PCA Algorithm Research Articles

Suppress ambient temperature interference strategy based on SnO2 gas semiconductor sensor using dynamic temperature modulation mode and principal component analysis algorithm

Semiconductor gas sensors have been widely used in environmental monitoring, toxic gas monitoring, respiratory marker monitoring and other fields due to their low price and high sensitivity etc. An easily overlooked parameter, ambient temperature, is a major factor affecting the accuracy of the sensor. In this paper, a new idea to suppress ambient temperature interference is proposed. Dynamic temperature modulation method is used to extend the dimension of output information, so that both ambient temperature and target gas concentration can be expressed simultaneously. The ambient temperature component is separated by coordinate transformation and component elimination. The data inverse transformation is carried out to achieve the purpose of suppressing the ambient temperature interference. Compared with the performance optimization of gas-sensing materials and additional temperature sensors, the first advantage of this method is to reduce the complexity and power consumption of sensor devices. In addition, the use of PCA algorithm also reduces the complexity of the recognition algorithm. At the same time, more intuitive data reduces the cost of parameter optimization.

Classification of T6 Tempered 6XXX Series Aluminum Alloys Based on Machine Learning Principles

Aluminum alloys are widely used in each sector of engineering because of their lower density coupled with higher strength compared to many existing alloys of other metals. Due to these unique characteristics, there is acceleration in demand and discovery of new aluminum alloys with targeted properties and compositions. Traditional methods of designing new materials with desired properties, like ‘domain specialists and trial-and-error ' approaches, are laborious and costly. These techniques also lead to the expansion of alloy search area. Also, high demand for recycling of aluminum alloys requires fewer alloy groups. We suggest a machine learning design system to reduce the number of grades in the 6XXX series of aluminum alloys by collecting the features involving chemical composition and tensile properties at T6 tempering state. This work demonstrates the efficiency of grouping the aluminum alloys into a number of clusters by a combined PCA and K-means algorithm. To understand the physics inside the clusters we used an explainable artificial intelligence algorithm and connected the findings with sound metallurgical reasoning. Through machine learning we will narrow down the search space of 6XXX series aluminum alloys to few groups. This work offers a useful method for reducing compositional space of aluminum alloys.

Automatic Gate Opening System with Face Recognition

Despite the limitations of current technology, facial recognition from still or moving sources is developing into a useful tool for law enforcement, security, and counterterrorism applications. Currently, facial recognition shows promise and has been used in a small number of applications, The effectiveness of a facial recognition algorithm is examined in this assessment, specifically with regards to factors such as age, gender, ethnicity, facial characteristics, and lighting levels. Face recognition is being used more frequently as a non-intrusive identity verification technique. In this paper, we propose for the first time a strongly privacy-enhanced face recognition system that effectively uses secure multiparty computation techniques to conceal both the biometrics and the result from the server performing the matching operation. These issues can be easily solved by Explainable AI (XAI), and the solutions are clear to the end users. The proposed system is entirely implemented on a Raspberry Pi, enabling a full embedded application. With the aid of open-source libraries for training, defining, and running machine learning models like Tensorflow.js, Keras, and OpenCV, the application was created using Python and HTML on PyCharm/Visual Studio Code. An entire application can be run on a microcontroller, like the Raspberry Pi, which enables plug-and-play operation of the system at any time. The face detection subsystem, which is based on an improved PCA algorithm, can identify faces in moving vehicles and communicate with the vehicle's owner via GSM. The car's location is sent via GPS. This is the most reliable car security system because we can still locate the car even when it is lost and we can see the thief's face. The owner of the car is free to give the password to new drivers as well. This makes the car security system safer and more comfortable.

Comprehensive characterization of pyroptosis phenotypes with distinct tumor immune profiles in gastric cancer to aid immunotherapy.

Pyroptosis is a form of programmed cell death that is essential for immunity. Herein, this study was conducted to uncover the implication of pyroptosis in immunomodulation and tumor microenvironment (TME) in gastric cancer. Prognostic pyroptosis-related genes were extracted to identify different pyroptosis phenotypes and pyroptosis genomic phenotypes via unsupervised clustering analysis in the gastric cancer meta-cohort cohort (GSE15459, GSE62254, GSE84437, GSE26253 and TCGA-STAD). The activation of hallmark gene sets was quantified by GSVA and immune cell infiltration was estimated via ssGSEA and CIBERSORT. Through PCA algorithm, pyroptosis score was conducted. The predictors of immune response (TMB and IPS) and genetic mutations were evaluated. The efficacy of pyroptosis score in predicting immune response was verified in two anti-PD-1 therapy cohorts. Three different pyroptosis phenotypes with different prognosis, biological pathways and tumor immune microenvironment were established among 1275 gastric cancer patients, corresponding to three immune phenotypes: immune-inflamed, immune-desert, and immune-excluded. According to the pyroptosis score, patients were separated into high and low pyroptosis score groups. Low pyroptosis score indicated favorable survival outcomes, enhanced immune responses, and increased mutation frequency. Moreover, low pyroptosis score patients displayed more clinical benefits from anti-PD-1 and prolonged survival time. Our findings uncovered a nonnegligible role of pyroptosis in immunomodulation and TME multiformity and complicacy in gastric cancer. Quantifying the pyroptosis score in individual tumors may tailor more effective immunotherapeutic strategies.

DDoS Risk Assesment using Machine Learning Techniques

The result of exploiting holes in safeguarding Internet of Things (IoT) devices, the amount of cyber- attacks and data breaches has skyrocketed across various corporations, companies, and sectors. Because its capacity to extract and learn deep characteristics of known assaults and detect novel attacks without the need for manual feature engineering, machine learning is used in cyber-attack detection. Despite the use of improved Machine Learning (ML) techniques for intrusion detection, the assault remains a huge danger to the Internet. The primary target of this research is to identify and detect assaults on the network. The expansion of social networks is now increasing on a daily basis. However, detecting the assaults is a difficult task. By examining the information in the KDDCUP Dataset, this project will dynamically detect the attack. The method of feature scaling is employed to standardize a range of independent variables or data constituents. The feature reduction PCA technique is used to locate the directions of highest variance in high-dimensional data and project it onto a new subspace with the same or less dimensions than the original one. Finally, the ML classification technique is used to categorise the data. Based on the assaults and the typical event, the final report is created.. Key Words: DDOS, Machine learning, Cyber Attacks,PCA Algorithm.

Effective full connection neural network updating using a quantized full FORCE algorithm

This paper presents a new training algorithm that can update the situation of layers’ network, and therefore, connections, neurons, and firing rate of neurons based on FORCE (first-order reduced and controlled error) training algorithm. The Quantized Full FORCE algorithm (QFF) also updates the number of neurons and connections between different layers in the network per iteration in a way that the whole firing rate of each layer is updated via selecting the best neurons and combining strong features. The update method is sequential, so that with each instance passing through the network, the network structure is updated with the Full FORCE algorithm. The algorithm updates the structure of networks with a multiple/single middle layer of the supervised version of feed forward networks such as Multilayer perceptron (MLP), changing them into partially-connected networks. A combination of principal component analysis PCA and Linear Discriminant Analysis (LDA) algorithms has been used to cluster the network input features. The paper focuses on the deep supervised MLP network with backpropagation (BP) and various datasets and its comparison with other MLP based stat of art methods and hybrid evolutionary algorithms. We achieved 98.15 percent accuracy for facial expression 98.6 and 97.7 percent for Wisconsin breast Cancer and Iris Flower in respectively. The training algorithm employed in the study enjoys a lower computational complexity while yielding faster and more accurate convergence, starting with a very low level of errors of 0.009 in comparison with the full connection network and it solves the challenge of getting stuck in local minima and poor convergence of Gradient Decent with BP.

Identification of potential key genes of TGF-beta signaling associated with the immune response and prognosis of ovarian cancer based on bioinformatics analysis

BackgroundTGF-beta signaling is a key regulator of immunity and multiple cellular behaviors in cancer. However, the prognostic and therapeutic role of TGF-beta signaling-related genes in ovarian cancer (OV) remains unexplored. MethodsData of OV used in the current study were sourced from TCGA and GEO databases. Consensus clustering was applied to classify OV patients into different clusters using TGF-beta signaling-related genes. Differentially expressed genes (DEGs) between different clusters were screened by the “limma” R package. Prognostic genes were screened from DEGs by univariate Cox regression, followed by the construction of the TGF-beta signaling-related score. The prognostic value of TGF-beta signaling-related score was evaluated in both training and testing OV cohorts. Moreover, the immune status, GSEA and therapeutic response between low- and high-score groups were performed to further reveal the potential mechanisms. ResultsBy consensus clustering, OV patients were classified into two clusters with different tumor immune environments. After differential expression and univariate Cox regression analyses, GMPR, PIEZO1, EMP1, CXCL13, GADD45B, SORCS2, FOSL2, PODN, LYNX1 and SLC38A5 were selected as prognostic genes. Using PCA algorithm, the TGF-beta signaling-related score of OV patients was calculated based on prognostic genes. Then OV patients were divided into low- and high-TGF-beta signaling-related score groups. We observed that the two score groups had significantly different survivals, tumor immune environments and expressions of immune checkpoints. In addition, GSEA results showed that immune-related pathways and biological processes, like chemokine signaling pathway, TNF signaling pathway and T cell migration were significantly enriched in the low-score group. Moreover, patients in the low- and high-score groups had remarkably different sensitivity to chemo- and immunotherapy. ConclusionFor the first time, our study identified ten prognostic genes associated with TGF-beta signaling, constructed a prognostic TGF-beta signaling-related score and investigated the effect of TGF-beta signaling-related score on OV immunity and therapy. These findings may enrich our knowledge of the TGF-beta signaling in OV prognosis and help to improve the prognosis prediction and treatment strategies in OV.

Multiscale Bayesian PCA for robust process modeling of a Fischer–Tropsch bench scale process

Building a good process model is critical for process monitoring technologies which ensure process safety, reliability, and profitability. In this work, we develop a Multiscale Bayesian PCA (MS-BPCA) algorithm and use it to model a real Fischer–Tropsch (FT) pilot plant. We demonstrate the algorithm’s superior and robust performance in dealing with autocorrelation and noise contamination compared to BPCA and Multiscale PCA. The modeling performance of BPCA depends on the accuracy of the likelihood and prior parameters. Therefore, we explore various techniques (multiscale filtering, polynomial approximation, and PCA) to empirically estimate these parameters. Furthermore, we study the limitations of the BPCA modeling algorithm when its assumptions of gaussian data and independent noise are violated. Finally, this work will compare the modeling performance of all techniques (PCA, Bayesian PCA, Multiscale PCA, and Multiscale Bayesian PCA) with real data collected from a state-of-the-art bench-scale Fischer–Tropsch (FT) process.

Significance of methylation-related genes in diagnosis and subtype classification of renal interstitial fibrosis

BackgroundRNA methylation modifications, such as N1-methyladenosine/N6-methyladenosine /N5-methylcytosine (m1A/m6A/m5C), are the most common RNA modifications and are crucial for a number of biological processes. Nonetheless, the role of RNA methylation modifications of m1A/m6A/m5C in the pathogenesis of renal interstitial fibrosis (RIF) remains incompletely understood.MethodsFirstly, we downloaded 2 expression datasets from the GEO database, namely GSE22459 and GSE76882. In a differential analysis of these datasets between patients with and without RIF, we selected 33 methylation-related genes (MRGs). We then applied a PPI network, LASSO analysis, SVM-RFE algorithm, and RF algorithm to identify key MRGs.ResultsWe eventually obtained five candidate MRGs (WTAP, ALKBH5, YTHDF2, RBMX, and ELAVL1) to forecast the risk of RIF. We created a nomogram model derived from five key MRGs, which revealed that the nomogram model may be advantageous to patients. Based on the selected five significant MRGs, patients with RIF were classified into two MRG patterns using consensus clustering, and the correlation between the five MRGs, the two MRG patterns, and the genetic pattern with immune cell infiltration was shown. Moreover, we conducted GO and KEGG analyses on 768 DEGs between MRG clusters A and B to look into their different involvement in RIF. To measure the MRG patterns, a PCA algorithm was developed to determine MRG scores for each sample. The MRG scores of the patients in cluster B were higher than those in cluster A.ConclusionsUltimately, we concluded that cluster A in the two MRG patterns identified on these five key m1A/m6A/m5C regulators may be associated with RIF.

New Media Interactive Design Visualization System Based on Artificial Intelligence Technology

The experimental results show that the average cumulative contribution rate of this algorithm was 92.78%, while that of the traditional algorithm was 88.88%. In contrast, the average cumulative contribution rate of this algorithm was improved by 3.9%. In terms of classification accuracy, the average classification accuracy of this algorithm was 94.99%, while the traditional algorithm was 90.98%. In contrast, the average classification accuracy of this algorithm was improved by 4.01%. In terms of dimension reduction time, the average dimension reduction time of this algorithm was 3.46s, while that of the traditional algorithm was 6.43s. In contrast, the average dimension reduction time of this algorithm was shortened by 2.97s. It can be seen from the data that the improved PCA algorithm can effectively improve the classification accuracy and cumulative contribution rate of the visualization system, shorten the dimension reduction time, and improve the system's ability to process data.

Quality Information Detection of Agaricus bisporus Based on a Portable Spectrum Acquisition Device.

As one of the most popular edible fungi in the market, the quality of Agaricus bisporus will determine its sales volume. Therefore, to achieve rapid and nondestructive testing of the quality of Agaricus bisporus, this study first built a portable spectrum acquisition device for Agaricus bisporus. The Ocean Spectromeper was used to calibrate the spectral data of the device, and the linear regression analysis method was combined to analyze the two. The results showed that the Pearson correlation coefficient of significance between the two was 0.98. Then, the spectral data of Agaricus bisporus were collected, the spectral characteristic wavelength of Agaricus bisporus was extracted by the SPA and PCA algorithms, and the moisture content and whiteness prediction models based on a BP neural network and PLSR, respectively, were built. The parameters of the BP neural network model were optimized by SSA. The R2 values for the final moisture content and the predicted whiteness were 0.95 and 0.99, and the RMSE values were 5.04% and 0.60, respectively. The results show that the portable spectral acquisition and analysis device can be used for the accurate and rapid quality detection of Agaricus bisporus.

IT Diagnostics of Parkinson’s Disease Based on the Analysis of Voice Markers and Machine Learning

The results of studying the parameters of the spectra of speech signals by machine learning with the use of neural networks are presented. This study was carried out in order to confirm experimentally the possibility of performing an assessment of these parameters for the detection of Parkinson’s disease in the early stages (IT diagnostics). During the study, the public database was used, which systematized the spectra of vowel sounds uttered by patients with Parkinson’s disease. The applied method is binary data classification. In the course of the study, the speech data spectrum was first preprocessed, which consisted of filtering it in order to remove its noise components and eliminate bursts and gaps in it. Then the parameters of the processed spectrum of speech data were determined: average value, maximum and minimum, peak, wavelet coefficients, MFCC and TQWT. After that, the object was selected using the PCA algorithm. The model was trained using the Knn and Random Forest algorithms, as well as the Bayesian neural network. The Bayesian optimization algorithm and the GridSearch method were used to find the best model hyperparameters. It has been established that when using Knn, Random Forest and Bayesian neural network, it is possible to increase the accuracy of recognition of Parkinson’s disease by 94.7; 88.16 and 74.74 %, respectively. A similar study by other scientists showed that the recognition accuracy of data sets was only 86 %.

Cross-correlation Forecast of CSST Spectroscopic Galaxy and MeerKAT Neutral Hydrogen Intensity Mapping Surveys

Cross-correlating the data on neutral hydrogen (H i) 21 cm intensity mapping with galaxy surveys is an effective method to extract astrophysical and cosmological information. In this work, we investigate the cross-correlation of MeerKAT single-dish mode H i intensity mapping and China Space Station Telescope (CSST) spectroscopic galaxy surveys. We simulate a survey area of ∼300 deg2 of MeerKAT and CSST surveys at z = 0.5 using Multi-Dark N-body simulation. The PCA algorithm is applied to remove the foregrounds of H i intensity mapping, and signal compensation is considered to solve the signal loss problem in H i-galaxy cross power spectrum caused by the foreground removal process. We find that from CSST galaxy auto and MeerKAT-CSST cross power spectra, the constraint accuracy of the parameter product ΩH I b H I r H I,g can reach ∼1%, which is about one order of magnitude higher than the current results. After performing the full MeerKAT H i intensity mapping survey with 5000 deg2 survey area, the accuracy can be enhanced to <0.3%. This implies that the MeerKAT-CSST cross-correlation can be a powerful tool to probe the cosmic H i property and the evolution of galaxies and the Universe.

Construction of a smart grid load forecasting platform based on clustering algorithm

Abstract With the rapid development of economic levels, the demand for electricity usage in various industrial sectors has continued to rise. In order to adequately meet the growth in demand for electricity usage at each user end within the power system, accurate forecasting of electricity load is required. This paper uses the K-Means algorithm as the basis, combined with the Canopy algorithm, whose simple and efficient advantages can be used as the initial clustering step for demanding clustering algorithms. The PCA algorithm is used to reduce the dimensionality of the residential multidimensional electricity consumption feature set, and after optimization, the feature vector weights are calculated to obtain the appropriate selection to determine the training and testing samples. The improved K-Means algorithm carries out five clustering tests, and its average accuracy is above 97%. The average relative error is then used as an indicator to compare the algorithm with the time series method, curve extrapolation method, and WKNR method for analysis. The average relative error of the smart grid load forecasting method proposed in this paper is 0.87%, while the average relative errors of the other three algorithms are 1.64%, 1.57%, and 0.93%, respectively. The distribution of relative errors falls less than 1% more than the other methods. It can be seen that the improved K-Means algorithm has higher prediction accuracy and also makes the smart grid load testing platform more practical due to its simple implementation principle.

Fault Detection Based on Complete Information Principal Component Analysis for Electric Submersible Pump

Electric submersible pump (ESP) is the key production equipment for the offshore oil industry. As a result of poor working condition, fault and failures happen frequently and affect the production effectiveness. It is significant to detect the fault and failure in time. The fault detection for ESP mainly depends on the expert experience for decades. The data-driven fault detection methods are widely utilized in recent years with the rapid development of sensor technology, where the PCA is the most widely used methods. However, the data collected from the front line faces the imprecision problem, which reduces the accuracy of data-driven fault detection methods. The interval arithmetic is an effective way to handle imprecision data. However, traditional PCA and interval PCA algorithms cannot handle nonlinear problem. Aiming at this problem, the complete information principal component analysis methods are adopted for the fault detection of electric submersible pump in this paper. Compared with PCA and CPCA methods, the CIPCA method has better detection robustness when more inaccurate point value data.

A multi-spectral image-based high-level classification based on a modified SVM with enhanced PCA and hybrid metaheuristic algorithm

In the context of Precision Agriculture (PA), an extremely fine crop classification has recently grown in importance. Satellite photos are often used to identify land use. Furthermore, this classification poses new difficulties since in addition to utilising the multi-temporal features of the multi-spectral images; it also calls for pixel-based examination and a greater number of trainings. Multiple machine learning methods are applied to multi-spectral and multi-temporal satellite data in this paper to create crop categorization models. The UAV platform is used for monitoring the crop field. The multispectral photo is caught as the intake figure which is taken out of the dataset. In precision agriculture, the pre-processing is performed for removing the noise content present in the input image. Feature section is done through PCA and the kernel modified SVM is used for classification. The implementation is performed using PYTHON platform. Paddy and Wheat are selected for the similarity. The accomplishment of the considered methodology is paralleled with and without the optimization, and also compared with the existing methods like Naïve bayes (NB), K- Nearest Neighbour (KNN), K- means and Random Forest (RF).

Speech Segmentation Based on the Computation of Local Signal Manifold Dimension

A new computational method of unvoiced and voiced speech segmentation is proposed from the perspective of local linear manifold analysis of speech signals. It is based on the estimation of the dimension of short-time linear subspace. The subspace dimensional characteristics of the single phoneme signal are studied. The local signal vector set is analyzed by using the PCA algorithm to estimate the dimension of the data matrix formed by framing. The local PCA is used to analyze the speech signal to achieve the segmentation of unvoiced and voiced pronunciation. Simulation experiments prove the effectiveness of the proposed method.

On the optimality of the Oja’s algorithm for online PCA

In this paper we analyze the behavior of the Oja’s algorithm for online/streaming principal component subspace estimation. It is proved that with high probability it performs an efficient, gap-free, global convergence rate to approximate an principal component subspace for any sub-Gaussian distribution. Moreover, it is the first time to show that the convergence rate, namely the upper bound of the approximation, exactly matches the lower bound of an approximation obtained by the offline/classical PCA up to a constant factor.

Automatic Attendance Management System using Face Recognition

Face recognition is among the most productive image processing applications and has a pivotal role in the technical field. In the recent time automated face recognition has become a trend and has been developed very much, this is mainly due to two reasons first it is due to availability of modern technologies and second is due to the ability to save time using face recognition in the process of taking attendance of students. Its usage will grow vast in the future as it saves a lot of time. It consumes a lot of time to take attendance manually and few might also fake the attendance, in order to prevent time consumption and avoid faking the attendance. Face recognition is used to identify the person present in the class and mark their attendance , this is done with the help of image or video frame. We proposed an automatic attendance management system using techniques such as PCA algorithm. The face detection and recognition will automatically detect the students in the classroom and mark the attendance by recognizing the person. The traditional process of making attendance and present biometric systems are vulnerable to proxies. This paper is therefore proposed to tackle all these problems. The proposed system makes the use of Haar classifiers, KNN, CNN, SVM, Generative adversarial networks, and Gabor filters. After face recognition attendance reports will be generated and stored in excel format. The system is tested under various conditions like illumination, head movements, the variation of distance between the student and cameras. After vigorous testing overall complexity and accuracy are calculated. The Proposed system proved to be an efficient and robust device for taking attendance in a classroom without any time consumption and manual work. The system developed is cost-efficient and need less installation

Construction of a Diagnostic m7G Regulator-Mediated Scoring Model for Identifying the Characteristics and Immune Landscapes of Osteoarthritis

With the increasingly serious burden of osteoarthritis (OA) on modern society, it is urgent to propose novel diagnostic biomarkers and differentiation models for OA. 7-methylguanosine (m7G), as one of the most common base modification forms in post transcriptional regulation, through which the seventh position N of guanine (G) of messenger RNA is modified by methyl under the action of methyltransferase; it has been found that it plays a crucial role in different diseases. Therefore, we explored the relationship between OA and m7G. Based on the expression level of 18 m7G-related regulators, we identified nine significant regulators. Then, via a series of methods of machine learning, such as support vector machine recursive feature elimination, random forest and lasso-cox regression analysis, a total of four significant regulators were further identified (DCP2, EIF4E2, LARP1 and SNUPN). Additionally, according to the expression level of the above four regulators, two different m7G-related clusters were divided via consensus cluster analysis. Furthermore, via immune infiltration, differential expression analysis and enrichment analysis, we explored the characteristic of the above two different clusters. An m7G-related scoring model was constructed via the PCA algorithm. Meanwhile, there was a different immune status and correlation for immune checkpoint inhibitors between the above two clusters. The expression difference of the above four regulators was verified via real-time quantitative polymerase chain reaction. Overall, a total of four biomarkers were identified and two different m7G-related subsets of OA with different immune microenvironment were obtained. Meanwhile, the construction of m7G-related Scoring model may provide some new strategies and insights for the therapy and diagnosis of OA patients.