Principal component analysis (PCA) is one of the proposed methods to reduce the size of the data set that can be used for both one and two-dimensional data. Regarding the lack of sparsity property in the base vectors, Sparse PCA has been proposed, which maintains the properties of standard PCA and simultaneously forces some of the elements of the base vectors to zero. In this paper, due to the sparsity in base vectors that cause some dataset values to be ineffective in moving to new space, two algorithms are presented in one-dimensional and two-dimensional mode to remove redundancy from raw data. In the one-dimensional algorithm, redundancy is detected between signal layers and then removed from all set observations. In a two-dimensional algorithm, the significance of the row and the column of the dataset images are detected and the less important ones are eliminated directly from raw data. One of the most important advantages of proposed algorithms, which can be read as non-uniform sampling methods, is to preserve the appearance of signals. After removing the raw data redundancy by the two algorithms presented, new data with fewer dimensions can be used in other applications such as dataset recognition, compression, and so on.

Background and Objectives: The primary purpose of recommender systems is to estimate the users' desires and provide a predicted list of items based on relevant data. Recommender systems that suggest items to users face two cold start and Sparse data challenges. Methods: This paper aims to propose a novel method to overcome such challenges in recommender systems. Singular value decomposition is a popular method to reduce Sparse data in recommender systems by reducing dimensions. However, the basic singular value decomposition can only extract those feature vectors of users and items that may be recommended with lower recommendation precisions. Notably, using the similarity criteria between entities can reduce cold start to resolve the singular value decomposition problem by extracting more refined factor vectors. Besides, considering the context's dimensions as the third dimension of the matrix requires using another flexible algorithm, such as tensor factorization, which offers a viable solution to minimize the Sparse data challenge. This study proposes TCSSVD, a novel method to resolve the challenges mentioned above in recommender systems. First, a two-level matrix is obtained using the similarity criteria between the user and the item to reduce the cold start challenge. In the second step, the contextual information is used by tensor in two-level singular value decomposition to reduce the challenge of Sparse data. Results: For reviewing the proposed method, these two data sets, IMDB and STS, were used because of applying user and item features and contextual information. The RMSE criterion (95% accuracy) was used to investigate the predictions' accuracy. However, since the user's rating of the item is particularly important in recommender systems, compared with other methods, such as tensor factorization, HOSVD, BPR, and CTLSVD, the TCSSVD method uses the following criteria: Precision, Recall, F1-score, and NDCG. Conclusion: The findings indicated the positive effect of using the innovative similarity criteria on the extraction of user and item attributes to reduce the complications deriving from the cold start challenge. Also, the use of contextual information through the tensor in the TCSSVD method reduced the complications related to Sparse data. The results improve the recommendation accuracy of the recommender systems.

This study aims to illustrate the problem of (Quasi) Complete Separation in the Sparse data pattern occurring medical data. We presented the failure of traditional methods and then provided an overview of popular remedial approaches to reduce bias through vivid examples. Penalized maximum likelihood estimation and Bayesian methods are some remedial tools introduced to reduce bias. data from the Tehran Thyroid and Pregnancy Study, a two-phase cohort study conducted from September 2013 through February 2016, was applied for illustration. The bias reduction of the estimate showed how sufficient these methods are compared to the traditional method. Extremely large measures of association such as the Risk ratios along with an extraordinarily wide range of confidence interval proved the traditional estimation methods futile in case of Sparse data while it is still widely applying and reporting. In this review paper, we introduce some advanced methods such as data augmentation to provide unbiased estimations.

This paper introduces a Sparse inversion methodology for large-scale magnetic survey data. The minimum support constraint is used in the stabilizer term and leads to models with sharp boundaries. The subsurface under the survey area is divided into a large number of cubes with fixed geometry and unknown susceptibility. In this case, the number of model parameters is much larger than the number of data. Then, transforming from the model space to the data space yields a much smaller system of equations that can be solved quickly. The conjugate gradient algorithm is used to obtain the numerical solution of this system of equations. The proposed algorithm has been applied on a synthetic model consisting of multiple bodies, and also, on real data from Tigh Nuo Ab area in south of Birjand, Iran. Both synthetic and real cases have demonstrated the efficiency of the presented algorithm....

Determining and supplying environmental water requirements (EWRs) of ecosystems including wetlands is one of the most effective ways to mitigate wetlands degradation and to ensure the provision of ecosystem services. This study aims at determining EWR of the Kanibrazan International wetland, south of Lake Urmia, using a combined hydro-ecological approach. The wetland’, s water and vegetation areas were extracted using long-term satellite data. Moreover, frequency analysis of the inundated areas, investigation of the vegetation life cycle, and estimation of evapotranspiration (ET) from the wetland were conducted. Subsequently, through multi-season field surveys from summer 2015 to fall 2016, the wetland’, s hydrography and spatial distribution of vegetation and birds’,habitat in the wetland were obtained. Then, indicator vegetation and winter bird species were identified and used to determine appropriate inundation areas and volumes. Finally, satellite-derived inundated areas and ETs were used in an inverse water balance model to calculate the associated inflow regime to the wetland. Results show that in a normal year an annual volume of 16. 5 MCM, with a tow-peak hydrograph in mid-fall and late spring is required to be supplied as EWR of the Kanibrazan wetland. The findings of this study can be applied in the planning of the surface and groundwater water resources feeding wetland and to better manage wetland’, s water quality.

The neoclassical growth model is tested by use of panel data procedure in this research. In the econometric test, simoultanously time series and cross detection will be compared on the basis of panel data method through which their observed points increase and consequently the estimation efficiency will be increased. The examination of neoclassical growth theory has been done with reference to external & internal factors of 52 selected countries from 1960 to 2000. The independent variable of model has been selected on the basis of the result of previous research which explains the result in three separate models: developed countries, developing countries, and whole countries. These factors are such as: Gross National Products with lag of period, work force age, growth rate, education level, the change of capital accumulation and economic trade volum. The consequences of this research is that: neoclassical growth model can explain the major part of economic growth of the countries with use of internal variables. Also with the use of panel procedure of fixed effect, we can see the fundamental differences and structure of the growth process for different countries; and show how the economic, and social conditions affect on the growth.

Polarimetric synthetic aperture radar (PolSAR) images contain so much information about the characteristics of the targets of the desired area with high resolution. Nowadays, using this data for terrain classification is known as a hot topic of interest for researchers. Recently, Sparse representation-based technique as a powerful tool in the field of signal processing, has attracted a lot of attention. Therefore, in the first step, the structure of a Sparse representation-based classifier is proposed. On the other hand, according to recent research results, ensemble classifier as an effective approach has more capabilities compare to single-classifiers. Therefore, in the next step, an ensemble classifier with Naïve Bayes combination rule is presented by using the Sparse representation-based classifier and other diverse single-classifiers. Finally, an optimum ensemble classifier is proposed by using multiple objective particle swarm optimization (MOPSO) and considering accuracy and reliability as objective functions. The experimental results over a benchmark PolSAR image demonstrate the effectiveness of the proposed algorithms compared to the existing techniques.