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检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

共 1333 条记录，以下是91-100 订阅

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Transfer learning based COVID-19 detection Using Radiological Images 2

Transfer learning based COVID-19 detection Using Radiologica...

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2nd IEEE international conference on Applied Electromagnetics, signal processing, and Communication, AESPC 2021

作者： Mahapatra, Aurosweta Pahadsingh, Sasmita Kar, T.

ISBN: (纸本)9781665442992

Artificial Intelligence is a process that enables machines to imitate human behaviour. Both machine learning and Deep learning are subsets of AI. The basic difference between ML(machine learning) and DL(Deep learning) is that in machine learning manually defining of features is done to get the desired outcome whereas in deep learning the neural network learns of its own and publishes the result. In the present crisis due to COVID-19 pandemic the contagious power of virus has led to huge encounter of cases on daily basis. This stimulates the need for specialised and accurate methods to detect COVID-19 cases. The contribution of deep learning to this problem has been significant. The application of deep learning concepts has shown its emence importance and utility in medical domain for detection of COVID-19 cases using CT scan and X-Ray images of lungs. Our proposed method compares the accuracy of multiple pretrained models in predicting COVID-19 infected cases for a specific dataset of radiological images using three distinct optimizers for each model. This research aims to determine which model, together with its associated optimizer, is most suitable for identifying COVID-19 infected cases from radiological lungs images. © 2021 IEEE.

关键词： Computerized tomography

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Particle Swarm Optimization (PSO) based 2-DoF-PID power system stabilizer design for damping out low frequency oscillations in power systems 2

Particle Swarm Optimization (PSO) based 2-DoF-PID power syst...

引用

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

作者： Acharya, Debasis Das, Dushmanta Kumar Rai, Ankur NIT Nagaland Department of EEE Dimapur Nagaland797103 India

ISBN: (纸本)9781728107448

Low frequency oscillation (LOF) could be a serious concern in grid. It may occur due to tiny signal perturbation. It will cause complete grid collapse. Power system stabilizer (PSS) is used to help the system by rising the damping of grid. During this paper, particle swarm optimization (PSO) based two-degree of freedom PID stabilizer is planned to enhance the damping of the system. The stabilizer parameters are tuned utilizing PSO. Integral square error (ISE) is picked as the objective criterion for the plan. To execute this, IEEE type DC1 exciter with single machine unbounded bus (SMUB) system is considered. The execution of the PSO based 2-DoF-PID is compared with PSO based PI, PID stabilizers. To check the viability of the structured stabilizers, comparatives investigation is finished with some current outcomes. © 2019 IEEE.

关键词： Damping

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Pneumonia Radiograph Diagnosis Utilizing Deep learning Network 2

Pneumonia Radiograph Diagnosis Utilizing Deep Learning Netwo...

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IEEE 2nd international conference on Electronic Information and Communication Technology (ICEICT)

作者： O'Quinn, Wesley Haddad, Rami J. Moore, David L. Georgia Southern Univ Dept Elect & Comp Engn Statesboro GA 30458 USA

ISBN: (纸本)9781538692981

Pneumonia is a life-threatening respiratory disease caused by bacterial infection. The goal of this study is to develop an algorithm using Convolutional Neural Networks (CNNs) to detect visual signals for pneumonia in medical images and make a diagnosis. Although Pneumonia is prevalent, detection and diagnosis are challenging. The deep learning network AlexNet was utilized through transfer learning. A dataset consisting of 5659 images was used for training, and a preliminary diagnosis accuracy of 72% was achieved.

关键词： Artificial Intelligence Deep learning Medical Imaging Image processing Pneumonia Radiograph processing

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Research on Classification of Malicious Code Visualized Target Detection 2

Research on Classification of Malicious Code Visualized Targ...

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2nd international conference on signal processing and Intelligent Computing, SPIC 2024

作者： He, Zipeng Zhao, Yuntao Yang, Jianjun Shenyang Ligong University School of Information Science and Engineering Shenyang China Unit 6409 of the People's Liberation Army Fushun China

ISBN: (纸本)9798350368888

With the generation of a large number of variant malicious codes in various forms, traditional detection techniques can no longer accurately detect these unknown malicious codes, and the malicious code visualization method can express the core of malicious codes in image features, which can more accurately identify the malicious codes. Firstly, we summarize the current malicious code detection methods, then introduce the malicious code visualization and the data set required for the experiment, and finally use the deep learning method to classify and detect the visualized malicious code images, aiming to test the effect of the malicious code visualization and a detection method that is based on the deep learning method, and help developing malicious code detection technology. © 2024 IEEE.

关键词： Adversarial machine learning

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Improved extreme learning machine and its application in SAR target recognition 2

Improved extreme learning machine and its application in SAR...

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2nd international conference on Image and Video processing, and Artificial Intelligence (IPVAI)

作者： Jian, Chen Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350108 Peoples R China

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

In this paper, An improved algorithm for the extreme learning machine is proposed and applied to SAR target *** order to solve the influence of the noise and spatial distribution of the training samples on the calculation of the classification plane, different penalty factors are given to different training samples,and according to this, the "weighted extreme learning machine" is proposed. And then,the kernel function is introduced into the "extreme learning machine" to improve the ability of nonlinear function approximation. Considering that the general training algorithm of the weighted extreme learning machine is slow and consumes a lot of computer memory when the number of training samples is large, a training method based on conjugate gradient algorithm is proposed. The test on "banana benchmark data" shows that the weighted extreme learning machine based on the conjugate gradient method can complete the convergence in the number of iterations far less than the number of samples, and the calculation speed is much faster than the traditional algorithm. Finally, this proposed algorithm is applied to SAR target recognition. The test on MSTAR data set shows that the proposed algorithm is not only extremely fast in SAR target recognition, but also has better recognition performance than support vector machine. general limit learning machine. BP neural network and other algorithms.

关键词： Extreme learning machine Conjugate Gradient Method Synthetic aperture radar Target recognition

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Meteorite Hunting Using Deep learning and UAVs 2

Meteorite Hunting Using Deep Learning and UAVs

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2nd international conference on signal processing and Information Security, ICSPIS 2019

作者： Alowais, Aisha Naseem, Safa Dawdi, Takwa Abdisalam, Mariam Elkalyoubi, Yusra Adwan, Anas Hassan, Khawla Fernini, Ilias Sharjah Academy for Astronomy Space Sciences and Technology University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781728138732

In this paper, we present an automated meteorite detection system that employs an autonomous Unmanned Aerial Vehicle (UAV). It is programmed to recognize and locate meteorites using machine learning. In this design, the UAV carries a Single Board Computer (SBC), through which realtime data processing of the live video feed from an optical camera is performed. The integration of a GPS module into the system enables localization of the detected meteorites. The onboard processing, carried out by the SBC, involves analyzing each frame from the video and running it through our deep learning model. This model is trained on images of different types of commonly found meteorites, via Transfer learning performed on Convolutional Neural Networks (CNN). The results were promising, with an accuracy of approximately 90%. Future enhancements, including the addition of an infrared sensor to capture thermal images, are also discussed. © 2019 IEEE.

关键词： Drones

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Application and optimization of intrusion detection system based on Artificial Intelligence 2

Application and optimization of intrusion detection system b...

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2nd IEEE international conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Zhu, Kaidi Wu, Xiangwen Yu, Sun Huo, Da Li, Rui Pei, Xikai Second Res Inst CAAC Chengdu Peoples R China Inner Mongolia Autonomous Reg Civil Airports Grp Hulun Buir Branch Hulunbeir Peoples R China North China Regionnal Air Traff Management Bur CA Beijing Peoples R China China Civilaviat Adm Air Traff Management Bur Beijing Peoples R China

ISBN: (纸本)9781728199481

The traditional perimeter intrusion detection system will be faced more challenges in the new era, such as high-accuracy positioning, low energy dependence, high environmental robustness and so on. This paper introduces a new type of artificial intelligence intrusion detection technology. The RF sensor chip is embedded in the detection cable, and the radio frequency electromagnetic environment is formed in space through the sending and receiving of radio frequency signal, which is used to sense the surrounding environment to detect electromagnetic *** advanced space-time signal processing technology and machine learning technology, the influence of environmental interference on the system is effectively eliminated, and the intrusion detection system based on artificial intelligence algorithm has lower false alarm rate and higher environmental robustness.

关键词： image processing machine learning Intrusion Detection Technology Microwave array technology

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A Dynamical System for Online learning of Periodic Movements of Unknown Waveform and Frequency

A Dynamical System for Online Learning of Periodic Movements...

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2nd Biennial IEEE RAS-EMBS international conference on Biomedical Robotics and Biomechatronics (BioRob 2008)

作者： Gams, Andrej Righetti, Ludovic Ijspeert, Auke Jan Lenarcic, Jadran IJS Jamova Cesta 39 Ljubljana 1000 Slovenia Ecole Polytech Fed Lausanne CH-1015 Lausanne Switzerland

ISBN: (纸本)9781424428823

The paper presents a two-layered system for learning and encoding a periodic signal onto a limit cycle without any knowledge on the waveform and the frequency of the signal, and without any signal processing. The first dynamical system is responsible for extracting the main frequency of the input signal. It is based on adaptive frequency phase oscillators in a feedback structure, enabling us to extract separate frequency components without any signal processing, as all of the processing is embedded in the dynamics of the system itself. The second dynamical system is responsible for learning of the waveform. It has a built-in learning algorithm based on locally weighted regression, which adjusts the weights according to the amplitude of the input signal. By combining the output of the first system with the input of the second system we can rapidly teach new trajectories to robots. The systems works online for any periodic signal and can be applied in parallel to multiple dimensions. Furthermore, it can adapt to changes in frequency and shape, e.g. to non-stationary signals, and is computationally inexpensive. Results using simulated and hand-generated input signals, along with applying the algorithm to a HOAP-2 humanoid robot are presented.

关键词： Dynamical systems

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Digital Recognition of Street View House Numbers Based on DCGAN 19

Digital Recognition of Street View House Numbers Based on DC...

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2nd international conference on Image and Graphics processing (ICIGP) / 5th international conference on Virtual Reality

作者： Zhong, Juping Gao, Jing Chen, Rongjun Li, Jun Guangdong Polytech Normal Univ Coll Comp Sci Guangzhou 510665 Guangdong Peoples R China Guangdong Hengdian Informat Technol Co Ltd Guangzhou 510635 Guangdong Peoples R China

ISBN: (纸本)9781450360920

Deep learning algorithms have surpassed human resolution in applications such as face recognition and object classification. However, it can only produce very blurred, lack of details of the image. Generative Adversarial Network is a game training of minimax antagonism between generator G and discriminator D, and ultimately achieves Nash equilibrium. We use deep convolutional GAN that recognizes sequence numbers and without split characters. First we use convolution network to extract character features. Second we construct a convolution neural network to recognize digits of natural scene house number. DCGAN is used to improve the resolution of the number of fuzzy houses, so as to extract more abundant data features in data set training. It can better recognize the numbers in the natural street.

关键词： Image processing machine learning deep convolutional generative adversarial networks street view house numbers

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Mobile Health Solutions for Effective Symptom Detection and Disease Management Using machine learning 2

Mobile Health Solutions for Effective Symptom Detection and ...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Choudhary, Santosh Kumar Kumar, Pradeep Vaishnavi, Udipi Patchala, Deekshita Dept. of ECE VNRVJIET Hyderabad500090 India

ISBN: (纸本)9798350366570

People in today's world are so busy with work and other commitments that they rarely have time to see doctors for illnesses that initially seem minor but eventually become life-threatening. In the above situations, where patients do not have easy access to healthcare or face difficulties in utilizing it, this can be very useful in helping them understand the urgency of their health condition. The suggested algorithm makes use of machine learning to forecast diseases based on user-provided symptoms. The flow of the disease diagnosis support system using symptoms is as follows: First, data pre-processing is performed, and several machine learning classification models, including K Nearest Neighbor, are then applied to the data, Random Forest, and Support Vector machine (SVM). The accuracy of each model is calculated to aid in predicting the disease based on the mentioned symptoms. The predicted disease is then sent back to the application, where it is displayed to the user. Our model primarily focuses on seasonal infections and diseases. © 2024 IEEE.

关键词： Adversarial machine learning

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