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.

Steganography is the practice of hiding data, such as images, videos, or text, within a cover image without it being detectable by the human eye. Several factors, such as the capacity, security, and robustness of the technique, are essential when transferring information using this method. In this study, we propose a new approach to image steganography that improves the Least Significant Bit (LSB) technique by utilizing images of 24 bits in each pixel. Improving the capacity and security of LSB-based steganography requires a combination of techniques, such as indirect embedding, embedding in multiple channels, and applying cryptographic and compression techniques. This approach conducts by encrypting each compressed secret message bit with the most significant bit of the red channel and then saving the output bit (hidden bit) in the least significant bit of the (green/blue) channel according to the row value (odd/ even). To further security, the suggested approach uses multi-level encryption; employs RSA to encrypt the secret message before applying the Huffman compression and encrypting the hidden bit by (XOR/XNOR) in the embedding method based on the row value of the pixel. Meanwhile, it is planned to use the Huffman coding technique to shorten the length of the encrypted message that will be inserted in the cover image. For the color photos in this work, the standard images were acquired from a standard dataset (USC-SIPI). The suggested approach performs better when measured in terms of mean square error (MSE), peak signal-to-noise ratio (PSNR), and comparison to findings from related prior efforts.

In this paper, we introduce and study the concept of weak gamma continuous modules as a generalization of continuous gamma modules. A gamma submodule of a gamma module is called weak gamma summand if there exists submodule of and there exists an ideal of where and . An module is called weak gamma extending module if every submodule of is essential in a weak gamma summand. An module is called weak gamma continuous module if is it weak gamma extending module and if for each submodule of is isomorphic to a direct summand of , then is weak gamma summand of . Also it is called weak gamma quasi-continuous module if is weak gamma extending module and if and are direct summands of with , then is a weak gamma summand of . Many properties and results of the these modules are given

Over the past few years, ear biometrics has attracted a lot of attention. It is a trusted biometric for the identification and recognition of humans due to its consistent shape and rich texture variation. The ear presents an attractive solution since it is visible, ear images are easily captured, and the ear structure remains relatively stable over time. In this paper, a comprehensive review of prior research was conducted to establish the efficacy of utilizing ear features for individual identification through the employment of both manually-crafted features and deep-learning approaches. The objective of this model is to present the accuracy rate of person identification systems based on either manually-crafted features such as DCT, DWT, DFT, PCA, LBP, SURF, SIFT, etc., or deep learning techniques such as CNN, DNN, Alex Net CNN, VGG-16, SVM, Squeeze Net, Google Net, MobileNetV2, etc. The effort will make it easier for researchers, especially those who are new to the field, to have a brief understanding of the trend of employing deep learning in a trustworthy biometric for the identification and recognition of human identification.

Cardiovascular disease prediction is a serious challenge for clinical data analysis. This study examines deep learning-based categorization strategies for heart disease. Deep learning algorithms are employed with echocardiograms to categorize heart disease. This paper uses echocardiography to predict and identify heart abnormalities, with the help of decision-making and forecasting, based on the copious data the healthcare sector has provided. Medical experts can forecast clinical outcomes, which helps them choose the best course of action. In-depth longitudinal electronic health records are a rich source of historical data with complex patterns that have the potential to be leveraged by machine learning to improve physicians' prediction abilities (EHR) significantly. Most contemporary medical specialties rely on imaging as one of the most data-rich components of electronic health records when making treatment decisions (EHRs). Only a few tasks in medical image processing and reconstruction have seen success using machine and deep learning, including registration, segmentation, and feature extraction. Cardiac imaging sequence analysis must incorporate the extraction of spatial and temporal characteristics to forecast crucial information throughout time correctly.

There have been several security solutions based on artificial intelligence (AI), such as intrusion detection systems (IDS),cyberattacks are increasing because big data is increasing by using the internet on all sides of life, therefore, unbalanced data poses a serious problem in intrusion detection systems. The proposed detection system that is based on deep learning Convolutional Neural Network(CNN )partitions data into training and testing., Creating the classifier model for the Principal Component Analysis (PCA )technique of reducing features, is required for the development of intelligent analytic tools that need data pre-treatments and deep learning algorithm-performance enhancement. The UNSW-NB15 data set is used According to experimental findings, We employed a number of evaluation tools to assess the proposed NIDCNN strategy relying on the UNSW-NB15 data set that takes 30% of it for testing and after processing this part of the data became used to evaluate the proposed system. Measures such as a classifier's F-Score, precision, and sensitivity (Recall) are evaluated. Classifier performs better than the other approaches at determining if the data stream is normal or malicious. which is used to assess deep learning's effectiveness, the suggested model results from a high level of accuracy. The experimental findings demonstrate the suggested system's ability to accelerate the intrusion detection process while reducing memory and CPU usage. Experimental results prove the theoretical considerations.Because the UNSW-NB15 data set contains a wide range of patterns that accurately represent contemporary real network traffic, New NIDS algorithms can therefore be assessed using it.

In this study, a method was developed to adjust the procedures of analysis of variance for the one-way balanced random effects model when error limits across classes are not independent, while the assumption of independence between the error terms among the different classes is essential in the analysis of variance. The variance-covariance structure and the correlation coefficients for the error limits were defined. The was assumed as the correlation coefficient of error terms within the class and the is the correlation coefficient for error terms in different classes. The work performed by deriving correction factor and calculating the expectation of the mean sum of squares of errors and treatments as well as correcting the f-statistic.

Iris research is focused on developing techniques for identifying and locating relevant biometric features, accurate segmentation and efficient computation while lending themselves to compression methods. Most iris segmentation methods are based on complex modelling of traits and characteristics which, in turn, reduce the effectiveness of the system being used as a real time system. This paper introduces a novel parameterized technique for iris segmentation. The method is based on a number of steps starting from converting grayscale eye image to a bit plane representation, selection of the most significant bit planes followed by a parameterization of the iris location resulting in an accurate segmentation of the iris from the original image. A lossless Hexadata encoding method is then applied to the data, which is based on reducing each set of six data items to a single encoded value. The tested results achieved acceptable saving bytes performance for the 21 iris square images of sizes 256x256 pixels which is about 22.4 KB on average with 0.79 sec decompression average time, with high saving bytes performance for 2 iris non-square images of sizes 640x480/2048x1536 that reached 76KB/2.2 sec, 1630 KB/4.71 sec respectively, Finally, the proposed promising techniques standard lossless JPEG2000 compression techniques with reduction about 1.2 and more in KB saving that implicitly demonstrating the power and efficiency of the suggested lossless biometric techniques.

The objective of this paper is to introduce and investigate two families of analytical and bi-univalent functions, and , with respect to symmetric conjugate points that are defined in the open unit disk and connected to a series of beta-negative binomial distributions. For functions in each of these families, we look into upper bounds for the initial Taylor-Maclaurin coefficients and .

Let be a ring. A right -module is called JS- -injective (where is any right -module) if every right -homomorphism from a submodule of J J into extends to [9]. A ring is called right JS-injective if is JS- -injective. The right JS-injective rings are studied in this paper. Many characterizations and properties of this type of rings are obtained.

Deep learning is a branch of machine learning that focuses on the development and refinement of complex neural networks for data analysis, prediction, and decision-making. Deep learning models use numerous layers of artificial neurons to automatically extract important features from raw data, making them superior at many tasks to typical machine learning models. Deep learning models' success in these fields has enhanced state-of-the-art performance and created new research and application prospects. Deep learning has been popular due to its capacity to tackle complicated issues in computer vision, natural language processing, speech recognition, and decision-making. In this study, we discuss deep learning techniques and applications, including recurrent neural networks, long short-term memory, convolutional neural networks, generative adversarial networks, and autoencoders. We also demonstrate deep learning's use in various fields. Deep learning has transformed artificial intelligence by enabling computers to learn from enormous datasets and accomplish complex tasks. As a result, scientists and engineers in fields as diverse as medicine, farming, manufacturing, and transportation have increased their focus on developing deep-learning methods and software. Current research trends and potential future paths in deep learning are also highlighted.

Many articles studied best trigonometric approximation and many researchers worked on the neural network approximation, but no one related the best trigonometric approximation to neural network approximation. We define trigonometric activation function, then we use it to obtain neural network, which we use it as a best approximation for functions in spaces for . That what we shall introduce in our work have.

Data compression is necessary for the storing, transfer, and manipulation of digital data in today's rapidly changing world due to the widespread use of medical technology and the vast data creation by various medical modalities. Many scientists and engineers have proposed new compression processes, methods, and algorithms in recent years. There is a discussion of the current state of these compression strategies, including their recent advances in the form of hybrid techniques, performance requirements, legal challenges, and the potential to advance medical technology.

In this paper, The property of subordination is used to define the subclass bounds on the coefficients. The upper bound for the functions in this class is provided for . The fourth Hankel determinant for a class is obtained. Some new results also are obtained.

The job scheduling problem is used in industry, manufacturing, job planning, and the network environment to manage users’ jobs on the right machines with different limitations. There are three types of job scheduling problems. Firstly, job shop scheduling means each job is executed on a determined sequence of machines specified previously. While in the flow shop scheduling problem, each job can be carried on one idle sequencing using a job queue. The third type, open shop, the sequence of jobs can be carried out on any free machine. This paper describes the various types of job scheduling problems and the various swarm intelligence algorithms, particularly cuckoo search algorithm, that can used to solve them.

With the rise of social media on the web, sentiment analysis has become one of the most important areas of study. Today, millions of people share their thoughts, ideas, feelings, and opinions on social media sites like Twitter and Facebook. Sentiment analysis, also called opinion mining, is mostly about classifying and predicting how people feel about a certain target. It involves putting text documents or sentences into groups based on how positive or negative they are about a certain topic. Researchers always find "natural language processing" to be one of the most interesting topics. To solve different problems and improve the accuracy of different applications, it is always helpful to know the exact meaning of what is being said in a conversation. Sentiment analysis uses natural language processing (NLP) and learning models like machine learning and deep learning algorithms to figure out how people feel about the data given. Sentiment analysis looks at sarcasm because sarcasm is a way for people to say how they feel about something without saying it directly. People means the exact opposite of what the sentence says at first glance. Sarcasm is hard to figure out because each sarcastic sentence is different. This paper will talk about what has been done in the field of sarcasm detection, the different techniques used, and the problems that still need to be solved.

Today's world is heading towards complete digital transformation, and with all its advantages, this transformation involves many risks, the most important of which is phishing. This article proposes a system that extracts features from all parts of the email, initially brought from different data sets, and uses one of the machine learning algorithms (K-means algorithm) to extract the valuable features, as used four methods to calculate the distance in the K-means algorithm. This work used SVM as a classifier to classify emails into phishing and legitimate and tuned its parameters to obtain a high percentage of accuracy. The proposed model gave accuracy equal to 98.8 %.

Due to the great and rapid development in the electronic field, the spread of the Internet, and the diversity of social media, the spread and transmission of data have become an important matter in different areas of life. The video is one of the most important of this information, which includes a lot of information that requires storing and retrieving a large database. Therefore, video structure analysis is the basis for facilitating the video search process based on its contents, indexing, and retrieval. In this research, we will present a survey of the video structure analysis process, the basic concepts and all related processing steps, the procedures used in each method, and the most prominent works used in each step.