Principle Component Analysis Approach Research Articles

Impact Assessment of Integrated Pest Management Technology on Kharif Paddy Cultivation in Vidarbha Region of Maharashtra, India

The Eastern Vidarbha region consist of Bhandara, Gondia, Gadchiroli and Chandrapur and some part of the Nagpur district is famous for specialized farming and paddy as major crop. The total area in Maharashtra state was 15.53 lakh hectares with annual production 34.81 lakh tons of rice (Anonymous 2023-24). About 80.00 per cent gross cropped area in this region is under paddy. IPM has been proved to be a cost minimization technique. Losses in Paddy yield the tune 10 to 25 per cent occurs due to attack of insect pest and disease. Being the topic of such high importance from cultivators point of view, the research topic has been selected i.e. Impact Assessment of Integrated Pest Management technology on Kharif Paddy cultivation in Vidarbha region of Maharashtra State its economic investigation in different level of adoption of IPM technologies. The study was carried out with the main goals of determining the extent to which recommended technology has been adopted in Kharif paddy production by using principle component analysis approach and for develop the composite index, examining the input utilization of Kharif paddy at varying levels of IPM adoption, and workout cost effectiveness and profitability of kharif paddy at different level of adoption of IPM technology. The study was conducted in the districts of Gadchiroli in the Vidarbha region of Maharashtra State, India. Three tahasil, Gadchiroli, Chamorshi and Dhanora were chosen from these districts, and a total of 120 farmers were selected from seven villages, namely Gadchiroli, Indala, Hirapur, Chamorshi, Krishna Nagar, Heti, Kanartola. The primary data, which cover the years 2023–2024. Total 120 farmers, 25 farmers were classified as high adopters, 76 farmers were classified as medium adopters, and 19 farmers were classified as low adopters. As a result of input usage, the group with a high adoption level utilized the most human labour. Farmyard manure is used most in the high adopter group (49.87 quintal per hectare), followed by low adopters (31.73 qtl/ha) and medium adopters (34.45 qtl/ha) because the farmers can apply only owned farm FYM. The reasons of lower used of FYM, due to shortage of cattle’s population. The reasons of low adopter, high expenditure of Integrated pest management components was the 89.00 per cent farmers can use chemically control that means 2 to 3 spraying was use in kharif paddy in low adopter group. The B:C ratio at Cost ‘A1’ was 1.79, 1.85 and 1.92 in low, medium and high adopters, respectively, while B:C ration at Cost ‘C2’ for low, medium, high adopter were 1.12,1.19,1.25 respectively. The results concluded that the low adopters group are not making more profit. It indicates that, as adoption of technology increases the yield level of crop and so that the net returns also increases. The reduction in per quintal production cost at Cost ‘C2’ over low adopter group were Rs. 188.06/-. Paddy productivity has been reported increased by 19.84 per cent with the reduction in the cost of Rs. 7375.89 in high level of adoption of Integrated Pest Management Technology. The findings of the present study with regards to overall adoption of integrated pest management practices of paddy by paddy growers were observe for wide adoption of IPM technology in Kharif Paddy, Government should take initiative for production of bio-agents and available the pheromane trap and yellow sticky trap through establishing unit in Government Agricultural Offices and Krishi Vigyan Kendra and encourage the rural youth for establishing Units at taluka level.

Image Fusion of Multi-Temporal Images of Eyes Feature using Hybrid Approach of PCA and SWT

A wide variety of information is represented by an image feature, which may be applied in various applications such as mage fusion, video processing, medical diagnosis, traffic safety monitoring, visual surveillance, feature matching, image segmentation, pattern matching, person identification, sentiment analysis, human computer interaction and many more fields. This paper focused on multi-temporal feature extraction of eyes feature from human face images captured in two different time slots to reduce the semantic gap from images and to improve the image quality by image fusion using PCA, SWT and hybrid approach of PCA and SWT algorithm. In this paper, comparative study of multi-temporal image fusion using Principle Component Analysis (PCA), Stationary Wavelet Transform (SWT) algorithms and hybrid approach of PCA and SWT are employed and its experimental results are evaluated with its performance analysis. Image fusion performance is compared based on eight quantitative quality measures as SSIM, MSE, NAE, CC , SC , AD, SD and MI.The outcomes of comparison show that employing the hybrid approach of PCA+SWT transform can improve image fusion performance. The applicability of this work approach may have several uses when the utilization of human facial features is feasible.

ANALYSIS OF DECISION-MAKING FACTORS EQUITY INVESTMENT IN INDONESIA


 
 
 
 Invetment decision for an investor is influenced by various factors. Ideally, investors need to have various considerations before they finally make an investment. The importance of knowing the factors that shape the decision to choose stocks for investors in Indonesia is the purpose of this research. There were 185 respondents who fulfilled the purposive sampling technique as a sample collection technique. The research instrument was a questionnaire as primary data. The data analysis technique was carried out by factor analysis with the principle component analysis approach and SPSS as a research tool. The stages of factor analysis are carried out by compiling a correlation matrix, extraction factors, rotation factors, and naming factors. The result is that there are two factors that are generated as forming factors for investing in stocks in Indonesia, namely investment management experience; investment management knowledge.
 
 
 

The Improvement of Artificial Neural Network Accuracy Using Principle Component Analysis Approach

An important problem in a classification system is how to get good accuracy results. A way to increase the accuracy of a classifier system is to improve the number of input data attributes. Improving the number of input data attributes can be done using the Principal Component Analysis (PCA) method. The aim of this research is to reduce the number of input data attributes to increase the accuracy in a mushroom classification system. The research method used in this study started from collecting datasets from Kaggle.com related to mushroom-classification, then the data visualization process was carried out using pie charts then a dimension reduction process was carried out to reduce the number of variables using the PCA method. The next step is the training and testing of the artificial neural network. The architecture of artificial neural network used is backward error propagation with the number of hidden layers as much as 2 layers with the number of cells as many as 3 and 2. The training data used is 80%, while the testing data is 20%. Based on the test results, obtained an accuracy of 100% with 150,000 iterations and using 11 input variables from 22 existing input variables. By adding Principal Component Analysis part of the development that can improve the accuracy and performance of Artificial Neural Networks

Predictive Maintenance with PCA Approach for Multi Automated Railroad Crossing System (ARCS) in The Framework of Prognostic and Health Management (PHM) Planning

The existence of level crossings between railroads and road vehicles that do not have gates in areas far from crowds, such conditions require gates that are made automatically to avoid accidents. The Automated Railroad Crossing System (ARCS) is an automatically activated railroad crossing gate where train arrival information is obtained through sensors. For one level crossing, there are several electronic devices installed in the automatic railroad crossing system. It is planned that the automatic railroad crossing system will be installed at several level crossings. The problem is how to estimate the time to perform automatic railroad crossing maintenance at several different locations, For this reason, it is necessary to know the estimated remaining useful life (Remaining Useful Life) of the subsystems. The purpose of this research is to find the estimated remaining useful time (RUL) of the subsystem in the automatic railroad crossing system in order to estimate the time to perform maintenance. The process that is carried out to obtain the remaining useful time is through the Prognostic Health Management System development plan, while the analysis of the estimated remaining useful time is carried out using Principle Component Analysis (PCA), the results of this simulation show promising results to determine the estimated value of the remaining useful time. If it can be known the estimated remaining time of the benefit, it is hoped that the maintenance plan for each automatic railroad crossing system can be carried out more efficiently.

Effectiveness of the entropy weight method to evaluate abiotic stress tolerance in citrus rootstocks

Aim of study: The entropy weight method (EWM) is considered one of the most reliable and scientific approaches when decision making is influenced by multiple factors. However, there are no reports on the application of EWM in the evaluation of abiotic and biotic stress tolerance in crops. In this study, abiotic stress via saline water irrigations was imposed on different citrus rootstocks. The relative stress tolerance levels of rootstocks were ascertained using EWM and compared with standard fuzzy membership approach and the factor analysis. Area of study: Punjab Agricultural University Regional Research Station Abohar, India, 2017-2019. Material and methods: In a pot culture study, about 1½ yr-old rootstock seedlings were exposed to saline water irrigations with 4 and 6 dS m-1 electrical conductivity (EC) for 60 days. Saline water response index for mineral composition of plant parts, physiological and biochemical attributes of rootstocks were calculated for each salinity level over 2 dS m-1 conductivity water, considered as control and subjected to further analysis. Main results: At 4 EC, the entropy weight and membership function value of the rootstocks ranged 0.758-0.998 and 0.682-0.731, respectively. The corresponding values at 6 EC ranged between 0.759-0.991 and 0.391-0.728, respectively. Following EWM, the rootstocks were rated for their relative tolerance levels as: Rangpur Lime>Cleopatra>Volkamer Lemon=Rough Lemon>Carrizo at 4 EC salinity level. At 6 EC, the order was: Cleopatra>Rangpur Lime>Volkamer Lemon>Rough Lemon>Carrizo. The results were consistent between EWM and standard principle component analysis approaches. Research highlights: The study suggests that the comprehensive evaluation of crop genotypes for abiotic stress tolerance is also feasible with the EWM.

Cyber threat intelligence using PCA-DNN model to detect abnormal network behavior

Security issues are the most critical challenges facing new technologies associated with the internet of things (IoT), big data, and cloud computing. A secure and efficient intrusion detection system (IDS) is crucial to detect security threats. Existing IDSs are known to suffer from many problems, most notably the high rate of false positive alerts, the long time required to detect attacks, and the inability to detect zero-day attacks, which can ruin companies. The weakness of IDS backend engines costs companies time in the investigation process. This paper proposes and enhances IDS detection mechanisms via two processes: using a deep neural network (DNN) model with new features for threat detection based on two assumptions related to handling zero-day attacks, with low computing power and resources, and a comprehensive solution for detection by merging the DNN model and principle component analysis (PCA) to increase security and performance. The proposed detection mechanism combines DNN, PCA, statistical, and knowledge-based approaches to offer significantly greater efficiency than existing IDS, as indicated by analytical and software results. A simulation model is used with up-to-date web attacks, distributed denial of service (DDoS), denial of service (DoS), brute force, insider infiltration, Botnet, and Heartbleed attacks. The proposed detection techniques for large networks are analyzed and complexity in the design is avoided by reducing the number of DNN model layers, thus minimizing detection time delay and false positives, while increasing security against network attacks. Integrating the proposed DNN with PCA, an innovative contribution, introduces robust IDS to significantly improve the detection time delay and security performance. The proposed model showed a 98% accuracy rate. To best of our knowledge, the highest accuracy rate stated based on a large number of attacks is 97%, which makes our model state of art.

Efficiency Evaluation of Major Coastal Ports in China: Application of the Three-stage DEA Model

With the increasing competition between ports, how to improve port efficiency and promote the sustainable development of ports becomes the focus of attention. The paper applies the three-stage data envelopment analysis (T-S DEA) model to measure the efficiency of top 10 Chinese coastal ports, including the scale efficiency (SE) and technical efficiency (TE). Besides, the principle component analysis approach is adopted to reduce the influence of redundant information of evaluation indicators on results. The stochastic frontier analysis regression model is applied to eliminate the effects of environmental factors and stochastic errors under similar exterior conditions. The results show: (1) The ports efficiency (SE and TE) in different regions is quite different; the efficiency of Chinese coastal ports is generally low, which is mainly caused by scale inefficiency; (2) The SE and TE are improved and the input and output redundancy ratio are decreased significantly by comparing the results of traditional DEA model. (3) Shanghai, Ningbo-Zhoushan, Shenzhen, Hong Kong and Kaohsiung have relatively high comprehensive efficiency. This study provides valuable information for estimating port efficiency and establishing competitive strategies in the future.

Primary components in shaping koperasi masjid’s participation level: Employing a principle component analysis approach

Koperasi masjid present themselves as a part of muamalah through Sharia economic facilitation. This study was conducted to analyse the cooperative members’ participation level in relation to the implementation of a cooperative business program. A survey using a case study approach was conducted at koperasi Masjid Hafshotul Iman, Masjid Taqwa and Masjid Darul HikmahThe survey employed a purposive sampling method with the participation of the cooperative’s management (chairman, secretary, and treasurer) and the masjid’s members. Ten respondents were purposively chosen from each mosque and a total 30 respondents were acquired. The distributed questionnaire was a closed one, measured with a Likert scale. The results were analysed using factor analysis and the Principal Component Analysis approach (PCA). Based on the results of the factor analysis, the members’ participation level in cooperative service can be classified into three primary factors. Some of the variables displayed a strong correlation with Factor 1, namely motivation, management performance, RAT performance, and supervisor performance. The second primary factor was formed by activeness in participating in cooperative activities. Lastly, the third primary factor encompassed three variables with a strong correlation, namely age, education, and membership duration.

Neural networks and principle component analysis approaches to predict pile capacity in sand

Determination of pile bearing capacity from the in-situ tests has developed considerably due to the significant development of their technology. The project presented in this paper is a combination of two approaches, artificial neural networks and main component analyses that allow the development of a neural network model that provides a more accurate prediction of axial load bearing capacity based on the SPT test data. The retropropagation multi-layer perceptron with Bayesian regularization (RB) was used in this model. This was established by the incorporation of about 260 data, obtained from the published literature, of experimental programs for large displacement driven piles. The PCA method is proposed for compression and suppression of the correlation between these data. This will improve the performance of generalization of the model.

Neural networks and principle component analysis approaches to predict pile capacity in sand

Determination of pile bearing capacity from the in-situ tests has developed considerably due to the significant development of their technology. The project presented in this paper is a combination of two approaches, artificial neural networks and main component analyses that allow the development of a neural network model that provides a more accurate prediction of axial load bearing capacity based on the SPT test data. The retropropagation multi-layer perceptron with Bayesian regularization (RB) was used in this model. This was established by the incorporation of about 260 data, obtained from the published literature, of experimental programs for large displacement driven piles. The PCA method is proposed for compression and suppression of the correlation between these data. This will improve the performance of generalization of the model.

Dietary amino acids and incidence of hypertension: A principle component analysis approach

The current study aimed to investigate the association between dietary amino acid patterns and incidence of hypertension, using principal components factor analyses. This study was conducted within the framework of Tehran Lipid and Glucose Study on 4288 adults, who were free of hypertension at baseline (2008–2011) and were followed for three years (2011–2014). Principal component factor analyses were conducted based on eight amino acid groups and three amino acid patterns were extracted. The first pattern was characterized by branched chain, aromatic, and alcoholic amino acids, and proline. Acidic amino acids and proline were highly loaded in the second pattern and the third was characterized by sulphuric and small amino acids. Adjusted odds ratio of the highest quartile of the first pattern was 1.83 (95%CI: 1.21–2.77, P for trend = 0.002) compared to the lowest one. The first pattern had high positive correlation with dietary intakes of animal protein and dairy, but was negatively correlated with plant protein, fruit, and vegetable. There was no significant association for the second and third patterns. Findings indicate that the dietary amino acid pattern, rich in branched chain, aromatic, and alcoholic amino acids, and proline could increase the risk of hypertension.

Non-parametric estimation of particle size distribution from spectral extinction data with PCA approach

Non-parametric estimation of particle size distribution (PSD) is carried out with the help of spectral extinction method and Tikhonov regularization method. Meanwhile, a selection principle of optimal measurement wavelengths on the basis of principle component analysis (PCA) approach is proposed to improve the retrieval accuracy. Results show that the optimally selected wavelengths can ensure retrieval accuracy, compared with randomly selected wavelengths. Then, four common PSDs, namely Log-Normal (L-N), Rosin-Rammer (R-R), Normal (N-N) and Gamma distributions, are estimated under different random measurement errors. Numerical tests show that the present methodology can be successfully used to reconstruct the PSDs with high stability in the presence of random errors and low susceptibility to the shape of distributions. Finally, the PSD of aerosol measured over Harbin in China and the PSD of microalgae cell are retrieved, respectively. The study shows that increasing the number of subintervals of the particle size range will improve the retrieval accuracy as well as increase the cost of experiment, so the number of subintervals should be selected reasonably. Moreover, to obtain more reliable results, the measurement sample size should be as large as possible. As a whole, the present methodology provides a reliable and effective technique to estimate the PSDs non-parametrically from the spectral extinction signals.

Tunable Diode Laser Absorption Spectroscopy- Based Detection of Propane for Explosion Early Warning by Using a Vertical Cavity Surface Enhanced Laser Source and Principle Component Analysis Approach

It is important in the petrochemical industry that there are high sensitivity, high accuracy, low-power consumption, and intrinsically safe methods for the detection of propane gas, to provide early warning of potential explosion hazards in oil–gas storage and transportation. Tunable diode laser absorption spectroscopy (TDLAS) technology has the potential to provide an excellent basis both for early warning of explosion hazards and additionally to achieve precise, quantitative detection. In this paper, an approach to TDLAS detection of propane by using vertical cavity surface enhanced laser (VCSEL) technology, coupled with principle component analysis (PCA) is reported. In the evaluation carried out, the minimum detectable concentration could reach as low as 300 ppm and relative errors of the gas concentrations measured in this way were all below 5%. The experimental results obtained demonstrate that the use of VCSEL sources and PCA together solves difficulties often seen in the quantitative detection of propane by TDLAS at wavelengths close to 1684 nm. The approach also provides a strong experimental basis for the development of sensor systems based on TDLAS for propane and other combustible gas early warning of explosions, which makes it have real applicability in the petrochemical industry and oil–gas storage and transportation.

Principle component analysis: Robust versions

Modern problems of optimization, estimation, signal and image processing, pattern recognition, etc., deal with huge-dimensional data; this necessitates elaboration of efficient methods of processing such data. The idea of building low-dimensional approximations to huge data arrays is in the heart of the modern data analysis. One of the most appealing methods of compact data representation is the statistical method referred to as the principal component analysis; however, it is sensitive to uncertainties in the available data and to the presence of outliers. In this paper, robust versions of the principle component analysis approach are proposed along with numerical methods for their implementation.

Evaluation of electronic service quality using an integrated fuzzy clustering and principle component analysis approach: an empirical case study

This study uses data mining techniques to improve decision making process in various functions of electronic services provided by insurance companies. This study focuses on incentive packages recommended automatically by the system to the most qualified customers. Initially, customers are clustered based on their profiles. The fuzzy clustering techniques are used for this study. Each cluster is formed by customers with common characteristics according to their historical transactions. In each cluster, another data mining technique called principal component analysis is used to rank customers according to some variables which indicate the qualification of customer (customer satisfaction and expectation) to promote the insurance services and initiative packages.

Evaluation of electronic service quality using an integrated fuzzy clustering and principle component analysis approach: an empirical case study

This study uses data mining techniques to improve decision making process in various functions of electronic services provided by insurance companies. This study focuses on incentive packages recommended automatically by the system to the most qualified customers. Initially, customers are clustered based on their profiles. The fuzzy clustering techniques are used for this study. Each cluster is formed by customers with common characteristics according to their historical transactions. In each cluster, another data mining technique called principal component analysis is used to rank customers according to some variables which indicate the qualification of customer (customer satisfaction and expectation) to promote the insurance services and initiative packages.

Improving the Classification Accuracy in Detecting Hardware Trojan in ALU Using PCA

Present day electronic chips are outsourced by the manufacturer and done by third-parties. Such practices are widely prevalent as it works out to be more profitable in terms of cost and effort to complete these processes offshore in VLSI foundries. This comes with a potential risk of compromised security due to hardware Trojans. There are existing methodologies which aim at tacking the issue of Hardware Trojans by detecting it. Hardware Trojan detection by decomposing the design into blocks to detect combinational Trojan and Sequential Trojan is proposed in this paper. By comparing the leakage and total power values consumed by both the design i.e. with and without the presence of the hardware Trojans, we can identify the presence of the hardware Trojans. Moreover if the measurement noise gets high it masks or hides the effect of the variation in power profile, which leads to a wrong decision. So by taking the measurement noise under consideration the classification is effectively done for magnifying the differences by using Principle Component Analysis approach and tested with 8, 16 and 32 bit ALU designs.

Principle Component Analysis of two-particle correlations in PbPb and pPb collisions at CMS

A Principle Component Analysis (PCA) of two-particle azimuthal correlations as a function of transverse momentum (pT) is presented in PbPb collisions at 2.76 TeV and high-multiplicity pPb collisions at 5.02 TeV. The data were recorded using the CMS detector at the LHC. It was shown that factorization breaking of two-particle azimuthal correlations can be attributed to the effect of initial-state fluctuations. Using a PCA approach, Fourier coefficients of observed two-particle azimuthal correlations as a function of both particles' pT are characterized into leading and sub-leading mode terms. The leading modes are essentially equivalent to anisotropy harmonics (vn) previously extracted from two-particle correlation methods as a function of pT. The sub-leading modes represent the largest sources of factorization breaking. In the context of hydrodynamic models, they are a direct consequence of initial-state fluctuations. The results are presented over a wide range of centrality and event multiplicity. The results are connected to the measurement of pT-dependent flow factorization breaking.

Direct reconstruction of dynamical dark energy from observational Hubble parameter data

Reconstructing the evolution history of the dark energy equation of state parameter $w(z)$ directly from observational data is highly valuable in cosmology, since it contains substantial clues in understanding the nature of the accelerated expansion of the Universe. Many works have focused on reconstructing $w(z)$ using Type Ia supernova data, however, only a few studies pay attention to Hubble parameter data. In the present work, we explore the merit of Hubble parameter data and make an attempt to reconstruct $w(z)$ from them through the principle component analysis approach. We find that current Hubble parameter data perform well in reconstructing $w(z)$; though, when compared to supernova data, the data are scant and their quality is worse. Both $\Lambda$CDM and evolving $w(z)$ models can be constrained within $10\%$ at redshifts $z \lesssim 1.5$ and even $5\%$ at redshifts 0.1 $\lesssim$ z $\lesssim$ 1 by using simulated $H(z)$ data of observational quality.