bluuebunny/crossref_metadata_2025 huggingface dataset:
Advance in Algebraic curves: Application to Computer Graphing
Authors: Mahdi Ahmed Mohsin
Published: October 2023

Differential geometry, a branch of mathematics that studies the geometry of curves, surfaces, and manifolds (the higher-dimensional analogs of surfaces). Curve is a smoothly flowing line (non sharp changes) or a curve must bend (change direction) but in Mathematics a straight line also a curve. We will present new definitions and theorems.
Performance Evaluation of Ryu Controller in Software Defined Networks
Authors: Albu-Salih Alaa Taima
Published: October 2023

Software-defined network (SDN) is based on separating data plane from control plane and decision-making processes to centralize network control. The OpenFlow (OF) protocol is the most common and widely used in software-defined network for communicating and controlling switches. With this protocol, the switch learns the routing information from the controller and then passes data packets based on this information. One of the most important components of the SDN is the controller, which is the smartest component of the network. Many controllers have been developed since the advent of SDN. One of the most important and famous controllers is Ryu. Due to the importance of the Ryu controller in SDN, in this article, the performance of the Ryu controller in terms of latency and throughput are evaluated. Cbench is used for measuring throughput and latency of this controller
Security and Privacy in IoT Healthcare System: A systematic review
Authors: Atiyah Rana Fadhel , Al-Mejibli Intisar
Published: October 2023

As technology advances worldwide, several governments and health organizations develop or implement mobile health systems and applications to treat and monitor patients. Several health systems based on the Internet of Things (IoT) applications have been built to monitor patients remotely. Different mechanisms and techniques have been used in healthcare applications, such as centralized, decentralized, and hybrid. In addition, many communication technologies were used, such as Bluetooth, GPS, WiFi, and others. This systematic analysis discusses these aspects, focusing on user security, privacy, effective data extraction, and confidential handling. This review investigates all researches published between 2015 and 2021, where four databases (IEEE Xplore, ScienceDirect, Research Gate, and Springer) were considered. A detail of used exclusion criteria and selection procedure to assess the collected publications were presented. Only thirteen papers have matched the criteria thoroughly examined and included in this study. The findings are provided throughout the papers to highlight gaps and concerns in an IoT-based healthcare system that will be constructed to effectively decrease potential dangers during messaging while preserving total user privacy and security.
MANCOVA Model For Heteroscedasticity And Unbalance Cell Size: Framework
Authors: Kadhim Noor Abdul kareem , Naji Huda Zaki
Published: October 2023

Multivariate analysis of covariance models are widely used in human societies, principally in health, ecology, environmental science and psychological perspective. As such, the researchers face the task of seeking for ways to improve the performance of these models to respond to the requirements which are treating. This paper proposes an extension of two way MANOVA to two way MANCOVA model under effect one covariate factor for heteroscedastic and unbalanced cell sizes. A type III sums squares of an influence is computed that adjusted for all other influences for suggested two way MANCOVA model, no matter in which order they are included. As classical tests could dangerously biased when homogeneity violates the assumption of cell covariance matrices, the suggested model implemented the modified tests obtained by Zhang & Xiao (2012) to test the hypothesis of model
Improved Kidney Stone Detection from Ultrasound Images Using GVF Active Contour
Authors: Mosa Qusay Omran
Published: October 2023

Medical image segmentation is of large significance in supporting information about human body structures which assist physicians in correct diagnosis to determine doing radiotherapy or surgeries. Therefore, accurate interest region detection in ultrasound images represents a challenging function and hence needs to apply more trusted tools to gain the best segmentation and classification of kidney stones. This challenge in ultrasound images includes many factors like low contrast, occlusions, signal deviations, and noise made it difficult to determine these stone’s boundaries. This paper applied gradient vector flow (GVF) model which has a large capture range to identify the image boundaries of kidney stones region and estimate variation in stone measurement to prepare a suitable treatment diagnosis.
A Survey of Transform Coding for High-Speed Audio Compression
Authors: Jabbar Zinah J. , George Dr. Loay E.
Published: October 2023

The necessity for audio compression continues to be a critical issue., as it is associated with lowering the data capacity of one of the most widely distributed digital files transmitted between remote parties, audio compression remains a critical topic. The effectiveness of the two sound pressure units in this article has been tested. Because of this  industry's importance,, which is reflected in storage capacity and transportation needs, Over the last two decades, sound pressure has been the topic of intense investigation. The rapid expansion of the computer industry is leading to a growing demand for high- quality audio data; As a result, the development of sound compression methods, of which there are two types: lossy and lossless, is critical. The purpose of this study is to explore sound compression techniques and to highlight the suitability and applications of each approach.
A Survey for Emotion Recognition Based on Speech Signal
Authors: Hameed Fatima A. , Georgeb Dr. Loay E.
Published: October 2023

There are many characteristics of human beings, such as fingerprints, DNA, and retinal pigmentation that are essential. A person's voice is unique to each individual. Humans use speech to communicate their thoughts and feelings. The process of determining one's mental state involves expressing one's basic emotions in words. A person's emotions play a significant part in his or her daily existence. In order to convey one's thoughts and feelings to others, it is essential to use this method. Emotions can be discerned from speech information because humans have a built-in ability to do so. The selection of an emotion recognition body (speech database), the identification of numerous variables connected to speech, and the selection of a suitable classification model are the main hurdles for emotion recognition. To identify emotions, an emotion identification system analyzes auditory structural elements of speech. The analysis is based on multiple research papers and includes an in-depth examination of the methodology and data sets. The study discovered that emotion detection is accomplished using four distinct methodologies: physiological signal recognition, facial expression recognition, a variety of speech signals, and text semantics in both objective and subjective databases such as JAFFE, CK+, Berlin emotional database, and SAVEE. In general, these techniques enable the identification of seven basic emotions. To determine the emotion, the audio expression for eight emotions (happy, angry, sad, depressed, bored, anxious, afraid, and apprehensive), all published research maintain an average level of accuracy. The major goal of this survey is to compare and contrast numerous previous survey methodologies, which are backed up by empirical evidence. This study covered signal collection processing, feature extraction, and signal classification, as well as the pros and downsides of each approach. It also goes over a number of strategies that may need to be tweaked at each step of speech emotion recognition.
A SURVY of video datasets for anomaly detection and human activity recognition
Authors: Abd al-rasheed Viean fuaad , Shatib Dr. Narjis Mezaal
Published: October 2023

The computer vision researchers concentrated on the automation of the surveillance system. Many datasets suited for diverse applications have been proposed by research in this subject. in a number of different domains of application Human action recognition may be used effectively, which has the potential to improve many facets of daily life.These contain, among other things, preventing violent act and detecting crimes such as murder, stealing, and property damage, as well as predicting pedestrian activity in traffic. And this study addresses the properties of public datasets used for Human Action Recognition. Researchers develop these distinct anomaly datasets as a result of the availability of security cameras installed in various areas. For researchers to comprehend and develop in this field, It is required to review anomaly detection video datasets. Therefor This study presents a survey for video surveillance activity recognition.
Review on swarm intelligent techniques and their applications in different area
Authors: Ibrahim Noor khalid , Shati Narjis Mezaal
Published: October 2023

Broadly, swarm intelligence (SI) algorithms are considered as nature-inspired techniques improved depending on the idea of communications between living entities such as birds' flocks, Ant Colony, and fish, which means deliberates the group behavior evolving through self-organizing of population individuals. SI has been stimulated via the surveillance of group behavior in its populations in nature because their behavior appears to have the ability to solve complex tasks and optimization problems. The fitness function which is based on SI has been improved to solve combinatorial and mathematical optimization problems by using these algorithms. This means, these techniques work based on the behaviors of individuals in their population so the observation, swam algorithms can be employed for solving the different problems in various applications such as in the medical systems or to enhance the performance of other application systems. In this paper, some swarm intelligent methodologies are reviewed and concerns with their applications in some areas are mentioned.
On P -Clean Rings
Authors: Ramzib Esraa H. , Mohammed Akram S.
Published: October 2023

If all the elements of a ring W  can be written as a sum of pure and idempotent elements", the ring is said to be P-clean. In this paper, we introduce the P-clean ring nation and look into some of its fundamental properties, examples, and relationship to the clean ring
Pixel Based Techniques for Gray Image Compression: A review
Authors: Abeda Zahraa. H. , AL-Khafaji Ghadah K.
Published: October 2023

Currently, with the huge increase in modern communication and network applications, the speed of transformation and storing data in compact forms are pressing issues. Daily an enormous amount of images are stored and shared among people every moment, especially in the social media realm, but unfortunately, even with these marvelous applications, the limited size of sent data is still the main restriction's, where essentially all these applications utilized the well-known Joint Photographic Experts Group (JPEG) standard techniques, in the same way, the need for construction of universally accepted standard compression systems urgently required to play a key role in the immense revolution.

This review is concerned with Differential pulse code modulation (DPCM) and pixel-based techniques, where the spatial domain is exploited to compress images efficiently in terms of compression performance and preserving quality. The new pixel-based method overcomes predictive coding constraints with fewer residues and higher compression ratios.
Image Compression using Polynomial Coding Techniques: A review
Authors: Mahdi Noor S. , AL-Khafaji Ghadah
Published: October 2023

Compression solves the two serious issues of size and transmission efficiently, and various standard techniques are available, such as GIF, JPEG, MPEG, and MP3, in which these techniques combine efficiency, ease of use, and speed with the ability to fulfill a variety of requirements, but there's always a necessity and demand for new innovative techniques to improve the digital realm. Polynomial coding is one of the simple promising compression techniques of modeling base that composed of deterministic part (coefficients) and probabilistic part (residual) to represent image information compactly, there are many models were developed to improve the discipline of this technique performance, where This paper review focused on researchers' efforts to compress grayscale and color images using linear and non-linear lossless and lossy techniques.
AsthmaONTO :An Ontology System for Diagnosing asthma disease
Authors: Kadim Fatima Bassim , Othman Zainab I.
Published: October 2023

Asthma is a common chronic inflammatory disease of the airways in the lungs. According to World Health Organization Asthma is often not properly diagnosed or treated, especially in low-income and lower-middle-income countries. People with untreated asthma can experience disturbed sleep, daytime fatigue, and poor concentration. Detecting the disease at the right time can help to reduce the risk it may cause. Using Semantic Web technologies, this project aims to improve asthma disease diagnosis. We created a domain ontology (AsthmaONTO) that encompasses asthma disease domain knowledge. The ontology includes terminology, relationships, and properties that can be used in the diagnosis of asthma. To diagnose asthma disease and predict the risk of asthma, a set of rules has been developed based on valid links between ontology concepts. It also offers therapy options and recommendations. A domain expert submitted a sample set of individuals with asthma disease, which was used to evaluate the proposed system. The system correctly diagnosed 48 of the 54 cases patients with asthma (ratio of correctness is 88.8%), according to the findings.
Some Properties Of Soft Modular Space
Authors: Huniwi Rand Raheem , Al-Mayahi Noori F.
Published: October 2023

In this paper, we will present a definition of soft modular space and some properties of soft modular (convergent, continuous, bounded) are explained instead of the prevailing definition.
Machine Learning-Based Effective Detection Scheme of Fake News
Authors: Kamel Khalid Abood , Waleed Jumana
Published: October 2023

Today, extremely large amounts of false news are consistently uploaded by malevolent people with fraudulent intentions, endangering democracy, justice, and public confidence while having highly harmful social impacts on both individuals and society. Due to the inherent uncontrollable posting processes of social media sites (such as Facebook, Twitter, and Snapchat), this is particularly pertinent to them. The need for earlier false news identification has considerably fueled efforts in academia and business to create more accurate fake news detection techniques. Unfortunately, there isn't much information available regarding how news spreads. There are benefits and drawbacks to relying on social media to follow the news. Social media platforms do make it possible for information to flow swiftly among users. However, these websites could be used to disseminate "fake news," which is low-quality content that contains errors. The widespread dissemination of false information has an extremely damaging effect on both people and society. As a result, the detection of fake news posted on numerous social media platforms has recently emerged as a highly regarded field of study. In this paper, an effective detection scheme of fake news based on the commonly utilized techniques of machine learning has been presented. This proposed scheme involves diverse phases; Dataset preprocessing phase, extracting the phase of the feature, and Naive Bayes (NB) and K-nearest Neighbor are used in the categorization step (KNN). The obtained results in this presented scheme exhibit that the utilization of the NB classification technique exceeds the K-NN classification technique with an accuracy of 94% using the same dataset.
Interactive Situated Autonomic Multi-Agents System-Comprehensive Survey
Authors: Ali Mohammed , Obied Ali
Published: October 2023

Multi-Agent Systems (MAS) are made up of autonomous entities called agents. Agents collaborate to complete tasks, but their intrinsic capacity to learn and make independent judgments allows them to be more flexible. Agents learn new contexts and behaviors through their interactions with other agents as well as the environment. Agents then exploit their knowledge to determine and carry out an action on the environment in order to accomplish their assigned objective. Because of its versatility, MAS is well suited to solving issues in a wide range of areas, including computer science, civil engineering, electrical engineering, etc. Developing cooperative MAS necessitates tackling a variety of issues, especially coordination among agents. Consequently, this paper discusses several interaction ways among agents in many disciplines.  Index Terms—multi-agent system(MAS), Robotic Process Automation, situated agents, interaction.
Pre Diagnosing the Stroke Using Deep Learning
Authors: Atshan Makarem S. , Nemer Zainab N.
Published: October 2023

Stroke is a long-term disability that affects people all around the world. A stroke deprives the brain of oxygen and nutrients, causing brain cells to stop working or die. Radiologists classify them using Magnetic Resonance Imaging(MRI) of the brain for people with stroke. We've presented a solution based on Deep Learning Algorithms such as Convolution Neural Networks (CNN) and Transfer Learning (TL). We divided MRI into two categories in this work (stroke and normal). In this paper, a 4-layer CNN is used from beginning to end with a modification of the EfficientNetB0 network by adding two layers to make the extracted features more diverse.  CNN and pre-trained EfficientNetB0 were used to train our architecture. The EfficientNetB0 achieves an accuracy of 99.6%, while CNN achieves an accuracy of 99.2%. The newly created EfficientNetB0 could be a highly important decision-making tool in stroke research and brain diagnostic testing. As a physician's assistant, the proposed method works.
Touchless Hand Sanitizer Mobile Robot Application: Survey and Dataset Preparation for Deep Learning Recognition
Authors: Khaleel Rand Z. , Raheem Firas A.
Published: October 2023

The COVID-19 pandemic spreads throughout the world, many sanitizer dispenser devices included robots designed to minimize human contact and as less as possible to minimize the speared of this pandemic or any germs among humans. In many positions, these sanitizers dispensers are important to sterilize and help people from infection such as patients and medical staff in hospitals, teachers, and students in schools or universities, etc. In this paper, many studies of sanitizer dispenser devices including mobile robot applications for touchless hand sanitizers especially in the coronavirus pandemic have been explained. The major aim of this survey is to compare and contrast many previous survey methodologies. This study covered the introduction to robot applications, hand sanitizers dispenser device including a mobile robot, and object detection-based deep learning techniques that recognizes people. Object detection with the deep learning method is discussed because it is the most important feature of the hand sanitizer device, which is based on a mobile robot application. Furthermore, in this work, the proposed block diagram of dataset processing has been instigated using the mini-computer Raspberry Pi version 4, Raspberry Pi camera system, and LabelImg graphical tool. These dataset outputs files of each image (label.xml and image.jpg) are prepared and classed into 7 classes to use as inputs to the next future work of deep learning. In future work, we want to create a mobile robot hand sanitizer that recognizes the people using a deep learning approach. The python programing language has been used. The contribution of this work is to create, annotate and prepare the dataset for Deep Learning while the previous related works depended on existing dataset that taken from website .
Statistical Analysis of Aluminum Doped Titanium Dioxide
Authors: RASHEED Mohammed , Sarhan Mohammed Abdelhadi , Jaber Ahmed Shawki , Bouras Dikra , Al-Darraji , Mustafa Nuhad , Rashid Ahmed
Published: October 2023

In the present research, titanium dioxide and Al nanoparticles were purchased from a Chinese company, then the aluminum materials were added in five ratios that are (0, 1, 2, 3, 5) wt.%. The dry press method was used through a hydraulic press from one direction, using a mold of diameter 10 mm to obtain a ceramic compressed dimension that was firing at a temperature of 900 °C. The thermal conductivity is studied for all samples that include pure with the added proportions and compared between them when adding the proportions for aluminum. The ratio of 2 wt.% is the highest conductivity among the used ratios. The mechanical properties included the hardness when adding percentage from aluminum toTiO2 2 wt. % ratio showed is the highest hardness value between other ratios, the samples surface, taking surface pictures of the samples through an optical microscope.
Sf-covering dimension and sf-local dimension
Authors: Shaker Ghazwan Farhan , Hussain Al-Abdulla Raad Aziz
Published: October 2023

The aim of this paper is to introduce new types of dimension functions which are sf-covering.dimension,sf-dim X, and sf-local dimension, sf-loc dim X , by using semi feebly open (sf-open) sets, with examples and theorems. The relationships between them and other concepts will be studied like - -space, -regular space and -normal space.
Double a-g-Transformation and Its Properties
Authors: Abbood Ahmed Mahdi , Geem Methaq Hamza
Published: October 2023

In this paper, we introduce a new transformation , namely , a-g-transformation  and denote it as . This transformation consider as generalized to double g-transformation. We defined it as the following form :
On Differential and Integration of g-g-Transformation
Authors: Fadhil Tiba Maytham , Geem Methaq Hamza
Published: October 2023

In this work we present an integral transform, namely g-g-transform and defined as :

This transformation consider generalized for g-transformation which defined as :

We study differential and integration of this transformation. Also we find the inverse of this transformation by using g-g-transformati of integration . we use differential of g-g-transormation for finding g-g-transformation of some function of the form  .
STRUCTUREAL DEASIGN OF SECURE E-COMMERCE WEBSITES EMPLOYING MULTI-AGENT SYSTEM
Authors: Abdul Hussien Farah Tawfiq , S. Rahma Abdul Monem , Bdul Wahab Hala Bahjat A
Published: October 2023

The vast growth in electronic trading and monetary transactions results to increase the  importance of E-commerce applications rapidly. In order to accomplish the fully functioning, reliable, scalable, secure, efficient and user-friendly E-commerce applications, sufficient system analysis and design processes are important. Shopping over the internet requires offering secure payment method.

Providing security for each individual online consumer at the same time (particularly for large websites) is a time-consuming effort that can result in a variety of issues, including response delays, customer orders being lost, and system freeze or crash, all of which impair system performance.

This work seeks to present a new multi-agent system prototype structure that solves the challenge of security while avoiding issues that might degrade system performance. By creating integrated system taking into account the three parties the e-commerce, the e-bank and the customer, the main concentration is to create an archeticture that helps in decreasing the website traffic and supportng the  security with effective performance. To simulate the real systems a recommendation system is created based on the customer behaviors to increase system performance in helping the customers to find their favorite products and solve problems like cold start, sparsity of user‐item matrix, scalability, and changes in user interest.

This is done by creating a commercial environment (e-commerce and e-bank websites) and a software agent that is installed on the client's device to handle the purchase and ciphering procedures without the need for the consumer to intervene. The security process involve preprocessing phase followed by a modified lightweight AES employing chaotic tent map to generate encryption key.
Traffic Load Balancing based on Congestion Avoidance (TLBCA) in high-speed computer networks
Authors: Khudhair Ahmed Malik , Nassar Khulood A.
Published: October 2023

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++.
Neuro-Fuzzy System for Heart Failure Prediction
Authors: Khaleel Wijdan A. , Chiad Adala M.
Published: October 2023

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.
SOLVING VOLTERRA-FREDHOLM INTEGRAL EQUATIONS BY QUADRATIC SPLINE FUNCTION
Authors: Salim Sarfraz Hassan , Saeed Rostam Karim , Faraj Jwamer Karwan Hama
Published: October 2023

Using the quadratic spline function, this paper finds the numerical solution of mixed Volterra-Fredholm integral equations of the second kind. The proposed method is based on employing the quadratic spline function of the unknown function at an arbitrary point and using the integration method to turn the Volterra-Fredholm integral equation into a system of linear equations with respect to the unknown function. An approximate solution can be easily established by solving the given system. This is accomplished with the help of a computer program that runs on Python 3.9..
Using Logistic Regression Models and Artificial Neural Networks to Study the Factors Affecting the Academic Achievement of University Student
Authors: Hamid Imad Yagoub , Altaher Abdelgadir Elameen , Alosaimi Mohammed Theeb
Published: October 2023

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.
On The Statistically Convergent For Quadruple Sequence Spaces of Fuzzy Numbers Described by Triple Maximal Orlicz Functions
Authors: Hussein Aqeel Mohammed
Published: October 2023

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
Explicit aspect extraction techniques: Review
Authors: Akram Arwa , Sabir Aliea Salman
Published: October 2023

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.
Solving Linear Programming Problems Involving Hexagonal and Octagonal Fuzzy Numbers via Two Ranking Functions
Authors: Mitlif Rasha Jalal , Hasen Saba S. , Ouda Eman Hassan
Published: October 2023

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.
Use Two Kind Hybridization of the Chaotic Peafowl Algorithm with the Hummingbird Algorithm
Authors: Alhamdany Yahya A. , Mitras Ban Ahmed
Published: October 2023

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.
Fourier Transform with Noisy Image
Authors: Muthana Rasha , Mohammed Nora Ahmed
Published: October 2023

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.
On Triple g-Transformation and Its Properties
Authors: Hadi AL-Owaidi Rabab Jasim , Geem Metaq Hamza
Published: October 2023

In this paper, we introduce a new transformation , namely , triple g-transformation  and denote it as . This transformation consider as generalized to some types of triple transformations. We defined it as the following form :
Designing the electronic payment system for the UOITC university using Zain Cash wallet
Authors: Abdulhussein Reem Razzaq , Kamil Wail Khaled
Published: October 2023

The payment system is a suggested project that will construct an adequate information management system, for the University of Information Technology and Communication . The proposed system will be sufficient for documenting cash payments of university fees, and eliminating mistakes caused by the manual approach of collecting student  payment information. The main objective of this project is to design and implement an electronic payment system(EPS). allows students of private government University in addition  evening students at the university, to pay tuition fees using the electronic payment system through the Zain Cash Wallet service. The computer-based payment documentation system of the UOITC was implemented using PHP languages, MYSQL database. Results indicate that the design of an efficient system enhances the payment system of the UOITC; the work satisfied all the objectives intended.
A Survey of Medical Image Analysis Based on Machine Learning Techniques
Authors: Al Gharrawi Ruaa Jasim , Al-Joda Alyaa Abdulhussein
Published: October 2023

Machine learning is a result of the availability and accessibility of a massive amount of data collected via sensors and the internet. The concept of machine learning demonstrates and spreads the fact that computers can improve themselves. Deep learning is causing a paradigm shift in medical image analysis. A medical image is a visual representation of the interior of a body, typically used for diagnostic or therapeutic purposes. Researchers and policymakers interested in healthcare outcomes should read this; this research provides an overview of machine learning at a high level. Computer vision is the field of using computer algorithms to understand and analyze visual data, and machine learning is a key tool for developing these algorithms. This review discovered that there are three varieties of machine learning strategies: supervised, unsupervised, and semi-supervised, and they seem to be gaining traction in risk assessment, disease prognosis, and image-based diagnosis, with increasing success. Convolutional neural networks (CNNs), k-means clustering, random forests, transductive learning, and support vector machines are among the most commonly used algorithms. Image analysis using CNN is the most effective method for medical imaging.
Age Invariant Face Recognition Model Based on Convolution Neural Network (CNN)
Authors: Ibrahem Muntadhar Hussien , Abdulameer Mohammed Hasan
Published: October 2023

Building an intelligent system similar to the human perception system in face recognition is still an active area of research, despite the advancements in technologies and face recognition research carried out when age changes. Deep learning algorithms have outperformed conventional methods in with regard to accuracy and effectiveness of recognition a variety of difficulties, including position, expression, lighting, and aging. But aging is one of the problems that affects the face the most, as it plays a significant role which directly affects facial features, so we notice some people who are very difficult to distinguish and may not be known at all because of the strong change in their features. As a result, we researched deep learning techniques generally and the convolutional neural network (CNN) specifically. This strategy is employed by a number of significant stages: The first side, includes preparing the dataset related to the subject of the study, Isolate the data between training, validation and testing. As for the second part of the work, data preprocessing, such a data augmentation, Normalization, Face detection, and resizing. After then, begin a features extraction operation by the convolution neural network (CNN) that is suggested. After all that, the classification stage begins, which was done by using the (SoftMax) function, because we have approximately (570) classes. In the testing phase, we perform the task of checking the two images entered whether they belong to the same person or not. In this paper, adopted the (Age) and (FG-Net) datasets, Finally, the verification accuracy rate for the proposed system reached 98.7 % on the (Age) dataset, and reached 99.4 % on the (FG-Net) dataset.
Modification Fuzzy Artificial Neural Networks For Solving Fuzzy Singular Perturbation Problems With Boundary Condition
Authors: Al-Janabi Tabark Aqeel , Mohammed  Al-Abrahemee Khalid Mindeel
Published: October 2023

Throughout this work through the using of a neuro-fuzzy system, we have developed a new technique. This updated approach is known neuro – fuzzy system method (MNFS). to develop a numerical method for resolving (FSPPs) for ordinary differential equations with BC. The activation function for hyperbolic tangents used to determine the hidden units' sigmoid function and the parameters of a fuzzy neural network and its formula is:                           .Standard training algorithms and analytical techniques were contrasted with the suggested strategy. Our research revealed the provided approach stands out for its excellent accuracy of the results, low error rate, and much faster speed than that of conventional methods. A number of examples are used to demonstrate the suggested strategy.
2-Semi-Bounded Linear Operators
Authors: Azeez Ahmed M , Shaakir Laith K
Published: October 2023

In this Article, we introduced a new definition of 2- semi bounded operator in 2- inner product space. Then, we investigate a new Space of bounded operators and proved it as vector space. After that we show this space as Banach space. Finally, we discussed some properties of this space.
Build Network Intrusion Detection System based on combination of Fractal Density Peak Clustering and Artificial Neural Network
Authors: Alkafagi Salam Saad
Published: October 2023

Imbalanced data poses a serious problem in intrusion detection systems. In this article, we propose a network intrusion detection system based on fractal density peak clustering and an artificial neural network (FD-ANN). The proposed detection system consists of three parts: data clustering based on the density-peak clustering (DPC) method, using the fractal concept as a membership weight of all data to the cluster, and a neural network to classify the data. The DPC method uses categorization of the tare data into subgroups with strongly correlated attributes to reduce the size of the training data and the imbalance of the sample. Each subgroup has its neural network to train the data. Based on fractal membership weights, the output of all classifiers of the sub-neural networks is combined using the aggregation function. The benchmarks of this model are based on the data sets NSL-KDD and UNSW-NB15. The proposed solution outperforms other known classification approaches in terms of overall accuracy, recall, precision, and F1 score.
The Role of Artificial Intelligence in Medicine Applications
Authors: Betti Ahlam Abbas
Published: October 2023

The goal of research on artificial intelligence aims to make machines and software similar to human performance, so measuring the degree to which an artificial intelligence system can resemble human capabilities is used to determine the types of artificial intelligence. Thus, by comparing the machine with humans in terms of versatility and performance, it becomes possible to categorize artificial intelligence, with multiple types of artificial intelligence, where artificial intelligence that can perform human-like functions with equal levels of efficiency will be considered as a sophisticated type of artificial intelligence, while Artificial intelligence with limited functionality and performance is considered a simpler and less sophisticated type Based on this scale, and in general, artificial intelligence can be classified. Depending on the species classification of AI-enabled devices based on their similarity to the human brain, and their ability to "think" and possibly "feel" like humans. According to this classification system, there are four types of artificial intelligence systems: “interactive machines, limited memory machines, theory of mind, and self-aware artificial intelligence
A General Overview on the Categories of Image Features Extraction Techniques: A Survey
Authors: Sameer Rafal Ali
Published: October 2023

In the image processing’s field and computer vision it’s important to represent the image by its information. Image information comes from the image’s features that extracted from it using feature detection/extraction techniques and features description. Features in computer vision define informative data. For human eye its perfect to extract information from raw image, but computer cannot recognize image information. This is why various feature extraction techniques have been presented and progressed rapidly. This paper presents a general overview of the feature extraction categories for image.
Solving Fractional Partial Differential Equations by Triple g-Transformation
Authors: Hadi AL-Owaidi Rabab Jasim , Geem Metaq Hamza
Published: October 2023

In this article, we use triple g-transformation for solving fractional partial differential equations. There are many studies about finding solutions of fractional partial differential equation by using many  transformations like Laplace transform, Fourier transform and Elzaki transform. In this paper we use triple g-transformation  because this transformation can be used to solve most of the fractional partial differential equations by choosing the appropriate functions .
A Certain Class of Analytic Functions Associated with Beta Negative Binomial Distribution Defined on Complex Hilbert Space
Authors: Hussein Dhirgam Allawy , Lauibi Yusra Mohammed
Published: October 2023

In this work , we show and research a new class of univalent and analytic functions with negative coefficients linked to the beta negative binomial distribution on a complicated Hilbert space. We find fascinating geometric features such as the convex set, coefficient estimates, distortion and growth theorems, starlikeness and convexity radii, and describe the extreme points for functions in this class.
The Cubic Rank Transmuted Gumbel Distribution
Authors: ELHertaniy Doaa Abed , Bashir Hamid Abdel Rahim
Published: October 2023

A Cubic Rank Transmuted Gumbel distribution (CTGD) in this research is extend the work of cubic transmuted distribution families. CTGD improves the flexibility of transmuted distributions and allows for the modeling of more complex data. Its hazard rate function, moment-generating function, moments,  quantile function, entropy and  order statistics,  are only a few of the key statistical characteristics that we examine. Finally, the Cubic Transmuted Gumbel Distribution is  applied to three real datasets to test its applicability and evaluate how well estimate approaches function for the CTGD, Gumbel(G), and transmuted Gumbel (TG) distributions. The observed  results demonstrated that, for the used data sets, CTGD provides a superior fit than G and TG distributions.
Grey Wolf Optimization for Facial Emotion Recognition: Survey
Authors: Marzouk Hadeel F. , Mosa Qusay O.
Published: October 2023

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.
A Secure and High Capacity Image Steganography Approach Using Huffman Coding and RSA Encryption
Authors: Kadhem Eman Lateef , Baawi Salwa Shakir
Published: October 2023

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.