Recommendation System

The most challenging problem in human resources specially in the IT digital services company, is to assign the best collaborator’s in the adequate project , then ensure the delivery’s performance.in this paper we aim to develop à recommandation System using based-content and collaborative filtering in order to recommend potential profiles for a new job offer. The Principal parts of this recommandation is the matching between job offer of new project and collaborators profiles and the scoring using AHP method. In the first step we propose a model of criteria to measure collective skills , we validate by a survey realized in the IT service company , we analyze the data collected using PCA method (Principal Component Analysis).the results indicate six factors to measure collective skills of each collaborator (Technical skill, Proactivity ,Integrity, Cooperation, Communication and Benevolence/Interpersonal Relationship), these factors are used in AHP function to give score for each collaborator then allow the recommendation for the adequate project.

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Identification of Calculus Bovis and its mixed varieties by ultra-high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC-Q/TOF-MS) combined with the principal component analysis (PCA) method

Journal of Pharmaceutical and Biomedical Analysis ◽

10.1016/j.jpba.2019.112979 ◽

2020 ◽

Vol 179 ◽

pp. 112979 ◽

Cited By ~ 3

Author(s):

Wen-Xi Liu ◽

Xian-Long Cheng ◽

Xiao-han Guo ◽

Xiao-ru Hu ◽

Feng Wei ◽

...

Keyword(s):

Mass Spectrometry ◽

Principal Component Analysis ◽

High Performance Liquid Chromatography ◽

Liquid Chromatography ◽

High Performance ◽

Time Of Flight ◽

Principal Component ◽

Flight Mass Spectrometry ◽

Pca Method ◽

Tof Ms

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The Design of Index about Corporate Governance Based on PCA Method

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.926-930.4085 ◽

2014 ◽

Vol 926-930 ◽

pp. 4085-4088

Author(s):

Chuan Jun Li

Keyword(s):

Principal Component Analysis ◽

Corporate Governance ◽

Principal Components ◽

Principal Component ◽

Component Analysis ◽

Contribution Rate ◽

Variance Contribution ◽

Pca Method

This article uses the PCA method (Principal component analysis) to evaluate the level of corporate governance. PCA is used to analyze the correlation among 10 original indicators, and extract some principal components so that most of the information of the original indicators is extracted. The formulation of the index of corporate governance can be got by calculating the weight based on the variance contribution rate of the principal component, which can comprehensively evaluate corporate governance.

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Patch-Based Principal Component Analysis for Face Recognition

Computational Intelligence and Neuroscience ◽

10.1155/2017/5317850 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 10

Author(s):

Tai-Xiang Jiang ◽

Ting-Zhu Huang ◽

Xi-Le Zhao ◽

Tian-Hui Ma

Keyword(s):

Principal Component Analysis ◽

Face Recognition ◽

Nearest Neighbor ◽

Spatial Information ◽

Principal Component ◽

Component Analysis ◽

Two Dimensional ◽

Face Images ◽

Pca Method ◽

Local Spatial Information

We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.

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Deteksi Formalin Pada Buah Tomat (Lycopersicum esculentum Mill) Dengan Teknologi Hidung Elektronik (Electronic Nose)

Jurnal Ilmiah Mahasiswa Pertanian ◽

10.17969/jimfp.v4i2.10935 ◽

2019 ◽

Vol 4 (2) ◽

pp. 359-366

Author(s):

Irfan Maibriadi ◽

Ratna Ratna ◽

Agus Arip Munawar

Keyword(s):

Principal Component Analysis ◽

Electronic Nose ◽

Principal Component ◽

Component Analysis ◽

Lycopersicum Esculentum ◽

Olfactory Ability ◽

Pca Method ◽

The Difference ◽

Electronic Nose Technology

Abstrak, Tujuan dari penelitian ini adalah mendeteksi kandungan dan kadar formalin pada buah tomat dengan menggunakan instrument berbasis teknologi Electronic nose. Penelitian ini menggunakan buah tomat yang telah direndam dengan formalin dengan kadar 0.5%, 1%, 2%, 3%, 4%, dan buah tomat tanpa perendaman dengan formalin (0%). Jumlah sampel yang digunakan pada penelitian ini adalah sebanyak 18 sampel. Pengukuran spektrum beras menggunakan sensor Piezoelectric Tranducer. Klasifikasi data spektrum buah tomat menggunakan metode Principal Component Analysis (PCA) dengan pretreatment nya adalah Gap Reduction. Hasil penelitian ini diperoleh yaitu: Hidung elektronik mulai merespon aroma formalin pada buah tomat pada detik ke-8.14, dan dapat mengklasifikasikan kandungan dan kadar formalin pada buah tomat pada detik ke 25.77. Hidung elektronik yang dikombinasikan dengan metode principal component analysis (PCA) telah berhasil mendeteksikandungan dan kadar formalin pada buah tomat dengan tingkat keberhasilan sebesar 99% (PC-1 sebesar 93% dan PC-2 sebesar 6%). Perbedaan kadar formalin menjadi faktor utama yang menyebabkan Elektronik nose mampu membedakan sampel buah tomat yang diuji, karena semakin tinggi kadar formalin pada buah tomat maka aroma khas dari buah tomat pun semakin menghilang, sehingga Electronic nose yang berbasis kemampuan penciuman dapat membedakannya.Detect Formaldehyde on Tomato (Lycopersicum esculentum Mill) With Electronic Nose TechnologyAbstract, The purpose of this study is to detect the contents and levels of formalin in tomatoes by using instruments based on Electronic nose technology. This study used tomatoes that have been soaked in formalin with a concentration of 0.5%, 1%, 2%, 3%, 4%, 5% and tomatoes without soaking with formalin (0%). The samples in this study were 18 samples. The measurements of the intensity on tomatoes aroma were using Piezoelectric Transducer sensors. The classification of tomato spectrum data was using the Principal Component Analysis (PCA) method with Gap Reduction pretreatment. The results of this study were obtained: the Electronic nose began to respond the smell of formalin on tomatoes at 8.14 seconds, and it could classify the content and formalin levels in tomatoes at 25.77 seconds. Electronic nose combined with the principal component analysis (PCA) method have successfully detected the content and levels of formalin in tomatoes with a success rate at 99% (PC-1 of 93% and PC-2 of 6%). The difference of grade formalin levels is the main factor that causes Electronic nose to be able to distinguish the tomato samples tested, because the higher of formalin content in tomatoes, the distinctive of tomatoes aroma is increasingly disappearing. Thereby, the Electronic nose based on the olfactory ability can distinguish them.

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Spatial Distribution of Fluoride in Drinking Water in Dhamar City, Yemen

International Journal of Environment ◽

10.3126/ije.v10i1.38402 ◽

2021 ◽

Vol 10 (1) ◽

pp. 49-63

Author(s):

Hefdhallah Al Aizari ◽

Rachida Fegrouche ◽

Ali Al Aizari ◽

Saeed S. Albaseer

Keyword(s):

Drinking Water ◽

Anthropogenic Activities ◽

Groundwater Resources ◽

Principal Component ◽

World Health ◽

Residential Areas ◽

Central Plateau ◽

The World ◽

Pca Method ◽

Health Organization

The fact that groundwater is the only source of drinking water in Yemen mandates strict monitoring of its quality. The aim of this study was to measure the levels of fluoride in the groundwater resources of Dhamar city. Dhamar city is the capital of Dhamar governorate located in the central plateau of Yemen. For this purpose, fluoride content in the groundwater from 16 wells located around Dhamar city was measured. The results showed that 75% of the investigated wells contain fluoride at or below the permissible level set by the World Health Organization (0.5 – 1.5 mg/L), whereas 25% of the wells have relatively higher fluoride concentrations (1.59 – 184 mg/L). The high levels of fluoride have been attributed to the anthropogenic activities in the residential areas near the contaminated wells. Interestingly, some wells contain very low fluoride concentrations (0.30 – 0.50 mg/L). Data were statistically treated using the principal component analysis (PCA) method to investigate any possible correlations between various factors. PCA shows a high correlation between well depth and its content of fluoride. On the other hand, health problems dominating in the study area necessitate further studies to investigate any correlation with imbalanced fluoride intake.

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Tree crown size as a measure of tree biosocial position in 135-year-old oak (Quercus L.) stand

Folia Forestalia Polonica ◽

10.1515/ffp-2016-0004 ◽

2016 ◽

Vol 58 (1) ◽

pp. 31-42 ◽

Cited By ~ 1

Author(s):

Katarzyna Kaźmierczak ◽

Bogna Zawieja

Keyword(s):

Principal Component ◽

Tree Height ◽

Projection Area ◽

Tree Crown ◽

Diameter At Breast Height ◽

Breast Height ◽

Crown Length ◽

Pca Method ◽

Crown Projection Area

AbstractThe paper presents an attempt to apply measurable traits of a tree – crown projection area, crown length, diameter at breast height and tree height for classification of 135-year-old oak (QuercusL.) trees into Kraft classes. Statistical multivariate analysis was applied to reach the aim. Empirical material was collected on sample plot area of 0.75 ha, located in 135-year-old oak stand. Analysis of dimensional traits of oaks from 135-year-old stand allows quite certain classification of trees into three groups: pre-dominant, dominant and co-dominant and dominated ones. This seems to be quite promising, providing a tool for the approximation of the biosocial position of tree with no need for assessment in forest. Applied analyses do not allow distinguishing trees belonging to II and III Kraft classes. Unless the eye-estimation-based classification is completed, principal component analysis (PCA) method provided simple, provisional solution for grouping trees from 135-year-old stand into three over-mentioned groups. Discriminant analysis gives more precise results compared with PCA. In the analysed stand, the most important traits for the evaluation of biosocial position were diameter at breast height, crown projection area and height.

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Age Classification Based on Feature Fusion

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.519-520.644 ◽

2014 ◽

Vol 519-520 ◽

pp. 644-650

Author(s):

Mian Shui Yu ◽

Yu Xie ◽

Xiao Meng Xie

Keyword(s):

Feature Fusion ◽

Principal Component ◽

Directional Pattern ◽

Gabor Wavelet ◽

Support Vector ◽

Classification Problems ◽

Pca Method ◽

Multi Class Classification ◽

Global And Local ◽

Fusion Theory

Age classification based on facial images is attracting wide attention with its broad application to human-computer interaction (HCI). Since human senescence is a tremendously complex process, age classification is still a highly challenging issue. In our study, Local Directional Pattern (LDP) and Gabor wavelet transform were used to extract global and local facial features, respectively, that were fused based on information fusion theory. The Principal Component Analysis (PCA) method was used for dimensionality reduction of the fused features, to obtain a lower-dimensional age characteristic vector. A Support Vector Machine (SVM) multi-class classifier with Error Correcting Output Codes (ECOC) was proposed in the paper. This was aimed at multi-class classification problems, such as age classification. Experiments on a public FG-NET age database proved the efficiency of our method.

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A data-driven approach to measuring epidemiological susceptibility risk around the world

Scientific Reports ◽

10.1038/s41598-021-03322-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Alessandro Bitetto ◽

Paola Cerchiello ◽

Charilaos Mertzanis

Keyword(s):

Factor Model ◽

Principal Component ◽

Data Driven ◽

Dynamic Factor ◽

Country Level ◽

Epidemiological Risk ◽

Data Driven Approach ◽

Managerial Implications ◽

Total Variability ◽

Pca Method

AbstractEpidemic outbreaks are extreme events that become more frequent and severe, associated with large social and real costs. It is therefore important to assess whether countries are prepared to manage epidemiological risks. We use a fully data-driven approach to measure epidemiological susceptibility risk at the country level using time-varying information. We apply both principal component analysis (PCA) and dynamic factor model (DFM) to deal with the presence of strong cross-section dependence in the data. We conduct extensive in-sample model evaluations of 168 countries covering 17 indicators for the 2010–2019 period. The results show that the robust PCA method accounts for about 90% of total variability, whilst the DFM accounts for about 76% of the total variability. Our index could therefore provide the basis for developing risk assessments of epidemiological risk contagion. It could be also used by organizations to assess likely real consequences of epidemics with useful managerial implications.

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Music and Kansei

Advances in Computer and Electrical Engineering - Kansei Engineering and Soft Computing ◽

10.4018/978-1-61692-797-4.ch010 ◽

2010 ◽

pp. 180-198 ◽

Cited By ~ 1

Author(s):

Shigekazu Ishihara ◽

Mitsuo Nagamachi ◽

Jun Masaki

Keyword(s):

Recommendation System ◽

Computer Software ◽

Principal Component ◽

Theoretical Aspect ◽

Psychological Research ◽

Theoretical Research ◽

Evaluation Methodology ◽

Small Range ◽

Japan Society ◽

Physiological Measurement

In this chapter, at first the authors review the researches on music in Japan Society of Kansei Engineering. Music related researches are classified into 6 categories; Kansei evaluation methodology, music psychological research, physiological measurement, music theoretical research, Kansei music system and recommendation system. Then, the authors present their approaches for research Kansei on melody and rhythm from the music theoretical aspect. A mode, one of the most elemental structures in music, is a sequence of n musical tones, arranged from the 12 tones, that fall within a one-octave range and are chosen and arranged according to the rules of that mode. In this chapter, the authors analyze, using sound tracks composed automatically by computer software, the relationships between modes and Kansei. “Melodic range” is defined as the high and low extent of the tone movements in a mode. Mode and melodic ranges were the parameters they controlled for evaluation by Kansei. Eighteen sample tracks were automatically composed from combinations of six modes and three ranges. Forty-seven Kansei word pairs were used in the research questionnaire. The results of principal component analysis and an analysis of variance reveal a contrast between tracks with major modes and a larger range and tracks with minor modes and small range. The authors also found that modes and ranges can independently or synergistically affect the Kansei. Based on their results, they have developed a real-time melody recognition program that identifies the mode and its corresponding Kansei from music. The authors also studied rhythm with programmed drum patterns and found that the fluctuation of drum beats relates to the degree of activity, with the interval and complexity of the rhythmic variations relating to the strained to bright axis of a principal components loading map.

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