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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 3245 条记录，以下是121-130 订阅

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EEG-Driven machine learning Framework for Student Cognitive Load Classification

EEG-Driven Machine Learning Framework for Student Cognitive ...

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Smart Systems for applications in Electrical Sciences (ICSSES), international conference on

作者： Varunkumar MS Siddharth Gupta Pramod TC Department of Artificial Intelligence and Data Science Siddaganga Institute of Technology Tumkur Karnataka India Department of Computer Science & Engineering Siddaganga Institute of Technology Tumkur Karnataka India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Understanding differences in learning abilities is essential for improving education. This paper presents a framework to differentiate between learners using EEG data. The framework identifies critical factors that influence learning experiences by applying data-driven approaches and statistical methods. It includes analyses such as band power, entropy calculation, and coherence measurement across EEG channels. The extracted features are used to train a classifier and to identify cognitive behavior patterns. The framework's accuracy is verified using cross-validation, classification reports, and confusion matrices. The experimental analysis shows major improvements in cognitive pattern understanding and identification. This framework provides educators with actionable insights to design personalized learning strategies. By enhancing the understanding of individual learning dynamics, the framework has the potential to optimize educational outcomes across diverse learner profiles.

关键词： Support vector machines Accuracy Education machine learning Coherence Feature extraction Brain modeling Cognitive load Electroencephalography Entropy

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Enhancing Cross-Domain data Analytics Through Multi-source Transfer learning 3rd

Enhancing Cross-Domain Data Analytics Through Multi-source T...

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3rd international conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： Li, Jiarong Wang, Jingge Wang, Jingyang Xu, Chihan Wang, Hao Zhang, Chaobo Liang, Xiaojun Ding, Wenbo Gui, Weihua Pengcheng Laboratory Shenzhen 518055 China Tsinghua-Berkeley Shenzhen Institute Shenzhen International Graduate School Tsinghua University Shenzhen 518055 China RISC-V International Open Source Laboratory Shenzhen 518055 China Advanced Technology Research Institute Beijing Institute of Technology Beijing 100081 China School of Future Technology South China University of Technology Guangzhou 511442 China School of Automation Central South University Changsha 410083 China

ISBN: (纸本)9789819645053

The rapid evolution of machine learning and computer vision has given rise to numerous new tasks, primarily driven by deep convolutional neural networks’ ability to create complex mappings between feature space X and label space Y. These tasks often necessitate large-scale, human-labeled datasets for supervised training of the network. However, manual labeling is both costly and time-consuming. To overcome this hurdle, it is imperative to develop algorithms that can leverage existing labeled datasets to extract knowledge about the target domain. While most prior research has focused on single-source transfer learning, our study delves into multi-source transfer across various domains and tasks. The paper presents an improved cross-domain data analytics model that supports multi-source transfer learning by training a classifier to re-weight different sources and adjusting the rich and intricate information among sources to boost target learning. Experiments involving two source domains transferring to a single target domain showcase the effectiveness of our methodology. The model’s potential applications in image recognition include object recognition, medical imaging diagnosis, and so on. Future research could explore the scalability of the model to accommodate larger datasets and further refine the transfer learning process for more efficient knowledge extraction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional Neural Network Deep learning Image Classification Multi-source Transfer learning

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Advancements, Methods, and Obstacles in Emotion recognition with Multimodal Approaches

Advancements, Methods, and Obstacles in Emotion Recognition ...

引用

Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Arunkumar M Athiban R Selva Jothi M S Pavithra Dept of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Emotion recognition has gained an avid curiosity in the research area due to its broad range of applications in human-computer interaction particularly in real time emotion recognition, sentiment analysis, and affective computing. Deep learning techniques are advancing exponentially, automating feature engineering and transcending traditional machine learning techniques. Appeal of the above models can be highly beneficial but also raise certain technical and ethical concerns that are not often discussed enough in the existing studies. While highlighting the recent advancements, the methods involved and obstacles in emotion recognition, this review also proposes future directions in developing a standard unified framework for multimodal approaches. Categorizing and analysing the recent studies depends upon the model types employed, data modalities used and the type of fusion techniques used, this review was able to gain insights into the effect of recent multimodal approaches addressing technical and ethical concerns. Utilization of fusion techniques in multimodal models is still not reliable but they better convey the connection between different modalities and have a huge potential for development in the future. This review provides insights into the state of art multimodal methods using modalities like facial expressions, body gestures, vocal cues, text, and physiological signals for researchers to understand the trends and future directions in this field.

关键词： Deep learning Emotion recognition Ethics Sentiment analysis Affective computing Reviews Real-time systems Physiology Reliability Standards

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SRv6-INT Enabled Network Monitoring and Measurement: Toward High-Yield Network Observability for Digital Twin 3rd

SRv6-INT Enabled Network Monitoring and Measurement: Toward...

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

作者： Zhang, Xu Feng, Chuan Han, Pengchao Liu, Wei Gong, Xiaoxue Zhang, Qiupei Guo, Lei School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China School of Information Engineering Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9789819616978

Network monitoring and measurement is an important part of realizing the network digital twin. However, it introduces the problem of high cost when obtaining the status data of physical networks. Therefore, to efficiently achieve network observability, we first introduce potential techniques and network scenarios for implementing network measurements. Then, a High-Yield Segment Routing over IPv6 based In-band Network Telemetry (HY-SRv6-INT) system is proposed in this paper. In particular, we design the HY-SRv6-INT system architecture. It consists of IPv6 switches implemented by the P4 language and a Software-Defined Networking (SDN) controller containing the HY-SRv6-INT application. Next, we experimentally demonstrate a packet-level path-associated network measurement process. In addition, the performance of the HY-SRv6-INT system is also quantitatively evaluated to verify its advantages. Finally, we discuss some research challenges and open issues for future network telemetry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Internet protocols

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AI-Based Automated Subtitle Generation System for Multilingual Video Transcription and Embedding

AI-Based Automated Subtitle Generation System for Multilingu...

引用

Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： K. Penyameen G.M Siva Suriya Rajan A. Arshath Ahamed S. Yugesh Ram J John Shiny A Periya Nayaki Department of Information Technology Velammal College of Engineering and Technology Madurai Tamilnadu

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The subtitle generator system proposed in this project is an intelligent tool towards automation of subtitle production for video content., using the most advanced speech recognition and transcription and video processing technologies. U sing integration with machine learning models such as Whisper by OpenAI with multimedia tools including FFmpeg and MoviePy., it manages the production of subtitles that track up and are perfectly synchronized with spoken content within video files. It gives standard SRT file generation with direct embedding abilities into videos., hence reducing the total manual effort and time taken in the production of subtitles. Support to transcribe into multiple languages is also given with an easy Graphical User Interface GUI that provides ease of interaction with the application. This solution to video accessibility., particularly for the purpose of using more than one language., is scalable and automated to provide the best possible approach in media industry subtitle generation.

关键词： Accuracy Production Speech recognition machine learning Streaming media Media Generators data models Multilingual Graphical user interfaces

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Recommendation System Using Emotion Detection

Recommendation System Using Emotion Detection

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Susindra S Sweta B Keerthikha Shri R K. Brindha School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The need for a personalized user experience brought recommendation systems to the forefront of digital innovation. However, traditional approaches tend to often forget human emotions, which represent a critical driver in decision making. The paper proposes a new framework in which the detection of emotions becomes part of the recommendation system through advanced facial expression analysis and sentiment evaluation. The paper discusses a hybrid architecture combining emotion recognition via CNN and a collaborative filtering technique for dynamic recommendations. The study outcome demonstrated an enormous improvement in user engagement and satisfaction. It further underlines the transformative potential of emotion-aware systems.

关键词： Industries Emotion recognition Technological innovation Sentiment analysis Decision making machine learning User experience Real-time systems Internet of Things Recommender systems

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Musical Instrument recognition Using machine learning Approaches

Musical Instrument Recognition Using Machine Learning Approa...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Chethan B K Bharath K N Kiran Puttegowda Manjula A V Sunil Kumar D S Anitha K Yathiraj G R Department of Computer Science Engineering (AI&ML) Vidyavardhaka College of Engineering Mysuru India Department of ECE Dayananda Sagar Academy of Technology and Management Bengaluru India Department of ECE Vidyavardhaka College of Engineering Mysuru India Department of ECE The National Institute of Engineering Mysuru India Department of Computer Applications East Point College of Higher Education Bengaluru India Department of ECE GSSS Institute of Engineering Technology for Women Mysuru India Department of CSE Coorg Institute of Technology Ponnampet India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

This work focuses on musical instrument recognition in miscellaneous audio sources, a fundamental issue of the machine auditory system. We contrast traditional machine learning techniques against deep learning strategies in a clinical dataset solved under a supervised multi-label, multi-output classification paradigm. To enhance the performance of the system, we adjust the analysis parameters for window size, hop size, and label threshold levels. Furthermore, we explore the benefits of auxiliary feature sets as alternative data representations, specifically: Auditory features are based on two categories: (1) four-band spectral decomposition of stereo sound data, left, right, mid, and side channels and (2) the harmonic and residual model of the audio signal. Two methods for fusing the models trained on these representations are suggested and we use the best of these methods to increase recognition accuracy. These enhancements lead to a vast improvement; with a 14.25 % increase M-F score for the macro average and a 24.17 % improvement in the exact match ratio overall compared to the baseline.

关键词： Deep learning Integrated circuits Communication systems Instruments data visualization Auditory system Harmonic analysis Acoustics data models Background noise

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Development of an Automatic Speech recognition System for Irula Language: A machine learning Approach

Development of an Automatic Speech Recognition System for Ir...

引用

international Symposium on Advanced Computing and Communication (ISACC)

作者： M. Krishnaveni P. Subashini R.Janani R.Komalavalli P.Prabhusundhar G. T. Prabavathi Centre for Machine Learning and Intelligence Avinashilingam University for Home Science and Higher Education for Women Coimbatore Tamil Nadu India Gobi Arts & Science College Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The Irula language, which is predominantly spoken by the Irula tribes in southern India, offers specific challenges because of its linguistic features and limited resources. To address these challenges, this research study presents the development of an Automatic Speech recognition (ASR) system for Irula, which accelerates the conversion of spoken language into English text. In the proposed system, the data is collected and pre-processed, features are extracted, and acoustic and language models are developed for efficient translation. The spectral subtraction noise removal method is used to reduce the background noise from collected speech data during the data pre-processing. The Mel Frequency Cepstral Coefficients (MFCC) are then used to extract the features which represent the characteristics of speech signals. Acoustic modelling is implemented using Hidden Markov Models (HMMs) and their fine-tuned version. A pre-trained language model such as GPT is used for accurate translation. Finally, the web application is developed for translating Irula language into English text to increase the accessibility for Irula speakers.

关键词： Translation Noise Hidden Markov models machine learning Linguistics Feature extraction Acoustics Mel frequency cepstral coefficient Intelligent systems Automatic speech recognition

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Real Time Automated Sign Language Detection and Translation Using AI and ML Driven Hand Gesture recognition

Real Time Automated Sign Language Detection and Translation ...

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Disruptive Technologies (ICDT), international conference on

作者： Khushu Jain Yuvraj Panwar Devanshu Vats Shuchi Mala Amity School of Engineering and Technology Amity University Noida India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

This paper discusses an automatically generated real-time framework towards the conversion of Indian Sign Language into text using artificial intelligence and machine learning methodologies related to hand-gesture recognition. This system uses a CNN model to classify gestures and it processes live video input from the webcam to extract hand gesture features using MediaPipe, trying to bridge this gap in communication for the hearing-impaired community with that of the mainstream society. Thorough analyses include the system architecture, methodologies of data collection, model training, and testing processes iteratively, along with challenges such as changing lighting conditions, hand placement, and gesture speed, and strategies used to overcome these challenges. Promising results show high accuracy in real-time recognition, pointing out the potential of the framework to enhance communication accessibility. Subsequent research endeavors will prioritize the augmentation of the system to facilitate word-level recognition, incorporate multilingual functionalities, and improve its robustness for utilization across varied environments, thereby promoting wider applicability and empowering individuals with special abilities.

关键词： Hands Training Sign language Translation Accuracy Text recognition Feature extraction Real-time systems Robustness data models

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A No-Code Approach to Classifying Neurological Disorders Using Orange data mining

A No-Code Approach to Classifying Neurological Disorders Usi...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Yabez Davidraj P Elavarasan S Vivek T Ramesh Munirathinam Department of Biomedical Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Neurodegenerative diseases are progressive disorders causing neuron damage, leading to cognitive and motor impairments. This study presents a machine learning approach for classifying multiple neurological disorders using MRI data with the Orange data analytics software. A total of 165 MRI images representing conditions such as Alzheimer’s disease, Huntington’s disease, motor neuron disease, and Pick’s disease were sourced from the AANLIB database. Image features were extracted using the VGG-16 deep learning model, and classification was conducted via Support Vector machine (SVM) with polynomial, linear, radial basis function (RBF), and sigmoid kernels. The polynomial and sigmoid kernels achieved the highest classification accuracy of 93.3%, outperforming the linear and RBF kernels. These findings highlight that MRI-based classification using deep learning features and SVM in Orange can effectively differentiate neurological disorders, showing promise for early and accurate diagnosis.

关键词： Support vector machines Neurological diseases Accuracy Magnetic resonance imaging Neurons Motors Polynomials Software Kernel Alzheimer's disease

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