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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"

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2nd international conference on signal and Data processing, ICSDP 2022

2nd International Conference on Signal and Data Processing, ...

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2nd international conference on signal and Data processing, ICSDP 2022

ISBN: (纸本)9789819914098

The proceedings contain 48 papers. The special focus in this conference is on signal and Data processing. The topics include: Process Mining-Based Behavioral Modeling of Learners in Self-paced learning Environment;blockchain Scalability: Solutions, Challenges and Future Possibilities;Solution of Unit Commitment Problems in GAMS Computational Environment;taxonomy and Implications of machine learning for Internet of Things: Qualities, Uses and Algorithms;auto Organizer: A machine learning-Based Tool for Automatic Organization of Files;twitter Spam Detection Using Different machine learning Techniques;on the Role of Perceptual Information in Image Classification;bengali Text Classification Based on Probability Measure;comprehensive Analysis on the Performance of Antenna Design Using machine learning Techniques;design and Analysis of Novel Miniaturized Metamaterial Structures for Multiband Applications;hybrid Particle Swarm Optimization Based Deep learning Model for the Stage Classification of Lung Cancer;Interference Cancellation by Using Viterbi Algorithm for Space Base AIS System;a Stacked Multichannel Feature Map Based U-Net Model for Brain Tumor Segmentation;spatial Attention Gated U-Net Structure for Capsule Endoscopy Image Super-Resolution;feature Engineering and Selection for the Identification of Fake News in Social Media;early Detection of Pathological Myopia in Fundus Images Using Deep learning;an Investigation on the Extractive Summarization of Kannada Text;rice Plant Leaf Disease Detection—A Comparison of Various Methodologies;combating Fake News with machine learning and Deep learning Methods;Employing Soft Computing-Based GGA-MLP for Hyperparameter Optimization in COVID-19-Infected Lung Image Data Classification;Design of Compact UWB MIMO Antenna with High Isolation Using Square Swirl Shape EBG Structure;forecasting with Fuzzy Time Series and Variation.

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Lung image segmentation by generative adversarial networks 2

Lung image segmentation by generative adversarial networks

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

作者： Cai, Jiaxin Zhu, Hongfeng Xiamen Univ Technol Sch Appl Math Xiamen Peoples R China

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

Lung image segmentation plays an important role in computer-aid pulmonary diseases diagnosis and treatment. This paper proposed a lung image segmentation method by generative adversarial networks. We employed a variety of generative adversarial networks and use its capability of image translation to perform image segmentation. The generative adversarial networks was employed to translate the original lung image to the segmented image. The generative adversarial networks based segmentation method was test on real lung image data set. Experimental results shows that the proposed method is effective and outperform state-of-the art method.

关键词： Image segmentation lung image analysis machine learning deep learning generative adversarial networks

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2nd international conference on Soft Computing and signal processing, ICSCSP 2019

2nd International Conference on Soft Computing and Signal Pr...

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2nd international conference on Soft Computing and signal processing, ICSCSP 2019

ISBN: (纸本)9789811524745

The proceedings contain 70 papers. The special focus in this conference is on Soft Computing and signal processing. The topics include: Human Pose Estimation and Activity Classification Using machine learning Approach;implementation of Interpolation in Credit Card Fraud Detection;A Statistical-Semantic PSO Model for Customer Reviews-Based Question Answering Systems;low-Cost IoT Based Spirometer Device with Silicon Pressure Sensor;recommending Research Article Based on User Queries Using Latent Dirichlet Allocation;an Applied Time Series Forecasting Model for Yield Prediction of Agricultural Crop;design and Implementation of the Protocol for Secure Software-Based Remote Attestation in IoT Devices;label Assignment and Sentimental Analysis for a Product Review on Twitter Data;preserving the Privacy of Audio File Using Ideal Secret Sharing Scheme with Cloud Storage;hadoop Performance Acceleration by Effective Data and Job Placement;modified Quick Fuzzy Hypersphere Neural Network for Pattern Classification Using Supervised Clustering;third Eye: Assistance for Reading Disability;enhanced Attach Procedure for Prevention of Authentication Synchronisation Failure Attack;static Load Balancing Algorithms in Cloud Computing: Challenges and Solutions;a Survey on Text Question Responsive Systems in English and Indian Languages;vehicle Type Classification Using Deep learning;Deep learning Approach: Detection of Replay Attack in ASV Systems;acoustic Scene Classification Using Convolutional Neural Network;classification of Microscopic Algae: An Observational Study with AlexNet;Innovative Secure Authentication Interface for Hadoop Cluster Using DNA Cryptography: A Practical Study;a Novel True Colour Image Bit Modification Technique for Image Steganography;A GRU-Based Neural machine Translation Followed by Proper Noun Transliteration;real-Time Suspicious Activity Detection.

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Extracting Critical Data from Medical Images Based on machine learning 2

Extracting Critical Data from Medical Images Based on Machin...

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2nd international conference on Advanced Algorithms and signal Image processing, AASIP 2022

作者： Wang, Sixuan Chen, Jingyi Gong, Shan Sichuan University Pittsburgh Institute Sichuan China

ISBN: (纸本)9781510660571

machine learning can be applied to cancer diagnosis, neurosurgery, radiotherapy, and so on. Its high accuracy and efficiency can improve the hospitals' overall efficiency and save diagnostic time. This paper aims to provide an algorithm for extracting critical information from X-ray medical images. The extraction process was divided into two parts: 1) the pixel coordinates of cervical vertebra nodes via automatic localization and 2) patient-related data using OCR (optical character recognition). The extracted pixel coordinates and the text information in the image were then validated. Compared with manual processing of medical images, the proposed algorithm in this study provides higher efficiency, which can better serve doctors to evaluate patients' medical images for further diagnoses. In the future, more and more medical images can be analyzed in a more intelligent and efficient way. © 2022 SPIE.

关键词： Python

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Emotion Recognition using machine learning Models on Electroencephalogram (EEG) Data 2

Emotion Recognition using Machine Learning Models on Electro...

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2nd international conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Asha, S. Roshini, S. Sai Saravanan, S. Vignesh, K. Saveetha Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350325799

Analyzing Electroencephalogram (EEG) data using cutting-edge machine learning techniques to comprehend emotional states. EEGs are non-invasive and have been shown to illuminate the complex neural mechanisms at play during affective experiences in the human brain. Everything is addressed in detail, from data collection and organization to feature extraction for model training and evaluation. It has the potential to increase the accuracy of diagnosing mental health disorders and has additional applications in psychology, human-computer interaction, and healthcare. The proposed machine learning algorithms and refined data processing strategies are being investigated to improve the accuracy and applicability of EEG-based emotion identification. This is the case despite the challenges posed by factors such as the signal's complexity, individual variation, and the need for robust feature extraction. Due to the interdisciplinary nature of the discipline and the ongoing collaboration of its practitioners, EEG analysis has the potential to shed light on underexplored aspects of the human emotional experience. © 2023 IEEE.

关键词： Electroencephalography

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Improvement of Embedding Channel-Wise Activation in Soft-Attention Neural Image Captioning 2018

Improvement of Embedding Channel-Wise Activation in Soft-Att...

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

作者： Li, Yanke Hong Kong Polytech Univ Dept Appl Math Hong Kong Peoples R China

ISBN: (纸本)9781450365291

The paper dives into the topic of image captioning with the soft attention algorithm. We first review relevant works on the captioned topic in terms of background introduction and then explains the original model in details. On top of the plain soft attention model, we propose two approaches for further improvements: SE attention model which adds an extra channel-wise activation layer, and bi-directional attention model that explores two-way attention order feasibility. We implement both methods under limited experiment conditions and in addition swap the original encoder with state-of-art structure. Quantitative results and example demonstrations show that our proposed methods have achieved better performance than baselines. In the end, some suggestions of future work on top of proposed are summarized for a purpose of completeness.

关键词： Scene Understanding Image Captioning Deep learning Soft Attention

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Food Image Recognition for Price Calculation using Convolutional Neural Network 2019

Food Image Recognition for Price Calculation using Convoluti...

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3rd international conference on Digital signal processing (ICDSP) / 2nd international conference on Computer Graphics and Virtuality (ICCGV)

作者： Nordin, Md Jan Aziz, Norshakirah Xin, Ooi Wei Univ Kebangsaan Malaysia Fac Informat Sci & Technol CAIT Bangi Malaysia Univ Teknol PETRONAS High Performance Cloud Comp Ctr HPC3 Comp Informat Sci Dept Seri Iskandar Malaysia Univ Teknol PETRONAS Comp Informat Sci Dept Seri Iskandar Malaysia

ISBN: (纸本)9781450362047

This project is attempting to solve the issue of unfair and inconsistent food price being charged in economy rice or mixed rice that widely seen in the cafe of hawker stall in Malaysia. The main cause of the problem is the absence of standardized price list of the food which causes the pricing of the mixed rice remains unknown. Hence, the authors had decided to propose this project by utilizing convolutional neural network (CNN) algorithm and develop a web application to ease the vendor as well as to provide transparency to the buyer on the food price being charged. CNN model is trained to classify the different types of food. The food price will be stored in a database of the web application in order to calculate the food price with the recognized food in the machine learning model. The outcome of this project is a customized web application for Village 3 Cafe, UTP with a trained CNN classification model at the backend.

关键词： Food image recognition convolutional neural network (CNN) machine learning

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Literature reviews on applying artificial intelligence/machine learning to software engineering research problems: Preliminary 2

Literature reviews on applying artificial intelligence/machi...

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2nd Software Engineering Education Workshop, SEED 2019

作者： Muenchaisri, Pornsiri Department of Computer Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand

This paper is aimed to explore the application of Artificial Intelligence/machine learning (AI/ML) to software engineering research problems. Which activities of software engineering use AI/ML the most for solving research problems? The scope of the paper is to preliminary review research papers published in Asia-Pacific Software Engineering conference 2018 (APSEC 2018) proceedings and researches conducted at the Department of Computer Engineering, Chulalongkorn University (CPCU). The author manually reviews papers with some keywords such as machine learning, neural network, and natural language processing. The result shows that machine learning is used in coding and software quality improvement activities more than other activities. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Application programs

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Useful Tips and Tricks on Digital Data processing with Discrete Fourier Transform 2

Useful Tips and Tricks on Digital Data Processing with Discr...

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2nd international conference on Computing Methodologies and Communication (ICCMC)

作者： Sindhura, Bokkisam Ashwin, S. Rajkumar, G. Elamaran, V Sankar, M. SASTRA Deemed Univ Dept ECE Sch EEE Thanjavur India D Batu Technol Univ Dept E&TC Vathar Tarf Vadgaon Maharashtra India

ISBN: (纸本)9781538634523

The main focus of this study is to explore the basics on the discrete Fourier transform (DFT) strongly. This article exemplifies few important fundamental concepts behind DFT with a hands-on approach. The understanding signal frequencies in terms of the sampling frequency (Fs) of the discrete-time system with many examples are detailed with interestingly. This one will establish a thorough knowledge to design digital filters appropriately. Also, a few more tips and tricks such as the DFT of the repeated sequence and spectral leakage are demonstrated with examples clearly using the Matlab R2016b tool. The computer-based learning style helps the student community a lot to apply and explore their mind in recent and innovative digital signal processing (DSP) applications. This kind of study motivates student group with self-learning and hence reducing the teaching load.

关键词： Digital data processing fast Fourier transforms Spectral leakage Matlab

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Measurement of dielectric loss factor based on LabVIEW signal processing module 2

Measurement of dielectric loss factor based on LabVIEW signa...

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2nd international conference on machine learning, Big Data and Business Intelligence, MLBDBI 2020

作者： Hu, Ronghui Zhao, Aiming Yu, Chao Shi, Yawei Zhang, Xing Shanghai Dianji University Shanghai200000 China

ISBN: (纸本)9781728196381

Online monitoring of the dielectric loss of capacitive equipment is an effective method to judge its insulation status. Because the higher harmonics and noise signals in the power grid will bring errors to the measurement of dielectric loss, in order to improve the accuracy of the measurement, this paper designs a FIR digital filter based on LabVIEW to remove the harmonic and noise signals, in order to ensure the measurement For speed and accuracy, an algorithm program of sine wave parameter method (including least square method and trigonometric function integration method) based on LabVIEW is designed, and the dielectric loss angle is obtained. The two algorithms are simulated under different signal-to-noise ratios and multiple complete cycles. The simulation results show the feasibility and effectiveness of the two methods. The two algorithms are simulated under a non-integer period. The simulation results show that the trigonometric function integration rule must be calculated under the complete period, and the least square method does not have this requirement. © 2020 IEEE.

关键词： Dielectric losses

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