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Image recognition of Wild Birds in Yunnan Based on Improved Mobile Deep learning 5

Image Recognition of Wild Birds in Yunnan Based on Improved ...

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5th international conference on pattern recognition and Artificial Intelligence, PRAI 2022

作者： Li, Yucheng Zhou, Hua Zhao, Yili Yang, Huaiyuan Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

the bird resources in Yunnan Province have great ornamental value and economic value. these variable and colorful features bring many challenges to their intelligent recognition and efficient management. the primary requirement of bird image intelligent recognition is convenience, portability and rapidity. this paper proposes MobileNet to recognize the bird images because MobileNet has smaller scale, fewer parameters and faster speed. the experiment results show MobileNet also have higher accuracy and can be applied in the bird watching and wild bird recognition. therefore, this method has good practicability and can be generalized to the identification of other wild animals. © 2022 IEEE.

关键词： Image recognition

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Inevitable Exposure: Analysing the Privacy Paradox in the Age of Digital Connectivity with machine learning Paradigms

Inevitable Exposure: Analysing the Privacy Paradox in the Ag...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Ming Cheung Stephanie Ng James Zhang Chee Peng Lim School of Art Nanjing University of Information Science and Technology Nanjing China Institute for Intelligent Systems Research and Innovation Deakin University Geelong Australia

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

this study investigates the privacy paradox, a phenomenon whereby individuals express concerns about privacy while actively engaging in activities that compromise their personal data. through a topic analysis of video transcripts from Douyin, China's most popular short video-sharing platform, we investigate the application of text analytics to examine the privacy paradox phenomenon. We leverage BERTopic, a state-of-the-art topic modelling technique, and the Meta Llama 3 Large Language Model to conduct a topic analysis of discourses related to privacy issues in Douyin videos. Our analysis generates useful insights into where and how the privacy paradox manifests in various aspects of daily life. the findings underscore the need to put in place proactive data protection measures to address public concerns about privacy.

关键词： Privacy Analytical models Soft sensors Large language models Keyword search data protection machine learning Media data models pattern recognition

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Online Election Management System Using machine learning 5

Online Election Management System Using Machine Learning

引用

5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Selvaganesh, M. Logesh, P. Manishkumar, K. Ece Sri Ramakrishna Engineering College Coimbatore India

ISBN: (纸本)9798350321425

the Online Election Management System is a web-based application. the system has a centralized database to keep the details or records of all the voters, candidates and final Results. the System was implemented based on image classification technique and sending the passwords to voters through email, for login. the System uses HTML and CSS for front-end designing and for backend python programming language and python's web framework called Django for authentication and authorization, database management. the major work in the system is image recognition, for image recognition Convolutional Neural Network (CNN) is used. CNN needs less data to train the model and efficiency is more compared with other classification algorithm YOLO. On using this application, the voter need not go to the booth. So, it is user convenient and also it is easy for the admin to manage the voters and candidate information. It is secure because it uses a two-step verification method. First the user has to login using credentials then using image classification, it recognizes the exact voter. the system prepares the dataset at the time of user registration the user credentials by generating the password randomly for the voter and sending the voter credentials to the voter registered mail id. this research work gives a complete solution for an online voting system with higher accuracy and lesser time. © 2023 IEEE.

关键词： Python

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Multi-Sensor and Deep learning based Ankle Joint Motion Intention Prediction under Diverse Gait Speed Conditions 20

Multi-Sensor and Deep Learning based Ankle Joint Motion Inte...

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IEEE 20th international conference on Automation Science and Engineering (CASE)

作者： Gong, Jumin Wei, Dunwen Gao, Tao Liu, Zekun Univ Elect Sci & Technol China Dept Mech & Elect Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9798350358513;9798350358520

Motion intention prediction is an essential challenge in improving the efficacy of exoskeleton human-machine interactions. Traditional gait phase detection and motion pattern recognition methods can only identify the current gait state, lacking the ability to adjust motion parameters based on step speed dynamically, and real-time performance is difficult to guarantee. In this paper, we proposed a deep learning model TCN-LSTM, which integrated Time Convolutional Network (TCN) and Long Short-Term Memory Network (LSTM), to predict human ankle joint angles under different walking speeds based on data from Inertial Measurement Unit (IMU) and Goniometer (GON). We selected 5 subjects in a public dataset for experimental validation. Experimental results demonstrated that the proposed model could accurately and rapidly predict ankle joint angles. the MAE of the individual-specific model was 1.191 degrees, outperforming the 3 baseline models: LSTM (1.750 degrees), Gated Recurrent Unit (GRU) (1.653 degrees), and TCN (1.340 degrees). the Mean Absolute Error (MAE) of the general model is reduced by 14.3% compared to the 3 baseline models. the prediction error of TCN-LSTM with GON is reduced by 13.69%. Furthermore, the average prediction time of our model is only 3.7ms. therefore, the accuracy and real-time performance of the model can meet the continuous prediction of human ankle joint angle.

关键词： Long short-term memory

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Contextual Analysis Using Deep learning for Sensitive Information Detection 5

Contextual Analysis Using Deep Learning for Sensitive Inform...

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

作者： Liang, Yaxin Gao, Erdi Ma, Yuhan Zhan, Qishi Sun, Dan Gu, Xingxin University of Southern California Los Angeles United States New York University New York United States Johns Hopkins University Baltimore United States Marquette University Milwaukee United States Washington University in St. Louis St. Louis United States Northeastern University Boston United States

ISBN: (纸本)9798331527662

this project intends to study A semantic feature extraction algorithm that combines semantic feature-oriented lexical vector representation with attention mechanism (A-ELMO). Firstly, the fast comparison of the dictionary tree reduces the comparison of invalid features as much as possible and greatly improves the speed of retrieval. A language pattern lexical vector (ELMo) model for context analysis is established. the method uses a dynamic word vector to describe the context, which improves the extensibility of the system. the attention mechanism is introduced to enhance the recognition degree of the sensitive features in the image. We improved detection rate of sensitive data. Compared to the traditional word-level emotion parsing algorithm, the proposed algorithm shows significant improvements in accuracy. Additionally, the accuracy of this algorithm surpasses that of traditional keyword comparison algorithms. the experimental results demonstrate that the proposed algorithm has clear advantages in enhancing the sensitivity and detection ability of the sensor. © 2024 IEEE.

关键词： Contrastive learning

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End-to-End Tibetan U-Tsang Dialect Based Speech recognition Research

End-to-End Tibetan U-Tsang Dialect Based Speech Recognition ...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Jingbo Wang Jie Zhu School of Information Science and Technology Tibet University Lhasa Tibet China Collaborative Innovation Center for Tibet Informatization by MOE and Tibet Autonomous Region Lhasa Tibet China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

To address the challenge of how to improve the accuracy and efficiency of low-resource speech recognition, we chose the Tibetan U-Tsang dialect as the object of study and increased the diversity of training data through data augmentation techniques thereby achieving the goal of improving the efficiency of speech recognition. In order to better save the information in the feature map, we use the stepwise convolution combined with the activation function approach for feature extraction; the attention layer of the Conformer model is enhanced by introducing the rotational position encoding and the enhanced model can be better adapted to different lengths. the experimental results show that our end-to-end model has a significant improvement in recognition effect, with a word error rate of only 18.73%, which effectively improves the accuracy of speech recognition in Tibetan U - Tsang dialect, and provides a new solution for speech recognition in low-resource languages.

关键词： Adaptation models Accuracy Error analysis Convolution Training data Speech recognition machine learning Feature extraction data augmentation Encoding

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machine learning Based Cardiovascular Disease Prediction 3

Machine Learning Based Cardiovascular Disease Prediction

引用

3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Saraswathi, P. Prathiksaa, J.G. Prabha, M. Jhoshitha, N.A. It Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798350330724

Heart disease is an illness that affects the heart and manifests itself in a variety of ways, including blood vessel issues, irregular heartbeats, and diseases of the heart muscle or valves. In recent years, researchers have found that machine learning can help to detect heart problems by analyzing digital electrocardiogram (ECG) data. By using this technology, doctors can detect heart issues earlier and save lives. the process involves analyzing large sets of medical data, which includes information about patients' medical history. Logistic Regression, Support Vector machine (SVM), and Random Forest are most widely utilized machine learning techniques to perform classification. Also, it predicts whether a patient has heart ailments or not. In this research, Decision Tree Classifiers are modeled along with the mentioned techniques which predict the heart related issues. A performance measure like accuracy of each technique is analyzed and the best one is identified to guess the heart disease. © 2023 IEEE.

关键词： Electrocardiograms

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the Effect of data Transformation Techniques on machine learning Performance: A Case Study on Student Dropout Prediction

The Effect of Data Transformation Techniques on Machine Lear...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Yeong Chuan Liew Khai Yin Lim Ting Yee Lim Choo Jun Tan Koh Kiong Chai Xinjie Deng Faculty of Computing and Information Technology Tunku Abdul Rahman University of Management and Technology Penang Malaysia NEUON AI Sdn Bhd Sarawak Malaysia Institute for Intelligent Systems Research and Innovation Deakin University Geelong Australia

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

this study investigates the effects of data transformation techniques on the performance of various machine learning classification models. Specifically, the standard scaler (or Z-score) and min-max scaler techniques coupled with several machine learning and statistical algorithms, including K-Nearest Neighbors, Naive Bayes, Support Vector machine, and Logistic Regression (LR), are examined systematically. Based on a set of student performance data from an institution, application of the resulting model to predicting student dropout rate is demonstrated. the empirical results indicate that LR with a standard scaler yields the highest AUC (area under the receiver operating characteristic curve) score of 89.95%.

关键词： Support vector machines Logistic regression machine learning algorithms machine learning Receivers Predictive models Nearest neighbor methods Prediction algorithms data models Standards

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machine learning Techniques for pattern recognition in High-Dimensional data mining

Machine Learning Techniques for Pattern Recognition in High-...

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Electronic Information Engineering and Computer Communication (EIECC), international conference on

作者： Pochun Li Northeastern University Boston USA

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

this paper proposes a frequent pattern data mining algorithm based on support vector machine (SVM), aiming to solve the performance bottleneck of traditional frequent pattern mining algorithms in high-dimensional and sparse data environments. By converting the frequent pattern mining task into a classification problem, the SVM model is introduced to improve the accuracy and robustness of pattern extraction. In terms of method design, the kernel function is used to map the data to a high-dimensional feature space, so as to construct the optimal classification hyperplane, realize the nonlinear separation of patterns and the accurate mining of frequent items. In the experiment, two public datasets, Retail and Mushroom, were selected to compare and analyze the proposed algorithm with traditional FP-Growth, FP-Tree, decision tree and random forest models. the experimental results show that the algorithm in this paper is significantly better than the traditional model in terms of three key indicators: support, confidence and lift, showing strong pattern recognition ability and rule extraction effect. the study shows that the SVM model has excellent performance advantages in an environment with high data sparsity and a large number of transactions, and can effectively cope with complex pattern mining tasks. At the same time, this paper also points out the potential direction of future research, including the introduction of deep learning and ensemble learning frameworks to further improve the scalability and adaptability of the algorithm. this research not only provides a new idea for frequent pattern mining, but also provides important technical support for solving pattern discovery and association rule mining problems in practical applications.

关键词： Support vector machines Deep learning machine learning algorithms Accuracy Scalability Robustness Classification algorithms Association rule learning data mining Random forests

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Sugarcane Bud Detection Using YOLOv5 6th

Sugarcane Bud Detection Using YOLOv5

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

作者： Sindhe, P. Swathi Hegadi, Ravindra S. Department of Computer Science Central University of Karnataka Kadaganchi Karnataka Kalaburagi India

ISBN: (纸本)9783031530814

this paper addresses the labor-intensive and wasteful nature of the traditional sugarcane bud cutting method. To overcome these challenges, the proposed approach leverages YOLOv5 technology for sugarcane bud identification. the machine-learning model is trained with diverse data samples, enabling it to accurately distinguish between sugarcane buds and other elements in the images. the implementation yields the best of 79% accuracy in bud detection. By automating the process, the proposed method significantly reduces labor and time requirements while minimizing sugarcane wastage. this innovation presents promising implications for the sugarcane industry, as it streamlines bud identification and optimizes resource utilization. Furthermore, the adoption of YOLOv5 technology can potentially extend to other agricultural domains, offering opportunities for enhanced crop management and sustainable farming practices. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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