Modified Principal Component Analysis Research Articles

AN OPTIMIZED BIDIRECTIONAL CONVOLUTIONAL RECURRENT NEURAL NETWORK ARCHITECTURE WITH GROUP-WISE ENHANCEMENT MECHANISM OF SENTIMENTS FOR THE PERSPECTIVE OF CUSTOMER REVIEW SUMMARIZATION

Customer reviews play pivotal roles in consumers’ purchase decisions, but the sheer volume of text data can be overwhelming. In existing system, while ensemble methods can enhance performance, the associated computational complexity and resource intensiveness should be carefully considered, and appropriate measures should be taken to address these challenges in the context of Customer Review Summarization. This study introduces a Particle Swarm Optimization (PSO) based optimized architecture for a Bidirectional Convolutional Recurrent Neural Network (BiCRNN) with a group-wise enhancement mechanism tailored for Customer Review Summarization and named as OBiCRNN. This model is designed for the perspective of customer review summarization, aiming to effectively capture sentiments and generate concise summaries. The integration of PSO optimizes the network parameters, enhancing the learning process. Feature extraction is done by Modified Principal Component Analysis (MPCA) which uses correlated feature sets and extracts most informative features for given datasets. BiCRNN utilizes bidirectional LSTM and GRU layers for comprehensive context understanding, while the group-wise enhancement mechanism categorizes sentiment-related features, amplifying essential sentiments and attenuating less relevant ones. With this novel approach, the architecture leverages both PSO and BiCRNN for an advanced framework in customer review summarization where outcomes demonstrate the effectiveness of the deep learning (DL) model in producing coherent and informative summaries, enhancing the accessibility of customer feedback for both consumers and businesses. The study contributes to the field of natural language processing (NLP) and customer sentiment analysis, offering a scalable solution for managing the wealth of information present in online customer reviews.

Big Data Analytics Challenges and Opportunities in Heart Disease Recognition: Novel Dimensionality Reduction with Classification Approach

Due to the current technological growth, a number of strategies have been developed, and more are being developed to eliminate problems that arise in many fields. Big Data techniques are employed to effectively stored health data due to the continual and massive volume of data created by the human body. Furthermore, the most important procedure is the classification of health data since it must be carried out precisely in order to diagnose cardiac disease early. The database images are various in size to reduce the dimension the Modified Principal Component Analysis (MPCA) Algorithm is used. the proposed MPCA algorithm is act as a feature selection model to pick features. One of the best and most effective techniques for classifying medical data is the Modified Deep Convolutional Neural Network (MDCNN). It has been shown to work for a variety of hospitalized patients. Consequently, the simulation results show that this proposal enhances classification accuracy in experimental research for the detection of heart ailment. Hence, the proposed method leads to an efficient usage of the resources and cost reduction. This approach assists the physician in taking suitable decision for giving a better treatment at right moment.

Examining ALS: reformed PCA and random forest for effective detection of ALS

ALS (Amyotrophic Lateral Sclerosis) is a fatal neurodegenerative disease of the human motor system. It is a group of progressive diseases that affects the nerve cells in the brain and spinal cord that control the muscle movement of the body hence, detection and classification of ALS at the right time is considered to be one of the vital aspects that can save the life of humans. Therefore, in various studies, different AI techniques are used for the detection of ALS, however, these methods are considered to be ineffectual in terms of identifying the disease due to the employment of ineffective algorithms. Hence, the proposed model utilizes Modified Principal Component Analysis (MPCA) and Modified Random Forest (MRF) for performing dimensionality reduction of all the potential features considered for effective classification of the ALS presence and absence of ALS causing mutation in the corresponding gene. The MPCA is adapted for capturing all the Low-Importance Data transformation. Furthermore, The MPCA is objected to performing three various approaches: Covariance Matrix Correlation, Eigen Vector- Eigenvalue decomposition, and selecting the desired principal components. This is done in aspects of implying the LI (Lower-Importance) Data Transformation. By choosing these potential components without any loss of features ensures better viability of selecting the attributes for ALS-causing gene classification. This is followed by the classification of the proposed model by using Modified RF by updating the clump detector technique. The clump detector is proceeded by clustering approach using K-means, and the data reduced by their dimension are grouped accordingly. These clustered data are analyzed either for ALS causing or devoid of causing ALS. Finally, the model’s performance is assessed using different evaluation metrics like accuracy, recall, F1 score, and precision, and the proposed model is further compared with the existing models to assess the efficacy of the proposed model.

Multi-view Image Fusion Using Ensemble Deep Learning Algorithm For MRI And CT Images

Medical image fusions are crucial elements in image-based health care diagnostics or therapies and generic applications of computer visions. However, the majority of existing methods suffer from noise distortion that affects the overall output. When pictures are distorted by noises, classical fusion techniques perform badly. Hence, fusion techniques that properly maintain information comprehensively from multiple faulty pictures need to be created. This work presents Enhanced Lion Swarm Optimization (ESLO) with Ensemble Deep Learning (EDL) to address the aforementioned issues. The primary steps in this study include image fusions, segmentation, noise reduction, feature extraction, picture classification, and feature selection. Adaptive Median Filters are first used for noise removal in sequence to enhance image quality by eliminating noises. The MRIs and CT images are then segmented using the Region Growing–based k -Means Clustering (RKMC) algorithm to separate the images into their component regions or objects. Images in black and white are divided into image. In the white image, the RKMC algorithm successfully considered the earlier tumour probability. The next step is feature extraction, which is accomplished by using the Modified Principal Component Analysis (MPCA) to draw out the most informative aspects of the images. Then the ELSO algorithm is applied for optimal feature selection, which is computed by best fitness values. After that, multi-view image fusions of multi modal images derive lower-, middle-, and higher-level image contents. It is done by using Deep Convolution Neural Network (DCNN) and the Tissue-Aware Conditional Generative Adversarial Network (TAcGAN) algorithm, which fuses the multi-view features and relevant image features, and it is used for real-time applications. ELSO +EDL algorithm gives better results in terms of accuracy, Peak Signal-To-Noise Ratio (PSNR), and lower Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) when compared to other existing algorithms.

Four-Channel Phase and Quadrature Self-Injection-Locked (PQSIL) Radar for Displacement Monitoring Using a Modified Principal Component Analysis (MPCA) Method

Four-Channel Phase and Quadrature Self-Injection-Locked (PQSIL) Radar for Displacement Monitoring Using a Modified Principal Component Analysis (MPCA) Method

Raman identification of adulteration in poly-alpha-olefin synthetic lubricant using principal component analysis and two-dimensional correlation spectroscopy

Poly-alpha-olefin (PAO) synthetic lubricating oil provides significant advantages over mineral oil. Highly discerning methods of PAO adulteration with inexpensive mineral oil is crucial to promote energy conservation and emission reduction and safeguard the rights of consumers. In this research, conventional principal component analysis (PCA), modified principal component analysis (MPCA) and two-dimensional correlation spectroscopy (2D-COS) with temperature as perturbation factor were used for Raman identification of PAO adulteration with 5 %, 20 % and 50 % mineral oil (150 N). The influence of spectrum normalization on identification was also studied. When using maximum intensity normalization, the modified principal component analysis is more efficient than the conventional PCA and can distinguish the adulterated oil from the authentic PAO even when the adulteration ratio decreases to 5 %. Comparably, the conventional PCA can distinguish the adulteration with 5 % mineral oil by using peak area normalization. These results suggest that the area normalization is prior to the maximum intensity normalization for PCA identification, as attributed to that the maximum intensity normalization might weaken the intensity difference of strongest peak among different samples. By either peak area or maximum intensity normalization, the difference between the authentic and the adulterated PAO can be detected by synchronous 2D-COS, in which both the autocorrelation peak at 2843 cm−1 and the negative cross correlation peak at (2843, 2935 cm−1) become weakened along with the increase of adulteration ratio. This work can provide important technical support to ensure the safe operation of equipment and crack down on the illegal trade of counterfeit synthetic lubricants.

Clustering-based image segmentation for optimal image fusion using CT and MRI images

As opposed to using many unrelated photographs to depict the same scene, image fusions combine multiple, similar images to generate a single, unified image with greater detail. Imaging sensors and the need for a wideband signal to transmit most source images limit their resolution. This study suggests new methods of fusing medical pictures from different modalities in order to increase image quality and, by extension, the accuracy with which brain tumors can be detected and identified. Improved convolutional neural network (ICNN) and region growth-based [Formula: see text]-means clustering (RKMC) are used in the suggested strategy to boost the quality of brain image fusions obtained from Computed tomography scanned image (CTSI) and magnetic resonance imaging (MRI) in this study. The first stages of this task consist of eliminating noise, segmenting images, extracting and selecting features, and fusing images. AMF (Adaptive Median Filtering) are first used to eliminate noise from MRI images and CTSI of the brain, improving the image quality. With the help of the RKMC algorithm, MRI image and CTSI scans can be segmented into their constituent pieces, which can then be seen either as grayscale images or as pictures of objects. The RKMC algorithm is able to adequately account for the possibility of tumors in white images. More useful image features can be extracted with the use of MPCA (Modified Principal Component Analysis). Afterward, features with the highest fitness values are chosen by using AFO (Adaptive Firefly Optimization). Image fusions of multimodal images are carried out using ICNN, which generates the image’s lower-, middle-, and higher-level contents. Incorporating important and relevant image characteristics from all viewpoints and perspectives improves feature training and testing. The results show that the proposed RKMC+ICNNs outperform the state-of-the-art approaches in terms of accuracy, PSNR, RMSE, and runtime.

Extended Principal Component Analysis for Spatiotemporal Filtering of Incomplete Heterogeneous GNSS Position Time Series

When ordinary principal component analysis (PCA) is employed to analyze the position time series of a regional GNSS station network, the GNSS time series are assumed to be homogeneous, and the missing data in the time series must be restored beforehand. To directly process incomplete and heterogeneous GNSS position time series, we develop the extended PCA (EPCA) and weighted EPCA approaches to solving for the missing values based on the best low-rank approximation in the spatiotemporal domain. The proposed approaches are used to process the real GNSS position time series of 24 stations in North China spanning 2011 to 2019 and successfully extract the common mode errors (CMEs). The proposed approaches are compared with modified PCA (MPCA) and weighted MPCA, in which an additional optimization criterion needs to be introduced in the frequency domain. The results show that EPCA can extract more CMEs than MPCA for both the unweighted and weighted cases. Consequently, EPCA outperforms MPCA in reducing noise and improving the accuracy of site velocity estimates. Repeated simulation experiments show that the CMEs extracted by EPCA are closer to the simulated true values than those extracted by MPCA. When the formal errors of the time series are considered, both weighted EPCA and weighted MPCA outperform their unweighted counterparts, and the former outperforms the latter. In addition, EPCA is computationally more efficient than MPCA since fewer unknowns need to be estimated.

Hybrid Deep-GAN Model for Intrusion Detection in IoT Through Enhanced Whale Optimization

IoT networks emerging as a significant growth in modern communication technological applications. The network formed with sensor nodes with resource restrictions in complexity, open wireless transmission features lead them prone to security threats. An efficient Intrusion Detection System aids in detecting attacks and performs crucial counter act to promise secure and reliable function. However, for the reason of the widespread nature of IoT, the intrusion detection system is supposed to carry out in discrete form with fewer fascination on common manager. In order to conquer these issues, Distributed – Generative Adversarial Network (D-GAN) with Enhanced Whale Optimization – Distributed deep learning based on Artificial Neural Network (EWO-HDL+ANN) is proposed. Here the GAN can detect internal attacks and the D-GAN is capable of detecting both internal and external attacks effectively. Transfer By Subspace Similarity is engaged to carry out. After that the preprocessed data is fed into feature extraction stage. Modified Principal Component Analysis (MPCA) is applied to feature extraction, which is used to extract new features that are enlightened. Then, feature selection is executed by Enhanced Whale Optimization Algorithm, which is used to choose significant and superfluous features from the dataset. It gets better the classification accuracy through the greatest fitness value. Then the intrusion detection is evaluated by applying HDL+ANN algorithm used to detect the attacks powerfully. The experimental conclusion proves that the introduced EWO-DDL+ANN method provides enhanced intrusion detection system in the view of greater accuracy, precision, recall, f-measure and low False Positive Rate.

Improved Procedure for Multi-Focus Images Using Image Fusion with qshiftN DTCWT and MPCA in Laplacian Pyramid Domain

Multi-focus image fusion (MIF) uses fusion rules to combine two or more images of the same scene with various focus values into a fully focused image. An all-in-focus image refers to a fully focused image that is more informative and useful for visual perception. A fused image with high quality is essential for maintaining shift-invariant and directional selectivity characteristics of the image. Traditional wavelet-based fusion methods, in turn, create ringing distortions in the fused image due to a lack of directional selectivity and shift-invariance. In this paper, a classical MIF system based on quarter shift dual-tree complex wavelet transform (qshiftN DTCWT) and modified principal component analysis (MPCA) in the laplacian pyramid (LP) domain is proposed to extract the focused image from multiple source images. In the proposed fusion approach, the LP first decomposes the multi-focus source images into low-frequency (LF) components and high-frequency (HF) components. Then, qshiftN DTCWT is used to fuse low and high-frequency components to produce a fused image. Finally, to improve the effectiveness of the qshiftN DTCWT and LP-based method, the MPCA algorithm is utilized to generate an all-in-focus image. Due to its directionality, and its shift-invariance, this transform can provide high-quality information in a fused image. Experimental results demonstrate that the proposed method outperforms many state-of-the-art techniques in terms of visual and quantitative evaluations.

Accurate liver disease prediction system using convolutional neural network

Objectives: To introduce the technique which can ensure the accurate and reliable prediction of liver disease by adapting the deep learning technique. Methods: In this work Modified Convolutional Neural Network based Liver Disease Prediction System (MCNN-LDPS) is introduced for the accurate liver disease prediction outcome. In the proposed research work, Dimensionality reduction is carried out using Modified Principal Component Analysis. Optimal feature selection is carried out using Score based Artificial Fish Swarm Algorithm (SAFSA). In SAFSA algorithm, information gain and entropy values are taken as input values which proved accurate outcome. This research method has been analysed over Indian Liver patient dataset. Findings: The analysis of the research work proves that the proposed method MCNN-LDPS obtains better outcome in terms of increased accuracy, precision. Here comparison analysis proved that MCNN-LDPS obtains 4.05% increased accuracy, 21.23% F-measure, 4.22% precision and 34.26% recall. This research method has been compared with the existing Multi layer Perceptron Neural Network (MLPNN) for the performance analysis. Novelty: The major limitation of CNN is its inability to encode Orientational and relative spatial relationships, view angle. CNN do not encode the position and orientation of data. Lack of ability to be spatially invariant to the input data sample. This is resolved in this research work by combining the genetic algorithm with the CNN method. Keywords: Liver Disease Prediction; Feature Selection; Information Gain; Entropy; Convolutional neural network; Dimensionality Reduction

Advanced Multifocus Image Fusion algorithm using FPDCT with Modified PCA

Image fusion has been performed and reported in this paper for multi-focused images using Frequency Partition Discrete Cosine Transform (FP-DCT) with Modified Principal component analysis (MPCA) technique. The image fusion with decomposition at fixed levels may be treated as a very critical rule in the earlier image processing techniques. The frequency partitioning approach was used in this study to select the decomposition levels based on the pixel intensity and clarity. This paper also presents the modified PCA technique which provides dimensionality reduction. The wide range of quality evaluation metrics was computed to compare the fusion performance on the five images. Different techniques such as PCA, wavelet transforms with PCA, Multiresolution Singular Value Decomposition (MSVD) with PCA, Multiresolution DCT (MRDCT) with PCA, Frequency partitioning DCT (FP-DCT) with PCA were computed for comparison with the proposed FP-DCT Modified PCA (MPCA) technique. Images obtained after fusion process obtained by the method proposed shows enhanced visual quality, negligible information loss and discontinuities in the image than compared to other state of the art methods.

Image Enhancement based on Fusion using 2D LPDCT and Modified PCA

The images play a vital role in various fields of applications; medical field is the one, where images more widely used in diagnosis. Best image data analysis results if the quality of the image is high. To attain best image quality some popular techniques are available, among that image fusion is one of the technique, it enhances the information of the image by selecting and merging the significant information from two or more similar multi-focus images. Using the features of image fusion a new technique is proposed in this paper. In proposed technique, fusion of sources images with 2D Laplacian Pyramid Discrete Cosine Transformation (2D LP - DCT) and Modified Principal Component Analysis (MPCA). In this, two similar multi-focus images are considered, first, they undergone to 2D LP-DCT and then MPCA technique. The 2D LP-DCT enhances important image features, which are best utilized in image fusion and results good image quality. In Modified PCA, the concept of dimensionality reduction is used. The experimental results indicate that the suggested strategy can produce fused images with good visual quality and computational effectiveness than other state-of-the-art works.

A Data-Driven Learning Approach for Nonlinear Process Monitoring Based on Available Sensing Measurements

A data-driven learning approach is proposed to monitor nonlinear processes based only on the available sensing measurements in this paper. To achieve this aim, locally weighted projection regression (LWPR) is used to establish the model of the underlying nonlinear process with local linear models, in which the modified principal component analysis (MPCA) could be further applied for process monitoring. Moreover, the normalized weighted mean of all the proposed test statistics is employed to detect the possible abnormalities. A detailed discussion is made on principal-component-analysis-based approaches as well as on the reason of selecting MPCA under LWPR framework. The Tennessee Eastman process is first employed to demonstrate the superiority of MPCA. A numerical simulation example and an industrial benchmark of an autosuspension system are finally utilized to validate the effectiveness of the proposed scheme.

Trend extraction of rail corrugation measured dynamically based on the relevant low-frequency principal components reconstruction

Rail corrugation dynamic measurement techniques are critical to guarantee transport security and guide rail maintenance. During the inspection process, low-frequency trends caused by rail fluctuation are usually superimposed on rail corrugation and seriously affect the assessment of rail maintenance quality. In order to extract and remove the nonlinear and non-stationary trends from original mixed signals, a hybrid model based ensemble empirical mode decomposition (EEMD) and modified principal component analysis (MPCA) is proposed in this paper. Compared with the existing de-trending methods based on EMD, this method first considers low-frequency intrinsic mode functions (IMFs) thought to be underlying trend components that maybe contain some unrelated components, such as white noise and low-frequency signal itself, and proposes to use PCA to accurately extract the pure trends from the IMFs containing multiple components. On the other hand, due to the energy contribution ratio between trends and mixed signals is prior unknown, and the principal components (PCs) decomposed by PCA are arranged in order of energy reduction without considering frequency distribution, the proposed method modifies traditional PCA and just selects relevant low-frequency PCs to reconstruct the trends based on the zero-crossing numbers (ZCN) of each PC. Extensive tests are presented to illustrate the effectiveness of the proposed method. The results show the proposed EEMD-PCA-ZCN is an effective tool for trend extraction of rail corrugation measured dynamically.

Simultaneous Multiparameter Measurement in Pulsed Eddy Current Steam Generator Data Using Artificial Neural Networks

In-service inspection of complex systems such as nuclear steam generator (SG) tubes and their surrounding support structures is challenged by overlapping degradation modes. In these complex systems the simultaneous and accurate measurement of more than two interdependent parameters is difficult using standard statistical regression analysis tools. Recently, artificial neural networks (ANNs) have been investigated for dealing with the complex relation between inspection data and defect properties. In this paper, pulsed eddy current data were obtained using a single driver with an array of eight pick-up coils configured for inspection of Alloy-800 SG tube fretting, accompanied by tube offset within a simulated corroding ferromagnetic support structure. Time-voltage data were processed by a modified principal component analysis (MPCA) to reduce data dimensionality, and MPCA scores were input into an ANN that simultaneously targeted four parameters associated with support structure hole size, tube off-centering in two dimensions, and fret depth. The neural network was trained, tested, and validated on experimental data and provided estimates within 2% of hole inner diameter (ID) and 3% of fret depth targets. The estimates of hole ID and tube position were further improved when fret depth was used as an input, as might occur if fret depth inspection results are available.

Modified principal component analysis: an integration of multiple similarity subspace models.

We modify the conventional principal component analysis (PCA) and propose a novel subspace learning framework, modified PCA (MPCA), using multiple similarity measurements. MPCA computes three similarity matrices exploiting the similarity measurements: 1) mutual information; 2) angle information; and 3) Gaussian kernel similarity. We employ the eigenvectors of similarity matrices to produce new subspaces, referred to as similarity subspaces. A new integrated similarity subspace is then generated using a novel feature selection approach. This approach needs to construct a kind of vector set, termed weak machine cell (WMC), which contains an appropriate number of the eigenvectors spanning the similarity subspaces. Combining the wrapper method and the forward selection scheme, MPCA selects a WMC at a time that has a powerful discriminative capability to classify samples. MPCA is very suitable for the application scenarios in which the number of the training samples is less than the data dimensionality. MPCA outperforms the other state-of-the-art PCA-based methods in terms of both classification accuracy and clustering result. In addition, MPCA can be applied to face image reconstruction. MPCA can use other types of similarity measurements. Extensive experiments on many popular real-world data sets, such as face databases, show that MPCA achieves desirable classification results, as well as has a powerful capability to represent data.

Pulsed eddy current detection of cracks in F/A-18 inner wing spar without wing skin removal using Modified Principal Component Analysis

Stress corrosion cracks may develop between fasteners in the aluminum inner wing spars of F/A-18 Hornet aircraft. These fasteners secure carbon-fibre/epoxy composite wing skin, of nominal 13mm thickness, to the spar. Inspection of the spar through the wing skin is required in order to avoid wing disassembly. Pulsed eddy currents, generated by a probe design that utilizes the ferrous fastener as a flux conduit, are demonstrated as having the capability of detecting simulated cracks within the spar with the wing skin present. Differentially connected pickup coils are used to sense differences in response due to asymmetries in induced eddy current fields arising in the presence of cracks. To overcome variability in PEC signal response due to variability in magnetic coupling to the fastener, a large measurement set was analyzed using Modified Principal Component Analysis (PCA). The modified PCA minimizes residual sum of squares to extract eigenfunction scores, which are used to detect the presence of cracks ending on one side of the fastener hole. The ability of the method to sense simulated cracks independent of confounding measurement parameters was demonstrated.

Mimicking the olfactory system by a thickness-shear-mode acoustic sensor array

Mimicking the mammalian olfactory system by an array of twelve thickness-shear-mode (TSM) acoustic wave sensors with different adsorptive materials to form an artificial odour-sensing system has been investigated. The adsorptive materials chosen include polyethylene glycol-20M, hexadecyltriphenyl phosphonium iodide, N-(2-hydroxylbenzyl) dodecylamine, tween 65, dinonyl phthalate, polyethylene glycol 400-monostearate, epoxy resin, tri- n-octylphosphinoxid, di-iso-octyl phthalate, polyethylene glycol-6000, thionalid and dimethylpropaneldiol succinate. The frequency shift data obtained from the sensor array responding to the odours originated from different groups of organic compounds such as ketones (acetone, butanone, acetophenone, cyclohexanone, and acetylacetone), esters (methyl acetate, ethyl acetate, n-propyl acetate, and n-butyl acetate), and aldehydes (formaldehyde, benzaldehyde, glyoxal and furfural) are transformed to relative values to compensate the effect of the variation of the actual coating frequency shift, Δf. The relative frequency shift data matrices are subjected to modified principal component analysis (MPCA) treatment with or without data preprocesssing. The odours of different organic molecule samples are easily recognized from the two-dimensional displays of the first two principal components. The discrimination of aromas originated from different essential oils from France (Adoucissant TM663, Atlantic TM3387, and Spar Tacus TM5970) and different fruits such as apples, bananas, oranges, and pears is also possible.

Multi-echelon repair level analysis MERLA : Simon L. Devries and Scott A. Wicker. Proc. a. Reliab. Maintainab. Symp., 126 (1986)

Stress corrosion cracks may develop between fasteners in the aluminum inner wing spars of F/A-18 Hornet aircraft. These fasteners secure carbon-fibre/epoxy composite wing skin, of nominal 13 mm thickness, to the spar. Inspection of the spar through the wing skin is required in order to avoid wing disassembly. Pulsed eddy currents, generated by a probe design that utilizes the ferrous fastener as a flux conduit, are demonstrated as having the capability of detecting simulated cracks within the spar with the wing skin present. Differentially connected pickup coils are used to sense differences in response due to asymmetries in induced eddy current fields arising in the presence of cracks. To overcome variability in PEC signal response due to variability in magnetic coupling to the fastener, a large measurement set was analyzed using Modified Principal Component Analysis (PCA). The modified PCA minimizes residual sum of squares to extract eigenfunction scores, which are used to detect the presence of cracks ending on one side of the fastener hole. The ability of the method to sense simulated cracks independent of confounding measurement parameters was demonstrated.