Land cover maps derived from satellite images play a key role in regional and national land cover assessments. In order to compare maximum likelihood and minimum distance to mean classifiers, LISS-III images from IRS-P6 satellite were acquired in August 2008 from the western part of Isfahan. First, the LISS-III image was georeferenced. The Root Mean Square error of less than one pixel was the result of registration. After creating false color composite and calculating transformed divergence index, the images were classified using maximum likelihood and minimum distance to mean classifiers into six categories including river, bare land, agricultural land, urban area, highway and rocky outcrops. The results of classification showed that the dominant land cover type is urban area, occupying about 6821.1 ha representing 38.86% of total area. The accuracy of maximum likelihood and minimum distance to mean classifiers was obtained using error matrix and Kappa analysis. According to the results, the maximum likelihood algorithm had an overall accuracy of 94.93% and the minimum distance to mean method was 85.25% accurate. The results illustrate that the maximum likelihood method is superior to minimum distance to mean classifier.

Aim: This research aims to model land-use changes using the MDC, MD, and ML algorithms technique.Materials & Methods: Landsat 5 and 8 satellite images were used to compare the MDC, MD, and ML algorithms in five classes between 1987 and 2021 and estimate the model's accuracy. This was done using the overall accuracy coefficient, kappa coefficient, manufacturer's accuracy, user's accuracy, and addition and omission error.Finding: With the change of land-use to residential areas in 2021 compared to 1987, not only were agricultural lands damaged, but it also caused a change in the bed of the studied basin. The flood discharge at the outlet of the basin is reduced, and due to the change and encroachment of the riverbed, the width of the basin decreases. As a result, the capacity of the flood to pass decreases, and the flood intensifies.Conclusion: The area of residential areas in 1987 compared to 2021 (from 0.08 to 3.12) ) has increased, which has led to instability among the land uses of the studied area. Such changes and developments can have negative effects on the environment and natural resources of the Midawood-Dalon basin. They will cause the spread of risks and damages caused by natural disasters such as river flooding. Also, by comparing the overall accuracy coefficient and the Kappa coefficient, for 1987, the maximum likelihood algorithm with the overall accuracy coefficient (33.34) and Kappa coefficient (0.13), and for 2021, the Mahalanoi distance algorithm with the overall accuracy coefficient (55.29) and Kappa coefficient (0.45) with higher accuracy than other methods.Innovation:  The land-use changes that have taken place have caused the inappropriate dispersion of land-use (rainy land, pastures, water resources) and human areas in such a way that a part of the basin area has changed use due to the city's physical growth.

This paper introduces new similarity classifier using the Heronian mean, and the generalized Heronian mean operators. We examine the use of these operators at the aggregation step within the similarity classifier. The similarity classifier was earlier studied with other operators, in particular with an arithmetic mean, generalized mean, OWA operators, and many more. The two classifier here are tested on four real world data sets, i. e., echocardiogram, fertility, horse-colic, and lung cancer. Three previously studied similarity classifier are used as benchmarks to the new approaches. We observe that the similarity classifier with a generalized Heronian mean produces good classification results for the tested data sets, and is therefore more suitable for use in these classification problems.

A multi-objective particle swarm optimization (MOPSO) algorithm has been used to design a classifier which is able to optimize some important pattern recognition indices concurrently. These are Reliability, Score of recognition, and the number of hyperplanes. The proposed classifier can efficiently approximate the decision hyperplanes for separating the different classes in the feature space and dose not has any over-fitting and over-learning problems.Other swarm intelligence based classifiers do not have the capability of simultaneous optimizing aforesaid indices and they also may suffer the over-fitting problem.The experimental results show that the proposed multi-objective classifier can estimate the optimum sets of hyperplanes by approximating the Pareto-front and provide the favorite user's setup for selecting aforesaid indices.

In this paper, the problem of classification of motor imagery EEG signals using a sparse representation-based classifier is considered. Designing a powerful dictionary matrix, i.e. extracting proper features, is an important issue in such a classifier. Due to its high performance, the Common Spatial Patterns (CSP) algorithm is widely used for this purpose in the BCI systems. The main disadvantages of the CSP algorithm are its sensibility to noise and the over learning phenomena when the number of training samples is limited. In this study, to overcome these problems, two modified form of the CSP algorithms, namely the DLRCSP and GLRCSP have been used. Using the adopted methods, the average detection rate is increased by a factor of about 7.78 %. Also, a problem of the SRC classifier which uses the standard BP algorithm is the computational complexity of the BP algorithm. To overcome this weakness, we used a new algorithm which is called the SL0 algorithm. Our classification results show that using the SL0 algorithm, the classification process is highly speeded up. Moreover, it leads to an increase of about 1.61% in average correct detection compared to the basic standard algorithm.