is crucial to employ network intrusion detection techniques to monitor these networks. this paper presents an intrusion detection model, SEW-MBiGD, which integrates data processing and fusion neural networks to address data imbalance and insufficient feature learning in existing models. Firstly, to balance the dataset and mitigate the influence of edge data, the model employs Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbors (ENN) algorithms for data preprocessing, while also utilizing Wasserstein Generative Adversarial networks (WGAN) to generate minority class data. the proposed intrusion detection model is based on Bidirectional Gated Recurrent Unit (BiGRU) and multi-head self-attention (MHSA) mechanisms, which effectively manage long sequence data and capture correlations between data and global features. Experimental results demonstrate the efficacy of the proposed SEW-MBiGD model outperforming baseline models in achieving a balanced dataset and classification tasks. © 2023 IEEE.

关键词： Internet of things

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Revolutionizing Home Connectivity with IoT-Enabled Smart Mirrors for Internet Browsing and Smart Home Integration 5

Revolutionizing Home Connectivity with IoT-Enabled Smart Mir...

引用

5th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Prabha, M. Manicka Jegadeesan, S. Jayavathi, S.D. Rajkumar, G. Vinoth Jothi, J. Nirmal Krishnan, R. Santhana SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Information Technology Madurai India JP College of Engineering Department of Electronics and Communication Engineering Ayikudi Tenkasi India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350395235

this research study explores the transformative potential of IoT-Enabled Smart Mirrors to redefine home connectivity by enabling seamless internet browsing and smart home integration. It introduces a novel model algorithm, IoT-ViSense (IVS), which leverages IoT sensor data fusion with vision-based Convolutional Neural networks (CNN) for real-time environmental monitoring and security. A comprehensive simulation analysis of IoT-ViSense is conducted and compared against existing algorithms using pertinent simulation metrics. IoT-Enabled Smart Mirrors serve as multifunctional interfaces, integrating personalized weather updates, news, calendar schedules, and real-time compliments. they also incorporate features such as a built-in camera for capturing high-quality selfies, on-demand workout sessions facilitated by fitness experts, and the ability to browse the internet, make calls, and interact with other connected devices. the proposed IoT-ViSense (IVS) algorithm combines IoT sensor data with Vision-based CNN analysis to enhance environmental monitoring and security. It continuously processes data from IoT sensors and camera feeds, detecting anomalies and patterns in real-time. By fusing these data streams, IVS enhances security by identifying unauthorized access and suspicious activities while concurrently optimizing resource usage and ensuring comfortable living and working environments. the simulation analysis of IoT-ViSense (IVS) is compared with suitable existing algorithms, assessing its efficiency in terms of accuracy, response time, and resource optimization. Results reveal that IoT-ViSense (IVS) outperforms other algorithms, demonstrating its prowess in achieving a harmonious blend of smart home integration and real-time environmental monitoring. © 2024 IEEE.

关键词： Convolutional neural networks

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Energy-Efficient Data Collection and Trajectory Design for UAV-Enabled Wireless sensor Network 5

Energy-Efficient Data Collection and Trajectory Design for U...

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5th IEEE international conference on Electronics Technology, ICET 2022

作者： Zhang, Shanxin Cao, Runyu Jiang, Zefeng Jiangnan University School of Internet of Things Engineering Wuxi China Jiangnan University School of Artificial Intelligence and Computing Wuxi China

ISBN: (纸本)9781665485081

In wireless sensor networks (WSNs), unmanned aerial vehicles (UAV) has become an effective data collection technology, but the large amount of energy consumption generated in the data collection process seriously reduces the life of the wireless sensor network. Aiming at the problem of high energy consumption in the data collection process of wireless sensor networks, an energy-efficient data collection strategy based on sensor nodes (SNs) wake-up scheduling and UAV trajectory design is designed. Based on this scheme, a problem of minimizing the weighted energy consumption of UAV and SNs is constructed. this problem is described as a mixed integer non-convex programming problem, which is usually difficult to solve. Aiming at this problem, a joint optimization algorithm based on path discretization and successive convex approximation is proposed to obtain a local optimal solution that satisfies the KKT condition. Experimental results show that our proposed scheme can achieve significant network energy saving, and can achieve a flexible trade-off between the energy consumption of UAV and SNs. © 2022 IEEE.

关键词： Convex optimization

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Comparison of sensor-MAC (S-MAC), Timeout-MAC (T-MAC), Berkeley-MAC (B-MAC), and Modified ML-MAC (M2L-MAC) for Wireless sensor networks 5th

Comparison of Sensor-MAC (S-MAC), Timeout-MAC (T-MAC), Berk...

引用

5th international conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2023

作者： Dhaka, Raghuveer Singh Khurana, Manju Lamba, Sonu CSED Thapar Institute of Engineering and Technology Punjab Patiala147004 India

ISBN: (纸本)9783031609343

Synchronous medium access control (MAC) protocols are crucial in wireless sensor networks since they provide energy-efficient data exchanges. this study introduces a modified multi-layer MAC protocol to optimize network lifetime, energy consumption, throughput, and latency of the network. In this article, the nodes inside each wireless subnet are partitioned into five levels by randomly distributing one-fifth of the nodes from each subnet to each layer. In this, initially, the nodes specified in all layers i.e. 1, 2, 3, 4, and 5 are in a dormant state. When the communication process begins, the nodes specified in layer 1 switch to an active state, while the nodes in other layers remain in a dormant state. In a similar fashion, when the nodes in layer 2 communicate, the nodes in the alternate layers i.e. 1, 3, 4, and 5 remain inactive. this active and inactive state is carried out throughout the entire process for all the successor layers assumed in the proposed method. the utilization of sensor nodes in the modified ML-MAC protocol is characterized by a significantly limited duration of listening/active periods, which effectively mitigates energy consumption during the communication process. the protocol is implemented and simulated using MATLAB. When compared to the different MAC protocols, our proposed modified ML-MAC protocol shows considerable improvements in network lifespan, latency, energy consumption, and throughput. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： MATLAB

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Analysis and Design of Refractive Index sensor Based on Waveguide Coupling 5

Analysis and Design of Refractive Index Sensor Based on Wave...

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5th international conference on Intelligent Control, Measurement and Signal processing, ICMSP 2023

作者： Wang, Qiang Zhu, Chuanhui Yan, Shubin Wu, Taiquan School of Electrical and Control Engineering North University of China Taiyuan030051 China School of Electrical Engineering Zhejiang University of Water Resources and Electric Power Hangzhou310018 China

ISBN: (纸本)9798350336030

this paper presents a new refractive index sensor based on the Fano resonance in optical phenomena. the sensor has two main parts: a metal-insulator-metal (MIM) as the base waveguide and a V -shaped ring resonant cavity with a convex outer ring (VRROR) as the coupling structure. the sensing characteristics of the structure are rationalized by applying the finite element method, and the specific effects of the various parameters in the structure on the sensitivity(S) and superiority of the VRROR are analyzed. the final sensitivity of the VRROR type sensor reaches 2960 nm/RIU, and the figure of merit (FOM) under the corresponding system reaches 51.3. © 2023 IEEE.

关键词： Finite element method

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Generative Adversarial networks: A Comprehensive Review 1

引用

5th international conference on Computer and Communication Technologies, IC3T 2023

作者： Kotha, Pavani Janardhan Babu, V. Ankam, Sreejyothsna Department of CSE SVPCET Puttur India Department of AI SVPCET Puttur India Department of AIML GRIET Bachupally Hyderabad India

ISBN: (数字)9789819997046

ISBN: (纸本)9789819997039

Numerous models in the deep learning field have been created as a result of the rise in processing capacity. A generative model called Generative Adversarial networks (GAN) first appeared in 2014. Many architectures of GAN have been proposed in the process of research conducted on GAN. Any GAN architecture is the result of the competition between two networks, the Generator and Discriminator, to determine the distribution of the sampled data. this process helps in many applications like text to image conversion, style transfer, generating new images, attribute transfer, photo enhancement. this paper helps in knowing about the main working principle of any GAN architecture, recent advances, and applications of GAN. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Generative adversarial networks

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Dynamic Image Representations for Crowd Anomaly Detection using Generative Adversarial networks 5

Dynamic Image Representations for Crowd Anomaly Detection us...

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5th international conference on Computer and Applications, ICCA 2023

作者： Mahmoud, Samar Arafa, Yasmine Abdelmohsen, Maha Modern Science and Arts University Department of Computer Science Cairo Egypt University of Greenwich Department of Computing and Information Systems London United Kingdom

ISBN: (纸本)9798350303254

Anomaly detection within crowded environments is a key challenge in the crowd behaviour understanding and computer vision fields. Application of crowd anomaly detection has improved recently, however, advancements of accuracy and computation (processing power and time) are still required. In this paper, we present an approach to crowd behaviour anomaly detection using dynamic image representations as an alternative to optical flow extractions for temporal development feature extraction. the features are used in conjunction with image-to-image translation using CGANs for anomaly detection within crowds, the proposed framework is evaluated on standard benchmark datasets. the experimental results obtained have demonstrated the efficacy of this approach in comparison to the state-of-the-art crowd anomaly detection methods. © 2023 IEEE.

关键词： Anomaly detection

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Energy Efficient Access Scheduling Mechanism for Monitoring on Power Line Supported by Wide Band Based IoT networks 5

Energy Efficient Access Scheduling Mechanism for Monitoring ...

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5th IEEE international conference on Electronics Technology, ICET 2022

作者： Lu, Yang Zhai, Di Yang, Junbo State Grid Smart Grid Research Institute Co. Ltd Global Energy Interconnection Research Institute Co. Ltd Beijing102209 China

ISBN: (纸本)9781665485081

Wireless sensor networks play import roles in the Internet of thing (IoT). In the electric power company, wide band based IoT networks are used to monitor the power lines. In such a power line monitoring IoT (PL-IoT) network, how to reduce the energy consumed by sensor nodes is a key problem, since the wireless sensor nodes on the power lines are difficult to be repaired or replaced once deployed. In this paper, we focus on a PL-IoT network, and propose. By allocating special time slots to protect low-energy nodes, the network stability period of WSN is prolonged;the cluster head is selected through the energy distance ratio, and the energy of high-energy nodes is fully utilized to prolong network effective lifetime. Special time slots are allocated to collect burst signals, which solves the problem that traditional protocols cannot process burst signals. © 2022 IEEE.

关键词： Internet of things

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