Journal of Al-Qadisiyah for Computer Science and Mathematics

Federated Learning was designed to allow collaborative learning without revealing raw data as worries about machine learning privacy grew. Vertical Federated Learning (VFL) may be utilized for a distributed dataset with the same sample ID space but differs in feature space. And may be used in a wide variety of real-world contexts when parties have the same set of samples but only have partial attributes. Achieving privacy will be a result of this technique's capacity.

The research aims to build sports models to find the concentration of uranium in herbs using twelve herbs from medicinal herbs various uses by human use crime methods theory definition and some numerical methods different like Novel and Hermitian where we got mathematical models implicitly through it effect amount uranium herbs, and we got on miniatures mistake percentage almost non-existent.

problems. Many different experiments have shown that the evolutionary algorithm Strength Pareto Evolutionary Algorithm 2 (SPEA2) outperforms other approaches, and this makes it an excellent candidate for inclusion in the final design. According to Strength, one of SPEA2's core concepts, the population is sorted into niches based on the Pareto Front idea. With regard to outcomes this technique has a flaw that is mitigated by adding a fitness density estimator. Weakness in Strength is addressed with an approach called Strength by objective, which aims to include solution who do not dominate or are dominated by others inside the process. In this paper, the results will show a clear superiority of the proposed method comparing with the original method in solving the multi-objective Knapsack problem using three sizes of the Knapsack and the problem size of 750 items which is generated randomly. The comparison results between the proposed method and the other algorithms show outperformance of the Proposed algorithm using the dominance inductor showed the superiority of the proposed algorithm by a percentage of more than 1 %, Thanks to a highly diverse population and the inclusion of solutions that can be improved, the (SPEA2) algorithm's performance has been vastly enhanced by the Objective sorting mechanism

Congestion, and how to manage it or prevent its occurrence, is one of the most crucial and cutting-edge subjects in the world of networks since it has a significant influence on the computer network and the quality of service (QoS). Network efficiency is decreased by congestion, which frequently results in service interruptions. In order to preserve the continuity of data flow in the network, it is necessary to design approaches and processes to avoid congestion or to minimize its effects. Congestion are avoided at two levels, knot and link, by using different techniques, including close loop or open loop. This paper proposed a new technique (TLBCA) to distribute packets arriving at a specific node on the links connected to it, which leads to the same destination, to avoid congestion on one of the links and out of service, which then leads to more load on the other links until the network collapses. TLBCA is based on the popular algorithm Round Robin. ECMP and TinyFlow, the two most widely used comparable algorithms in this sector, performed around 20% worse than the approach when it was simulated using OMNET++.

The assignment models are one of the effective operations research techniques for enhancing the educational process and obtaining the desired learning outcomes. Universities organize the criteria for distributing accepted students in accordance with the central admission plan issued by the Ministry of Higher Education to their departments based on of the student’s qualifications represented in the total of his grades in the general examinations for secondary studies as a criterion that qualifies him to compete with other students, in addition to the student’s choices and the department plan. In this paper, we discuss the assignment problem (AP) in the allocation of students to college departments depending on the degree of comparison. Three techniques of assignment problem were used, namely; the Hungarian technique, the Alternative technique, and the new technique presented by Haleemah Jawad Kadhim in 2021, to find the best allocation

This study aimed to identify the most important factors affecting the academic achievement of a university student and then build a predictive model using these factors to predict the student's academic status. The ordinal logistic regression method was used to identify the influencing factors, and neural network technique to build the prediction model. It was applied to a sample of 188 students at Al-Yamamah University in the Kingdom of Saudi Arabia. The analyzed data were collected using a questionnaire of three main axes: representing student related factors, family, and academic factors. The results of the logistic regression model showed that 10 variables had a significant effect on the student's academic achievement: age, cumulative average at high school, the distance of residence, work besides studying, daily study hours, frequent absences, fear of exams, the economic level of the family, parents divorce, and family follow-up. As for the MLP network model with architecture (18-28-3), whose inputs were the significant variables chosen by the logistic model, the values of all performance indicators were supportive of the model's quality and predictive ability, where the correct classification rate values in the training data reached 96%, validity 93% and testing 90%. The results of the classification table were also high, with the correct classification of the three categories of the target variable: 100% good, 91.95% fair, 82.35% poor. The total percentage of correct classification was 94.68%. All these indicators confirm the validity and feasibility of using this model as an initial tool for predicting the academic status of a university student.

Heart failure is one of the dangerous heart diseases that infect humans and may cause death. This disease causes damage to the heart muscle, and it becomes unable to pump blood in the body as well as it should. Therefore, the condition of heart failure patients must be predicted as soon as possible in order to help the patients to live longer lives by offering appropriate therapy. Based on that, the aims of this paper are to use medical records to predict the state of a patient with heart failure if he/she will die or not, and extract the important features that have a direct effect on the patient's state. This paper used a dataset of 299 heart failure patients and applied the Neuro-Fuzzy systems (NFS) to this dataset. This prediction is made by testing each two feature together in the dataset and feeding it to the NFS system to determine its effect on the patients. In this paper, the accuracy and confusion matrix is used to evaluate the system's performance. The experimental results show that the system yielded 100% accuracy when the two-feature, serum creatinine and ejection fraction, are tested together, so it can be used alone to predict whether patients with heart failure will survive.

In this paper, we present some properties of the difference for quadruple sequences spaces of fuzzy complex numbers related to the space defined by triple maximal Orlicz functions and discuss some properties such as completeness, solidity, monotonicity, symmetric, convergence-freeness, and so on.

Mango is a drupe fruit which plays an active role in the economy of different countries. Classification process is a fundamental process in: diseases detection domain, sorting and grading. Previously, farmers can detect mango's diseases, identification ripe and unripe mango by their eyes, but it is inaccurate, waste of time and effort. AI technology helping farmers get high quality agricultural crops, the essential idea of AI in agriculture is its flexibility, reliability, speedy performance and applicability. AI technology improves enterprise performance and productivity by automating processes or tasks that once required human skill. AI can also understand data on a scale that no human can achieve, this ability can bring great advantages in the field of agriculture. In this paper, a review for application of artificial intelligence in mango classification and mango diseases identification have been presented.

In this paper ,we give the statistically convergent for quadruple sequence spaces of fuzzy numbers defined by the triple maximal Orlicz function and shows explore properties such as semi-normed space, complete semi-normed space, and others

Enhancement is most interesting parts in image processing field. It uses to enhance the structural appearance for picture without the degradation in the original input image. The enhancement techniques become the important key and simply extraction features by removing noise and another items inside an image. Several enhancement techniques have achieved with different and inaccurate results. The aim of this paper, the nature images quality was improved by the many enhancement techniques like of Histogram Equalization (HE), Local Histogram Equalization (LHE), Adaptive Histogram Equalization (AHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dualistic Sub-Image Histogram Equalization (DSIHE) and Recursive sub-image histogram equalization (RSIHE). In evaluation step, the performance of all these techniques are examined by values measurements of SSIM (Structural Similarity Index Matrix), Entropy, Peak Signal-to-Noise Ratio (PSNR) and Signal to Noise Ratio (SNR). The comparisons of the better existing results are given because to explain the best possible technique that can be used as suitable image enhancement. The results of the enhanced of 15 nature images have showed DSIHE technique has a batter a values of SSIM and Entropy with 0.9885 and 59975 respectively. Overall, based on the PSNR and SNR values, the CLAHE technique are recorded values higher than of other six techniques in 21.2952 and 192932 values.

Let be an infinite dimensional separable complex Hilbert space, and be a bounded linear operator. is called codisk mixing operator, - mixing operator, if for any non-empty open subsets of , there are and such that for all . In this paper, we studied a necessarily and sufficiently conditions of - mixing operators, and discused the direct sum of two - mixing operators.

In the previous few years, Finger knuckle (Fk) has received a lot of interest as a biometric trait in recent years. It will provide economic human identification performance due to its distinct difference between human-specific alternatives of visible lines, wrinkles, and ridges spread on the surface external of all finger knuckles. The foundation for most biometric systems is Fks. This report presents a thorough analysis of the pertinent Finger knuckle investigations. The foundation for most biometric systems is Fks. The identification system through finger knuckles usually contents of 4 steps, specifically image Acquisition, image preprocessing, feature extraction, and have matched. There are numerous methods used during this research at each level. The paper is likely to highlight these methods used in the PolyU database

Many types of ambiguous numbers have been studied in many mathematical fields. Two of these types, the obscure number hexagonal and octagonal are widely used, especially in mathematical programming. A linear fuzzy number (LFN), which generalizes these two types, is presented in this paper. Based on the definition of the order function by the total value, two ranking function for this linear fuzzy number is used. Arithmetic operations with their properties are entered on ambiguous numbers as a special case. It also turns out that the operations proposed on LFN are acceptable generalizations of conventional arithmetic operations on real numbers.

In this research, we presented the concept of Strongly Pseudo Nearly Quasi 2-Absorbing submodules . Which is a generalization of the concepts of 2-Absorbing and Quasi 2-Absorbing submodules and the ideal is not Strongly Pseudo Nearly Quasi 2-Absorbing submodulesand we gave an example of that . We also noted that is STPNS-2-Absorbing ideals under several conditions. Also in this part we introduce the characterization of the concept of STPNS-2-Absorbing ideals by special kind of submodules.

Finding the face key-points location or which is also called Face alignment has gotten a lot of interest in the last twenty years because of its wide range of applications in the major of automatic face analysis. Nonetheless, this task has proven an extremely challenging task in an unconstrained environment because of multiple confusing factors, such as occlusions, position, illumination, and expression. However, this task is still an open problem, even though researchers still developing a great many techniques to face the challenges of this problem, in this survey, a critical review of the current literature in the face alignment field had already been presented focusing on those methods tackling the total of this topic's challenges and difficulties under uncontrollable circumstances. Especially, the existing face alignment techniques have been categorized existing referring to more than the type of categorizing. Then represent a survey of the recent related works in face alignment and lastly, we discuss the general steps of the face alignment system.

The principal aim is to improve medical diagnosis through segmented images. Thus, medical image segmentation become one of the key technologies in computer-aided diagnosis. Active contours, or snakes, have been widely used for image segmentation purposes. However, high noise sensitivity and poor performance over weak edges are the most acute issues that hinder the segmentation accuracy of these curves, particularly in medical images. To overcome these issues, a novel external force that integrates gradient vector flow (GVF) field forces and the traditional snake function is proposed in this research. In addition, a novel technique is applied to limit the boundary of the initial contour by set four initial points around the medical issue and then connecting them by polynomial curves. Moreover, the positive effect of Particle Swarm Optimization (PSO) on calculating the final active contour area and its percentage to the entire image area is proved in this work.

In this research, we presented the notion of Strongly Pseudo Nearly Quasi 2-Absorbing submodules . Which is a generalization of the notions of 2-Absorbing and Quasi 2-Absorbing submodules and stronger than the notions (Nearly 2-Absorbing, pseudo 2-Absorbing , Nearly Quasi 2-Absorbing and pseudo Quasi 2-Absorbing) submodules. Give us the properties, characterizations and examples of this new concept. We studied the relationship between the notion of Strongly Pseudo Nearly Quasi 2-Absorbingsubmodules and notion (Nearly 2-Absorbing, pseudo 2-Absorbing , Nearly Quasi 2-Absorbing and pseudo Quasi 2-Absorbing) submodules with special types of modules.

Sentiment analysis is gathering opinion keywords, such as aspects, opinions, or features- and figuring out their semantic perspective relations. More specifically, aspect-based sentiment analysis – (ABSA) is a subfield of natural language that focuses on phrases associated with aspects and detects the sentiment that belongs to each one. Aspect extraction and sentiment analysis are the two fundamental functions in ABSA, and the different classes of aspects are explicit and implicit. To shed some light on this problem, we discuss aspect extraction tasks and classify explicit aspect extraction strategies into two categories: supervised, unsupervised and semi-supervised. This article explores prior research and their approaches by reviewing works from 2016 to 2021 and comparing numerous elements comprehensively, including classifying systems, classifier methods, datasets, and performance.

Ad-hoc networks have unique properties, making it impossible to execute the current existing network security policy successfully. Based on studying the Ad-hoc network, the Cloud Security theory will be applied to it. We apply Direct Blind Authentication theory and trusted computing platform hardware modules to optimize the Ad-hoc networks' authentication links. The program successfully addresses the security concerns of Ad-hoc nodes, strengthening the Ad-hoc network's defenses.

In this work we will study the concept of Extend Nearly Pseudo Semi-2-Absorbing submodules in multiplication modules and characterization of Extend Nearly Pseudo Semi-2-Absorbing ideals by of Extend Nearly Pseudo Semi-2-Absorbing submodules.

The noise is a major problem in computer vision because it affects image quality and impede the illation. In this paper, we suggest method works on instantaneous transformations that are used in the analysis of the signals representing the image and the frequencies they contain, through which the image can be retrieved or the areas containing the noise where the image is transformed within the complex range and then the original image. Result of this paper are 0.9804 in case SSIM measure and 32.9850 in case PSNR measure.

Our main goal in this note is to introduce several characterizations of Almost approximaitly nearly quasiprime submodule in class of multiplication modules. Moreover, we characterized Almost approximaitly nearly quasiprime submodules by theirs residual in class of multiplication modules with the help of some types of modules as projective, faithful, content and -regular modules. And we characterized almost approximaitly nearly quasiprime ideal by almost approximaitly nearly quasiprime of the form .

Innovations and new methods for solving long and difficult mathematical puzzles are essential to advances in many branches of research and knowledge. As a result, experts have proposed intelligent algorithms, which are determined by their ability to quickly and efficiently answer the most difficult mathematical puzzles. To achieve the greatest results in this worksheet, we used two different strategies. The first method involved integrating the Goshawk Optimization algorithm (NGOA) with fuzzy logic (FL), while the second method was based on two hybrids, the first by linking communities and the second by linking the equations between the Black Widow Optimization algorithm (BWOA) with the Northern Goshawk optimization algorithm (NGOA). Then we applied both techniques to the basic functions of ten algorithmic functions to get the results.

The wide spread of living organisms in nature and how they obtain food has prompted many scientists to form mathematical models that simulate these organisms. These models were used to solve math problems that take a long time and effort to solve, but these models were sometimes weak and required modification. In this research we used two methods to reach the optimal solution, the first method we used the Peafowl optimization algorithm (POA) with the chaos function and the chaotic tent function to reach the optimal solution and this was the first step in the work, the second method we hybridized the first step by adding the artificial hummingbird algorithm (AHA) The hybridization was of two types, the first by linking communities and the second by linking equations, and we got the optimal solution using 1000 iterations in the two steps, which resulted in producing the optimal solution.

In this article, we will present a generalization on the (2-Absorbing, Semi-2-Absorbing, Pseudo-2-Absorbing, Pseudo Semi-2-Absorbing and Nearly-2-Absorbing) submodules. We will study the relationship between this generalization and the basic generalizations studied previously. We have provided many Propositions, Remarks, Examples and characterizations in this article.