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检索条件"任意字段=2nd International Conference on Signal Processing and Machine Learning, SPML 2019"

共 886 条记录，以下是1-10 订阅

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Proceedings of 2019 2nd international conference on signal processing and machine learning, spml 2019

Proceedings of 2019 2nd International Conference on Signal P...

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2nd international conference on signal processing and machine learning, spml 2019

ISBN: (纸本)9781450372213

The proceedings contain 19 papers. The topics discussed include: the development and trend of ECG diagnosis assisted by artificial intelligence;learning how to avoiding obstacles for end-to-end driving with conditional imitation learning;multi-scale fusion and channel weighted CNN for acoustic scene classification;multi-source radar data fusion via support vector regression;multi-scale deep convolutional nets with attention model and conditional random fields for semantic image segmentation;discrete sidelobe clutter determination method based on filtering response loss;deep neural network-based scale feature model for BVI detection and principal component extraction;and an attention-enhanced recurrent graph convolutional network for skeleton-based action recognition.

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CONF-spml 2022 - Proceedings of the 2nd international conference on signal processing and machine learning

CONF-SPML 2022 - Proceedings of the 2nd International Confer...

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2nd international conference on signal processing and machine learning, CONF-spml 2022

The proceedings contain 16 papers. The topics discussed include: melanoma classification using feature extraction methods and machine learning approaches;strategies to solve the problem of information cocoon â€" research progress of cross-domain recommendation algorithm based on mining the potential interests of users;the development of ray tracing and its future;convolutional neural network and its application in handwritten digit and traffic sign recognition;a two-step rumor detection and classification method using machine learning;lip reading using multi-dilation temporal convolutional network;comprehensive survey on video denoise methods;optimization of CNN and LSTM based application on RC frame and long-span structural health monitoring;comparison of underlying algorithms in recommendation systems;and can PID make the electronic stability program more effective? research on co-simulation of electronic stability program.

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2023 2nd international conference on Paradigm Shifts in Communications Embedded Systems, machine learning and signal processing, PCEMS 2023

2023 2nd International Conference on Paradigm Shifts in Comm...

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2nd international conference on Paradigm Shifts in Communications Embedded Systems, machine learning and signal processing, PCEMS 2023

ISBN: (纸本)9798350310719

The proceedings contain 97 papers. The topics discussed include: affective computing for emotion identification using dual-stage filtered multi-channel EEG signals;investigation of the direct source to drain tunneling in 5 nm nanotube junctionless field effect transistor;analysis and implementation of approximate 4-2 compressors for approximate multipliers;commercial clustering of Indian bamboo species using machine learning techniques;optimizing MRC tasks: understanding and resolving ambiguities;analyzing word stress and its effects on Assamese and Mizo using machine learning;anomaly detection by predicting future frames using convolutional LSTM in video surveillance;and suppression of false-terms in Wigner-Ville distribution using time and frequency windowing.

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2nd international conference on machine Intelligence and signal processing, MISP 2019

2nd International Conference on Machine Intelligence and Sig...

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2nd international conference on machine Intelligence and signal processing, MISP 2019

ISBN: (纸本)9789811513657

The proceedings contain 36 papers. The special focus in this conference is on machine Intelligence and signal processing. The topics include: Real-time RADAR and LIDAR sensor fusion for automated driving;generalizing streaming pipeline design for big data;Adaptive fast composite splitting algorithm for MR image reconstruction;extraction of technical and non-technical skills for optimal project-team allocation;modified flower pollination algorithm for optimal power flow in transmission congestion;Intelligent condition monitoring of a CI engine using machine learning and artificial neural networks;bacterial foraging optimization in non-identical parallel batch processing machines;healthcare information retrieval based on neutrosophic logic;Convolutional neural network long short-term memory (CNN + LSTM) for histopathology cancer image classification;a novel approach for music recommendation system using matrix factorization technique;forecasting with multivariate fuzzy time series: A statistical approach;nature-inspired algorithm-based feature optimization for epilepsy detection;a combined machine-learning approach for accurate screening and early detection of chronic kidney disease;backpropagation and self-organizing map neural network methods for identifying types of eggplant fruit;head pose prediction while tracking lost in a head-mounted display;recommendation to group of users using the relevance concept;ACA: Attention-based context-aware answer selection system;dense and partial correspondence in non-parametric scene parsing;audio surveillance system;mopsa: Multiple output prediction for scalability and accuracy;Generation of image captions using VGG and resnet CNN models cascaded with RNN approach;impact of cluster sampling on the classification of landsat 8 remote sensing imagery;deep neural networks for out-of-sample classification of nonlinear manifolds;FPGA implementation of LDPC decoder.

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Research on the detection and analysis of weak signals with low SNR based on the Welch algorithm 24

Research on the detection and analysis of weak signals with ...

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2nd international conference on Communication Networks and machine learning, CNML 2024

作者： Wu, Bin Zhang, Wanqing Zhang, Jun Yu, Jing Huangfu, Taoyu Hebei Key Laboratory of Marine Perception Network and Data Processing School of Computer and Communication Engineering Northeastern University at Qinhuangdao Hebei Qinhuangdao China Southeast University Jiangsu Nanjing China

ISBN: (纸本)9798400711688

Submarines are vital for military defense and national security in strategic communication systems. Power Spectral Density (PSD) characterizes signal spectrum frequency dependence and is crucial for analyzing broadband random signals. This study proposes a simplified PSD estimation method using the Welch algorithm for detecting weak signals in noisy environments, particularly in submarine communications. MATLAB simulations demonstrate that the Welch algorithm with a Blackman window function and 512-point overlap improves variance performance and resolution, especially in low signal-to-noise ratio conditions. The results underscore the algorithm’s effectiveness in enhancing signal detection in submarines. © 2024 Copyright held by the owner/author(s).

关键词： MATLAB

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Optimizing DDoS Attack Detection Using machine learning 2

Optimizing DDoS Attack Detection Using Machine Learning

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2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Aggarwal, Saransh Behera, Bhagrajyoti Singh, Murari Kumar Sharma, Ajeet Kumar School of Computing Science and Engineering Sharda University Greater Noida India School of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (纸本)9798331530389

The increase in the Distributed Denial of Service attack (DDoS) leads to a significant threat to the network security. Inability to timely and accurately detect DDoS attacks disrupts services offered by companies and the government causing financial losses. Additionally, prolonged DDoS attacks can spoil user experience and degrade company reputation. The recent literature reveals various techniques for detecting DDoS attacks, including pre-trained machine learning and deep learning algorithms. This research paper addresses this challenge by tweaking random forest algorithms for maximum accuracy using CICDDoS2019 dataset. The methodology includes collecting of dataset, data processing, feature scaling and training multiple machine learning algorithms to identify maximum accuracy for detecting DDoS attacks. The result shows improvement of accuracy and precision of machine learning models used. Future work may include training the model for other types of attacks, real time implementation of the model developed, developing a hybrid approach for detection and protection from DDoS attack. © 2025 IEEE.

关键词： Network security

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Classifying Music-Induced Emotion Using Multi-Modal Ensembles of EEG and Audio Feature Models

Classifying Music-Induced Emotion Using Multi-Modal Ensemble...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Paukner, Philipp Ripoll, Marisa Sabir, Dilvan Erdoğan, Deniz Onat Sacchetto, Luca Diepold, Klaus TUM School of Computation Information and Technology Technical University of Munich Arcisstr. 21 Munich80333 Germany

ISBN: (纸本)9798350368741

In this paper, we present our submission to the EEG-Music Emotion Recognition Challenge at ICASSP 2025. Our work focused on Task 2, where the objective was to classify the emotional state of subjects in a discrete valence-arousal space while they listen to music. Our proposed solution adopts an ensemble approach, integrating electroencephalography (EEG) signals, raw audio signals, and song features. We incorporated a diverse set of models, including Audio Spectrogram Transformer (AST) [1] and a dedicated EEG model. By combining insights from these modalities, we aimed to capture the interplay between music and emotional responses. We achieved a balanced accuracy of 41.34% on held-out data, improving the baseline by more than 11% and thus 2nd place in the competition. © 2025 IEEE.

关键词： Audio EEG Ensemble machine learning Multi Modality Music Induced Emotion

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TalTech Systems for the PROCESS signal processing Grand Challenge

TalTech Systems for the PROCESS Signal Processing Grand Chal...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Erik Illaste Tanel Alumäe Tallinn University of Technology Estonia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The PROCESS Challenge aims to detect cognitive decline, including early stages like mild cognitive impairment, through spontaneous speech. This paper describes TalTech’s systems prepared for the challenge that applied machine learning models incorporating multimodal features to address both regression and classification tasks. For regression, the Lasso model achieved an RMSE of 2.54 on the test set, achieving 2nd place in the challenge. For classification, the XGBoost model achieved a macro F1 score of 0.61, placing 6th. These results demonstrate the potential of integrating diverse speech-based features and predictive modeling for scalable, early detection of cognitive decline.

关键词： Speech analysis machine learning signal processing Predictive models Feature extraction Acoustics Speech processing Dementia

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Optimizing DDoS Attack Detection Using machine learning

Optimizing DDoS Attack Detection Using Machine Learning

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Computational Intelligence, Communication Technology and Networking (CICTN), international conference on

作者： Saransh Aggarwal Bhagrajyoti Behera Murari Kumar Singh Ajeet Kumar Sharma School of Computing Science and Engineering Sharda University Greater Noida India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

关键词： Training machine learning algorithms Accuracy Computational modeling Companies Network security Denial-of-service attack User experience Computer crime Protection

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Advancing Techniques for Deepfake Detection and Evaluation: Challenges and Innovations

Advancing Techniques for Deepfake Detection and Evaluation: ...

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Computational Intelligence, Communication Technology and Networking (CICTN), international conference on

作者： Pradum Kumar Singh Sourav Sharma Ritu Sachdeva School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

The proliferation of deepfakes-also termed artificial intelligence-generated synthetic media poses unprecedented challenges to the digital authenticity of media, for media integrity, and to societies’ trust in its credibility. Despite such significant technological advancements, current methodologies for detecting deepfakes remain somewhat fragmented, reactive, and often unable to keep pace with rapidly evolving technologies in generative AI. This article considers a comprehensive multi-modal approach toward deepfake detection by integrating an advanced machine learning algorithm, novel statistical correlation techniques, and forensic image analysis that would fill in the critical gaps in the existing frameworks. By using an extensive dataset of 10,000 synthetic and authentic media samples from diverse domains, we developed a hybrid neural network architecture that attained 94.3% accuracy on identifying AI-generated content. Our methodology utilized ensemble learning by combining spatial-temporal inconsistency detection, biological signal analysis, and advanced feature extraction algorithms. Key findings demonstrate significant vulnerabilities in current deepfake generation models, with our proposed technique successfully identifying subtle artifacts and inconsistencies across multiple generative AI platforms. This research conclusively demonstrates that proactive, adaptive detection strategies are critical to mitigating the potential risks associated with synthetic media, providing a robust framework for future technological interventions in digital forensics and media authentication.

关键词： Deepfakes machine learning algorithms Generative AI Neural networks Digital forensics signal processing algorithms Authentication Media Feature extraction Information integrity

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