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共 134 条记录，以下是1-10 订阅

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2024 ieee 5th international conference on pattern recognition and machine learning, prml 2024

2024 IEEE 5th International Conference on Pattern Recognitio...

引用

5th ieee international conference on pattern recognition and machine learning, prml 2024

ISBN: (纸本)9798350355925

the proceedings contain 76 papers. the topics discussed include: a method for Chinese sarcasm detection based on enhanced cross-entropy and regularization;CNN-BiGRU-attention: a time series-based traffic flow prediction model;inevitable exposure: analyzing the privacy paradox in the age of digital connectivity with machine learning paradigms;research on data fusion algorithms for non-stop overload detection on highways;deep learning based automatic detection algorithm of atrial fibrillation implemented on FPGA;the effect of data transformation techniques on machine learning performance: a case study on student dropout prediction;a novel semi-supervised learning method using causal margin adaptation for imbalanced classification;and revolutionizing requirements elicitation: deep learning-based classification of functional and non-functional requirements.

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2024 ieee the 5th international conference on pattern recognition and machine learning

2024 IEEE the 5th International Conference on Pattern Recogn...

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pattern recognition and machine learning (prml), ieee international conference on

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Text-to-CQL Based on Large Language Model and Graph pattern Enhancement 5

Text-to-CQL Based on Large Language Model and Graph Pattern ...

引用

5th ieee international conference on pattern recognition and machine learning, prml 2024

作者： Zhao, Xinke Yilahun, Hankiz Hamdulla, Askar School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (纸本)9798350355925

With the wide application of knowledge graphs in various scenarios and Neo4j graph databases becoming excellent knowledge graph carriers, Cypher (CQL for short) has become the most popular graph database query language. However, when performing graph database retrieval, the complex pattern and syntax make constructing CQL statements a complicated and time-consuming task. therefore, similar to Text-to-SQL, it is necessary and urgent to study an effective method for end-to-end transformation of natural languages into CQL. there have been many advances in Text-to-SQL for traditional relational databases, but these methods cannot be well applied to Text-to-CQL, so there is still a lack of effective work on Text-to-CQL for graph databases. In recent years, as large language models have been widely applied to different tasks with good results, and considering that the fundamental difference between CQL and SQL is the representation of graph patterns. We propose in this paper PA-LLM, a Text-to-CQL method that utilizes large language models combined with graph patterns enhancement, which combines large language models and subdivides the graph patterns into three categories according to their respective characteristics. they are simple query patterns, multi-hop query patterns, and function query patterns. For different graph patterns, the method optimizes the process of generating CQL for the model, which can be subdivided into four sub-methods, simple pattern enhancement method, multi-hop pattern enhancement method, function pattern enhancement method, and entity-relationship enhancement method. the experimental results show that the method improves the quality of the CQL statements generated by the model, including the logical accuracy ACCLX and the execution result accuracy ACCEX, and achieves better results on the SpCQL dataset proposed by the National Defense University of Science and Technology (NDUST). It provides an effective solution for the task of converting CQL t

关键词： Knowledge graph

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conference Committee

Conference Committee

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pattern recognition and machine learning (prml), ieee international conference on

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Generative-CNN for pattern recognition in Finance 24

Generative-CNN for Pattern Recognition in Finance

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5th international conference on AI in Finance

作者： Natarajan, Jeevesh Wang, Wayne Jiang, Yaqiao Zhang, Zeqi Ye, Huanhui Kuang, Lingxi Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9798400710810

Convolutional neural networks (CNNs) have achieved considerable success across a spectrum of computer vision tasks, with applications ranging from healthcare to automated driving. Recent literature has also explored its potential utility in trading and risk management within the finance industry. Despite their versatility, CNNs are substantially constrained by their data-hungry nature. the lack of well-labeled image datasets poses a major challenge to the widespread adoption of CNNs in financial machine learning research across academia and industry. To address these concerns, this work presents Generative-CNN, a novel approach that utilizes a generative adversarial network (GAN) to synthetically generate images to enhance the performance of a CNN with applications in trading.

关键词： Generative Adversarial Networks Convolutional Neural Networks Candlestick patterns Financial Time Series Image Classification

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5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024 - Proceedings

5th International Conference on Sustainable Communication Ne...

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

ISBN: (纸本)9798331530013

the proceedings contain 286 papers. the topics discussed include: video transmission using wireless optical communication in underwater;unveiling the dark patterns in e-commerce web sites;brain cancer detection using a fusion of convolutional neural networks and random forests in deep vision systems;advancements and applications in object detection and pattern recognition;machine learning strategies for customer churn prediction in competitive enterprises;a review on facial expression recognition models, datasets, and future direction of research;advanced gradient boosting techniques for predicting obesity risk: a comprehensive machine learning approach;and reviewing facial insight - a smart approach to identity unveiling through active facial patches and multi-task cascaded convolutional networks.

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CAE-YOLOV8: Occlusion Object Detection Based on Improved YOLOv8 5

CAE-YOLOV8: Occlusion Object Detection Based on Improved YOL...

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5th international conference on machine learning and Computer Application, ICMLCA 2024

作者： Liu, Ruiyu Zhang, Hanlin Liu, Zhe Chen, Dan Maynooth international college Fuzhou university Fuzhou China College of Electrical Engineering and Automation Fuzhou University Fuzhou China

ISBN: (纸本)9798331530334

Occlusion recognition plays a vital role in smart manufacturing and industrial automation. this paper proposes an enhanced YOLOv8-based model designed to accurately recognize and locate occluded objects in industrial environments, assisting robots in precise grasping tasks. the model integrates the AKConv module and the Effciou loss function, which improve feature extraction and bounding box regression, especially in complex and highly occluded scenarios. Experimental results confirm that the modified YOLOv8s_Effciou model achieves a balanced performance across different levels of occlusion, showing notable robustness in challenging environments with high occlusion. © 2024 ieee.

关键词： Object detection

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Smart Facial Attendance Management System Using machine learning 5

Smart Facial Attendance Management System Using Machine Lear...

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

作者： Joshi, Harnish Bharatia, Nihar Sakariya, Urmil Garg, Dweepna Goel, Parth Department of Computer Engineering Changa388421 India Department of Computer Science and Engineering Changa388421 India

ISBN: (纸本)9798331530013

Face recognition systems are essential in practically every industry in our digital age. One biometric that is frequently utilized is face recognition. It is helpful for security and has a ton of other advantages, identity, and authentication. Despite being less accurate than fingerprint and iris recognition, it is nonetheless commonly utilized because it is a non-invasive and contact-free technique. Additionally, facial recognition systems can be used to track attendance in workplaces, colleges, and schools, among other places. By solving problems with manual attendance procedures, like time consumption and the possibility of proxy attendance, this system seeks to develop an effective facial recognition-based classroom attendance system. As a result, this mechanism becomes more necessary. there are four components to this system capture face, train model, detect and recognize faces, update attendance record, and face detection. the photos of the children in the classroom are what make the data folder. the Haar-Cascade classifier and the Local Binary pattern Histogram technique are used, respectively, for face detection and recognition. the suggested technique achieves approximately 80% accuracy when three to four students are present. Faces from live streaming video are identified and discovered. As a quick and safe substitute for manual attendance procedures, attendance is tracked in XLS sheets and forwarded to the appropriate faculty members at the conclusion of the session. © 2024 ieee.

关键词： Local binary pattern

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Advancements and Applications in Object Detection and pattern recognition 5

Advancements and Applications in Object Detection and Patter...

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

作者： Govinda Rajulu, G. Savija, J. Shereen Priscila, S. Anish, T.P. Nalini, M. Siva Subramanian, R. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology India Artificial Intelligence and Data Science Velammal Engineering College Chennai India Easwari Engineering College Department of IT Chennai India R.M.K. College of Engineering and Technology Dept of CSE Tiruvallur India S A Engineering College Dept of CSE India R.M.K College of Engg and Tech Dept of CSE India

ISBN: (纸本)9798331530013

Object detection and pattern detection are fundamental problems in computer vision, and have real-world uses such as in autonomous vehicles, healthcare, and security. Over the recent years, improvements made in the deep learning framework have democratized these fields and made it easier for researchers to build accurate models. CNN has become very popular in object detection and is currently implemented through features architectures like Faster RCNN and the Single Shot MultiBox Detector (SSD). When it comes to pattern recognition, especially in sequential data, Recurrent Neural Networks (RNN), especially Long Short- Term Memory (LSTM) network outperforms the rest. However, some of the important issues that remain unresolved include issues of robustness to varying environmental conditions and the question of computation cost. From the results and analysis of the research, it can be concluded that the following directions for future studies can be distinguished, enhance the model's robustness with methods such as domain adaptation, create new, less complex architectures for real-time applications. Also, multi-modal fusion and unsupervised learning are the two areas with a brighter future in the field of automating machine learning. In sum, more advanced object detection and pattern recognition will continuously push the development and usability of these technologies for safer, more efficient, and intelligent systems at different domains. © 2024 ieee.

关键词： Intelligent systems

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English Pronunciation Transformation Text Model Based on Decision Tree pattern recognition Algorithm 5

English Pronunciation Transformation Text Model Based on Dec...

引用

5th international conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Wang, Yingying Maanshan Teacher's College Ma'anshan243000 China

ISBN: (纸本)9798350385649

With the development of various technologies, the speech recognition system is gradually improved to meet the various requirements. In this study, the focus is on English pronunciation transformation text model based on decision tree and pattern recognition algorithm. In the proposed algorithm, 3 aspects are considered. Aspect 1: the decision tree algorithm is improved, this study considers the multivariate joint partitioning can improve the overall performance of the integrator by bringing more diverse individuals into the integrator, then the targeted normal form distance function is considered for the better distance measurement. Aspect 2: the speech signal features are designed to make the better signal classification. Aspect 3: the novel noise removal algorithm is proposed to make the audio-text transformation are efficient. In the experiment section, the accuracy is selected as the measurement and the models such as Neural Networks (NN) and Support Vector machine (SVM) are considered. the accuracy of the proposed algorithm reaches more than 99% on average. © 2024 ieee.

关键词： Decision trees

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