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检索条件"任意字段=2nd International Conference on Signal Processing and Machine Learning, SPML 2019"

共 886 条记录，以下是51-60 订阅

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Generation of Netlist from a Hand drawn Circuit through Image processing and machine learning 2

Generation of Netlist from a Hand drawn Circuit through Imag...

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

作者： Mohan, Akshatha Mohan, Athulya Indushree, B. Malavikaa, M. Narendra, C.P. Electronics and Communication Bangalore Institute of Technology Bangalore India

ISBN: (纸本)9781665442909

Circuit diagrams are used to depict electronic or electrical circuits graphically. It is simple for everyone to put their thoughts on paper. However, in order to conduct simulations in the different available tools, the circuit model needs be in digital form. This project presents several image processing and machine learning approaches for the conversion of hand-drawn circuits to netlists. Rather than training the dataset for all components, a technique based on the length ratios of a few of lines was employed to identify elements such as a voltage source, ground, and capacitor. Various image processing techniques are used to eliminate noise and prepare pictures for further processing. HOG feature extraction is utilized throughout the training and segmentation stages to detect resistor, diode, and inductor components. The final stage is to construct a netlist from the detected elements, wires, and their locations, as well as the identified nodes. © 2022 IEEE.

关键词： Feature extraction

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A Network Intrusion Detection System Based on Ensemble machine learning Techniques 2

A Network Intrusion Detection System Based on Ensemble Machi...

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

作者： Parkar, Prachiti SAP Fieldglass SAP Labs India Bangalore India

ISBN: (纸本)9781665442992

Technology is growing exponentially than ever before;it helps us to make unforeseen progress but at the same time these advancements also pose a grave danger to software systems in terms of security. While paving a path for advancements, these developments are simultaneously lending a helping hand to hackers to hack into systems illegally and misuse the data. While the hackers cannot be stopped, their activities can be identified and prevented. In this paper, I have proposed a simple and effective technique to detect network intrusions and protect valuable data by using machine learning algorithms. The novelty in this approach is that the proposed system used the same classifiers in feature selection which were used later for the main classification step, this proved to be fundamental in achieving high accuracy. Moreover, I have successfully simulated attacks in real-time to test our application. Finally, I concluded with fetched results and insights for future research. © 2021 IEEE.

关键词： Cybersecurity

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Credit Card Fraud Detection Using machine learning Techniques 2

Credit Card Fraud Detection Using Machine Learning Technique...

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2nd international conference on Paradigm Shifts in Communications Embedded Systems, machine learning and signal processing, PCEMS 2023

作者： Vejalla, Indrani Battula, Sai Preethi Kalluri, Kartheek Kalluri, Hemantha Kumar VFSTR University India RVR&JC College of Engineering India SRM University AP India

ISBN: (纸本)9798350310719

There are many types of fraud in our daily life. One of the frauds occurring these days is credit card fraud. When people around the globe make credit card transactions, there will also be fraudulent transactions. To avoid credit card fraud, we must know the patterns and how the fraud values differ. This paper proposed credit card fraud detection using machine learning based on the labeled data and differentiating the fraudulent and legitimate transactions. The experiment was conducted using supervised machine-learning techniques. © 2023 IEEE.

关键词： Crime

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A Study on ELM(Election pledge management for Local governors Model) Based on machine learning -Focused on On-Nara Document System- 2019

A Study on ELM(Election pledge management for Local governor...

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2nd international conference on Software Engineering and Information Management (ICSIM) / 2nd international conference on Big Data and Smart Computing (ICBDSC)

作者： Lee, Hong-Jae Han, Kyeong-Seok Kwon, Tae-Hyun Han, Sang-Ung Soongsil Univ Dept IT Policy Management Seoul 06978 South Korea Soongsil Univ Dept Management Seoul 06978 South Korea

ISBN: (纸本)9781450366427

The background of this paper is new social trend of more public's interest in the implementation of the pledge of the local governors who were elected by citizens. In these days the election pledge for enhanced local governmental policies became more important. The objective of this paper is to suggest the model of election pledge management for local government heads based on machine learning focused on On-Nara document system. The system is currently used by Korean governmental organizations for document processes. The methods to prove a comparative advantage of the proposed model are the comparison tests between As-Is system and To-Be system based on a few criteria such as time, efficiency and extraction rate. Through this model, local governors could present systematic goals and road map of pledges in order to get closer to citizens and local residents. In other words, this study proposes a model, so called ELM(Election pledge management for Local governors Model), for efficiently extracting necessary data from planned and implemented details of pledge projects that are prepared in the form of unstructured documents. We carried out research to prove empirically our machine learning-based model is more efficient than current semi-manual system with some automated processes in order to manage efficiently the pledge project implementation of local governors to get the results. In conclusion, this research proved that the proposed model is more competitive than the existing models. In the 4 th industrial revolution era the new approach using machine learning and big data will become more popular.

关键词： machine learning Big Data Natural Language processing Unstructured Database Election pledge management Text mining

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Kernel-optimized Based machine for Image Recognition

Kernel-optimized Based Machine for Image Recognition

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2nd international conference on Robot, Vision and signal processing (RVSP)

作者： Wang, Yun-Heng Fu, Ping Harbin Inst Technol Dept Automat Test & Control Harbin 150006 Peoples R China Heilongjiang Inst Sci & Technol Sch Elect & Control Engn Harbin Peoples R China

ISBN: (纸本)9781479931842

Kernel learning is an important research topic in the machine learning area. Research on self-optimization learning of kernel function and its parameter has an important theoretical value for solving the kernel selection problem widely endured by kernel learning machine, and has the same important practical meaning for the improving of kernel learning systems. In this paper, we focus on two schemes: kernel optimization algorithm and procedure, the framework of kernel self-optimization learning. Finally, the proposed kernel optimization is applied into popular kernel learning methods including KPCA, KDA and KLPP. Simulation results demonstrate that the kernel self-optimization is feasible to improve various kernel-based learning methods.

关键词： kernel self-optimization kernel discriminant analysis kernel locality preserving projection

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Prediction and Data Analysis of Price Based on PSO with Extreme learning machine Algorithm and Particle Swarm Optimization 2

Prediction and Data Analysis of Price Based on PSO with Extr...

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2nd international signal processing, Communications and Engineering Management conference, ISPCEM 2022

作者： Chen, Huanyun Zhao, Weiming Private Hualian College Guangzhou China

ISBN: (纸本)9781665492713

The changes in the trading market are affected by many factors, and traditional forecasting methods are more and more difficult to meet people's needs. In order to improve the accuracy of prediction, this paper proposes an extreme learning machine algorithm based on particle swarm optimization for prediction and analysis. First, the fluctuation data of previous years are collected, and the closing price of the stock is determined as an experiment and simulated to establish a learning sample, and then the learning sample is input. Based on the online extreme learning machine model optimized by particle swarm optimization, the prediction model is established. The results show that the optimized extreme learning machine algorithm, compared with models such as ELM, PSO-BP and DE-ELM, has better surface prediction accuracy and fitting effect, and can more accurately describe the change trend, which is more efficient than other methods. © 2022 IEEE.

关键词： Forecasting

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Autonomous Intelligence for FMV ISR Sensors With A Human In The Loop Decision Support System 2018

Autonomous Intelligence for FMV ISR Sensors With A Human In ...

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2nd international conference on Vision, Image and signal processing (ICVISP)

作者： Lewis, Rory Univ Colorado Colorado Springs Dept Comp Sci Colorado Springs CO 80918 USA

ISBN: (纸本)9781450365291

As the United States Air Force moves towards autonomous labelling of FMV from ISR sensors, it has experienced unforeseen technical and legal challenges. In terms of the technical challenges, this research effort identifies these obstacles and presents solutions for them with detailed step-by-step analysis of the processes, its testing and prototypes. In terms of the legal challenges, the USAF's goals of infusing artificial intelligence into autonomous labelling of FMV is also being challenged by a formidable, looming legal threat of new laws that will force the USAF to include 'humans in the loop' of its artificial intelligence and machine learning systems [20], [7], [15]. Again, we analyze these legal threats and present solutions to allow inclusion of a human in the loop. It is important to note that our solution to these technical and legal challenges form a two-pronged solution that yields a Bench to Battlefield, Government off-the-shelf (GOTS) autonomous FMV labelling system that will, as time goes by, learn and grow in its ISR identification abilities.

关键词： Artificial intelligence machine learning unmanned aircraft systems

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Research on signal processing Methods for Gas Ultrasonic Flowmeters 2

Research on Signal Processing Methods for Gas Ultrasonic Flo...

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2nd international conference on machine learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023

作者： Wan, Yong China Coal Technology and Engineering Group Chongqing Research Institute Chongqing400039 China State Key Laboratory of Gas Disaster Monitoring and Emergency Technology Chongqing400039 China

ISBN: (纸本)9798350328530

On the basis of the difficulty in determining the feature point in the signal processing of gas ultrasonic flowmeter,a variable threshold based zero-crossing detection signal processing method is proposed to determine a feature wave of the signal accurately. Thus the propagation time of ultrasonic signal is calculated,and the gas flow rate is measured *** method is realized real time in a signal processing system with DSP and FPGA as dualcore for the gas ultrasonic *** judging the 'step', the error caused by noise interference is eliminated,and the stability of the measurement results is enhanced. With piecewise linear calibration,the non-linear error is corrected,and the measurement accuracy of the system is improved. The experimental results of gas flow calibration verify the effectiveness of the method and the system. © 2023 IEEE.

关键词： Flow of gases

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ISR-Brain machine Intelligence for Unmanned Aircraft Systems 2018

ISR-Brain Machine Intelligence for Unmanned Aircraft Systems

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2nd international conference on Vision, Image and signal processing (ICVISP)

作者： Lewis, Rory Univ Colorado Colorado Springs Dept Comp Sci Colorado Springs CO 80918 USA

ISBN: (纸本)9781450365291

This paper presents a system for extrapolating knowledge and classification rules from existing ISR FMV and creating an ISR-Brain. As combat operations have grown to depend upon assured, live ISR support during operations, US forces are presented with formidable challenges to integrate artificial intelligence (AI) capabilities with existing ISR systems. The common challenge being the variance at which advances in commercial and academic AI are deployed compared to rate of speed that innovative AI systems are developed and utilized in military domains. ISR, USAF and SOCOM need to develop a means to seamlessly integrate military and commercial state-of-the-art systems. The ISR-Brain presented will be capable of converting classifiers in existing ISR FMV to machine learning rules for real time ISR sensor, multi-source, multi-enclave data and adaptable with ongoing research efforts with A2, SOCOM, REDO, MITRE and Project MAVEN to develop and test and ISR-Brain to enable the system to integrate with all ISR sensors and predict future Troops in Contact events (TIC) and IED events.

关键词： Artificial intelligence machine learning unmanned aircraft systems

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Honey Adulteration Detection using Hyperspectral Imaging and machine learning 2

Honey Adulteration Detection using Hyperspectral Imaging and...

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

作者： Al-Awadhi, Mokhtar A. Deshmukh, Ratnadeep R. Department of Computer Science and IT Dr. Babasaheb Ambedkar Marathwada University Aurangabad India

ISBN: (纸本)9781665442909

This paper aims to develop a machine learning-based system for automatically detecting honey adulteration with sugar syrup, based on honey hyperspectral imaging data. First, the floral source of a honey sample is classified by a botanical origin identification subsystem. Then, the sugar syrup adulteration is identified, and its concentration is quantified by an adulteration detection subsystem. Both subsystems consist of two steps. The first step involves extracting relevant features from the honey sample using Linear Discriminant Analysis (LDA). In the second step, we utilize the K-Nearest Neighbors (KNN) model to classify the honey botanical origin in the first subsystem and identify the adulteration level in the second subsystem. We assess the proposed system performance on a public honey hyperspectral image dataset. The result indicates that the proposed system can detect adulteration in honey with an overall cross-validation accuracy of 96.39%, making it an appropriate alternative to the current chemical-based detection methods. © 2022 IEEE.

关键词： Hyperspectral imaging

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