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

共 8124 条记录，以下是481-490 订阅

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7th EAI international conference on Robotics and networks, ROSENET 2023

7th EAI International Conference on Robotics and Networks, R...

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7th EAI international conference on Robotics and networks, ROSENET 2023

ISBN: (纸本)9783031644948

the proceedings contain 15 papers. the special focus in this conference is on Robotics and networks. the topics include: KineFormer: Solving the Inverse Modeling Problem of Soft Robots Using Transformers;multiple Node Localization in Cognitive Radio-Based Wireless sensor networks Using Grid Search;systematic Review of Network Slicing Resource Management in 5G;comparatively Studying Modern Optimizers Capability for Fitting Vision Transformers;Comparing LSTM and Transformer for Video Depth Estimation;style Transfer to Enhance Data Augmentation for Facial Action Unit Detection;best Image processing for Higher Face Detection Rate Using Haar Cascades;prediction of Road Traffic Accident Severity Using Machine Learning Techniques in the Case of Addis Ababa;detection of Misinformation Related to Pandemic Diseases Using Machine Learning;Test the Capability of Arduino TinyML for Machine Learning;Comparison of Different Methods for Estimation of Arterial Blood Pressure Using PPG Signals;Explainable AI for Discovering Disease Biomarkers: A Survey;analytical Approach for Mitigating Phishing Attempts.

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sensor Interfacing with Multicore Aurix TC397 Board 8th

Sensor Interfacing with Multicore Aurix TC397 Board

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8th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Ankad, Sahana Patil, Varsha Bhat, Sahana Channalli, Shriraksha Basawaraj Nissimagoudar, Prabha C. Kle Technological University Karnataka Hubli India

ISBN: (纸本)9789819766772

AURIX Board Multicore Application for Effective Temperature and Humidity sensor Interface. the creation of a multicore application for simultaneous temperature and humidity sensor data processing and acquisition on an AURIX board is presented in this paper. the program takes advantage of the platform’s multicore capabilities to handle data and communicate with sensors efficiently by using distinct cores. Communication and the retrieval of raw data from every sensor are made easier by dedicated drivers. Preprocessing and meaningful unit conversion are applied to acquired data before it is shown in real time over a UART interface. Compared to single-core implementations, this multicore technique clearly increases efficiency and responsiveness, opening the door for future applications integrating a variety of sensor kinds and capabilities. this paper highlights the advantages of parallel processing and offers insightful information on multicore programming for sensor interfacing on the AURIX platform. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Humidity sensors

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Meta-heuristic Artificial Humming Bird Algorithm Based Energy Efficient Cluster Head Selection (MAHA-EECHS) in Wireless sensor networks 5

Meta-heuristic Artificial Humming Bird Algorithm Based Energ...

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5th international conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Kusla, Vipan Brar, Gurbinder Singh Garg, Vikash Kumar Bansal, Ankit Kaushal, Rajesh University Ludhiana Department of Computer Science Ct Punjab India University Ludhiana Department of Cse Ct Punjab India Sliet Department of Computer Science and Engineering Punjab Longowal India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781665475242

A wireless sensor network (WSN) improves wireless communication by using hundreds or thousands of nodes to gather data. the lifespan of the nodes and balanced energy consumption are the major issues in the WSN. Long-term WSN efficiency requires optimising node energy. Selecting the optimal node as the cluster head improves energy usage in wireless sensor networks. the Artificial Hummingbird algorithm is used in this paper to identify the best cluster head selection in homogenous wireless sensor networks. the proposed algorithm's innovation lies in the fact that it takes into account a number of parameters like residual energy, intra-cluster distance, and balanced cluster formation while choosing a CH from a homogeneous sensor network. the performance analysis of the proposed algorithm considers four parameters: average energy consumption, total energy consumption, first node death, and residual energy. When compared to other algorithms, MATLAB-based simulation analyses show that the proposed algorithm MAHA-EECHS outperforms them. © 2023 IEEE.

关键词： Base stations

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X-Ray Insights: Comprehensive Dataset for Bone Fracture Detection Across Diverse Anatomical Regions 5

X-Ray Insights: Comprehensive Dataset for Bone Fracture Dete...

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5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Bhuria, Ruchika Gupta, Sheifali Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331504403

Effective diagnosis and treatment in medical imaging depend on exact identification of bone fractures. this paper presents a fresh approach for bone fracture diagnosis with MobileNetV2, a lightweight and effective convolutional neural network derived from deep learning. Leveraging transfer learning, the model fine-tunes a pre-trained MobileNetV2 architecture using an X-ray picture dataset. the approach consists in several phases: data collecting, pre-processing, model construction, training, evaluation, and application. Resizing X-ray images to 224 × 224 pixels, standardizing pixel values, and using data augmentation methods to improve model resilience are among pre-processing tasks. Custom classification layers-Dense and Dropout layers-along with some layer freezing to preserve pre-trained features-extend the MobileNetV2 model to solve the classification job. Built on binary cross-entropy loss function and Adam optimizer, the model is trained on a well-split dataset including test, validation, and training sets. Evaluation shows that the model is efficient;a classification report reveals exceptional performance criteria. the model achieves precision, recall, and F1-score of 0.95 for both fractured and non-fractured classes, therefore suggesting great accuracy in bone fracture diagnosis. the model's general accuracy is 0.95;macro and weighted averages regularly show performance over all classes. With its great accuracy and efficiency, which improve diagnosis capability and patient outcomes, this MobileNetV2-based model presents a dependable tool for medical professionals that fits very well for clinical. © 2024 IEEE.

关键词： Deep neural networks

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Solar panel surface dust detection method based on deep learning 24

Solar panel surface dust detection method based on deep lear...

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5th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Gong, Boao Ma, Bohan Department of Electronic and Information Engineering University of Liaoning Technology Liaoning Huludao China

ISBN: (纸本)9798400718007

With increasing environmental pollution, solar energy has become a widely adopted source of clean energy. However, maintaining solar panels can be costly, especially due to surface dust accumulation caused by environmental factors. Dust on the panel surface obstructs light, reducing the cells' light absorption, which subsequently lowers photocurrent and the system's overall efficiency. Current dust detection methods for photovoltaic components fall into two main categories: one is a method of estimating dust accumulation based on the output power of the panel, by combining prior information;the other is a direct detection method for the dust condition on the panel surface, including infrared detection, image processing technology, and deep learning methods. In this paper, we propose a novel convolutional neural network architecture based on the EfficientNet framework, customized for photovoltaic dust detection. the primary modification involves replacing the adaptive average pooling layer between the feature extractor and classifier with a rotary attention pooling layer (RAP) that leverages rotational position encoding. Experimental results demonstrate that our model achieves 87.31% accuracy in detecting dust on solar panel surfaces. Under the same experimental conditions and dataset, this model outperforms other deep learning models in both detection accuracy and overall performance. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Convolutional neural networks

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Challenges and Resolutions for Integrating PV Solar Plants into the Grid using AI Techniques in a Uniform Environment 5

Challenges and Resolutions for Integrating PV Solar Plants i...

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5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Kant, Surya Singh, Sarabjit Mehra, Komal Vichoray, Chandan Kharayat, Pushpendra Singh Soni, Mukesh Graphic Era Hill University Deptt. of Electronics & Comm. Engg Bhimtal India Rimt University Deptt. of Electrical Engg Punjab Mandi Gobindgarh India Ramdeobaba University School of Management Nagpur India Graphic Era Deemed to be university Department of Electrical Engineering Dehradun248002 India Dr. D. Y. Patil School of Science & Technology Dr. D. Y. Patil Vidyapeeth Pune Tathawade Pune India

ISBN: (纸本)9798331504403

Advancements in processing power, tools, and data generation have enabled the integration of artificial intelligence (AI) into photovoltaic (PV) systems. this study proposes a novel PV paradigm utilizing fuzzy rule-based algorithms to automate monitoring and diagnosis of solar panel efficiency issues. By integrating AI, this approach offers improved accuracy and precision in evaluating PV system performance. the study investigates the impact of AI and fuzzy rule-based algorithms on PV system value by analyzing factors such as efficiency, light intensity, output electrical power, temperature, and humidity. this research compares the benefits and drawbacks of AI-based methods with conventional approaches, identifying potential applications and future directions. By exploring the integration of AI and fuzzy rule-based algorithms, this study contributes to the optimization and enhancement of photovoltaic systems. © 2024 IEEE.

关键词： Fuzzy Logic Maximum Power Point Tracking (MPPT) Neural networks (NNs) Photovoltaic (PV)

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Learning with Balanced Criss-Cross Attention for Cross-Modality Crowd Counting 23

Learning with Balanced Criss-Cross Attention for Cross-Modal...

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5th international conference on Information Technology and Computer Communications, ITCC 2023

作者： Zeng, Xin Zhang, Wanjun Wang, Huake Bian, Xiaoli Zhengzhou Vocational College of Finance and Taxation Zhengzhou China School of Computer and Information Engineering Henan University Kaifeng China School of Information and Communications Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798400700583

Cross-modality crowd counting is one of the most essential tasks in multimedia and image processing, which usually uses multi-sensor information as input in neural networks. Various approaches have been proposed to extract the alignment and relationships between the different modalities in the task of crowd counting. In this work, we explore how to further remedy the cross-modal discrepancies and learn latent relevance across different modalities. We present a novel RGBT crowd counting framework, namely Balanced Criss-Cross Attention Network (BCANet), to overcome the above limitations. To bridge the two modalities, we introduce a Balanced Criss-Cross Attention (BCA) module to encode complementary information across modalities. Lastly, we evaluate our BCANet via extensive experiments and demonstrate that it consistently achieves state-of-the-art results on RGBT-CC and DroneRGBT datasets. © 2023 ACM.

关键词： Image processing

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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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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...

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

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

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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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