IntroductionThe rapid growth of cities and the process of industrialization have created numerous environmental problems across many parts of the world. It is essential for planners and managers to be aware of changes in land cover and land use over extended periods to evaluate and predict the impacts caused by these changes. Remote sensing is an effective tool for monitoring land use changes in urban areas and their surroundings. Tehran has expanded significantly over the last few decades due to population growth and migration, leaving substantial effects on the surrounding environment. Consequently, this study presents a model based on the decision tree algorithm to classify and monitor land use changes using images from TM and MSS sensors in the western region of Tehran between 1975 and 2011. Materials and methodsIn this study, one MSS sensor image and three TM sensor images from the Landsat satellite, all taken in June, were used along with ancillary data, specifically a digital elevation model extracted from the 1:25000 topographic map of the Mapping Organization. After pre-processing, land cover indices, including vegetation index, DT method, and its combination with the maximum likelihood classification method, were used to extract land use classes. The accuracy of the classified images obtained from the DT was evaluated using the kappa coefficient and overall accuracy, and finally, the changes in different land use classes over time were calculated using the image comparison method. Results and discussionAccording to this study's findings, the overall classification accuracy for 2011 is 82%. The results of change monitoring indicate a positive and increasing trend in the density of built-up land over the 36-year period, while other land types have decreased. The density of the built-up land class in 1975, with an area of 2166 hectares (equivalent to 8%), increased to 8125 hectares (29%) by 2011. In total, the percentage of relative change is 21%, equivalent to 5959 hectares. By examining the land use changes in the west of Tehran from 1975 to 2011, shown in the maps, it is evident that urban development and increased demand for various services, coupled with a lack of adequate space, have led to the destruction of green spaces in the western part of Tehran, replaced by other land uses. ConclusionThis research aimed to monitor land use/cover in the west of Tehran with high classification accuracy using a model based on the DT algorithm combined with the maximum likelihood classification method. Multi-temporal satellite images from the Landsat satellite’s TM and MSS sensors, along with ancillary data, were used to conduct the research. After preparing a land use map for each period, a map depicting land cover and land use changes was extracted. The results of this research indicate that remote sensing data combined with classification techniques have a high capability to extract various types of land use maps and evaluate land use changes. Moreover, Landsat’s MSS and TM sensor data prove to be suitable and cost-effective tools for depicting and analyzing land use/cover changes over time. Additionally, the findings highlight that using a branching or multi-stage method for classifying satellite images offers advantages such as reduced processing time, improved accuracy in small classes, and the ability to use different data sources, feature sets, and algorithms at each decision-making stage. 

In computer science, a binary decision diagram is a data structure that is used to represent a Boolean function and to consider a compressed representation of relations. This paper considers the notation of T.B.T (total binary truth table), and introduces a novel concept of binary decision (hyper)tree and binary decision (hyper)diagram, directly and in as little time as possible, unlike previous methods. This study proves that every T.B.T corresponds to a binary decision (hyper)tree via minimum Boolean expression and presents some conditions on any given T.B.T for isomorphic binary decision (hyper)trees. Finally, for faster calculations and more complex functions, we offer an algorithm and so Python programming codes such that for any given T.B.T, it introduces a binary decision (hyper)tree.

Stratified sampling is one of the most widely used sampling designs. In some cases, it is up to the researcher to determine the boundaries of the strata, and in some cases, the population is already stratified. The optimal classification is obtained for a situation of strata boundries, where the variance of the population mean (or total) estimator reaches its lowest value. In traditional methods, the variance of the estimator is considered as a function of the strata boiundries for the response variable, in order to reach the minimum of the variance, equations are obtained which are often solved by numerical methods. The first deficiency of this method is not considering all auxiliary variables. For example, in estimating the average income, classifying the society based on factors such as gender and job history can not only increase the efficiency of the estimator, but also make the interpretability and generalizability of the results easier. The second one is complex equations that do not have a closed and understandable solutions n this paper, we have tried to construct the optimal classification based on a new criterion that is a combination of variance and a penalty for increasing the number of strata, so that important auxiliary variables in the formation of the decision tree determine the boundries of the strata. The classification process starts from the saturated tree and with successive pruning until reaching the root node, the number of strata decreases, the optimal stratification is achieved based on the introduced combined criterion.

Rapid urban growth and industrialization have caused many environmental problems in a number of cities around the world. Knowledge about land cover/land use changes in the long term is very important for urban managers and policy makers in order to evaluate and predict the resulting problems. Remote sensing is an effective tool for monitoring these changes in urban areas and its periphery. Over recent decades, Yasouj City has developed and affected its surrounding environment due to urban growth and immigration. The objective of this research is to develop a decision tree and data mining based conceptual model for land cover change detection using a Landsat Thematic Mapper (TM) and ancillary data in the central Section of Boyerahmad County from 1990 to 2009. Based on findings of the study, the overall six-class classification accuracies for 1990 and 2009 were, respectively, 93.16% and 93.37%. The overall accuracy of land cover change maps, generated from post-classification change detection methods and evaluated using two approaches, ranged from 85.6% to 86.98%. The maps also showed that between 1990 and 2009 the urban area increased by approximately 19.28% while agriculture and forest decreased by 31.76% and 7.32% respectively.

We present new links between some remarkable martingales found in the study of the binary Search tree or of the bisection problem, looking at them on the probability space of a continuous time binary branching process.

Elimination of redundancies in the memory representation is necessary for fast and efficient analysis of large sets of fuzzy data. In this work, we use MTBDDs as the underlying data-structure to represent fuzzy sets and binary fuzzy relations. This leads to elimination of redundancies in the representation, less computations, and faster analyses. We also extended a BDD package (BuDDy) to support MTBDDs in general and fuzzy sets and relations in particular. Representation and manipulation of MTBDD based fuzzy sets and binary fuzzy relations are described in this paper. These include design and implementation of different fuzzy operations such as max, min and max-min composition. In particular, an efficient algorithm for computing max-min composition is presented. Effectiveness of our MTBDD based implementation is shown by applying it on fuzzy connectedness and image segmentation problem. Compared to a base implementation, the running time of the MTBDD based implementation was faster (in our test cases) by a factor ranging from 2 to 27. Also, when the MTBDD based data-structure was employed, the memory needed to represent the final results was improved by a factor ranging from 37.9 to 265.5. We also describe our base implementation which is based on matrices.

This study investigates the ability of tax evasion prediction of listed companies in Tehran Stock Exchange (TSE) by decision tree Algorithms. Statistical population of this study is all companies listed in TSE from 2005 to 2016. Statistical sample includes 1081 year-company. Data was analyzed by One-Way ANOVA and decision tree Algorithms. In this regard, research data test was done by using SPSS and Weka softwares. The research results showed that the best performances are respectively as what follows here: Random Forest, REPtree, J48, LMT, decision Stump, and Random tree. In addition, the One-Way ANOVA showed that differences in the efficiency of decision tree Algorithms are statistically significant.

Consideration of water quality and implementation of appropriate actions for preventing of water resources pollution is a very important issue in Iran because of surface water deficit. Sustainable development of agriculture is impossible without considering of surface water quality. Water quality control is a noteworthy issue in irrigation scheduling program of agricultural land. Since surface water quality monitoring and assessment is very expensive and time consuming. Thus, finding a cheap, simple and relatively exact method which can predict the water quality class base on minimum hydro chemical parameters would be very useful. decision tree as one of the data mining techniques classify data sets based on a tree structure and uses for prediction base on extracting the exiting patterns and roles among data sets. In this study, the decision tree method was used to classify water quality in some hydrometrics stations located at southern side of Sahand Mountain, including Chekan, Girmizigol, Shishovan, Tazekand and Moghanjig. The water quality classes were defined based on if-then rules. The results showed that the decision tree method is able to predict the water quality classes based on small number of hydro chemical parameters with high accuracy.