Journal of Al-Qadisiyah for Computer Science and Mathematics

This study explores the field of scientific data extraction using online scraping techniques, with a specific focus on the Springer and Nature archives within the University of Basrah's setting. This study aims to explicate the theoretical underpinnings of web scraping, emphasizing its importance in the acquisition of structured data from online sources. This study explores the many issues presented by dynamic content, captchas, and IP blocking and proposes novel solutions for each of these obstacles. The university's research objectives were supported by a rich dataset that was carefully constructed through a painstaking approach encompassing data collection, preparation techniques. The results highlight the effectiveness of web scraping, significant influence of preprocessing. This study not only enhances the existing body of academic research methodology but also advances the University of Basrah's pursuit of data-driven and influential scholarly pursuits.

In the present article, we introduce two families and of holomorphic and bi-univalent functions associating -pseudo-starlike and convex functions with Sakaguchi type functions defined by Gegenbauer polynomials. We derive the initial Maclaurin coefficients estimates and determinate the Fekete-Szeg problem of functions in these families.

Oil and gas production forecasting has always been a hot topic in the petroleum industry. Production forecasting in this sector aims to estimate future production rates, facilitating operational planning, production optimization, and resource allocation for companies. Scientists have traditionally attempted to forecast oil and gas production using methods such as Numerical Reservoir Simulation (NRS) and Decline Curve Analysis (DCA). However, these methods present challenges including time-consuming processes lasting hours or even days, uncertain accuracy, reliance on accurate static models, and uncertainty in dynamic model parameters. In this research, aim to address these limitations by leveraging machine learning models for production forecasting. These models enable faster and more precise decision-making by accurately predicting future outcomes based on historical data. Our study employs three models: Decision Trees (DTR), Random Forest (RFR), and XGBoost. In this reserch utilize the Python programming language and a dataset from wells in New York State, USA. Experimental results demonstrate that the RFR model achieves the highest accuracy (99%) for oil and gas production compared to the XGBoost and DTR models.

In this paper, notions of some separation axioms by using D-set in bitopological space. We studied some of the fundamental properties and relations among types of -bitopological spaces where =0,1.

The significance of detecting network traffic anomalies in cybersecurity cannot be overstated, especially given the increasing frequency and complexity of computer network attacks. As new Internet-related technologies emerge, so do more intricate attacks. One particularly daunting challenge is represented by dictionary-based brute-force attacks, which require effective real- time detection and mitigation methods. In this paper, we investigate Secure Shell or Secure Socket Shell, is a network protocol that gives users, particularly system administrators, a secure way to access a computer over an unsecured network(SSH)and File Transfer Protocol is a standard network protocol used for the transfer of files from one host to another over a TCP-based network, such as the Internet (FTP) brute-force attack detection by using Our research focuses on using the machine learning approach to detect SSH and FTP brute-force attacks. A reasonably thorough investigation of machine learners' efficacy in identifying brute force assaults on SSH and FTP is made possible by employing several classifiers. Brute-force assaults are a popular and risky method of obtaining usernames and passwords. Applying ethical hacking is an excellent technique to examine the effects of a brute-force assault. This article discusses many defense strategies and approaches to using brute-force assaults. The pros and cons of several defense strategies are enumerated, along with information on which kind of assault is easiest to identify. we made use of machine learning classifiers: Naive Bayes, Random Forest, Logistic Regression, we determined that the Random Forest algorithm achieved the highest level with an accuracy the contribution lies in demonstrating the feasibility of training and evaluating basic Random Forest models with two independent variables to classify CSE-CIC-IDS2018 dataset.

In [1], the authors introduced a property of rings called the f(x),g(x)-clean property. We investigate some new results about this property. In particular, we prove that the ring of matrices over a ring R is f(x),g(x)-clean if the ring R is f(x),g(x)-clean as well. We demonstrate, among many other things, that a ring's f(x),g(x)-cleanness may always be passed to R[[x]], but not always to its polynomial ring R[x].

In this paper, we aim to show many results from the weakly pseudo-primary-2-absorbing sub-module, including triple-zero of the weakly pseudo-primary-2-absorbing sub-module. Furthermore, weakly pseudo-primary-2-absorbing triple-zero have been characterized in some module types, including content, finitely generated, and multiplication modules.

This study presents a comprehensive exploration of machine learning (ML) techniques for predicting vulnerabilities in websites, which is a critical aspect of modern cybersecurity. With the advancement of digital threats and the complexity of cyber-attacks, conventional security strategies have become increasingly inadequate. By employing machine learning algorithms such as Random Forest and Gradient Boosting, this study formulates models adept at identifying potential vulnerabilities within the website code. This approach responds to the escalating demand for enhanced security measures, in the face of increasingly sophisticated digital threats. By integrating anomaly detection findings through the Isolation Forest algorithm, this study enriches the training dataset, enabling models to adapt to both known and emerging vulnerability patterns

The aim of this paper is to analyze the temperature and magnetohydrodynamic (MHD) peristaltic flow of non-Newtonian Sutterby fluid through a porous wavechannel under the influence of rotation. The problem formula is non-linear and non-homogeneous partial differential equations. By using the perturbation technique, we solved the momentum equation under the assumptions of a long wavelength and an extremely low Reynolds number. After getting the solution, we have used the "Mathematica 13" program to analyze the results through graphs, and it have been studied the effect of the rotation, magnetic field, viscosity of the fluid and average radius of the tube on the fluid movement and its temperature.

Multiple fixed points common to fuzzy spaces b-metric are presented in this work. We provide an appropriate requirement for a sequence on the fuzzy space b-metric to be Cauchy, which is a significant outcome. As a result, we streamline the numerous theorem of point fixed proofs on a fuzzy space for b-metrics with established contraction conditions.

Increasing coverage and reducing the energy consumed in wireless sensor networks is an interesting field for researchers since the discovery of wireless sensor networks and it is an open problem. Activating the lowest connectivity range for each node individually depending on its remaining power and the place it covers, while maximizing coverage by placing it in the optimal place will reduce the energy consumed and increase the lifetime of wireless sensor networks. In this paper, we find an approach to increase coverage by optimizing correlation and residual energy using genetic algorithms by placing each node in the optimal position to maximize coverage, assuming that each node has a different energy from the other nodes. We will use drones to carry and move sensors to optimal position. The aim of the proposed work is to cover the largest area of a given region by using the least range of connection and increasing the Lifetime. We ran a succession of simulations and found the proposed model better than the strategies we found in literature.

Many studies during the last decade tried to provide an automatic technique for facial recognition and identification challenge especially in security systems. In this study, we suggested two methods for student attendance problem based on image processing and machine learning algorithms. The first method uses Haar cascade classifier with the Local Binary Patterns Histograms (LBPH) model and the second method composed from Histograms of Oriented Gradient (HoG) followed by the Convolutional Neural Network (CNN) model. Both methods take a collection of random student images taken from low quality sources as input. A set of image processing filters are first applied on images to enhance the method of extracting the face boundary. Then, each model will be trained using random images from student dataset. The trained model is tested using testing set. The results showed that the method that employ CNN model with HoG provides high accuracy value of 98.44%. While, the accuracy of LBPH model with Haar Cascade classifier is 95.63%.

In this paper the definition of fuzzy anti-norm is modified the authors have studied some properties of convergence and continuity in fuzzy anti-normed spaces. Firstly, the authors have given some definitions and theorems, such as fuzzy anti-normed, fuzzy anti–continuous, and fuzzy anti-convergent sequence and Cauchy sequence on fuzzy anti-normed space and definitions of open ball and closed ball. The authors have presented some examples by using these definitions. Additionally, the authors have given a definition, sequentially fuzzy anti-continuous. and some of their properties are studied.

Swarm robotics is a distinctive type of multi-robotic system that relies on local communication among the swarm members to generate a desired global behaviour. This implies a lack of global information, requiring robots to sense and communicate using sensors and actuators located on their bodies. Consequently, the robots within the swarm must leverage collective intelligence to solve the problem at hand, as no individual robot can accomplish the task independently. This article provides an overview of swarm robotics in general, highlighting its characteristics that distinguish it from other multi-robotic systems and simultaneously serve as motivation to adopt a swarm robotics approach. A closer examination of collective decision making within swarm robotics and its design problem also provided, classifying design methods into manual design and automatic approaches. The most commonly used automatic approaches to design collective decision making in swarm robotics are explained, along with a mention of the benefits and drawbacks of such approaches. However, this review does not cover aspects such as the swarm collective behaviours – except collective decision making – and the swarm robotics tasks

Early warning of fires is very important to reduce loss of life and property. It is a technology that has become one of the most important design elements for smart cities in the future. Most current solutions rely on temperature and smoke sensors, which have limited detection ranges and scenarios and long response times. These are used inside some buildings and cannot be used in the external environment, as they are not a preferred solution. In addition to the above, sensor systems are costly because of the hardware and installation requirements, and they require regular upkeep. With the development of artificial intelligence and learning machines, fire detection has been used based on deep learning. Widely. Therefore, a solution to computer vision technology is proposed, which uses a convolutional neural network (CNN), Reducing false alarms. The use of computer vision techniques is certainly better than sensor-based systems, by utilizing security camera footage at specific periods to detect a fire when it breaks out. Our system achieved excellent results with an average predictive accuracy of 98% on the dataset. This accuracy and low cost make it a good alternative to systems that use sensors. The contribution of this study is to improve the detection process by conducting a classification process instead of detecting an object (fire), which requires complex mathematical calculations, large resources, and a long time.

In this article, we will investigate the Galerkin sinc approximation to solve the single pseudo-Poisson problem and in this method we will reach a linear system. We will solve this system by carefully choosing the length of the steps and the number of nodal points and with two methods; the first method, which is the usual method, is theoretically flawed and not practical and computationally efficient. Therefore, to solve the mentioned system, we introduce the orthogonalization technique, which solves both theoretical and computational problems. Numerical approximation is obtained whose accuracy is exponential and of order where N is a transaction parameter and c is a constant independent of N. In the final part, we give some numerical examples of individual pseudo-Poisson problems to demonstrate the efficiency of the method.

The modern world is full of data of all kinds; however, the vast amount of video and image data available provides the data set needed for facial recognition. Facial recognition is crucial in safety and surveillance systems that analyze visual data and millions of images. Using facial recognition software, a person's identity can be verified through a variety of media images. For facial recognition, there is a variety of algorithms available. The article presents an approach to a face recognition framework using Haar Cascade, a biometric technology in safety and surveillance systems. It investigates combining standard machine learning techniques for face detection and identification with Raspberry Pi face detection, a cost-effective and easy-to-use embedded system. The system detects faces from indirect and direct images, achieving high speed using the latest Raspberry Pi 4 and Python libraries. The work demonstrates a machine-learning-based design method and a complete embedded system. The face detection accuracy is 92%, and the average time is 0.35 compared to the local binary model (LBP). Many facial recognition algorithms on the web and in literature reviews are vulnerable to image attacks. These methods are very effective in identifying faces in webcams, video streams, images, and videos. This system's use of the Raspberry Pi 4 and advanced Python libraries results in fast and accurate real-time face detection. This paper extends the work first presented at the 12th Iranian/Second International Conference on Machine Vision and Image Processing (MVIP).

In this study, we provide the triple sequences space of fuzzy numbers identified by simple elliptic Orlicz function and discuss some properties like the space (ℓ∞)???? 3 is complete,

Malware attacks on mobile devices are becoming more common and more complicated every year. Malware writers see the open-source Android app as their main target because so many people use it. Artificial intelligence is used by most of the literature's mobile malware detection methods to find ransomware. Our research, on the other hand, makes it clear that most of the earlier studies used different metrics and models, as well as different datasets and classification features that came from static, dynamic, or hybrid analysis strategies. This makes comparing the different suggested detection methods more difficult and may also make the results less certain. The goal of this work is to solve the problem of AI-powered malware detection by sorting current methods and approaches into three groups: the type of dataset, the type of detection method used (machine learning models, deep learning models, and Behavioral Analysis model), and how well the method works. In this way, we suggest a convergent plan that can be used as a basis for future methods of finding malware on Android and as a solid standard for artificial intelligence work in this area.

This review presents a comprehensive analysis of contemporary scholarship pertaining to instant messaging (IM) user behavior and security protocols. Through meticulous selection, the authors highlight critical studies that illuminate optimized message consumption strategies and delve into the evolving landscape of IM security models. Focusing on the past four years, the review meticulously dissects cutting-edge advancements in this domain. A significant insight emerges: achieving optimal communication security necessitates the synergistic convergence of three fundamental techniques: end-to-end encryption for data confidentiality, decentralized authentication for independent user verification, and zero-knowledge proof for identity obscurity. The review postulates that the simultaneous integration of these elements within the application architecture is paramount for robust privacy and heightened security in the realm of IM.

Deep learning is highly effective in dealing with complex tasks such as image classification and recognition. However, finding the optimal architecture's hyperparameters for Convolutional Neural Networks (CNNs) to achieve the best performance and parameter regularization can be challenging. Metaheuristic optimization algorithms can be utilized to find solutions in this context. In this research, a computerized CNN was adjusted using an improved Salp Swarm Algorithm (SSA) to enhance crucial CNN settings, like dropout rate, hidden units, learning rate, and batch size. The refined design was tested on two standard datasets. MNIST and Fashion MNIST. The outcomes displayed model performance achieving accuracy levels of 99.6% for MNIST and 94.08% for Fashion MNIST. This tuned system outperformed the existing practices by 0.2% and 0.04% for each dataset while also cutting down on computational expenses. The fusion of SSA with CNNs displayed adaptability and resilience opening up possibilities, in image classification and consistently delivering outstanding outcomes.

Recently, the research on Electroencephalogram (EEG) signals have been obtained more focus at the same time the EEG signal is regarded as the basis for the prediction of diagnosis disease and the brain behavior. EEG is as significant tool for many conditions that can be recorded the brain human waves which accommodate the brain activity. In the recent decades, EEG data has been extensively applied in the approaches of data analysis such as time series analysis. With the considerable achievement of deep learning (DL) implement on the time series data, multiple studies have been began applying deep learning algorithms on the processing of EEG signal. Several deep learning techniques that assistant in the detection various psycho-neuro disorders, have been proposed in order to automate EEG detection and classification with great development in multiple applications of EEG signals. Also, different machine learning (ML) algorithms have been presented in such research for brain signals identification and classification in the era of Artificial intelligence (AI). In an attempt to summarize the EEG signal processing techniques, we have performed a literature review around deep learning algorithms for decoding the human’s brain activity as well as diagnosis disease and clarified particulars about several deep learning algorithms. We also conducted some of ML papers about EEG signals classification. Based on the achievement results of the research mentioned in this article appears an advanced scientific development in terms of deep learning.