Journal of Al-Qadisiyah for Computer Science and Mathematics

Captioning an image is the process of using a visual comprehension system with a model of language, by which we can construct sentences that are meaningful and syntactically accurate for an image. The goal is to train a deep learning model to learn the correspondence between an image and its textual description. This is a challenging task due to the inherent complexity and subjectivity of language, as well as the visual variability of images. Computer vision and natural language processing are both used in the difficult task of image captioning. In this paper, an end to end deep learning-based image captioning system using Inception V3 and Long-Short Term Memory (LSTM) with an attention mechanism is implemented. Expansive experimentation has been realized on one of the benchmark datasets named MS COCO, and the experiential results signify that this intended system is capable of surpassing diverse related systems concerning the extensively utilized measures of evaluation, and the accomplished results were 0.543, 0.87, 0.66, 0.51, 0.42 for Meteor and BLEU(B1-B4), respectively.

In this paper, statistical convergence will be investigated. Then it sheds light on the study of some important concepts, characteristics and results of the previous study. In addition, we introduced a definition in limit and continuity which is the statistical limit and statistical continuous functions.

Human face expression Recognition is one of the most effective forms of social communication. Generally, facial expressions are a simple and obvious way for people to express their feelings and intentions. Typically, the goal of facial expression recognition is to categorize facial expressions into specific classes of expression labels. This paper presents a survey of facial emotion expression classification based on different machine learning and deep learning mechanisms and optimization algorithms. In order to evaluate the basic emotion of a person's face, a technology called facial expression recognition employs a computer as a helper with specific algorithms. Seven basic emotions were represented by facial expressions, including a smile, sadness, anger, disgust, surprise, fear, and a natural expression. In this paper, the focus is on using the Grey Wolf algorithm for selection of the optimal features from feature extraction from input image faces to recognize human facial emotions. In most studies, the FER system was applied to popular datasets such as the JAFEE database and the Cohn-Kanade database.

In this paper, Veita-Pell polynomials (VPPs) are first presented with some interesting properties. These properties are utilized to construct a general explicit formula of their operational matrix of derivative. Then an appropriate direct technique is suggested for solving quadratic optimal control problems based on VPPs and the idea of state parameterization algorithm. The resulting performance index optimal value shows the proposed method is able to provide a good treatment with fast convergence. The effectiveness of the presented method is illustrated by solving three numerical examples. The obtained results show that as the number of basis functions VPPs increase the error in the solution by the present method will be decreased and it may exactly close with the analytical one. This is the main modification of the algorithm and this contribution in the using special basis functions in obtaining an approximate solution with minimum number of VPPs and satisfactory accuracy.

Problems of large keys in the on-lattice ring signature link due to reduce to the lattice, based on the problem of fault-tolerant learning on the ring (RLWE), according to "Homomorphic Commitment→∑-Protocol→Fiat-Shamir Transformation" reconstructs a linkable ring signature scheme on a lattice. First construct a base “Homomorphic commitment scheme” on polynomial ring of RLWE difficult, and use the Fiat-Shamir transformation method. The method converts the ∑-protocol into a linkable ring signature scheme, and suggests linkable ring signature scheme model. It is resistant to quantum computer attacks. compared with the previous linkable ring signature schemes on lattices, proposed methods with high computational, simple and less time frame because the ring elements in the scheme are taken from small polynomials which represents as case of application in computer science.

We introduce and investigate SAS-injective modules as a generalization of small injectivity. A right module over a ring is said to be SAS- -injective (where is a right -module) if every right-homomorphism from a semiartinian small right submodule of into extends to . A module is said to be SAS-injective, if is SAS- -injective. Some characterizations and properties of SAS-injective modules are given. Some results on small injectivity are extended to SAS-injectivity.