Biotechnological analysis of DNA microarray genes provides valuable insights into the discovery and treatment of diseases such as cancer. It may also be crucial for the prevention and treatment of other genetic diseases. However, due to the large number of features and dimensions in a DNA microarray, the “curse of dimensions” problem is very common. Many machine learning methods require an effective subset of input genes to achieve high accuracy. Unfortunately, extracting features (genes) is an inherently NP-hard problem. Recently, the use of metaheuristics to overcome the NP-hardness of the feature extraction problem has attracted the attention of many researchers. In this paper, we use the combination of fuzzy entropy and Giza Pyramid Construction (GPC) for feature selection. First, redundant features in the microarray dataset are removed using the fuzzy entropy approach. GPC is then used to reduce the execution time. This results in the selection of a near-optimal subset of genes for cancer detection. Dimensionality reduction with GPC followed by classification with Convolutional Neural Network (CNN) creates a synergy to increase efficiency. The proposed method is tested on five well-known cancer patient datasets: leukemia, lymphoma, MLL, ovarian, and SRBCT. The performance of CNN was also measured with four well-known classifiers, including K-nearest neighbor, naïve Bayesian, decision tree, and logistic regression. Our results show that, on average, CNN has the highest accuracy, recall, precision, and F-measure in all datasets.

In Optical Character Recognition (OCR), state-of-the-art algorithms are applied to the same databases to compare performance and cost. Various benchmark databases have been created recently in Persian script to facilitate OCR application development. Unfortunately, there is a lack of coherent categorization and systematic identification in Persian and other languages about how to choose the databases to provide a suitable platform for evaluation. This article provides an analytical framework called DB-QM (DataBase Qualitative Measurement) to achieve a macro vision for assessing Persian OCR databases. In our proposed framework, three components are available: First, a categorization of Persian databases is proposed. Therefore, the databases are considered from their content point of view. Second, several quantitative and qualitative evaluation criteria are introduced and categorized based on the nature of databases. Finally, a discussion about the strengths and weaknesses of databases is made on the proposed criteria. It concerns a comparison among databases and the critical points about how to select one for testing algorithms. In addition, the main challenges around database improvement have been taken into account. Our analytical discussion not only clarifies the superiority of one database to another but provides a diverse discipline on how to use the appropriate databases. Also, critical challenges for the enhancement of new databases are highlighted.

A large volume of unstructured data, especially text data, is generated and exchanged daily. Consequently, the importance of extracting patterns and discovering knowledge from textual data is significantly increasing. As the task of automatically recognizing the relations between two or more entities, semantic relation extraction has a prominent role in the exploitation of raw text. This article surveys different approaches and types of relation extraction in English and the most prominent proposed methods in Persian. We also introduce, analyze, and compare the most important datasets available for relation extraction in Persian and English. Furthermore, traditional and emerging evaluation metrics for supervised, semi-supervised, and unsupervised methods are described, along with pointers to commonly used performance evaluation datasets. Finally, we briefly describe challenges in extracting relationships in Persian and English and dataset creation challenges.

In most traditional machine learning algorithms, the training and testing datasets have identical distributions and feature spaces. However, these assumptions have not held in many real applications. Although transfer learning methods have been invented to fill this gap, they introduce new challenges as negative transfers (NTs). Most previous research considered NT a significant problem, but they pay less attention to solving it. This study will propose a transductive learning algorithm based on cellular learning automata (CLA) to alleviate the NT issue. Two famous learning automata (LA) entitled estimators are applied as estimator CLA in the proposed algorithms. A couple of new decision criteria called merit and and attitude parameters are introduced to CLA to limit NT. The proposed algorithms are applied to standard LA environments. The experiments show that the proposed algorithm leads to higher accuracy and less NT results.

In this paper we present a novel and efficient method, called shoulder point detection (SPD), for computing a planar rational quadratic Bézier curve to approximate a target shape defined by a set of dense and noisy data points. Our contribution is utilizing from one of the exclusive properties of Conic Splines, called the shoulder point(SP) for speed up of the curve fitting process. The SPD can be summarized in the following two steps: first, one data point of input data set is detected as a shoulder point through a heuristic approach. Then in step2, detected shoulder point is utilized to generate a quadratic rational Bézier curve as fitting result of data set. Splitting the input data points into the some segments and applying the proposed method locally can guarantee the accuracy of fitting process. We show that SPD is significantly faster than other data fitting methods used currently in the field of curve fitting since the fitting results are reasonably accurate.

Building an audio-visual data corpus is one significant step in audio-visual research. One of the most challenging tasks in computer science is computer-aided speech therapy and language learning. Developing computer applications for training and rehabilitation of the handicapped and helping the hearing and speaking-impaired by facial speech synthesis are among the most helpful, state-of-the-art roles of computer technology in today's human-machine interacting systems. To date, there have been no audio-visual corpora in Persian language, in that it makes it difficult or even impossible for researchers to carry out studies in the area. This paper gives an indication of the collected Persian audio-visual data corpus. AVA is a comprehensive, systematic collection of both continuous speech and isolated spoken utterances in Persian language. The goal of this project is to facilitate audio-visual research in the language through this data corpus which is available upon request.

Recently, Web based facial appearance systems have received more attention by various aspects and applications like online systems. Therefore there is a request for a system to have an ability of predict and render different appearance effects on facial images for online systems. Here, an E-appearance system is proposed to predict the essential effects of facial images with different appearance. Given facial image data of the same person in two different appearance, quotient image then capture appearance characteristic of image. Then, together with warping technique we map the characteristic to any other particular persons face in order to generate new facial appearance. The technique makes experiments on different facial appearance.

Abstract Facial rejuvenation is the process of reversing the aging effects on the human face digitally. This paper generalizes a new approach for facial rejuvenation in adults image. Applications of facial rejuvenation are widespread. They include face recognition, education, entertainment, telecommunications, Psychology, criminal objects, Cosmetic arts and it can be used as an aid for medical cosmetics surgery and the reconstruction of the face. This paper proposes a novel facial rejuvenation modeling algorithm with two techniques. These techniques discuss the facial deformation based on the face anthropometrics theory and remove wrinkles based on what we called wrinkles inpainting. For example if we have been given a few different faces, we need to be able to compare the difference between the facial characteristics of the youth and the aged, then from there onwards, define a set of outlines which are going to be the basis of the simulation of Face Rejuvenation. The first is the geometric deformation details like skin texture, which differs between the aged and the youth. The second is anthropometrics data change. It was developed in the face anthropometrics measurement theory. Then together with warping technique we map the characteristics to any other particular persons' face in order to generate more expressive and convincing facial rejuvenation. The original contribution and advantage of this paper are that, the proposed methods are simple to implement, reliable, in which they required only one source image without needing to collect a lot of images and their computation are fast for interactive environment. Copyright © 2004 John Wiley & Sons, Ltd.