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共 777 条记录，以下是141-150 订阅

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Deep neural network for MIMO-SCMA detection 5

Deep neural network for MIMO-SCMA detection

引用

2023 5th international conference on Information Science, Electrical, and Automation Engineering, ISEAE 2023

作者： Zhang, Shiwei Ge, Wenping Key Laboratory of Signal Detection and Processing College of Information Science and Engineering Xinjiang University Xinjiang Urumqi830017 China

ISBN: (纸本)9781510667440

this article introduces deep learning into the multiple-input multiple-output (MIMO) sparse code multiple access (SCMA) system and proposes a MIMO-SCMA detection scheme based on deep neural networks (DNN) to improve bit error rate (BER) performance. the DNN learns the codebook of each user through channel feature learning on different transmission antennas. the fully connected DNN is designed as the decoder at the receiving end, which does not require traditional multi-antenna detection and multi-user detection, and can obtain user data with one decoding operation. the encoder and decoder are trained using an end-to-end training method. All learning models of the DNN are generated offline and the learned models are used for online testing. In this model, the received signal and channel coefficients are set as input data, and the label corresponding to the transmitted symbol is set as output data for offline learning. After offline learning is completed, the model can be deployed online with fixed weights and biases. through simulation experiments, the proposed DNN encoder-decoder method can reduce the BER and computational complexity of the receiver in the MIMO-SCMA system. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep neural networks

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EMG signal Classification for Human Hand Rehabilitation via Two machine learning Techniques: kNN and SVM 5

EMG Signal Classification for Human Hand Rehabilitation via ...

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5th IEEE international conference on Advanced Systems and Emergent Technologies (IC_ASET)

作者： Briouza, Sami Gritli, Hassene Khraief, Nahla Belghith, Safya Singh, Dilbag Univ Tunis El Manar Natl Engn Sch Tunis Lab Robot Informat & Complex Syst RISC Lab LR16ES BP 37 Tunis 1002 Tunisia Univ Carthage Higher Inst Informat & Commun Technol Tunis 1164 Tunisia Gwangju Inst Sci & Technol Sch Elect Engn & Comp Sci Infonet Lab Gwangju South Korea

ISBN: (纸本)9781665427357

In the last few years, electromyography (EMG) has shown a lot of potential in therapy and rehabilitation applications of the human limbs though exoskeletons and prosthesis. the use of machine learning (ML) techniques has made huge contributions to biomedical signals classification. Many ML methods have been adopted and the results were very promising. In this paper, we choose two different ML classifiers: the k-Nearest Neighbors (kNN) and the Support Vector machine (SVM). the main goal is to compare their performance using different combinations of time-domain features. this crucial strategy allows to choose the adequate features in order to obtain good model performance. the experimental results demonstrate that the SVM classifier is more efficient where it gives a higher accuracy compared to the kNN technique.

关键词： Rehabilitation Human hand machine learning SVM kNN

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A Safe Transfer learning Approach Inspired by Desensitization therapy 5

A Safe Transfer Learning Approach Inspired by Desensitizatio...

引用

5th international conference on Information Science, Parallel and Distributed Systems, ISPDS 2024

作者： Huang, Qing Zhao, Chong Liu, Ziqi Yang, Tao China West Normal University School of Electronic Information Engineering Nanchong China China West Normal University School of Computer Science Nanchong China China Tobacco Sichuan Industrial Co. Ltd Chengdu China

ISBN: (纸本)9798350352719

Transfer learning is a mainstream method to solve data scarcity tasks, but it faces the threat of defect inheritance, which makes the student model vulnerable to adversarial example attacks. Traditional solutions include model slicing and model reconstruction. However, due to the uninterpretability of deep neural networks, model slicing methods often adopt a conservative way, which leads to the limited resistance of the model to adversarial example attacks. Inspired by the theory of the biomedical field, this paper proposes a node optimization method based on desensitization therapy (STL-DMP). In STL-DMP, the student model is regarded as an allergic person, the abnormal state of the student model after being attacked by the adversarial sample is regarded as an allergic reaction, the interference is regarded as an allergen, model weights are regarded as sensitizing-cell activity mediators, and the loss function is regarded as an immune regulatory signal. Based on the above statement, STL-DMP uses interference (allergens) to prepare adversarial samples (vaccines). the student model (allergy) is exposed to a specific perturbation (allergen) by training (treatment) with dedicated adversarial examples (vaccines). the model weights (sensitizing-cell activity mediators) are gradually weakened (consumed to an equilibrium value), thus eliminating the threat of adversarial sample attacks (sensitized state). Experiments show that STL-DMP improves the resistance of the model to adversarial attacks while maintaining accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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Health status of battery for substation 5

Health status of battery for substation

引用

5th international conference on Green Energy, Environment, and Sustainable Development, GEESD 2024

作者： Fan, Ziming Yang, Jun Xun, Hua Co. Ltd. Inner Mongolia Power Research Institute Branch Inner Mongolia Hohhot010000 China

ISBN: (纸本)9781510683006

Due to the increasing degree of substation automation and intelligence and the promotion of unattended operation, the role of batteries is becoming more and more important. Battery life assessment methods usually rely on physical models and experimental data. However, due to the complexity and high cost of the experimental process, as well as the complexity of the model and the limitations of the hypothesis, these methods are often limited. By collecting a large number of battery operation data, machine learning and statistical analysis techniques are used to construct a prediction model, so as to more accurately evaluate the battery life and improve the evaluation efficiency. © 2024 SPIE.

关键词： Batteries Data modeling Power grids Performance modeling Mathematical optimization Data processing Reliability Education and training Mathematical modeling Systems modeling

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Detecting Autism Spectrum Disorder from Functional Magnetic Resonance Imaging Data with an Ensemble machine learning Method 5

Detecting Autism Spectrum Disorder from Functional Magnetic ...

引用

5th international conference on Intelligent Medicine and Image processing, IMIP 2023

作者： Sun, Chenxi Sun, Duanchen Liu, Zhi-Ping Shandong University School of Control Science and Engineering Jinan Shandong250061 China Shandong University School of Mathematics Jinan Shandong250100 China

ISBN: (纸本)9798350337396

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder caused by functional impairment of the brain. the prevalence of this disease in the population has been increasing in recent years. Early identification of ASD can help children improve with early behavioral interventions. However, traditional testing methods are very expensive and time-consuming. machine learning methods based on functional Magnetic Resonance Imaging(fMRI) have emerged as a research direction for autism-assisted diagnosis. In this paper, we developed an ensemble approach to distinguish ASD from normal controls by a stacking strategy, integrating the results of multiple machine learning classifiers. the dataset included 569 subjects from different centers in the study. We extracted the functional connectivity (FC) as features and removed the redundant ones by SVM-RFE, building an ensemble model for prediction by the stacking strategy. Specifically, KNN, NB, RF, GBDT, and XGBoost were used as base models, and logistic regression was employed as a meta-model. the experimental results illustrated that the ensemble method could improve the evaluation metrics of prediction results by about 2% on average compared with individual classifiers. thus, the stacking strategy has better classification performance for intelligent diagnosis of ASD via fMRI. © 2023 IEEE.

关键词： Diseases

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Missing Values for Classification of machine learning in Medical data 5

Missing Values for Classification of Machine Learning in Med...

引用

5th IEEE international conference on Artificial Intelligence and Big Data, ICAIBD 2022

作者： Ren, Lijuan Wang, Tao Seklouli, Aicha Sekhari Zhang, Haiqing Bouras, Abdelaziz Lyon France Chengdu University of Information Technology School of Software Engineering Chengdu China

ISBN: (纸本)9781665499132

Missing values are an unavoidable problem for classification tasks of machine learning in medical data. With the rapid development of the medical system, large scale medical data is increasing. Missing values increase the difficulty of mining hidden but useful information in these medical datasets. Deletion and imputation methods are the most popular methods for dealing with missing values. Existing studies ignored to compare and discuss the deletion and imputation methods of missing values under the row missing rate and the total missing rate. Meanwhile, they rarely used experiment data sets that are mixed-type and large scale. In this work, medical data sets of various sizes and mixed-type are used. At the same time, performance differences of deletion and imputation methods are compared under the MCAR (Missing Completely At Random) mechanism in the baseline task using LR (Linear Regression) and SVM (Support Vector machine) classifiers for classification with the same row and total missing rates. Experimental results show that under the MCAR missing mechanism, the performance of two types of processing methods is related to the size of datasets and missing rates. As the increasing of missing rate, the performance of two types for processing missing values decreases, but the deletion method decreases faster, and the imputation methods based on machine learning have more stable and better classification performance on average. In addition, small data sets are easily affected by processing methods of missing values. © 2022 IEEE.

关键词： Classification (of information)

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Video Origin Camera Identification using Ensemble CNNs of Positional Patches 5

Video Origin Camera Identification using Ensemble CNNs of Po...

引用

IEEE 5th international conference on Multimedia Information processing and Retrieval (MIPR)

作者： Veksler, Maryna Aygun, Ramazan Akkaya, Kemal Iyengar, Sitharama Florida Int Univ Dept Elec & Comp Engn Miami FL 33174 USA Kennesaw State Univ Dept Comp Sci Marietta GA 30060 USA Florida Int Univ KF Sch Comp & Inf Sci Miami FL 33199 USA

ISBN: (数字)9781665495486

ISBN: (纸本)9781665495486

the use of multimedia devices has increased immensely with the availability of affordable mobile IoT technologies. Consequently, video capturing applications are incorporated in many crucial sectors including crime investigations and surveillance. In such applications, it is important to ensure the integrity of the video data and verify its source for digital forensics purposes. In this paper, we tackle the problem of video source camera identification to validate the origin of video data that may come from numerous types of cameras. Specifically, we propose a novel approach based on ensemble of convolutional neural networks (CNNs) for detection of video source. In our approach, the video is analyzed using patches obtained from I-frames after splitting each frame into quadrants and training a CNN per quadrant for location awareness. Our experimental results demonstrate that our model brings significant improvement for video source identification accuracy compared to existing machine learning based techniques.

关键词： Training Location awareness Surveillance Digital forensics machine learning Information processing Streaming media

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A machine learning-Based Approach for Enhancing Student learning Experiences 5th

A Machine Learning-Based Approach for Enhancing Student Lear...

引用

5th international conference on Soft Computing and signal processing, ICSCSP 2022

作者： Bhaskar, T. Tribhuvan, R.R. Department of Computer Engineering Sanjivani College of Engineering Maharashtra Kopargaon India

ISBN: (纸本)9789811986680

the tried-and-true learning method of outcome-based learning (OBL) is based on a specified set of goals. there are three parts to OBL: educational objectives, program outcomes, and course outcomes (COs). At the end of each course, faculty members may take several ML-based recommended actions to enhance the quality of learning and instruction. Some students may be encouraged to engage in dangerous activities due to the sheer volume of classes and professors engaged. As a consequence of this research, multiple machine learning algorithms may be used to predict the correct course of action based on college course requirements and academic records. Different issue change strategies and versatile methodologies, for example, one-versus-all, parallel importance, naming power set, series arrangement, and custom order ML-KNN, might be utilized to a dataset to tackle the issue. At the Institute of Computer and Information Sciences, the suggested recommender ML-based framework is used as a contextual investigation to help academic staff improve learning quality and instructional approaches. According to the findings, the recommended recommendation method provides a greater number of options for enhancing students’ educational experiences. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Pedestrian Trajectory Prediction Based on Improved Social Spatio-Temporal Graph Convolution Neural Network 5

Pedestrian Trajectory Prediction Based on Improved Social Sp...

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5th international conference on machine learning and Natural Language processing (MLNLP)

作者： Yang, Jikun Han, Chao Anhui Polytech Univ Sch Elect Engn Wuhu Peoples R China

ISBN: (纸本)9781450399067

Pedestrian trajectory prediction is a key technology in the field of autonomous driving. the trajectory of pedestrians is not only affected by the surrounding objects, but also by the social interaction between adjacent pedestrians. Aiming at the problem that pedestrian interaction visual blind area is easy to be ignored in pedestrian trajectory prediction, a convolution network algorithm based on spatio-temporal graph is proposed. Firstly, Pedestrian feature information is obtained by spatio-temporal graph. then the connection weights between irrelevant points are screened out according to the visual blind area. Finally, the time extrapolation convolution neural network (TXP-CNN) is used to predict the future trajectory of pedestrians. through experiments on two public datasets (Eth and UCY), average displacement error (ADE) and the final displacement error (FDE) of the proposed model on the dataset are 0.42 and 0.73, respectively.

关键词： machine vision trajectory prediction blind area screening double Gaussian distribution time-extrapolator convolution neural network

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Area and Delay Optimized Approximate Parallel Prefix Adders for Image processing Applications 5th

Area and Delay Optimized Approximate Parallel Prefix Adders ...

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5th international conference on Electrical and Electronics Engineering, ICEEE 2024

作者： Dharani, G. Pavitra, Y.J. Department of Electronics and Communication Engineering PES University Bangalore India

ISBN: (纸本)9789819790364

Approximate computing enhances energy efficiency by allowing hardware to deliver sufficiently accurate results tailored to specific applications, prioritizing energy savings over absolute precision. An essential domain of interest lies in advancing adder units, crucial for applications ranging from machine learning to signal, image, and video processing. these units are also fundamental in more complex operations like subtraction, comparison, multiplication, squaring, and division. this paper focuses on synthesizing approximate parallel prefix adders. Unlike current methods that focus on specific architectures, the synthesizer generates all solutions meeting the designer's requirements, offering a range of delay, area, and error trade-offs. this automated exploration of design space leads to near-optimal solutions unattainable with traditional architectures. the synthesized adders achieve a 40–55% reduction in area and improve image quality metrics by up to 21.49% in PSNR and 24.14% in MSSIM for an image filter application compared to an exact parallel-prefix adders adder. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Video signal processing

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