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2nd international conference on signal processing and Communication, ICSPC 2019

ISBN: (纸本)9781728118499

The proceedings contain 82 papers. The topics discussed include: comparative analysis of various machine learning techniques for intrusion detection system;influence of assorted back barriers on AlGaN/GaN HEMT for 5G K-band applications;a low-cost Arduino based automatic irrigation system using soil moisture sensor: design and analysis;sequential segmentation of EEG signals for epileptic seizure detection using machine learning;classification of lung images using deep convolutional neural network;a four grade brain tumor classification system using deep neural network;feature selection and classification for analysis of breast thermograms;multi-frame image super-resolution by interpolation and iterative backward projection;state of art of network on chip;and a hybrid secure and energy efficient cluster based intrusion detection system for wireless sensing environment.

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machine learning on Biomedical Images: Interactive learning, Transfer learning, Class Imbalance, and Beyond 2

Machine Learning on Biomedical Images: Interactive Learning,...

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2nd IEEE international conference on Multimedia Information processing and Retrieval (MIPR)

作者： Khan, Naimul Mefraz Abraham, Nabila Hon, Marcia Guan, Ling Ryerson Univ Ryerson Multimedia Res Lab Toronto ON Canada

ISBN: (纸本)9781728111988

In this paper, we highlight three issues that limit performance of machine learning on biomedical images, and tackle them through 3 case studies: 1) Interactive machine learning (IML): we show how IML can drastically improve exploration time and quality of direct volume rendering. 2) transfer learning: we show how transfer learning along with intelligent pre-processing can result in better Alzheimer's diagnosis using a much smaller training set 3) data imbalance: we show how our novel focal Tversky loss function can provide better segmentation results taking into account the imbalanced nature of segmentation datasets. The case studies are accompanied by in-depth analytical discussion of results with possible future directions.

关键词： Training Image color analysis Biomedical imaging Computer architecture Feature extraction Rendering (computer graphics) machine learning

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Supervised machine learning approaches for medical data classification 2

Supervised machine learning approaches for medical data clas...

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2nd international conference on Artificial Intelligence and signal processing, AISP 2022

作者： Dalai, A.K. Jena, A.K. Ramana, B.V. Maneesha, B. Panda, Nibedan School of Computer Science and Engineering VITAP University India Department of Information Technology Aditya Institute of Technology and Management AP Tekkali532201 India Department of CSE Presidency University Yelahanka Bengaluru560064 India

ISBN: (纸本)9781665442909

Recently there is an emergent curiosity among researchers to apply machine learning algorithms over diversified real world complications to get simpler *** notion behind this briefing is to represent the basic machine learning algorithms and its applicability in current research. Broadly machine learning algorithms falls to the category of either supervised or unsupervised learning technique. In this paper we have discussed supervised machine learning techniques with its simplicity to apply over various problem areas and simultaneously the challenges for such algorithms. Furthermore SVM and Random Forest (RF) are utilised learn, categorise, and compare cancer, liver, diabetes, iris, and heart data in this study. For all considered data sets, the results of SVM and RF are compared. The results are properly analysed in order to develop better prediction learning techniques. © 2022 IEEE.

关键词： Support vector machines

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Critical Dimension of Word2Vec 2

Critical Dimension of Word2Vec

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2nd international conference on Innovations in Electronics, signal processing and Communication, IESC 2019

作者： Das, Shuvayanti Ghosh, Sohan Bhattacharya, Shubham Varma, Rajat Bhandari, Dinabandhu Heritage Institute of Technology Kolkata India

ISBN: (纸本)9781728107448

Word embeddings are an efficient way of representing text such that they can be used by different machine learning Algorithms. Word2Vec is one such word embedding model. Although it is highly efficient, this model can take up a lot of space to store the vector representations. So defining the appropriate dimension of this model is very important to improve its performance in memory restricted devices. In this work, we present an empirical approach to decide the dimension of the word embeddings for a specific set of documents (corpora) to a critical value such that the representation of the words still preserve their original semantic and syntactic meanings. In this context, principal component analysis (PCA) has been used to compare the precision of vectors represented in different dimensions. © 2019 IEEE.

关键词： Principal component analysis

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Sequential Segmentation of EEG signals for Epileptic Seizure Detection using machine learning 2

Sequential Segmentation of EEG Signals for Epileptic Seizure...

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2nd international conference on signal processing and Communication, ICSPC 2019

作者： Ahmad, Zeba Karin Singh, Vikram Khan, Yusuf Uzzaman Aligarh Muslim Unversity Department of Electrical Engineering Aligarh202002 India

ISBN: (纸本)9781728118499

The problem of epilepsy has grown exponentially and is now considered as one of the most prevailing neurological disorders affecting around 50 million people around the globe. Epilepsy is identified by analyzing the interictal activity present in the EEG signal. Visual analysis of EEG is a tedious process and subject to human error. This work proposes a robust method to ease the burden of intractable seizures by automatic recognition of ictal epileptiform activity in the EEG of epileptic patients. The classification between EEG having an epileptic seizure and non-seizure is done using various machine learning algorithms. The classifiers used are Simple Decision tree, Quadratic Discriminant, Medium Gaussian SVM, Bagged Trees, and Subspace k-NN. The performance is assessed using 10-fold cross-validation. © 2019 IEEE.

关键词： Electroencephalography

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A Semantic Internet of Things Framework using machine learning Approach based on Cloud Computing 2018

A Semantic Internet of Things Framework using Machine Learni...

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2nd international conference on Digital signal processing (ICDSP) / international conference on Computer Graphics and Virtuality (ICCGV)

作者： Ding, Pei-Wun Hsu, I-Ching Natl Formosa Univ 64 Wenhua Rd Huwei Township 632 Yunlin Taiwan

ISBN: (纸本)9781450364027

A major limitation of existing Semantic Web applications is the lack of automatic generation linked data for personal needs. Internet of Things (IoT) can provide automatic sensing data to improve this limitation. The study addresses this issue by defining a Semantic Internet of Things Framework (SIOTF), which is implemented on Hadoop-based cloud computing ecosystem to provide efficiency in dealing with a mass of sensing data. The SIOTF is composed of four modules: Internet of Things module, Naive Bayesian Classification module, Open Data Service module, and Semantic Web module. The proposed SIOTF is used to develop a Culture Sharing Cloud Platform (CSCP) that provides customized culture information for personnel needs. To demonstrate the feasibility of CSCP, the experimental results illustrate the efficiency and effectiveness of the proposed approach.

关键词： Cloud Computing IoT Semantic Web machine learning

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Classification of Hand Movements via EMG using machine learning Methods for Prosthesis 2

Classification of Hand Movements via EMG using Machine Learn...

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2nd international conference on Artificial Intelligence and signal processing, AISP 2022

作者： Karuna, M. Guntur, Sitaramanjaneya Reddy Department of Electronics and Communication Engineering Vignan’s Institute of Information Technology India Department of Biomedical Engineering Vignan’s Foundation for Science Technology and Research India

ISBN: (纸本)9781665442909

The recognition of hand movements using surface electromyography (sEMG) and a machine learning technique is becoming increasingly significant to control a prosthetic hand in a rehabilitation facility for people who have had their hands amputated in order to regain lost capability. However, in real life, controlling a prosthetic hand utilizing non-invasive methods is still a challenge. Existing research results are limited and not meeting the needs of amputee. The objective of this work is to fulfill the gap by proposing empirical mode decomposition (EMD) based machine learning (ML) classifier to recognize hand movements of the Ninapro dataset, this benchmark standard is used to evaluate four classifiers by comparing the performance accuracy results. The outcome of this work is better movement recognition achieved using one of the four distinct classifiers. © 2022 IEEE.

关键词： Empirical mode decomposition

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Discrete sidelobe clutter determination method based on filtering response loss 2

Discrete sidelobe clutter determination method based on filt...

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2nd international conference on signal processing and machine learning, spml 2019

作者： Wen, He Duan, Chongdi Wang, Weiwei Li, Yu China academy of space technology Xi'an Xi'an710100 China

ISBN: (纸本)9781450372213

For air moving target detection with space-based radar (SBR), discrete sidelobe clutter is generally caused by strong scattering points at the sidelobe direction in the observation scene, which is difficult to discern from moving targets as a result of its strong power and special Doppler feature. To solve the above problem, the discrete clutter determination method based on filtering response loss is proposed. Firstly, the power of the potential target is calculated after clutter suppression, and then the power loss of the potential target is obtained by giving comparison to the initial power. Finally, in accordance with the criterion that power loss of discrete sidelobe clutter is high while that of the moving target is low after clutter suppression, with which the discrete sidelobe clutter can be identified based on the adaptive threshold. Simulation results with the real measured data show the feasibility and effectiveness of the proposed method. © 2019 Association for Computing machinery.

关键词： Space time adaptive processing

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Extreme learning machine Based Defect Detection for Solder Joints

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JOURNAL OF INTERNET TECHNOLOGY 2020年第5期21卷 1535-1543页

作者： Ma, Liyong Xie, Wei Zhang, Yong Feng, Xijia Harbin Inst Technol Sch Informat Sci & Engn Harbin Heilongjiang Peoples R China Harbin Inst Technol Dept Civil Engn Harbin Heilongjiang Peoples R China

The quality of solder joints is essential for electronic products, and the detection of defects in solder joints is critical to the quality control of electronic products. A vision inspection is developed to detect defects of solder joints in automatic line. Extreme learning machine is applied to identify defective solder joints from qualified ones. Five low level features and three advanced features are employed as input features. The low-level features include roundness, roughness, entropy, contrast and histogram of oriented gradient. The advanced features include grey-level co-occurrence matrix, local binary pattern, and segmentation-based fractal texture analysis. To solve unbalanced samples problem, Gaussian mixture model based dense estimation scheme is proposed to adjust the classification super plane for extreme learning machine. The experimental results demonstrate that the proposed defect detection method is more efficient than neural network, support vector machines, common extreme learning machine and convolutional neural network-based methods, and it has real-time performance to meet the equirement of the actual production line.

关键词： Defect detection Solder joints Extreme learning machine Gaussian mixture model

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Ecg Classification using machine learning Techniques and Smote Oversampling Technique 2020

Ecg Classification using Machine Learning Techniques and Smo...

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2nd international conference on Image processing and machine Vision / international conference on Pattern Recognition and machine learning (IPMV)

作者： Zhong, Zhang Xing Michael, Akotonou J. Lun, Zhao Jie Yue, Dong Hong Taiyuan Univ Technol Dept Software Engn Taiyuan Peoples R China

ISBN: (纸本)9781450388412

In this paper, automatic classification of Atrial Fibrillation (AF) based on single lead ECG signal was proposed using three different classification algorithm AdaBoost, K-Nearest Neighbors (KNN) and Support Vector machine (SVM). SMOTE technique was applied as data oversampling techniques. Many features were extracted and Minimum Redundancy Maximum Relevance (MRMR) algorithm was used to select relevant features. 5834 records were selected from the Physionet Challenge 2017 dataset for this experiment. Classification using oversampling technique yields best results for all classifiers involved. AdaBoost on oversampling data yields the best accuracy of 98.8%.

关键词： ECG AdaBoost KNN MRMR SMOTE SVM

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