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检索条件"任意字段=5th International Conference on Signal Processing and Machine Learning, SPML 2022"

共 783 条记录，以下是461-470 订阅

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Preliminary Results on the Use of Classification Trees to Predict Non-suicidal Self-injury with Data Collected through a Mobile App

Preliminary Results on the Use of Classification Trees to Pr...

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15th international conference on Bio-inspired Systems and signal processing (BIOsignalS) held as part of 15th international Joint conference on Biomedical Engineering Systems and Technologies (BIOSTEC)

作者： Capra, Chiara Marti-Puig, Pere Vega Moreno, Daniel Llunas, Laia Nicolaou, Stella Schmidt, Carlos Sole-Casals, Jordi Univ Vic Univ Cent Catalunya Data & Signal Proc Res Grp Carrer Laura 13 Vic Spain beHIT Carrer Mata 1 Barcelona Spain Fundacio Sanitaria Igualada Hosp Igualada Dept Psychiat & Mental Hlth Igualada Spain

ISBN: (纸本)9789897585524

machine learning (ML) integrated with technology has been a breakthrough in mental health, bringing clinical improvements both for the patient and for the clinician. Among these, real-time patient symptoms' tracking through ecological momentary assessment (EMA) data can be a valuable tool to forecast symptomatology at the individual-patient level for specific disorders, among which non suicidal self-injury. We aimed at applying classification trees to predict non-suicidal self-injury (NSSI) with EMA data collected through a mobile app. A database of 40 patients diagnosed with borderline personality disorder (BPD) with NSSI (N=22), and a subclinical group of students with NSSI (N=19) was analysed. EMA data was collected by the Sinjur app. Two classification trees were used as models. For the first tree, training results reported 69,7% of accuracy, whereas test results reported 59,3% of accuracy, 87,5% of sensitivity and 58,78% of specificity. For the second tree, training results reported 67,9% of accuracy, whereas test results reported 65,2% of accuracy, 85% of sensitivity and 64,8% of specificity. We concluded that real-time patient monitoring via a mobile app can be a valuable tool for making technology-based predictions at the individual patient level. this promising data needs to be built upon in future studies and needs major translation in the everyday clinical practice to demonstrate its real-world efficacy and later, to be translated to the enterprise world.

关键词： machine learning Digital Mental Health Non-suicidal Self-injury Applied AI

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Off-Grid Error Calibration for Single Snapshot DOA Estimation Based on Sparse Bayesian learning

Off-Grid Error Calibration for Single Snapshot DOA Estimatio...

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

作者： Haosheng Fu Yuanyuan Wang Fengzhou Dai National Laboratory of Radar Signal Processing Xidian University Xi'an China

In this work, the grid mismatch problem for direction of arrival (DOA) estimation of array is studied. To solve the grid mismatch impact on the DOA estimation accuracy, a novel off-grid model for DOA estimation using sinc interpolation is proposed in this paper. Specifically, the off-grid error is represented as parameter to be estimated in the observation model. Under the framework of sparse Bayesian learning (SBL), the off-grid error is estimated via the variational Bayesian expectation maximization (VBEM) and eliminated by updating the grids with sinc interpolation. the simulation results demonstrate superior performance of the proposed method over state-of-the-art methods are reported, as well.

关键词： Interpolation Adaptation models Direction-of-arrival estimation Simulation Estimation signal processing algorithms signal processing

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Handling Imbalanced and Overlapped Medical Datasets: A Comparative Study

Handling Imbalanced and Overlapped Medical Datasets: A Compa...

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international conference on Multimedia, signal processing and Communication Technologies, (IMPACT)

作者： Mohammad Sarosh Basit Adeeba Khan Omar Farooq Yusuf Uzzaman Khan Mohammad Shameem Dept. of Computer Engineering ZHCET Aligarh Muslim University Aligarh India Dept. of Electronics Engineering ZHCET Aligarh Muslim University Aligarh India Dept. of TB and Chest Diseases JNMCH Aligarh Muslim University Aligarh India

ISBN: (纸本)9781665476485

An imbalanced dataset with class overlapping is a challenging issue in medical research. Imbalanced data points lead to overfitting for the majority class while overlapped classes cause misclassification for both classes. Hence, this combination makes it challenging for classic machine learning algorithms to define a decision boundary between minority and majority classes. In our study, different algorithms with different techniques have been compared for example oversampling, undersampling, combined over and under sampling, and the ensemble methods to deal with class imbalance along with class overlapping. Two well-known highly imbalanced and overlapped medical datasets are used to compare the performance of different approaches and performance is evaluated by sensitivity and specificity. On the sleep apnea dataset, oversampling combined with ensemble classifier AdaBoost with the specificity and sensitivity of 0.72 and 0.46 which proved better than other techniques and classifiers. On the diabetes dataset, SMOTE-TOMEK oversampling combined with the Random Forrest classifier with the specificity and sensitivity of 0.91 and 0.77 proved to be better than all the combinations that have been tried for the classification with minimal number of features.

关键词： Sensitivity machine learning algorithms signal processing algorithms signal processing Sensitivity and specificity Sleep apnea Communications technology

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Identification of Robust Features for Classifying Spam and Ham Images using Transfer learning

Identification of Robust Features for Classifying Spam and H...

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international conference on signal processing and Information Security (ICSPIS)

作者： Abigail Copiaco Husameldin Mukhtar Leena El Neel Tasnim Nazzal University of Dubai UAE Dubai UAE

ISBN: (纸本)9781665492669

the utilization of transfer learning for image classification has gathered popularity and satisfactory results according to recent works. Nonetheless, exploring the extraction of image-based features prior to training have not been thoroughly investigated. In this work, we perform the classification of Spam versus Ham images through transfer learning using pre-trained neural network models, such as AlexNet and GoogleNet. Alongside this, we explore and compare the results obtained from image-based features, including Discrete Cosine Transform (DCT), Histograms, and Fast Fourier Transform (FFT), in order to identify the optimum approach. Experimental results suggest an accuracy of 98.56% for the top performing methodology, involving the use of DCT features with the GoogleNet model.

关键词： Training Histograms Fast Fourier transforms Transfer learning Neural networks Information security signal processing

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Radar Plot Classification Based on machine learning 21

Radar Plot Classification Based on Machine Learning

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5th international conference on Electronic Information Technology and Computer Engineering, EITCE 2021

作者： Liu, Zhengcheng Qi, Yongmei Dai, Xiao The 723 Institute of CSIC Yangzhou China

ISBN: (纸本)9781450384322

Clutter is the inherent environment of radar signal detection and processing. On the one hand, too many clutter points will start a false track, on the other hand, clutter plots will be incorrectly associated with the track, resulting in track errors. therefore, how to further distinguish target plots and clutter plots after target detection is an important and difficult problem in radar data processing. Aiming at the clutter data mixed in the plot data output by signal processing, this paper extracts the multi-dimensional feature parameters of radar plot on the radar measured data set, classifies the radar real target and false target by using the traditional support vector machine, fully connected neural network and convolution neural network respectively, and compares the effects of three different classification methods. © 2021 ACM.

关键词： Support vector machines

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Combining Temporal and Spectral Features for Improving Hand Gesture Classification from sEMG signals

Combining Temporal and Spectral Features for Improving Hand ...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Elbaz Siham Moufassih Mustapha Agounad Said Ait Yous Mohamed Physics Department Laboratory of Metrology and Information Processing Faculty of Sciences Ibn Zohr University Agadir Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Robotic prosthetic hands are being developed to restore some individuals’ capacity to manipulate objects in their daily lives. Currently, more focus is being placed on applying machine learning algorithms to enhance the dexterity of myoelectric prostheses; due to their high precision and sophisticated control capabilities. these algorithms aim to identify and classify hand gestures from a surface electromyography (sEMG), which will be used later as a command of an object. In this work, we suggest a method for categorizing different hand gestures. One of the advantages of this approach is that it can perform well based on a few features from both the time and frequency domains. We use these features with two well-known machine learning algorithms in the literature: Support Vector machine (SVM) and K-Nearest Neighbors (KNN). Subsequently, we assess how well these algorithms perform using a multi-channel sEMG dataset comprising hand gestures simulated by 40 participants, including movements such as the neutral state, wrist extension and flexion, grip, ulnar and radial deviation, abduction and adduction of all fingers, supination, and pronation. Four sEMG signal channels are used to record these gestures. SVM attained an accuracy of 95.94%, whereas KNN achieved 95.5%, according to the average accuracy rates derived from 40 subjects and 10 hand movements.

关键词： Hands Support vector machines Wrist Accuracy machine learning algorithms Frequency-domain analysis Nearest neighbor methods Feature extraction Electromyography Surface treatment

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High-Resolution Imaging from Gapped Data Based on Fast Sparse Bayesian learning

High-Resolution Imaging from Gapped Data Based on Fast Spars...

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

作者： Yuanyuan Wang Haosheng Fu Fengzhou Dai National Laboratory of Radar Signal Processing Xidian University Xi'an China

High-resolution imaging from gapped data has become a research hotspot in radar imaging field. Among many imaging algorithms, sparse Bayesian learning (SBL) is more robust and has greater estimation accuracy, which attracts active interest from researchers. Unfortunately, the inversion and multiplying operations are involved in each iteration of SBL lead to heavy computational complexity when they are implemented directly. In this paper, we propose a fast Fourier dictionary (FD)-based SBL algorithm to solve high-resolution imaging from gapped data, greatly reducing the calculation cost. Finally, the experimental results verify the effectiveness of the proposed method.

关键词： signal processing algorithms High-resolution imaging Imaging Radar imaging signal processing Time measurement Bayes methods

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A Multi-channel Speech Separation System for Unknown Number of Multiple Speakers

A Multi-channel Speech Separation System for Unknown Number ...

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

作者： Chao Peng Yiwen Wang Xihong Wu Tianshu Qu State Key Laboratory of Media Convergence Production Technology and Systems Key Laboratory on Machine Perception (Ministry of Education) School of Intelligence Science and Technology Peking University Beijing China

this paper presents a multi-channel speech separation system for an unknown number of speakers. It can be applied to cases with a different number of speakers using a single model by iterative speech separation based on beam signal. It first determines the spatial directions where speakers are located (Direction of Arrival, DOA), and then the beam signals in each direction are obtained with spectral features, spatial features, and directional features by deep neural networks. Finally, the iterative speech separation is performed on the basis of the beam signals. Experimental evaluations show that the proposed method is better than the multi-channel Permutation Invariant Training (PIT) and Deep Clustering (DPCL) for an unknown number of speakers and the one-and-rest speech separation method. Besides, the system can still keep a relatively good separation performance even though the number of speakers is enlarged to 9.

关键词： Training Deep learning Direction-of-arrival estimation Array signal processing Neural networks Estimation Feature extraction

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Improved Bayesian learning Algorithms for recovering Block Sparse signals With Known and Unknown Borders

Improved Bayesian learning Algorithms for recovering Block S...

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

作者： Neda Haghighatpanah Ramy H. Gohary Department of Systems and Computer Engineering Carleton University Ottawa ON Canada

ISBN: (纸本)9781665482387

this paper presents two novel Bayesian learning recovery algorithms for block sparse signals. Two cases are considered. In the first case, the signals within each block are correlated and the block borders are known. In the second case, the block borders are unknown and the signal elements are uncorrelated. Unlike their existing counterparts, the proposed algorithms obtain the optimal block covariances from the data estimated in the previous iterations. Furthermore, the decision to declare a block as zero is based on hypothesis testing. For the second case, we introduce a new prior model which is characterized by elastic dependencies among neighbouring signal elements. Using this model, we develop a novel Bayesian learning algorithm which iterates between estimating the dependencies among the signal elements and updating the Gaussian prior model. Numerical simulations illustrate the effectiveness of the proposed algorithms.

关键词： Simulation signal processing algorithms signal processing Fasteners Numerical simulation Bayes methods Numerical models

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Parallel Computation of Hadamard Product for Dynamic Dataset on Google Colab 5th

Parallel Computation of Hadamard Product for Dynamic Datas...

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5th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

作者： Shariar, Sarjil Hasan, K. M. Azharul Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh

ISBN: (纸本)9789811641763

Hadamard product, the element-wise product of tensors, is an important tool for many real-life applications such as machine LSTM, multimodal learning, object recognition, and segmentation. In this paper, we propose a faster processing of hadamard product by the parallel computation on graphics processing unit (GPU) for dynamic data. Most of the computations for real-life data are multidimensional and dynamic which expand during run time. In this scheme, we use dynamic memory allocation that allows further expansion of the dimension size and high memory utilization based on the notion of extendable array. Since high dimensionality is difficult to handle, we use a dimension conversion technique where the n-dimensional array structure is transformed into a 2-dimensional structure. Furthermore, to handle the future data, we apply the converted extendable array structure to get Scalable Hadamard Product (SHDP) for dynamic dataset. then, we implement a parallel algorithm for the parallel processing of the SHDP that partitions and distributes the element-wise product during runtime. We utilize the maximum available GPU threads for the faster performance of the hadamard product. We found good speed-up for the proposed partitioning while applying to "Google Colab". © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graphics processing unit

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