Journal of Al-Qadisiyah for Computer Science and Mathematics

NLP refers to the computer-based study of language when referring to textual data such as texts or reviews. Natural language processing frequently seeks to give otherwise unstructured natural language a representation of the text that gives it structure. Marketing, social media, and customer relationship management are just a few of the businesses that heavily rely on sentiment analysis (SA), one of the methods used in opinion mining, typically assesses a textual review's structure to see if it conveys a positive or negative impression .In this paper, the challenge was to convert unstructured texts into structured texts for drug datasets, and interlocking between sentiment analysis, the mechanism that was adopted in the paper starts with several stages. The first stage is data preprocessing with natural language processing techniques, and the next stage is the prediction step with classification models are logistic regression(LR), random forest(RF), Naïve Bays(NB), and Support Vector Machine (SVM), the best Result of prediction accuracy 92%,for random forest .

The purpose of this paper, is to introduce a new concepts which are induced n-Bi-Hom -Lie algebra, -Center, ( , ) -Center, ( , ) Hom - derivation, ( , ) Q-Hom , ( , ) Central Hom - derivation, ( , ) Hom - Centroid and give the condition to construct induced n- Bi-Hom -Lie algebra, studied Generalized Hom -derivations on direct sum of ideals and we studied the relation between Hom , Hom and Q Hom , Q Hom , G Hom

Voice isolation, a prominent research area in the field of speech processing, has garnered a great deal of attention due to its prospective implications in numerous domains. Deep neural networks (DNNs) have emerged as a potent instrument for addressing the challenges associated with vocal isolation. This paper presents a comprehensive study on the use of DNNs for voice isolation, focusing on speech recognition and speaker identification tasks. The proposed method uses frequency domain and time domain techniques to improve the separation of target utterances from background noise. The experimental results demonstrate the efficacy of the proposed method, revealing substantial improvements in voice isolation precision and robustness. This study's findings contribute to the increasing corpus of research on voice isolation techniques and provide valuable insights into the application of DNNs to improve speech processing tasks.

In the present study, an innovative approach is proposed to solve a special case of fractional foam drainage using the Laplace residual power series technique (LRPS) in conjunction with the Caputo operator for determining the fractional derivative. The study provides extensive guidelines for utilizing this approach to solve time-fractional nonlinear formulas. The effectiveness and validity of the proposed method are investigated and established by comparing the obtained results with the accurate responses using graphs. The study also confirms that the accuracy of the proposed technique increases with the number of items in the combined solution of the problems, as demonstrated by the convergence of the correlation between the obtained solutions and the actual solutions for the special case of fractional foam drainage formula. The research findings suggest that the proposed technique is not only accurate and uncomplicated but also highly adaptable, making it suitable for addressing both linear and nonlinear situations.

In this work, general formula of set solutions for system of Euler's equation in dimension m are derived by using Elzaki transformation. Also, supported examples are presented utilizing formula derived.

A new design approach called microservices-based architecture is quickly emerging as one of the most efficient ways to re-architect aging enterprise systems and reengineer them into new modern systems . Microservice architecture is essential for creating high-quality, scalable, high-performance, and easy-to-maintain software. The newest method of creating new applications or reorganizing existing systems is the microservice-based architecture. It is thought to be more efficient in a number of areas than the more traditional service-oriented design, including: such as maintainability, dependability, scalability, and agility. Software Engineering developed an architecture with independent and autonomous components known as the microservice architecture in response to the requirement to enhance and evolve software architecture design. Online exams are crucial components of education. It minimizes the vast amount of material resources and is both quick and effective. However, monolithic design, which was used to create online exam systems, has more issues and is incompatible with cloud computing, distribution, or new technologies. This paper presents on evolving and reusability a legacy enterprise system (Online Exam) using a microservice architecture. the study's contributions To update a legacy enterprise system, feature-driven microservice-specific transformation rules are adopted. Performance, maintainability, scalability, and testability are prioritized when comparing a historical monolithic architecture to a microservice architecture.

Let be a diffeomorphism map on a closed smooth manifold for dimension . We explain in this work any chain transitive set of generic diffeomorphism , if a diffeomorphism has another type of shadowing property is called, the eventual shadowing property on locally maximal chain transitive set, then is hyperbolic. In general, the eventual fitting shadowing property is not fulfilled in hyperbolic dynamical systems (satisfy in case L is Anosov diffeomorphism map) . In this paper, several concepts were presented. These concepts can be re-examined on other important spaces, and their impact on finding dynamical characteristics that may be employed in solving some mathematical problems.

In almost all fields of numerical analysis, spline functions are the most effective tool for polynomials employed as the fundamental method of approximation theory. Additionally, existence, uniqueness, and error boundaries are required for the spline creation in the g-spline interpolation issue. Mathematics, physics, biology, engineering, signal processing, systems identification, control theory, finance, and fractional dynamics have all shown an interest in fractional differential equations, also work in social sciences including economics, finance, and dietary supplements. It is crucial to find both close and accurate solutions of fractional differential equations. To find solutions of fractional differential equations, several analytical and numerical techniques have been developed. In this paper, we extend the five-degree spline (0,4) lacunary interpolation on uniform meshes. The outcomes, uniqueness and error boundaries for generalize (0,4) Lacunary interpolation using five- degree splines. These generalizes outperform the usage of the (0,4) five splines for interpolation.

Vehicle detection is crucial to the management of Intelligent Transportation Systems of urban traffic. Optical character recognition is many systems use, like the automatic number plate recognition system (NPR). Using various techniques and methodologies, such as convolution or deep neural networks, morphological operations, optical character recognition, and edge detection, such a system has been widely utilized to identify car license plates. Numerous applications algorithms of vehicle identification become possible after the development of many helpful techniques in computer vision and the availability number of related images as a dataset. In this paper, the various plate numbers of the specific vehicle will be and reviewed. Vehicle detection systems suffer from a variety of difficulties and constraints such as the all the surrounding state of clarity for the license plate, non-standard formats of the plate, complex situations, camera quality and clarity, the position and distance of the camera, the distortion in the image, contrast issues, reflections and the state day and night images As a result, numerous methods for comprehending and assessing the idea behind the vehicle number plate recognition systems have been presented with simply explaining the importance and drawbacks of each system.

In this paper present and study the notion of approximately 2-Absorbing submodules as generalization of 2-Absorbing submodules. Many new properties of this class obtain.

The use of biological and behavioural characteristics has significantly increased in recent years. Like fingerprints, faces, iris, and others in numerous crucial applications in security and electronic governance. Where recently, Research has concentrated on illuminating the recognition of iris images obtained in different fields across spectra by sensors that can capture double iris images in various areas (environment/lighting) by infrared Nearby and visible illumination (NIR, VIS) due to features unique in the iris texture that differ for the same individual, between their left and right eyes. But there is challenging to match NIR and VIS photographs due to the spectrum differences between pictures captured by near-infrared and visible light. We suggest utilizing the Histogram of Oriented Gradient (HOG) approach to extract meaningful information from images of the iris and implement Normalization to reduce differences in illumination, also apply the LDA for dimensionality reduction and K-Nearest Neighbor and Support Vector Machine as a classifier. The experimental findings were implemented on the PolyU bi-spectral dataset, and the results show that the suggested approach achieved exceptional matching performance by KNN, SVM on (Right to Left) for each (NIR, VIS) or versa (99.14%,99.04%, 97.44%,96.38%) respectively.

Because providing billions of objects with network connectivity, the Internet of Things (IoT) enables the collection and transfer of real-time data for intelligent applications. Therefore, IoT enables remote access and control of connected devices when there is a sufficient network infrastructure. Additionally, the introduction of software-defined networking (SDN) presents capabilities that allow internet providers and users to control and connect network equipment wirelessly, even as enabling a global perspective on the network, that has previously become a soaring interest area due to its extensive use for various applications and systems, including wireless sensor nodes, medical equipment, delicate home sensor systems, and some other connected IoT devices. In order to create worldwide connectivity between the Internet of Things (IoT) and based on the SDN architecture in the medical contexts, this paper's contribution is to outline some relevant directions. Additionally, we provide a model based on software defined network principles that depicts interactions between a group of people each of whom have a Nano network within their bodies and the medical services via the local network of a medical institution. For everybody electrical engineers to data engineers the requirement to integrate everything in a global setting is a significant problem. As a result, the cloud is useful for handling the instantaneous sharing of information. In terms of health care, the effort is also stated in terms of IoT architecture and services. IoT's current prospects for the healthcare sector are quite promising. Due to its capacity for sensing and measuring, it is also highly well-liked. From the smallest sensor to the massive amounts of data gathered, this revolution is completely changing how we view healthcare.

Cardiovascular function analysis is crucial for illness diagnosis, risk assessment, and therapy selection in clinical cardiology. Doctors may identify cardiac disorders such as right ventricular failure, hypertrophic cardiomyopathy, and dilated cardiomyopathy using a variety of imaging modalities that allow them to spot pathological alterations. The optimum course of therapy may be chosen more quickly thanks to accurate automation of the relevant duties. Artificial neural networks and deep learning are the foundation of generative adversarial networks (GANs), which are methods for creating synthetic pictures. The potential capacity of the GANs to solve problems has attracted interest in addition to their inherent flexibility and the adaptability of deep learning, on which they are founded. This survey aims to examine the significance of medical imaging in the study and diagnosis of cardiac disease. Demonstrate the widespread adoption of GAN Network approaches in the field of magnetic resonance imaging (MRI) medical image analysis; Explain the recent segmentation application of generative adversarial networks. GANs in cardiovascular imaging additionally Identify the hurdles to the effective application of the GAN Network to medical imaging tasks and highlight particular contributions that address or get around these problems

Multi-label text classification has grown in popularity in recent years, with each document being assigned numerous categories simultaneously. The Arabic Language has a very complex morphology and a vibrant nature; nonetheless, there needs to be more research on this topic for the Arabic Language. As a result, this study aims to present a method for the multi-label classification of Arabic texts based on binary relevance and the label power set transformation method. Three classification classifiers: namely logistics regression(LR), Random forest (RF), and multinomial naïve Bays (MNB), were experimentally assessed in this thesis. Furthermore, chi-square feature selection was investigated to improve the performance of the proposed model. The experimental results are implemented in Python programming using the "RTANews" multi-label Arabic text classification dataset. The results suggest that binary relevance combined with logistics regression produces the best results. It performedell, with an averaged micro-Recall of 0.8646. At the same time, the best result was produced by label power-set with the same algorithm and metrics of 0.8418 for the suggested multi-label Arabic text classification model.

The idea of hiding information is to shield a powerful message in public records. The present application-based hidden information is sensitive information disguised in many testing sectors, such as watermarking, fingerprinting, and steganography, which is the art of covered or hidden writing, which is covert contact with coded communications to hide the presence of a document on a communication channel from hostile attackers. This paper presents hiding speech in a coded image, depending on the quantization and discrete wavelet transformation (DWT). The proposed method used the discrete wavelet transform on a three-color band of covered images. Moreover, the term AMBTC which refers to “Absolute Moment Block Truncation Coding” is used for hiding information. The secret information is a short speech (representing a password for example) embedded based on the quantization level modification technique, which will be hidden in a codded image. The three color-bands of the image are transformed using discreet wavelet transform all decomposed regions are used for hiding except the Low-Low band is kept without any change to get high quality of the stego-image with respect to original image. In the bitmap, the secret bits of a speech data are replaced. The experimental results produced an average of PSNR for tested images is about 35.0. Secret speech is compressed using discrete cosine transform (DCT) about (35%-40%), and the result is converted to a binary form that will be embedded in a color image.

The coronavirus (SARS-CoV-2), which causes severe acute respiratory syndrome (COVID-19), is the most current virus with the potential to kill a substantial number of people around the world. Current procedures are restrictive, time-consuming, inefficient, and obsolete. This is due to their exclusive reliance on the technical expertise of a radiologist or medical consultant. Deep learning and technological advancement have also enabled medical researchers and scientists to investigate various neural networks and algorithms to develop apps, tools, and gadgets that can assist medical radiologists. This paper aims to give an overview of deep learning techniques that have been used in chest radiography of COVID-19 and pneumonia cases, as well as the most important datasets that help researchers test algorithms.

The evaluation of metrics, the effects of machine learning, and the use of preprocessing techniques for image enhancement constitute the essence of image quality. Through a thorough investigation, this study seeks to identify the core components of image quality. First, various measures for gauging image quality are compared to see how well they work. These metrics offer precise evaluations of aspects of a picture such as sharpness, contrast, and color integrity. The effect of machine learning techniques on the quality of images is then examined. The study investigates the use of machine learning approaches to improve image quality by training models on substantial datasets and tailoring them for certain tasks. In addition, the function of preprocessing methods in picture enhancement is investigated. Before further processing or analysis, the image quality is improved using various techniques, including noise reduction, image denoising, and local entropy filtering. The findings of this work offer important new perspectives on the assessment of image quality measurements, the potential of machine learning for image enhancement, and the significance of preprocessing methods in producing higher image quality.