Principal Component Analysis Technique Research Articles

Using principal component analysis to explore managerial factors influencing lecturers' job performance: a perspective from Nigeria

Proper use of relevant managerial factors is fundamental to organisational performance. Employing the methodology of systematic review, we placed 92 English empirical articles on this subject into perspective. 26 managerial factors were found to be relevant to determining lecturers' performance in Nigerian universities. In furtherance to that, the study sent online survey to 322 lecturers in Nigerian tertiary institutions using Google form, soliciting the respondents to rank the identified managerial factors based on how they affect their lecturing job. Then, the study used the Principal Component Analysis technique to identify the managerial factors with the highest variance. Motivation, recruitment process, adoption of strategic management practices and organisational support to employees were found to contribute 73.6% of the total variance. This indicates that these four factors were the top predictors of job performance among lecturers in Nigerian public universities. Motivation was found to explain 58% of the total variance. The study can go a long in way by providing tertiary institution managers with a guide on formulating robust human resource policies. The policies when formulated are expected to yield good results, since the input of the policies originated from those the policies affect the most.

Identifying the Factors Affecting the Survival Rate of Kidney Transplant Patients in Isfahan Using Classification Techniques

Purpose: 10% of the world's population suffers from chronic kidney disease and millions of deaths occur annually due to lack of access to appropriate treatment in the world. Kidney transplantation is associated with several problems. These problems, including kidney rejection, the consequences of surgery, drug poisoning, and infectious diseases can reduce the chances of survival of these patients. The science of classification has been proposed in recent years to reduce medical errors due to inexperience, reduce the workload of physicians and provide a suitable model for making better decisions.
 Materials and Methods: The data set includes information about patients for whom kidney transplantation was performed in Isfahan. The data set includes 2554 patients and 38 attributes. The techniques used in this study will include random forest, Principal Component Analysis (PCA), and Support Vector Machine (SVM).
 Results: Among the studied techniques, PCA technique in three classes out of four classes had better performance than other techniques. The syndrome has the highest recurrence among traits. Five attributes include syndrome, blood type, dialysis time, weight, and age.
 Conclusion: The results showed that the PCA method in the case of non-numerical data has a good performance in identifying attributes. Also, five attributes that affect the survival rate of kidney transplant patients were identified.

Prediction of Unknown Terrorist Group Names Responsible for Attacks in Turkey

In this paper, the dataset of real incidents that occurred in Turkey between 2013 and 2017 and are regarded as acts of terrorism without any doubt according to Global Terrorism Database (GTD) are used to predict the group names responsible for unknown attacks. Principal Component Analysis (PCA) technique was used for feature selection. A novel voting method between five classification algorithms such as Random Forests, Logistic Regression, AdaBoost, Neural Network, and Support Vector Machine was used to predict the names. The results clearly demonstrate that the classification accuracy of all classifiers studied in this paper improved when PCA was used to select features as compared to selecting features without using PCA. The prediction of terrorist group names with PCA based feature reduction and the original features is carried out and the results are compared.

Trace Elements and Heavy Metal Contents in West Algerian Natural Honey.

Analysis of trace elements and heavy metals in honey is essential for honey quality and safety and also monitoring environmental pollution. This study aimed to evaluate the composition of thirty-seven honey samples of different botanical origins (14 multifloral and 23 unifloral) obtained from beekeepers located in the west region of Algeria. Inductively coupled plasma-mass spectrometry (ICP-MS) and atomic absorption spectroscopy (AAS) methods were used to determine the levels of 19 elements in honey (K, Na, Ca, Mg, Mn, Cu, Fe, Zn, V, Cr, Co, As, Ru, Rh, Cd, W, Pt, Au, and Pb). Ru, Rh, Pt and, Au were not detected in any of the tested honey samples. The most abundant minerals were K, Ca, Na, and Mg ranging within 153.00-989.00 mg/kg, 33.10-502.00 mg/kg, 13.30-281.00 mg/kg, and 20.80-162.00 mg/kg, respectively. Fe, Mn, Zn, and Cu were the most abundant heavy metals while Pb, V, Cr, W, Co, and Cd were the lowest ones (<1 mg/kg) in the honey samples surveyed. Several honey types, lavender, rosemary, mild white mustard, thyme, milk thistle, carob tree, orange tree, Euphorbia, Eucalyptus, camphor, jujube tree, sage, and harmal, were studied, and the statistical analysis was carried out using principal component analysis (PCA) and hierarchical cluster analysis (HCA) techniques to evaluate the data. The results showed that the analyses of mineral content were sufficient to determine the floral origin and their variability may be related to geochemical and geographical differences. On other hand, all elements detected were at levels below safe thresholds.

Assessment of Surface Water Quality Using the Water Quality Index (IWQ), Multivariate Statistical Analysis (MSA) and Geographic Information System (GIS) in Oued Laou Mediterranean Watershed, Morocco

Surface water is used for a variety of purposes, including agriculture, drinking water, and other services. Therefore, its quality is crucial for irrigation, human welfare, and health. Thus, the main objective is to improve surface water quality assessment and geochemical analysis to evaluate anthropogenic activities’ impact on surface water quality in the Oued Laou watershed, Northern Morocco. Thirteen surface water samples were characterized for 26 physicochemical and biological parameters. In this aspect, emerging techniques such as multivariate statistical approaches (MSA), water quality indices (WQI), irrigation water quality (IWQI), and Geographic Information System (GIS) were employed to identify the sources of surface water pollution, their suitability for consumption, and the distribution of surface water quality. The results showed that the major ion concentrations were reported in the following order: Ca2+, Na+, Mg2+, and K+; and HCO3− &gt; CO32− &gt; Cl− &gt; SO42− &gt; NO3− &gt; F− &gt; PO43− &gt; NO2−. It was also demonstrated that almost all parameters had concentrations lower than World Health Organization (WHO) limits, except for bicarbonate ions (HCO3−) and the biochemical oxygen demand for five days (BOD5), which exceeded the WHO limits at 120 mg/L and 3 mg/L, respectively. Furthermore, the types of Ca2+-HCO3− (Calcium-Bicarbonate) and Ca2+-Mg2+-HCO3− (Calcium-Magnesium-Bicarbonate) predominated in surface water. The Principal Component Analysis (PCA) indicates that the Oued Laou river was exposed to two forms of contamination, the first being attributed to anthropogenic activities such as agriculture, while the second reflects the water-sediment interaction. The Hierarchical Cluster Analysis (HCA), reflecting the mineralization in the study area, has classified the samples into four clusters. The Inverse Distance Weighting (IDW) of the WQI indicates that 7.69% and 38.46% of the surface water represent, respectively, excellent and good quality for drinking. At the same time, the IWQI revealed that 92.30% of the water surface is good for irrigation. As a result, the combination of WQIs, PCA, IWQI, and GIS techniques is effective in providing clear information for assessing the suitability of surface water for drinking and its controlling factors and can also support decision-making in susceptible locations such as the Oued Laou river in northern Morocco.

Geographical variations in outreach of information and communication technology and rural development in West Bengal, India

AbstractThe article investigates the Geographical variations in the outreach of information and communication technology (ICT) diffusion and its effects on rural development across 341 Community Development (CD) blocks of West Bengal. The study is based on secondary data. Secondary data has been collected from Districts Statistical Handbook, Primary Census Abstract, District Census Handbook, and Socio‐Economic and Caste Census. The principal component analysis technique has been used for ICT diffusion index calculation. ArcMap 10.1 has been used for Mapmaking to understand the spatial intensity of the ICT index. The study finds that West Bengal falls within the least zone (0.07–0.18) of ICT diffusion in India due to multi‐dimensional socio‐economic factors. Only 35 CD blocks out of 341 are located in the high ICT diffusion zone (0.49–0.79) in West Bengal, which is 6.87 percent area to the total area. The study concluded that the higher the ICT diffusion higher is the rural development in West Bengal. The study recommends that the government invest more money in ICT in rural development.

Application of Principal Component Analysis for Steel Material Components

In this research, we made use of the principal component analysis (PCA) technique, which is a multivariate statistical method that transforms a fixed number of correlated variables into a fixed number of orthogonal, uncorrelated axes known as principal components by making use of orthogonal transformation. In other words, the PCA technique converts correlated variables into uncorrelated axes. To minimize the dimensionality of a data set that included a large range of connected variables while yet keeping as much variance within the data set as possible, we employed a method called principal component analysis (PCA). This allowed us to analyze eleven steel components. This is accomplished by reworking the unique variables into a brand new set of uncorrelated variables known as principal components (PC). The principal components are ordered in such a way that they preserve the majority of the variation that is found in all of the unique variables. This is done by reworking the unique variables into a brand new set of uncorrelated variables called principal components (PC). We are able to come to the conclusion that the five principal components that collectively account for approximately sixty-seven percent of the variance in all of the data are the best principal components because this percentage represents the best principal aspect of all of the 11 principal components.

Soil Quality Assessment of Different Land Use Types Based on TOPSIS Method in Hilly Sandy Area of Loess Plateau, Northern China.

In order to combat land desertification and to evaluate the soil quality of different cover land types, and thereby determine the best land use strategy for vegetation restoration, this research comprehensively analyzed the soil quality of different land use/cover types in the hilly sandy area of the Loess Plateau by using the Kruskal-Wallis test (K-W test) and principal component analysis (PCA) technique for order preference by the similarity to ideal solution (TOPSIS), and the potential ecological risk index (RI). The result indicated that the cropland abandonment over a period of time could improve the soil quality to a certain extent; however, the process of natural restoration was very slow compared with that of the planted mixed shrubs. The soil quality of all land use/cover types in the hilly sandy area of Youyu County clearly improved after 10-25 years of revegetation, but the degree of improvement varied greatly with the different planted species and their combinations. The low levels of soil organic matter, total nitrogen and available phosphorus were the major limiting factors affecting soil quality improvement under different land use/cover types, not metal toxicity. Pioneer species of H. rhamnoide, C. korshinskii and Pinus sylvestris var. mongolica, etc. are vital to vegetation restoration of the study area. Revegetation using these species is therefore recommended to combat future desertification in this area.

Business Incubators – A Cure or Curse – A Study focused on the Perception of Young Entrepreneurs in Oman

Business incubators support the young entrepreneurs to improve their business idea and make their ventures successful. Though this is true, a good number of young entrepreneurs do not focus on joining these incubators due to their differences in perception towards business incubators. This study attempts to throw light on the perceptions of young and prospective Omani entrepreneurs towards business incubators, by focusing on 250 young individuals, following tertiary education in the higher education institutions (HEIs) in Oman. The information collected were analyzed using Principal Component Analysis and Multiple Linear Regression techniques. The results showed that individuals’ perception towards the support mechanism available at the incubators along with professional guidance and network support could play a positive role in encouraging them to join the incubation program, while assumed negativity related to the possibility of losing their cherished vision and possible increase in startup costs, could play a negative role on the likelihood of joining business incubators. These findings will help the incubators, Universities and policy makers to redefine their business practices to improve the effectiveness of their services.

Asset management-based resilience index formulation for pavements via principal components analysis

PurposeThis paper extends the authors’ previous research work investigating resilience for municipal infrastructure from an asset management perspective. Therefore, this paper aims to formulate a pavement resilience index while incorporating asset management and the associated resilience indicators from the authors’ previous research work.Design/methodology/approachThis paper introduces a set of holistic-based key indicators that reflect municipal infrastructure resiliency. Thenceforth, the indicators were integrated using the weighted sum mean method to form the proposed resilience index. Resilience indicators weights were determined using principal components analysis (PCA) via IBM SPSS®. The developed framework for the PCA was built based on an optimization model output to generate the required weights for the desired resilience index. The output optimization data were adjusted using the standardization method before performing PCA.FindingsThis paper offers a mathematical approach to generating a resilience index for municipal infrastructure. The statistical tests conducted throughout the study showed a high significance level. Therefore, using PCA was proper for the resilience indicators data. The proposed framework is beneficial for asset management experts, where introducing the proposed index will provide ease of use to decision-makers regarding pavement network maintenance planning.Research limitations/implicationsThe resilience indicators used need to be updated beyond what is mentioned in this paper to include asset redundancy and structural asset capacity. Using clustering as a validation tool is an excellent opportunity for other researchers to examine the resilience index for each pavement corridor individually pertaining to the resulting clusters.Originality/valueThis paper provides a unique example of integrating resilience and asset management concepts and serves as a vital step toward a comprehensive integration approach between the two concepts. The used PCA framework offers dynamic resilience indicators weights and, therefore, a dynamic resilience index. Resiliency is a dynamic feature for infrastructure systems. It differs during their life cycle with the change in maintenance and rehabilitation plans, systems retrofit and the occurring disruptive events throughout their life cycle. Therefore, the PCA technique was the preferred method used where it is data-based oriented and eliminates the subjectivity while driving indicators weights.

Hydrogeochemistry and Water Quality Assessment in the Thamirabarani River Stretch by Applying GIS and PCA Techniques

The primary objective of this research is to assess the hydrogeochemical features and water quality of the Thamirabarani river stretch, located in southern India. Thirty-five water samples from the Thamirabarani river stretch were obtained from the districts of Tirunelveli and Thoothukudi. Twelve water quality parameters were measured during the pre-monsoon and post-monsoon periods of 2020 and 2021. The analytical results were verified with BIS and WHO standards to evaluate the water for drinking purposes. A Geographic Information system (GIS) was applied to know the spatial variation of the hydrogeochemical properties over the research area. Moreover, the Water Quality Index was calculated and it was revealed that 15% of the water samples used are outstanding, 35% are fit for potable use, 25% are poor, 15% water are very poor, and 10% unfit for consumption. Principal Component analysis (PCA) was performed to find out the dominant factors and their variance coverage for the overall water quality. The PCA results indicate that a water sample in Zone 1 is known for its alkalinity. The water qualities in Zone 2 and Zone 3 were affected by anthropogenic factors and industry wastes. More sea water intrusion was observed in Zone 4 in the water quality of the Thamirabarani river basin.

Stresses in the Lunar Interior: Insights From Slip Directions in the A01 Deep Moonquake Nest

AbstractWe probe the present‐day stresses in the lunar interior by examining the slip directions of moonquakes in the A01 nest. In this nest, some deep moonquakes appear to slip “backwards,” in the opposite direction to other events. We assess whether these changes in slip direction result from a spatial variation in the tectonic stress or from a temporal variation in the tidal stress. To test these two options, we first show that a dominant tectonic stress implies deep moonquakes can only slip in one direction: forwards and backwards, while a dominant tidal stress could allow moonquakes to slip in more directions: any combination of forwards, backwards, left, and right. Then we look for the number of slip directions; we separate the deep moonquake waveforms into slip directions using a principal component analysis technique. We find two slip directions present in the A01 deep moonquake nest. The moonquakes slip in a variety of directions as time evolves. This observation implies that the tidal stresses drive deep moonquakes. Additionally, these results place a new constraint on the magnitude of the tectonic stresses at depth; they must be smaller than the modeled tidal stress of ∼0.1 MPa.

Assessment of urban river water quality using modified NSF water quality index model at Siliguri city, West Bengal, India

Rivers are the source of freshwater for any urban community and hence, monitoring of river water is an obligatory yet challenging task. This study was conducted in a subtropical urban river in India with the view of developing a quantitative approach to assess its water quality (WQ) status. For the purposes of this study, water samples were collected from five locations across the Mahananda River main streams encompassing both urbanised and non-urbanised parts of the Siliguri city during April to June of 2021 and collected samples were analysed for fourteen common WQ indicators: pH, Temperature, Conductivity, TDS, Turbidity, Total Hardness (TH), DO, BOD, COD, NO 3 − , PO 4 3− , Cl − , Fecal Coliform (FC) and E. coli for assessing water quality. In order to obtain WQ status, the present study utilised the modified national sanitation foundation (NSF) water quality index (WQI) model, whereas the crucial WQ indicators were identified using the principal components analysis (PCA) technique. All WQ indicators were considered to compute the NSF-WQI except water pH and TH. Most WQ indicators were breached the guideline values of the Bureau of Indian Standards (BIS) and Indian Standards (IS) for surface water. The modified NSF-WQI results revealed that the Mahananda River water quality was “good” to “medium” quality and the water is only suitable for limited purposes under certain conditions. The findings of this study provided evidence that the river WQ is heavily influenced by urban pressures because relatively “good” WQ was found at the sampling location of the outer part of the urban area. The results of this research could be effective in improving the Mahananda River's water quality and maintaining its complex ecosystem in order to ensure sustainable urban growth.

End-to-end deep auto-encoder for segmenting a moving object with limited training data

&lt;span lang="EN-US"&gt;Deep learning-based approaches have been widely used in various applications, including segmentation and classification. However, a large amount of data is required to train such techniques. Indeed, in the surveillance video domain, there are few accessible data due to acquisition and experiment complexity. In this paper, we propose an end-to-end deep auto-encoder system for object segmenting from surveillance videos. Our main purpose is to enhance the process of distinguishing the foreground object when only limited data are available. To this end, we propose two approaches based on transfer learning and multi-depth auto-encoders to avoid over-fitting by combining classical data augmentation and principal component analysis (PCA) techniques to improve the quality of training data. Our approach achieves good results outperforming other popular models, which used the same principle of training with limited data. In addition, a detailed explanation of these techniques and some recommendations are provided. Our methodology constitutes a useful strategy for increasing samples in the deep learning domain and can be applied to improve segmentation accuracy. We believe that our strategy has a considerable interest in various applications such as medical and biological fields, especially in the early stages of experiments where there are few samples.&lt;/span&gt;

Additive hazards model with time-varying coefficients and imaging predictors.

Conventional hazard regression analyses frequently assume constant regression coefficients and scalar covariates. However, some covariate effects may vary with time. Moreover, medical imaging has become an increasingly important tool in screening, diagnosis, and prognosis of various diseases, given its information visualization and quantitative assessment. This study considers an additive hazards model with time-varying coefficients and imaging predictors to examine the dynamic effects of potential scalar and imaging risk factors for the failure of interest. We develop a two-stage approach that comprises the high-dimensional functional principal component analysis technique in the first stage and the counting process-based estimating equation approach in the second stage. In addition, we construct the pointwise confidence intervals for the proposed estimators and provide a significance test for the effects of scalar and imaging covariates. Simulation studies demonstrate the satisfactory performance of the proposed method. An application to the Alzheimer's disease neuroimaging initiative study further illustrates the utility of the methodology.

Water Desalination Plant Fault Detection using Artificial Neural Network

&lt;p&gt;Water famine is very cruel. The desalination plant was brought in to alleviate the water shortage. A desalination plant has been set up in many places around the world. In the view of expanding worldwide need of fresh water, the usage of desalination plant becomes more common. There are more than twenty-five thousand desalination plants around the world. Many industries require treated water for production, water treatment, and other functions. Water quality is occasionally insufficient or does not satisfy the quality criteria for manufacturing causes. So, the enterprises utilize desalination systems to purify water for their own usage. It makes the water safe to drink as well as suitable for a variety of industrial applications. The fault occurring in the desalination plant, it slows down the processing the speed and reduce the output rate. In this study, focuses on the faults like not under system control, electrical fault, pump fault, control valve fault, inaccurate signal, old data fault, derived fault and transmitter fault. The proposed artificial neural network with the single and double component fault (ANN S-DCF) is introduced to detect the faults occurring in the desalination plant. The faults are splitted into two catagories and characteristics of each fault are trained in the artificial neural network. The result of this work achieves the best accuracy comparing to the existing techniques of SVR (support vector regression), PCA (principal component analysis) and DPLS (dynamic partial least square) method. This study achieves the accuracy rate of 96%, precision rate of 93% and sensitivity rate of 95% respectively with low complexity and high operational speed.&lt;/p&gt;&#x0D;

A deep learning approach for facial emotions recognition using principal component analysis and neural network techniques

AbstractIn this work, advanced facial emotions are recognized using Neural network‐based (NN) PCA methodology. The earlier models are cannot detect facial emotions with moving conditions but the CCTV and other advanced applications are mostly depending on moving object‐based emotion recognition. The blurring, mask, and moving object‐based facial image are applied to the training process, and at the testing condition, real‐time facial images are applied. The PCA is extracting features and pre‐processing the images with NN deep learning process. The proposed facial emotion recognition model is most useful for advanced applications. The design is finally verified through confusion matrix computations and gets measures like accuracy 98.34%, sensitivity 98.34% Recall 97.34%, and F score 98.45%. These output results compete with present models and outperformance the methodology.

A Real-Time Method to Estimate the Operational Condition of Distribution Transformers

In this article, an unsupervised learning method is presented with the objective of modeling, in real-time, the main operating modes (OM) of distribution transformers. This model is then used to assess the operational condition through use of two tools: the operation map and the health index. This approach allows, mainly, for a reduction in the need for the interpretation of results by specialists. The method used the concepts of k-nearest neighbors (k-NN) and Gaussian mixture model (GMM) clustering to identify and update the main OMs and characterize these through operating mode clusters (OMC). The evaluation of the method was performed using data from a case study of almost one year in duration, along with five in-service distribution transformers. The model was able to synthesize 11 magnitudes measured directly in the transformer into two latent variables using the principal component analysis technique, while preserving on average more than 86% of the information present. The operation map was able to categorize the transformer operation into previously parameterized levels (appropriate, precarious, critical) with errors below 0.26 of standard deviation. In addition, the health index opened the possibility of identifying and quantifying the main abnormal variations in the operating pattern of the transformers.

Machine learning prediction of the mechanical properties of refractory multicomponent alloys based on a dataset of phase and first principles simulation

In this work, a dataset including structural and mechanical properties of refractory multicomponent alloys was developed by fusing computations of phase diagram (CALPHAD) and density functional theory (DFT). The refractory multicomponent alloys, also named refractory complex concentrated alloys (CCAs) which contain 2–5 types of refractory elements were constructed based on Special Quasi-random Structure (SQS). The phase of alloys was predicted using CALPHAD and the mechanical property of alloys with stable and single body-centered cubic (BCC) at high temperature (over 1,500°C) was investigated using DFT-based simulation. As a result, a dataset with 393 refractory alloys and 12 features, including volume, melting temperature, density, energy, elastic constants, mechanical moduli, and hardness, were produced. To test the capability of the dataset on supporting machine learning (ML) study to investigate the property of CCAs, CALPHAD, and DFT calculations were compared with principal components analysis (PCA) technique and rule of mixture (ROM), respectively. It is demonstrated that the CALPHAD and DFT results are more in line with experimental observations for the alloy phase, structural and mechanical properties. Furthermore, the data were utilized to train a verity of ML models to predict the performance of certain CCAs with advanced mechanical properties, highlighting the usefulness of the dataset for ML technique on CCA property prediction.

The quality of life in an urban innovation ecosystem: analyzing talents’ perception in Southern Brazil

PurposeTalented people with interpersonal skills and competencies are pivotal for creating knowledge, innovation and organizational effectiveness, contributing to local development. In this regard, the quality of life is a critical factor in attracting and retaining talented people in any region. This study aims to analyze talents’ perception of the quality of life in an urban innovation ecosystem. This study considers talents to be the students from Science, Technology, Engineering and Mathematics programs.Design/methodology/approachThis study analyzes 263 students from three of the country’s most important universities located in the city of Porto Alegre in southern Brazil. This study examines the data using principal component analysis and cluster techniques.FindingsThe results indicate five clusters. The “Love for the city” and the “Mixed” ones portray high levels of a sense of belonging to the city, but differ, for example, in their perception on city infrastructure. Conversely, both the “Worried about education” and the “Worried about commercial services” ones express low levels of a sense of belonging in the city. The “Security” cluster portrays the highest level on security issues in the city. The cluster analyses provide detailed information on the factors valued by talents in urban innovation ecosystems.Originality/valueTo date, to the best of the authors’ knowledge, this study is the first that uses cluster techniques to measure talents’ perception of the quality of life in an urban innovation ecosystem. The findings contribute to mapping talents’ perception and building profiles which may support the development of policies and programs to attract and retain qualified people in innovation ecosystems.