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

共 777 条记录，以下是661-670 订阅

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A Technique For Determining the Early Detection For Cervical Cancer 5

A Technique For Determining The Early Detection For Cervical...

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5th IEEE international conference on signal processing, Computing and Control (ISPCC)

作者： Deswal, Anjali Dhawan, Sanjeev Singh, Kulvinder Kurukshetra Univ Univ Inst Engn & Technol UIET Dept Comp Sci & Engn Kurukshetra 136119 Haryana India

ISBN: (纸本)9781728139883

Cervical cancer is one of the most common types of cancer among women. More importantly, cervical cancer is because by Human papillomavirus (HPV) and having regular Pap smear tests can detect HPV infection and pre-cancerous cells. Treatment of HPV can stop cervical cancer before it develops into cancer cells. Since the patterns of development of cervical cancer provide multiple opportunities for detection and treatment. this paper is divided into six sections. Section 1 and 2 discuss cervical cancer and various existing screening for cervical cancer. Section 3 provides a brief overview of the work done so far in the field of cervical cancer. Section 4 discusses the proposed mechanism and the results are described in section 5. Finally, section 6 discusses the conclusion drawn from the whole work. In this paper, an attempt has been made to propose an early detection of cervical cancer system based on some existing parameters such as smoking, the number of sexual partner and age of the person. this work has been implemented in Eclipse and analysed using Weka.

关键词： Cervical cancer Early detection machine learning Weka

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Proceedings of 5th IEEE international conference on signal processing, Computing and Control, ISPCC 2019

Proceedings of 5th IEEE International Conference on Signal P...

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5th IEEE international conference on signal processing, Computing and Control, ISPCC 2019

ISBN: (纸本)9781728139869

the proceedings contain 70 papers. the topics discussed include: an efficient design of spiral shaped MEMS energy harvester for low power electronic applications;generalized framework for higher-order fractional derivatives - from Riemann-Liouville to Atangana-Baleanu;mitigation of power system transients in a multi-machine system using unified power flow controller;automated textural features based skin disease diagnosis system;machine learning based soil moisture prediction for internet of things based smart irrigation system;detection, quantification and analysis of Neofabraea leaf spot in olive plant using image processing techniques;and proposed techniques for detection of time and frequency synchronization in GSM systems.

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Music Genre Classificatio Using Novel Song Structure Derived Features

Music Genre Classificatio Using Novel Song Structure Derived...

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international conference on Computer Science and Engineering (UBMK)

作者： Rustem Ozakar Eyup Gedikli Department of Computer Engineering Erzurum Technical University Erzurum Turkey

ISBN: (数字)9781728175652

ISBN: (纸本)9781728175669

Rapid grow of the digital music content and service providers worldwide everyday increases the importance of music genre classification. Most genre classification still relies heavily on human effort. signal processing combined with machine learning methods aims to solve this problem autonomously for decades. In this work, we introduce novel high-level features derived from song structures and examine their performance through both CNN and a Voting Classifier. Results show that these features alone increases the classification accuracy significantly compared to random prediction and has potential of use in combination with other various features.

关键词： Hafnium compounds Global Positioning System Feature extraction machine learning signal processing Music information retrieval

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Few-Shot learning to Classify Android Malwares

Few-Shot Learning to Classify Android Malwares

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IEEE international conference on signal and Image processing (ICSIP)

作者： Laha Ale Longzhuang Li Dulal Kar Ning Zhang Aayasha Palikhe Texas A&M University-Corpus Christi Corpus Christi TX USA

ISBN: (数字)9781728168968

ISBN: (纸本)9781728168975

Mobile phones have become a target for cybercrime where malicious apps are developed to acquire sensitive information or corrupt data. To mitigate this issue and to improve the security in mobile devices, many machine learning methods have been developed to detect and classify Android malware. One problem with the existing methods is that they all assume a large dataset is available to train the learning models. In this paper, a model named few-shot malware classification (FSMC) is proposed to classify Android apps by using only a few training cases for each class. For three small datasets, the experimental results demonstrate that the FSMC model is able to achieve significantly higher accuracy compared to the existing 2-way 1-shot and 2-way 3-shot malware classification methods.

关键词： Training Neural networks machine learning Programming Malware Security Smart phones

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Surface EMG Classification of Basic Hand Movement 5

Surface EMG Classification of Basic Hand Movement

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5th international conference on Advances in Biomedical Engineering (ICABME)

作者： Omama, Youssef Haddad, Christelle Machaalany, Maroun Hamoudi, Ahmad Hajj-Hassan, Mohamad Abou Ali, Mohamad Hamawy, Lara Lebanese Int Univ Dept Biomed Engn Beirut Lebanon Medconsul Co Biomed Engn Dept Beirut Lebanon

ISBN: (纸本)9781728123141

In recent decades, enormous improvements in the rehabilitation of the amputee have been made. A major development is that the prosthetic restoration of a limb or an extremity re-establishes the functional mobility of the patient. therefore, myoelectric control devices become popular as they mimic the appearance and replace the function of a missing limb simultaneously. For this reason, the overall goal of this project is to classify motion patterns, especially of different types of contractions in order to drive a below-elbow prosthetic arm. Firstly, the acquisition part is designed to capture surface EMG signals using Myoware sensor acquisition board placed on the biceps and triceps and to perform the required signal processing. Secondly, an analysis is performed on the obtained signals, and feature extraction is implemented accordingly. After that, the EMG classification is done using an Artificial Neural Network (ANN) computing system. the ANN is a machine learning method made of many layers, and trained to recognize the muscle contractions. the weights and the activation function are updated to predict seven gestures output. thus, the input and the output are manipulated to perform a specific task accurately and the algorithm is considered a real time system due to its fast response.

关键词： Myoware Arduino Mega Artificial Neural Network (ANN) machine learning signal processing Amputee

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Evaluation of Dental Prosthesis Colors Using a Neural Network

Evaluation of Dental Prosthesis Colors Using a Neural Networ...

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IEEE international conference on signal and Image processing (ICSIP)

作者： Kazuya Ueki Hayata Wakamatsu Yasuaki Hagiwara School of Information Science Meisei University Tokyo Japan

ISBN: (数字)9781728168968

ISBN: (纸本)9781728168975

One of the most important tasks in the work of dental technicians is to reproduce the color of dental prostheses. Experienced dental technicians can correctly estimate the true color from a photograph of the patient's teeth taken by the dentist, but this is a very difficult process for inexperienced dental technicians. In this study, using machine learning techniques, we aimed to develop an automatic color evaluation system that possesses the same skill as experienced dental technicians. We prepared 62 images of the patient's teeth that were annotated by experienced dental technicians, and we attempted to estimate the true color by using a neural network. We confirmed that it is difficult to completely evaluate the true tooth color, but some color candidates could be obtained correctly.

关键词： Image color analysis Neural networks Teeth machine learning Dentistry Task analysis Prosthetics

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Classification of human heart signals by novel feature extraction techniques for rescue application 5

Classification of human heart signals by novel feature extra...

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5th international conference on Image Information processing (ICIIP)

作者： Saha, Rajesh Singh, Ratnesh Kumar Kumar, Rajeev Singh, Ghanapriya Goel, Tushar Pal, Pankaj Kumar NIT Uttarakhand Dept Elect Engn Srinagar Garhwal India

ISBN: (纸本)9781728108995

In the paper, the methodology of detecting the heart rate signal wirelessly with regards to use it as an applications as an emerging tool for health care in emergency and surveillance application is presented. A novel feature extraction technique is presented to detect human heart signal or electrocardiogram (ECG) signals wirelessly and classify the signal as normal or abnormal. By the analysis of the heart rate signal the presence of an alive human person below the debris can be detected. After detection of heart rate signals via sensors, the challenge lies in classifying the human heart rate signals as normal or abnormal. Heart signals or Electrocardiogram (ECG) is the primary signal used to diagnose and detection of heart disease abnormality. In this work, ECG signals are de-noised and then analyzed to extract feature from it by a novel technique which is combining Discrete Wavelet Transform (DWT) and nonlinear, linear statistical feature. To verify the efficiency of extracted feature, here used various machine learning (ML) methods namely as Decision Trees (DT), Discriminant Analysis (DA), Logistic Regression (LR), Nearest Neighbor (KNN), Support Vector machine (SVM) and Ensemble have been used as a classifiers and their results are compared. KNN reports the lowest accuracy of 81.60% on MIT- BIH Arrythmia Database. SVM and Ensemble reports the highest accuracy of 97.40%.

关键词： ECG machine learning Discrete Wavelength Transform Features

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Sparse Blind Compressed Sensing Based on Bilinear Generalized Approximate Message Passing

Sparse Blind Compressed Sensing Based on Bilinear Generalize...

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IEEE international conference on signal and Image processing (ICSIP)

作者： Jingjing Si Yinbo Cheng Jiaoyun Wang School of Information Engineering Yanshan University Qinhuangdao China Ocean College Hebei Agricultural University Qinhuangdao China

ISBN: (数字)9781728168968

ISBN: (纸本)9781728168975

By combining the bilinear nature of both the bilinear generalized approximate message passing (BiGAMP) methodology and the sparse blind compressed sensing (BCS) problem, a novel sparse BCS scheme is proposed. Based on BiGAMP, this scheme learns the sparse dictionary and sparse coefficients directly from the undersampled measurements, and then recovers the unknown signals by taking the standard compressed sensing as post-processing. Numerical investigation shows that this BiGAMP-based sparse BCS scheme performs better than the direct sparse BCS method, and is more adaptive to the signal under consideration than the standard CS with dictionary learning.

关键词： Runtime Message passing machine learning Sensors Sparse matrices Standards Compressed sensing

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Lightweight and Modeling Attack Resistant PUFs Authentication Based on Portion Mapping

Lightweight and Modeling Attack Resistant PUFs Authenticatio...

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IEEE international conference on signal and Image processing (ICSIP)

作者： Ziheng Chen Bing Li Yan Zhang Minzhang Gu Pengwei Yuan Xiaoyong Cheng School of Microelectronics Southeast University China School of Cyber Science and Engineering Southeast University China Southeast University Shenzhen Research Institute China

ISBN: (数字)9781728168968

ISBN: (纸本)9781728168975

Physical Unclonable Functions provide an efficient authentication scheme over traditional security-related fields based on inherent manufacturing variations. While the transmission channel of authentication protocol is exposed where adversaries collect challenge response pairs and machine learning methods are performed on these data, that result in false certification. In this paper, a novel PUFs-based authentication protocol was proposed which has resistance against machine learning attack and keep hardware consumption low-overhead. A response attenuator based on Von Neumann corrector is applied in this protocol to mapping specific portion relation between challenge and response. this scheme provides verifier with inherent noisy CRPs while the same CPRs turn out to be irrelevant to adversaries due to portion mapping of CRPs. We present the implementation on 28nm FPGA as well as ES modeling attack results. this protocol reduces prediction rate of 2-XOR APUF from 75% to 57% with only hundreds of 6-input Look-Up-Tables and registers.

关键词： Resistance Attenuators Protocols Authentication machine learning Hardware Field programmable gate arrays

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Rotating Element Bearing Fault Diagnosis Using Discrete Cosine Transform and Supervised machine learning Algorithm 5

Rotating Element Bearing Fault Diagnosis Using Discrete Cosi...

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5th international conference on Computer, Communication, Chemical, Materials and Electronic Engineering, IC4ME2 2019

作者： Bhakta, Kangkan Sikder, Niloy Nahid, Abdullah-Al Islam, M M Manjurul Khulna University Electronics Communication Engineering Discipline Khulna Bangladesh Khulna University Computer Science Engineering Discipline Khulna Bangladesh University of Ulsan School of Electrical Electronics and Computer Engineering Ulsan Korea Republic of

ISBN: (纸本)9781728130606

Motors are the driving force of our industrial world, as they power approximately 85% of all rotating machines. this revolutionary invention has been through radical changes before entering into the commercial industries, and their present forms are very reliable, to say the least. However, despite being so robust, induction motors are not entirely fault-proof and are more vulnerable to the internal faults than the external ones. Among the internal faults, certain types of bearing faults are more frequent, and their effects range from various performance-related issues to hard motor breakdowns. Fortunately, the recent advancements in the fields of Digital signal processing and machine learning allow us to detect these bearing faults and Figure out their origins, which in turn enables us to preserve their health and take measures against breakdowns. through vibration analysis, this paper proposes a powerful method to detect these faults and differentiate among them based on the location of their occurrence within the bearing. Utilizing a well-known signal processing technique called Discrete Cosine Transform and Decision Tree classifier, this method is capable of classifying the motor bearing states with a 99.4% accuracy. © 2019 IEEE.

关键词： Decision trees

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