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Artykuły w czasopismach na temat „PCA ALGORITHM”

Autor: Grafiati

Data publikacji: 11 września 2023

Data aktualizacji: 25 lipca 2025

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Angela, Nadya, and Robertus Setiawan Aji Nugroho. "COMPARISON BETWEEN DEEP NEURAL NETWORK AND PRINCIPAL COMPONENT ANALYSIS ALGORITHM IN FACE RECOGNITION." Proxies : Jurnal Informatika 5, no. 1 (2024): 50–63. http://dx.doi.org/10.24167/proxies.v5i1.12445.

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Face recognition is one technology that is commonly used now. Therefore, various algorithms continue to be developed to obtain maximum results with minimum costs. One of them is the Deep Neural Network or DNN algorithm. While DNN requires a large dataset to train the algorithm, another algorithm called the Principal Component Analysis (PCA) algorithm works good in a smaller dataset. These algorithms are compared to know which algorithm has the better result in given circumstances. Later the accuracy, speed, and optimality of the algorithms are analyzed. By comparing these algorithms, we could

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Dinanti, Aldila, and Joko Purwadi. "Analisis Performa Algoritma K-Nearest Neighbor dan Reduksi Dimensi Menggunakan Principal Component Analysis." Jambura Journal of Mathematics 5, no. 1 (2023): 155–65. http://dx.doi.org/10.34312/jjom.v5i1.17098.

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This paper discusses the performance of the K-Nearest Neighbor Algorithm with dimension reduction using Principal Component Analysis (PCA) in the case of diabetes disease classification. A large number of variables and data on the diabetes dataset requires a relatively long computation time, so dimensional reduction is needed to speed up the computational process. The dimension reduction method used in this study is PCA. After dimension reduction is done, it is continued with classification using the K-Nearest Neighbor Algorithm. The results on diabetes case studies show that dimension reducti

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Subiyanto, Subiyanto, Dina Priliyana, Moh Eki Riyadani, Nur Iksan, and Hari Wibawanto. "Face recognition system with PCA-GA algorithm for smart home door security using Rasberry Pi." Jurnal Teknologi dan Sistem Komputer 8, no. 3 (2020): 210–16. http://dx.doi.org/10.14710/jtsiskom.2020.13590.

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Genetic algorithm (GA) can improve the classification of the face recognition process in the principal component analysis (PCA). However, the accuracy of this algorithm for the smart home security system has not been further analyzed. This paper presents the accuracy of face recognition using PCA-GA for the smart home security system on Raspberry Pi. PCA was used as the face recognition algorithm, while GA to improve the classification performance of face image search. The PCA-GA algorithm was implemented on the Raspberry Pi. If an authorized person accesses the door of the house, the relay ci

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Zhang, Kang, Yongdong Huang, and Cheng Zhao. "Remote sensing image fusion via RPCA and adaptive PCNN in NSST domain." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 05 (2018): 1850037. http://dx.doi.org/10.1142/s0219691318500376.

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In order to improve fused image quality of multi-spectral (MS) image and panchromatic (PAN) image, a new remote sensing image fusion algorithm based on robust principal component analysis (RPCA) and non-subsampled shearlet transform (NSST) is proposed. First, the first principle component PC1 of MS image is extracted via principal component analysis (PCA). Then, the component PC1 and PAN image are decomposed by NSST to get the low and high frequency subbands, respectively. For the low frequency subband, the sparse matrix of PAN image by RPCA decomposition is used to guide the fusion rule; for

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Hadiprakoso, Raden Budiarto, and I. Komang Setia Buana. "Performance Comparison of Feature Extraction and Machine Learning Classification Algorithms for Face Recognition." IJICS (International Journal of Informatics and Computer Science) 5, no. 3 (2021): 250. http://dx.doi.org/10.30865/ijics.v5i3.3333.

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Face recognition is a highly active research topic in pattern recognition and computer vision, with numerous practical applications. Face recognition can provide the most natural interaction experience similar to the way humans can recognize others. This paper presents a performance comparison of various machine learning approaches and feature extraction algorithms. The feature extraction algorithm used is Principal Component Analysis (PCA), Latent Dirichlet Allocation (LDA), and a combination of PCA-LDA. The method used is to take a dataset sample and then evaluate and compare machine learnin

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MR., VRISHABH J. SHAH, SHAILESH S. PENKAR MR., and DEEPA MANOJ MRS. "PCA: THE DETECTION OF INTUITIVENESS." JournalNX - A Multidisciplinary Peer Reviewed Journal 3, no. 3 (2017): 92–94. https://doi.org/10.5281/zenodo.1462705.

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The study of science brain continually receives information which it processes, evaluates, and compare to the stored information and makes appropriate decisions. This technology serves to detect information in the brain using P300 mermer algorithm along with pattern classification algorithm as a means of detecting the attention, information processing, and memory-related responses to these presentations as revealed by brain waves with the help of biological neural network. https://journalnx.com/journal-article/20150195

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Dolan, Matthew T., Sung Kim, Yu-Hsuan Shao, and Grace L. Lu-Yao. "Authentication of Algorithm to Detect Metastases in Men with Prostate Cancer Using ICD-9 Codes." Epidemiology Research International 2012 (August 22, 2012): 1–7. http://dx.doi.org/10.1155/2012/970406.

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Background. Metastasis is a crucial endpoint for patients with prostate cancer (PCa), but currently lacks a validated claims-based algorithm for detection. Objective. To develop an algorithm using ICD-9 codes to facilitate accurate reporting of PCa metastases. Methods. Medical records from 300 men hospitalized at Robert Wood Johnson University Hospital for PCa were reviewed. Using the presence of metastatic PCa on chart review as the gold standard, two algorithms to detect metastases were compared. Algorithm A used ICD-9 codes 198.5 (bone metastases), 197.0 (lung metastases), 197.7 (liver meta

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Zhao, Wenjing, Yue Chi, Yatong Zhou, and Cheng Zhang. "Image Denoising Algorithm Combined with SGK Dictionary Learning and Principal Component Analysis Noise Estimation." Mathematical Problems in Engineering 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/1259703.

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SGK (sequential generalization of K-means) dictionary learning denoising algorithm has the characteristics of fast denoising speed and excellent denoising performance. However, the noise standard deviation must be known in advance when using SGK algorithm to process the image. This paper presents a denoising algorithm combined with SGK dictionary learning and the principal component analysis (PCA) noise estimation. At first, the noise standard deviation of the image is estimated by using the PCA noise estimation algorithm. And then it is used for SGK dictionary learning algorithm. Experimental

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9

Subramaniam, Ashwin, and Byung-Joo Oh. "Mushroom Recognition Using PCA Algorithm." International Journal of Software Engineering and Its Applications 10, no. 1 (2016): 43–50. http://dx.doi.org/10.14257/ijseia.2016.10.1.05.

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Li, Mingfei, Zhengpeng Chen, Jiangbo Dong, et al. "A Data-Driven Fault Diagnosis Method for Solid Oxide Fuel Cell Systems." Energies 15, no. 7 (2022): 2556. http://dx.doi.org/10.3390/en15072556.

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In this study, a data-driven fault diagnosis method was developed for solid oxide fuel cell (SOFC) systems. First, the complete experimental data was obtained following the design of the SOFC system experiments. Then, principal component analysis (PCA) was performed to reduce the dimensionality of the obtained experimental data. Finally, the fault diagnosis algorithms were designed by support vector machine (SVM) and BP neural network to identify and prevent the reformer carbon deposition and heat exchanger rupture faults, respectively. The research results show that both SVM and BP fault diag

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Vu, L. G., Abeer Alsadoon, P. W. C. Prasad, and A. M. S. Rahma. "Improving Accuracy in Face Recognition Proposal to Create a Hybrid Photo Indexing Algorithm, Consisting of Principal Component Analysis and a Triangular Algorithm (PCAaTA)." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 01 (2017): 1756001. http://dx.doi.org/10.1142/s0218001417560018.

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Accurate face recognition is today vital, principally for reasons of security. Current methods employ algorithms that index (classify) important features of human faces. There are many current studies in this field but most current solutions have significant limitations. Principal Component Analysis (PCA) is one of the best facial recognition algorithms. However, there are some noises that could affect the accuracy of this algorithm. The PCA works well with the support of preprocessing steps such as illumination reduction, background removal and color conversion. Some current solutions have sh

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Trivedi, Gargi, and Dr Rajesh Sanghvi. "Optimizing Image Fusion Using Modified Principal Component Analysis Algorithm and Adaptive Weighting Scheme." International Journal of Advanced Networking and Applications 15, no. 01 (2023): 5769–74. http://dx.doi.org/10.35444/ijana.2023.15103.

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Image fusion is an important technique for combining two or more images to produce a single, high-quality image. Principal component analysis (PCA) is a commonly used method for image fusion. However, existing PCA-based image fusion algorithms have some limitations, such as sensitivity to noise and poor fusion quality. In this paper, we propose a modified PCA algorithm for image fusion that uses an adaptive weighting scheme to improve the fusion quality. The proposed algorithm optimizes the fusion process by selecting the principal components that contain the most useful information and weighi

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Moghaddasi, Zahra, Hamid A. Jalab, Rafidah Md Noor, and Saeed Aghabozorgi. "Improving RLRN Image Splicing Detection with the Use of PCA and Kernel PCA." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/606570.

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Digital image forgery is becoming easier to perform because of the rapid development of various manipulation tools. Image splicing is one of the most prevalent techniques. Digital images had lost their trustability, and researches have exerted considerable effort to regain such trustability by focusing mostly on algorithms. However, most of the proposed algorithms are incapable of handling high dimensionality and redundancy in the extracted features. Moreover, existing algorithms are limited by high computational time. This study focuses on improving one of the image splicing detection algorit

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Adamu, Nuraddeen, Samaila Abdullahi, and Sani Musa. "Online Stochastic Principal Component Analysis." Caliphate Journal of Science and Technology 4, no. 1 (2022): 101–8. http://dx.doi.org/10.4314/cajost.v4i1.13.

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This paper studied Principal Component Analysis (PCA) in an online. The problem is posed as a subspace optimization problem and solved using gradient based algorithms. One such algorithm is the Variance-Reduced PCA (VR-PCA). The VR-PCA was designed as an improvement to the classical online PCA algorithm known as the Oja’s method where it only handled one sample at a time. The paper developed Block VR-PCA as an improved version of VR-PCA. Unlike prominent VR-PCA, the Block VR-PCA was designed to handle more than one dimension in subspace optimization at a time and it showed good performance. Th

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15

Xu, Ke. "Application of portrait recognition system for emergency evacuation in mass emergencies." Journal of Intelligent Systems 30, no. 1 (2021): 893–902. http://dx.doi.org/10.1515/jisys-2021-0052.

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Abstract A portrait recognition system can play an important role in emergency evacuation in mass emergencies. This paper designed a portrait recognition system, analyzed the overall structure of the system and the method of image preprocessing, and used the Single Shot MultiBox Detector (SSD) algorithm for portrait detection. It also designed an improved algorithm combining principal component analysis (PCA) with linear discriminant analysis (LDA) for portrait recognition and tested the system by applying it in a shopping mall to collect and monitor the portrait and establish a data set. The

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Lasarev, Michael R., Eric R. Bialk, David B. Allen, and Patrice K. Held. "Application of Principal Component Analysis to Newborn Screening for Congenital Adrenal Hyperplasia." Journal of Clinical Endocrinology & Metabolism 105, no. 8 (2020): e2930-e2940. http://dx.doi.org/10.1210/clinem/dgaa371.

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Abstract Purpose Newborn screening laboratories are challenged to develop reporting algorithms that accurately identify babies at increased risk for congenital adrenal hyperplasia (CAH) due to 21-hydroxylase deficiency (21OHD). Screening algorithms typically use cutoff values for a key steroid(s) and include considerations for covariates, such as gestational age or birth weight, but false-positive and false-negative results are still too frequent, preventing accurate assessments. Principal component analysis (PCA) is a statistical method that reduces high-dimensional data to a small number of

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Zhaxi, Cai Rang, and Yue Guang Li. "A Novel Face Recognition Algorithm." Advanced Materials Research 718-720 (July 2013): 2055–61. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.2055.

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This paper firstly analyzes the principle of face recognition algorithm, studies feature selection and distance criterion problem, puts forward the defects of PCA face recognition algorithm and LDA face recognition algorithm. According to the deficiencies and shortcomings of PCA face recognition algorithm and LDA face recognition algorithm, this paper proposes a solution -- PCA+LDA. The method uses the PCA method to reduce the dimensionality of feature space, it uses Fisher linear discriminant analysis method to classification, the realization of face recognition. Experiments show that, this m

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Wang, Zhenyi, Yalei Wang, and Xiaoliang Jin. "Prediction of Grade Classification of Rock Burst Based on PCA-SSA-PNN Architecture." Geofluids 2023 (June 13, 2023): 1–11. http://dx.doi.org/10.1155/2023/5299919.

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The uncertainty and complexity of rock burst brings great difficulties to the prediction of rock burst grades. In order to estimate the risk grades of rock burst, an integrated method combining principal component analysis (PCA) and sparrow search algorithm (SSA) with probabilistic neural network (PNN) was proposed. Considering that the in situ stress of rock mass, the strength of rock, and the strength of rock mass are the key influencing factors of rock bursts, the maximum in situ stress σ max , maximum tangential stress σ θ , rock strength σ ci , rock mass strength σ cm , and three rock bur

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Patil, Shweta, and S. S. Katariya. "Facial Expression Recognition using PCA Algorithm." Communications on Applied Electronics 3, no. 3 (2015): 22–24. http://dx.doi.org/10.5120/cae2015651904.

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Schmitt, Eric, and Kaveh Vakili. "The FastHCS algorithm for robust PCA." Statistics and Computing 26, no. 6 (2015): 1229–42. http://dx.doi.org/10.1007/s11222-015-9602-5.

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Wang, Yang, and Qiang Wu. "Sparse PCA by iterative elimination algorithm." Advances in Computational Mathematics 36, no. 1 (2011): 137–51. http://dx.doi.org/10.1007/s10444-011-9186-3.

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Huang, Dong, Zhang Yi, and Xiaorong Pu. "A new local PCA-SOM algorithm." Neurocomputing 71, no. 16-18 (2008): 3544–52. http://dx.doi.org/10.1016/j.neucom.2007.10.004.

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Ishida, Emille E. O. "Kernel PCA for Supernovae Photometric Classification." Proceedings of the International Astronomical Union 10, H16 (2012): 683–84. http://dx.doi.org/10.1017/s1743921314012897.

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AbstractIn this work, we propose the use of Kernel Principal Component Analysis (KPCA) combined with k = 1 nearest neighbor algorithm (1NN) as a framework for supernovae (SNe) photometric classification. It is specially recommended for analysis where the user is interested in high purity in the final SNe Ia sample. Our method provide good purity results in all data sample analyzed, when SNR⩾5. As a consequence, we can state that if a sample as the Supernova Photometric Classification Challenge were available today, we would be able to classify ≈ 15% of the initial data set with purity higher t

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Albitar, Maher, Wanlong Ma, Kevin Diep, Ferras S. Albitar, Herbert A. Fritsche, and Neal D. Shore. "Using a combination of urine and plasma biomarkers for the development of a scoring sytem that can diagnose and predict prognosis of prostate cancer." Journal of Clinical Oncology 32, no. 4_suppl (2014): 163. http://dx.doi.org/10.1200/jco.2014.32.4_suppl.163.

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163 Background: Relying solely on serum prostate-specific antigen (sPSA) to screen for prostate cancer (PCa) can lead to unnecessary biopsies. Biomarkers from urine and plasma were isolated to develop a detection scoring system for the presence of prostate cancer as well as to better predict aggressiveness. Methods: Urine and plasma specimens were analyzed from 141 patients (61 newly diagnosed PCa patients, 60 benign prostate hyperplasia (BPH) patients, and 20 post-prostatectomy patients) using polymerase chain reaction (PCR) for the levels of UAP1, PDLIM5, IMPDH2, HSPD1, PCA3, PSA, TMPRSS2, E

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He, Shi Jun, Shi Ting Zhao, Fan Bai, and Jia Wei. "A Method for Spatial Data Registration Based on PCA-ICP Algorithm." Advanced Materials Research 718-720 (July 2013): 1033–36. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1033.

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The spatial data which acquired by 3D laser scanning is huge, aiming at the iteration time is long with classic ICP algorithm, a improved registration algorithm of spatial data ICP algorithm which based on principal component analysis (PCA) is proposed in this paper (PCA-ICP), the basic principle and steps of PCA-ICP algorithm are given. The experiment results show that this method is feasible and the iterative time of PCA-ICP algorithm is shorter than classical ICP algorithm.

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Guojiang, Wang, and Yang Guoliang. "Facial Expression Recognition Using PCA and AdaBoost Algorithm." International Journal of Signal Processing Systems 7, no. 2 (2019): 73–77. http://dx.doi.org/10.18178/ijsps.7.2.73-77.

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Luo, Rui, Qingxiang Zeng, and Huashan Chen. "Artificial Intelligence Algorithm-Based MRI for Differentiation Diagnosis of Prostate Cancer." Computational and Mathematical Methods in Medicine 2022 (June 28, 2022): 1–10. http://dx.doi.org/10.1155/2022/8123643.

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The rapid increase in prostate cancer (PCa) patients is similar to that of benign prostatic hyperplasia (BPH) patients, but the treatments are quite different. In this research, magnetic resonance imaging (MRI) images under the weighted low-rank matrix restoration algorithm (RLRE) were utilized to differentiate PCa from BPH. The diagnostic effects of different sequences of MRI images were evaluated to provide a more effective examination method for the clinical differential diagnosis of PCa and BPH. 150 patients with suspected PCa were taken as the research objects. Pathological examination re

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Efeoğlu, Ebru. "An Artificial Intelligence-Based Hybrid Approach to Detect the Type of Buried Objects with Broad Frequency Band Antenna Systems." Firat University Journal of Experimental and Computational Engineering 3, no. 3 (2024): 362–78. http://dx.doi.org/10.62520/fujece.1476716.

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Knowing the type of buried object before excavation prevents unnecessary excavation. Moreover, it saves time and money. In this study, an experiment set was prepared for the detection of buried objects. The experimental set was composed of an antenna that sends and receives electromagnetic waves in a wide frequency band, software that records and processes reflections, and a sandbox. In the study, metallic and non-metallic objects with different depths, sizes and shapes were buried in this sand pool and measurements were taken along a profile. 2D images were created from the measurements and i

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JANKOVIC, MARKO, and HIDEMITSU OGAWA. "TIME-ORIENTED HIERARCHICAL METHOD FOR COMPUTATION OF PRINCIPAL COMPONENTS USING SUBSPACE LEARNING ALGORITHM." International Journal of Neural Systems 14, no. 05 (2004): 313–23. http://dx.doi.org/10.1142/s0129065704002091.

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Principal Component Analysis (PCA) and Principal Subspace Analysis (PSA) are classic techniques in statistical data analysis, feature extraction and data compression. Given a set of multivariate measurements, PCA and PSA provide a smaller set of "basis vectors" with less redundancy, and a subspace spanned by them, respectively. Artificial neurons and neural networks have been shown to perform PSA and PCA when gradient ascent (descent) learning rules are used, which is related to the constrained maximization (minimization) of statistical objective functions. Due to their low complexity, such al

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Karhunen, Juha. "Stability of Oja's PCA Subspace Rule." Neural Computation 6, no. 4 (1994): 739–47. http://dx.doi.org/10.1162/neco.1994.6.4.739.

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This paper deals with stability of Oja's symmetric algorithm for estimating the principal component subspace of the input data. Exact conditions are derived for the gain parameter on which the discrete algorithm remains bounded. The result is extended for a nonlinear version of Oja's algorithm.

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Asiedu, Louis, Felix O. Mettle, and Joseph A. Mensah. "Recognition of Reconstructed Frontal Face Images Using FFT-PCA/SVD Algorithm." Journal of Applied Mathematics 2020 (October 5, 2020): 1–8. http://dx.doi.org/10.1155/2020/9127465.

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Face recognition has gained prominence among the various biometric-based methods (such as fingerprint and iris) due to its noninvasive characteristics. Modern face recognition modules/algorithms have been successful in many application areas (access control, entertainment/leisure, security system based on biometric data, and user-friendly human-machine interfaces). In spite of these achievements, the performance of current face recognition algorithms/modules is still inhibited by varying environmental constraints such as occlusions, expressions, varying poses, illumination, and ageing. This st

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Li, Qiao Yan, and Hai Yan Quan. "The Dimension Reduction Method of Face Feature Parameters Based on Modular 2DPCA and PCA." Applied Mechanics and Materials 687-691 (November 2014): 4037–41. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.4037.

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In face recognition algorithms, Principal Component Analysis (PCA) is one of classical algorithms. But PCA algorithm needs to convert each sample matrix into vectors, which leads to a large amount of calculations in solving high-rank matrix. The essence of Modular Two-dimensional Principle Component Analysis (2DPCA) is that original images are divided into modular images, and image covariance matrix is constructed directly from the sub-images by the optimal projection matrix. But the number of features is still large and correlation still exists in feature extraction, which influences the spee

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Zeng, Qingxi, Wenqi Qiu, Pengna Zhang, Xuefen Zhu, and Ling Pei. "A Fast Acquisition Algorithm Based on Division of GNSS Signals." Journal of Navigation 71, no. 4 (2018): 933–54. http://dx.doi.org/10.1017/s0373463317000984.

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The acquisition of signals is a precondition for tracking and solution calculation in software-based Global Navigation Satellite System (GNSS) receivers. The Parallel Code phase Acquisition (PCA) algorithm can simultaneously obtain the correlation results at every sampling point. However, if the number of sampling points that needs processing is large, this method will lead to a heavy computational load. Thus, we improve the process of the PCA algorithm and propose a novel algorithm that divides the signals intoK(Kis a constant) parts to achieve correlation and obtains the correlation results

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Riyadi, Andrian, and Muhammad Febri Andriyanto. "The Use of Technology in Improving Understanding of Student Learning Performance Patterns." Journal of Automotive Technology and Education 2, no. 1 (2025): 1–9. https://doi.org/10.21831/jate.v2i1.913.

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This study aims to analyze the patterns of students' academic performance based on final semester exam scores, attendance, and task participation using the Principal Component Analysis (PCA) approach and the k-means clustering algorithm. The data used in the study were collected from 50 students and included exam scores, attendance rates, and task participation. The PCA method was employed to reduce the dimensionality of the data, resulting in two principal components (PC1 and PC2) that explained 74.57% of the data variability. Subsequently, the k-means algorithm was applied to cluster student

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Yang, Wenkao, Jing Wang, and Jing Guo. "A Novel Algorithm for Satellite Images Fusion Based on Compressed Sensing and PCA." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/708985.

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This paper studies the image fusion of high-resolution panchromatic image and low-resolution multispectral image. Based on the classic fusion algorithms on remote sensing image fusion, the PCA (principal component analysis) transform, and discrete wavelet transform, we carry out in-depth research. The compressed sensing (CS) abandons the full sample and shifts the sampling of the signal to sampling information that greatly reduces the potential consumption of traditional signal acquisition and processing. We combine compressed sensing with satellite remote sensing image fusion algorithm and pr

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Yi, Ting-Hua, Kai-Fang Wen, and Hong-Nan Li. "A new swarm intelligent optimization algorithm: Pigeon Colony Algorithm (PCA)." Smart Structures and Systems 18, no. 3 (2016): 425–48. http://dx.doi.org/10.12989/sss.2016.18.3.425.

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Karuna, Yepuganti, Saritha Saladi, and Budhaditya Bhattacharyya. "Brain Tissue Classification using PCA with Hybrid Clustering Algorithms." International Journal of Engineering & Technology 7, no. 2.24 (2018): 536. http://dx.doi.org/10.14419/ijet.v7i2.24.12155.

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Distinct algorithms were developed to segment the MRI images, to satisfy the accuracy in segmenting the regions of the brain. In this paper, we proposed a novel methodology for segmenting the MRI brain images using the clustering techniques. The Modified Fuzzy C-Means (MFCM) algorithm is pooled with the Artificial Bee Colony (ABC) algorithm after denoising images, features are extracted using Principal Component Analysis (PCA) for better results of segmentation. This improves the ability to extract the regions (cluster centres) and cells in the normal and abnormal brain MRI images. The compara

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Kenechukwu, Nwosu Ifeanyi, Kene Tochukwu Anyachebelu, and Muhammad, Umar Abdullahi. "Detection of Fraudulent Health Insurance Claims Based on Decision Tree with Principal Component Analysis." Asian Journal of Research in Computer Science 16, no. 4 (2023): 49–66. http://dx.doi.org/10.9734/ajrcos/2023/v16i4370.

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Fraudulent health insurance claims pose a significant challenge to insurance companies and healthcare providers, leading to substantial financial losses and compromised service quality. In this study, we focused on detecting fraudulent health insurance claims using the decision tree algorithm and principal component analysis (PCA). The objective was to gain valuable insights and extract meaningful patterns from the dataset to enhance fraud detection capabilities. We developed a comprehensive method that employed the decision tree algorithm to build a decision tree-based model and the PCA for d

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Gore, Pritee. "Identification and Detection of Sugarcane Crop Disease Using Image Processing." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (2021): 378–80. http://dx.doi.org/10.22214/ijraset.2021.36635.

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Sugarcane is a renewable, natural agriculture resource and it is most important crop of India. Sugarcane Crop is a perennial crop which results into less labour and high yields. Sugarcane crop is one of the main pillar for Indian economy. Nowadays there are different diseases which affecting the sugarcane plants in diverse areas. So In this work we are going to use machine learning algorithms and image processing for sugarcane leaf disease detection. Machine learning is a trending area where the technological benefits can be imparted to the agriculture field also. In this we are going to use P

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Zhang, Hongyang, Zhouchen Lin, Chao Zhang, and Junbin Gao. "Relations Among Some Low-Rank Subspace Recovery Models." Neural Computation 27, no. 9 (2015): 1915–50. http://dx.doi.org/10.1162/neco_a_00762.

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Recovering intrinsic low-dimensional subspaces from data distributed on them is a key preprocessing step to many applications. In recent years, a lot of work has modeled subspace recovery as low-rank minimization problems. We find that some representative models, such as robust principal component analysis (R-PCA), robust low-rank representation (R-LRR), and robust latent low-rank representation (R-LatLRR), are actually deeply connected. More specifically, we discover that once a solution to one of the models is obtained, we can obtain the solutions to other models in closed-form formulations.

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Abdel-Qader, Ikhlas, Lixin Shen, Christina Jacobs, Fadi Abu Amara, and Sarah Pashaie-Rad. "Unsupervised Detection of Suspicious Tissue Using Data Modeling and PCA." International Journal of Biomedical Imaging 2006 (2006): 1–11. http://dx.doi.org/10.1155/ijbi/2006/57850.

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Breast cancer is a major cause of death and morbidity among women all over the world, and it is a fact that early detection is a key in improving outcomes. Therefore development of algorithms that aids radiologists in identifying changes in breast tissue early on is essential. In this work an algorithm that investigates the use of principal components analysis (PCA) is developed to identify suspicious regions on mammograms. The algorithm employs linear structure and curvelinear modeling prior to PCA implementations. Evaluation of the algorithm is based on the percentage of correct classificati

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Huang, Ke Wang. "Experimental Study of FPCA on its Generalization Performance in Image Classification." Applied Mechanics and Materials 496-500 (January 2014): 2299–302. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.2299.

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The theoretical study of FPCA shows that FPCA algorithm has better generalization performance than existing PCA and its extended algorithms. But this theoretic conclusion was not confirmed by existing experimental results because of the problems of evaluation criterion. Introducing the idea of clustering performance criterion of LDA, we proposed a general performance metrics for PCA and performed numbers of experimental studies to compare FPCA with existing PCA and its extended algorithms by using our metrics. We found in the feature extraction of image samples that FPCA really has better gene

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Yu, Cheng Bo, Jun Tan, Lei Yu, and Yin Li Tian. "A Finger Vein Recognition Method Based on PCA-RBF Neural Network." Applied Mechanics and Materials 325-326 (June 2013): 1653–58. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1653.

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This paper puts forward a finger vein classification algorithm which combines Principal Component Analysis (PCA) with Radial Basis Function (RBF) neural network algorithm, named the PCA-RBF algorithm. Use the training sample to reduce PCA dimensions, and abstract the main component of the image. Because of the advantages of RBF neural network classifying, put finger vein images into different classes, and then use the shortest distance to recognize. Through the experiment result comparing with Back Propagation (BP) neural network, PCA-RBF neural network is better in finger vein recognition. Th

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Maafiri, Ayyad, and Khalid Chougdali. "Robust face recognition based on a new Kernel-PCA using RRQR factorization." Intelligent Data Analysis 25, no. 5 (2021): 1233–45. http://dx.doi.org/10.3233/ida-205377.

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In the last ten years, many variants of the principal component analysis were suggested to fight against the curse of dimensionality. Recently, A. Sharma et al. have proposed a stable numerical algorithm based on Householder QR decomposition (HQR) called QR PCA. This approach improves the performance of the PCA algorithm via a singular value decomposition (SVD) in terms of computation complexity. In this paper, we propose a new algorithm called RRQR PCA in order to enhance the QR PCA performance by exploiting the Rank-Revealing QR Factorization (RRQR). We have also improved the recognition rat

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Zhang, Ning Li, Yan Ma, Xiang Fen Zhang, and Yan Lu Xu. "An Improved PCA-SIFT Algorithm by Fuzzy K-Means for Image Matching." Applied Mechanics and Materials 644-650 (September 2014): 4291–96. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.4291.

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Image matching plays an important role in computer vision. The features extracted by SIFT algorithm have high stability invariant to scale, rotation and light, so it is the most popular algorithm for image matching. While SIFT algorithm also has its disadvantages of high dimensional data and time-consuming. To solve this problem, the traditional method employs PCA algorithm to reduce dimensionality of the descriptors. While PCA is a linear dimensionality reduction algorithm which means that it can only be used for linear distributed data. This paper employs the fuzzy K-means algorithm to impro

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Chen, Xiaojuan, Haoyu Yu, Jingyao Xu та Funan Gao. "An SNR Enhancement Method for Φ-OTDR Vibration Signals Based on the PCA-VSS-NLMS Algorithm". Sensors 24, № 13 (2024): 4340. http://dx.doi.org/10.3390/s24134340.

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To improve the signal-to-noise ratio (SNR) of vibration signals in a phase-sensitive optical time-domain reflectometer (Φ-OTDR) system, a principal component analysis variable step-size normalized least mean square (PCA-VSS-NLMS) denoising method was proposed in this study. First, the mathematical principle of the PCA-VSS-NLMS algorithm was constructed. This algorithm can adjust the input signal to achieve the best filter effect. Second, the effectiveness of the algorithm was verified via simulation, and the simulation results show that compared with the wavelet denoising (WD), Wiener filterin

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Liu, Biao, Rongping Tan, Baogao Tan, Chenhui Huang, and Keqin Yang. "Super-Resolution Reconstruction Algorithm-Based MRI Diagnosis of Prostate Cancer and Evaluation of Treatment Effect of Prostate Specific Antigen." Concepts in Magnetic Resonance Part A 2022 (October 15, 2022): 1–7. http://dx.doi.org/10.1155/2022/5447347.

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MRI of prostate cancer (PCa) was performed using a projection onto convex sets (POCS) super-resolution reconstruction algorithm to evaluate and analyze the treatment of prostate-specific antigen (PSA) and provide a theoretical reference for clinical practice. A total of 110 patients with PCa were selected as the study subjects. First, the modified POCS algorithm was used to reconstruct the MRI images, and the gradient interpolation algorithm was used instead of the traditional bilinear algorithm to preserve the edge information. The diagnostic and therapeutic effects of MRI examination, PSA ex

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Juneja, Jyoti, and Avani Chopra. "GLCM and PCA Algorithm based Watermarking Scheme." International Journal of Computer Applications 180, no. 48 (2018): 24–29. http://dx.doi.org/10.5120/ijca2018917261.

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Akram, Noreen, and Naeem Abbas. "Automated Facial Expression System using PCA Algorithm." International Journal of Computer Applications 182, no. 9 (2018): 32–36. http://dx.doi.org/10.5120/ijca2018917681.

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Gómez-Pedrero, José A., Julio C. Estrada, Jose Alonso, Juan A. Quiroga, and Javier Vargas. "Incremental PCA algorithm for fringe pattern demodulation." Optics Express 30, no. 8 (2022): 12278. http://dx.doi.org/10.1364/oe.452463.

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