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

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Performance Analysis of Machine Learning Algorithms With Clustering Protocol in Wireless sensor networks 5

Performance Analysis of Machine Learning Algorithms With Clu...

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5th international conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Gantassi, Rahma Masood, Zaki Lim, Sol Sias, Quota Alief Choi, Yonghoon Chonnam Natl Univ Dept Elect Engn Gwangju 61186 South Korea

ISBN: (纸本)9781665456456

In wireless sensor networks (WSN), machine learning (ML) algorithms have an important role in cluster head (CH) selection according to several quality of service (QoS) metrics. this paper provides a comprehensive review and a case study on an experimental testbed of the implementation of various ML algorithms within various clustering protocols in WSNs.

关键词： Clustering protocol Machine Learning Quality of Service Wireless sensor networks

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Oracle Bone Script Intelligent Recognition: Automatic Segmentation and Recognition of Original Rubbing Single Characters 5

Oracle Bone Script Intelligent Recognition: Automatic Segmen...

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5th international conference on Electronic Communication and Artificial Intelligence, ICECAI 2024

作者： Tang, Haotian Tang, Rui Wang, Hanchen South China University of Technology Guangzhou China

ISBN: (纸本)9798350386943

Oracle Bone Script is the earliest mature writing system discovered in China to date. It is the source of Chinese characters and the root of excellent traditional Chinese culture, playing a significant role in promoting and inheriting Chinese culture. therefore, the automatic recognition and extraction of Oracle Bone Script characters have become an important research topic. However, due to the diversity of characters, irregular arrangement, and complex background interference, the automatic extraction of Oracle Bone Script faces many challenges. Traditional image processing techniques struggle with the complexity of Oracle Bone Script rubbings. Moreover, the scarcity of Oracle Bone Script images puts deep learning methods in a predicament of insufficient training data. this paper focuses on the research of segmentation and recognition of single characters in Oracle Bone Script rubbings. Firstly, image processing algorithms are used to normalize, expand, and denoise the Oracle Bone Script image dataset, building a preprocessing model for Oracle Bone Script images. then, the YOLOv5 model is used as the baseline model for transfer learning to modify and debug the model, training a segmentation model for Oracle Bone Script single characters. Compared to existing models for Oracle Bone Script extraction, our model has faster training efficiency and higher extraction accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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A Pick and Place Mechanism with Vision to Sort Objects of Different Colour and Size 5

A Pick and Place Mechanism with Vision to Sort Objects of Di...

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5th IEEE international conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Shweta, J. Kochuvila, Sreeja Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350379884

the traditional pick and place mechanisms in manufacturing and automation often lack adaptability, precision, and cost-effectiveness, when handling objects of different sizes and colours. this paper describes an innovative pick and place system that employs vision sensors to enhance the size and colour detection capabilities. the proposed system utilises a pre-trained monocular depth detection model along with a custom-made size detection algorithm that can measure object's dimensions accurately without having to resort to stereo vision systems. For colour detection, we adopted HSV colour space to ensure strong operation even under different lighting scenarios. A smart phone camera is used as a vision sensor to test the performance of the system. It is demonstrated that the proposed system greatly increases the pick and place machine's efficiency and versatility, thereby making it more appropriate for today's fast-changing industrial settings. © 2024 IEEE.

关键词： Stereo image processing

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Semantic Understanding in Natural Language processing: Application of Graph Neural networks in Relationship Extraction 5

Semantic Understanding in Natural Language Processing: Appli...

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5th international conference on Computers, Information processing and Advanced Education, CIPAE 2024

作者： Shan, Ying School of Chinese Ethnic Minority Languages and Literatures Minzu University of China Beijing100081 China

ISBN: (纸本)9798331527662

Semantic understanding in the prospects of natural language processing (NLP) is designed to interpret and comprehend the meaning of the prospects of text beyond words. A fundamental application of the systems revolves around relationship extraction, designed to play a fundamental role in identifying and categorizing semantic relationships between entities and text. Graph Neural networks (GNNs) to this effect are considered powerful tools within this domain that help model complex and relational data. the representation of texts into graphs through nodes that correspond to the input words signifies the syntactic and semantic relationships drawn through NLP. GNNs, therefore helping capture contextual and intricate dependencies and information through a process that enhances the extraction accuracy mainly by leveraging on the structural properties of the system. Semantic understanding in NLP, therefore handles different elements that include the non-sequential systems of language that help integrate information in varied document prospects. therefore, this article seems to draw an understanding of NLP through applying GNN in relationship extraction. Findings established that GNNs in relationship extraction remain a promising development contributing to the accurate and sophisticated semantic understanding of NLP. © 2024 IEEE.

关键词： Knowledge graph

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Ventilation Equipment and Facility Reliability Analysis Based on Evidence theory and Neural Network 5

Ventilation Equipment and Facility Reliability Analysis Base...

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

作者： He, Meisheng Lu, Weixia Hunan Institute of Traffic Engineering Hunan Hengyang China

ISBN: (纸本)9798350336030

this article proposed a new solution through the combined use of evidence theory and neural network method for the evaluation of the reliability of the mine ventilation equipment and facilities. First, the outputs of multiple BP neural network serve as the basic probability distribution function of the evidence theory, after which the results are fused according to the rules of evidence combination, from which the security degree of the ventilation system can be figured out. the example shows that the evaluation results by this method are reasonable and objective, and very effective for the reliability evaluation of mine ventilation equipment and facilities. In light of the output instability of single BP neural network, we can select multiple BP networks, and through the comparison of the uncertainty degree of different lines of BP network after their fusion, we are able to analyze the impact of the selected lines of BP network on network instability. © 2023 IEEE.

关键词： Neural networks

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Sentiment Analysis of Tibetan Short Texts Based on Graph Neural Network with Keyphrases Integration 5

Sentiment Analysis of Tibetan Short Texts Based on Graph Neu...

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5th IEEE international conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Zhang, Jing Ren, Hang Yang, Jin Qun, Nuo Ni, Shengqiao School of Information Science and Technology Tibet University Collaborative Innovation Center for Tibet Informatization by MOE and Tibet Autonomous Region Lhasa China School of Information Science and Technology Tibet University Lhasa China School of Cyber Science and Engineering Sichuan University Chengdu China

ISBN: (纸本)9798350355925

Sentiment analysis is a core task in the field of natural language processing. Despite considerable development over time, existing sentiment classification techniques for Tibetan texts still face challenges such as insufficient text comprehension and low recognition rates of emotional words. To address these issues, this paper proposes a sentiment analysis method for Tibetan short texts based on the fusion of keyphrases with graph neural networks. this method employs graph neural networks to model the textual structure and enhances sample representation by extracting keyphrases. It also utilizes an iterative heterogeneous sample alternating training strategy to further improve the model's understanding of textual semantics. Experimental results confirm that this method has improved both accuracy and F1 score by 1.7% compared to the baseline, demonstrating its effectiveness in the sentiment analysis of Tibetan short texts. © 2024 IEEE.

关键词： Graph neural networks

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Recognition of Handwritten English Alphabets Using a Lightweight Convolutional Neural Network 5

Recognition of Handwritten English Alphabets Using a Lightwe...

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5th international conference on Sustainable Communication networks and Application, ICSCNA 2024

作者： Vijay, M. Prathyusha, Goddilla Prasanthi, Garnepudi Likhitha, Gollapalli Suji, Kamatham Priya, Jarropalli Dhathri Krishnankoil India

ISBN: (纸本)9798331530013

Recognition System for Handwritten Characters Convolutional neural network customization. A key problem in pattern recognition is handwritten character recognition, which has several applications in fields including license plate recognition, postal automation, and document digitization. In this study, we present a novel Convolutional Neural Network (CNN) architecture that focuses on the 26 uppercase English alphabetic letters, with the aim of effectively recognizing handwritten characters. the model automatically extracts and processes hierarchical information from input images using a sequence of convolutional and pooling layers. We then use fully connected layers for decision-making. three convolutional layers (32, 64, and 128 filter sizes) make up the design. Maxpooling layers follow, reducing the spatial dimensions without compromising important features. Following the output feature map flattening, the model employs two dense layers. the model uses ReLU activations in layers with 512 and 256 neurons, respectively, to capture intricate interactions. Twenty-six neurones with a softmax activation function for categorization make up the last layer. this architecture strikes a balance between classification accuracy and computing efficiency, specifically for the demands of character recognition. the experimental findings demonstrate the model's capacity to generalise successfully to handwritten characters across various writing styles and noise levels. With common handwritten character datasets, the suggested system achieves competitive accuracy, demonstrating its potential for high accuracy and minimal processing overhead in real-world applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Vulnerable Object Detection Using Machine Learning 5

Vulnerable Object Detection Using Machine Learning

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5th international conference on Sustainable Communication networks and Application, ICSCNA 2024

作者： John Shiny, J. Abhishek, M. Mahendravarman, N. Velammal College of Engineering and Technology Tamilnadu Madurai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

this machine language project on object detection vulnerability is to have the safety and wellbeing of toddlers by using the advantages of machine learning techniques for identifying and assessing potentially dangerous objects in an environment that contains little children. this system uses advanced computer vision and deep learning algorithms, particularly Convolutional Neural networks (CNNs) in the analysis of real-time images or video feeds from cameras mounted in homes, daycares, or play areas. the deep learning models comprise a very good training set with high variability and diversity in images of ordinary objects and identified threats. the dataset consists of a wide range of objects that fall into one of the following categories: dangerous sharp items such as scissors and knives, dangerous small objects capable of causing choking, or poisonous substances or toxic materials if ingested. through such data processing, the CNN- based system is trained to identify and classify such hazardous things with a precision level of almost 100%. After training, it can monitor the environment continuously with the help of live video feeds. the system immediately alerts a caregiver, parents, or administrative personnel once it identifies an object that fits into one of the pre-defined hazard categories, based on the deployment scenario. that alert would then give them timely information to provide for safety concerns. this project aims to minimize the risks of accidents and injuries by providing an effective and automatic means to monitor the environments wherein the toddlers are physically active. Integrating sophisticated object detection technology with proper safety protocols will create a more secure environment for young children through reduced accidents and evidence of a secure space for play and living. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning Models for Real-Time Object Recognition: Transforming Autonomous Vehicle Safety and Navigation Capabilities 5

Deep Learning Models for Real-Time Object Recognition: Trans...

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5th international conference on Sustainable Communication networks and Application, ICSCNA 2024

作者： Sharanya, C. Karthikeya, K.V. Antony Saviour, Mariya Princy Gomathi, P.S. Pandi, V. Samuthira Prakalya, S.B. Sathyabama Institute of Science and Technology Department of ECE Tamil Nadu Chennai India AT&T Telangana Hyderabad India St.Joseph University Department of ECE Chennai India V.S.B. Engineering College Department of ECE Karur India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331530013

Real-time object recognition has been transformed by deep learning models, which have contributed to revolutionary increases in the capabilities of autonomous cars in terms of both safety and navigation. Object identification and classification systems are able to process enormous volumes of sensor data with an unparalleled level of speed and accuracy. this is made possible by the utilization of convolutional neural networks (CNNs) and advanced neural architectures. the potential of deep learning to recognize objects, pedestrians, road signs, and barriers in dynamic situations is highlighted in this research, which investigates the vital role that deep learning plays in autonomous vehicle systems. Several important difficulties, such as the optimization of the model, the efficiency of the computation, and the robustness in a variety of lighting and environments, are addressed. When it comes to enhancing vehicle decision-making, collision avoidance, and overall navigation performance, we also examine the impact that these models have on these areas. We demonstrate, by means of our research, that real-time deep learning-based object recognition is essential to the advancement of the dependability and safety of autonomous cars, hence paving the way for transportation systems that are both more secure and energy efficient. © 2024 IEEE.

关键词： Convolutional neural networks

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Biological Profile Estimation of the Human Skeleton Based Forensic Identification Using Deep Learning Image Classification 5

Biological Profile Estimation of the Human Skeleton Based Fo...

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

作者： Dutta, Sriparna Keerthana, R. Shekar, D Chandra Kumar, S. Satheesh Institute of Aeronautical Engineering Computer Science and Information Technology Hyderabad Dundigal India

ISBN: (纸本)9798331518097

this research study proposes an innovative methodology for the automated estimation of biological profiles through deep learning techniques, with a particular focus on the classification of age and gender derived from chest X-ray imaging. Utilising Convolutional Neural networks (CNNs) within the TensorFlow/Keras framework, this investigation confronts the inherent limitations associated with traditional forensic anthropology, such as examiner bias, protracted processing durations, and the variability encountered in instances of poorly preserved skeletal remains. the proposed system capitalises on chest X-rays to extend the analytical reach of skeletal assessments beyond the typically examined osseous structures, attaining an impressive 96% accuracy in gender classification alongside negligible error rates in age estimation. these findings highlight the capacity of CNNs to effectively extract and scrutinise intricate anatomical features, thereby facilitating accurate forensic profiling. In contrast to conventional methodologies, this paradigm significantly reduces the necessity for human involvement, presenting a more objective, efficient, and scalable alternative for forensic identification. the incorporation of this system into a user-centric interface enhances its applicability in practical contexts, further illustrating its practical significance. Although this study reveals considerable advancements, subsequent endeavours will concentrate on augmenting the diversity of the dataset and investigating sophisticated architectures to improve generalisability and performance metrics. this research accentuates the transformative potential inherent in AI-driven methodologies within forensic science and medical imaging, establishing a solid foundation for future innovations in these domains. © 2024 IEEE.

关键词： Convolutional neural networks

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