The use of raw radiography results in lung disease identification has not acceptable performance. Machine learning can help identify diseases more accurately. Extensive studies were performed in classical and deep learning-based disease identification, but these methods do not have acceptable accuracy and efficiency or require high learning data. In this paper, a new method is presented for automatic interstitial lung disease identification on radiography images to address these challenges. In the first step, patient information is removed from the images; the remaining pixels are standardized for more precise processing. In the second step, the reliability of the proposed method is improved by Radon TRANSFORM, extra data is removed using the Top-hat filter, and the detection rate is increased by DISCRETE Wavelet TRANSFORM and DISCRETE COSINE TRANSFORM. Then, the number of final features is reduced with Locality Sensitive Discriminant Analysis. The processed images are divided into learning and test categories in the third step to create different models using learning data. Finally, the best model is selected using test data. Simulation results on the NIH dataset show that the decision tree provides the most accurate model by improving the harmonic mean of sensitivity and accuracy by up to 1.09times compared to similar approaches.

Time series is a type of data with complex structure. Analysis of time series is used in sciences such as meteorology, economics, geology, marine science, medicine and engineering widely. So, Because of time series applications in various sciences, the interest to analyze these data has been increased. On the other hand by developing information gathering technologies such as mobile, GPS and sensors, and Access to large volumes of time series data, we always require methods to extract useful information from large datasets. Thus, data mining is an important method for solving this problem. Clustering analysis as the most commonly used function of data mining, has attracted many researchers in computer science. Clustering is a strong instrument for knowledge discovery and it provides useful information about existing patterns in datasets. In general, the purpose of clustering is representing large datasets by a fewer number of cluster centers. It simplifies large datasets and thus is an important step in the process of knowledge discovery and data mining. Fuzzy C-means (FCM) clustering is one of the most important classic clustering methods that have been used in many researches. The main disadvantage of this method is the high probability of getting trapped in local optima especially in facing high-dimensional data such as time series. Furthermore Euclidean distance is the most commonly used similarity measure in Fuzzy C-means but sometimes, its necessary to use another similarity/dissimilarity measures instead of Euclidean distance. In this paper in order to compensate the shortcomings of Fuzzy C-means algorithm, we used one of the existing evolutionary algorithms. Evolutionary algorithms has gained huge popularity in the field of pattern recognition and clustering recently. Among the existing evolutionary algorithms, the differential evolution algorithm as a strong, fast and efficient global search method has been attracted the attention of researchers. In this paper, we proposed a technique for clustering time series data using a combination of Fuzzy C-means and differential evolution (DE) approach and we considered dynamic time warping (DTW) as distance measures between time series. Also, in this method we used DISCRETE COSINE TRANSFORM ((DCT)) to time series dimension reduction. Finding all elements of cluster centers using differential evolution is time consuming and the large number of unknown parameters related to the cluster centers will reduce the efficiency and the speed of differential evolution algorithm. So, for reducing the search space, the most important DISCRETE COSINE TRANSFORM coefficients of the cluster centers were recognized as the main unknown clustering problem in the proposed method and differential evolution algorithm tries to determine the near optimal DISCRETE COSINE TRANSFORM coefficients of cluster centers by minimizing the Fuzzy C-means objective function. Experimental results over two popular data sets indicate the superiority of the proposed technique compared to fuzzy C-means and a clustering algorithm based on differential evolution without using dimension reduction techniques. Comparing the run time of the methods, the proposed method is slower than the Fuzzy C-means clustering algorithm, but due to the use of DISCRETE COSINE TRANSFORM method to reduce unknowns, it operates faster than differential evolution without using dimension reduction techniques.

One of problems of OFDM systems, is the big value of peak to average power ratio. To reduce it, any attempt have been done amongst which, random phase updating is an important technique. In contrast to paper, since power variance is computable before IFFT block, the complexity of this method would be less than other phase injection methods which could be an important factor. Another interesting capability of random phase updating technique is the possibility of applying the variance of threshold power. The operation of phase injection is repeated till the power variance reaches threshold power variance. However, this may be a considered as a disadvantage for random phase updating technique. The reason is that reaching the mentioned threshold may lead to possible system delay. In this paper, in order to solve the mentioned problem, (DCT) TRANSFORM is applied on subcarrier outputs before phase injection. This leads to reduce the number of required carriers for reaching the threshold value which results in reducing system delay accordingly.

Image retrieval is one of the most applicable image processing techniques, which have been used extensively. Feature extraction is one of the most important procedures used for interpretation and indexing images in content-based image retrieval (CBIR) systems. Reducing the dimension of feature vector is one of the challenges in CBIR systems. There are many proposed methods to overcome these challenges. However, the rate of image retrieval and speed of retrieval is still an interesting field of research. In this paper, we propose a new method based on the combination of Hadamard matrix, DISCRETE wavelet TRANSFORM (HDWT2) and DISCRETE COSINE TRANSFORM ((DCT)) and we used principal component analysis (PCA) to reduce the dimension of feature vector and K-nearest neighbor (KNN) for similarity measurement.The precision at percent recall and ANR are considered as metrics to evaluate and compare different methods.Obtaining results show that the proposed method provides better performance in comparison with other methods.

Purpose: Different views of an individuals’ image may be required for proper face recognition.Recently, DISCRETE COSINE TRANSFORM ((DCT)) based method has been used to synthesize virtual views of an image using only one frontal image. In this work the performance of two different algorithms was examined to produce virtual views of one frontal image.Materials and Methods: Two new methods, based on neural networks and principle component analysis (PCA) were used to make virtual views of an image. The results were compared with those of the (DCT)-based method. Two distance metrics, i.e. mean square error (MSE) and structural similarity index measure (SSIM), were used to measure and compare image qualities. About 400 data were used to evaluate the performance of the new proposed methods.Results: The neural networks fail to improve the quality of virtually produced images. However, principle component analysis improved the quality of the synthesized images about 3%.Conclusion: Principle component analysis is better than both (DCT)-based and neural network methods for synthesizing virtual views of an image.