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

共 769 条记录，以下是471-480 订阅

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Research on machine learning Classification of Mild Traumatic Brain Injury Patients Using Resting-State Functional Connectivity 6th

Research on Machine Learning Classification of Mild Traumati...

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6th international conference on Cognitive Systems and signal processing, ICCSIP 2021

作者： Li, YuXiang Shen, Hui Xie, Hongwei Hu, Dewen College of Intelligence Science and Technology National University of Defense Technology Hunan Changsha China

ISBN: (纸本)9789811692468

In recent years, the incidence of mild traumatic brain injury (mTBI) has been increasing, especially in the military because of soldiers’ special working environment. Current diagnostic systems relying too much on patients’ self-description leads to improper diagnosis and treatment. Finding biomarkers of mTBI becomes significant for diagnostic accuracy improvement. We used machine learning methods to extract highly discriminative functional connectivity features and to discriminate mTBI patients from healthy controls. 31 mTBI patients and 31 healthy controls with matching age, gender and education level underwent resting-state functional magnetic resonance imaging. A promising classification accuracy of 75.81% was achieved using resting-state functional connectivity as features. Moreover, some functional connectivities between certain brain regions of the cerebellum and the sensorimotor were found to exhibit the highest discriminative power, which might provide a new idea for the discovery of stable biomarkers of mTBI. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Biomarkers

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Enabling Efficient Federated learning via Ternary Quantization over Fading Channels

Enabling Efficient Federated Learning via Ternary Quantizati...

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international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Ada Irem Pekdemir Ferkan Yilmaz Hakan Ali Çırpan Istanbul Technical University Electronics and Communication Engineering Istanbul Turkey Radar Electronic Warfare and Intelligence Systems Division ASELSAN A.Ş. Ankara Turkey

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

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： Resistance Quantization (signal) Accuracy Federated learning Computational modeling signal processing algorithms Doping Rayleigh channels Semiconductor process modeling Uplink

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Age and Gender Prediction Using Paralinguistic Features of EEG signals and machine learning

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

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international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Reema Alamr Walaa Alsumari Turky Alotaiby College of Computer Science and Information Technology King Saud University Riyadh Saudi Arabia Institute of Artificial Intelligence and Robotics Technologies King Abdulaziz City for Science and Technology Riyadh Saudi Arabia

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

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： Support vector machines Radio frequency Accuracy Feature extraction Brain modeling Electroencephalography Vectors Brain-computer interfaces Recording Random forests

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Optimal Feature Identification and Major Depressive Disorder Prediction through Correlation-Based machine learning Approach

Optimal Feature Identification and Major Depressive Disorder...

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international conference on Electrical Information and Communication Technology (EICT)

作者： Mozaddid Ul Hoque Babar Ashikur Rahman Bhuyain Jannatul Ferdouse Md. Sohrawordi Emran Ali Department of CSE HSTU Dinajpur Bangladesh School of IT Deakin University Melbourne Australia

Major Depressive Disorder (MDD) is a mental health disorder characterized by continuous feelings of sadness, diminished interest in activities, and a range of physical and emotional symptoms that profoundly affect a person’s daily life and functioning. Early detection of MDD is crucial for improved treatment outcomes. the detection of Major Depressive Disorder (MDD) relies on extracting several features from raw EEG signals. this study presents a machine learning-based approach to identify MDD using minimal EEG signal features, employing a correlation analysis with various correlation coefficient values. the proposed method achieved an impressive accuracy of 84% with SVM, showing 91.5% sensitivity, 75.5% specificity, and 85.7% f1-score with best correlation coefficient value. To evaluate performance, 5-fold cross-validation with inter-subject variability is utilized. the study extracts several feature sets using different coefficient values and assesses their performance with diverse coefficient values, aiming to identify the most effective features for early diagnosis of MDD.

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Using Data Mining Techniques to Analyze Facial Expression Motion Vectors 6th

Using Data Mining Techniques to Analyze Facial Expression Mo...

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6th international conference on Dynamics of Information Systems, DIS 2023

作者： Roshanzamir, Mohamad Alizadehsani, Roohallah Roshanzamir, Mahdi Shoeibi, Afshin Gorriz, Juan M. Khosravi, Abbas Nahavandi, Saeid Acharya, U. Rajendra Department of Computer Engineering Faculty of Engineering Fasa University Fasa74617-81189 Iran Deakin University GeelongVIC Australia Department of Electrical and Computer Engineering University of Tabriz Tabriz Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain Department of Signal Theory Networking and Communications Universidad de Granada Granada Spain Swinburne University of Technology HawthornVIC3122 Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

ISBN: (纸本)9783031503191

Automatic recognition of facial expressions is a common problem in human-computer interaction. While humans can recognize facial expressions very easily, machines cannot do it as easily as humans. Analyzing facial changes during facial expressions is one of the methods used for this purpose by the machines. In this research, facial deformation caused by facial expressions is considered for automatic facial expression recognition by machines. To achieve this goal, the motion vectors of facial deformations are captured during facial expression using an optical flow algorithm. these motion vectors are then used to analyze facial expressions using some data mining algorithms. this analysis not only determined how changes in the face occur during facial expressions but can also be used for facial expression recognition. the facial expressions investigated in this research are happiness, sadness, surprise, fear, anger, and disgust. According to our research, these facial expressions were classified into 12 classes of facial motion vectors. We applied our proposed analysis mechanism to the extended Cohen-Kanade facial expression dataset. Our developed automatic facial expression system achieved 95.3%, 92.8%, and 90.2% accuracy using Deep learning (DL), Support Vector machine (SVM), and C5.0 classifiers, respectively. In addition, based on this research, it was determined which parts of the face have a greater impact on facial expression recognition. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data mining

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Measurement of Vacuum Interrupter Shell thickness Based on Terahertz Time-Domain Spectroscopy and BP Neural Network

Measurement of Vacuum Interrupter Shell Thickness Based on T...

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Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), international conference on

作者： Zijian Zhu Ying Zhao Hui Li Chaohai Zhang Jiangsu Key Laboratory of New Energy Generation and Power Conversion Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

the vacuum interrupter serves as the core component of a vacuum circuit breaker, and the thickness of its shell is a crucial indicator for evaluating its quality. In order to achieve accurate measurement of the vacuum interrupter’s thickness, a BP neural network model is constructed. this model allows for obtaining the thickness measurement results directly through input and output relationship mapping, without the need to calculate the refractive index. this approach effectively avoids errors in refractive index caused by uneven microstructure of ceramics. Experimental results demonstrate that after BP neural network training, prediction accuracy is further improved. When compared with five other machine learning models, the BP neural network exhibits superior performance in thickness prediction, achieving an accuracy rate of 99.9975%. therefore, this study proposes a method for measuring terahertz vacuum arc extinguishing outer shell thickness based on BP neural network technology. this method can effectively enhance measurement accuracy and precision, thereby holding significant importance for ensuring safe operation and maintenance of vacuum circuit breakers.

关键词： Accuracy Interrupters Neural networks Refractive index Vacuum technology machine learning Time measurement Ceramics Time-domain analysis thickness measurement

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A 16-Channel GRU based On-Chip Seizure Classifier for Closed Loop Neuromodulation

A 16-Channel GRU based On-Chip Seizure Classifier for Closed...

引用

IEEE international conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Lakshmi Iyer Laxmeesha Somappa Department of Electrical Engineering IIT Bombay Mumbai India IIT Bombay Centre for Semiconductor Technologies (SEMIX) India

ISBN: (数字)9798350383638

ISBN: (纸本)9798350383645

the advancement of implantable on-chip machine learning (ML) classifiers enables the treatment of neurological disorders through closed-loop responsive neurostimulation. However, the hardware realization of the ML algorithm demands area and power efficiency along with low processing latency. this paper explores the time-series nature of the ECoG signal and introduces a scalable machine-learning model based on the Unidirectional Gated Recurrent Unit (GRU) for onset seizure detection. A 16-channel time-division multiplexed (TDM) GRU classifier is implemented on 65nm CMOS technology. the proposed SoC uses only 3 spectral features of different physiological frequency bands which were extracted using an FIR filter. Evaluated on the electroencephalography (EEG) data from the CHB-MIT scalp EEG database, the proposed implementation achieved a sensitivity of 93.1%, specificity of 89.1%, f1-score of 93.8% and an energy efficiency of 2 µJ/classification at 1.2 V supply voltage.

关键词： Sensitivity Time series analysis machine learning Time division multiplexing Feature extraction Brain modeling Electroencephalography

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Sound Event Localization and Detection Based on Iterative Separation in Embedding Space

Sound Event Localization and Detection Based on Iterative Se...

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IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Zeyu Yuan Donghang Wu Xihong Wu Tianshu Qu Key Laboratory on Machine Perception (Ministry of Education) School of Artificial Intelligence Peking University Peking China

Current Sound Event Localization and Detection(SELD) methods mainly adopt the output format from SELDnet that the Direction Of Arrival(DOA) prediction is for each category rather than event, thus these methods cannot handle the simultaneous occurrence of the same type of sound event in different directions. Although track-wise based methods could detect the homogeneous overlap, they are still limited to the need to know the maximum number of overlapping sound sources. In order to solve these problems, we propose a SELD method based on iterative separation in embedding space: Sep-SELD. Our localization and detection are performed on each single event, instead of locating and detecting all events at the same time. this is done by introducing separation in the embedding space. Meanwhile, to deal with the inconsistent and potential unknown number of active events in different frames, the separation is performed in an iterative manner. We conduct experiments on the DCASE2020 TASK3 dataset, and the results show that the proposed method has comparable performance to track-wise methods and flexibility to handle overlapping events without retraining from scratch.

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AF-KNN: A machine learning-based Algorithm for Automatic Recognition of Logistics Numbering Images

AF-KNN: A Machine Learning-based Algorithm for Automatic Rec...

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IEEE international conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Lanqi Yang Jianjun Wang Ao Chen Wenjia Ma Institute of Cyberspace Security University of Chinese Academy of Sciences Beijing China China United Network Communications Co. Ltd. Heilongjiang Branch Harbin China College of Information Science and Technology Donghua University Shanghai China School of Electronics & Information Engineering Tiangong University Tianjin China

With the development of IoT intelligence and automation, the demand for efficient and accurate recognition of logistics numbering image processing is increasing. In response to the high computational complexity of IoT environments and the need for higher efficiency, precision, and real-time recognition, this paper proposes an adaptive ensemble algorithm called Adaptive Fusion-KNN, based on KNN. AF-KNN integrates two weak classifiers based on KNN and utilizes machine learning methods to adaptively train these weak classification models. It dynamically adjusts its own weight in the voting process based on the error rate from the previous round of training, thereby ensuring more reliable label prediction results from the strong classifier. the introduction of this algorithm provides a theoretical basis for efficient, accurate, and automated classification of data in IoT application scenarios.

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ICBSP 2021 - Proceeding of 2021 6th international conference on Biomedical Imaging, signal processing

ICBSP 2021 - Proceeding of 2021 6th International Conference...

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6th international conference on Biomedical Imaging, signal processing, ICBSP 2021

ISBN: (纸本)9781450385817

the proceedings contain 10 papers. the topics discussed include: compare derived U-Nets using for retinal vessels segmentation;information compensation based on pulse coupled neural network;deep learning framework for detecting positive lymph nodes of gastric cancer on histopathological images;enhancement of COVID-19 segmentation using machine learning analyses of lung imaging;searching efficient models for human activity recognition;an efficient EEG microstate analysis method for emotion study;3D-printing-oriented mechanical redesign of a hand exoskeleton system for rehabilitative tasks;overview of Alzheimer’s disease research;and research on the relationship between race and the COVID-19.

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