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Multiple Colour Detection of RGB Images Using machine learning Algorithm 5th

Multiple Colour Detection of RGB Images Using Machine Learni...

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5th international conference on Applied Informatics, ICAI 2022

作者： Awotunde, Joseph Bamidele Misra, Sanjay Obagwu, David Florez, Hector University of Ilorin Ilorin Nigeria Østfold University College Halden Norway Universidad Distrital Francisco Jose de Caldas Bogota Colombia

ISBN: (纸本)9783031196461

Colour detection is the act of identifying the name of any colour. Color detection is required for object recognition, and it is also utilized in a variety of picture editing and sketching programs. machine learning (ML) has been proof useful in this area, and lot of researches have been done. this has been utilized in fields of neural networks and digital image processing recently. RGB images multiple colour recognition are powerful tools for various images for images and sketches. there have been several suggested regression models that use crop image characteristics and image indices, however, they have not been properly tested for accuracy and adaptation effectiveness for multiple colors. therefore, this paper proposes K- Nearest Neighbour (KNN) classifier for efficient colour detection of RGB images. the KNN algorithm is a prominent ML technique and neural network classification technique. the KNN classifier is utilized to segregate distinct colors in the RGB images. the paper utilized colour histogram for feature extraction to find the features that most relevant pattern that define certain colours. the feature extraction further improved the efficacy and accuracy of KNN classifier’s in the classification of RGB images. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Color

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Sparse Representation of Sound Field Based on K-SVD

Sparse Representation of Sound Field Based on K-SVD

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IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Yuan Liu Wenqiang Liu Yongchang Li School of Mathematical Sciences and Key Laboratory of Architectural Acoustic Environment of Anhui Higher Education Institutes Anhui Jianzhu University Hefei China

Sparse representation has been applied to nearfield acoustic holography in order to provide an accurate reconstruction with a small number of sampling points. However, the performance of the application is based on the sparsity of the signal to be reconstructed. In this study, K-SVD dictionary learning method is used to construct a basis of the sound field and the sound field can be sparsely represented by the learned dictionary. the samples of K-SVD are the sound fields obtained by simulations rather than those measured in practice, by taking the advantage of the equivalent source method. Compared with other sparse bases used in the existing nearfield acoustic holography, the dictionary learned by K-SVD is more flexible and the prior of the sound field is not necessary. the numerical simulation results demonstrate the validity of advantage of the proposed method.

关键词： Dictionaries machine learning Holography signal processing Numerical simulation Acoustic measurements Acoustics

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Variational Autoencoder-Based Deep Neural Network for Coronary Heart Disease Risk Prediction 1

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17th international conference on Intelligent Information Hiding and Multimedia signal processing (IIH-MSP) / 14th international conference on Frontiers of Information Technology, Applications and Tools (FITAT)

作者： Amarbayasgalan, Tsatsral Park, Kwang Ho Davagdorj, Khishigsuren Ryu, Keun Ho theera-Umpon, Nipon Chungbuk Natl Univ Sch Elect & Comp Engn Database & Bioinformat Lab Cheongju 28644 South Korea Ton Duc Thang Univ Fac Informat Technol Ho Chi Minh City 700000 Vietnam Chungbuk Natl Univ Dept Comp Sci Coll Elect & Comp Engn Cheongju 28644 South Korea Chiang Mai Univ Inst Biomed Engn Chiang Mai 50200 Thailand

ISBN: (数字)9789811910579

ISBN: (纸本)9789811910579;9789811910562

Coronary heart disease (CHD) is one of the top causes of global mortality. Most patients cannot be diagnosed at the early stage because it does not give any symptoms for many years. If CHD gets worse, it will require advanced treatments, such as heart transplant and stent surgery. therefore, it is useful in preventing CHD by predicting high-risk people who will suffer from CHD. In this study, we have proposed a variational autoencoder (VAE)-based deep neural network (DNN) model for predicting CHD risk. We improved the performance of DNN model by enriching training dataset using VAE-based data generation. the proposed method has been compared with machine learning algorithms on Korea National Health and Nutrition Examination Survey (KNHANES) dataset. As a result, the proposed method outperformed the compared classifiers. the performance measurements include accuracy, precision, recall, F1-score, and AUC score which reached 0.851, 0.882, 0.809%, 0.844, and 0.851, respectively.

关键词： Coronary heart disease Deep neural network machine learning Variational autoencoder

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Metal and Metal Oxide Nanoparticle Image Analysis Using machine learning Algorithm 5th

Metal and Metal Oxide Nanoparticle Image Analysis Using Mach...

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5th EAI international conference on Big Data Innovation for Sustainable Cognitive Computing, BDCC 2022

作者： Bannigidad, Parashuram Potraj, Namita Gurubasavaraj, Prabhuodeyara Department of Computer Science Rani Channamma University Karnataka Belagavi India Department of Chemistry Rani Channamma University Karnataka Belagavi India

ISBN: (纸本)9783031283239

Nanomaterials are used in almost every field of engineering. Synthesis techniques and conditions greatly affect the properties of synthesized nanomaterials. Identifying the nanomaterial from FESEM and TEM images with bare eyes is an exceedingly impossible task. Digital image processing techniques play a vigorous part in identifying the size and structure, and classifying them precisely helps scientists and investigators to use them in numerous applications. the advantages of digital image processing techniques increase the precision of object recognition in computer vision and pattern recognition. the proposed technique extracts various textural features such as kurtosis, skewness, and entropy from boron, iron, and silver nanoparticle images. the classification is done by using PNN and K-NN classifiers. the K-NN classifier has an accuracy of 80.00% for boron, 86.67% for iron, and 93.33% for the silver nanoparticle images, and the PNN classifier has an accuracy of 86.67% for boron, 93.33% for iron, and 93.33% for silver nanoparticle images. Hence, based on the experimentation, the proposed study suggested that the PNN classification with texture features is the best classifier used to classify the boron, iron, and silver nanoparticle images as compared to the K-NN classifier. Further, the results also are established manually with chemical experts, which proves the exhaustiveness of the proposed method. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Synthesis (chemical)

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An Advanced and Ideal Method for Tumor Detection and Classification from MRI Image Using Gamma Distribution and Support Vector machine 5th

An Advanced and Ideal Method for Tumor Detection and Classif...

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5th international conference on Soft Computing and signal processing, ICSCSP 2022

作者： Aruna, S.K. Sambandam, Rakoth Kandan thaiyalnayaki, S. Vetriveeran, Divya —Kengeri Campus Karnataka Bangalore India Tamilnadu Chennai India

ISBN: (纸本)9789811986680

As indicated by a measurable report distributed by the registry of central brain tumor at United States (CBTRUS), roughly 59,550 individuals were recently diagnosed to have essential benign and essential harmful brain tumors in 2017. Besides, in excess of 91,000 individuals, in the United States alone, were living with an essential harmful cerebrum tumor and 367,000 were living with an essential kind brain tumor. the task of detecting the position of the tumor in the body of the patient is the starting point for a medical treatment in the diagnosis process. the main aim of this study is to design a computer system, which is able to detect the tumor presence in the digital images of the brain in the patient and to accurately define its borderline. In this proposed model, gamma distribution method is used for training, testing, and for the feature extraction process, while SVM, support vector machine is used for the classification process. Most of the algorithms find it difficult to segment the tumors that were present in the edges. But with the help of gamma distribution along with the use of edge analysis, it is easier to identify those tumor areas that are present in the edges, thus making it easier for the preprocessing process. Gamma distribution also provides us with high accuracy, and it can also point the exact location of the tumor than compared to other algorithms. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Tumors

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Investigation into Bearing Fault Classification using Various Feature Set Combinations in KNN

Investigation into Bearing Fault Classification using Variou...

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international conference on Multimedia, signal processing and Communication Technologies, (IMPACT)

作者： Zaufisha Saleem Mantaza Mudassir Sidra Khanam Department of Mechanical Engineering Zakir Hussain College of Engineering and Technology Aligarh Muslim University Aligarh India

ISBN: (纸本)9781665476485

In many rotating types of machinery, rolling element bearings are considered "crucial", as rotating components cause the majority of failures, with proper performance monitoring; imminent failures can be identified and corrected. At the initial phase, there may exist many distinct features embedded in the data that may be able to identify a bearing fault which are non-traceable by using the physical models. Due to which, machine learning (ML) algorithms are currently the growing area of research for diagnostic and prognostic purposes. this work presents a study on the fault classification accuracy by K-Nearest Neighbor (KNN) machine learning classifier wherein the effect of different Time domain, Frequency domain and a combination of Time and Frequency domain features individually and in combinations have been investigated. the classification accuracy of Fine, Medium, and Coarse KNN have been examined and from the results obtained, Fine KNN has been used to further study the classification accuracy for various feature set combinations. Furthermore, features have been ranked using One-way ANOVA and their classification accuracies obtained using those top ranked features and their combinations have been investigated.

关键词： Time-frequency analysis Rolling bearings machine learning signal processing Object recognition Multimedia communication Time-domain analysis

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A Novel Path Recommendation Algorithm for Efficient and Secure Government Affairs System

A Novel Path Recommendation Algorithm for Efficient and Secu...

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IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Xinyu Yao Yubo Song Xingmin Zou Qianhui Lu Chao-Hsien Hsieh School of Cyber Science and Engineering Qufu Normal University Qufu Shandong China

Government organizations are digitizing their services. the purpose is to improve efficiency and provide better services for users. But, every platform is inefficient to connect with each other so far. therefore, with the characteristics of resource sharing, fast computing speed, and high reliability of distributed system, this paper develops a security and efficient government affairs system with a novel path recommendation algorithm. the purpose is to recommend appropriate government agency for offline affair of user. the algorithm contains three functions, distance priority, time priority, and mixed average priority. then, the system recommends the appropriate standard by comparing the total cost of the three functions. However, this paper retrieves the location of real government departments and then generates users with uniform distribution for simulation experiments. there are some specific experimental results in this paper. First, as the user interval is smaller, the proportion gap between government agencies receiving users is getting smaller as well. Second, as the user interval is large, moderate, and small, the system then will select distance priority function, time priority function, mixed average priority function for recommendation, respectively.

关键词： machine learning algorithms Government Standards organizations signal processing algorithms machine learning signal processing Blockchains

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Driver Drowsiness Monitoring System Using Visual Behavior And machine learning

Driver Drowsiness Monitoring System Using Visual Behavior An...

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international conference on Multimedia, signal processing and Communication Technologies, (IMPACT)

作者： A. Swathi Ashwani Kumar V. Swathi Y. Sirisha D. Bhavana Shaik Abdul Latheef A. Abhilash G. Mounika Department of CSE Sreyas Institute of Engineering and Technology Hyderabad Telangana India Faculty of Engineering and Technology SRM Institute of Science and Technology NCR Campus Ghaziabad UttarPradesh India

ISBN: (纸本)9781665476485

One of the leading causes of car accidents is drowsy driving. the driver drowsiness detection system detects fatigue and helps to avoid accidents. Classic methods include vehicle-based, behavior-based, and physiological-based techniques. Some of these solutions, however, are inconvenient for the driver, while others necessitate the purchase of costly sensors and devices. As a result, this project demonstrates how to build low-cost, real-time driver drowsiness with long-term accuracy. In this system, drowsiness is detected using a webcam. this webcam captures the driver's frames using image processing techniques. For each frame, the eye aspect ratio (EAR) and mouth opening ratio (MOR) is calculated. the computed values and the observed threshold values are used to detect drowsiness.

关键词： Visualization Webcams Mouth Ear machine learning Fatigue Real-time systems

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A Study and Comparison of Different Sparse Bayesian learning Algorithms in DOA Estimation

A Study and Comparison of Different Sparse Bayesian Learning...

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IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Yuyang Shao Hui Ma Hongwei Liu National Lab of Radar Signal Processing Xidian University Xi'an China

the direction of arrival (DOA) is a typical sparse parameter estimation problem. Its solution methods include greedy algorithm, norm minimization method and Bayesian estimation, in which the Bayesian methods are superior in estimation accuracy, but huge amount of computation has become the bottle-neck. this paper analyzes and compares the computation complexity of sparse Bayesian learning (SBL), multi-task sparse Bayesian learning (MSBL) and inverse-free sparse Bayesian learning (IFSBL) in DOA estimation. Simulations are also provided and prove that IFSBL is much better than SBL and MSBL in operational efficiency.

关键词： Direction-of-arrival estimation Minimization methods Simulation signal processing algorithms Estimation signal processing Multitasking

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Weather Prediction with machine learning Techniques: A Systematic Mapping

Weather Prediction with Machine Learning Techniques: A Syste...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Sonia Rainu Nandal Seema Kharb Kamaldeep Joshi Computer Science and Engineering University Institute of Engineering and Technology Maharshi Dayanand University Rohtak India Department of Computer Science and Engineering SRM University Sonepat India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

Weather Prediction techniques are widely used to predict the almost accurate weather at a particular place on a particular time. After getting success in various fields like image processing, sound prediction etc. machine learning is doing well in weather prediction as well. In this paper, a survey in the form of systematic mapping was conducted for Weather Prediction with machine learning techniques covering the time period of 2011-2022. After compiling a pool of 2394 papers and applying a set of inclusion and exclusion criteria, a final pool of 2227 relevant papers were obtained. these existing publications were downloaded on goggle drive and a systematic analysis was done with the help of Python pyBibx library.

关键词： Surveys Systematics Environmental science Wind speed Image processing machine learning learning (artificial intelligence)

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