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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 1791 条记录，以下是41-50 订阅

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Comparative Analysis on Prediction Model of Hate Speech Detection Based on Social Media data Using Artificial Intelligence 4

Comparative Analysis on Prediction Model of Hate Speech Dete...

引用

4th international conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Kaware, Pravin D. Raut, Anjali B. Department of Computer Science & Engineering H.V.P.M's College of Engineering & Technology Amravati India

ISBN: (纸本)9798331530402

the exponential expansion of social media networking platforms has magnified the dissemination of hate speech, thereby requiring strong recognition systems to minimize its influence. the present work offers a comparative examination of many prediction models for the recognition of hate speech using social media data, employing Artificial Intelligence (AI) methodologies. the paper intends to assess the efficiency of several machine learning (ML) and Deep learning (DL) algorithms for accurately detecting hate speech. the various strategies are evaluated using known datasets and measures including accuracy, recall, precision, and Fl-score. In addition, useful insights on the most effective methods for identifying hate speech are presented. the findings suggest that deep learning approaches are exceptionally effective at catching the contextual nuances of hate speech. Al-based hate speech detection apps improve accuracy and efficiency. Such systems are critical for creating a safer and more inclusive online. © 2025 IEEE.

关键词： Deep learning

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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 ...

引用

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

引用

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...

引用

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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Generation of Synthetic Image Anomalies for Analysis 4th

Generation of Synthetic Image Anomalies for Analysis

引用

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

作者： Breuss, David Rusy, Karel Goetzinger, Maximilian Jantsch, Axel Vienna Univ Technol ICT Gusshausstr 27 A-1040 Vienna Austria Christian Doppler Lab Embedded Machine Learning Vienna Austria Schneider Elect Power Drives GmbH Ruthnergasse 1 A-1210 Vienna Austria

ISBN: (纸本)9783031821493;9783031821509

Many powerful anomaly detection algorithms are based on machine learning and rely on datasets for training and evaluation. However, anomalous samples are often rare in real-world datasets and might not be representative of anomalies encountered in the field. In this paper, we propose a synthetic anomaly generation methodology that focuses on generating large numbers of synthetic anomalies in images with defined variances in size, shape, and texture, achieving higher diversity scores than the state-of-the-art. To demonstrate the value of the proposed generation methodology for in-depth performance analysis, we generate anomalies in three MVTec AD datasets, which we then use to analyze and evaluate several anomaly detection algorithms. While all analyzed anomaly detection algorithms showed strong recall rates on these datasets, significant sensitivity differences regarding an anomaly's size, shape, and texture are observable through the analysis with our synthetic datasets. While we observed some algorithms' robustness towards different anomaly shapes and textures, others showed differences in recall rates of up to 80% points for some pixel manipulation methods. the results demonstrate the value of our synthetic anomalies, as they boost the capability to scrutinize anomaly detection algorithms.

关键词： Synthetic data Anomalies Image Generation Analysis

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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...

引用

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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the Effect of a Weighted Mean Pooling on the Performance of Convolution Neural Network 4th

The Effect of a Weighted Mean Pooling on the Performance of ...

引用

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

作者： Shadoul, Inas Hossen, Abdulnasir Mesbah, Mostefa Al-Hmouz, Rami Sultan Qaboos Univ Dept Elect Engn Coll Engn Al Khoud Oman

ISBN: (纸本)9783031821493;9783031821509

Convolutional Neural Networks (CNNs), a deep learning application, are powerful tools particularly suited for image processing and classification applications. Pooling is a major component of CNNs and significantly influences learning. In this step, data is reduced in size through a specific algorithm or technique, resulting in the reduction of the computational load on the layers. the various pooling techniques in CNNs have specific uses, features, and effects, some desirable and others counterproductive to the training goal. Using one pooling technique can produce results that other techniques cannot. Some cases can benefit differently from different pooling techniques. this raises the question of whether combining these pooling techniques could achieve a collective positive impact, potentially leading to performance gains beyond those achievable by individual techniques used separately. A control parameter is added to optimize the selection of the pooling method or could be a weighted combination of more than one method. the results show that the presented method guarantees the same performance as a single pooling layer at least and could be improved when weighted pooling layers are involved in some datasets.

关键词： machine learning Convolution Neural Network (CNN) Pooling Mixed pooling Image classification

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Advanced Wheat Disease Detection: Utilizing an Integrated CNN-SVM Model for Leaf Image Classification 4

Advanced Wheat Disease Detection: Utilizing an Integrated CN...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Kumar, Ravi Ranjan Rana, Gautam Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331540364

Artificial Intelligence (AI) and machine learning (ML) has significantly advanced gender recognition technology, which automates the identification of a person's gender from various forms of data. this study explores the development and evaluation of a gender recognizer system leveraging the EfficientNet B0 architecture and transfer learning. the model processes input images through several steps, including dataset preprocessing, feature extraction, and classification. the effectiveness of the model is demonstrated through performance metrics such as precision, recall, F1 score, and accuracy. the results indicate a high accuracy rate of 93%, with precision and recall scores showcasing the model's robust and reliable performance in predicting gender. Additionally, the ethical considerations, including privacy concerns and potential biases in gender recognition algorithms, are discussed, emphasizing the need for fairness and transparency in the deployment of such technologies. © 2024 IEEE.

关键词： Transfer learning

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