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

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Use machine learning to Predict the Running Time of the Program 2021

Use Machine Learning to Predict the Running Time of the Prog...

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2021 5th international conference on Computer Science and Artificial Intelligence

作者： Xinyi Li Yiyuan Wang Ying Qian Liang Dou East China Normal University China

ISBN: (纸本)9781450384155

the prediction of program running time can be used to improve scheduling performance of distributed systems. In 2011, Google released a data set documenting the vast amount of information in the Google cluster. However, most of the existing running time prediction models only consider the coarse-grained characteristics of the running environment without considering the influence of the time series data of the running environment on the prediction results. Based on this, this paper innovatively proposes a model to predict the running time of the program, which predicts the future running time through historical information. At the same time, we also propose a new data processing and feature extraction scheme for Google cluster data sets. the results show that our model greatly outperforms the classical model on the Google cluster data set, and the root-mean-square error index of running time under different prediction modes is reduced by more than 60% and 40%, respectively. We hope that the model proposed in this paper can provide new research ideas for cloud computing system design.

关键词： machine learning Runtime prediction Cloud computing Time series

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Automated Cell Recognition using Single-cell RNA Sequencing with machine learning 21

Automated Cell Recognition using Single-cell RNA Sequencing ...

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2021 5th international conference on Computational Biology and Bioinformatics

作者： Chengqi Xu Yuetian Chen Yiyang Cao Southern University of Science and Technology China Rensselaer Polytechnic Institute USA The University of British Columbia Canada

ISBN: (纸本)9781450387446

this paper investigates the superiority and limitations of different dimensionality reduction schemes and classification methods in specific single-cell RNA sequencing (scRNA-seq) data sets. With systematic analysis as well as variables-controlled experiments, a pipeline was constructed from rpkm data to final cell type recognition and multiple dimension reduction methods are applied (including PCA, AutoEncoder, ISOMAP, and the combination algorithm of PCA+t-SNE) and multiple classifiers (Random Forest and Support Vector machine, etc.) to obtain the accuracy difference of multiple solutions. By comparing the variation of different models and parameters on the final classification accuracy, this paper summarizes and outlook the information loss and classification effects of different processing schemes on the data set and seeks to find the best combination from them. Using the combination of PCA+SVM, this work obtained 53.13% global maximum accuracy and based on this result to further explore the possibility of improving accuracy and model transfer learning in a wider range of applications.

关键词： Classification Dimension Reduction sc-RNA Seq

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ICBIP 2020 - Proceedings of 2020 5th international conference on Biomedical signal and Image processing

ICBIP 2020 - Proceedings of 2020 5th International Conferenc...

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5th international conference on Biomedical signal and Image processing, ICBIP 2020

ISBN: (纸本)9781450387767

the proceedings contain 16 papers. the topics discussed include: full tooth contour recognition and model reconstruction method based on CT images;a robust optic disc localization algorithm in retinal images based on support vector machine;hybrid method for biomedical image Poisson denoising;analysis and application of optical illusion images;nonrigid registration of multimodal images using local structural descriptors;multimodal image fusion based on random projection and joint sparse representation;real-time fluorescent image analysis of SlipChip-based microfluidic devices;a method for automatic tracking of cell nuclei with weakly-supervised mitosis detection in 2D microscopy image sequences;and discriminative color space learning for face anti-spoofing via convolutional neural networks.

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Handwritten Character Recognition of MODI Script using Convolutional Neural Network Based Feature Extraction Method and Support Vector machine Classifier 5

Handwritten Character Recognition of MODI Script using Convo...

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5th IEEE international conference on signal and Image processing, ICSIP 2020

作者： Joseph, Solley George, Jossy Dept. of Computer Science Bangalore India Carmel College for Women Goa India

ISBN: (纸本)9781728168968

Deep learning based algorithms are used in various pattern recognition tasks, including character recognition. Convolutional Neural Network (CNN) is effectively implemented for character recognition and is one of the best performing deep learning models. CNN can be used for character recognition directly or it can be used for extracting features in the character recognition process. Implementation of a feature extraction method using CNN autoencoder for MODI script character recognition is discussed in the paper. the extracted features are then subjected to Support Vector machine (SVM) for the purpose of classification. the On-the-fly data augmentation method is used to add variability and generalization of the data set. MODI Script is an ancient Indian script and was used for writing Marathi until 1950. Various libraries and temples in India and abroad have a large collection of MODI documents. Character recognition related research of MODI script is still in infancy and research and development is necessary to extract the information from MODI manuscripts stored in various libraries. the performance of the proposed method, which uses CNN autoencoder as a feature extractor and an SVM based classifier gives very high accuracy and is better compared to the most accurate MODI character recognition method reported so far. © 2020 IEEE.

关键词： Character recognition

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2020 international conference on Advanced Technologies for signal and Image processing, ATSIP 2020

2020 International Conference on Advanced Technologies for S...

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5th international conference on Advanced Technologies for signal and Image processing, ATSIP 2020

ISBN: (纸本)9781728175133

the proceedings contain 97 papers. the topics discussed include: target recognition from ISAR image using polar mapping and shape matrix;nuclei segmentation approach for digestive neuroendocrine tumors analysis using optimized color space conversion;face emotion recognition from static image based on convolution neural networks;monaural speech separation based on linear regression optimized using gradient descent;melanoma skin cancer detection using deep learning and classical machine learning techniques: a hybrid approach;EWMA kernel generalized likelihood ratio test for fault detection of chemical processes;AI-based pilgrim detection using convolutional neural networks;and multi-label learning embedding approach based on multi-temporal spectral signature for hyperspectral images classification.

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Pandemic rumor identification on social networking sites: A case study of COVID-19 21

Pandemic rumor identification on social networking sites: A ...

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Proceedings of the 2021 5th international conference on Natural Language processing and Information Retrieval

作者： Mohsan Ali Iqbal Murtza Ayesha Ejaz National Center for Cybersecurity Air University Islamabad Pakistan Department of Creative Technologies Air University Islamabad Pakistan Department of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9781450387354

Digital Rumors, because of the ease and innovations in social networking technologies, has become an important issue. these rumors become a critical issue in a disaster, epidemic, or pandemic. Considering classification power of conventional and deep learning techniques, we propose a hybrid learning technique that identifies rumors effectively. For this, TF-IDF description has been used to build a stack of multiple conventional learning techniques; logistic regression, Naïve Bayes, and random forest. Whereas, word-embedding features have been used for purpose of deep learning; LSTM and LSTM-RNN. the combination of LSTM and RNN makes this study unique in the field of rumor detection. With LSTM and RNN gated architectures, huge series rumor tweets may be efficiently managed. To aggregate the decisions, the labels of deep learning and the stack of conventional learning have been combined using majority voting based ensemble classification. To evaluate the performance of the proposed technique, we used publically available standard COVID-19 RUMOR dataset. the proposed technique obtains 99.02% accuracy, which shows its effectiveness. the dataset utilized and the ensemble model created for rumor identification distinguish our work from existing methods.

关键词： Covid-19 Deep learning Ensemble learning Long-short term memory machine learning Natural language processing

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Indoor Seat Occupancy Classification with Wi-Fi Channel State Information and machine learning Methods 2021

Indoor Seat Occupancy Classification with Wi-Fi Channel Stat...

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2021 4th international conference on Sensors, signal and Image processing

作者： Yichuan Zhang Jiefeng Li Hang Wang Jilin University China

ISBN: (纸本)9781450385725

Keeping a distance by monitoring the seat occupancy is an essential way to prevent the spread of virus inside a room. However, most current human sensing methods need customized devices, so a cheaper way of indoor seat occupancy classification is in need. Recent researches indicate that Wi-Fi channel state information (CSI) can be utilized for indoor human sensing without wearable sensors. this paper proposes a multi-person seat occupancy classification method based on machine learning and Wi-Fi CSI received by commercial network interface card. We designed an experimental scenario of 5 seats and 2 individuals, and use commercial Wi-Fi devices to build a multi-input multi-output (MIMO) system indoors to acquire an adequate dataset. then a pipeline consists of phase calibration, linear interpolation, outlier removal and threshold de-noising was applied to preprocess the raw CSI amplitude and phase data. After sliding window feature extraction, convolutional neural network (CNN) and some conventional machine learning methods, such as naive Bayes (NB), decision tree (DT), support vector machine (SVM) and K-nearest neighbors (KNN), are used to classify seat occupancy, among which CNN performs the best, with a classification accuracy of 95%.

关键词： Wi-Fi sensing machine learning Channel state information Human sensing

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Comparison of Norm-Based Feature Selection Methods on Biological Omics Data 21

Comparison of Norm-Based Feature Selection Methods on Biolog...

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Proceedings of the 5th international conference on Advances in Image processing

作者： Jiayuan Song Zheng Liu Harbin Normal University China Tongji University China

ISBN: (纸本)9781450385183

Feature selection methods have become significant methods when analyzing high-throughput biological data due to the nature of large p and small n problems. One of the most crucial categories of feature selection methods is norm-based approaches because they can reduce the magnitude of coefficients and enhance the sparsity of selected features. there are many norm-based feature selection methods with different merits and demerits. therefore, the specific choice of norm-based methods for omics data has become a problem. In our work, we mainly concentrate on the comparison and evaluation of two popular norm-based methods, namely Lasso and Ridge regression. the regression with norm is Lasso Regression and the regression with norm is Ridge Regression. the results indicate that Ridge Regression performs better than Lasso Regression when dealing with high throughput TCGA datasets.

关键词： machine learning Ridge regression Feature selection Lasso regression

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Delay Root Cause Analysis and 3D Modeling of LTE Control Communication Using machine learning

Delay Root Cause Analysis and 3D Modeling of LTE Control Com...

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Artificial Intelligence of things (ICAIoT), international conference on

作者： Sameer Qutaiba Mohammed Muhammad Ilyas Department of Computer Engineering Altinbas University Istanbul Turkey

this study investigates and evaluates delay root cause analysis and 3D modeling of LTE control communication utilizing sophisticated machine learning for network testing. the research studied LTE protocols for 5th-generation mobile telephony and provided guidelines for controlling LTE frequency for background knowledge, although it used an independent technique that did not employ LTE standards. 512 elements of input-output MIMO were employed for 100-GHz and 128 elements for mid-band sub-6-GHz. LOS is always 0.5. this paper is about LTE, not 3D modeling of LTE control path loss type communication using machine learning. this work’s route loss depends on cross-pol beam LTE polarization (±45o). the receiver (Rx) operations and transmitter (Tx) activities in the estimated distance of 0.5 km at an approximate altitude of 15.25 m. Distance, handover authentication, rain, atmosphere, and sub-6GHz vs 100GHz weather conditions affect path loss. the methodology has enhanced the spatial variety by boosting transmitting power and transmitting efficiency. Authorizing and sanctioning ANN-based LTE frequency for both mid-band sub-6-GHz and 100-GHz is possible due to its planning and development using open-source material and strategy with high transmission power and rate under doubtful handover confirmation using MIMO input/yield receiving wires. this theory examines LTE innovation dimensioning as unbiased for various handover verification and allows input boundary alterations for various organization arrangement setups for LTE recurrent data transmission from 6 GHz to 100 GHz for three climate sorts. this cycle should be seen as an undeniable level way to examine LTE networks under various air conditions. Using signal handling tool compartment and explicit AI-based ANN calculation from AI toolkit in MATLAB R2019a, it is possible to create a result answer for three climate types in a dataset with an LTE communication level of exactness of downpour assimilation and abundance folia

关键词： Root cause analysis three-dimensional displays Wires Authentication Artificial neural networks Handover Delays

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2020 international conference on Wireless Communications, signal processing and Networking, WiSPNET 2020

2020 International Conference on Wireless Communications, Si...

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5th international conference on Wireless Communications, signal processing and Networking, WiSPNET 2020

ISBN: (纸本)9781728152844

the proceedings contain 41 papers. the topics discussed include: detection of potato disease using image segmentation and machine learning;Wyner-Ziv coding of chroma in wireless capsule endoscopy image compression using deep side information generation;brain signal analysis for mind controlled type-writer using a deep neural network;evaluation of brain signal analysis for subjective aesthetic-appreciation using type-2 fuzzy sets;development of budget friendly wireless access point to use in littoral environments;implementation and performance analysis of multiuser detector for massive MIMO OFDM system over Rayleigh channel;antenna array beam scanning and SINR visualization on a map for 5G urban macro-cell test environment;optimal spectrum sensing in cognitive radio systems using signal segmentation algorithm;and backscatter-assisted wireless powered communication networks with multiple antennas.

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