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检索条件"任意字段=4th International Conference on Machine Learning and Data Minining in Pattern Recognition"

共 1778 条记录，以下是171-180 订阅

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Enhanced Feline Facial recognition: Advancing Cat Face Detection with YOLOv8 and TensorRT 4

Enhanced Feline Facial Recognition: Advancing Cat Face Detec...

引用

4th international conference on Computer Vision and pattern Analysis, ICCPA 2024

作者： Wang, Teng University College Durham University DurhamDH1 3LE United Kingdom

ISBN: (纸本)9781510682528

In the field of computer vision, the ability to accurately detect and recognise animal features in various environments is an area of growing interest and application. this study presents an advanced cat face detection system utilising the You Only Look Once Version 8 (YOLOv8) architecture, enhanced with TensorRT optimisation for real-time processing. the approach involves a comprehensive data augmentation process to improve detection accuracy across diverse cat breeds and environmental conditions. Performance evaluation is based on quantifiable metrics;the optimised model achieves a notable reduction in inference time from 50.1ms to 0.9ms and a decrease in GPU power usage from 77 watts to 63 watts, without compromising accuracy. the accelerated processing speed and reduced power requirements make the system highly suitable for real-time applications, such as pet monitoring systems or behaviour analysis tools, where rapid and accurate detection is paramount. the research highlights the potential of deep learning algorithms in precise animal feature recognition and contributes to the field of computer vision by addressing challenges in small, diverse object detection. © 2024 SPIE.

关键词： Object detection data modeling Performance modeling Education and training Facial recognition systems Animals Computer vision technology Deep learning

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Predictive Analytics in Sports: Using machine learning to Forecast Outcomes and Medal Tally Trends at the 2024 Summer Olympics 4

Predictive Analytics in Sports: Using Machine Learning to Fo...

引用

4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Moolchandani, Jhankar Chole, Vikrant Sahu, Santosh Kumar, Rahul Shukla, Anshita Kumar, Ashok Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Madhya Pradesh Gwalior India

ISBN: (纸本)9798350387490

this paper focuses on using machine learning approaches in predicting the medal projections and analyzing the medal distribution pattern in the 2024 Summer Olympics. Due to the availability of a large number of variables both at the individual athlete level and the country level as well as event specific measures, various statistical models are trained to predict the number of medals and unearthing traits that are associated with success in Olympics. the evaluation of the predictions of the models is done using linear regression and random forest along with support vector classifier, as well as artificial neural network classifiers. Using the research findings, it becomes evident that predictive analytics has the capacity of offering a better understanding of the performance of sport teams and formulation of strategies for future competition events. As is observed from the above findings the random forest model sampled the highest accuracy of 99. 034 percent in the general set out of the four models;thus, it is recommended ideal for complicated set data investigations Based on the results of the experiments, it is clearly observed that the random forest has the highest accuracy of 99. 034. © 2024 IEEE.

关键词： Predictive analytics

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Algorithmic study of multi-sensor fusion-based vehicle navigation: comparative analysis of Kalman filter and machine learning models 4

Algorithmic study of multi-sensor fusion-based vehicle navig...

引用

4th international conference on Computer Vision and pattern Analysis, ICCPA 2024

作者： Zhang, Jiayang Fuzhou University Fuzhou China

ISBN: (纸本)9781510682528

the purpose of this study is to investigate the development and evaluation of a high integrity navigation system for vehicular applications, focusing on the fusion of Global Positioning System (GPS) and Inertial Measurement Unit (IMU) data. the study compares the similarities and differences in performance between the Kalman filter (a traditional GPS/IMU integration method) and machine learning models. the experiments are based on KITTI GPS/IMU sequences, and the impact of these methods on the performance of the navigation system is evaluated by introducing different noise levels. First, a Kalman filter is used to fuse the GPS/IMU data and the estimation error of the trajectories is investigated by adjusting the noise level. Second, a machine learning model is introduced to compare its performance under different parameter configurations using random forest regression as an example. In addition, the effects of different parameters on the performance of the two methods are analyzed, which provides an important reference for choosing a suitable navigation system. the results show that the Kalman filter model basically outperforms the machine learning model in terms of mean square error (MSE) and mean absolute error (MAE) of trajectory estimation. However, the random forest regression model performs best after tuning. the paper concludes with a comparison between the Kalman filter and the random forest regression model, emphasizing the robustness and adaptability of the random forest regression model in GPS/IMU fusion. the research results provide insights for the selection and design of navigation systems. © 2024 SPIE.

关键词： Kalman filters

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2023 4th international conference on Artificial Intelligence and data Sciences: Discovering Technological Advancement in Artificial Intelligence and data Science, AiDAS 2023 - Proceedings

2023 4th International Conference on Artificial Intelligence...

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4th international conference on Artificial Intelligence and data Sciences, AiDAS 2023

ISBN: (纸本)9798350318432

the proceedings contain 65 papers. the topics discussed include: final year project repository with automated classification using TF-IDF;identification of road surface defects using multiclass support vector machine;adaptive ids concept with PRBS multi inputs multi outputs (MIMO) and matched filtering algorithm;3D visualization of femoral shaft fractures;harnessing natural language processing for mental health detection in Malay text: a review;a survey on using spatio-temporal networks for rainfall prediction;authorship attribution in Bahasa Indonesia using twitter dataset on political topic;implementing vision transformer to model emotions recognition from facial expressions;BERT-BiLSTM architecture to modelling depression recognition for Indonesian text from English social media;and Xception based transfer learning combined with residual block for facial emotion recognition.

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Balancing Class Distribution in Microarray Analysis Using GenAI 4th

Balancing Class Distribution in Microarray Analysis Using Ge...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Anurekha, G. Amutha, S. Nivethika, K. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9783031821554;9783031821561

Microarrays are sophisticated datasets that have a substantial number of features and a limited number of samples. this can lead to a class imbalance problem. Balancing class distribution is an important research domain. In this paper, a novel variational auto encoder model is proposed for class imbalance mitigation. the proposed model employs an adversarial loss function to generate simulated data. the proposed model is evaluated using the autism gene expression dataset. the obtained experimental results prove that the data generated from the proposed model have obtained noteworthy results compared with the original dataset by employing three different classifiers.

关键词： Class imbalance Deep learning Microarray Simulated data Variational autoencoder

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Research on Face recognition System Based on Intelligent machine learning Algorithm 4

Research on Face Recognition System Based on Intelligent Mac...

引用

4th IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Zou, Xiaosong School of Design and Art Jingdezhen Ceramic University Jingdezhen China

ISBN: (纸本)9798350359589

this paper introduces a real-time face detection technology based on TMS320C6201. through the confidential communication between each subsystem, the synchronization of each subsystem is completed, and the real-time face recognition, feature code extraction, and the closest face matching are carried out. Firstly, Grabcut foreground extraction method is used for pre-background segmentation of recognized images to reduce external interference, and then face detection and identification are carried out according to the segmentation effect. A parallel MPI program is developed by transforming the traditional serialization-based face information updating method into a parallel method. this paper applies existing MPI-based methods and existing web-based facial information acquisition methods to improve the efficiency of existing face recognition technologies. It realizes the distributed processing of the update algorithm in the original face recognition system and enhances the ability of the system to process a large amount of data to achieve the purpose of improving the system performance. the experimental results show that the system combining grab cut and Adaboost algorithm can improve the recognition rate and detection rate, and the recognition speed is faster. © 2024 IEEE.

关键词： Facial recognition grid card algorithm identification simulation test

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Towards Exploring Adversarial learning for Anomaly Detection in Complex Driving Scenes 1

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4th international conference on Deep learning theory and Applications (DeLTA)

作者： Habib, Nour Cho, Yunsu Buragohain, Abhishek Rausch, Andreas Tech Univ Clausthal Inst Software & Syst Engn Arnold Sommerfeld Str 1 D-38678 Clausthal Zellerfeld Germany

ISBN: (数字)9783031390593

ISBN: (纸本)9783031390586;9783031390593

One of the many Autonomous Systems (ASs), such as autonomous driving cars, performs various safety-critical functions. Many of these autonomous systems take advantage of Artificial Intelligence (AI) techniques to perceive their environment. But these perceiving components could not be formally verified, since, the accuracy of such AI-based components has a high dependency on the quality of training data. So machine learning (ML) based anomaly detection, a technique to identify data that does not belong to the training data could be used as a safety measuring indicator during the development and operational time of such AI-based components. Adversarial learning, a sub-field of machine learning has proven its ability to detect anomalies in images and videos with impressive results on simple data sets. therefore, in this work, we investigate and provide insight into the performance of such techniques on a highly complex driving scenes dataset called Berkeley DeepDrive.

关键词： Adversarial learning Artificial intelligence Anomaly detection Berkeley DeepDrive (BDD)

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Image recognition Using machine learning for Classifying Finger Print recognition using direction filtering 4

Image Recognition Using Machine Learning for Classifying Fin...

引用

4th international conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Bhargava, Neeraj Bhargava, Ritu Taterh, Swapnesh Rathore, Pramod Singh Bhowmick, Apoorva Mds University Ajmer India Ajmer India Amity University Rajasthan India Manipal University Jaipur Jaipur India United Nations Secretariate New YorkNY10017 United States United Nations Secretariate Baghdad Iraq

ISBN: (纸本)9798350393248

Image processing is the new trend of implementing the image classification and many kinds of researchers are being working on this kind of implementation to process the data in the form of images. the current paper is focusing on identifying the images and processing the fingerprints which hold the secret information. We learn about steganography preparation, that involves the images which consists of secret information, and we need to decrypt that kind of information with some tools and techniques. Here in this article, we are focusing on the new concept of image processing which can consider the fingerprint of the human to hide secret information. this kind of information is very sensitive and can be shared with anyone and the concept behind photo-response non- uniformity is the way of linking pictures to the camera sensors which are having well established finger printing information. the information we transform from one user to the other user in the government organizations must be confidential and we are performing better PRNU with machine learning and the implementation consists of different neural network approaches and these approaches are more likely to work with existing approaches of PRNU. machine learning and image processing will lead the concept of highest secured data transmission with the data encrypted in the form of fingerprint. © 2023 IEEE.

关键词： Cryptography

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Intelligent Bone Fracture recognition Framework: Convolutional Neural Network(CNN): Note: Sub-titles are not captured in Xplore and should not be used 4

Intelligent Bone Fracture Recognition Framework: Convolution...

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4th IEEE international conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Mishra, Debani Prasad Bhal, Arpita Kumar, Ayush Pradhan, Anirudh International Institute of Information Technology Bhubaneswar India International Institute of Information Technology Bhubaneswar India

ISBN: (纸本)9798331528171

In this study, we introduce a method to diagnose bone disease using advances in machine learning (ML) and deep learning (DL) technologies. Our scheme combines several different technologies with convolutional neural networks (CNN) to distinguish healthy bones. this new hybrid model represents a breakthrough in bone diagnosis and provides a significant performance in computation time compared to existing deep CNN models. the main innovation is that we use an improved sharp-edge algorithm to accurately identify cracks in X-ray images. then the grayscale image and the corresponding image are input into the algorithm for training and evaluation. We perform a performance test evaluating our method against a state-of-the-art deep CNN model using skeletal muscle data. Our results showed the best performance in fracture diagnosis, achieving irregularity, F1 score, and recall of 96.12%, 99%, and 98%, respectively. this demonstrates the effectiveness of combining algorithms to improve CNN performance, demonstrating the potential of our method to revolutionize bone diagnosis in clinical practice. © 2024 IEEE.

关键词： Convolutional neural networks

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Application of Deep learning Neural Networks in Sports Shoe Brand Classification Task 4

Application of Deep Learning Neural Networks in Sports Shoe ...

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4th international conference on Computer, Big data and Artificial Intelligence, ICCBD+AI 2023

作者： Li, Biao Sun, Guang Sheng, Haoyu Liu, Junjun Hunan University of Finance and Economics School of Information Technology and Management Changsha China Central South University School of Computer Science and Engineering Changsha410083 China

ISBN: (纸本)9798350373233

In recent years, deep learning has gained widespread attention and importance due to its outstanding performance in image feature learning. the rapid development of machine learning and deep learning has provided a powerful tool for handling complex classification problems and has achieved significant results. the development of neural network algorithms has also benefited from the progress in deep learning research. Deep learning is a machine learning method based on multi-layer neural network structures, which improves the modeling capability for complex data and tasks by increasing the depth and complexity of the network. For the task of classifying shoe brands, deep learning can help neural network models better understand features such as appearance, texture, and brand logos of shoes, as well as identify other everyday items to assist in their classification. After identifying the items, using different optimizers can improve the accuracy of classification, enabling more effective classification decisions. © 2023 IEEE.

关键词： Image recognition

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