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Journal articles on the topic 'Machine learning algorithms for denoising'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Li, Zeyu. "Image Denoising based on Deep Learning." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 1245–51. http://dx.doi.org/10.54097/hset.v39i.6749.

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Image denoising has always been one of the research hotspots in the field of image processing, which aims to remove the noise from the imaging device or external noise environment and other interfering factors in the image to restore the noisy image to the original clean and noise-free image. Mature algorithms and machine learning techniques have been developed previously for different application situations and specific computer vision works. Image denoising based on deep learning can adaptively learn image content and is suitable for image denoising tasks in high-noise environments. This pap

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Tang, Ke. "Comparative analysis of voice denoising using machine learning and traditional denoising." Applied and Computational Engineering 30, no. 1 (2024): 118–24. http://dx.doi.org/10.54254/2755-2721/30/20230083.

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Noise often affects the content of an audio signal, and noise reduction techniques can help retrieve the original speech content. In recent years, AI-based noise reduction has witnessed rapid development. This article provides a brief introduction to the background and principles of several AI-based noise reduction methods. One of the mentioned methods is an end-to-end time-domain deep learning speech division algorithm, which utilizes a multi-layer CNN network framework. Due to the need for deep network architectures to extract features, it involves a higher computational load. Traditional no

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Fu, Shaokun, Yize Wu, Rundong Wang, and Mingzhi Mao. "A Bearing Fault Diagnosis Method Based on Wavelet Denoising and Machine Learning." Applied Sciences 13, no. 10 (2023): 5936. http://dx.doi.org/10.3390/app13105936.

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There are a lot of interference factors in the operating environment of machinery, which makes it ineffective to use traditional detection methods to judge the fault location and type of fault of the machinery, and even misjudgment of the fault location and type may occur. In order to solve these problems, this paper proposes a bearing fault diagnosis method based on wavelet denoising and machine learning. We use sensors to detect the operating conditions of rolling bearings under different working conditions to obtain datasets of different types of bearing failures. On the basis of using the

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Kalyani, Akhade, Ghodekar Sakshi, Kapse Vaishnavi, Raykar Anuja, and Wadhvane Sonal. "A Survey on Image Denoising Techniques." International Journal of Innovative Science and Research Technology (IJISRT) 9, no. 2 (2024): 2. https://doi.org/10.5281/zenodo.10686007.

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In the digital era of the world images are vital part of life and media. This survey explores a wide array of image denoising methods, spanning traditional and contemporary approaches. The review encompasses classical filters, statistical methods, and modern machine learning-based algorithms, with a focus on their principles, advantages and limitations. Through a systematic examination of the literature, we categorize the denoising techniques based on their underlying methodologies and applications. Insights are drawn from comparative analyses, highlighting the trade-offs and performance varia

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Rajput, Savita, Apurva Ware, Karan Umredkar, and Prof Jaya Jeshwani. "Digital Watermarking Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 2081–85. http://dx.doi.org/10.22214/ijraset.2022.40991.

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Abstract: Digital watermarking is a technique used for the information of the images that provides security for the confidentiality. The repetitions of the multimedia objects (i.e. audio, video, text, etc.) have been protected by some of the developed digital watermarking techniques. Digital Watermarking is the process of concealing messages in digital contents in order to verify the rightful owner of the copyright protection. In this paper we have proposed a method that would assist its users to embed a watermark to the cover image based on an adaptive approach in a much robust way while main

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Rajput, Savita, Apurva Ware, Karan Umredkar, and Prof Jaya Jeshwani. "Digital Watermarking Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 2081–85. http://dx.doi.org/10.22214/ijraset.2022.40991.

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Abstract: Digital watermarking is a technique used for the information of the images that provides security for the confidentiality. The repetitions of the multimedia objects (i.e. audio, video, text, etc.) have been protected by some of the developed digital watermarking techniques. Digital Watermarking is the process of concealing messages in digital contents in order to verify the rightful owner of the copyright protection. In this paper we have proposed a method that would assist its users to embed a watermark to the cover image based on an adaptive approach in a much robust way while main

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Gupta, Ankita, Lakhwinder Kaur, and Gurmeet Kaur. "Drought stress detection technique for wheat crop using machine learning." PeerJ Computer Science 9 (May 19, 2023): e1268. http://dx.doi.org/10.7717/peerj-cs.1268.

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The workflow of this research is based on numerous hypotheses involving the usage of pre-processing methods, wheat canopy segmentation methods, and whether the existing models from the past research can be adapted to classify wheat crop water stress. Hence, to construct an automation model for water stress detection, it was found that pre-processing operations known as total variation with L1 data fidelity term (TV-L1) denoising with a Primal-Dual algorithm and min-max contrast stretching are most useful. For wheat canopy segmentation curve fit based K-means algorithm (Cfit-kmeans) was also va

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Wu, Dingming, Xiaolong Wang, and Shaocong Wu. "A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction." Entropy 23, no. 4 (2021): 440. http://dx.doi.org/10.3390/e23040440.

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The trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short-term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Therefore, we used discrete wavelet transform (DWT)-based denoising to denoise stock data. Denoising the stock data assisted us to eliminate the influences of short-term random events on the continuous trend of the stock. The denoised data showed more stable trend characteristics and smoothness. Extreme l

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Liu, Zhongyuan, Xian Zhang, Diquan Li, Shupeng Liu, and Ke Cao. "Noise Elimination for Wide Field Electromagnetic Data via Improved Dung Beetle Optimized Gated Recurrent Unit." Geosciences 15, no. 1 (2025): 8. https://doi.org/10.3390/geosciences15010008.

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Noise profoundly affects the quality of electromagnetic data, and selecting the appropriate hyperparameters for machine learning models poses a significant challenge. Consequently, the current machine learning denoising techniques fall short in delivering precise processing of Wide Field Electromagnetic Method (WFEM) data. To eliminate the noise, this paper presents an electromagnetic data denoising approach based on the improved dung beetle optimized (IDBO) gated recurrent unit (GRU) and its application. Firstly, Spatial Pyramid Matching (SPM) chaotic mapping, variable spiral strategy, Levy f

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Li, Qi, Ruiqi Lin, Yu Zhang, Wei Ba, and Wei Lu. "A novel wavelet threshold denoising and deep belief network fault detection algorithm." Insight - Non-Destructive Testing and Condition Monitoring 63, no. 10 (2021): 610–17. http://dx.doi.org/10.1784/insi.2021.63.10.610.

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For oil pipeline leakage fault detection problems, a novel negative pressure wave (NPW) leak detection method based on wavelet threshold denoising and deep belief network (Wavelet-DBN) is proposed. Firstly, the wavelet threshold denoising method is used to deal with the sample pressure signal, and the results of wavelet denoising with different wavelet basis functions and different decomposition levels are compared. The optimal parameters are selected for wavelet denoising and the characteristic information of a pipeline pressure signal is extracted. Secondly, in order to improve the accuracy

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Bezerra, Francisco Elânio, Geraldo Cardoso de Oliveira Neto, Gabriel Magalhães Cervi, et al. "Impacts of Feature Selection on Predicting Machine Failures by Machine Learning Algorithms." Applied Sciences 14, no. 8 (2024): 3337. http://dx.doi.org/10.3390/app14083337.

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In the context of Industry 4.0, managing large amounts of data is essential to ensure informed decision-making in intelligent production environments. It enables, for example, predictive maintenance, which is essential for anticipating and identifying causes of failures in machines and equipment, optimizing processes, and promoting proactive management of human, financial, and material resources. However, generating accurate information for decision-making requires adopting suitable data preprocessing and analysis techniques. This study explores the identification of machine failures based on

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Han, Chang. "The Analysis about Compressed Sensing Reconstruction Algorithm Based on Machine Learning Applied in Interference Multispectral Images." Advances in Multimedia 2021 (November 26, 2021): 1–6. http://dx.doi.org/10.1155/2021/8020473.

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Interferometric multispectral images contain rich information, so they are widely used in aviation, military, and environmental monitoring. However, the abundant information also leads to the disadvantages that longer time and more physical resources are needed in signal compression and reconstruction. In order to make up for the shortcomings of traditional compression and reconstruction algorithms, the stacked convolution denoising autoencoder (SCDA) reconstruction algorithm for interference multispectral images is proposed in this paper. And, the experimental code based on the TensorFlow sys

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Fan, Zhiyong, Quansen Sun, Feng Ruan, Kai Hu, and Jin Wang. "A Novel Extreme Learning Machine based Denoising Algorithm." International Journal of Signal Processing, Image Processing and Pattern Recognition 9, no. 2 (2016): 159–66. http://dx.doi.org/10.14257/ijsip.2016.9.2.114.

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Ramírez Vélez, J. C., C. Yáñez Márquez, and J. P. Córdova Barbosa. "Using machine learning algorithms to measure stellar magnetic fields." Astronomy & Astrophysics 619 (November 2018): A22. http://dx.doi.org/10.1051/0004-6361/201833016.

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Context.Regression methods based on machine learning algorithms (MLA) have become an important tool for data analysis in many different disciplines.Aims.In this work, we use MLA in an astrophysical context; our goal is to measure the mean longitudinal magnetic field in stars (Heff) from polarized spectra of high resolution, through the inversion of the so-called multi-line profiles.Methods.Using synthetic data, we tested the performance of our technique considering different noise levels: In an ideal scenario of noise-free multi-line profiles, the inversion results are excellent; however, the

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Xu, Jiang, Siqian Liu, Zhikui Chen, and Yonglin Leng. "A Hybrid Imputation Method Based on Denoising Restricted Boltzmann Machine." International Journal of Grid and High Performance Computing 10, no. 2 (2018): 1–13. http://dx.doi.org/10.4018/ijghpc.2018040101.

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Data imputation is an important issue in data processing and analysis which has serious impact on the results of data mining and learning. Most of the existing algorithms are either utilizing whole data sets for imputation or only considering the correlation among records. Aiming at these problems, the article proposes a hybrid method to fill incomplete data. In order to reduce interference and computation, denoising restricted Boltzmann machine model is developed for robust feature extraction from incomplete data and clustering. Then, the article proposes partial-distance and co-occurrence ma

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Bsoul, Qusay, and Malik Jawarneh. "Olive Leaf Disease Detection using Improvised Machine Learning Techniques." Semarak International Journal of Machine Learning 5, no. 1 (2025): 64–73. https://doi.org/10.37934/sijml.5.1.6473a.

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Plants are integral to human life, and so, plant health is important. Regularly monitoring of plant health and plant disease detections are important in property agriculture. In agriculture, the use of image processing techniques run by computers in solving agricultural problems is increasingly common, particularly in the classification and identification of crop disease. Such usage could preserve the technical and commercial well-being of agriculture. This study demonstrated the application of support vector machine and image processing-enabled approach to detect and classify Olive leaf disea

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Bsoul, Qusay Bsoul, and Malik Jawarneh. "Olive Leaf Disease Detection using Improvised Machine Learning Techniques." Semarak International Journal of Machine Learning 5, no. 1 (2025): 64–73. https://doi.org/10.37934/sijml.5.1.6473.

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Plants are integral to human life, and so, plant health is important. Regularly monitoring of plant health and plant disease detections are important in property agriculture. In agriculture, the use of image processing techniques run by computers in solving agricultural problems is increasingly common, particularly in the classification and identification of crop disease. Such usage could preserve the technical and commercial well-being of agriculture. This study demonstrated the application of support vector machine and image processing-enabled approach to detect and classify Olive leaf disea

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Pradumn Kumar, Praveen Kumar Shukla. "Deep Learning in Computer Vision: A Critical Review." Journal of Electrical Systems 20, no. 3 (2024): 5540–62. http://dx.doi.org/10.52783/jes.6453.

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Today, a machine learning tool has gained a lot of consideration as a data analysis and image processing tool, with encouraging results. Massive amounts of data are being generated in every field or any domain at a distressing rate. This review paper provides a brief overview of current technologies and a conceptual explanation of the development of computer vision with image processing and the usage of specific regions in their respective fields. Computer vision with deep learning allows researchers to investigate pictures and videos to attain essential facts, recognize facts on occasions, or

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Rymarczyk, Tomasz, and Grzegorz Kłosowski. "Applying Machine Learning Algorithms to Solve Inverse Problems in Electrical Tomography." MATEC Web of Conferences 210 (2018): 02016. http://dx.doi.org/10.1051/matecconf/201821002016.

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The article presents four selected methods of supervised machine learning, which can be successfully used in the tomography of flood embankments, walls, tanks, reactors and pipes. A comparison of the following methods was made: Artificial Neural Networks (ANN), Supported Vector Machine (SVM), K-Nearest Neighbour (KNN) and Multivariate Adaptive Regression Splines (MAR Splines). All analysed methods concerned regression problems. Thanks to performed analysis the differences expressed quantitatively were visualized with the use of indicators such as regression, error of mean square deviation, etc

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Ajay, P., B. Nagaraj, R. Arun Kumar, Ruihang Huang, and P. Ananthi. "Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm." Scanning 2022 (June 6, 2022): 1–9. http://dx.doi.org/10.1155/2022/1200860.

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Hyperspectral microscopy in biology and minerals, unsupervised deep learning neural network denoising SRS photos: hyperspectral resolution enhancement and denoising one hyperspectral picture is enough to teach unsupervised method. An intuitive chemical species map for a lithium ore sample is produced using k -means clustering. Many researchers are now interested in biosignals. Uncertainty limits the algorithms’ capacity to evaluate these signals for further information. Even while AI systems can answer puzzles, they remain limited. Deep learning is used when machine learning is inefficient. Su

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Nair, Priyanka, Devesh Kumar Srivastava, and Roheet Bhatnagar. "Noise reduction in Hyperion high dynamic range hyperspectral data using machine learning and statistical techniques." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 6 (2024): 6913. http://dx.doi.org/10.11591/ijece.v14i6.pp6913-6928.

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Numerous remote sensing applications rely heavily on hyperspectral imagery, but it is frequently plagued by noise, which degrades the data quality and hinders subsequent analysis. In this research paper, we present an in-depth analysis of noise removal techniques for hyperspectral imagery, specifically for data acquired from the Hyperion EO-1 sensor. Setting off with obtaining Hyperion data and the pre-processing stages, the paper discusses the acquisition and denoising of Hyperion data. The hyperspectral data considered is in the high dynamic range (HDR) format, which maintains the original i

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Vidunath, Janitha, Chamath Shamal, Ravindu Hiroshan, Udani Gamlath, Chamira U. S. Edussooriya, and Sudath R. Munasinghe. "Identification of Elephant Rumbles in Seismic Infrasonic Signals Using Spectrogram-Based Machine Learning." Applied System Innovation 7, no. 6 (2024): 117. http://dx.doi.org/10.3390/asi7060117.

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This paper presents several machine learning methods and highlights the most effective one for detecting elephant rumbles in infrasonic seismic signals. The design and implementation of electronic circuitry to amplify, filter, and digitize the seismic signals captured through geophones are presented. The process converts seismic rumbles to a spectrogram and the existing methods of spectrogram feature extraction and appropriate machine learning algorithms are compared on their merit for automatic seismic rumble identification. A novel method of denoising the spectrum that leads to enhanced accu

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Pompapathi, Manasani, Shaik Khaleelahmed, Malik Jawarneh, et al. "Effective crop categorization using wavelet transform based optimized long short-term memory technique." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 2309–18. https://doi.org/10.11591/eei.v14i3.7748.

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Effective crop categorization is important for keeping track of how crops grow and how much they produce in the future. Gathering crop data on categories, regions, and space distribution in a timely and accurate way could give a scientifically sound reason for changes to the way crops are organized. Polarimetric synthetic aperture radar dataset provides sufficient information for accurate crop categorization. It is essential to classify crops in order to successfully. This article presents wavelet transform (WT) based optimizedlong short-term memory (LSTM) deep learning (DL) for effective crop

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Oyewola, David Opeoluwa, Emmanuel Gbenga Dada, Sanjay Misra, and Robertas Damaševičius. "Predicting COVID-19 Cases in South Korea with All K-Edited Nearest Neighbors Noise Filter and Machine Learning Techniques." Information 12, no. 12 (2021): 528. http://dx.doi.org/10.3390/info12120528.

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The application of machine learning techniques to the epidemiology of COVID-19 is a necessary measure that can be exploited to curtail the further spread of this endemic. Conventional techniques used to determine the epidemiology of COVID-19 are slow and costly, and data are scarce. We investigate the effects of noise filters on the performance of machine learning algorithms on the COVID-19 epidemiology dataset. Noise filter algorithms are used to remove noise from the datasets utilized in this study. We applied nine machine learning techniques to classify the epidemiology of COVID-19, which a

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Zhang, Jingsi, Xiaosheng Yu, Xiaoliang Lei, and Chengdong Wu. "A Multi-Feature Fusion Model Based on Denoising Convolutional Neural Network and Attention Mechanism for Image Classification." International Journal of Swarm Intelligence Research 14, no. 2 (2023): 1–15. http://dx.doi.org/10.4018/ijsir.324074.

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Spatial location features extracted by denoising convolutional neural network. At this time, an attention mechanism is introduced into denoising convolutional neural network. The dual attention model of local area is presented from two dimensions of channel and space—channel attention mechanism weights channel and spatial attention mechanism weights location. A variety of machine learning methods are used to classify and train different features. Multi-semantic features and heterogeneous features are fused by adaptive weighted fusion algorithm. Finally, the data sets Cifar-10, STL-10, Cifar-10

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Hao, Zhonghua, Shiwei Ma, Hui Chen, and Jingjing Liu. "Dataset Denoising Based on Manifold Assumption." Mathematical Problems in Engineering 2021 (January 18, 2021): 1–14. http://dx.doi.org/10.1155/2021/6432929.

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Learning the knowledge hidden in the manifold-geometric distribution of the dataset is essential for many machine learning algorithms. However, geometric distribution is usually corrupted by noise, especially in the high-dimensional dataset. In this paper, we propose a denoising method to capture the “true” geometric structure of a high-dimensional nonrigid point cloud dataset by a variational approach. Firstly, we improve the Tikhonov model by adding a local structure term to make variational diffusion on the tangent space of the manifold. Then, we define the discrete Laplacian operator by gr

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Kłosowski, Grzegorz, Tomasz Rymarczyk, and Edward Kozłowski. "Tomographic image correction with noise reduction algorithms." MATEC Web of Conferences 252 (2019): 09001. http://dx.doi.org/10.1051/matecconf/201925209001.

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This article presents an original approach to improve the results of tomographic reconstructions by denoising the input data, which affects output images improving. The algorithms used in the research are based on autoencoders and Elastic Net - both related to artificial intelligence or machine-learning developed controllers. Due to the reduction of unnecessary features and removal of mutually correlated input variables generated by the tomography electrodes, good quality reconstructions of tomographic images were obtained. The simulation experiments proved that the presented methods could be

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Treder, Kevin P., Chen Huang, Judy S. Kim, and Angus I. Kirkland. "Applications of deep learning in electron microscopy." Microscopy 71, Supplement_1 (2022): i100—i115. http://dx.doi.org/10.1093/jmicro/dfab043.

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Abstract We review the growing use of machine learning in electron microscopy (EM) driven in part by the availability of fast detectors operating at kiloHertz frame rates leading to large data sets that cannot be processed using manually implemented algorithms. We summarize the various network architectures and error metrics that have been applied to a range of EM-related problems including denoising and inpainting. We then provide a review of the application of these in both physical and life sciences, highlighting how conventional networks and training data have been specifically modified fo

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Sedik, Ahmed, Mohamed Marey, and Hala Mostafa. "WFT-Fati-Dec: Enhanced Fatigue Detection AI System Based on Wavelet Denoising and Fourier Transform." Applied Sciences 13, no. 5 (2023): 2785. http://dx.doi.org/10.3390/app13052785.

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As the number of road accidents increases, it is critical to avoid making driving mistakes. Driver fatigue detection is a concern that has prompted researchers to develop numerous algorithms to address this issue. The challenge is to identify the sleepy drivers with accurate and speedy alerts. Several datasets were used to develop fatigue detection algorithms such as electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG), and electromyogram (EMG) recordings of the driver’s activities e.g., DROZY dataset. This study proposes a fatigue detection system based on Fast Fourier

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Hanan A. R. Akkar, Wael A. H. Hadi, Ibraheem H. Al-Dosari, Saadi M. Saadi, and Aseel Ismael Ali. "Classification Accuracy Enhancement Based Machine Learning Models and Transform Analysis." Communications - Scientific letters of the University of Zilina 23, no. 2 (2021): C44—C53. http://dx.doi.org/10.26552/com.c.2021.2.c44-c53.

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The problem of leak detection in water pipeline network can be solved by utilizing a wireless sensor network based an intelligent algorithm. A new novel denoising process is proposed in this work. A comparison study is established to evaluate the novel denoising method using many performance indices. Hardyrectified thresholding with universal threshold selection rule shows the best obtained results among the utilized thresholding methods in the work with Enhanced signal to noise ratio (SNR) = 10.38 and normalized mean squared error (NMSE) = 0.1344. Machine learning methods are used to create m

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Liu, Meizhuang, Faxian Cao, Zhijing Yang, Xiaobin Hong, and Yuezhen Huang. "Hyperspectral Image Denoising and Classification Using Multi-Scale Weighted EMAPs and Extreme Learning Machine." Electronics 9, no. 12 (2020): 2137. http://dx.doi.org/10.3390/electronics9122137.

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Recently, extended multi-attribute profiles (EMAPs) have attracted much attention due to its good performance while applied to remote sensing images feature extraction and classification. Since the EMAPs connect multiple attribute features without considering the pixel-based Hyperspectral Image (HSI) classification, homogeneous regions may become unsmooth due to the noise to be introduced. To tackle this problem, we propose the weighted EMAPs (WEMAPs) to reduce the noise and smoothen the homogeneous regions based on weighted mean filter (WMF). Then, we construct multiscale WEMAPs to product mu

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Antara, Fahmida Ahmed, ASM Shamsul Arefin, Md Tamjid Rayhan, and Sabbir Ahmed Chowdhury. "Detection of Schizophrenia from EEG Signals using Dual Tree Complex Wavelet Transform and Machine Learning Algorithms." Bangladesh Journal of Medical Physics 15, no. 1 (2022): 8–27. http://dx.doi.org/10.3329/bjmp.v15i1.63559.

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This research was conducted with the aim to detect schizophrenia automatically from EEG signals using machine learning algorithms. The 16 electrode EEG data were collected from the online repository where 43 schizophrenic and 39 healthy persons’ dataset is available. By applying Low Pass Filter and Total Variation Denoising method, raw EEG signals were denoised and were decomposed into beta, alpha, theta and delta waves by using Dual Tree Complex Wavelet Transform. To apply machine learning algorithms, five features: mean, median, standard deviation, energy and kurtosis were considered for all

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Zhang, Zheng-Yong, Jian-Sheng Su, and Huan-Ming Xiong. "Technology for the Quantitative Identification of Dairy Products Based on Raman Spectroscopy, Chemometrics, and Machine Learning." Molecules 30, no. 2 (2025): 239. https://doi.org/10.3390/molecules30020239.

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The technologies used for the characterization and quantitative analysis of dairy products based on Raman spectroscopy have developed rapidly in recent years. At the level of spectral data, there are not only traditional Raman spectra but also two-dimensional correlation spectra, which can provide rich compositional and characteristic information about the samples. In terms of spectral preprocessing, there are various methods, such as normalization, wavelet denoising, and feature extraction. A combination of these methods with appropriate quantitative techniques is beneficial to reveal the dif

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Rai, Ankush, and Jagadeesh Kannan R. "BI-DIRECTIONAL RECURRENT NEURAL NETWORK FOR IMPROVING MULTISPECTRAL IMAGE DENOISING." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (2017): 272. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19678.

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While procuring images form satellite the multispectral images (MSI) are often prone to noises. finding a good mathematical description of the learning based denoising model is a difficult research question and many different research accounted in the literature. Many have attempted its use with the application of neural network as a sparse learned dictionary of noisy patches. Also, this approach allows several algorithm to optimize itself for the given task at hand by using machine learning algorithm. In this study we present an improved method for learning based denoising of MSI images. Recu

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Chrétien, Stéphane, and Pascal Bondon. "Projection Methods for Uniformly Convex Expandable Sets." Mathematics 8, no. 7 (2020): 1108. http://dx.doi.org/10.3390/math8071108.

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Many problems in medical image reconstruction and machine learning can be formulated as nonconvex set theoretic feasibility problems. Among efficient methods that can be put to work in practice, successive projection algorithms have received a lot of attention in the case of convex constraint sets. In the present work, we provide a theoretical study of a general projection method in the case where the constraint sets are nonconvex and satisfy some other structural properties. We apply our algorithm to image recovery in magnetic resonance imaging (MRI) and to a signal denoising in the spirit of

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Tiwari, Pradeep Kumar, K. Kannan, Duggineni Veeraiah, et al. "Security Protection Mechanism in Cloud Computing Authorization Model Using Machine Learning Techniques." Wireless Communications and Mobile Computing 2022 (July 26, 2022): 1–12. http://dx.doi.org/10.1155/2022/1907511.

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Because of the vast number of applications and the ambiguity in application methods, handwritten character recognition has garnered widespread recognition and increased prominence in the community of pattern recognition researchers ever since it was first developed. This is due to the fact that application methods can be quite ambiguous. Computer in the cloud, on the other hand, allows for suitable network access on demand to a shared pool of customizable computing resources and digital devices. According to those knowledgeable in the subject, the standard filtering techniques are not enough w

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Su, Wenbin, Yifei Zhang, Hongbo Wei, and Qi Gao. "Denoising and Dehazing an Image in a Cascaded Pattern for Continuous Casting." Metals 12, no. 1 (2022): 126. http://dx.doi.org/10.3390/met12010126.

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Automatic vision systems have been widely used in the continuous casting of the steel industry, which improve efficiency and reduce labor. At present, high temperatures with evaporating fog cause images to be noisy and hazy, impeding the usage of advanced machine learning algorithms in this task. Instead of considering denoising and dehazing separately like previous papers, we established that by taking advantage of deep learning in a modeling complex formulation, our proposed algorithm, called Cascaded Denoising and Dehazing Net (CDDNet) reduces noise and hazy in a cascading pattern. Experime

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Wang, Yukun, Aiying Zhao, Xiaoxue Wei, and Ranran Li. "A Novel Ensemble Model Based on an Advanced Optimization Algorithm for Wind Speed Forecasting." Energies 16, no. 14 (2023): 5281. http://dx.doi.org/10.3390/en16145281.

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Concerning the vision of achieving carbon neutral and peak carbon goals, wind energy is extremely important as a renewable and clean energy source. However, existing research ignores the implicit features of the data preprocessing technique and the role of the internal mechanism of the optimization algorithm, making it difficult to achieve high-accuracy prediction. To fill this gap, this study proposes a wind speed forecasting model that combines data denoising techniques, optimization algorithms, and machine learning algorithms. The model discusses the important parameters in the data decompo

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Wei, Xing, and Ruifang Sui. "A Review of Machine Learning Algorithms for Retinal Cyst Segmentation on Optical Coherence Tomography." Sensors 23, no. 6 (2023): 3144. http://dx.doi.org/10.3390/s23063144.

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Optical coherence tomography (OCT) is an emerging imaging technique for diagnosing ophthalmic diseases and the visual analysis of retinal structure changes, such as exudates, cysts, and fluid. In recent years, researchers have increasingly focused on applying machine learning algorithms, including classical machine learning and deep learning methods, to automate retinal cysts/fluid segmentation. These automated techniques can provide ophthalmologists with valuable tools for improved interpretation and quantification of retinal features, leading to more accurate diagnosis and informed treatment

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De Fazio, Roberto, Lorenzo Spongano, Massimo De Vittorio, Luigi Patrono, and Paolo Visconti. "Machine Learning Algorithms for Processing and Classifying Unsegmented Phonocardiographic Signals: An Efficient Edge Computing Solution Suitable for Wearable Devices." Sensors 24, no. 12 (2024): 3853. http://dx.doi.org/10.3390/s24123853.

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The phonocardiogram (PCG) can be used as an affordable way to monitor heart conditions. This study proposes the training and testing of several classifiers based on SVMs (support vector machines), k-NN (k-Nearest Neighbor), and NNs (neural networks) to perform binary (“Normal”/”Pathologic”) and multiclass (“Normal”, “CAD” (coronary artery disease), “MVP” (mitral valve prolapse), and “Benign” (benign murmurs)) classification of PCG signals, without heart sound segmentation algorithms. Two datasets of 482 and 826 PCG signals from the Physionet/CinC 2016 dataset are used to train the binary and m

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Le, Xiao Yan. "A Web Text De-Noising Algorithm Based on Machine Learning." Applied Mechanics and Materials 536-537 (April 2014): 516–19. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.516.

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The Web has become a huge information resources distributed information space, contains a huge amount of various types of Web documents.Search engine, it is difficult to meet different user requirements for the elaboration of the retrieval results.To noise method, this paper proposes a text first to find out the noise page information, then according to the rules of human interaction way to generate a denoising, finally find and remove the noise, the experiments has been proved this method to be effective on improving the accuracy of classification.

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Yussif, Abdul-Mugis, Haleh Sadeghi, and Tarek Zayed. "Application of Machine Learning for Leak Localization in Water Supply Networks." Buildings 13, no. 4 (2023): 849. http://dx.doi.org/10.3390/buildings13040849.

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Water distribution networks (WDNs) in urban areas are predominantly underground for seamless freshwater transmission. As a result, monitoring their health is often complicated, requiring expensive equipment and methodologies. This study proposes a low-cost approach to locating leakages in WDNs in an urban setting, leveraging acoustic signal behavior and machine learning. An inexpensive noise logger was used to collect acoustic signals from the water mains. The signals underwent empirical mode decomposition, feature extraction, and denoising to separate pure leak signals from background noises.

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Xu, Zhou, Dongdong Ye, Jianjun Chen, and Haiting Zhou. "Novel Terahertz Nondestructive Method for Measuring the Thickness of Thin Oxide Scale Using Different Hybrid Machine Learning Models." Coatings 10, no. 9 (2020): 805. http://dx.doi.org/10.3390/coatings10090805.

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Effective control of the thickness of the hot-rolled oxide scale on the surface of the steel strip is very vital to ensure the surface quality of steel products. Hence, terahertz nondestructive technology was proposed to measure the thickness of thin oxide scale. The finite difference time domain (FDTD) numerical simulation method was employed to obtain the terahertz time-domain simulation data of oxide scale with various thickness (0–15 μm). Added Gaussian white noise with a Signal Nosie Reduction (SNR) of 10 dB was used when simulating real test signals, using four wavelet denoising methods

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Yang, Jingmin, Shanghui Deng, Li Xu, and Wenjie Zhang. "Calibration-Free 3D Indoor Positioning Algorithms Based on DNN and DIFF." Sensors 22, no. 15 (2022): 5891. http://dx.doi.org/10.3390/s22155891.

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The heterogeneity of wireless receiving devices, co-channel interference, and multi-path effect make the received signal strength indication (RSSI) of Wi-Fi fluctuate greatly, which seriously degrades the RSSI-based positioning accuracy. Signal strength difference (DIFF), a calibration-free solution for handling the received signal strength variance between diverse devices, can effectively reduce the negative impact of signal fluctuation. However, DIFF also leads to the explosion of the RSSI data dimension, expanding the number of dimensions from m to Cm2, which reduces the positioning efficie

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Tamilselvi, C., Md Yeasin, Ranjit Kumar Paul, and Amrit Kumar Paul. "Can Denoising Enhance Prediction Accuracy of Learning Models? A Case of Wavelet Decomposition Approach." Forecasting 6, no. 1 (2024): 81–99. http://dx.doi.org/10.3390/forecast6010005.

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Denoising is an integral part of the data pre-processing pipeline that often works in conjunction with model development for enhancing the quality of data, improving model accuracy, preventing overfitting, and contributing to the overall robustness of predictive models. Algorithms based on a combination of wavelet with deep learning, machine learning, and stochastic model have been proposed. The denoised series are fitted with various benchmark models, including long short-term memory (LSTM), support vector regression (SVR), artificial neural network (ANN), and autoregressive integrated moving

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Mohammad-Djafari, Ali. "Regularization, Bayesian Inference, and Machine Learning Methods for Inverse Problems." Entropy 23, no. 12 (2021): 1673. http://dx.doi.org/10.3390/e23121673.

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Classical methods for inverse problems are mainly based on regularization theory, in particular those, that are based on optimization of a criterion with two parts: a data-model matching and a regularization term. Different choices for these two terms and a great number of optimization algorithms have been proposed. When these two terms are distance or divergence measures, they can have a Bayesian Maximum A Posteriori (MAP) interpretation where these two terms correspond to the likelihood and prior-probability models, respectively. The Bayesian approach gives more flexibility in choosing these

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Kittisuwan, Pichid. "Speckle Noise Reduction of Medical Imaging via Logistic Density in Redundant Wavelet Domain." International Journal on Artificial Intelligence Tools 27, no. 02 (2018): 1850006. http://dx.doi.org/10.1142/s0218213018500069.

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In the digital world, artificial intelligence tools and machine learning algorithms are widely applied in analysis of medical images for identifying diseases and make diagnoses; for example, to make recognition and classification. Speckle noises affect all medical imaging systems. Therefore, reduction in corrupting speckle noises is very important, since it deteriorates the quality of the medical images and makes tasks such as recognition and classification difficult. Most existing denoising algorithms have been developed for the additive white Gaussian noise (AWGN). However, AWGN is not a spe

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Golkar Amoli, Mehdi, Mahdi Hasanlou, Ruhollah Taghizadeh Mehrjardi, and Farhad Samadzadegan. "Exploring the Potential of PRISMA Satellite Hyperspectral Image for Estimating Soil Organic Carbon in Marvdasht Region, Southern Iran." Remote Sensing 16, no. 12 (2024): 2149. http://dx.doi.org/10.3390/rs16122149.

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Soil organic carbon (SOC) is a crucial factor for soil fertility, directly impacting agricultural yields and ensuring food security. In recent years, remote sensing (RS) technology has been highly recommended as an efficient tool for producing SOC maps. The PRISMA hyperspectral satellite was used in this research to predict the SOC map in Fars province, located in southern Iran. The main purpose of this research is to investigate the capabilities of the PRISMA satellite in estimating SOC and examine hyperspectral processing techniques for improving SOC estimation accuracy. To this end, denoisi

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Zhang, Shaohui, and Weihua Li. "Bearing Condition Recognition and Degradation Assessment under Varying Running Conditions Using NPE and SOM." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/781583.

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Manifold learning methods have been widely used in machine condition monitoring and fault diagnosis. However, the results reported in these studies focus on the machine faults under stable loading and rotational speeds, which cannot interpret the practical machine running. Rotating machine is always running under variable speeds and loading, which makes the vibration signal more complicated. To address such concern, the NPE (neighborhood preserving embedding) is applied for bearing fault classification. Compared with other algorithms (PCA, LPP, LDA, and ISOP), the NPE performs well in feature

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Jóźwik-Wabik, Piotr, Krzysztof Bernacki, and Adam Popowicz. "Comparison of Training Strategies for Autoencoder-Based Monochromatic Image Denoising." Sensors 23, no. 12 (2023): 5538. http://dx.doi.org/10.3390/s23125538.

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Monochromatic images are used mainly in cases where the intensity of the received signal is examined. The identification of the observed objects as well as the estimation of intensity emitted by them depends largely on the precision of light measurement in image pixels. Unfortunately, this type of imaging is often affected by noise, which significantly degrades the quality of the results. In order to reduce it, numerous deterministic algorithms are used, with Non-Local-Means and Block-Matching-3D being the most widespread and treated as the reference point of the current state-of-the-art. Our

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