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检索条件"任意字段=6th International Conference on Signal Processing and Machine Learning, SPML 2023"

共 763 条记录，以下是11-20 订阅

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Towards Attention-based ANN Models for Tabular Data Regression: A Use Case of Water Desalination 6

Towards Attention-based ANN Models for Tabular Data Regressi...

引用

6th international conference on Communications signal processing and their Applications

作者： Ragab, Abdallah Sheta, Omar ElBatt, Tamer Mohamed, Essam Amer Univ Cairo Dept Comp Sci & Engn New Cairo Egypt Amer Univ Cairo Inst Global Hlth & Human Ecol Ctr Excellence Water New Cairo Egypt

ISBN: (纸本)9798350384826;9798350384819

In this paper, we propose a novel machine learning model applied to water desalination processes as a potential use case. machine learning models show great promise in diverse fields, unlike model-based, often complex mathematical tools, due to their model-free nature and ability to learn from new data. In particular, we start with a classic artificial neural network (ANN) model and explore the potential of boosting its prediction performance using recently introduced attention-based models. this is among the early work to explore attention models for tabular data regression. Moreover, to the best of the authors' knowledge, it is the first to apply attention-based ANN models for tabular data regression to air gap membrane distillation (AGMD) water desalination processes. To test the merits of our proposed approach and better understand potential trade-offs, we rely on a public dataset representing AGMD water desalination processes to predict key system parameters. We demonstrate the superior performance of our proposed model compared to a classic ANN-based baseline model in [1], which is attributed primarily to incorporating attention.

关键词： machine learning ANNs Attention AGMD water desalination performance results

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Enabling Efficient Federated learning via Ternary Quantization over Fading Channels 6

Enabling Efficient Federated Learning via Ternary Quantizati...

引用

6th international conference on Communications signal processing and their Applications

作者： Pekdemir, Ada Irem Yilmaz, Ferkan Cirpan, Hakan Ali Istanbul Tech Univ Elect & Commun Engn Istanbul Turkiye ASELSAN AS Radar Elect & Intelligence Syst Div TR-06830 Ankara Turkiye

ISBN: (纸本)9798350384826;9798350384819

Federated learning(FL) performance degrades when noise and fading affect uplink and downlink transmissions between users and the Fusion Center (FC). this research addresses a gap by investigating the impact of these channel effects on global machine learning (ML) models. We propose a method to improve communication and computation efficiency in FL using ternary quantization and a doping ratio. We simulate an additive noise channel with Rayleigh fading in both uplink and downlink transmissions. Two algorithms, Stochastic Gradient Descent with Momentum (SGDM) and Gradient Descent Backpropagation (GRDBP), are used to train local ML models. ML model parameters are then quantized with three values and power-adjusted before transmission. the doping ratio explores how combining user's local ML model with the global ML model at different ratios affects re-learning. Our simulations show that a doping ratio of 0.5 or higher significantly improves the system's resistance to additive noise and channel fading, leading to higher accuracy. Further, the combination of GRDBP, ternary quantization, and a high doping ratio is shown to be particularly effective under these challenging channel conditions. this technique enables FL systems to achieve faster, more accurate performance while maintaining user data privacy.

关键词： Federated learning machine learning ternary quantization wireless communication

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Age and Gender Prediction using Paralinguistic Features of EEG signals and machine learning 6

Age and Gender Prediction using Paralinguistic Features of E...

引用

6th international conference on Communications signal processing and their Applications

作者： Alamr, Reema Alsumari, Walaa Alotaiby, Turky King Saud Univ Coll Comp Sci & Informat Technol Riyadh Saudi Arabia King Abdulaziz City Sci & Technol Inst Artificial Intelligence & Robot Technol Riyadh Saudi Arabia

ISBN: (纸本)9798350384826;9798350384819

the development of brain-computer interfaces (BCI) has sparked significant interest in leveraging electroencephalography (EEG) data for diverse applications. One of the applications is age and gender prediction of individuals. this paper introduces a novel approach that harnesses the potential of machine learning models, specifically Support Vector machines (SVM) and Random Forest (RF), in conjunction with paralinguistic feature extraction to achieve highly accurate age and gender prediction. this study used a dataset consisting of EEG recordings from 64 subjects in a relaxed position, with both open and closed eyes. By extracting paralinguistic features from the EEG signals, subtle variations in brain activity associated with age and gender differences are captured. through extensive experimentation and rigorous evaluation, the SVM model demonstrated exceptional performance, achieving an accuracy of 99.61% in both age and gender estimation. these remarkable results highlight the effectiveness of the proposed approach and the potential of paralinguistic feature extraction on EEG data as robust indicators of age and gender.

关键词： Brain-computer interfaces EEG Paralinguistic features Age prediction Gender prediction Support Vector machines Random Forest

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ANFIS-Based Deployment for Precise Electricity Load Forecasting: A Netherlands Case Study 6

ANFIS-Based Deployment for Precise Electricity Load Forecast...

引用

6th international conference on Communications signal processing and their Applications

作者： Marzbani, Fatemeh Osman, Ahmed Hassan, Mohamed S. Amer Univ Sharjah Dept Ind Engn Sharjah U Arab Emirates Amer Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates

ISBN: (纸本)9798350384826;9798350384819

Over the past years, electricity demand has exhibited a consistent upward trend. the time series data for electricity load are distinguished by their intermittent nature. Hence, accurate prediction of electricity demand is essential for the efficient operation of the electricity grid. this paper employs a two-step prediction framework for short-term predictions of electricity load for a case study in the Netherlands. In the first phase, historical data set is preprocessed and subjected to a feature engineering process. Subsequently, an Adaptive Neuro-Fuzzy Inference System (ANFIS) predictive model is applied to the preprocessed data to provide 15 minutes ahead electricity load forecast. In this stage, two different learning algorithms, namely hybrid and backpropagation, are used to train ANFIS and a comparison between the obtained results is conducted. the results indicate that the hybrid algorithm outperform the backpropagation in terms of prediction accuracy.

关键词： Electricity load ANFIS forecast time series machine learning

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machine learning and Audio signal processing for Predictive Maintenance: A review 6

Machine Learning and Audio Signal Processing for Predictive ...

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6th IEEE Colombian conference on Automatic Control (CCAC)

作者： Prieto, Rommel Bravo, Diego Univ Cauca Phys Dept Popayan Colombia

ISBN: (纸本)9798350324723

this work shows a systematic review of literature of machine learning and audio processing for applications in preventive maintenance. It intends to prove how audio signal processing combined with machine Leanring techniques can produce a powerfool tool to detect anomalies and malfunctions in electromechanical devices. the document describes a review of art state and the algorithms that can be used for preventive maintenance applications. When reviewed, the literature proves that machine learning and Deep learning approaches provide high accuracy results as tools for PdM, and that Support Vector machines, k-Nearest Neighbors and Convolutional Neural Networks are the most used approaches as they achieve the highest evaluation metrics, and prove sound, vibration and current to be the most popular signals to train ML-PdM oriented models.

关键词： Audio signal processing machine learning Predictive Maintenance Support Vector machines Industry 4.0 Classification Algortithms

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Beam Prediction in 5G and 6G Systems: A Deep learning Approach 6

Beam Prediction in 5G and 6G Systems: A Deep Learning Approa...

引用

6th international conference on Communications signal processing and their Applications

作者： Hassanein, Shaimaa Shouman, Omar Yaacoub, Elias Al-Ali, Abdulaziz Khattab, Tamer Qatar Univ Comp Sci & Engn Doha Qatar Qatar Univ IPL Lab Elect Engn Doha Qatar

ISBN: (纸本)9798350384826;9798350384819

Exploiting beamforming techniques to direct radio waves into narrow beams targeted at users are essential in 5G and 6G to ensure high data rate requirements with low latency. However, beamforming in a dynamic environment is challenging due to the high sensitivity to shift in the receiver location. therefore, an adaptive beamforming approach is needed. In this paper, we utilize DeepSense 6G and 5G MIMO datasets for optimal beam prediction using deep learning approaches. We introduce a simple approach to transform radar input into vector embeddings in the DeepSense 6G dataset. Moreover, we address the severe minority classes problem in the 5G MIMO dataset. Finally, we conduct a thorough analysis of the state-of-the-art models. the proposed model shows significant improvements in the Top-1, Top-3, Top-5, weighted average and distance based accuracies.

关键词： Beamforming 6G Sub-Terahertz Beam prediction mmWave communication Convolutional Neural Network (CNN) machine learning (ML)

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NR-V2X Quality of Service Prediction through machine learning with Nested Cross-Validation Scheme 6

NR-V2X Quality of Service Prediction Through Machine Learnin...

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6th international conference on Communications signal processing and their Applications

作者： Yazici, Ibrahim Gures, Emre Acibadem Turk Telekom R&D Dept Istanbul Turkiye Czech Tech Univ Fac Elect Engn Prague Czech Republic

ISBN: (纸本)9798350384826;9798350384819

the proliferation of connected vehicles and the advent of New Radio (NR) technologies have ushered in a new era of intelligent transportation systems. Ensuring reliable and low-latency communication between vehicles and their surrounding environment is of utmost importance for the success of these systems. this paper presents a novel approach to predict Quality of Service (QoS) in Vehicle-to-Everything (V2X) communications through nested cross-validation. Our methodology employs several machine learning (ML) methods to predict some QoS metrics, such as packet delivery ratio (PDR), and throughput, in NR-based V2X scenarios. In ML employment, nested cross-validation approach, unlike conventional cross-validation approach, prevents information leakage from parameter selection into hyperparameter selection, and this results in getting more robust results in terms of overfitting. the study utilizes real-world NR-V2X datasets to train and validate the proposed ML methods. through extensive experiments, we demonstrate the efficacy of our approach in accurately predicting QoS parameters, even in dynamic and challenging vehicular environments. In summary, our research contributes to the advancement of NR-based V2X communication systems by introducing employment of ML methods with a novel approach for QoS prediction. the combination of accurate predictions through nested cross-validation not only enhances the reliability of communication in connected vehicles' landscape but also has a supportive role for stakeholders to make informed decisions for the optimization and management of vehicular networks.

关键词： machine learning V2X nested cross-validation Quality of Service prediction

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ECG signal denoising based on wavelet threshold algorithm 23

ECG signal denoising based on wavelet threshold algorithm

引用

6th international conference on signal processing and machine learning, spml 2023

作者： Hu, Chengyu Guo, Jianxin Wang, Zheng The School of Electronic Information Xijing University Xi an China

ISBN: (纸本)9798400707575

this article discusses the application of wavelet threshold algorithms in signal denoising, which is crucial in removing noise interference in signal processing. the basics of wavelet theory and statistical characteristics of non-Gaussian noise are introduced, and existing threshold equations are improved and perfected for non-Gaussian noise backgrounds. the improved algorithm is then applied to the denoising of weak life signals, specifically electrocardiogram signals, and the results are analyzed. the combination of boosted wavelet and threshold functions is used for improvement, and the adaptive threshold of the Brige-Massart strategy is applied to process the electrocardiogram signal. Finally, the boosted wavelet threshold algorithm is found to be suitable for denoising complex Gaussian and non-Gaussian noise and can achieve good denoising effects. © 2023 ACM.

关键词： Wavelet transforms

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Unsupervised machine learning for Anomaly Detection in Wi-Fi Based IoT Networks 6

Unsupervised Machine Learning for Anomaly Detection in Wi-Fi...

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6th international conference on Communications signal processing and their Applications

作者： Zahra, Fatima Tu Bostanci, Yavuz S. Soyturk, Mujdat Marmara Univ VeNIT Lab Istanbul Turkiye BigTRI Bilisim AS Istanbul Turkiye Marmara Univ Dept Comp Engn Istanbul Turkiye

ISBN: (纸本)9798350384826;9798350384819

With the advent of 5G, there has been a significant surge in the deployment of Internet of things (IoT) devices. this proliferation, while promising, has simultaneously highlighted crucial security vulnerabilities of IoT devices due to their inherent lack of robust security features. this research focuses on enhancing the security of the IoT ecosystem through continuous monitoring to identify potential network problems and cyber threats, which often present themselves as anomalous network patterns. this paper evaluates seven unsupervised machine learning (ML) models for anomaly detection in Wi-Fi networks, utilizing data from an IoT test bed under both normal and anomalous conditions. the models are trained on real, unlabeled IoT network data, aiming to detect irregularities in link quality and connectivity. the findings are based on the assessment of these models using a separate, labeled dataset for validation. the models are subsequently deployed and assessed within the IoT system through real-time evaluation and their performance is compared. this method improves both the security and operational reliability of IoT networks by proactively identifying and resolving issues, thereby reducing downtime and ensuring the continuous operation of IoT systems.

关键词： IoT 5G anomaly detection unsupervised ML Wi-Fi networks cybersecurity

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EEG signal processing Using Deep learning for Motor Imagery Tasks: Leveraging signal Images 6th

EEG Signal Processing Using Deep Learning for Motor Imagery ...

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6th international conference on Biomedical Engineering

作者： Amran, Husna Najeha Markom, Marni Azira Awang, Saidatul Ardeenawatie Adom, Abdul Hamid Tan, Erdy Sulino Mohd Muslim Markom, Arni Munira Univ Malaysia Perlis Fac Elect Engn & Technol Arau 02600 Perlis Malaysia Univ Technol Mara Malaysia UiTM Fac Engn Kampus Shah Alam Shah Alam 40450 Selangor Malaysia Univ Malaysia Perlis Ctr Excellence Adv Sensor Technol Arau 02600 Perlis Malaysia

ISBN: (纸本)9783031803543;9783031803550

A novel approach to processing electroencephalography (EEG) signals has emerged, leveraging the utilization of signal images. the application of deep learning techniques in bypassing complex signal and image processing tasks has generated significant interest in this field. However, challenges remain in signal image processing, particularly in handling significant features and image sizes. this study presents a comprehensive investigation of EEG motor imagery signal processing, focusing on the classification of three tasks: eating, drinking, and seeking assistance. Fast Fourier Transform (FFT) is employed to extract signal image features, which are subsequently utilized in a deep learning framework. EEG data were collected from five subjects, and four transfer functions of deep learning models, namely VGG16, VGG19, ResNet50, and ResNet101, were employed for training and classification purposes. the performance of the four models was meticulously evaluated and compared. Notably, VGG16 exhibited superior performance in accurately classifying the EEG motor imagery tasks, achieving an impressive accuracy of 90%, sensitivity of 84%, and specificity of 92%. In conclusion, this study underscores the efficacy of EEG signal image processing through deep learning-based classification techniques. the findings highlight the potential of utilizing signal images in EEG analysis for motor imagery tasks, thereby contributing to the advancement of brain-computer interface technology and enhancing our understanding of neural dynamics.

关键词： Electroencephalography (EEG) signal Image Fast Fourier Transform (FFT) Convolution neural network (CNN) Deep learning

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