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共 2833 条记录，以下是201-210 订阅

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Biometric Muzzle pattern recognition for Cattle Identification using Deep learning 6

Biometric Muzzle Pattern Recognition for Cattle Identificati...

引用

6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Jayakumar, Nithya Nithish Kumar, R. Ruthrakumar, A. Saikavya, K. Department of Information Technology K. S. Rangasamy College of Technology Tiruchengode India

ISBN: (纸本)9798350398496

the ability to identify specific cows is a critical aspect of keeping cattle. Registration is essential for the production, distribution, and breeding of cows. It is dangerous to brand cows in the ear in the traditional manner. To solve this issue, we recommend using a biometric scanner for identification. Numerous studies have shown that the muzzle pattern can be used to distinguish animals and eliminate inconsistent patterns. this strategy might be effective in reducing spurious matching muzzle patterns. On the tip of their nose or muzzle, the majority of animals have a distinctive pattern. An animal's characteristic pattern is obvious to the unaided eye once it is born. Animals can be recognized by human muzzle patterns, just like with fingerprints. Systems for identifying animals are needed, as they would be useful for submitting loan and insurance applications. Based on a biometric muzzle scanner, the approach of machine learning, image processing, and computer vision has been assessed for identification purposes. © 2023 IEEE.

关键词： Biometrics

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Fast Sparse Dynamic Time Warping 26

Fast Sparse Dynamic Time Warping

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Hwang, Youngha Gelfand, Saul B. LG Display Seoul South Korea Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Dynamic time warping (DTW) has been applied to a wide range of machine learning problems involving the comparison of time series. An important feature of such time series is that they can sometimes be sparse in the sense that the data takes zero value at many epochs. this corresponds for example to quiet periods in speech or to a lack of physical activity. However, employing conventional DTW for such sparse time series runs a full search ignoring the zero data. Sparse dynamic time warping (SDTW) was previously developed which yields the exact DTW solution while reducing the time complexity by the order of a suitably defined sparsity ratio. this paper focuses on the development and analysis of a fast approximate algorithm for dynamic time warping based on the SDTW framework. We call this fast sparse dynamic time warping (FSDTW). this study includes numerical experiments which compare the performance and complexity of FSDTW with DTW, SDTW and other algorithms that approximate DTW for sparse time series. It is shown that FSDTW reduces the time complexity relative to SDTW by the order of the sparsity ratio with negligible error relative to the exact DTW distance.

关键词： Geometry Costs Heuristic algorithms Time series analysis machine learning Approximation algorithms pattern recognition

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FEPC: Fairness Estimation Using Prototypes and Critics for Tabular data 26

FEPC: Fairness Estimation Using Prototypes and Critics for T...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Giloni, Amit Grolman, Edita Elovici, Yuval Shabtai, Asaf Ben Gurion Univ Negev Dept Software & Informat Syst Engn Beer Sheva Israel

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

A machine learning (ML) fairness estimator, which is used to assess an ML model's fairness, should satisfy several conditions when used in real-life settings. Specifically, it should: i) support a comprehensive fairness evaluation that explores all ethical aspects;ii) be flexible and support different ML model settings;iii) enable comparison between different evaluations and ML models;and iv) provide reasoning and explanations for the fairness assessments produced. Existing methods do not sufficiently satisfy all of the above conditions. In this paper, we present FEPC (Fairness Estimation using Prototypes and Critics for tabular data), a novel method for fairness assessment that provides explanations and reasoning for its assessments by using an adversarial attack and customized fairness measurement. Given an ML model and data records, FEPC performs a comprehensive fairness evaluation and produces a fairness assessment for each examined feature. FEPC was evaluated using two benchmark datasets (ProPublica COMPAS and Statlog datasets) and a synthetic dataset containing two features, one of which is biased and one of which is fair, and compared to existing fairness assessment methods. the evaluation demonstrates that FEPC satisfies all of the conditions, making it suitable for real-life settings, and outperforms existing methods.

关键词： Maximum likelihood estimation Prototypes machine learning Benchmark testing Cognition data models pattern recognition

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Emotion Decoding: An Extensive Examination of Electroencephalogram Signals Using Explainable machine learning 6

Emotion Decoding: An Extensive Examination of Electroencepha...

引用

6th international conference on Electrical Engineering and Information and Communication Technology, ICEEICT 2024

作者： Nag, Anindya Mondal, Hirak Raihan Kabir, Sheikh Ridwan Islam, Md. Rabiul Ahmed, Md. Estiak Hasan Jamil, S.M. Computer Science and Engineering Discipline Khulna University Khulna9208 Bangladesh North Western University Department of Computer Science and Engineering Khulna9100 Bangladesh Islamic University Department of Biomedical Engineering Kushtia7003 Bangladesh

ISBN: (纸本)9798350385779

Emotion plays a substantial impact on an individual's mental processes and social interactions. It functions as a connection between an individual's emotions and their behaviors, or it may be stated that it occasionally impacts one's life choices. Given the individual variations in emotional patterns and their manifestations, the investigation of these phenomena should rely on methodologies that are applicable across diverse populations. In order to improve accuracy and pick specific features, the process of emotion detection utilizing brain waves or EEG data necessitates the utilization of effective signal processing techniques. Researchers have been working on several methods of human-machine interface technologies for a considerable period. In recent years, they have achieved significant progress in the automated comprehension of emotions through brain signals. Our research involved the classification and testing of three emotional states using EEG signals collected from the widely accessible EEG Brainwave dataset: Feeling Emotions from kaggle, utilizing seven machine learning techniques. this study presented a methodology that employed machine learning to identify emotions using the EEG Brainwave dataset. the study also conducted a thorough evaluation of several machine learning algorithms to assess their overall effectiveness. the study employed the Local Interpretable Model-Agnostic Explanations (LIME) to generate interpretable predictions and gain insights into the elements that influence the model's predictions. Despite the interpretability of the ML models, the XGBoost classifier had the greatest accuracy of 99% in recognition. © 2024 IEEE.

关键词： Electroencephalography

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DBGAN: A data Balancing Generative Adversarial Network for Mobility pattern recognition 25th

DBGAN: A Data Balancing Generative Adversarial Network for M...

引用

25th international conference on Big data Analytics and Knowledge Discovery (DaWaK)

作者： Zhang, Ke Liu, Hengchang Clarke, Siobhan Trinity Coll Dublin Dublin D02 R123 Ireland Univ Elect Sci & Technol China Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9783031398308;9783031398315

Mobility pattern recognition is a central aspect of transportation and data mining research. Despite the development of various machine learning techniques for this problem, most existing methods face challenges such as reliance on handcrafted features (e.g., user has to specify a feature such as "travel time") or issues with data imbalance (e.g., fewer older travelers than commuters). In this paper, we introduce a novel data Balancing Generative Adversarial Network (DBGAN), which is a specifically designed attention mechanism-based GAN model to address these challenges in mobility pattern recognition. DBGAN captures both static (e.g., travel locations) and dynamic (e.g., travel times) features of different passenger groups, and avoids using handcrafted features that may result in information loss, based on a sequence-to-image embedding method. Our model is then applied to overcome the data imbalance issue and perform mobility pattern recognition. We evaluate the proposed method on real-world public transportation smart card data from Suzhou, China, and focus on recognizing two different passenger groups: older people and students. the results of our experiments demonstrate that DBGAN is able to accurately identify the different passenger groups in the data, with the detected mobility patterns being consistent with the ground truth. these results highlight the effectiveness of DBGAN in overcoming data imbalance in mobility pattern recognition, and demonstrate its potential for wider use in transportation and data mining applications.

关键词： Mobility pattern recognition data imbalance Kernel embedding Sequence to image Generative adversarial networks Self-attention

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An Efficient Momentum Framework for Face-Voice Association learning 6th

An Efficient Momentum Framework for Face-Voice Association L...

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6th Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Qiu, Yuanyuan Yu, Zhenning Gao, Zhenguo Huaqiao Univ Dept Comp Sci & Technol Xiamen 361021 Fujian Peoples R China Key Lab Comp Vis & Machine Learning Fujian Prov Xiamen 361021 Fujian Peoples R China

ISBN: (纸本)9789819984282;9789819984299

Cross-modal face-voice association is an active field that utilizes biometric features for cross-modal information retrieval. the primary approach for addressing this task involves utilizing contrastive learning to construct a modality-agnostic subspace. However, many existing contrastive learning methods in cross-modal research tend to neglect the significance of symmetrical information within heterogeneous data. this oversight leads to the generation of different negative examples for each identity in a random mini-batch. Furthermore, the length of negative examples in contrastive learning is coupled with the mini-batch size and is limited by the GPU memory size. To address these issues, this paper introduces an innovative Cross-Modal Momentum Contrast (CMMC) algorithm, which leverages queues to provide sufficient and symmetric information. Moreover, we propose an update strategy to maintain the consistency of negative example information throughout the training process. By combining the operations mentioned above, our proposed CMMC can effectively improve the correlation between face and voice data. Extensive experiments conducted on two datasets confirm the superiority of our framework and demonstrate its competitive performance compared to state-of-the-art methods.

关键词： Cross-modal Face-voice association Momentum contrastive learning

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Fake News Investigation Using Ensemble machine learning Techniques 6th

Fake News Investigation Using Ensemble Machine Learning Tech...

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6th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2023

作者： Jain, Jai Dubey, Vansh Sama, Lakshit Kumar, Vimal Singh, Simarpreet Budhiraja, Ishan Arora, Ruchika Bennett University Uttar Pradesh Greater Noida India Capgemini Engineering Melbourne Australia SR University Ananthasagar Telangana Hasanparthy Hanumakonda India

ISBN: (纸本)9783031530814

the classification of any information as true or false has piqued the curiosity of researchers all around the world. Different types of studies are done to document the impact of misleading and fake news on the general public, as well as people’s reactions to such news. Falsified news or fabricated posts are any textual or visual content that is fake/false that is created in order for readers to believe in anything that isn’t true. For instance, a news item headlined "Beasts in White Aprons" was recently circulated on the microblogging platform-Facebook, by an acknowledged reporter from Srinagar, J &K, and many began to believe it, despite the fact that it was completely false. therefore, the main goal of this research is to apply various machine learning models to distinguish between real and fraudulent news. By using several machine learning models to discriminate between authentic and false news, we add to the expanding body of research on identifying fake news in this work. Our model performs better in scenarios in which there is limited data. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision trees

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Dialogue Generation with Skeleton Extraction and Causal Intervention 6

Dialogue Generation with Skeleton Extraction and Causal Inte...

引用

6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Wan, Zhiyun University of Science and Technology of China School of Data Science Hefei China

ISBN: (纸本)9798350325485

Retrieval and re-generate, which generates a natural language sentence through re-writing a retrieved sentence using a sequence-To-sequence generation structure, has been proven to be effective in dialogue response generation. However, the rewriting component tends to keep irrelevant information from the retrieval results, leading to low relevance in the re-written results. To address this problem, we propose a new framework (name), which extracts the skeleton from the retrieval results and uses it along with the query to generate responses. Specifically, we use the invariant learning method to capture the skeleton of the retrieval results. this solution employs causal intervention methods to identify noises in the retrieval results, enabling the generation component aware of what to keep and what to generate in the re-writing stage. Experimental results show that our approach improves the relevance and diversity of the generated results compared to the existing generative models. © 2023 IEEE.

关键词： learning systems

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Collection Storage and mining of Consumer Behavior data Editing Based on Face recognition Technology

Collection Storage and Mining of Consumer Behavior Data Edit...

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6th international conference on Computing Methodologies and Communication, ICCMC 2022

作者： Lin, Haibo Liu, Chao Guangdong University of Science and Technology College of Management Guangdong Dongguan 523083 China Guilin University of Aerospace Technology College of Management Guangxi Guilin 541004 China

ISBN: (纸本)9781665410281

this paper is based on face recognition to study the association pattern and its application in consumption data. For credit card consumption data, the data mining method is compared. the data mining technology is used to conduct customer segmentation, the selection of core indicators of customer value calculation and the calculation and analysis of value. the facial features are used as an anonymous secret key to store the user's consumption history. When customers arrive in the store, they can quickly and timely provide accurate consumer recommendation services, which can greatly improve the accuracy of sales services, improve customer satisfaction, merchant service quality, and operating profits, and provide data basis and decision support for future consumer business development deserves the value of research. © 2022 IEEE.

关键词： Face recognition

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Using Transformers to Classify Arabic Dialects on Social Networks 6

Using Transformers to Classify Arabic Dialects on Social Net...

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6th international conference on pattern Analysis and Intelligent Systems, PAIS 2024

作者： Adel, Berhoum Eddine Meftah, Mohammad Charaf Laouid, Abdelkader Chait, Khaled Kara, Mostefa University of El Oued University of El Oued Liap Laboratory El-Oued Algeria National Higher School of Mathematics Scientific And Technology Hub of Sidi Abdellah Algiers16093 Algeria University of El Oued Liap Laboratory El-Oued Algeria

ISBN: (纸本)9798350350265

Arabic dialects are the history and civilization of Arab societies and distinguish people and cities from each other in cultures, traditions, and customs. Social media networks provide speakers of various dialects with spaces through comments and tweets to spread their culture and traditions in local and regional dialects. In this research, we propose an intelligent method that extracts attention-grabbing words in the text and extracts their features and connections after converting them into digital data for processing. this research classifies these texts using the latest natural language processing applications, namely transformers, based on the attention mechanism. We implemented two transformer models: BERT with Deep learning (Bidirectional Encoded Representations of Transformers) and DistillBERT with machine learning, a small, fast, cheap, and lightweight transformer model trained by the BERT Distilling Base. Both are pre-trained text encoders, and we implemented the new regularization approach for the final classification task. Our experiment used a text dataset for five Arabic dialects (Algerian, Tunisian, Egyptian, Levantine, and Gulf Arabic). Both models provide outstanding performance on the dataset regarding accuracy and speed. © 2024 IEEE.

关键词： Deep learning

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