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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3244 条记录，以下是391-400 订阅

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Power data Security Defense Technology Based on Behavior Profiling and Its Application in Cloud Environment 3rd

Power Data Security Defense Technology Based on Behavior Pro...

引用

3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Ren, Qingqing Li, Haosheng Tian, Haomiao Feng, Haonan Cao, Shu Xinjiang Uygur Autonomous Region State Grid Xinjiang Electric Power Co. Ltd. Information and Communication Company Urumqi830000 China

ISBN: (纸本)9789819616978

This article focuses on the security challenges faced by power data in cloud environments, particularly the risk of data leakage caused by abnormal user behavior. The study proposes an innovative power data security defense technology based on behavioral profiling. This technology constructs a user behavior model through fuzzy C-means clustering algorithm, analyzes real-time access and operation characteristics of power data, forms accurate behavior portraits, and uses machine learning algorithms to identify abnormal behavior patterns. The experimental results show that this technology has extremely high accuracy in detecting normal and abnormal electricity usage behavior, with most data points having an accuracy rate of over 90%, and some even reaching 100%, effectively distinguishing between normal and abnormal behavior. The research conclusion shows that the defense strategy based on behavioral profiling provides a new perspective and efficient means for the security protection of power data, significantly improving the security protection level of power data in cloud environments, and building a solid defense line for the digital transformation of the power industry and the safe operation of smart grids. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information leakage

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Digit recognition by the Implementation of Supervised learning Using a Convolutional Neural Network 3

Digit Recognition by the Implementation of Supervised Learni...

引用

3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Paliwal, Mrinal Kumar Chawla, Sunil Soni, Punit Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350342338

The recognition of handwritten numbers is a significant challenge in the field of computer vision, as it finds applications in various domains including banking, medical diagnostics, and handwriting recognition. In recent years, there has been a notable enhancement in our technological capabilities due to the progress made in internet accessibility and technology. Deep learning algorithms have emerged as valuable computational tools for addressing several challenges, including the task of digit recognition. The utilization of convolutional neural networks in the task of digit recognition is driven by their ability to acquire hierarchical representations from picture data. This scholarly article provides a comprehensive examination of the application of Convolutional Neural Networks (CNNs) in the field of machine learning for the purpose of digit recognition. This research examines the architectural components of Convolutional Neural Networks (CNNs), the training procedures employed, and the various methodologies employed for the purpose of digit recognition. The paper also provides an overview of the different datasets used for training and testing CNNs for digit recognition and a discussion of the results obtained from various studies. © 2023 IEEE.

关键词： Convolutional Neural Network Digit recognition MNIST database Neural Network Supervised learning

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Graptolite Image Classification Based on Feature Transfer and Mixup data Enhancement 3

Graptolite Image Classification Based on Feature Transfer an...

引用

3rd international conference on Computer Vision and pattern Analysis, ICCPA 2023

作者： Xu, Xiaoxiao Li, Chaofeng Liu, Honglin Institute of Logistics Science & Engineering Shanghai Maritime University Shanghai China Sinopec Petroleum Exploration and Production Research Institute Beijing China

ISBN: (纸本)9781510667563

Aiming at the problem of special shape of graptolites, insufficient training samples and manual identification of graptolites consumes a lot of time and energy, a new Graptolite image classification based on feature transfer and mixup data enhancement is proposed. First, we expand graptolite dataset by using traditional data enhancement and mixup to generate mixed samples, then use the deep learning model pretrained on the large-scale ImageNet dataset to construct a new transfer learning model through feature transfer. Finally, tenfold cross validation method is used to test the recognition accuracy of the model. The experimental results show that the classification accuracy of the proposed method on the graptolite dataset is 88.65%. Compared with the traditional data enhancement method and the pretrained model trained by ImageNet directly, the classification accuracy of graptolites is improved by 1.19%, which shows that the method has better recognition effect. © 2023 SPIE.

关键词： Image classification

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An Automatic Traffic Sign recognition and Classification Model Using Neural Networks 3rd

An Automatic Traffic Sign Recognition and Classification Mod...

引用

3rd international conference on machine learning, Internet of Things and Big data, ICMIB 2023

作者： Padhy, Rajalaxmi Samal, Alisha Dash, Sanjit Kumar Mishra, Jibitesh Odisha University of Technology and Research Bhubaneswar India

ISBN: (纸本)9789819939312

The significance of traffic symbol recognition technologies, which have played a key role in street security, has been the subject of much interest to researchers. To accomplish their assessment, specialists employed Artificial Intelligence, deep learning, and image processing tools. Convolutional Neural Networks (CNN) are deep learning-based designs that have sparked a new and ongoing research into traffic symbol classifications and recognition frameworks. The objective of this paper is to establish a CNN model that is suitable for insertion purposes and has a high level of order exactness. For the series of street symbols, we used an upgraded LeNet-5 model. The German Traffic Sign recognition Benchmark (GTSRB) information base will function as the framework for our model architecture, which outperformed existing models. GTSRB will have 99.84 percent accuracy. We decided to use a camera to verify the proposed model for an implanted application because of its softness and reduced number of boundaries (0.38 million) based on the improved LeNet-5 structure. The outcomes are advantageous, demonstrating the effectiveness of the discussed strategy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Activity recognition in Video Frames for Enhancing Human-Robot Collaboration: A machine learning Perspective 3

Activity Recognition in Video Frames for Enhancing Human-Rob...

引用

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

作者： Chaubey, Ved Prakash Sharma, Shamneesh Kumar, Aman Malik, Arun Malik, Praveen Batra, Isha UpGrad Education Private Limited Delivery and Student Success UpGrad Campus Karnataka Bangalore India School of Computer Science & Engineering Lovely Professional University Punjab India School of Electronics and Communication Engineering Lovely Professional University Punjab India

ISBN: (纸本)9798350330724

Human activity recognition is a prominent area of investigation within computer vision research. Its potential applications span diverse fields such as security monitoring, healthcare, and human-computer interaction. The identification and classification of human activity through the utilization of machine learning techniques has yielded a plethora of proposed models within this dynamic field of scientific inquiry. By employing diverse kinetic models that are linked to the learning of spatial or temporal features, the characteristics of a collection of image or video data are extracted. The primary aim of this model is to identify and classify ongoing activities. This objective has been successfully achieved by various deep-layer trained models that have been implemented in this field. To fulfill the specified criteria, the proposed approach utilizes the Long Recurrent Convolutional Network (LRCN) and Long Short-Term Memory (LSTM) convolutional neural network architectures. The implementation of Vision-Based Human Activity recognition utilizes the 3D Convolutional Neural Network (CNN) model. The model undergoes training utilizing the UCF-50 dataset. The classification of the photos by the model is contingent upon the action being performed. The model will receive videos as input, which will undergo appropriate pre-processing before being classified based on the activity. The vision-based human activity identification system offers several advantages, including but not limited to accuracy, cost-effectiveness, and user-friendliness. © 2023 IEEE.

关键词： Neural network models

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Research on Wordle’s Popularity, Pass Rate, and Difficulty Based on Deep learning 3rd

Research on Wordle’s Popularity, Pass Rate, and Difficulty ...

引用

3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Jian, Shuoqu Gu, Nan Huo, Sihan Zhao, Runhao Xu, Dan National University of Defense Technology Changsha China Sun-Yat-Sen University Guangzhou China

ISBN: (纸本)9789819624676

Wordle is a very popular word game, which is not only attracted many players, but researchers’ interests. However, the prediction, pass rate, and the distinction of difficulty are rare be studied. In this paper, we study the game popularity of Wordle by using LSTM to predict the popularity of the game based on previous scenarios. We use BP-neural network to predict the pass rate of Wordle. Moreover, we evaluate the game difficulty by TOP-SIS model, and train a classifier that can make classification on the Wordle puzzles. In experiment part, we use the data from to predict the popularity of the game on March 1, 2023, and analyze the difficulty of four game levels, predict the difficulty of some levels. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks

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Seasonal-Wise Occupational Accident Analysis Using Deep learning Paradigms 3rd

Seasonal-Wise Occupational Accident Analysis Using Deep Lear...

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3rd international conference on machine learning, Internet of Things and Big data, ICMIB 2023

作者： Nandhini, N. Anitha, A. School of Information Technology and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819939312

In recent years, occupational accidents causes a huge loss of human life and the development of the economy of the country. Many techniques are evolved for automating the safety precautions for employees in the industrial sectors such as mining, metals, construction, chemical, and electrical sections. However, the automation cannot be accurate as the data analysis is based on real-life data. Since the real-life data are imbalanced and uncertain, it is necessary to identify better tools to overcome these issues. Thus the proposed model utilizes SMOTE (Synthetic Minority Over-sampling Technique) for data balancing, whereas a rough set is used for identifying the significant features that help to maintain data consistency. The consistent data is then applied to the Deep Neural Network (DNN) for the classification process. The performance of the proposed model is checked against the evaluation metrics and compared with the existing deep learning models to exhibit the efficiency of the proposed model. Thus the findings of the proposed model may improve the abilities of safety professionals in the industrial sector to develop safety intervention activities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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Deep-learning-based research on detection algorithms for marine fish 3

Deep-learning-based research on detection algorithms for mar...

引用

3rd international conference on Computer Vision and data mining, ICCVDM 2022

作者： Li, Mengwei Wuhan Polytechnic University China

ISBN: (纸本)9781510661363

The identification of marine fish species is significant for marine fish research and conservation and the development and utilization of marine fish resources. To increase the speed and accuracy of detection of marine fish species in natural environments, this document proposes a fish recognition method based on the improved YOLOv5 model. First, the efficient channel attention mechanism module is incorporated into the backbone network to enhance the learning of local and global features of the feature map;then, the GIoU loss function is modified into the Focal-EIoU loss function to reduce the impact of low-quality samples on the detection effect. Under the same training conditions, the experimental results show that the improved YOLOv5s model achieves 94.6% detection precision, 93.8% recall rate, and 92.4% mean average precision. The enhanced fish detection method based on YOLOv5 proposed in this study has good accuracy and effectiveness and can meet the detection of marine fish species in a natural environment. © COPYRIGHT SPIE.

关键词： Object detection

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Analysis and prediction of student achievement in online teaching based on KNN 3

Analysis and prediction of student achievement in online tea...

引用

3rd international conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2022

作者： Li, Hongmei Zhang, Haihang Anhui Xinhua University China Hefei No. 11 Middle School China

ISBN: (纸本)9781510661301

Using learning analytics technology to mine online learning features can optimize the teaching process. On the basis of collecting students' online learning information, the similarity between features is firstly analyzed;secondly, machine learning algorithm is used to construct a student's learning performance prediction model, and the accuracy and K value of the model are analyzed;The teacher's teaching process reform puts forward the corresponding direction. © 2023 SPIE.

关键词： data mining

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Probability of Heart Disease using various machine learning Algorithms 3

Probability of Heart Disease using various Machine Learning ...

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3rd international conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Pillai, Suryanarayan Mogra, Saachi Shriman, Nakul Bakade, Kanchan SVKM's Nmims Mukesh Patel School of Technology Management and Engineering Department of Electronics and Telecommunication Mumbai India

ISBN: (纸本)9798350348347

Heart disease is the leading cause of death globally, requiring advanced predictive knowledge and skills. Healthcare systems lack effective analysis tools to uncover hidden patterns. Using Kaggle's dataset, an automated method aims to predict disease occurrences using data mining techniques, reducing costs and waiting times. Heart disease risk demands a sizable dataset that is excessively large and complex toprocess and analyze using traditional methods. Our goal is to identify an effective and precise method for estimating the probability of heart illness. The experimental results show the highest accuracy, Area under curve is best achieved by the random forest classifier which is about 83.96%, 92.35%, and the highest F1 score is achieved by SVM which is about 80.7%. © 2023 IEEE.

关键词： Heart

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