Iraqi Journal for Computer Science and Mathematics

A new curve is produced and graphically studied. The name of my daughter, Nada, has been given to this curve; in other words, Nada is used to describe this curve whenever it is used in this paper. “Nada’s curve” is a form of closed curve that is constructed when the circle’s diameter and the ellipse’s minor axis share the same length, and they are tangent by a point from a drawn line passing through the circumstances of the circle and ellipse. Then, from these two intersection points, the point of intersection of the vertical and horizontal lines is selected to determine a point of Nada’s curve. By sharing two vertically lined points on both the circle’s diameter and the ellipse’s minor axis, the parametric equation can be simplified and calculated easily, starting with an equation of the
circle: x2 +(y-1)2 =1. Cases of Nada’s curve were graphically investigated in this study. The surface generated by the revolution of Nada’s curve around its axis is called “Nadaoid.”

In this study, the gradation of involutive matrices, whose definitions were given before, is conducted. The solutions of the equation x2 = 1 in real numbers are ±1. Meanwhile, in the solution of the equation xk = 1 in real numbers, there is always the number ±1 that is independent of the power of k 2 Z+. This feature, which is revealed by this equation in real numbers, is the subject of the research. In particular, the kind of situation in which the equation would display in the matrices is determined. Initially, the second-order square matrices are studied by obtaining some of their properties. Then, new cases arising from the known addition, subtraction, multiplication, scalar multiplication, and division operations of this set of second-order and quadratic involutive matrices are investigated. Some properties of third- and second-degree involutive matrices, which can be provided and exist, are emphasized. Comparisons are performed on the involutive matrices. Examples, theorems, and lemmas emerging
between these two types of matrices are given. New concepts are introduced to the literature by using linear matrix equations and multiplications by the matrices

Blockchain and the Internet of Things (IoT), two of the most emerging technologies, are already reconfiguring our digital future, as described by the drastic change in the current network architecture. The incorporation of IoT has brought the objects around us to life, making them “smart” and capable of communicating with one another, thereby amassing massive data by consistently acquiring the physical world for analysis and intelligent action. It has made our dream of seamless integration of the digital and physical worlds a reality, altering the essence of our perception of the physical world. However, the issue with current IoT solutions is the requirement for a centrally controlled party (similar to a cloud server). This research covers the most essential implications of IoT’s most challenging scenarios, as revealed by the current studies, when connecting and communicating via the Internet. The vast amounts of generated sensitive data pose a significant risk to privacy and security; interestingly, the original architecture design calls for a decentralized system, such as the distributed or peer-to-peer (P2P) system. This scenario highlights the relevance of the blockchain, as it can offer a secure and trustworthy method of sharing information via a distributed/P2P model for achieving transparency, security, privacy, auditability, resilience, access authentication, data immutability, and so on. In this study, we explain how blockchain and IoT technologies are combined to overcome their specific shortcomings and ultimately achieve their maximum benefits. This research
also discusses the technical aspects of IoT and bitcoin. We present a comprehensive overview of both technologies, including the bloc

The density-based spatial clustering for applications with noise (DBSCAN) is one of the most popular applications of clustering in data mining, and it is used to identify useful patterns and interesting distributions in the underlying data. Aggregation methods for classifying nonlinear aggregated data. In particular, DNA methylations, gene expression. That show the differentially skewed by distance sites and grouped nonlinearly by cancer daisies and the change Situations for gene excretion on it. Under these conditions, DBSCAN is expected to have a desirable clustering feature i that can be used to show the results of the changes. This research reviews the DBSCAN and compares its performance with other algorithms, such as the traditional number of clustering, K-mean particle swarm optimization (PSO), and Grey–Wolf optimization (GWO). This method offers high performance for improvement. The DBSCAN algorithm also offers better results of clusters and gives better performance assessment according to the results shown in this study.

This study is concerned with the problem of estimating the reliability function of the parameters of the two-parameter Burr XII distribution when the data are complete. Concepts such as the likelihood estimator, bias, bias ratio, and mean square error are defined. The purpose of this research is investigate the properties of the double-stage shrinkage estimators (DSSEs) of the reliability function of Burr XII distribution and obtain the equations of bias, mean square error, and bias ratio. The relative efficiency of the Burr XII distribution of the proposed estimators is also investigated by deriving their equations. Numerical results show performance of our estimators to be better than those of the classical pooled estimators based on the relative efficiency criteria. In particular, our proposed DSSEs have better performance than the classical estimator and single-stage shrinkage estimators.

An important part of a cryptosystem is a cryptographic algorithm, which protects unauthorized attackers from obtaining private and sensitive data. This study is a research project on identifying cryptographic algorithms using deep learning techniques and categorizing cryptographic algorithms based on feature extraction. The research involves employing block cipher modes called electronic codebook with the encryption algorithms Blowfish and advanced encryption standard (AES), where the data will be encrypted using the same key and a different key. The model has been developed by changing the structure and parameters of the proposed model and the training rate of the data. This model will build several dense FCNN of n layers on regular fully connected neural networks. Its construction will consist of five hidden layers, with each layer consisting of 128 neurons and hidden layers activation Relu except for the output layer, which consists of two classifiers and the SoftMax activation function. FCNN is better able to classify big data. It is also more efficient in use, reducing complexity, with the ability to store training data. First, the fully connected neural network (FCNN) model was used to evaluate the categorization of the models. Then, all models, even the encryption forms, were evaluated utilizing true positive measurements for satisfactory classification of the identified encryption method and false positive measurements for incorrect classification. The effectiveness of the model was then calculated using the precision value, recall, loss, accuracy range, and F1-Score metrics using a confusion matrix. The FCNN model parameters will be changed to more effectively identify the encryption algorithm. In the proposed method, when using the same key, the accuracy was 81%, and when using a different key, the accuracy was 49%. The FCNN model’s adjusted weights and learning will be based on large data to define and assess encryption algorithms more effectively and efficiently

Today, cybersecurity is considered one of the most noteworthy topics that are circulated frequently among companies in order to protect their data from hacking operations. The emergence of cyberspace contributed to the growth of electronic systems. It is a virtual digital space through which interconnection is established between computers and smartphones connected within the Internet of Things environment. This space is critical in building a safe digital environment free of threats and cybercrime. It is only possible to make a digital environment with the presence of cyberspace, which contains modern technologies that make this environment safe and far from unauthorized individuals. Cybersecurity has a wide range of challenges and obstacles in performance, and it is difficult for companies to face them. In this report, the most significant practices, sound, and good strategies will be studied to stop cybercrime and make a digital environment that guarantees data transfers between electronic devices safely and without the presence of malicious software. This report concluded that the procedures provided by cybersecurity are required and must be taken care of and developed.

The exchange of data is not limited to personal text information or information about institutions and governments, but includes digital media transferred via the Internet including everything, whether texts, images or videos and audio, or animation. These media need high-security protection and high speed during its transmission from one site to another. In this study, a new method is suggested for hiding a gray-level image within a larger color image based on the proposed steganography map that merged chaotic function and randomize function. The size of the chaos and randomize functions is 16 bytes. Experimental results obtained a successful method based on mean squared error, signal-to-noise ratio, peak signal noise rate, embedding capacity, entropy, and histogram. This method can rapidly hide and extract ciphertext in and from the gray image. The original image and the stego image are difficult to distinguish because the correlation between them is very close to 1, indicating that attackers cannot easily differentiate these images with the naked eye. This condition can successfully hide information on the Internet.

People in the modern era spend most of their lives in virtual environments that offer a range of public and private services and social platforms. Therefore, these environments need to be protected from cyber attackers that can steal data or disrupt systems. Cybersecurity refers to a collection of technical, organizational, and executive means for preventing the unauthorized use or misuse of electronic information and communication systems to ensure the continuity of their work, guarantee the confidentiality and privacy of personal data, and protect consumers from threats and intrusions. Accordingly, this article explores the cybersecurity practices that protect computer systems from attacks, hacking, and data thefts and investigates the role of artificial intelligence in this domain. This article also summarizes the most significant literature that explore the roles and effects of machine learning and deep learning techniques in cybersecurity. Results show that machine learning and deep learning techniques play significant roles in protecting computer systems from unauthorized entry and in controlling system penetration by predicting and understanding the behaviour and traffic of malicious software.

Context: To increase the efficiency of wastewater treatment, modeling and optimization of pollutant removal processes are the best solutions. The relationship between input and output parameters in wastewater treatment processes (WWTP) are the complicated one, and it is difficult for designing of models using statistics. Artificial Intelligence (AI) models are generally more flexible when comparing with statistical models while modeling complex datasets with nonlinearity and missing data.

Objective: Studies on WWTP of AI based are increasing day by day. Therefore, it is crucial to systematically review the AI techniques available which are implemented for WWTP. Such kind of review helps for classifying the techniques that are invented and helps to identify challenges as well as gaps for future studies. Lastly, can sort out the best AI technique to design predictive models for WWTP.

Method: With the help of the most relevant digital libraries, the total number of papers collected are 1222 which are based on AI modelling on WWTP. Then the filtration of the papers is mainly based on the inclusion and exclusion criteria. Also, to identify new relevant papers, snowballing is the other technique applied.

Results: Finally selected 76 primary papers to reach the result that were published between 2004 and 2020.

Conclusion: ANN with MLP approach on BP algorithm become a supervised neural network called BPNN is the most used AI modelling for WWTP and around 40% of the experimental research done with BPNN. Then there are some limitations on AI modelling of WWTP using photoreforming which is the current study of WWTP represents a promising path for generating renewable and sustainable energy resources like chemicals and fuels.

This study deals with a special case in the geometry of 2D special plane curves. The research treaties the development of a method for locating points and drawing curves derived from moving points on two curved paths of conic sections, namely, circle and ellipse. The paper presents geometrical methods to determine points and nodes and to draw these melted-like curves in two cases. Parametric equations are also used to determine the ordinates of the points of each curve of the two cases of the curves. This research is the initial phase for the new type of curves and awaits future studies that will deal on 3D surfaces, such as a sphere, a right cone, or a right cylinder.

The mobile wireless sensor network (WSN) is an emerging field as it widens the scope of applications where sensor networks can be applied. Generally, mobility in wireless communications degrades how well each pair of nodes communicate with one another. In WSN, the effect is higher because nodes have limited communication and computational capabilities. Those limitations create a challenging environment for the operation of sensor node communications. This paper proposes an energy-efficient media access control (MAC) protocol with mobility adaptive throughput based on a carrier-sense multiple access with collision avoidance MAC mechanism called the SEEK-mobility adaptive protocol (SEEK-MADP). SEEK-MADP uses a unique control packet operation to transfer data packets through as many nodes as possible in one duty cycle. The control packets, SYNC and RTS, are merged into one packet (SEEK). SEEK is then transferred to the downstream nodes to establish a connection between a stream of nodes in a duty cycle. This process minimizes the energy consumed by the handshaking process between the connected nodes. To increase throughput, the data period is adaptive to minimize/maximize the data packets according to the movement speed of the sender/receiver nodes. The proposed algorithm is assessed via extensive simulations in mobile scenarios using Network Simulator version 2. The final results show that the SEEK-MADP outperforms MAC protocols, sensor MAC (S-MAC), and SMAC with adaptive listening, which have shown good performance in mobile scenarios. The performance of the proposed algorithm is better than that of IEEE 802.15.4 standard MAC mechanism at mobile scenarios.

Regression models explore the relationship between the response variable and one or more explanatory variables. It becomes practically challenging in real-life applications to model this relationship when the explanatory variables are linearly dependent. Conventionally, to avoid this issue, several shrinkage estimators are proposed. The ridge estimator is one of the most popular methods of estimation when there is linear dependency among the explanatory variables. In this study, we proposed a jackknifed version of the ridge estimator for the Bell regression model by jackknifing the ridge estimator to reduce the biasedness. The simulation and the application results revealed that the proposed estimator enhance the performance of the ridge estimator.

This paper represents a sequential design of controlling from a 3D chaotic system to a simple 6D hyperchaotic system by adding three linear controllers via state feedback control and coupling strategies. The new 6D system has a unique non-hyperbolic equilibrium point with four positive Lyapunov exponents (LE). Moreover, some dynamic properties are analyzed which inclined equilibrium and their stability, dissipative, conservative, and multistability. In addition, the corresponding analog electronic circuit is designed and implemented to verify the new simple 6D hyperchaotic system. Finally, the NI Multisim simulation results observed from the digital oscilloscope are consistent with the mathematical simulation based on the Matlab R2021a.

Breast cancer is one of the most common types of cancer among women, which requires building smart systems to help doctors and early detection of cancer. Deep learning applications have emerged in many fields, especially in health care, but there are still some limitations in this technique, such as the small number of classified medical images needed to train deep learning models, so this paper aims to find a solution to this problem by using new techniques for transfer learning by taking advantage of the presence of unclassified medical images of the same disease. The proposed approach was applied to the modified Xception model to classify the histological images of breast cancer in the ICIAR 2018 dataset into four classes: invasive carcinoma, in situ carcinoma, benign tumor, and normal tissue. The proposed approach has obtained 99%, 99.003%, 98.995%, 99%, 98.55%, and 99.14% for accuracy, precision, recall, F1-score, sensitivity, and specificity respectively. Our work has been compared with previous works.

The integral transforms has an essential role to solve the differential and integral equations. In this paper the Sadik and complex Sadik transforms are discussed in detail through an application study for solving initial value problem iincludes the system of ODEs and result explained that Sadik and complex Sadik transforms that closely related..

COVID-19 is a very dangerous pandemic attacking the respiratory organs of humans. It is
characterized by its contagious speed, especially with its last versions. Effectiveness of confrontation resides in a
strategy based on the speed of intervention, early detection, and appropriate and quick treatment. However, this
strategy requires more effort and enormous human, material, and financial resources. The latter situation requires
a much more efficient solution based on using new technologies. Through the integration of the internet of things
in healthcare, the quality of the latter will be improved. This integration requires suitable architecture represent
the foundation for the system that handles the data generated by this technology. To concretize this strategy, we
propose an approach based on IoT architecture inspired by the organization of the Algerian health structure for
fighting COVID-19. The architecture allows the organization to manage resources and ensure adequate resources.
The approach also consists of a semantic web-based system to handle the heterogeneity of data sources and exchange
it with different applications. The system is based on a proposed fuzzy ontology that helps to treat vague and imprecise
data that characterize the medical domain. The fuzzy ontology is developed by reusing a standard IoT ontology for
allowing the sharing and reuse of IoT data. It also uses the NEWS2 score system for defining membership functions.
We conducted an experimental study, and the results of the proposed approach were compared with those obtained
by physician assessment. The results show that the approach is effective, and even if COVID-19 disappears soon
according to the World Health Organization indications, the proposed approach will still be valid for any other
epidemic that may occur in the future or for any other disease

with the rapid spread of the use of video files cross the world before and during the pandemic, and the availability of capabilities and tools to create, store and transmit video files of high quality. The latest studies illustrate that video files are the highest data types that had been transmitted over the internet, during video transmission or broadcast from one point to the other or over the internet, videos may be damaged for several reasons, such as packet loss or jitter, in this paper, a proposed approach of Ridgelet transformation is presented to improve received video file quality, based on embedded watermark quality present in video frames. It is implemented by extracting three-color channels of video frames, i.e., Red (R), Green (G), and Blue (B). A selected watermark is included in blue channel (B) along with transmitting the video file from source to destination. However, the quality of watermark is measured with execution regarding watermark. Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) were adopted as measurement criteria. The evaluation results show interesting comparative values without noise, in comparison to applying salt, pepper, and Gaussian noise to video frames.

This research paper aims to investigate the current state of process efficiency in Iraqi universities
and propose a management information system (MIS) to improve it. A literature review was conducted to identify
the challenges faced by universities in Iraq, as well as the solutions proposed in the literature. The findings showed
that the main challenges in Iraqi universities include a lack of automation, poor data management, and inadequate
communication between departments. A comparison was also made between the challenges faced by Iraqi
universities and those faced by universities worldwide. To address these challenges, a survey was conducted to
gather data from university staff and students. The survey results were analyzed, and the findings were used to
design a MIS based on Oracle Application Express (APEX). The proposed system includes a student records
management system, a staff management system, and an automated workflow system to improve communication
and collaboration between departments. The proposed system was also found to be in line with the regulations and
policies of the Ministry of Higher Education in Iraq. The system architecture and database design were also
discussed in detail. The results of this research indicate that implementing a MIS using Oracle APEX can
significantly improve the process efficiency in Iraqi universities. This can lead to an increase in the quality of
education and a reduction in the administrative burden on staff and students.