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

共 8114 条记录，以下是241-250 订阅

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Dynamic Student Classification with Forgetting Mechanisms on Memory networks for Knowledge Tracing 5

Dynamic Student Classification with Forgetting Mechanisms on...

引用

5th international conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2024

作者： Huang, Guimin Qin, Xiangqian School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798350368284

Knowledge Tracing (KT), a technique for modeling students' knowledge levels and predicting their future question-answering performance based on their historical answer data, is one of the key research areas to strengthen the ability of personalized education. In recent years, memory networks have received more and more attention and application in the field of KT, however, the current KT model based on memory networks ignores the effect of students' learning sequence on the level of forgetting, and fails to model the forgetting behavior of students in the process of learning by using the characteristics of time intervals in the interaction data. therefore, in this paper, we propose a dynamic student classification with forgetting mechanisms on memory networks model (DSCFMN), which enhance the existing model by dynamically classifying students similar to their learning abilities at specified time intervals, incorporating forgetting factors and introducing a weight decay strategy. Experiments show that our model performs well on online education datasets, and the proposed model achieves better prediction results than existing knowledge tracking methods. © 2024 IEEE.

关键词： Contrastive Learning

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Layer-wise Relevance Backward Propagation for Fake Face Detection and Reconstruction 5

Layer-wise Relevance Backward Propagation for Fake Face Dete...

引用

5th international conference on Image processing and Capsule networks, ICIPCN 2024

作者： David, Muhumuza William, Mawejje Mark Bridget, Nabagereka Raudha, Nakayiza Hellen Nakibuule, Rose Marvin, Ggaliwango Makerere University Dept of Computer Science Arua Uganda Muni University Dept of Computer and Information Science Kampala Uganda

ISBN: (纸本)9798350367171

Deep learning techniques involving Generative Adversarial networks (GANs) enable artificially generated facial images. this alone presents difficulties in terms of authenticity resulting in ethical concerns among institutional applications for digital forensics, security and social media moderation. A question remains today, why would someone fake identity and how can we improve performance of the deep neural networks? this project initiated a novel approach called Layer-wise relevance backward propagation for fake face detection and reconstruction that determines the relevance of each layer to the final prediction by extracting the most important features in the input images while giving the reason as to how the model has come to the decision. Layer wise backward relevance propagation (LBRP) being the most effective explainable technique to give relevance scores, the Convolutional Neural Network (CNN) was trained using human face dataset from flickr-face-hq along with pretrained models models including VGG-16, Res-net, Mobile-net, Vision *** improve and monitor model performance, accuracy was consider along other evaluation metrics such as precision and finally Machine learning operations (MLOPs) was used for managing the pipeline resources of the model thus an accuracy 99.84% with VGG-16 was obtained along with different visualization of Grad-cam and more to give insights into each prediction made by the model. therefore due tho this effectiveness from the results obtained during image processing tasks we propose an algorithm that combines several techniques that ensure improved accuracy, precision through incorporation of interpretability using explainable methods involving LBRP and grad-cam for fake face detection and reconstruction of faces to enable capture informative features at different scales, integrating transfer learning and ensembled method for scalable performance. © 2024 IEEE.

关键词： Generative adversarial networks

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Cyber Bullying Detection Using Deep Learning and Natural Language processing 5

Cyber Bullying Detection Using Deep Learning and Natural Lan...

引用

5th international conference on IoT Based Control networks and Intelligent Systems, ICICNIS 2024

作者： Gowthami, S. Rajesh, Sudha Shagul, A.S. Papinaidu, M. Kumar, K Ashok Venkataramanaiah, B. Vignan's Institute of Information Technology Department of EEE Visakhapatnam India SRM Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Saveetha Engineering College Department of Mechanical Engineering Chennai India Aditya Institute of Technology and Management Department of English Tekkalii India Bhoj Reddy Engineering College for Women Department of ECE Hyderabad India Vel Tech Rangarajan Dr.sagunthala R&D Institute of Science and Technology Department of ECE Chennai India

ISBN: (纸本)9798331518097

Social media platforms have become one of the primary communication channels worldwide. Unfortunately, these platforms are also exploited for harmful activities, with cyber bullying being a significant concern, particularly among younger users. this research presents a novel model aimed at detecting instances of cyber bullying using deep learning techniques and Natural language processing (NLP). the research employs three datasets from Twitter, Instagram, and Facebook to predict cyber bullying behaviors through the Long Short-Term Memory (LSTM) algorithm. the findings demonstrate that the proposed model effectively identifies bullying instances, offering improvements over previous detection approaches. the model achieved accuracies of 96.64%, 94.49%, and 91.26% for the Twitter, Instagram, and Facebook datasets, respectively, highlighting its robustness across different social media platforms. © 2024 IEEE.

关键词： Tweets

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BDET 2023 - 2023 5th international conference on Big Data Engineering and Technology

BDET 2023 - 2023 5th International Conference on Big Data En...

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5th international conference on Big Data Engineering and Technology, BDET 2023

ISBN: (纸本)9798400707537

the proceedings contain 5 papers. the topics discussed include: DBSCAN-based malicious node detection to secure wireless sensor networks;a study of the impact of top manager’s Taisui on IPO;Can CEO’s environmental concern mitigate the impact of extreme weather on the performance of listed companies?;improved gray wolf optimization algorithm based on tanh activation inertia weights;and BERT-based federated learning to rank towards news retrieval.

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Design and implementation of Intelligent Water Cup system based on wireless sensor network 24

Design and implementation of Intelligent Water Cup system ba...

引用

5th international conference on Computer Information and Big Data Applications, CIBDA 2024

作者： Wang, Nan Liu, Yan Yan, Zhiqiang College of Computer Science Wuhan Donghu University WuHan430299 China College of Computer and Artificial Intelligence Wuhan University of Engineering Science Wuhan430200 China

ISBN: (纸本)9798400718106

this project has designed an intelligent water cup system based on based on wireless sensor network. the cup cover is provided with a memory area of a memory chip and an intelligent control system is embedded on the cup body, through intelligent sensors (temperature sensor, GPS sensor, pressure sensor, etc.) to achieve measurement and remind users to drink water. Its specific functions include: Using GPS sensors to monitor the location of the cup to avoid losing track;using temperature sensors can determine the ambient temperature and water temperature;through Bluetooth, transmission of basic information in the cup, including the cup of electricity, water temperature, water allowance and other information. the Intelligent Cup is also equipped with display, alarm, communication, storage, positioning and other modules, so as to achieve intelligent control, to bring more convenience to people’s lives. © 2024 ACM.

关键词： Wireless sensor networks

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Enhancing Polyphonic Character Disambiguation in Chinese Text processing with Bidirectional LSTM networks 5

Enhancing Polyphonic Character Disambiguation in Chinese Tex...

引用

5th international conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2024

作者： Li, Xuan Sun, Yu School of Computer Science and Technology Changchun University Jilin Changchun China School of Special Education Changchun University Changchun Jilin China

ISBN: (纸本)9798350368284

this study addresses the challenge of polyphonic character ambiguity in Chinese text processing through the development of a deep learning model capable of automatically identifying and resolving such ambiguities. the research begins with data collection and preprocessing, involving the acquisition of polyphonic character text data and a Chinese polyphonic character library, transformed into a suitable format for model input. Subsequently, a model architecture based on a bidirectional Long Short-Term Memory (LSTM) network was designed to enhance semantic understanding using 300-dimensional word embedding vectors. While achieving 82.98% accuracy on the test set, the model encountered high loss values during training, indicating potential issues like gradient vanishing. therefore, future efforts will concentrate on refining the model structure and training methodology to enhance numerical stability and generalization capabilities, aiming for improved performance in real-world polyphonic character disambiguation tasks. © 2024 IEEE.

关键词： Semantics

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Flow Field Super-Resolution Method Based on Graph Neural networks for Wing Profiles 5

Flow Field Super-Resolution Method Based on Graph Neural Net...

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5th international conference on Computer Engineering and Application (ICCEA)

作者： Wu, Pin Xu, Liji Liu, Zhitao Feng, Weibing He, Yaolong Shanghai Univ Shanghai Frontier Sci Ctr Mechanoinformat Sch Comp Engn & Sci Shanghai Peoples R China Shanghai Univ Sch Comp Engn & Sci Shanghai Peoples R China Shanghai Univ Shanghai Frontier Sci Ctr Mechanoinformat Sch Mech & Engn Sci Shanghai Peoples R China

ISBN: (纸本)9798350386783;9798350386776

the primary objective of flow field super-resolution technology is to enhance the resolution of flow field data while simultaneously reducing computational resource requirements through computational simulations and data processing methods. Building upon prior research, this study introduces a novel flow field super-resolution method composed of three essential components: an interpolation module, graph data preprocessing, and a graph neural network, referred to as RGHF. Within the interpolation module, the radial basis function interpolation method is employed. Furthermore, a pioneering graph convolution operation named GConv is introduced in the graph neural network and prediction modules. this approach effectively leverages low-resolution data to predict high-resolution data, resulting in reduced computational time while maintaining an acceptable level of precision. Experimental results on the RAE2822 airfoil dataset demonstrate an 81% reduction in computational time, along with robustness across varying flight conditions.

关键词： Graph Neural Network Flow Field Super-Resolution Interpolation Algorithm

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Proceedings - 2023 5th international conference on Intelligent Medicine and Image processing, IMIP 2023

Proceedings - 2023 5th International Conference on Intellige...

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5th international conference on Intelligent Medicine and Image processing, IMIP 2023

ISBN: (纸本)9798350337396

the proceedings contain 17 papers. the topics discussed include: application in tumor classification based on target morphological consistency patches adaptively extracted and priori guided feature extraction method;hierarchical construction of pulmonary vascular trees from CT images based on combined deep learning and graph topology optimization;identifying abnormal sessions via adversarial learning on heterogeneous information network;design of an epidemic prevention and control system based on SIR model: a community-based example;classification of skin tumor lesions based on transfer learning using dermoscopic images;deep neuroevolution to predict astrocytoma grade from functional brain networks;and two-stage method for automatic detection of pelvic landmarks.

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Poultry Pathogen Detection: Using Deep Learning for Coccidiosis Identification 5

Poultry Pathogen Detection: Using Deep Learning for Coccidio...

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5th international conference on Image processing and Capsule networks, ICIPCN 2024

作者： Harshitha, S. Likhitha, K. Varshini, Katta R G Mahure, Sonali Jagadish Yasaswi, Anaparthi New Horizon College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350367171

Poultry farming plays a crucial part in fulfilling the need for animal protein worldwide, but the industry faces significant challenges in controlling infectious diseases that impact bird health and productivity. Protozoan parasites of the genus Eimeria are the source of coccidiosis, which is common and economically significant poultry disease. Traditional methods of coccidiosis diagnosis rely on labor-intensive and time-consuming microscopic examination of fecal samples, leading to delayed detection and response. this study investigates the use of deep learning methods for the quick and precise diagnosis of coccidiosis in poultry. A novel deep learning model trained on a comprehensive dataset of microscopic images of fecal samples obtained from infected birds has been proposed in this study. this study presents an innovative approach to coccidiosi detection using the VGGNet architecture, a deep convolutional neural network renowned for its hierarchical feature learning capabilities. Leveraging this architecture, we explore the potential for automated identification of coccidia-infected samples through microscopic image analysis. this study evaluates the performance of the deep learning model against traditional diagnostic methods and also architectures like YOLOv5, ResNet and demonstrating its ability to significantly reduce the time required for coccidiosis detection without compromising accuracy. We also discuss the model's flexibility in a variety of environments and its potential for early identification, enabling prompt intervention and disease management. this research contributes to the ongoing efforts to modernize poultry disease diagnostics through the utilization of deep learning. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Control in Stable Diffusion through Example-based Fine-Tuning and Prompt Engineering 5

Enhancing Control in Stable Diffusion Through Example-based ...

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5th international conference on Image processing and Capsule networks, ICIPCN 2024

作者： Mallikharjuna Rao, K. Patel, Tanu International Institute of Information Technology LMCSI Data Science and Artificial Intelligence Naya Raipur India

ISBN: (纸本)9798350367171

Recent advancements in text-to-image generation allow the creation of diverse images from textual descriptions. However, personalizing these models for specific subjects remains challenging. Existing techniques like dreambooth address this somewhat, but they lack fine-grained control over the generated image. this work proposes a novel approach that combines dreambooth fine-tuning with prompt engineering for controllable, subject-specific image generation with Stable Diffusion. We leverage dreambooth to embed a unique identifier for a subject. By incorporating carefully crafted text prompts alongside dreambooth, users can guide the image generation process toward specific details like pose, environment, and lighting. this allows for highly customized image generation featuring the subject in diverse contexts, even if those elements weren't present in the initial reference images used for dreambooth training. We leverage an autogenous class-specific prior preservation loss function to ensure the subject's key characteristics are retained throughout the generation process. We demonstrate the effectiveness of our method on various tasks, including subject recontextualization, text-guided view synthesis, and artistic rendering. CLIP Score has been used as the evaluation metrics, this work establishes a new benchmark dataset specifically designed for subject-driven image generation using Stable Diffusion and prompt engineering, facilitating further research in this area. © 2024 IEEE.

关键词： Rendering (computer graphics)

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