جستجوی مقالات مرتبط با کلیدواژه "discriminant analysis" در نشریات گروه "علوم پایه"

The diversity of 18 tea (Camellia sinensis L.) clones was evaluated in a randomized complete block design with two replications at Fashalam tea station (Rasht) in 2017. Physiological, biochemical and crop traits such as chlorophyll a, chlorophyll b, total chlorophyll, carotenoid, reducing sugar, total protein content, proline, tea leaf yield and number of shoots were measured. The results of variance analysis showed a significant difference in the level of one percent between tea clones for all studied traits. Mean comparison of clones showed that 100 and 399 clones had the highest amount of green leaf yield and number of shoots and in terms of proline and chlorophyll a were in second rank. The lowest coefficient phenotypic and genotypic variation related to total protein trait (2.97 and 2.58) and the most related to proline (85.88 and 85.84), and other traits were in this range. The cluster analysis by Ward method was able to place 18 clones of the study in three groups and the percentage grouping accuracy of clones by discriminant analysis was 100%. Average of green leaves yield and different types of chlorophyll traits in clones of the first cluster and proline and protein traits in clones of third cluster showed a high value. The clones of these two groups could be used in planning breeding projects and deciding to select suitable parents in crossing programs to increase the leaf yield and proline (which has a role in stress tolerance) in tea. Also, clones 100 and 399 showed a high average for green leaf yield and second rank for proline traits and therefore were introduced as promising clones.

In this study, a discriminant analysis was carried out to compare the morphological variation among the Indian squid (Uroteuthis duvaucelii) collected from four regions in the Persian Gulf and Oman Sea (two regions in the Persian Gulf (Bushehr waters and Western Strait of Hormoz) and two regions in the Oman Sea (Sistan and Balochestan waters and Eastern Strait of Hormoz). The most important variables were investigated factor analysis with principle component analysis (PCA). Indian squids were sampled by stern bottom trawlers and further 21 morphological factors were investigated. The results indicated that in the main component analysis, two components were formed, the first and second components contains 54.24  and 81.50 % of the total variance respectively. The second component consists of four arms in the U.duvaucelii. Investigating the difference between groups by discriminant analysis method showed that three functions were formed (P<0.05) and based on  U.duvaucelii from the four regions can be divided into three groups, two inhabiting in the Persian Gulf, and one group in the Oman Sea.

Geochemical studies of stream sediments are one of the most common methods in mineral exploration, specifically used for gold prospecting. In this study, the univariate statistical parameters, such as the comparison of the maximum and minimum of each element with the background were used in order to identify the Influential elements. Then histogram and probability diagram were prepared. Subsequently influential components and Elements that are closely linked together, were identified using multivariate statistical methods such as Principal Component Analysis. These analyses indicate that six elements (including Au, Hg, Be, Mo, Ti, and Sn) are more important than others. The components of 3 and 7 as influential components were identified and these components were mapped. In the next step, the model of influential elements was creating on a logarithmic scale. The result of the modeling indicates that 80% of classification is correct. Subsequently, the gold-element data were divided into three sub-communities including background (code 0), the Community of background to the anomaly (code 1), and the anomaly-community (code 2). Finally, the gold distribution pattern based on these sub-communities was prepared that three parts of the region were presented as potential areas. For 8 elements the map of discriminant analysis shows that Au and As have opposite treatment.

Discriminant analysis is a classification method that can predict the group membership of a newly sampled observation. In discriminant analysis, classification of new observed data has an uncertainty. In this paper, the confidence degree is introduced to determine the confidence of classification of new observed data. Then, a Monte Carlo-based sensitivity analysis is applied to an assessment of the influence of financial variables on the overall variance in defined confidence degree. The variables that have a dominant influence on the confidence of new observed data are identified. The result show that in changing classification of the new branch of banks, the uncertainties in the long-term deposits and loans granted variables are more significant than the uncertainties in other bank’s variables in decision making. Also, the personnel, non-benefit deposit and non-performing loans variables have little effect. The results open the door to better making decision on establishing a new bank’s branches.

The purpose of this study is to evaluate the distribution pattern of heavy metals and their possible environmental effects on the human food chain، using statistical analysis of physical and chemical variables of the continental shelf sediments of the Gulf of Oman. Discriminant data analysis shows the flow pattern from west to east water that suggests the source of sediments on the continental shelf of Oman is the Makran region. Distribution and dispersal of elements in sediments of the study area depend on the element type، abundance of clay minerals، organic maters and carbonate. High level of As، Cr، and Rb in marine sediments can be a factor for the pollution of marine environment and for entering these elements in humans food chain. Cr in the near-shore sediments in the study area is relatively high and about 3 times more than standard. The high amounts of Rb (4 times more than standard) can be correlated with higher comment of carbonate in near shore sediments and can be entered into the food chain and drinking water as dissolved ions. Arsenic (As) does not show any correlation with physical and chemical variables، and does not depend on geological processes، therefore its increase in the region can be the reason of human activities.

In this paper, has been tried to discriminate wheat_cultivated fields from those crops that have similar characteristics to this class, spectrally and phonologically, in bahar_hamedan region. So based on cultivation calendar of study region, decided to apply bitemporal or dual_season multispectral spot5 images for this aim. Both images divided to discrete objects(polygpns) using multiresoluion segmentation algorithm. The zonal attributes of segments entered to discriminant analysis as variables. After the stepwise discrimination analysis, all the segments were classified using outputted discrimination functions. ‘’discrimination accuracy’’ and ‘’ classification accuracy’’ were defined as discrete concepts. Also, with assuming the DNs of each band pixels as variables “pixel based” discriminant analysis was applied. Comparison of both methods results indicated that generally, accuracy of pixel_based classification is upper than other method.