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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 1776 条记录，以下是31-40 订阅

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Multidimensional Time Series Analysis for Anomaly pattern Detection and Interpretation 4

Multidimensional Time Series Analysis for Anomaly Pattern De...

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

作者： Deng, Ziling Kang, Jiahua Wang, Xiaoxiao University of Electronic Science and Technology of China Computer Science and Engineering Chengdu China

ISBN: (纸本)9798350359589

the interpretability of multivariate time series anomaly detection is crucial for understanding the reasons behind anomalies, enhancing the usability and credibility of models, and ensuring successful real-world applications. Although there has been considerable research on the interpretability of time series, most are based on black-box models and have certain limitations in revealing the underlying patterns of anomalies. To address this issue, this paper focuses on identifying anomaly patterns in multivariate time series using a white-box machine learning approach, which overcomes the interpretability limitations of traditional methods in high-dimensional dynamic data processing. this approach innovatively enhances the application of Dynamic Time Warping (DTW) in higher dimensions to effectively capture anomaly patterns of any length, while identifying both short-term and long-term anomalies. Experiments on the Yahoo dataset demonstrate its high accuracy and reliability in anomaly pattern detection, particularly in explaining the reasons and impacts of identified anomaly patterns. this innovation marks a breakthrough in anomaly pattern interpretation, showing significant practicality and interpretability in real-world applications, and provides vital support for decision-making and predictive modeling in time series analysis. © 2024 IEEE.

关键词： Anomaly pattern Extraction data Interpretability Multidimensional Time Series pattern recognition

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4th international conference on Computational Methods in Science and Technology, ICCMST 2024

4th International Conference on Computational Methods in Sci...

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4th international conference on Computational Methods in Science and Technology, ICCMST 2024

ISBN: (纸本)9781032911571

the proceedings contain 167 papers. the special focus in this conference is on Computational Methods in Science and Technology. the topics include: the proposed framework for hybrid multilevel optimized machine learning classification in apple leaf disease detection;estimation of sediment load in Mahanadi River, India using artificial neural networks;prediction of water discharge in Mahanadi River basin, India using artificial neural networks;online misinformation detection using deep learning on social media: A survey and analysis;deep insight of medical images segmentation and classification by deep learning;human intrusion detection by optimize dragon deep belief network;human activity recognition using residual deep network with extreme learning;enhancing lung cancer diagnosis: A comprehensive analysis of machine learning and deep learning approaches;advanced perspectives on big data security: Navigating challenges in cloud computing;improved segmentation and classification of breast cancer using mammogram images with residual network based deep learning;analysis of early detection and prediction of diabetic retinopathy by deep learning;mustard and wheat mildew disease classification using deep learning;machine and deep learning approaches for heart disease risk assessment;uncovering agile software frameworks;calculate the asphalt pavement’s binder activity level using machine learning techniques;empowering healthcare with Swin Transformer V2: Advancing pneumonia diagnosis through deep learning;object detection in videos using deep learning methods: A survey;analysis of machine learning and deep learning techniques for credit card fraud detection in class imbalanced datasets;enhancing data security through an integrated approach of multiple key cryptography and steganography in the wavelet domain.

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4th international conference on Computational Methods in Science and Technology, ICCMST 2024

4th International Conference on Computational Methods in Sci...

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4th international conference on Computational Methods in Science and Technology, ICCMST 2024

ISBN: (纸本)9781032911571

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Use of machine learning in Object Detection for Visually Impaired Person 4

Use of Machine Learning in Object Detection for Visually Imp...

引用

4th international conference on Intelligent Technologies, CONIT 2024

作者： Munia, Tahmina Haque Taher, Kazi Abu Bangladesh University of Professionals Dhaka Department of Information and Communication Technology Bangladesh

ISBN: (纸本)9798350349900

thisreport presents an object detection model for visually impaired persons for object detection, face recognition, navigation and distance measurement. the system model uses a webcam to captures visual images. the images are compressed to a resolution of 300×300 pixels. the MobilenetV3 model is used for object detection which then are classified into human and non-human types. A face detection algorithm is used to recognize human faces. the distance measurement process uses the focal length of the camera and a calibration pattern to calculate the distance of the objects. the proposed object detection model achieved an impressive performance on the test dataset with an average precision of 92.5%, achieving an accuracy of more than 98% for human detection and approximately 91.7% for other objects. the proposed integrated model's high accuracy and precision make it a promising solution for the visually impaired people © 2024 IEEE.

关键词： Adversarial machine learning

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A Framework for Cognitive Load recognition Based on machine learning and Multimodal Physiological Signals by Wearable Sensors 4

A Framework for Cognitive Load Recognition Based on Machine ...

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4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Wang, Chenwei Li, Xinyu Huang, Jinyang Zhao, Peng Wang, Meng Zhuang, Guohang Sun, Xiao Hefei University of Technology School of Computer and Information Hefei China

ISBN: (纸本)9798350324303

Cognitive load recognition is of great significance to the study of emotional health, as excessive cognitive load not only impairs work and learning efficiency but also leads to mental pressure and fatigue, affecting overall well-being. Advances in wearable technology and understanding the role of physiological signals in assessing human health have enabled the possibility of cognitive load recognition using these signals. this work proposes a framework that combines multiple physiological signals (ECG, EDA, and PPG) collected through wearable sensors for cognitive load recognition. Heart rate variability (HRV) features are extracted from ECG signals, while statistical characteristics are extracted from EDA and PPG signals. the RFECV feature selection algorithm is utilized in the feature selection module to achieve optimal feature subset selection and redundant feature removal. the classification of cognitive load states is implemented based on the ASAPSO-LightGBM(Adaptive Simulated Annealing Particle Swarm Optimization-Light Gradient Boosting machine) machine learning model. We validated the effectiveness of the framework on two publicly available datasets (MAUS and WESAD). Experimental comparisons of feature combinations and machine learning models demonstrate that the proposed framework achieves superior performance, with recognition accuracy of 98.52% and 88.38% on the two datasets, respectively. © 2023 IEEE.

关键词： Feature Selection

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Human Activity recognition Using Sensor Measurements 4

Human Activity Recognition Using Sensor Measurements

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4th international conference on Expert Clouds and Applications, ICOECA 2024

作者： Karthick Saran, J. Mayilsamy, M. Riyash Ahamed, M. Munirathinam, Ramesh Karpagam Academy of Higher Education Department of Biomedical Engineering Coimbatore India

ISBN: (纸本)9798350385793

Accelerometer and gyroscope readings are found to be ubiquitously used for human activity recognition. these sensors are found to complement each other and increase the performance in determining the location and movement of an individual. In this work, the efficiency of multiple machine learning models is tested against six different human activities. Results show that the machine learning models have the ability of differentiating the human activities. the k-NN model is found to have an accuracy of 79.5% while the tree-based machine learning models resulted in an accuracy of 97.7%, which is comparatively high. the distance metric used in k-NN might have a significant role in determining the classes but it fails to handle the non-linearity in data. Finally, this study concludes that the tree based machine learning models are highly suitable for human activity recognition. © 2024 IEEE.

关键词： Adversarial machine learning

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Automating Forensic Analysis: Integrating machine learning Algorithms for Forensic Evidence and Serial Killer Profiling 4

Automating Forensic Analysis: Integrating Machine Learning A...

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4th international Mobile, Intelligent, and Ubiquitous Computing conference

作者： Hassan, Hana Ahmed Mohamed Abouelwafa, Mariam Mohamed Saber, Amira Alaa Ibrahim, Ahmed Ehab Shorim, Nada AbdElminaam, Diaa Salama Misr Int Univ Fac Comp Sci Cairo Egypt Benha Univ Fac Comp & Artificial Intelligence Banha Egypt

ISBN: (纸本)9798350367782;9798350367775

Serial murder investigations pose significant challenges due to the complexity of criminal patterns and behaviors. this study presents an integrated forensic analysis system using advanced artificial intelligence (AI) techniques to enhance the efficiency and accuracy of criminal investigations, particularly in serial murder cases. By applying various machine learning algorithms, including logistic regression, support vector machines (SVM), K-nearest neighbors (KNN), convolutional neural networks (CNN), and generative adversarial networks (GAN), the system analyzes key forensic evidence such as serial killer patterns, fingerprint recognition, face sketching, bloodstain analysis, and autopsy reports. Each module in the system contributes to narrowing down suspect profiles and linking potential cases using AI-driven pattern recognition and data analysis. the system demonstrated high performance across multiple forensic tasks, with logistic regression and SVM achieving 85% accuracy in serial killer pattern recognition, GAN achieving 99% accuracy in face recognition, and LSTM achieving 99.3% accuracy in bloodstain analysis. the integration of these AI techniques ensures robust, unbiased, and efficient forensic analysis, significantly reducing the time and labor required in traditional investigative methods. the findings of this study underscore the transformative potential of AI in forensic science, providing law enforcement with powerful tools to profile serial killers, analyze crime scenes, and identify critical evidence with precision. this unified AI-based approach holds promise for improving investigative outcomes and enhancing public safety by assisting in the timely resolution of complex murder cases.

关键词： Serial killer Artificial Intelligence Forensic Analysis Fingerprint face sketching Autopsy Report Bloodstain Glass Identification

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the Analysis of Network Course in Intelligent Image Acquisition Education System Based on Deep learning 4

The Analysis of Network Course in Intelligent Image Acquisit...

引用

4th international Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Ge, Qianqian Wei, Cuncun Wu, Dongyan Zhejiang Business Technology Institute Zhejiang Ningbo315012 China

ISBN: (纸本)9798331528676

the Internet, big data, and intelligent robots have transformed online education. this study introduces an Enhanced Convolutional Neural Network (CNN+) model optimized with refined weight initialization and strategic activation functions for image recognition. the CNN+ model achieves 99.5% accuracy on MNIST and 75.2% on CIFAR-10, with the Sigmoid-ReLU hybrid boosting performance to 99.2% and 72.3% respectively. these optimizations underscore the importance of strategic CNN architecture choices in enhancing performance, supporting advancements in online education platforms. the CNN+ model represents a significant breakthrough in image recognition for online education. ©2024 IEEE.

关键词： Adversarial machine learning

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Intelligent In-Car Emotion Regulation Interaction System Based on Speech Emotion recognition 4

Intelligent In-Car Emotion Regulation Interaction System Bas...

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4th international conference on Computer, Control and Robotics (ICCCR)

作者： Yang, Yuhan Zhang, Yan Zhong, Zhinan Dai, Wan Chen, Yunfei Chen, Mo Southeast Univ Sch Mech Engn Nanjing Peoples R China Nanjing Tech Univ Coll Art & Design Nanjing Peoples R China

ISBN: (纸本)9798350373141;9798350373158

With driving emerging as a common mode of transportation, the automotive industry has increasingly prioritized driving safety and experience. A substantial body of research focused on driving safety and the overall travel experience has underscored the pivotal role of emotions. In this article, we introduce an innovative in-car emotion recognition and interaction system, carefully crafted to intelligently respond to the emotional states of drivers. this system captures real-time emotional data through its user input layer and seamlessly integrates it into the technological architecture layer, residing within the vehicle's CPU. Leveraging cutting-edge deep learning models for emotion recognition, the system's outcomes trigger tailored emotion regulation strategies within the interaction feedback layer. Notably, our study introduces a groundbreaking speech fusion feature, MFCCs+, meticulously crafted for driving contexts. Furthermore, we have optimized the driving speech emotion recognition model using 1D-CNN, resulting in a remarkable 10% improvement in recognition accuracy. Subsequent validation experiments affirm the system's effectiveness in enhancing driving safety. In conclusion, the integration of emotion-based interaction solutions holds immense potential for elevating both driving safety and the overall travel experience within intelligent driving scenarios. this innovation promises to shape the future landscape of automotive travel, offering a safer and more enjoyable journey for all.

关键词： Human machine interaction (HMI) automotive travel feature selection speech emotion recognition machine learning emotion regulation

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machine learning and Deep learning Analysis of PCG data 4

Machine Learning and Deep Learning Analysis of PCG Data

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4th international conference on Artificial Intelligence and Signal Processing

作者： Kotipalli, Khyathi Devi Gayam, Vyshnavi Chandu, Hema Poojitha Abburi, Radha Samanta, Sibendu SRM Univ Dept CSE Amaravati Andhra Pradesh India SRM Univ Dept ECE Amaravati Andhra Pradesh India SRM Univ Dept ECE & CSE Amaravati Andhra Pradesh India

ISBN: (纸本)9798350350661;9798350350654

Cardiovascular diseases are some of the most common diseases today. A new estimate from the World Heart Federation (WHF) states that the number of deaths from cardiovascular diseases (CVD) increased from 12.1 million in 1990 to 20.5 million in the year 2021. In recent years, the field of healthcare has witnessed significant advancements in technology and data analysis techniques. Congenital abnormalities, diseases caused by impaired heart rhythm, vascular occlusion, post-operation arrhythmias, heart attacks and irregularities in heart valves are some of the various cardiovascular diseases. Early recognition of them is very important for obtaining positive results in treatment. One such area of research that holds great promise is the classification of heart sounds using phonocardiography (PCG). Classification of heart sounds has become increasingly important in enhancing diagnostic precision and enhancing patient care. the integration of machine and deep learning into medical diagnostics has emerged as a transformative avenue. machine and Deep learning techniques offer the potential to automate and increase the accuracy of cardiac sound analysis providing healthcare professionals with rapid and constant diagnostics support. this research contributes to efficient cardiac diagnostics, aiding the timely detection of abnormalities and enhancing patient care. It aspires to learn about the value of heart sound classification, the utilization of phonocardiography, and the application of deep learning and machine learning methods for enhancing the accuracy of classification.

关键词： Heart Sound classification machine learning Deep learning Phonocardiography Cardiovascular Diseases MFCCs GFCCs

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